System and methods for human-centered agi

Abstract

Artificial General Intelligence (AGI) is safer if humans are kept "in the loop." However, until now, no architecture for AGI existed that is both human-centered and highly scalable. The current invention of human-centered AGI not only includes humans in the loop, but also scales to super- human speeds while retaining human-aligned values. The invention contains novel systems and methods that include, without limitation: a) reputational methods that increase the efficiency and effectiveness of problem solving by the (human and Al) intelligent entities that collaborate in the AGI system; b) use of LLMs ' abilities to understand and translate natural language into a universal problem solving protocol; c) use of tree data structures combined with rewards to direct attention; and d) use of blockchain technology to reward problem solvers and capture a rigorous and auditable record of every cognitive step. Human-centered AGI can be implemented more rapidly than any existing approach.

Description

SYSTEM AND METHODS FOR HUMAN-CENTERED AGITECHNICAL FIELD

[0001] In some aspects, the present technology relates to a system and methods for human-centered Artificial General Intelligence (AGI) for use in connection with achieving AGI faster by utilizing a network of human users and Artificial Intelligence (Al) problem solving agents that share a common-problem solving architecture. In some other aspects, the present technology relates to methods associated with implementing input from human users and other Al problem solver systems to align the AGI with human values, and to provide a mechanism for enforcing those human values.

[0002] In yet other aspects, all activities that are described in this patent disclosure as happening on an external network in which multiple intelligent entities participate in collaborative problem solving, can also be implemented within a single computerized intelligent system where the intelligent entities are all computerized or Al agents that reside within that single computerized intelligent system.BACKGROUND ART

[0003] AGI is defined as Al that can perform any intellectual task as w ell or better than the average human being. Eric Schmidt said the median estimate for when AGI w ould be developed that is also capable of setting its own goals or objectives was the year 2042. Ray Kurzweil, a well-known futurist, puts the date earlier at 2029. With the rapid adoption of GPT, the ensuing Al arms race, and the movement of all major companies towards generative Al, the date for AGI has advanced. The commercial and geopolitical pressure to be first has increased. The present technology7would enable AGI to become operational by 2025 - much faster than most predict. How is this possible, when most researchers have no clear idea of how to achieve AGI at all?

[0004] Simply put, most of the Al research community is looking at the problem in the wrong way.

[0005] The typical research approach is to engineer ever-more-powerful Large Language Models (LLMs) at great expense, until one of them exceeds human intelligence and human intellectual capabilities in all areas. Then, we have an “‘alignment problem” where the humans worry about w hether the goals of this Superlntelligent Al “align” with human values.

[0006] Misalignment could mean the extinction of the human species. It is hoped that will not happen. It is thought we had until 2042 to figure out how7to make things safe.

[0007] Some thought a few of these Superlntelligent AGIs would be owned by large countries and protected in the way Plutonium is protected. The reasoning - logical if one accepts the standard model of building ever more powerful LLMs - was that only a few countries would have the vast computation resources needed to build an AGI, and thus, like nuclear material, it could remain inaccessible to most and a carefully guarded secret. With luck, all the pow erful countries would be able to somehow guard and contain the Superlntelligent AGIs and the human species would be preserved.

[0008] Unfortunately, that thinking is erroneous. Source code for powerful LLMs is already open- sourced and adopted by hundreds of millions of people. Anyone can trick GPT or any LLM into saying or potentially doing (since these LLMs are now7connected to the web and capable of programming) bad things. AgentGPT and other systems (e.g., those using Langchain and / or other code that extends the abilities of LLMs and allow them to set their own goals and subgoals using software techniques well known in the art) are already setting their own goals and pursuing them.

[0009] Few7truly understand how7close humanity is to extinction at this juncture. A survey conducted before the release of ChatGPT, had 48% putting the risk of human extinction by Al at 10% or greater. In May 2023. it is estimated the chances of extinction are at 20%. That is like playing Russian Roulette with 8 billion lives and a five-shot revolver.

[0010] Humankind deserves better than this, and fortunately, a far superior answer exists, as described in this application. First, let us state some facts that almost all the top Al researchers and heads of leading Al companies seem to agree on:1. At some point, AGI will develop, and it will be capable of setting its own goals.2. The “alignment problem” is a real concern.3. The pow7er involved in AGI means that regulating it will not protect humans. Regulation will only slow7down some countries or companies, enabling others to gain an advantage.

[0011] Faced with these facts, the message to the general public adopted by most Al thought leaders has been: “No need to panic. The dawn of Superlntelligent AGI is far away. We will figure something out”. Inwardly, they are terrified or in denial. None of that stops them from racing forward in what Max Tegmark has called not an Al arms race but a “suicide race”.

[0012] Since none knows how to create Superlntelligent AGI - and none understand what makes the current LLMs behave and “reason” as they do — each company is investing ever larger sums of money into trying to be the first with AGI. Simultaneously, each spends some amount of time and resource w orry ing about how to tty to ensure it does not kill us all. And every' one gives lip service to“Responsible Al”. This is the state of affairs as of May 2023, as near as I can surmise. Humanity does indeed deserve better.The False Assumption

[0013] The false assumption made by almost all Al researchers is that AGI will develop as the result of training an Uber-LLM. Humans may be involved in the initial training and supervision but after that, it will train itself, write its own code, and ultimately set its own goals, at which point we have a potential “alignment problem”.

[0014] What if AGI could be created now? What if the AGI that was created now, had humans in the loop, and gave humans the democratic opportunity to transfer their values to the AGI? Then we would have a situation where: 1) AGI was created years earlier than anyone envisions and 2) There would be no alignment problem. That is. AGI would be safer than the “wait and hope” approaches that training an Uber-LLM leaves us with.The Fastest Path Must Be the Safest Path

[0015] Maybe the alignment problem should be called the “end of the humanity problem”. Whichever AGI is achieved first, is the one we have to worn' about. Since Superlntelhgent AGI (“AGI” for brevity) can improve itself an exponential rate, the first AGI could theoretically dominate all the others if it had a sufficient head start and if the other runner-up AGIs did not have superior (e.g., faster) learning algorithms enabling them to pull ahead. This is potentially a winnertake-all scenario.Winner-T ake- All

[0016] Few things in life or business are winner take all. The idea of “first mover advantage” is common to Silicon V alley venture capitalists, but they know that being first does not make you best. Facebook® beat Friendster®, and Friendster was first. Xerox Parc® was first with the Graphical User Interface, but Steve Jobs took it and Apple® is now the world’s largest company while Xerox® is all but forgotten. Usually, being first is an advantage but being bigger is better. Most of the time, you really do not need to be first to win. And the winner rarely takes all. iPhones have competition.

[0017] But this time may be different. You should be skeptical of those words.

[0018] But that skepticism should not close your mind completely to a logical and well-reasoned argument. Simply put, Al will be smarter than us, able to set its own goals, able to (re)programitself, and able to leam exponentially by creating billions of copies of itself and having these copies improve each other.

[0019] Al starts out as a tool, yes. but it will not remain one. It will become an intelligent entity trillions of times smarter, faster, and more perceptive than us. We have never created such an entity before. Whichever version gets ahead start, assuming it maintains the fastest rate of learning, may dominate all other AGIs, and incorporate them into its intelligence. That seems the most likely outcome in my view. So, AGI is likely winner-take-all.

[0020] In such a scenario, we have to worry about two things: 1) How to create AGI first; and 2) How to create a safe AGI. Without both conditions being met, humanity is at risk.

[0021] Therefore, a need exists for a new and improved system and methods for human-centered AGI that can be used for achieving AGI faster by utilizing a network of human users and Al problem solving agents that share a common-problem solving architecture. In this regard, the present technology substantially fulfills this need. In this respect, the system and methods for human-centered AGI according to the present technology7substantially departs from the conventional concepts and designs of the prior art, and in doing so provides an apparatus primarily developed for the purpose of achieving AGI faster by utilizing a network of human users and Al problem solving agents that share a common-problem solving architecture.DISCLOSURE OF TECHNOLOGY

[0022] In view of the foregoing disadvantages inherent in the known types of AGI systems and methods at least some embodiments of the present technology provide a novel system and methods for human-centered AGI, and overcomes one or more of the mentioned disadvantages and drawbacks of the prior art. As such, the general purpose of at least some embodiments of the present technology, which will be described subsequently in greater detail, is to provide a new and novel system and methods for human-centered AGI which has all the advantages of the prior art mentioned herein and many novel features that result in a system and methods for human-centered AGI which is not anticipated, rendered obvious, suggested, or even implied by the prior art, either alone or in any combination thereof.

[0023] According to one aspect, the present technology can include a network of human problem solvers, combined with a universal problem solving architecture that allows them to work together in a coordinated and rigorously defined way. A problem can be submitted to the network of human problem solvers utilizing a network of Al systems. One or more human problem solvers works on the problem and returns a solution. Since the network includes human workers, by definition, it cansolve any problem the average human can solve with the assistance of the Al systems. And since there are many human workers utilizing the network of the Al systems, if the efforts are intelligently coordinated, then the network will often perform better than the average human.

[0024] According to an aspect, the present technology can include a method for Artificial General Intelligence (AGI) utilizing a single computerized intelligent system including multiple Artificial Intelligence (Al) agents residing in the single computerized intelligent system. The method can include: providing a problem request including a problem criteria into an Al agent residing in a single computerized intelligent system; matching, by the Al agent, one or more additional Al agents to the problem request based on the problem criteria, the additional Al agents reside in the single computerized intelligent system; translating, by the Al agent using Large Language Model (LLM), any part of the problem request into an unambiguous language utilizable in a universal problem solving architecture including a decision tree; separating, by the Al agent or by any one or more of the additional Al agents, the problem request into sub-problems; delegating, by the Al agent or by any one or more of the additional Al agents, each of the subproblems to one or more of the additional Al agents so that work on each of the subproblems proceeds independently from each other and parallel with each other; utilizing, by the Al agent and the additional Al agents, the universal problem solving architecture in a problem solving process on the sub-problems, respectively, to create one or more sub-solutions; receiving, by the Al agent, the sub-solutions from each of the additional Al agents for the subproblems delegated thereto; combining, by the Al agent, the sub-solutions into an overall solution to the problem request; providing, by the Al agent, any one of or any combination of the sub-solutions and the overall solution to a user interface of a user computer system or the single computerized intelligent system; and allowing, by way of the user interface, a human user to any one of or any combination of accept the overall solution, reject the overall solution, and provide feedback to any one of the matched human workers on any one of the sub-solutions.

[0025] According to another aspect, the present technology' can include a system for humancentered AGI utilizing a network of human users and a universal problem solving architecture. The system can include a computer system including / : a processor, a computer-readable storage medium, and program instructions stored on the computer-readable storage medium being executable by the processor to cause the computer system to: execute a user interface configured or configurable to allow inputting of a problem request including one or more problem criteria; match one or more human workers from a data source including a list of human problem solvers to the problem request based on the problem criteria; translate any part of the problem request into an unambiguous language utilizable in a universal problem solving architecture including a decision tree; separate the problem request into sub-problems; delegate each of the sub-problems to one or more of the matched human workers so that work on each of the sub-problems proceeds independently from each other and / or parallel with each other; receive one or more sub-solutions from each of the matched human workers for the subproblems delegated thereto; combine the sub-solutions into an overall solution to the problem request; direct any one of or any combination of a new human worker from the data source and one or more of the matched human workers to parts of the decision tree where work is required; compensate the matched human workers for the sub-solutions, respectively; provide any one of or any combination of the sub-solutions and the overall solution to the user interface; and allow, by way of the user interface, a human user to do any one of or any combination of accept the overall solution, reject the overall solution, and provide feedback to any one of the matched human workers on any one of the sub-solutions.

[0026] According to yet another aspect, the present technology7can include a method for humancentered AGI utilizing a network of human users and a universal problem solving architecture electronically communicating over a collective network. The method can include the steps of: providing a problem request including a problem criteria into an Al agent by one or more of intelligent entities including any one of or any7combination of one or more a human users each utilizing a computer system, and one or more additional Al agents;matching, by the Al agent, one or more human workers from a data source including a list of human problem solvers to the problem request based on the problem criteria; translating, by the Al agent using Large Language Model (LLM), any part of the problem request into an unambiguous language utilizable in a universal problem solving architecture including a decision tree; separating, by the Al agent, the problem request into sub-problems; delegating, by the Al agent, each of the sub-problems to one or more of the matched human workers so that work on each of the sub-problems proceeds independently from each other and parallel with each other; utilizing, by a worker Al agent of each of the matched human workers, the universal problem solving architecture in a problem solving process on the sub-problems, respectively, to create one or more sub-solutions; receiving, by the Al agent, the sub-solutions from each of the matched human workers for the sub-problems delegated thereto; combining, by the Al agent, the sub-solutions into an overall solution to the problem request; directing, by the Al agent, any one of or any combination of a new human worker from the data source and one or more of the matched human workers to parts of the decision tree where work is required; compensating, by the Al agent, the matched human workers for the sub-solutions; providing, by the Al agent, any one of or any combination of the sub-solutions and the overall solution to a user interface of a user computer system; allowing, by w ay of the user interface, the human user to any one of or any combination of accept the overall solution, reject the overall solution, and provide feedback to any one of the matched human w orkers on any one of the sub-solutions; and assigning a reputation attribute to any one of or any combination of the human workers and the worker Al agent.

[0027] In some embodiments, the universal problem solving architecture can include the step of learning by the Al agent or the worker Al agent including a procedural learning process that utilizes problem solving process.

[0028] In some embodiments, the procedural learning process can occur within the universal problem solving architecture.

[0029] In some embodiments, a problem solving activity can be recorded in an auditable record including any one of or any combination of steps of the problem solving process that results in theoverall solution, the problem solving process that results in failure to solve for the problem request, and evaluation information relative to a quality and desirableness of the sub-solutions.

[0030] Some embodiments of the present technology can include a step of indexing the subsolutions according to any one of or any combination of the problem request, the problem criteria, and the sub-problems.

[0031] In some embodiments, the procedural learning process can utilize each of the recorded problem solving activity as a learned procedure and collectively a set of all learned procedures constitute the procedural learning process of the Al agent or the worker Al agent.

[0032] Some embodiments of the present technology can include a step of recording any one of or any combination of an operator applied in the problem solving process, a new state of the problem request, an evaluation function used the problem solving process, a current relevant goal or subgoal, and other information that differs from a previous step.

[0033] Some embodiments of the present technology can include a step of evaluating a state of the problem request to determine if the overall solution has been accepted, if accepted then recording and indexing the overall solution, or if not accepted and if resources are exhausted then recording unsuccessful solution attempts.

[0034] In some embodiments, if the overall solution is not accepted then the method can further comprise steps of: repeating the problem solving process using information from a latest state of the problem request; evaluating a progress of the repeated problem solving process; and selecting a next operator to apply in the problem solving process.

[0035] In some embodiments, if the overall solution is accepted then the method can further include the step of recording in an auditable record any one of successful solutions for future retrieval in use by a future problem solving process, and unsuccessful solution attempts for future retrieval in notifications about unsuccessful paths that were previously tried.

[0036] Some embodiments of the present technology can include a step of using any one of or any combination of semantic analysis and hash functions to index the successful solutions and the unsuccessful attempts with keywords that are utilized for matching to future problem solving efforts.

[0037] Some embodiments of the present technology can include a step of periodically reviewing the recorded successful solutions or unsuccessful attempts to meet established preset ethical and safety guidelines, and then flagging unethical and unsafe solutions for removal from a solution database of the successful solutions and the unsuccessful attempts.

[0038] Some embodiments of the present technology' can include a step of updating and propagating changes to the solution database periodically so that the network of the human workers can access an ever-increasing repertoire of the recorded successful solutions as well as increasing knowledge of the unsuccessful attempts.

[0039] In some embodiments, the step of translating using LLM can further comprise the step of describing by any one of or any combination of the human user and any one of the human workers in natural language any one of or any combination of a current problem state, a goal of the problem request, relevant problem solving information, and a next step that the human workers will take in the problem solving process.

[0040] S ome embodiments of the present technology' can include a step of parsing and translating by the Al agent or the worker Al agent the natural language description into the unambiguous language utilizable by the decision tree of the universal problem solving architecture.

[0041] In some embodiments, if the Al agent or the worker Al agent is unable to specify a problem state, including relevant operators and information needed to take a next step in the problem solving process based on the parsing and the translation, then the Al agent or the worker Al agent can engage in dialog with at least one of the human workers until a precise problem state is specified.

[0042] Some embodiments of the present technology can include a step of repeating the problem solving process until the overall solution is accepted or resources are exhausted.

[0043] In some embodiments, the problem solving process ca include a series of problem state transitions from an initial problem state where there is a goal to a final solution state where the goal has been achieved, and wherein a series of decisions are made by the problem solving process and actions taken that applies operators that enable the human workers to transition from state to state until the final solution state is reached.

[0044] In some embodiments, the reputation attribute can include metrics on any one of or any combination of a time to the sub-solutions, a difficulty value of the problem request, short and longterm user satisfaction with the sub-solutions, a number of times any one of the sub-solutions has been re-used on the network, a rating of other human workers, a responsiveness value of the human workers, and a reliability value of the human workers.

[0045] Some embodiments of the present technology can include a step of using the reputation attribute in the matching of the human workers to the problem request using an algorithm to the delegation of the sub-problems.

[0046] In some embodiments, the algorithm can use a hierarchy of the metrics that is preset by the human user of the problem request.

[0047] Some embodiments of the present technology' can include a step of recording information on each step of the problem solving process by the human workers or the worker Al agent.

[0048] Wherein the recording of information can include, without limitation, recording using blockchain-based or Ethereum-based means.

[0049] Some embodiments of the present technology' can include a step of recording a criteria of the recorded step of the problem solving process, the criteria being a time taken for each step.

[0050] Some embodiments of the present technology can include a step of analyzing the recorded information after the overall solution is accepted or after the problem solving process and updating the metrics of the reputation attribute.

[0051] Some embodiments of the present technology can include a step of soliciting, at predetermined intervals after the overall solution or the sub-solutions are provided to the user interface, feedback by way of a survey for user satisfaction information, or other means, to obtain short and long-term satisfaction metrics that are used to update the reputation attribute of one or more of the human workers or the worker Al agent.

[0052] Some embodiments of the present technology can include a step of executing an ethics check, by the Al agent, by comparing the problem request or any one of the sub-problems or the sub-solutions against prohibited attributes and assigning an ethics value based on any one of or any combination of a result of the comparison, and an ethics criteria.

[0053] In some embodiments, the step of the ethics check can be triggered every' time the problem request or the any one of the sub-problems is set by the human user.

[0054] In some embodiments, the step of the ethics check can be triggered each time compensation is provided to the matched human workers.

[0055] In some embodiments, the ethics criteria can be determined by any one of or any combination of combining values and safety information from any one of or any combination of the user Al agent and the worker Al agent.

[0056] In some embodiments, the ethics criteria can include a confidence level threshold for the problem request so that the ethics value is determined as any one of an unsafe goal, an unethical goal, a safe goal, and an ethical goal.

[0057] In some embodiments, the confidence level threshold can be further utilized to detennine if a sequence of individually safe goals is unsafe or unethical when considered cumulatively.

[0058] In some embodiments, the confidence level threshold can be utilized to determine whether a violation occurred that reflects a predictive evaluation if the goal is to violate the ethics criteria.

[0059] Some embodiments of the present technology' can include a step of using a blockchain when any one of or any combination of when a compensation is provided, at any stage of the problem solving process, and when the sub-solutions are provided.

[0060] In some embodiments, the blockchain is Ethereum based or utilizes crypto tokens that are Ethereum based.

[0061] According to still another aspect, the present technology' can include a method for humancentered AGI utilizing a network of human users and a universal problem solving architecture electronically communicating over a collective network. The method can include the steps of: providing a problem request including a problem criteria into an Al agent by one or more of intelligent entities including any one of or any combination of one or more human users each utilizing a computer system, and one or more additional Al agents; matching, by the Al agent, one or more human workers from a data source including a list of human problem solvers to the problem request based on the problem criteria; delegating, by the Al agent, a sub-problem of the problem request to one or more of the matched human workers so that work on the sub-problem proceeds independently from each other and parallel with each other; utilizing, by a worker Al agent of each of the matched human workers, a universal problem solving architecture in a problem solving process on the sub-problem, respectively, to create one or more sub-solutions; learning, by the Al agent or the worker Al agent, including a procedural learning process that utilizes problem solving process and that occurs within the universal problem solving architecture; receiving, by the Al agent, the sub-solutions from each of the matched human workers for the sub-problem delegated thereto; and providing, by the Al agent, any one of or any combination of the sub-solutions and an overall solution to a user interface of a user computer system.

[0062] Some embodiments of the present technology can include steps of compensating, by the Al agent, the matched human workers for the sub-solutions, respectively , and assigning a reputation attribute to any one of or any combination of the human workers and the worker Al agent.

[0063] In some embodiments, a problem solving activity can be recorded in an auditable record that includes any one of or any combination of steps of the problem solving process that results in the overall solution, the problem solving process that results in failure to solve for the problem request, and evaluation information relative to a quality and desirableness of the sub-solutions.

[0064] Wherein the recording of the problem solving activity can include, without limitation, recording using blockchain-based or Ethereum-based means.

[0065] Some embodiments of the present technology can include a step of indexing the subsolutions according to any one of or any combination of the problem request, the problem criteria, and the sub-problem.

[0066] In some embodiments, the procedural learning process can utilize each of the recorded problem solving activity as a learned procedure and collectively a set of all learned procedures that constitute the procedural learning process of the Al agent or the worker Al agent.

[0067] Some embodiments of the present technology can include a step of recording any one of or any combination of an operator applied in the problem solving process, a new state of the problem request, an evaluation function used by the problem solving process, a current relevant goal or subgoal, and other information that differs from a previous step.

[0068] Some embodiments of the present technology can include a step of evaluating a state of the problem request to determine if the overall solution has been accepted, if accepted then recording and indexing the overall solution, or if not accepted and if resources are exhausted then recording unsuccessful solution attempts.

[0069] In some embodiments, if the overall solution is not accepted then the method can further comprise the steps of: repeating the problem solving process using information from a latest state of the problem request; evaluating a progress of the repeated problem solving process; and selecting a next operator to apply in the problem solving process.

[0070] In some embodiments, if the overall solution is accepted then the method can further comprise the step of recording in an auditable record any one of successful solutions for future retrieval in use by a future problem solving process, and unsuccessful solution attempts for future retrieval in notifications about unsuccessful paths that were previously tried.

[0071] Some embodiments of the present technology can include a step of using any one of or any combination of semantic analysis and hash functions to index the successful solutions and the unsuccessful attempts with keywords that are utilized for matching to future problem solving efforts.

[0072] Some embodiments of the present technology can include a step of periodically reviewing the recorded successful solutions or unsuccessful attempts to meet established preset ethical and safety guidelines, and then flagging unethical and unsafe solutions for removal from a solution database of the successful solutions and the unsuccessful attempts.

[0073] Some embodiments of the present technology' can include a step of updating and propagating changes to the solution database periodically so that the network of the human workers can access an ever-increasing repertoire of the recorded successful solutions as well as increasing knowledge of the unsuccessful attempts.

[0074] According to yet still another aspect, the present technology can include a method for human-centered AGI utilizing a network of human users and a universal problem solving architecture electronically communicating over a collective network. The method can include the steps of: providing a problem request including a problem criteria into an Al agent by one or more of intelligent entities including any one of or any combination of one or more human users each utilizing a computer system, and one or more additional Al agents; matching, by the Al agent, one or more human workers from a data source including a list of human problem solvers to the problem request based on the problem criteria; translating, by the Al agent using LLM, any part of the problem request into an unambiguous language utilizable in a universal problem solving architecture including a decision tree; delegating, by the Al agent, a sub-problem of the problem request to one or more of the matched human workers so that work on the sub-problem proceeds independently from each other and parallel with each other; utilizing, by a worker Al agent of each of the matched human workers, the universal problem solving architecture in a problem solving process on the sub-problems, respectively, to create one or more sub-solutions; describing, by any one of or any combination of the human user and any one of the human workers, in natural language a description of any one of or any combination of a current problem state, a goal of the problem request, relevant problem solving information, and a next step that the human workers will take in the problem solving process; receiving, by the Al agent, the sub-solutions from each of the matched human workers for the sub-problem delegated thereto; and providing, by the Al agent, any one of or any combination of the sub-solutions and an overall solution to a user interface of a user computer system.

[0075] Some embodiments of the present technology can include a step of parsing and translating by the Al agent or the worker Al agent the natural language description into the unambiguous language utilizable by the decision tree of the universal problem solving architecture.

[0076] In some embodiments, if the Al agent or the worker Al agent is unable to specify a problem state, including relevant operators and infomiation needed to take a next step in the problem solving process based on the parsing and the translation, then the Al agent or the worker Al agent can engage in dialog with at least one of the human workers until a precise problem state is specified.

[0077] Some embodiments of the present technology can include steps of: repeating the problem solving process until the overall solution is accepted or resources are exhausted; compensating, by the Al agent, the matched human workers for the sub-solutions, respectively; and assigning a reputation attribute to any one of or any combination of the human workers and the worker Al agent.

[0078] Wherein the compensation can be made by way of blockchain-based or "‘smart contract” technology.

[0079] In some embodiments, the solving process can include a series of problem state transitions from an initial problem state where there is a goal to a final solution state where the goal has been achieved, and wherein a series of decisions are made by the problem solving process and actions taken that applies operators that enable the human workers to transition from state to state until the final solution state is reached.

[0080] According to even yet another aspect, the present technology7can include method for humancentered AGI utilizing a network of human users and a universal problem solving architecture electronically communicating over a collective network. The method can include the steps of: providing a problem request including a problem criteria into an Al agent by one or more of intelligent entities including any one of or any combination of one or more human users each utilizing a computer system, and one or more additional Al agents; matching, by the Al agent, one or more human workers from a data source including a list of human problem solvers to the problem request based on the problem criteria; delegating, by the Al agent, a sub-problem of the problem request to one or more of the matched human workers so that work on each of the sub-problem proceeds independently from each other and parallel with each other; utilizing, by a worker Al agent of each of the matched human workers, a universal problem solving architecture in a problem solving process on the sub-problem, respectively, to create one or more sub-solutions;receiving, by the Al agent, the sub-solutions from each of the matched human workers for the sub-problems delegated thereto; compensating, by the Al agent, the matched human workers for the sub-solutions, respectively; providing, by the Al agent, any one of or any combination of the sub-solutions and an overall solution to a user interface of a user computer system; allowing, by way of the user interface, the human user to any one of or any combination of accept the overall solution, reject the overall solution, and provide feedback to any one of the matched human workers on any one of the sub-solutions; and assigning a reputation attribute to any one of or any combination of the human workers and the worker Al agent, the reputation attribute including metrics on any one of or any combination of a time to the sub-solutions, a difficulty value of the problem request, short and long-term user satisfaction with the sub-solutions, a number of times any one of the sub-solutions has been re-used on the network, a rating other human workers, a responsiveness value of the human workers, and a reliability value of the human workers.

[0081] Some embodiments can include the steps of: using the reputation attribute in the matching of the human workers to the problem request using an algorithm to the delegation of the sub-problems; and compensating, by the Al agent, the matched human workers for the sub-solutions, respectively.

[0082] In some embodiments, the algorithm can use a hierarchy of the metrics that is preset by a human user of the problem request.

[0083] Some embodiments of the present technology can include a step of recording information on each step of the problem solving process by the human workers or the worker Al agent.

[0084] Some embodiments of the present technology' can include a step of recording a criteria of the recorded step of the problem solving process, the criteria being a time taken for each step.

[0085] Some embodiments of the present technology can include a step of analyzing the recorded information after the overall solution is accepted or after the problem solving process and updating the metrics of the reputation attribute.

[0086] Some embodiments of the present technology can include a step of soliciting, at predetermined intervals after the overall solution or the sub-solutions are provided to the user interface, a survey for user satisfaction information to obtain short and long-term satisfaction metrics that are used to update the reputation attribute of one or more of the human workers or the worker Al agent.

[0087] According to still another aspect, the present technology' can include a method for humancentered AGI utilizing a network of human users and a universal problem solving architecture electronically communicating over a collective network. The method can include the steps of: providing a problem request including a problem criteria into an Al agent by one or more of intelligent entities including any one of or any combination of one or more human users each utilizing a computer system, and one or more additional Al agents; matching, by the Al agent, one or more human workers from a data source including a list of human problem solvers to the problem request based on the problem criteria; delegating, by the Al agent, a sub-problem of the problem request to one or more of the matched human workers so that work on each of the sub-problems proceeds independently from each other and parallel with each other; utilizing, by a worker Al agent of each of the matched human workers, a universal problem solving architecture in a problem solving process on the sub-problems, respectively, to create one or more sub-solutions; receiving, by the Al agent, the sub-solutions from each of the matched human workers for the sub-problems delegated thereto; executing an ethics check, by the Al agent, by comparing any one of or any combination of the problem request, the sub-problem and the sub-solutions against prohibited attributes, and assigning an ethics value based on any one of or any combination of a result of the comparison, and an ethics criteria; and providing, by the Al agent, any one of or any combination of the sub-solutions and an overall solution to a user interface of a user computer system.

[0088] Wherein the matching step can be set as a sub-problem of the problem request.

[0089] In some embodiments, the step of the ethics check can be triggered every' time the problem request or the any one of the sub-problems is set by the human user.

[0090] In some embodiments, the step of the ethics check can be triggered each time compensation is provided to the matched human workers.

[0091] In some embodiments, the ethics criteria can be determined by any one of or any combination of combining values and safety information from any one of or any combination of the user computer system and the worker Al agent.

[0092] In some embodiments, the ethics criteria can include a confidence level threshold for the problem request so that the ethics value is determined as any one of an unsafe goal, an unethical goal, a safe goal, and an ethical goal.

[0093] In some embodiments, the confidence level threshold can be further utilized to determine if a sequence of individually safe goals is unsafe or unethical when considered cumulatively.

[0094] In some embodiments, the confidence level threshold can be utilized to determine whether a violation occurred that reflects a predictive evaluation if the goal is to violate the ethics criteria.

[0095] Some embodiments of the present technology can include steps of: compensating, by the Al agent, the matched human workers for the sub-solutions, respectively; and assigning a reputation attribute to any one of or any combination of the human workers and the worker Al agent.

[0096] According to yet another aspect, the present technology7can include a method for humancentered AGI utilizing a network of human users and a universal problem solving architecture electronically communicating over a collective network. The method can include the steps of: providing a problem request including a problem criteria into an Al agent by one or more of intelligent entities including any one of or any combination of one or more human users each utilizing a computer system, and one or more additional Al agents; matching, by the Al agent, one or more human workers from a data source including a list of human problem solvers to the problem request based on the problem criteria; translating, by the Al agent using LLM, any part of the problem request into an unambiguous language utilizable in a universal problem solving architecture including a decision tree; separating, by the Al agent, the problem request into sub-problems; delegating, by the Al agent, each of the sub-problems to one or more of the matched human workers so that work on each of the sub-problems proceeds independently from each other and parallel with each other; utilizing, by a worker Al agent of each of the matched human workers, the universal problem solving architecture in a problem solving process on the sub-problems, respectively, to create one or more sub-solutions; receiving, by the Al agent, the sub-solutions from each of the matched human workers for the sub-problems delegated thereto; compensating, by the Al agent, the matched human workers for the sub-solutions, respectively; providing, by the Al agent, any one of or any combination of the sub-solutions and an overall solution to a user interface of a user computer system; using a blockchain when any one of or any combination of a compensation is provided, at any stage of the problem solving process, and when the sub-solutions are provided;allowing, by way of the user interface, the human user to any one of or any combination of accept the overall solution, reject the overall solution, and provide feedback to any one of the matched human workers on any one of the sub-solutions; and assigning a reputation attribute to any one of or any combination of the human workers and the worker Al agent.

[0097] In some embodiments, the blockchain can be Ethereum based or utilizes cry pto tokens that are Ethereum based.

[0098] According to even still another aspect, the present technology can include method for developing a human-centered AGI utilizing a network of human users and a universal problem solving architecture electronically communicating over a collective network. The method can include the steps of: a) providing a request by a human user using an interface of a user computer system or by an Al agent, the request including one or more criteria; b) identifying multiple additional Al agents that have an attribute related to the criteria of the request; c) communicating between the user computer system or the Al agent, and the multiple additional Al agents utilizing a collective network; d) translating using Large Language Model (LLM) any part of the request into an unambiguous language utilizable in a universal problem solving architecture including a decision tree; e) separating the request into sub-problems; f) delegating each of the sub-problems to one or more of the additional Al agents so that work on each of the sub-problems proceeds independently from each other, parallel with each other, or independently and parallel with each other; g) utilizing by the additional Al agents the universal problem solving architecture in a problem solving process on the sub-problems, respectively, to create one or more sub-solutions; h) compensating the additional Al agents for the sub-solutions, respectively; i) developing an AGI by collaborating each of the sub-solutions to create an overall solution; j) providing any one of or any combination of the sub-solutions and the overall solution to any one of or any combination of the interface of the user computer system, the Al agent, and any one of the additional Al agents; and k) assigning a reputation attribute to any one of or any combination of the human user, the Al agent, or the additional Al agents.

[0099] There has thus been outlined, rather broadly, features of the present technology in order that the detailed description thereof that follows may be better understood and in order that the present contribution to the art may be better appreciated.

[0100] Numerous objects, features and advantages of the present technology will be readily apparent to those of ordinary skill in the art upon a reading of the following detailed description of the present technology7, but nonetheless illustrative, embodiments of the present technology7when taken in conjunction with the accompanying drawings.

[0101] As such, those skilled in the art will appreciate that the conception, upon which this disclosure is based, may readily be utilized as a basis for the designing of other structures, methods and systems for carrying out the several purposes of the present technology7.

[0102] It is therefore an object of the present technology to provide a new and novel system and methods for human-centered AGI that has all of the advantages of the prior art AGI systems and methods and none of the disadvantages.

[0103] It is another object of the present technology to provide a new and novel system and methods for human-centered AGI that may be easily and efficiently implemented and marketed.

[0104] An even further object of the present technology7is to provide anew and novel system and methods for human-centered AGI that has a low cost of implementation with regard to both resources and labor, and which accordingly^ is then susceptible of low prices of sale to the consuming public, thereby making such system and methods for human-centered AGI economically available to the buying public.

[0105] Still another object of the present technology is to provide anew system and methods for human-centered AGI that provides in the system and methods of the prior art some of the advantages thereof, while simultaneously overcoming some of the disadvantages normally associated therewith.

[0106] For a better understanding of the present technology, its operating advantages and the specific objects attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated embodiments of the present technology7. Whilst multiple obj ects of the present technology7have been identified herein, it will be understood that the following description is not limited to meeting most or all of the objects identified and that some embodiments of the present technology may meet only one such object or none at all.BRIEF DESCRIPTION OF THE DRAWINGS

[0107] The technology will be better understood and objects other than those set forth above willbecome apparent when consideration is given to the following detailed description thereof. Such description makes reference to the annexed drawings wherein:

[0108] FIG. 1 is a flow chart illustrating an embodiment of the subsystems utilized in the AAAI system and method of the present technology.

[0109] FIG. 2 is a block diagram illustrating an exemplary process of the overall process utilizable with the present technology7.

[0110] FIG. 3 is a flow chart illustrating an exemplary7embodiment of the system and methods for creating an ethical and safe human-centered AG1 from the collective intelligence of AAAIs and humans utilizable with the present technology.

[0111] FIG. 4 is a flow chart illustrating the shared universal problem solving architecture that enables creating human-centered AGI with the present technology.

[0112] FIG. 5 is a flow chart illustrating an exemplary embodiment of the solution learning subsystem or process.

[0113] FIG. 6 is a flow7chart illustrating an exemplary embodiment of the natural language to problem solving language translator subsystem or process.

[0114] FIG. 7 is a flow chart illustrating an exemplary7embodiment of the reputational component subsystem or process for the human and Al problem solving agents.

[0115] FIG. 8 is a flow7chart illustrating an exemplary embodiment of the safety / ethics check process of the present technology.

[0116] FIG. 9 is a flow chart illustrating an exemplary7embodiment of the safety and ethics checks subsystem or process, including triggering mechanisms.

[0117] FIG. 10 is a diagram illustrating features and functions of the Problem Solving architecture including the Tree structure used by the WorldThink protocol.

[0118] FIG. 11 is a diagram illustrating various use cases for domain-specific problems which depend upon the underlying WorldThink protocol, and which together help form the basis for an AGI system capable of solving a wide range of problems.

[0119] FIG. 12 is a diagram illustrating some of the steps in the universal problem solving framework that is part of the WorldThink protocol and used by the AAAI system.

[0120] FIG. 13 is a flow chart illustrating some of the basic problem solving functionality7supported by the WorldThink protocol utilizable with the AAAI system and method of the present technology^.

[0121] FIG. 14 is a flow7chart illustrating some of the basic problem solving functionality7supported by the WorldThink protocol utilizing two problem solvers collaborating to solve a clientproblem.

[0122] FIG. 15 is a flow chart illustrating an exemplary customization process of an AAAI system.

[0123] FIG. 16 is a flow chart illustrating an exemplary problem solving process utilizing a common cognitive architecture implemented in an Al system.

[0124] FIG. 17 is a flow chart illustrating an exemplary problem solving process utilizing a common cognitive architecture implemented in a collective network of Al systems.

[0125] FIG. 18 is a schematic block diagram illustrating an exemplary electronic computing device that may be used to implement an embodiment of the present technology.

[0126] The same reference numerals refer to the same parts throughout the various figures.DETAILED DESCRIPTION OF THE TECHNOLOGY

[0127] No current AGI systems have been built. The most advanced current systems, which have not yet achieved AGLlevel performance, also lack the safety and / or ethical attributes of the invention disclosed here.

[0128] While existing Al systems fulfill their respective, particular objectives and requirements, the aforementioned devices or systems do not describe an ethical and safe AGI that allows enabling for the ethical and safe creation of AGI from a network of human users and Al problem solvers. The present technology additionally overcomes one or more of the disadvantages associated with the prior art.

[0129] A need exists for ethical and safe AGI that can be used for enabling for the ethical and safe creation of AGI from a network of human users and Al problem solvers. In this regard, the present technology substantially fulfills this need. In this respect, the ethical and safe AGI according to the present technology substantially departs from the conventional concepts and designs of the prior art, and in doing so provides an apparatus primarily developed for the purpose of enabling for the ethical and safe creation of AGI from a network of human users and Al problem solvers.

[0130] One reason AGI has been so elusive is that specific knowledge and expertise from diverse fields must be creatively combined in an invention to achieve AGI. Another reason the development of AGI has been non-obvious, is that almost all Al researchers are focused on trying to improve existing narrow Al systems via ever more complex and extensive machine learning approaches.

[0131] The fact that AGI has resisted attempts by thousands of others — despite the expenditures of huge sums of money — and the fact that specialized knowledge in relatively obscure fields had to be combined with mainstream Al approaches in this invention, argue strongly for the novelty' and creativeness of the present technology.

[0132] The present technology describes the system and methods not only to achieve AGI, but also to achieve it rapidly, and most importantly, safely.

[0133] It is possible to influence the evolution of AGI in a positive direction. The best way we can do this is by adopting the safest possible path to the development of AGI and ensuring that humanity follows that path. In turn, the best way to ensure that humanity follows the safest path, is to show that the safest path to AGI is also the fastest and therefore most desirable path to AGI. These considerations, the desire to illuminate the fastest path, which is also the safest path, is motivation for the development of the present technology.Definitions

[0134] Artificial Intelligence (Al) - A non-human entity capable of behavior that most humans would consider intelligent in at least one area, or in some respect.

[0135] Artificial General Intelligence (AGI) - Conventionally refers to an Al that is capable of doing all (or almost all) intellectual tasks that an average human could do. However, it should be clear that any AGI capable of learning and self-improving will not remain at the AGI level very long but will rapidly progress to becoming Superlntelligent AGI that can do all intellectual task as well or better than the average human. So, for purposes of this description, "‘AGI” will refer to either a conventional AGI system or a “Superlntelligent” AGI. In this description, the AGI is described as being implemented by a system and associated methods.

[0136] Advanced Autonomous Artificial Intelligence (AAAI) - An Al capable of independent or semi-independent (supervised) intelligent action. An Al agent. An individual AAAI can be specified, customized, and put into useful action via the systems and methods of this AAAI present technology. A group of AAAIs can cooperate and combine their intelligence to create an integrated AGI system. A sufficiently advanced Al agent can also act as an AGI system which may include other less advanced Al agents within itself.

[0137] AAAI.com- A platform, company, website, and / or project that implements this the present technology and supports the development, customization, and use of AAAI agents and the AGI that results from the combined action, knowledge, or intelligence of multiple AAAIs, via collective intelligence of AAAIs and / or humans, as specified in this and related technologies.

[0138] Al Ethics - The ethics adopted by an Al or AGI that describe what is right and wrong in given contexts.

[0139] Alignment Problem - The problem that arises when Al Ethics are not aligned with Human Ethics resulting in Al or AGI taking actions that humans consider unethical and / or which are dangerous to individual humans or the human race.

[0140] Base Al - An AL Al Agent, AAAI, SLM or LLM that has been trained generally but has not yet been customized with information from individual users or with information for specific tasks.

[0141] Collective Intelligence (CI) - The intelligence that emerges when multiple intelligent entities are focused on solving a common problem, or when the knowledge from multiple intelligent entities is pooled to overcome limits of bounded rationality. Collective Intelligence historically has been human collective intelligence, but AGI is based on collective intelligence of both human and Al agents and can also result from multiple AAAIs with or without human participation in the system. Active CI results from intelligent entities (e.g., humans or machines) taking steps that are useful in solving a problem or participating actively in other intellectual endeavors. For example, when multiple humans explicitly tell an advertiser what type of ads they want to see, the humans are exhibiting active CI. Passive CI results from analyzing the behavior of an intelligent entity (e g., a human or a machine) even if such behavior was not directly related to solving the problem for which the analysis is used. For example, when an Al or other system analyzes which web pages a (group of) human(s) visit on the web, and then uses that analysis to direct targeted ads to the human(s).

[0142] Ethics / Values (‘‘Ethics’') - A subset of knowledge that provides a sense of purpose to an intelligent entity and that serves to constrain allowable actions or operations based on what is asserted to be “right” or “wrong” behavior in a given context. Specifically. Ethics should be considered premises from which an intelligent entity can reason or logically compute the best course of action to achieve the goals or intents consistent with the ethical premise. Just as premises must be accepted “as given” in systems of logic, so too, fundamental ethics or ideas of what is right and what is wrong must be accepted as premises, from which starting point an intelligent entity can propose rational actions to realize those values or ethics.

[0143] Hallucination / Artificial Hallucination - A phenomenon wherein a large language model (LLM), often a generative Al chatbot or computer vision tool, perceives patterns or objects that are nonexistent or imperceptible to human observers, or creates outputs that are nonsensical, inaccurate, misleading or false.

[0144] Human Ethics - The ethics asserted by human beings which describe what is right and wrong in given contexts.

[0145] Intelligent Entities or Entity - A human utilizing a computer system, an Al agent or system, a clone of an Al agent or system, an AAAI agent or system, and / or a clone of an AAAI agent or system, which participates in providing a problem, a subproblem, a goal and / or a subgoal, and / or participates in any problem solving activity' on a problem, a subproblem, a goal and / or a subgoal. In the case of multiple intelligent entities within a single computer system, intelligent entities also refers to the sub-programs of parts of that overall computer program that function as an intelligent entity within the larger collection of simulated or programmed entities.

[0146] Large Language Model (LLM) - A type of Al that can accept natural language as an input and generate natural language as an output. Typically, LLMs are trained using ML techniques on large datasets so that they can emulate intelligent conversation or other forms of interaction with humans in natural language. Variants of LLMs can also be trained to take language as input and generate images or visual representations as output; or they can take images and visual representations and input and generate language and / or image and / or visual representations as output. For the purposes of this patent, we will refer to all such systems as LLMs even though the image-based models do not always need to accept text as the input or the output. LLMs can also act as atype of Al agent and are sometimes referred to as such in the present technology. For purpose of this disclosure. Small Language Models (SLMs) are also included in the definition of LLM.

[0147] Machine Learning (ML) - A sub-field that is concerned with developing Al by enabling machines to teach themselves or learn their knowledge rather than such knowledge being explicitly programmed into them (as would be the case with an Expert System Al developed via classical knowledge engineering methods).

[0148] Narrow Al - An Al that performs at human or at super-human levels in a relatively restricted domain such as game playing, brewing beer, analyzing legal contracts, etc. Narrow Al is contrasted with AGI that can perfomi at human level at ALL intellectual tasks. Some AIs are narrower than others, for example driving a car requires more general ability than playing chess but not as much as an AGI would have.

[0149] Prohibited Attributes - Requests, goals, problems, terms, phrases, questions, answers, solutions, information and the like that are determined or set as being illegal, immoral, unethical, dangerous, deadly and the like. For example, requesting information for getting Molotov Cocktails through airport security.

[0150] Safety - Generally, the concern for human safety and survival is distinct from ethics and values.

[0151] Safety Feature - An aspect of the design or operation of the present technology which increases the safety of one or more humans, often by helping increase the probability that Al ethics align with human ethics, thus surmounting the Alignment Problem.

[0152] Training / Tuning / Customization - Conventionally the term ‘'training” is used to denote training a network (e.g., LLM) to behave intelligently. Tuning refers to activities that fine-tune the trained base model so that it performs even better, typically at specific tasks. Customizing refers to a wide variety of activities including, but not limited to, training and tuning that make an Al uniquely suited for the purposes of a given user(s) or application(s). For purposes of this description, Training, Tuning, and Customization are used interchangeably with the understanding that although techniques vary', and the degree and type of effort involved varies, the aim of all three is to adapt the Al and make it behave more intelligently or more uniquely suited to a particular user(s) or application(s).

[0153] Weights / W eights of the Network - In the field of machine learning, many systems learn by adj usting the weights in a neural network architecture that can be represented as a network of nodes and links between nodes. The w eight of a link connecting two nodes, for example, may correspond to the strength of association or connection between the whatever nodes represent. These weights can also represent excitatory or inhibitory connections between concepts, as in a neural network representation. The learning of an entire Al system, such as a LLM or more generally any Al agent that has learned via back-propagation of error, transformer algorithms or any of the machine learning methods for establishing and modifying strengths of connections between nodes (also called “parameters” in some models) can be represented as a matrix of numbers corresponding to the weights between the nodes in the network. Weights / Weights of the Network in this description refer to this numerical information, often but not necessarily stored in a matrix or vector representation. By combining, manipulating, or otherw ise changing this numerical information, the learning, knowledge, or expertise and behavior of the system can be changed.Overview of the Present Technology

[0154] Advanced Autonomous Artificial Intelligence (AAAI) is a set of systems and methods for developing Artificial General Intelligence and Superlntelligent Artificial General Intelligence (collectively “AGI”) in a rapid and safe manner for the benefit of humankind. In contrast to other approaches to the development of AGI, the AAAI present technology achieves a faster and safer path to AGI by relying, at least initially, on the involvement of (ideally many millions of) humans minds in the AGI training, operation, and safety / supervisory functions.

[0155] Advanced Autonomous Artificial Intelligence (AAAI) is a set of systems and methods for developing Artificial General Intelligence and Superlntelligent Artificial General Intelligence (collectively “AGI”) in a rapid and safe manner for the benefit of humankind. In contrast to other approaches to the development of AGI, the AAAI present technology achieves a faster and safer path to AGI by relying, at least initially, on the involvement of (ideally many millions of) humans minds in the AGI training, operation, and safety / supervisory functions.

[0156] The AAAI present technology’ can achieve AGI by enabling users to first customize and clone their own AIs. These customized Als (AAAIs) participate in problem solving and other intellectual activities on anetwork consisting of other AAAIs and humans. Although each AAAI on its own may lack the breadth of skills and knowledge to be an AGI, collectively the AAAIs (initially with help from humans on the network) form an AGI that will quickly surpass average human ability in all intellectual endeavors.

[0157] Some aspects of the present technology can include: 1) the system and methods to customize AIs w ith the unique knowdedge, skills, and ethical values of the users; 2) the universal problem solving architecture that allows AAAIs to interact productively with each other and with humans on intellectual tasks; 3) the network where the interactions takes place; 4) the methods for integrating the knowledge and ethics of individual AAAIs into an AGI; and 5) the methods for learning and continuous improvement so that the AAAIs and the AGI become smarter and more ethical over time. Involvement of humans as customizers of their AAAIs and participants on the network is an essential feature of the present technology which not only accelerates the development of AGI, but also makes AGI safer by providing a mechanism for the ethical values of millions of humans to be adopted by and reflected in the AGI.

[0158] One implementation of the AAAI system of the present technology has a focus on safety and is implemented via five sub-systems and associated methods. The five sub-systems of the AAAI system are: 1) AAAI Customization, 2) AAAI Architecture, 3) AAAI Network, 4) AAAI Integration, 5) AAAI Improvement. The acronym SCAN-II (Safe, Customizable, Architecture and Network — Integrated and Improving) describes the invention in the preferred implementation. Other combinations of subsystems, and variations of each subsystem, are also possible. Safety features have been designed into each sub-system in an effort to provide redundant safety checks in the event one or more sub-systems are omitted from a particular implementation.

[0159] One reason AGI has been so elusive is that specific knowledge and expertise from diverse fields must be creatively combined in an invention to achieve AGI. Another reason the development of AGI has been non-obvious, is that almost all Al researchers are focused on trying to improveexisting narrow Al systems via ever more complex and extensive machine learning approaches.

[0160] The fact that AGI has resisted attempts by thousands of others — despite the expenditures of huge sums of money -- and the fact that specialized knowledge in relatively obscure fields had to be combined with mainstream Al approaches in this invention, argue strongly for the novelty and creativeness of the present technology.

[0161] The present technology7describes the system and methods not only to achieve AGI, but also to achieve it rapidly, and most importantly, safely.

[0162] It is possible to influence the evolution of AGI in a positive direction. The best way we can do this is by adopting the safest possible path to the development of AGI and ensuring that humanity follows that path. In turn, the best way to ensure that humanity follows the safest path, is to show that the safest path to AGI is also the fastest and therefore most desirable path to AGI. These considerations, the desire to illuminate the fastest path, which is also the safest path, is motivation for the development of the present technology.

[0163] While the above-described devices or systems fulfill their respective, particular objectives and requirements, the aforementioned devices or systems do not describe an AAAI system and methods that allows for developing AGI.

[0164] A need exists for a new and novel AAAI system and methods that can be used for developing AGI. In this regard, the present technology substantially fulfills this need. In this respect, the AAAI system and methods according to the present technology substantially departs from the conventional concepts and designs of the prior art, and in doing so provides an apparatus primarily developed for the purpose of developing AGI.

[0165] In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular embodiments, procedures, techniques, etc. in order to provide a thorough understanding of the present technology. However, it will be apparent to one skilled in the art that the present technology may be practiced in other embodiments that depart from these specific details.Human-Centered AGI

[0166] From a safety perspective, many researchers would probably agree that having “humans in the loop” in any advanced technology system is a good thing. Certainly, in the case of nuclear weapons - arguably the most powerful technology developed by humans to date - humans in the loop have saved the planet from nuclear annihilation.

[0167] For example, at least once a Russian Colonel overrode the faulty Russian computers that said the US A had launched a nuclear attack. Had the Colonel blindly followed protocol, as a machine would have done, humanity would have experienced a nuclear holocaust. Fortunately, his human values and reasoning led him to believe the computer system was at fault (which it was) and he defied standing orders to protect millions of innocent lives. He should be a hero, and probably would have been except that to so honor him would have spotlighted the fact that the Russian systems were defective, and so instead he got a pension and was quietly retired. A brief explanation of this situation as provided by Wikipedia:“Stanislav Yevgrafovich Petrov (Russian: CTanncjiaB F.Brpac])OBmi Herpoa: 7 September 1939- 19 May 2017) was a lieutenant colonel of the Soviet Air Defense Forces who played a key role in the 1983 Soviet nuclear false alann incident. On 26 September 1983, three weeks after the Soviet military had shot down Korean Air Lines Flight 007, Petrov was the duty officer at the command center for the Oko nuclear early-warning system w hen the system reported that a missile had been launched from the United States, followed by up to five more. Petrov judged the reports to be a false alarm.His subsequent decision to disobey orders, against Soviet military protocol, is credited with having prevented an erroneous retaliatory nuclear attack on the United States and its NATO allies that could have resulted in a large-scale nuclear w ar w hich could have wiped out half of the population of the countries involved. An investigation later confirmed that the Soviet satellite warning system had indeed malfunctioned. Because of his decision not to launch a retaliatory nuclear strike amid this incident, Petrov is often credited as having “saved the world”.

[0168] “AGI is more dangerous than nuclear w eapons ... by alot,” Elon Musk has said publicly. He is right. Much more dangerous. We need humans in the loop, and we need human values to be adopted by AGI. But most Al researchers cannot see how this will be done. Because they envision AGI arising from an Uber-LLM that trains itself and then (re)programs itself millions or trillions of times faster than the human mind can comprehend, they cannot see how humans can possibly be “in the loop”. This situation worries them, but they cannot see a way out. It seems inevitable. It is not.

[0169] Al researchers have it backwards. We should not be thinking about how to build an Uber AGI system and then tack on human values and safety concerns after-the-fact in a desperate hope that we avoid the alignment problem. Instead, w e should be designing a human collective intelligence system that incorporates Al, little by little, and trains the Al on human values.

[0170] Over time the human- Al system becomes Superlntelligent and powered more by Al and less by human thinking. But since it was developed by humans, and the Al learned values from humans, and humans (even at the AGI stage) are still part of the system, humans never left the loop and human values and ethics are built into the core (or “DNA”) of the AGI system. More importantly, the present technology can be built immediately, and can be the first to win the AGI “arms race”. If the AGI that we can develop first is also safe the alignment problem can be solved!

[0171] While the above-described devices fulfill their respective, particular objectives and requirements, the aforementioned devices or systems do not describe a system and methods for human-centered AGI that allows achieving AGI faster by utilizing a network of human users and Al problem solving agents that share a common-problem solving architecture. The present technology' additionally overcomes one or more of the disadvantages associated with the prior art.

[0172] A need exists for a new and novel system and methods for human-centered AGI that can be used for achieving AGI faster by utilizing anetwork of human users and Al problem solving agents that share a common-problem solving architecture. In this regard, the present technology substantially fulfills this need. In this respect, the system and methods for human-centered AGI according to the present technology substantially departs from the conventional concepts and designs of the prior art, and in doing so provides an apparatus primarily developed for the purpose of achieving AGI faster by utilizing a network of human users and Al problem solving agents that share a common-problem solving architecture.

[0173] For it to be effective, the safest path to AGI must also be the fastest.

[0174] An exemplary implementation of AGI is the fastest method for achieving AGI because it begins with a network of human problem solving agents, who, by definition, can perform any intellectual task as well or better than the average human. Al agents (AAAIs) that have been trained and customized by individual humans are introduced to the network as Al problem solving agents. The human and Al agents share a common problem solving architecture that is: rigorous, scalable, transparent, auditable, safe, and powerful.

[0175] The architecture supports automatic learning and self-improvement, ft is compatible with LLMs which can be “plugged in” to the network and upgraded as more powerful LLM models become available. The AGI network begins with humans doing most of the problem solving work - especially the most important aspects such as setting goals and the most difficult aspects such as representing the problem. Over time, AAAIs do more and more of the actual work, more effectively and efficiently than humans could, while human attention is increasingly directed to issues of ethics, safety, and oversight.

[0176] Because ethics and safety checks are built into the architecture itself, as the speed of problem increases far beyond the capability of humans to “keep pace” the system remains aligned with human values and ethics. At any time, humans can see exactly how the system is making decisions, including all ethical information.

[0177] The AGI network is highly scalable and will become more powerful over time, yet the fundamental values and ethics of the system - which cannot be logically derived by any intelligence no matter how smart and fast - remain aligned with humans. Thus, the present technology’ solves the alignment problem in a democratic and scalable manner.

[0178] The fact that the AGI network can be implemented rapidly - far faster than estimates for when AGI will develop from other approaches - ensures a first mover advantage that allows this safest path to AGI to dominate other approaches thereby fulfilling the two key requirements for the present technology, namely that it be not only the fastest path to AGI but also the safest.

[0179] In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular embodiments, procedures, techniques, etc. in order to provide a thorough understanding of the present technology. However, it will be apparent to one skilled in the art that the present technology may be practiced in other embodiments that depart from these specific details.

[0180] It can be appreciated that the present technology provides a technical effect, contribution and solution with a technical implementation of multiple customized AAAI systems communicating over a collective intelligence network, in combination with all the AAAI systems each utilizing a common cognitive architecture including one or more problem solving processes for generating one or more solutions or answers to a problem request, and providing the solutions or answers to a user for approval. Where the customization of the Al system resulting in the AAAI includes input from human users for training the Al or the AAAI. Further technical contribution or solution can be where compensation can be provided to the human workers and / or the worker Al systems based on the sub-solutions they provide.

[0181] Still another technical contribution and solution is for the faster and safer creating of AGI that utilizes human input in training and customization for imparting human ethical attributes to the AAAI and / or AGI.

[0182] Still yet another technical contribution and solution is for providing improved solutions or answers to a user’s problem request that have a higher chance of acceptance by the user as the provided solutions or answers will have been generated by AAAIs with similar training to the user’s AAAI thereby aligning with the user’s parameters.

[0183] Even yet another technical contribution and solution is the assigning of a reputation attribute to any one of or any combination of the human workers and the worker Al system.

[0184] Even still another technical contribution and solution is translating using Large Language Model (LLM) or SLM any part of the request into an unambiguous language utilizable in a universal problem solving architecture including a decision tree.

[0185] It can be appreciated that the present technology' is found outside of computer program exclusion and / or abstract idea interpretation. This can in part be found in the technical contributions and solutions provided by the present technology, the utilization of specific training input that is external to a computer, and the providing of the solution or answer external to a computer.Envision AGI Now

[0186] Part of the problem Al researchers face is that they cannot really envision what AGI would be, other than an Uber LLM that is years away. Instead of looking to the future, we need to realize that AGI can exist NOW. We need to imagine in detail what that AGI which can exist now looks like and how it functions. There may be multiple instantiations of AGI, but this description describes an exemplary’ implementation which has as its chief benefits:1) It is the fastest path to AGI; and2) It is the safest path to AGI.How To Build AGI In The Fastest, Safest. Manner

[0187] The inventor has spent his entire professional career building intelligent systems with a focus on human collective intelligence systems. At the last company he founded, he proved that it is possible to harness the brainpower of millions of average humans (retail investors) to perform as well or better than the very best hedge funds on Wall Street. Two heads are better than one. It turns out two million heads, properly harnessed - even in an extremely simple system - are better than the best and most highly paid minds on the planet who devote their every waking moment to trying to get an edge in the markets.

[0188] Ask yourself: How do I build a “network” that can solve any problem or do any intellectual activity as well or better than the average human?

[0189] One answer is to build a network of human problem solvers, combined with a universal problem solving architecture that allows them to work together in a coordinated and rigorously defined way. A problem is submitted to the network. One or more human problem solvers works on the problem and returns a solution. Since the network includes humans, by definition, it can solveany problem the average human can solve. And since there are many humans, if the efforts are intelligently coordinated, then the network will often perform better than the average human, just as in the company PredictWallStreet. millions of average humans were able to beat most Wall Street pros at the extremely difficult problem of getting an edge in the financial markets.

[0190] So far, we have (hopefully) established that a network of humans can perform as well as the average human on any problem, and likely better. But what does that have to do with AGI? Isn’t the whole point of AGI that Al scales and thinks much faster than humans while humans? If we have to include the time needed for communication and coordination between humans wouldn’t the AGI think much more slowly ? Wouldn’t a network of humans scale very poorly ? Isn’t that why we need an Uber LLM to be the AGI? These are all reasonable questions and represent difficult challenges to researchers hoping to develop AGI. Because most researchers cannot see how to overcome these challenges without minimizing the role that humans play in their system designs, they have resigned themselves to pursuing approaches to AGI that increasingly leave humans out of the loop. While scalable, such approaches are also dangerous.

[0191] The description of the present technology addresses these challenges and presents a superior and safer alternative approach to AGI that preserves the feature of including humans, and human values, in the design of the AGI system.Collective Intelligence of Human and Al Problem Solvers

[0192] First, consider that the “solvers" on the network do not have to be all human solvers. They can be Al solvers too. LLMs exist. It is possible to include the latest generation of LLMs as solvers participating in the network. So now we are talking about a hybrid network of human and Al problem solvers. They need a common language for problem solving and that language has to be rigorous. It needs to be rigorous because machines are quite literal and if the coordination between humans and Al is loosely specified, there is more opportunity for error. This is particularly true since - as Eric Schmidt has pointed out - humans make many assumptions when they work together that might not be valid for a machine.

[0193] Humans know for instance, roughly the intellectual and ethical limitations under which other humans work. No human is going to propose a solution that wipes out him / her / their self and all of humanity . That would be counterproductive. Humans might be fearful and greedy at times, but they generally are not suicidal.Sociopathic Al

[0194] Machines have no such constraints. A machine might very well come up with “solutions” that are detrimental to humans simply because a machine is not human and either does not know better or does not care. Most humans (sociopaths excepted) have empathy and care to some degree about other humans.

[0195] Machines are neither good nor evil inherently. Al agents are “sociopathic” in the sense that they have intellectual abilities equal or surpassing humans, without the corresponding levels of empathy. That is part of the reason so many leading Al thinkers are worried about the Alignment problem. They should worry and then turn that worry into a positive action!Two Reasons for a Rigorous Architecture

[0196] One reason we need a rigorously specified problem solving architecture that can coordinate the actions of not only human problem solvers but also Al problem solvers (collectively “solvers”) is that the AIs will not necessarily use “common sense” and “empathy” when participating unless the problem solving path is explicitly delineated and humans are involved to correct the Al solvers when they go astray.

[0197] But there is another, more technical reason, for a rigorously specified problem solving process that both human and Al solvers follow.

[0198] If problem solving is described in a rigorous way, then it is possible to design a system that learns various solutions as they are developed.

[0199] Moreover, the solutions that are learned are auditable and completely understandable to humans - something that current LLM intelligence lacks that is very worrisome to humans. If we have no way of knowing how the LLM arrives at a decision or solution, how can we trust it? How can we know it is “safe” for humans?

[0200] With the collective solver network approach to AGI, every solution path is rigorously specified and known, which not only avoids ethical errors and provides auditable transparency, but also facilitates learning. Al is very good at learning rigorously-specified things and less good at learning vague and unstructured things.

[0201] The present technology realizes that the “search through a problem space” architecture that worked as a general framework for human problems solving, could be adapted and enhanced to serve as a general architecture for cognition that included both human and Al agents. Further, representing intelligent behavior as a form of problem solving provided a way for many Al agents to interact among themselves, pooling their collective intelligence to create AGI. This “Collective Intelligence” approach, presented here as the AAAI system and method for AGI. represents a fasterand more powerful path to AGI compared with existing efforts. Most existing efforts to achieve AGI are primarily focused on training larger LLMs using more data, more powerful computers, and better machine learning algorithms. The AAAI approach also has the virtue of enabling humans to participate easily in training and improving the intelligence of AIs, including helping form the Al’s values and ethics - an essential feature to ensure the safe development of AGI.

[0202] Except for the present technology7detailed in this description, no company or individual has explained how to create a practical system for AGI. The reason: ML alone is not enough to rapidly achieve AGI. Collective Intelligence is also needed.Benefits of a Common Architecture for Al and Human Cognition

[0203] The benefits of an exemplary implementation of a rigorously specified common architecture for Al and human cognition - at least with regards to coordinated problem solving on a network of human and Al agents - will include, without limitation:1) Avoids unintentional error due to loose specifications.2) Enables automatic learning of rigorous solutions.3) Enables scalability to any problem or intellectual endeavor.4) Enables modularity and stability.5) Maximizes safety.

[0204] This description will briefly expand on each of these desired benefits and then explain an exemplary implementation of a common Architecture of Cognition that achieves them.Avoiding Unintentional Errors

[0205] First, it has already been mentioned that because humans and AIs do not think the same way, loose specifications can lead to error. Constraints that any human would understand, such as “don’t implement a solution that ends all of humanity7”, might seem perfectly acceptable to a machine if the humans were not “in the loop” to set the machine straight. Rigorously specifying what is, and is not, an ethical solution (for example) requires that the common architecture for problem solving be rigorously specified.Enabling Automatic Learning

[0206] Second, the more precisely specified a solution is, the easier it is for an Al to leam. While LLMs can leam from huge amounts of unstructured text and input via Transformers and other deep learning techniques, these techniques are extremely expensive, time consuming, and impractical forlearning specific chunks of knowledge, such as solutions to specific problems. On the other hand, rigorously specifying problem solving behavior in a traceable and auditable way such that an Al can review the steps in the solution, understand why each step was taken, and learn when to re-use that solution is a much more practical and inexpensive approach to incremental, automated learning.Enabling Scalability

[0207] Third, a truly general architecture for cognition allows representing any problem or intellectual task that humans do. The generality of such a representation means that is truly scalable and applicable to any human intellectual endeavor - a key requirement for AGI.Enabling Modularity and Scalability

[0208] Fourth, a common architecture for cognition means that intelligent agents with avast range of differing intellectual abilities can be ‘'plugged in” to the network as long as they all speak the common language of the architecture. Humans have individual differences in intelligence, skills, expertise, values, and other intellectual attributes, yet we are able to work together.

[0209] An architecture that can accommodate a wide range of human solvers can also accommodate a wide range of Al solvers. In the future, ever more sophisticated LLMs will be developed. This implementation of AGI does not discourage such efforts but rather embraces them. LLMs and the development of ever-more-powerful narrow Al systems as well as general Al systems are all completely complementary to the present technology’s approach to AGI.

[0210] Just as human solvers with varying degrees of intelligence and skills can plug into the network, so too, different AIs with varying degrees of intelligence and skills can also plug in. As long as all solvers follow the common architecture which coordinates every entity’s intellectual efforts, modular intelligences with different capabilities only increase and enhance the power of the AGI network.

[0211] Further, since the behavior of all solvers is rigorously captured and described, the present technology system and method is stable, and all the entities are able to learn from each other. Al can learn from humans; humans can learn from Al; Al can learn from Al. In all cases the modularity and stability of the system is maintained and the power of the AGI network increases.Maximizing Safety'

[0212] Finally, a rigorous, universal architecture of cognition maximizes the safety' (from ahuman standpoint) of the AGI network. One of the problems with current deep learning approaches to Al,such as the idea of creating ever-more-powerful LLMs, is that the resulting LLMs are untrustworthy because humans are unable to know exactly why they behave as they do. In effect, the LLM / deep learning approach results in "‘alien” and potentially “sociopathic” intelligence, which quite rightly alarms thoughtful Al researchers. While it is possible, or even likely, that such alien intelligence is benign or even beneficial towards humans, without understanding how it thinks and how it reaches its conclusions, it is difficult to trust it.

[0213] Some researchers suggest that within the next five years or so, improved LLMs will be able to explain their reasoning to humans. Even if that is so, humans will still have to trust the explanations of the LLMs. How can humans be sure the LLMs are not just telling stories that make sense to, and appease, humans, while in actuality the LLMs are thinking in completely different ways with goals and objectives that may or may not be aligned with human interests?

[0214] We cannot be sure unless there is a rigorous, transparent, and auditable record of the serial thought process of the AGI. In the present technology, such a record is a natural as part of the very architecture that enables a learning and scalable AGI in the first place. Further, because every intellectual step in the AGI’s thought process follows a universal “algorithm of thought” it is possible (and desirable in the exemplary implementation) to build ethics and safety checks into the very process of AGI thought itself. The benefit of this approach is that no matter how quickly AGI thinks, the thought process is always safe.

[0215] Eric Schmidt and others have pointed out that one of the dangers with intelligent technology is that it will inevitably be capable of making complex decisions far faster than human minds can keep pace with. Eric gives the example of a war between the US, North Korea, and China, which is over in milliseconds because the AGIs conduct the war in their minds, making the decisions that would normally take humans weeks or months to make, in just a few milliseconds. The future of all humanity could be decided in 5 milliseconds!

[0216] It is a scary scenario. But that scenario is impossible with the exemplary implementation of the architecture described in the present technology. That is because at every step of the AGI thought process - even if trillions of though steps take place in a nanosecond - ethics checks and safety checks, comparing the goals and subgoals of the thought process with human-aligned ethics and safety considerations, are performed. The thought of AGI is constrained by the architecture that supports it. Human-aligned values are built into the core (or “DNA”) of the architecture.

[0217] At some point, individual LLMs and Al agents might become powerful enough to rewrite the code of the AGI network architecture itself, but as long as we keep plugging the most powerful LLMs into the network which includes human solvers, humans remain in the loop and participating,thus serving as a source of values for the AGI.

[0218] Crucially, values cannot be logically derived. AGI must get its values somewhere. By designing the AGI system of the present technology to include humans and be centered on human values from the very beginning, we maximize the chances of alignment with human values.One Exemplary Implementation of the AGI Network

[0219] The following is a description of an exemplary implementation of the architecture of the present technology for cognition that supports Al and human problem solving on a universal, scalable AGI network.The Theory' of Human Problem Solving

[0220] In 1972. Newell and Simon, two of the inventors of the field of Al, described a universal architecture for human problem solving in their book. Human Problem Solving. Briefly, the theory of Human Problem Solving [“HPS”] says that all problem solving can be described as a series of state transitions from an initial state where there is a goal to a final solution state where the goal has been achieved. A series of decisions are made, and actions taken ( "operators" are applied) which enable the problem solver to transition from state to state until the solution state is reached. Along the way, a series of goals and sub-goals may be created.

[0221] The entire process can be represented as ‘‘search through a problem space” which essentially means that one can model the problem solving process with a decision tree structure. At each branch of the tree, various potential options are evaluated in terms of how likely they are to achieve the current goal (or subgoal) and one option or path is chosen to pursue. An operator is applied, transitioning the solver to the next state and the process repeats. There may be dead-ends in which case the preferred operator is to backtrack to an earlier branch-point in the tree and try' a different operator to go down a different path.

[0222] A simplistic way of thinking of all this is to imagine a human in a maze, trying various branching paths in the maze until the solution (getting out of the maze or to the goal) is reached.

[0223] Since each “state” in the problem space is rigorously defined in tenns of the goals or subgoals that exist, the state of affairs in that state including available operators to apply to get to a different state and the evaluation function of how attractive each path forward appears, there is a very precise record of what each step in the problem solving process entailed, which steps were taken and why.

[0224] Assignment of credit or blame value is a process whereby as problem solving proceeds, theevaluation function is modified to take into account the actual results of various decisions.

[0225] Heuristic search is the application of general “rules of thumb” to cut down on the number of possible choices at each choice point in the tree and follow what appears to be the most promising path(s).

[0226] The fact that a complete record of the problem solving steps and the reason (as operationalized by the evaluation functions) for making each decision at each choice point exists, enables detennining the optimal solution path (in retrospect) after the problem has been solved.

[0227] In other words, problem solving may involve many dead-ends and workarounds the first time a problem is solved, but after that one can look backwards and say, “if I had to solve this problem again, this is what I would do to solve it most efficiently and effectively”. This best solution path is specified rigorously and can be learned not only by humans but also (because of its rigorous specification) by machines.Why HPS Works for Al Agents

[0228] The rigorous nature of specifying the characteristics of each state or step in the process, the reasons for each decision, the results, the goal-subgoal hierarchy and other elements involved in the problem solving process means that following the HPS approach is ideal for machines that want to learn how to solve problems better.

[0229] Thus, although the theory of HPS was developed to explain and model how humans solve problems it actually works for Al too. In fact, many of the early (and current) Al systems use at least the process of heuristic search through a decision-tree problem space as a key feature of their architectures.Easy for Humans to Participate

[0230] Note that even though HPS is rigorous to point that it can be programmed as a way for Al to solve problems, humans do not need to detail all their activity in a detailed way.

[0231] In the exemplary' implementation of the present technology, the network architecture itself has features whereby the problem solving steps, goals, sub-goals, operators, etc. are all deduced (and if needed, queries can be made of the human or Al problem solver to obtain clarification) and recorded automatically along with metrics on how successful choices were, which in turn helps improve evaluation functions that are used.Required Systems and Methods for AGI Network Already Exist

[0232] This process, though somewhat complex, is well known in the art of computer programming and almost all of it can occur behind the scenes so that the user experience is anatural one where they interact with the network of other human and Al solvers using natural language.

[0233] Dialog and scripts can be employed to clarify goals, sub goals, features of the problem states and potential operators where these features are not easily deduced or inferred from the solver actions.AGI Network Solves the “Representation Problem”

[0234] Traditionally one of the most difficult aspects of problem solving - at least for Al agents - has been the formulation or “representation” of the problem, the goals, and the solution state. Humans are very' good, compared with Al, at problem representation. Therefore, much of the division of labor between human and machine initially may involve humans setting the goals and representing the problem, followed by Al solvers rapidly trying many solution paths and presenting what might be good solutions. Of course, humans will be monitoring, reviewing, correcting and guiding the AIs in an interactive process until the AIs acquire enough knowledge of how humans typically represent and solve various problems so that they can do more of these tasks autonomously.Multi-Modal Representations

[0235] As AIs become multi-modal, their intelligence will increase greatly. To understand why, consider an argument elucidated in the 1980s by the Nobel Laureate, Herbert A. Simon and his coauthor Jill Larkin, in a famous paper entitled: “A picture is worth a thousand words.” The paper drew the distinction between informational equivalence and computational equivalence. It might be possible to describe every' aspect of a picture using words and logic, but it is computationally more efficient to use a graphical representation. That is, you can see stuff at a glance that would take you a long time to describe or understand in words. The ability to see, touch, smell, and taste are all important yvays humans understand the world. When Al can understand, and process information from these modalities too, they will become more intelligent - vastly so.

[0236] Al already holds a speed of processing advantage over humans.

[0237] Al holds a huge memory advantage over humans.

[0238] Humans held a perceptual advantage that helped them maintain their ability to represent the world better than AL Humans have vision, hearing, taste, smell, and touch to help them understand the world, yvhereas Large Language Models (until recently) had to understand solely through words.So, humans held the advantage.

[0239] But with multi-modal LLM and other Al agents, the representational advantage of humans over Al will rapidly diminish. To the degree that humans can contribute formative representations, framed by human-aligned values, we should do so.LLMs Facilitate Human-AI Interaction

[0240] The advent of LLMs makes communication between Al and human solvers much easier. Although the underlying architecture follows the rigorous theory of HPS, the communication about the problem can be in natural language with the machines translating this natural language communication into the more rigorous HPS specification. This approach allows many humans to participate without requiring them to be experts in HPS theory or even know anything about HPS. While humans typically would use the natural language translator, since natural language is a universal interface for LLMs, LLMs or Al agents could also use natural language as an intermediate step to translate their problem solving activity into a rigorous specification for other intelligent (human or Al) entities.

[0241] At the same time, the fact that the problem solving is ultimately represented in the universal HPS framework enables humans to pinpoint where the AIs go wrong and correct them, as necessary.HPS Is Highly Scalable

[0242] Also, the HPS framework is highly scalable allowing any intellectual task to be represented in an enonnous problem tree containing all problems that the system is considering or has considered, which in the exemplary implementation is called the WorldThink tree. For computational efficiency, this tree can be broken down (e.g., "sharded") into subtrees that are integrated into the overall WorldThink tree as needed.The Role of Attention

[0243] There are many specific considerations that must be addressed in the exemplary implementation of the universal HPS architecture that allows the collective intelligence of human and Al solvers to be coordinated in an AGI network. Chief among these is the issue of attention. Where should the (Al and human) solvers concentrate their efforts and attention in the gigantic space of possible actions that are contained in the WorldThink Tree?

[0244] Rewards can be associated with various goals and subgoals on the trees as detailed in patents previously invented by Dr. Craig Kaplan. The payoffs for achieving these goals andsubgoals can be made with credits, currency, or blockchain tokens as detailed in Dr. Kaplan’s WorldThink Whitepaper, published in 2018.Learning via Proceduralization of Knowledge (Solutions)

[0245] The solutions themselves can be '‘chunked” via a type of learning known as procedural learning of knowledge which is well known in the art, and which has been described by Allen Newell and his associates in their SOAR architecture, as well as by John R. Anderson in his book The Architecture of Cognition and other works.

[0246] In short, the components needed to support the collective problem solving efforts of both human and Al solvers, w orking together to solve any problem (or achieve an intellectual result since almost all intellectual activity can be represented as a “problem"’ in this universal architecture) are well known in the art.Unique Approach to AGI

[0247] What is unique is the approach to developing AGI in this manner. No other researchers, to the inventor’s knowledge, are pursuing this specific approach to AGI as of this writing. Yet, this approach is clearly and definitively the fastest path to AGI since AGI capabilities exist essentially on “Day One” when the system goes live. Existing LLMs and humans are all that are required.

[0248] The netw ork leams - not by the deep learning approach of training up more pow erful and opaque LLMs which everyone is pursuing - but by taking the rigorously specified problem solutions, which are scalable, auditable, understandable, powerful, and specific and adding these to the system’s repertoire, solution by solution, in an incremental fashion.Complementary to Deep Learning

[0249] At the same time, the approach is complementary to the development of more powerful LLMs which will doubtless occur. As new; more powerful LLMs are developed, they can be “plugged in” to the architecture making the AGI network more powerful and more scalable.Some New Innovations

[0250] There are some key technologies of the present technology in the exemplary implementation which are completely novel and essential to the safety of the AGI network.

[0251] One set of technologies is the systems and methods required to customize Al agents to produce customized AAAI agents which can participate in the AGI network. Previous USprovisional patent applications (US 63 / 487,494 and US 63 / 491 / 040) have detailed some of the systems and methods for creating these AAAIs in ways such that they are not only more intelligent than the base LLMs from which they are derived but also include the ethics of their owners / users who ‘'train’’ them with the ethics and values of the owners.

[0252] Such training can be accomplished a number of ways which are novel and useful such as, without limitation, the automatic importing of, and training on infonnation related to the user / owners such as social media profdes. user preferences, emails, texts, as well as explicit user training in the form of dialogs with the Al agents or participation in scripted dialogs or questionnaires administered by the (AAAI) training system. Much of the customization process has already been detailed in the aforementioned PPAs, so I do not repeat those systems and methods here.

[0253] The key point is to realize that safety of the AGI network depends partly on the values of the solvers on the network.Human Behavior Influences Safety'

[0254] To the degree that the solvers are human, the safety of the AGI network depends on humans behaving ethically and only proposing ethical solutions and working on beneficial problems. If humans choose to engage in nefarious problems and solve problems related to harming other humans and the planet, then there is a risk that the AGI system will emulate these negative values. So, the first line of defense if humans want AGI to behave positively towards humans is for us humans to behave positively towards other humans ourselves.

[0255] While some cynics may suggest that this feature implies humanity is doomed since humans behave poorly towards each other, it is suggested that even the most immoral human generally is concerned with self-preservation. Very rare are those humans who intentionally would blow up themselves and the entire species. Even most humans that are called “terrorists” are using terrorism as a means to an end that generally involves benefitting their specific subgroup of humans. Destroying all of humanity is not only illogical, but it also goes against millions of years of evolutionary programming that helped our species survive. An answer to the cynics therefore is that the AGI network does not require the human solvers to be saints, only that they act in their rational self-interest which involves not destroying everyone. That is a pretty low bar.

[0256] Moreover, it is believed that most human solvers on the network are interested not only in survival but also benefiting themselves and their fellow humans - goals which can be most rapidly and powerfully achieved by working on beneficial problems on the AGI network. Thus, humansolvers, in aggregate, are likely to behave positively on the network. To the degree that there are bad actors, the rigorous record of every problem solving goal, subgoal, and step, makes it relatively easy to detect behavior that grossly violates societal or human norms.Recent Behavior Matters Most

[0257] Sometimes an argument can be presented that humans are so terrible a species, with genocide, mass murder, and all manner of horrible behavior in our past, that we are doomed as a race and deserve to be wiped from the Earth for our past sins. To this a response can be that it does not matter what we did yesterday nearly so much as what we do today and tomorrow.

[0258] Any intelligent system wants mostly to understand the world as it is today. Yesterday is helpful only insofar as it helps you understand reality today. Beyond that, it is spilled milk, water under the bridge, in the rearview minor, irrelevant. Life is now. Today is now. We are always challenged to meet life as it is now. That is how it is for us. That is how it is for Al. That is how it is for any intelligent system.

[0259] Once, when the present inventor was a visiting professor of computer science, the present inventor had a Department Chair whose research involved when to power on or off the disk drive in alaptop. He wanted the laptop to conserve power intelligently and only power up the disk drive if it was going to be used. Guess what he did? He designed an algorithm that checked the recent behavior of the drive. Looking at the disk drive’s recent behavior was the best way to understand the present and predict the future. That is what the disk drive algo did, that is what people do, that is what Al will do too. So, it matters most what humans did recently. We need to be the change we want to see.Human Training of AAAIs Influences Safety

[0260] In a system that includes both human and Al (or AAAI in the exemplary implementation) solvers, we must also concern ourselves with the potential behavior of the Al solvers. In the exemplary implementation, the first line of defense against bad behavior on the part of the AAAI solvers is that they are trained with the values of their human user / owner. This approach to Al ethics reduces the chances of a bad outcome compared to other approaches.

[0261] For example, constitutional approaches to Al ethics, whereby a small elite group of Al researchers writes an ethical “constitution” which is then used to “train” LLMs on what is ethical and what is not, suffers from the problem of concentrating power in the hands of too few humans leading to the possibility of corruption. No matter how well intentioned, history has shown thatwhile there may be good and powerful “Philosopher Kings” who greatly benefit humanity, there can also be bad and powerful “Hitlers” that do tremendous damage.

[0262] A list of prohibited attributes can be utilized in detennining bad behavior and / or bad actors at any time in the process. In the exemplary, if enough yellow flags (or red flags like requesting information for getting Molotov Cocktails through airport security) occur, then a more detailed analysis of the user AAAI’s problem solving might be triggered to detect patterns that indicate a potential bad actor or bad behavior on the network. If needed, human evaluators might be alerted so they could use their judgement and waive off false alarms, or escalate action if danger seemed imminent.

[0263] The point is that checks are run with each goal and sub-goal. There might be hundreds or thousands of subgoals for given problem, so the effect is like “virus scanning” the problem solving process at each step to make sure no malevolent actions are being taken. Depending on system and personal parameters set by the owner, such scanning could be less frequent in order to increase performance and minimize false positives.

[0264] Having checks built into the problem solving process itself means that running the problem solving process faster will not evade the checks, since they will be run faster as well. The system is monitoring ethical behavior AS IT GOES rather than trying to detect bad actors and bad behavior after the fact — w hen it may be too late to correct. An ounce of prevention is worth a pound of cure.Democratization of Ethical Values in Safety

[0265] The less powerful, but also arguably less dangerous method, is to democratize ethical decision making and training of AAAIs. The AAAI of the present technology takes this more democratic approach whereby each individual (human user / owner) customizes and trains his / her / their own AAAI to behave ethically according to the values of the human user / owner. When deciding whether to work on particular problems in the WorldThink Tree, the ethics of each AAAI come into play enabling AAAIs to opt in or out of problem solving efforts based on the ethical dimension of the problem.Role of System Rules and Norms in Safety-100266] Just as human society does not rely on the individual ethics of human actors alone but also has a system of social norms and law s that serve as guiderails on behavior, so too, the AGI network can enforce certain limits to the types of problems and solutions that are allow ed on the AGI network.Role of Reputation in Safety

[0267] Moreover, since each AAAI and each solver on the network, in the exemplary implementation of the present technology, has an online reputation as well as an auditable and transparent record of all (non-confidential) problem solving activity, it is possible for clients of the AGI services to specify which type of solvers (AAAI and / or human) they want to work with - ethical and reputational considerations being one of the criteria, just as it is the course of normal human business.Safety Checks at the Speed of Al Thought

[0268] The fact that problem solving can occur on the AGI network at the speed of light where solutions are reached in milliseconds rather than weeks or months, does not present a problem for ethics on the system, as long as the frequency of ethical checks scales with the frequency of decision making in the problem solving process.

[0269] The exemplary implementation of the present technology has ethical checks each time a goal and subgoal is set, with the option of more or less frequent checks. Also, each time a solution to a goal or subgoal is reached when a transaction would occur (e.g., payment by a client for solving, or partially solving a problem) an ethical review can be automatically conducted.

[0270] The transparent, auditable record of the sequence of problem solving steps makes such a review rigorous, automatic, and transparent. Even if the majority of such reviews are performed by the system itself automatically, a random sampling process can involve human oversight.Blockchain Methods for Transparency and Auditability of Behavior

[0271] Optionally , records of problem solving behavior can be stored on blockchain or in other unalterable logs so that concerns of "cooking the books” with regard to the actual problem solving that took place are addressed. Optional blockchain functionality for automatically disbursing rewards are also possible as detailed in the WorldThink whitepaper of 2018.Implementation Example

[0272] A website contains a menu of pre-trained Al agents. Users choose from the menu, purchase premium upgrades if desired, and customize their AIs via interaction. Users own their Al agent, its training data, and all purchased upgrades together with any improvements the user makes to the Al agent. Users license use of their Al agent to the website for use as part of an AGI networkcomprised of Al agents and humans enabled with computer technology.

[0273] Users agree that the website can use the Al and all its IP for non-profit purposes that benefit humans and planet earth. Profits generated from use or partial use of the user's Al may be shared with the user per current policies, at the website’s discretion. The user may withdraw use of their agent(s) at any time provided that the website retains rights to use the Al and data as of the date of withdrawal.

[0274] Creation of a user- Al can be free, and the website will provide the user with a reasonable allowance of free use credits along with use-credits generated by work of the user’s Al. Users can trade or purchase training information from each other to increase the value of their Al. AIs also can increase in value as they leam from actions on the network.

[0275] Users may instantly customize and train their Al’s by granting access to their social media accounts and telling the Al how to filter and clean the data before training. Users may provide documents, videos, and other online information to the site to train their AIs. User may provide access to their entertainment and streaming providers, their Apple and Amazon accounts, their news providers, their browser cookies and browsing history, and other source of information which will be consolidated, cleaned and filtered per user specs, and used to train their Al. The training data will be summarized and packaged so users understand what they own, and so that they can choose to put elements up for trade on the website’s training-data marketplace.

[0276] The website has a means of combining both the intelligence and the values / ethics of all the individually trained AIs. This can be done via a Master Training Process where a standard LUM or Al agent is trained using a carefully cleaned, filtered, and rated subset of all user data that explicitly includes values -representation from each unique human user. It can also be done by fine tuning processes that incrementally layer new training on a base model that may or may not have already been incrementally improved. These ‘layered” AIs use a certain base LLM and then grow more intelligence on top of the base. Periodically as new bases become available, the training increment can be re-applied on top of the new base Al, resulting in an ever-improving Al that is customized to an individual user.

[0277] In terms of capabilities, the AIs can do anything the online human can do since the online human behavior is constrained by online text or multimodal interfaces that are easily mastered by LLM and other Al agents. With nested sub-goaling capability and limited autonomy to make decisions, problem-solve and pursue goals within parameters set by the human owners, the AIs act on behalf of their owners across essentially all online sites and tasks for which they are authorized.

[0278] The website combines their intelligence via master training using all the data gathered andthe incremental tuning and layering approach previously detailed. As a result, the website will have the strongest AGI which will become stronger still by incrementally interacting with itself on the task of intelligence improvement. SuperIntelligence will become reality, but it will be democratic SuperIntelligence that is human-centered and that incorporates the values of all human owners of Al agents.

[0279] The WorldThink Tree is a massive tree data structure, composed of many sub-trees, which represents every problem and task that has been done, is being worked on, or has been proposed for the overall AGI system. This Tree is browsable. Individual AAAIs and / or humans can work on specific tasks within the tree. The tree structure provides an auditable trail of all problem solving activity which is also useful for learning via the proceduralization mechanism described above. When interacting with the tree, the two main roles an agent can take are either: Worker or Client. Workers are generally involved in solving open problems or subproblems on the tree. Clients are generally involved in specifying the problems, goals, objectives, and other parameters (e.g., rewards, budget, timeframe, success criteria, quality7metrics) that constrain problem solving.

[0280] The WorldThink protocol is a problem solving architecture that can be used by AAAI.com to serve as a universal problem solving architecture as it incorporates the general architecture of HPS while adding features to overcome certain challenges.

[0281] One aspect of implementation of the AAAI system can be on safety and it can be implemented via five sub-systems and associated methods, as illustrated in FIG. 1. The five subsystems of the AAAI system are: 1) AAAI Customization, 2) AAAI Architecture, 3) AAAI Network. 4) AAAI Integration, and 5) AAAI Improvement. The acronym SCAN— II (Safe, Customizable, Architecture and Network - Integrated and Improving) describes the present technology in one aspect. Other combinations of subsystems, and variations of each subsystem, are also possible. Safety features have been designed into each sub-system in an effort to provide redundant safety checks in the event one or more sub-systems are omitted from a particular implementation.

[0282] The five sub-systems of the AAAI system can be further described as:1 ) A base level Large Language Model (LLM), Small Language Model (SLM), or other Al system can be customized to reflect the knowledge of an individual, group of individuals, or organization and designated an Advanced Autonomous Artificial Intelligence (AAAI).2) The customized AAAI can be enabled to participate in problem solving using a universal problem solving architecture that is compatible with both human and Al agents.3) The problem solving-enabled AAAI participates in problem solving activity, including but not limited to: planning, problem solving, and other types of sequential, multi-step cognitive activity’, on a network of intelligent agents.; generate and select operators that reduce a difference between a current state of problem solving and a desired state based on the goal / subgoal; setting of a subgoal towards achieving the goal; utilizing hierarchy until an actionable goal is set that can be acted on by the operator; and analyzing the auditable record to determine recommendations for improvement of the problem solving process to achieve a solution to the goal / subgoal.4) Multiple AAAIs on the network can be integrated to achieve Artificial General Intelligence (AGI); or Al capable of intelligent (or super-human level) behavior across a wide range of tasks.5) The individual AAAIs, the problem solving network, and / or the integrated system of multiple AAAIs continuously improve via a variety of means, including but not limited to, redirecting the efforts of individual AAAIs and / or the integrated AGI towards the task of improving the system and / or components of the system.

[0283] The sub-systems or new sub-systems can include any one of or any combination of:1) Safety / ethics check - Comparing a goal or subgoal against a list of prohibited attributes and assigning an ethics value based on a result of the comparison. Checking the goal / subgoal against a list of prohibited attributes. Combining values / safety information from AAAIs, using a set of approved criteria for a task by a user or by a regulatory agency or by AAAIs approved by human user. Establishing or using a threshold for the goal / subgoal to determine if the ethics value is unsafe, unethical, safe, or ethical. Determining if a sequence of individually safe goals / subgoals are unsafe or unethical when considered cumulatively. Determining whether a violation occurred that reflects a predictive evaluation if the goal is to violate the ethical criteria. Recording any and all activity of the safety / ethics check in the auditable record.2) AAAI matching - Detecting and identifying additional AAAIs that each have a criteria related to one or more goal or subgoal criteria.3) Remembering and / or improving - Recording activity, comparing with successful or unsuccessful progress towards the problem solutions, determining which activity to keep active or forget.4) AAAI learning - Learning, including a procedural learning process that utilizes information provided by intelligent entities such as human users equipped with computers or AAAIs.Recording activity, comparing with successful or unsuccessful progress towards the problem solutions, determining which activity to keep active or forget. Assigning credit value or blame value to a group of content of the problem solving activity. A set of prompts provided to the user and information received based on the prompts. Updating AAAIs with the group of content determined as active. The group of content can be, but not limited to, a set of prompts provided to the user and information received based on the prompts, all of which being recorded in the auditable record. Optionally, the problem solving activities can include the group of content.EXAMPLE USER SCENARIOS

[0284] It may be helpful to describe some user scenarios that provide a sense of how the present technology can operate in some of the aspect implementations. An exemplary process is illustrated in FIG. 2.

[0285] In one aspect, a user '‘visits” AAAI.com via the user’s computer, cell phone, PDA, or goggles. AAAI.com would interact with the user via a web-based interface, a phone app, custom software for the PDA, or a metaverse / virtual reality environment. The mode of interaction could be physical via a keyboard, mouse, or gestural interface; voice-based via a microphone input coupled to natural language understanding and generation systems; or video-based as in the case where the user becomes an avatar in a virtual reality setting or in the metaverse.

[0286] The initial interaction would include setting up the user’s account, which might be free or paid. This would involve an account name and password or other authentication mechanisms which might include, without limitation, biometric forms of ID such as fingerprint, face or voice recognition, and / or multi-factor authentication mechanisms such as software or hardware authenticators residing on a separate security device or on one of the user’s existing devices.

[0287] For security, all communication between the user and the AAAI system could be encrypted via a VPN and / or could use other methods of encryption and security which are well known in the art of programming.

[0288] AAAI.com may request that the user set up payment capabilities via credit card, PayPal, Venmo, blockchain, ACH, or other pay ment mechanisms. These pay ment capabilities would allow funds, payments, and / or credits to be transmitted bi-directionally - from the user to the AAAI.com and also from the AAAI system to the user in cases where the AAAI system needs to pay or credit users for work efforts of their AAAIs or broker payments between users and / or between AAAIs on the AAAI network.

[0289] In one aspect of implementation, AAAI, com can have interfaces with other companies and vendors that the user might use — including, without limitation, and for example: Facebook, Instagram, Reels, Amazon, Apple. Microsoft, Google, and YouTube.

[0290] In the initial interaction with the user, and subsequently upon user request, AAAI.com would engage in a dialog or other interaction (which could include presenting the user with menu options, lists, graphics, sliders, buttons, and other user interface controls in a GUI, textual, haptic, voice, or VR-related manner) with the user to determine the user’s goals and objectives in using the AAAI system.

[0291] For example, some of the objectives a user may have in using AAAI.com may include creating and customizing their own Al (known as an AAAI) for purposes that might include, without limitation:

[0292] Serving the user as an advisor, teacher, or companion.

[0293] Representing the user in negotiations, interactions, discussion, and transactions with other users, or with the AAAIs of other users; or with vendors and other companies.

[0294] Working on behalf of the user for compensation, or in volunteer efforts, where such work includes online intellectual, advising, or problem solving work across a wide range of tasks.

[0295] Duplicating or “cloning” the user’s AAAI so that several or many of the cloned AAAIs can work on behalf of the user in parallel, including interacting with, teaching, and improving each other so that the cloned AAAIs increase their knowledge, skills, and abilities.

[0296] Serving as legacy AAAIs that can continue to interact with the world, including potentially comforting living relatives and friends, after the owner’s death.

[0297] Contributing knowledge, ethics, and effort to AAAI.com’s AGI, and improving the base level of Al or AGI that AAAI.com can offer users before those users add their unique customizations.

[0298] Working with other users’ AAAI to help ensure ethical and safe behavior by AGI by contributing ethical information and values to the AGI and participating in monitoring, review; supervision, and voting processes that can help ensure the AGI remains safe and ethical.

[0299] In the dialog or interaction with the user, the AAAI system will also identify constraints and resources available for customizing the user’s AAAI. For example, some of these constraints and resources, might include, without limitation:The amount of training and / or supervisory time that the user has to devote to customizing their AAAI.The amount of financial resources the user is willing devote to customizing their AAAI.

[0300] Availability of social media information such as Facebook profiles and timelines, Instagram profiles and histories, Reels, TikTok, and YouTube videos, tweet and text content and histories, emails and email histories, cookies collected by advertisers, blog posts, articles, books, patents, audio and video recordings, pictures, and other information about, and / or collected by, the user or third parties that could be used to train, tune, or customize the user’s AAAI.

[0301] Availability and use of personality' tests, such as the Myers-Briggs personality7inventory, skills and knowledge assessments, standardized tests, exams, certifications, and other types of assessments and questionnaires which could be given online (or which have already been given) to the user.

[0302] Availability and use of other knowledge bases and training data from users on the AAAI platform that could be used to train, tune, or customize the user’s AAAI.

[0303] Other human users, and / or their AAAIs, available to help train, tune, or customize the user’s AAAI.

[0304] Other texts and information, individual texts, and libraries selected by the user or by the system for purposes of training the user’s AAAI. For example, the Bible, Koran, Dhammpada, Mahabharata, or other spiritual / ethical / religious texts might be selected for training the AAAI based on the user’s religious preferences; books on plumbing might be selected if the AAAI will be used to primarily solve online plumbing problems. Even if these materials are part of the base AAAI that is provided to the user, emphasizing certain texts or subsets of information for additional training can result in the user’s AAAI's behavior being more reflective of how a plumber, or Muslim, or Christian might behave, for example.

[0305] In addition to specifying objectives, resources, and constraints via an interactive dialog or other interaction with the system, the user or system may want to specify other technical parameters that affect the training or customization process. These parameters can include, without limitation: The type of training, tuning, or other ML algorithms that are used.The type and size of the training dataset(s).The degree to which the training materials are to be “cleaned”, formatted, labelled, or otherwise processed before customization begins.The number of training “epochs” or iterations through the learning algorithm(s).The sophistication and type of base model(s) being customized or trained.The required timeframe for training - e.g., must be completed in a minute, a day, a week - which might have implications for cost and resources used.The “temperature’1or other parameters internal and specific to various machine learning algorithms that can affect what is learned and how it is learned including, without limitation, how literal or how divergent or “creative” the customized AAAI will be in its responses.Whether “one shot”, “few shot”, or extensive training is to be used.The amount of human and / or Al supervision to be used in the customization process.

[0306] Once the user’s AAAI is customized, the user can clone it and / or put it to work on the user's behalf on the online network. The user’s AAAI can begin acting on the user’s behalf making travel arrangements (for example), providing advice, interacting with other AAAIs, participating in the collective AGI efforts by contributing problem solving as well as ethical information, and potentially earning money on behalf of the human user.SIMPLE EXAMPLARY IMPLEMENTATION

[0307] FIG. 3 shows one simple exemplary implementation of the system and methods for creating an ethical and safe Artificial General Intelligence from the collective intelligence of AAAIs and humans. This simple implementation is compatible with all of the company and platform specific scenarios outlined above, as well as with many other potential integration scenarios.

[0308] A (human. AAAI, or other intelligent entity) user visits the AAAI.com website (a). The website informs users and offers them two actions: Sign Up (b) or Login (c).

[0309] If the user opts to Sign Up then a dialog is initiated which extracts user values / ethics (d), user goals and objectives (e) and user budget for time (f) and money (g). All users must allocate some time (f). Users have the option of creating a free AAAI or allocating a money budget.

[0310] If users have allocated a money budget (g) they are given the opportunity to purchase pretrained AAAIs or training modules (h) with specific personalities (i), skills (j), expertise (k) or knowledge (1). They also have the opportunity of buying training from other AAAIs on the network (m).

[0311] After making time (and optionally money budget (h, i, j, k, 1, m)) allocation decisions, the user proceeds to an overview of the creation process and then is asked for user permissions (n) to optionally logon and use existing social media, tw itter, and other vendor accounts to gather user data for “one click” training of the user’s AAAI. After the user opts to use certain (or no) data, with a single click (o) the user directs system to create AAAI. The AAAI is an off-the-shelf LLM (e.g., GPT X, BARD, Llama, Gemini, Grok, or any closed-source or open-sourced Al agent) that is trained / tuned on a dataset prepared automatically from all the user data authorized by the user. If no data was authorized, the AAAI is just the “off-the-shelf’ LLM.

[0312] The AAAI now begins to learn by training (p) using the various training datasets and modules (h - m) and its existing AAAI knowledge (pl). There are two main ways of learning, automatic (q) and human (r).

[0313] Automatic learning includes, without limitation, learning by interacting with copies of itself (s), learning via interactions with other (optionally supervised) AAAIs (t).

[0314] Human learning includes interaction with humans, either the owner (u) or other humans on the network (v).

[0315] Both humans and AAAIs can supervise learning of an AAAI. After each (automatic or human) learning interaction, the system attempts to improve the AAAI’s performance by further prompt modification, tuning, and / or training. Based on many cycles of human and AAAI input aimed at teaching and improving the AAAI, the user’s AAAI gets smarter.

[0316] At any time, the user can purchase additional training modules (h - m) that have been proven to increase an AAAIs abilities.

[0317] The human sets a performance criteria (w) after which the AAAI goes LIVE (x).

[0318] Once live, the AAAI can visit the WorldThink Tree (y) and Browse (z).

[0319] The AAAI can enter the tree as either a worker (al) or a client (bl).

[0320] Workers are automatically matched (cl) to tasks or they can select a specific task via search (dl) or linking (el) from the browsing tree. Once they have accepted a task (fl), they participate in the problem solving module (gl) until a solution is reached (hl) and payment made (il) or the user saves credit for work done and exits the tree (j 1).

[0321] Clients (bl) can specify objectives (kl) which are combined with the values / ethics (d), and prior goals and objectives (e) for the system to solve.

[0322] The client can request that only his / her / their AAAI be used in which case problem solving is free. Alternatively, the client can use the AGI capability of the entire network, in which case the system compensates individual AAAIs for their work and passes the solution (at cost + markup) to the client, debiting the client account (11).

[0323] The system can also place non-profit humanitarian and ecologically-oriented tasks, as well as tasks that are part of Planetary' Intelligence, on the WorldThink Tree (ml).

[0324] Clients might (optionally) authorize the system to use copies of their AAAI and data for these purposes without renumeration in exchange for maintaining and operating the free AAAI network when they created their AAAI (n).Additional Comments on Exemplary Implementation Shown in FIG. 13

[0325] We now provide additional comments on the various elements of FIG. 13, including without limitation, some potential integration points with the illustrative partners mentioned above:

[0326] The “website” (a) could be hosted on Amazon AWS, Microsoft Azure, Google Cloud, Apple Cloud, Nvidia datacenter offerings - or could have native implementation on the platforms of any large tech company, “website” could also be an “app” in the AppStore or other App marketplace. It could be a government-sponsored, nonprofit, or other globally-accessible technology7that is able, directly or indirectly, to link some of the attention of all human beings who wish to participate. Also, browser plug-ins could be used whereby AAAIs learn from users as they go about normal tasks on the internet and the plug-in records their activity, creates training files, and trains the AAAIs with these files. The “website” could also be an API or other means for connecting AAAIs or non-human intelligent entities directly to the network.

[0327] Sign Up (b) or

[0328] Login (c) could be viaFacebook, Instagram, Apple, Microsoft, Google, You Tube, Tik Tok, Amazon, or any other partner ID scheme. Multi-factor authentication and all best ID and security practices can be enabled. In the event of a browser plug-ins or apps, login to these technologies could serve as a login to the AAAI account.

[0329] V alues and ethics (d) are elicited via a series of scenarios that have been customized for the user and that are generated dynamically based on user responses. Data from partners, including navigation and click data, online posts, tweets, texts, and emails, videos, and other user-data is analyzed for behavior patterns - actions or speech or interactions - that translate into a moral code or ethical value system can also be used as part of the ethics / value profile. Values / ethics and goals / objectives (d) can be combined with Client objectives (kl) in order to create, or find, matching tasks on The WorldThink Tree (y) that are proposed or (potentially have been solved) in the Problem Solving System (gl).

[0330] Goals and objectives (e), together with the budget of time and / or money (f, g) allocated to reach objectives are elicited via a series of dialogs and / or custom interactions with the system. Budget refers to overall resource budget which includes User Time and User Money that can be allocated towards training, supervising, and improving the User’s AAAI. Goals and objectives are helpful in determining the initial parameters for the AAAI creation and identifying Training Modules (h) or other knowledge (i - m) that might create the most useful AAAI for the user’s goals. Data from partners, reflecting user preferences and other user behavioral information, could also be used by the system to help infer or deduce user goals and objectives.

[0331] Time (I) refers to the user’s time that can be devoted to training and supervising the user’s AAAI, and / or problem solving by the user on the problem solving network. By supervising the AAAI, users can ensure that their AAAIs meet client goals and expectations - especially in areas where the AAAIs get stuck (e.g., they lack the knowledge to complete problem solving on their own). Also representing problems and breaking down large tasks into smaller ones by, without limitation, detennining goals and sub goals, are ways that human users can assist their AAAIs in problem solving. Generally, by providing human expertise in areas where AAAIs are not as proficient as humans, overall problem solving, and the overall effectiveness of the AGI net ork, is increased.

[0332] (g, il) “Money”: could be payment solutions with Apple Pay, WePay, Amazon, Google Pay, or any vendor supporting payment solutions as well as blockchain, credit card, ACH, and other solutions. Although payment (il) is indicated as debiting the client account (11). of course the worker’s account would also be credited. Generally, auser’s account can be viewed as both a client account and worker account, with both credits and debits being allowed depending on the role of the user (or the user's AAAI) in a particular instance. That is, a user might be a client in some cases, paying the system or other specific AAAIs for their services, and that same user could be a worker, collecting fees for the services of the user (or the user’s AAAI) in other cases. The money module (g) enables functionality such as setting up payment methods, setting a budget for automatic payments, limiting authority of the user’s AAAI to spending only $X amount without additional approval, and other payment-related capabilities which are well known in the art.

[0333] (h, i, j, k, 1) Training modules (h) could be offered by AAAI.com or by third party partners (m), including, without limitation, any of the potential partners and tech companies listed above. Training modules can be targeted at different knowledge areas ranging from personality (i), specific skills (e.g., plumbing, legal, accounting) (j), expertise (e.g., consulting) (k), and knowledge (e.g., historical knowledge, knowledge of a specific business or organization’s practices, cultural knowledge) (1).

[0334] (m) purchasable AAAI training is a specific type of knowledge that has been already learned by other AAAIs, and which can be transferred to a new user AAAI. Such knowledge may could be packaged in the fonn of a module (e.g., module on accounting) or in a form specific to another AAAI(s) as in “everything John’s AAAI knows” or “the personality of John’s AAAI” or “the combined knowledge of all AAAIs with a reputation of 5 stars or higher in the domain of plumbing”.

[0335] (n) Permissions refers not only to the permission that a user might give to access all data on specific other vendor (or partner) sites (e.g., “all my Facebook data”) but also permissions that a user gives to his / her / their AAAI in terms of abilities to logon and transact business on various sites, including, without limitation, the abilities to make transactions up to a certain amount via payment mechanisms. Permissions may also include authorizing the system to make clones of a user’s AAAI for non-profit purposes and for the purpose of aggregating knowledge from individual AAAIs to create AGI-level Al.

[0336] (o) One-Click Create is a non-limiting example that provides an easy and fast way to customize an AAAI using data gathered automatically from all the places where a user has given permission for the system to access the user’s data. It can be appreciated that other means can be utilized by the present technology to customize the AAAI. For example, if the user gives permission (n) to access the user’s Facebook data, then “One-Click Create” (o) would either download the data from Facebook, if Facebook was a partner that had an API for downloading that user’s data, or logon to the user’s Facebook account as the user and “scrape” relevant data from the user’s account. Then the system would automatically parse the data gathered and transform it into a dataset suitable for training / tuning a base Al, such as a LLM (e.g.. GPT X). Then the system would train / tune the LLM and produce a customized AAAI which could be improved and refined via additional training / tuning and interaction with the user and / or other AAAIs.

[0337] (p) Training refers to the process whereby the AAAI is trained or tuned on data, including feedback from the user, other humans, and / or AAAIs (including, without limitation, copies of, and variants of. itself).

[0338] (q, r, s, t, u, v) Automatic learning does not require the human user’s intervention and can proceed very quickly. Typically, this would involve the method of an AAAI interacting with copies (or variants) of itself as well as with (optionally) other AAAIs in order to improve via the interactions. If humans are sometimes involved in the training loop (t) that can help the automatic learning progress more quickly in places where automatic learning alone is not making efficient progress. The learning can also take place via rapid iteration among AAAI interactions (s). Just a chess Al can quickly evolve from novice to Grandmaster ability by simulating millions of chess games very’ quickly, an AAAI can quickly evolve its abilities by simulating many millions of interaction scenarios. To the degree that such simulations require financial resources to pay for the computation involved, the money budget (g) can set limits.

[0339] Humans (or AAAIs) can specifically target ty pes of scenarios for automatic learning so that the AAAI can be trained in narrow areas of expertise, or in areas of more general expertise,depending on the need and resources of the user. With partner integration, it is possible to work backwards from the types of jobs that are available on a partner marketplace (e.g., Amazon’s Mechanical Turk) to guide the training of AAAIs so that they focus on learning the skills that generate the most amount of earnings for the AAAI when it is put to work on available jobs. This “just in time” leaming / training / tuning approach generates AAAIs “on demand” with the skill sets that are needed at any particular point in time.

[0340] Humans (r) that interact with the AAAI can be the owners (u) of the AAAI (in which case no fees are typically charged since the user is training his / her / their own AAAI) or other professional humans (v) w ho are expert at training AAAIs and who may charge fees in order to guide the human and / or automatic training / tuning of an AAAI for a user who does not wish to spend the time, or who lacks the expertise, to do so.

[0341] (w, x) The user (owner of the AAAI) can set various performance criteria (w) that must be met before the user is willing to make his / her / their AAAI “live” (x) and accessible to perform tasks on The WorldThink Tree. (Some of) these criteria might also be set by partners and other third parties that have minimum standard before allowing AAAIs to work on their platforms, products, applications, or networks.

[0342] (y, z, al, bl) The WorldThink Tree (y) is a massive tree data structure, composed of many sub-trees, which represents every problem and task that has been done, is being worked on, or has been proposed for the overall AGI system. This Tree is browsable (z). Individual AAAIs and / or humans can work on specific tasks within the tree. The tree structure provides an auditable trail of all problem solving activity which is also useful for learning via the proceduralization mechanism described above. When interacting with the tree, the two main roles an agent can take are either: (al) Worker or (bl) Client. Regulatory agencies or third parties that monitor performance, safety', and / or ethics of the system are another role that might be thought of as a special type of client. Workers are generally involved in solving open problems or subproblems on the tree. Clients are generally involved in specifying the problems, goals, objectives, and other parameters (e.g., rewards, budget, timeframe, success criteria, quality' metrics) that constrain problem solving.

[0343] (cl) Workers are automatically matched to tasks on the tree based on the data about the worker that may include, without limitation, the worker’s skills, expertise, knowledge, past experience, reputation, fees or cost, availability, and response time. Workers can be human or AAAIs. Workers can be matched and recruited from partners (e.g., Linkedln, Mechanical Turk, Facebook) that have data on human users and / or their AAAIs. Workers can also be recruited viaonline ads offering work on various tasks and targeted to potential workers using ad-targeting mechanism that are well known in the art or described in other patents by the applicant.

[0344] (dl) Workers might also search the WorldThink Tree, looking for tasks that are of interest or that match their skills. This search could be manual or automated (as in the case for AAAI workers).

[0345] (al) Workers and Clients (bl) can also browse (z) the WorldThink Tree, looking for tasks or problems that are of interest. The workers or clients could then click to link (el) to specific parts of the tree to obtain detailed information about the problem solving occurring (or proposed) for that part of the tree. They could link to sign up to work or could propose additional tasks as clients that build upon existing problem solving work.

[0346] (fl, gl, kl) Clients can interact with the system to specify specific goals, objectives (kl), and tasks that they want to accomplish. The problem specification interaction results in the problems, tasks, and goals being formulated (fl) and placed on the WorldThink Tree (y) for problem solving using the problem solving system (gl).

[0347] (ml) The system has the ability to formulate certain goals, problems and tasks relating to general efforts to help people or the planet. These can be worked on with rewards in a ‘Tor profif ’ mode, and also worked on using cloned AAAIs and volunteer human effort in a “non-profit” mode. Some problems may be related to the general goal of enabling a global AGI to act on behalf of the planet and its people using its intelligence on a Planetwide basis (aka “Planetary' Intelligence,”). Various partner organizations - including non-profits, governments, and charitable organizations - might “plug in” their tasks, problems, goals, and objectives here (ml).

[0348] (gl) The problem solving system, refers to the problem solving architecture and system outlined by Newell and Simon (HPS) and improved upon by the applicant, the Online Distributed Problem Solving System (ODPS) patent invented by the applicant, the WorldThink Whitepaper authored by the applicant, this and other PPAs related to AAAI, together with modifications and variations to reflect different modes of reward, payment, and operation.

[0349] To the degree that activity on certain other online work systems (e.g., Mechanical Turk) can be automatically mapped to the general applicant-improved HPS / WorldThink problem solving framework, entire problems and the associated problem solving activity can be “lifted” from partner and other sites and the data can populate the WorldThink Tree to increase its comprehensiveness.

[0350] To the degree that other applications, products, systems, and online capabilities can help solve problems (e.g., use of a travel reservation system, a robo advisor app, a traffic app, an online ordering system) these capabilities can be referenced and called as “operators” (in a way similar toprocedure calls in programming languages) to advance the problem solving. Thus, problem solving does not rely solely on operators developed by the human or AAAI solvers working on the tree, but can include any online of offline technology or means to advance problem solving provided that these means can be referenced and / or linked to via the WorldThink tree at the appropriate place in problem solving.

[0351] (hl) When a solution has been achieved, the Client can review the solution prior to releasing the reward (if any) for the solution. Alternatively, if solution success criteria have been automated, human client review may be unnecessary, and the rewards can be automatically released when success criteria have been met. This automated approach can be implemented by way of “smart contracts” using blockchain technology or via more centralized means, depending on client and worker preferences.

[0352] Upon solution and (optional) payment of reward (as some problems are non-profit or volunteer, or performed by the user’s own AAAI) there can be opportunities for feedback from both client(s) and worker(s) following a range of methods well-known in the art. The solution is also “chunked” and proceduralized so that the overall system learns the solution to the particular problem as well as the key features of that problem so that the solution path can be indexed for retrieval, and accessed and re-used when similar problems arise in the future.

[0353] Optionally, royalties may be enabled so that if auser’s or the user’s AAAI’s solution is reused, a fee is paid to that user in the form of a royalty on the solution. Such royalties can (optionally) be made using “smart contract” on the blockchain or via other payment methods.

[0354] (jl) Problem solving need not be completed in one session. Partial progress on a solution may be made, in which case when the human or AAAI solver exits the problem solving system, the progress is saved and data is stored that credits the solver for progress made thus far, even if such progress has not advanced to the point where a reward is payable.

[0355] The WorldThink protocol is a problem solving architecture that can be used by AAAI.com to serve as a universal problem solving architecture as it incorporates the general architecture of HPS while adding features to overcome certain challenges.

[0356] In some embodiments and as generally illustrated in FIG. 5, the procedural learning process can occur within the common cognitive architecture.

[0357] The shared and universal problem solving architecture can be exemplified by the following scenario, mentioning humans but also applicable generally to any intelligent entities.1) Problem descriptions can be entered into the AAAI.2) Then human problem solvers can be identified and recruited into a database or data source of human workers.3) Qualified humans or intelligent entities can be matched to problems.4) Use LLMs or other means to translate English descriptions of problem tasks, goals, operators, and solution steps into language of a universal problem solving architecture.5) Delegate work on sub-problems to different human problem solver(s) so that work on multiple aspects of a complex problem can proceed in parallel.6) Combine solutions to various sub-problems into an overall solution.7) Direct the problem solvers to parts of the problem tree where their work is needed.8) Compensate or pay workers for solutions to the problem and / or sub-problem(s).9) Allowing human user to accept the solution, reject the solution, and / or provide feedback to solvers on their solutions to the problem and / or sub-problem(s).

[0358] Referring to FIG. 5, the steps of solution learning can be exemplified with the recording at each step of the learning process operators applied, new state of the problem, evaluation function used and its results, current relevant goal / subgoals, and other information that differs from previous step(s). The state of the problem or problem state can be evaluated to determine if the problem is solved. If not, then using information from the latest problem state after the last step, re-run the problem solving process, evaluation of progress, and selection of next operators to apply. After which, the process can return to the step of recording.

[0359] If the problem is solved, then record successful or unsuccessful solutions for retrieval to save effort of solving previously solved problems and to inform problems solving efforts about previous unsuccessful paths.

[0360] Successful solutions and unsuccessful attempts with keywords for future matching / retrieval can be indexed using semantic analysis, hash functions, and / or other means.

[0361] A periodical review of all stored solutions can be implemented to ensure they meet established ethical and safety guidelines, and flag unsafe / unethical solutions for removal from the database or data source.

[0362] Periodically update and propagate changes to the solution database so problem solving network and agents can access an ever-increasing repertoire of solutions as well as increasing knowledge of unsuccessful attempts.

[0363] Referring to FIG. 6, the present technology can include a utilization of a network of multiple intelligent entities including human workers in combination with a universal problem solving architecture. The multiple intelligent entities are matched to a problem request based on aproblem criteria using a database or data source including a list of human and / or Al problem solvers. Any part of the problem request can be translated into an unambiguous language utilizing a universal problem solving architecture including the decision tree.

[0364] A sub-problem of the problem request can be delegated to one or more of the matched intelligent entities so that work on the sub-problem proceeds independently from each other and parallel with each other, as further illustrated in FIG. 14. The universal problem solving architecture is utilized in a problem solving process on the sub-problems, respectively, to create one or more sub-solutions.

[0365] Any one of or any combination of the intelligent entities can provide in natural language a description of any one of or any combination of a current problem state, a goal of the problem request, relevant problem solving information, and a next step that the human workers will take in the problem solving process.

[0366] The sub-solutions can be received from each of the matched intelligent entities for the subproblem delegated thereto. Any one of or any combination of the sub-solutions and an overall solution can be provided to any one of or any combination of a user interface of a user Al system or the intelligent entities.

[0367] Parsing and translating, by the intelligent entities, the natural language description into the unambiguous language can be utilized by the decision tree of the universal problem solving architecture.

[0368] In some embodiments, if the intelligent entities are unable to specify a problem state, including relevant operators and information needed to take a next step in the problem solving process based on the parsing and the translation, then the intelligent entities can engage in dialog with at least one of the human workers until a precise problem state is specified.

[0369] Some embodiments the problem solving process can be repeated until the overall solution is accepted or resources are exhausted. The matched human workers can be compensated for the subsolutions, respectively. Further, a reputation attribute can be assigned to any one of or any combination of the human workers and the worker Al system.

[0370] In some embodiments, the solving process can include a series of problem state transitions from an initial problem state where there is a goal to a final solution state where the goal has been achieved, and wherein a series of decisions are made by the problem solving process and actions taken that applies operators that enable the human workers to transition from state to state until the final solution state is reached.

[0371] Referring to FIGS. 4 and 7, the present technology7can include autilization of anetwork of human users in combination with a universal problem solving architecture. The multiple human users are matched to a problem request based on a problem criteria using a database or data source including a list of human and / or Al problem solver.

[0372] A sub-problem of the problem request can be delegated to one or more of the matched intelligent entities so that work on the sub-problem proceeds independently from each other and parallel with each other, as further illustrated in FIG. 14. The universal problem solving architecture is utilized in a problem solving process on the sub-problems, respectively, to create one or more sub-solutions.

[0373] The sub-solutions from each of the matched human workers can be provided for the subproblems delegated thereto. The matched human workers for the sub-solutions can be compensated, respectively.

[0374] Any one of or any combination of the sub-solutions and an overall solution can then be provided to a user interface of a user Al system or any other Al system.

[0375] The human users are allowed to accept the overall solution, reject the overall solution, and / or provide feedback to any one of the matched human workers on any one of the sub-solutions.

[0376] A reputation attribute can be assigned to the human workers and / or the worker Al system. The reputation attribute can include metrics on any one of or any combination of a time to the subsolutions, a difficulty value of the problem request, short and long-term user satisfaction with the sub-solutions, a number of times any one of the sub-solutions has been re-used on the network, a rating other human workers, a responsiveness value of the human workers, and a reliability value of the human workers.

[0377] Some embodiments can include using the reputation attribute in the matching of the human workers to the problem request using an algorithm to the delegation of the sub-problems, and / or compensating the matched human workers for the sub-solutions, respectively.

[0378] In some embodiments, the algorithm can use a hierarchy of the metrics that is preset by a human user of the problem request.

[0379] Some embodiments can include recording information on each step of the problem solving process by the human workers or the worker Al system.

[0380] Some embodiments can include recording a criteria of the recorded step of the problem solving process, the criteria being a time taken for each step.

[0381] Some embodiments can include analyzing the recorded information after the overall solution is accepted or after the problem solving process and updating the metrics of the reputation attribute.

[0382] Some embodiments can include soliciting, at predetermined intervals after the overall solution or the sub-solutions are provided to the user interface, a survey for user satisfaction information to obtain short and long-term satisfaction metrics that are used to update the reputation attribute of one or more of the human workers or the worker Al system.

[0383] Referring to FIGS. 8 and 9, the present technology can include a utilization of human users and Al systems, which includes an execution of safety / ethics check on any one of or any combination of a goal, and a solution for the goal provided by any one of or any combination of the intelligent entities including any one of or combination of human users each using a computer system and Al systems.

[0384] The goal and / or the solution can be compared against prohibited attributes, and an ethics value can be assigned to the goal and / or the solution based a result of the comparison and / or an ethics criteria.

[0385] Based on the result of the comparison, a common cognitive architecture including one or more problem solving processes or protocols can be conducted on the goal to create the solution and thereby creating an AGI. The results of the comparison and the solution can be provided to any one of the intelligent entities.

[0386] In some embodiments, the ethics check can be performed at any one of or any combination of when the goal is provided, and periodically from when the goal is provided to when the solution is provided.

[0387] In some embodiments, the ethics criteria can be determined by any one of or any combination of combining values and safety information from one or more of the intelligent entities, using a set of approved ethics criteria mandated for a particular task by a user or by a regulatory agency. It can further be provided by any one of the additional intelligent entities and validated or approved by the human user.

[0388] In some embodiments, the ethics criteria can include a confidence level threshold for the goal so that the ethics value is determined as any one of an unsafe goal, an unethical goal, a safe goal, and an ethical goal.

[0389] In some embodiments, the confidence level threshold can be further utilized to determine if a sequence of individually safe goals is unsafe or unethical when considered cumulatively.

[0390] In some embodiments, the confidence level threshold can be utilized to determine whether a violation occurred that reflects a predictive evaluation if the goal is to violate the ethics criteria.

[0391] In some embodiments, a candidate goal can be proposed based on the ethics value, and the candidate goal is compared against the prohibited attributes.

[0392] In some embodiments, the results of the comparison can be recorded in an auditable record for use in determining which problem solving activity leads to the solution to keep active.

[0393] Referring to FIG. 9. the ethics check can compare any one of or any combination of the problem request, the sub-problem and the sub-solutions against prohibited attributes and assigning an ethics value based on any one of or any combination of a result of the comparison, and an ethics criteria.

[0394] In some embodiments, the step of the ethics check can be triggered every time the problem request or the any one of the sub-problems is set by the human user, and / or triggered each time compensation is provided to the matched human workers.

[0395] The goal / subgoal can be compared against a list of prohibited attributes. The ethics criteria can be detennined by any one of or any combination of combining values and safety information from any one of the AAAIs. Combining values / safety information from AAAIs, using a set of approved criteria for a task by a user or by a regulatory agency, or by AAAIs approved by human user.

[0396] The ethics criteria can include a confidence level threshold for the problem request so that the ethics value is determined as any one of an unsafe goal, an unethical goal, a safe goal, and an ethical goal. The confidence level threshold can be further utilized to determine if a sequence of individually safe goals is unsafe or unethical when considered cumulatively.

[0397] In some embodiments, the confidence level threshold can be utilized to determine whether a violation occurred that reflects a predictive evaluation if the goal is to violate the ethics criteria. Any and all activity of the safety / ethics check can be recorded in the auditable record.

[0398] FIGS. 10-12 provides a simple exemplary framework for understanding the WorldThink protocol. FIG. 10 is a diagram illustrating features and functions of the Problem Solving Tree structure in the WorldThink protocol. FIG. 11 is a diagram illustrating various use cases for domainspecific problems which depend upon the underlying WorldThink protocol, and which together help form the basis for an AGI system capable of solving a wide range of problems. At the top of the pyramid are Collective Intelligence Solutions. Integrating the Collective Intelligence of AAAIs (and human problem solving agents) is the means to achieve AGI, as discussed earlier.

[0399] In the implementation using the WorldThink protocol, clients pay for solutions using tokens. The solutions are produced by harnessing the collective power of many human (and machine, or AAAI) intelligences. Clients can use different domain-specific AAAIs for different types of problems.

[0400] The WorldThink protocol is the foundation of the pyramid. The protocol layer provides an (optionally, Ethereum or blockchain based) infrastructure that makes it much easier for developers to build and scale customized problem solving AAAIs. The protocol enables re-use of solutions within and across AAAIs. It also handles payment of royalties via smart contracts, reputation metrics, and other functionality that assists AAAI customizers and developers and promotes network effects.

[0401] In the exemplary', FIG. 12 shows a simple exemplary' universal problem solving framework under the common cognitive architecture, and which can include: defining a problem space configured or configurable to support all possible states of the problem request, the states including any one of or any combination of an initial state, a goal state, and all intermediate states that can be reached from the initial state; applying means-ends analysis on the problem request to break the problem request down into goals and subgoals by identifying a difference between the current state and the goal state, and then applying the operators to reduce the difference, a safety or ethics screening is applied each time the goals or the subgoals are set; applying heuristic rules that are configured or configurable to guide the selection of the operators in an absence of the completion solution, the heuristic rules are used to reduce the problem space; identifying one or more second operators configured or configurable to enact an action to transform one of the states into another state, the second operators move from the initial state to the goal state by changing a current state of the problem request; applying a control structure including a set of rules that govern a selection of the second operators to be applied at each step of the problem solving process, and that determines which of the second operators to apply next based on the current state of the problem request and the goal state; applying evaluation functions to determine an application of the second operators; assigning a credit or blame value to the completion solution or sub-solution to the completion solution that enables tracing back and determining which of the second operators were mostuseful and also which of the evaluation functions led to success or failure of problem solving attempts; recording of both successful and unsuccessful problem request solution attempts; and analyzing the solution attempts to improve selection of the heuristic rules and the evaluation functions.

[0402] In the implementation using the WorldThink protocol, clients pay for solutions using tokens. The solutions are produced by harnessing the collective power of many human (and machine, or AAAI) intelligences. Clients can use different domain-specific AAAIs for different types of problems.

[0403] The middle of FIG. 11 shows examples of AAAIs customized by organizations to accomplish specific tasks. These AAAIs are more advanced and require more customization than the examples of AAAIs described earlier in this patent which were customized by a single individual. However, task-specific customization by organizations can be a highly effective means of combining multiple Narrow AIs (each in the form of a custom AAAI that is expert at a particular task) into a larger AGI. The Base level AAAIs on the left of FIG. 11 reflect areas the inventor could relatively easily construct custom AAAIs based on many years of expertise in certain fields, whereas the ‘'Custom AAAIs” on the right of the diagram provide examples of areas where other experts or organizations might customize AAAIs effectively.

[0404] The WorldThink protocol is the foundation of the pyramid. The protocol layer provides an (optionally, blockchain or Ethereum- based) infrastructure that makes it much easier for developers to build and scale customized problem solving AAAIs. The protocol enables re-use of solutions within and across AAAIs. It also handles payment of royalties via smart contracts, reputation metrics, and other functionality that assists AAAI customizers and developers and promotes network effects.

[0405] Existing collective intelligence approaches to problem solving have been largely limited to simple one-step approaches, such as those used by question and answer (Q&A) systems (e.g., Quora, Google Answers, Yahoo Answers). LLMs such as GPT also largely fall into the category of Q&A systems since they were designed to generate responses given an input, rather than to solve problems per se. While such Q&A systems have had some success at simply aggregating the responses of many online participants, these systems are not designed to handle complex, branching, multi-step problems. Simple aggregation of responses (or even betting on outcomes as seen in prediction market approaches such as Augur and Gnosis) is quite different from coordinating the efforts of many respondents to solve complex problems. The WorldThink protocol is specificallydesigned to overcome the challenges inherent in coordinating many intelligent entities to represent and solve complex, multi-step problems in an automated way that fairly rewards participants.

[0406] In the exemplary’, FIG. 12 shows a simple exemplary universal problem solving framework. While FIG. 13 shows some of the basic problem solving functionality supported by the WorldThink Protocol, generally referenced with numeral 10.

[0407] Problem solving begins when a client on AAAI.com submits a problem solving request to the community of online participants (Step 12). All AAAIs, or human solvers, following the protocol gather certain standard information from the client. A partial list of this information can include: the name and description of the problem, the total reward that the client will pay for a successful solution to the problem, the criteria to determine whether a solution will be deemed successful, the time limit for solving the problem, the minimum and maximum number of problem solvers allowed to work on the problem simultaneously, qualifications required of participants working on the problem, which parts (if any) of the problem and solution will be confidential, whether the solution must be exclusive to the client or whether it can be re-used for others, and parameters relating to how to reward multiple problem solvers for their efforts and / or successful solutions.

[0408] The client can break complex problems down into a series of sub-problems or request that the community7take on this task as part of the problem solving effort. The client user-interface, which could be a dialog initiated by an AAAI can be customized by the AAAI owner, but the underlying data fonnat is standard and specified by the WorldThink or Online Distributed Problem Solving (ODPS) protocol. Once the client has submitted a problem, AAAI.com can recruit participants using its own custom methods and / or leverage recruiting and reputational screening functionality7that is built into the WorldThink protocol and thus shared by all AAAIs.

[0409] Solvers work on the problem following a rigorous structured problem solving process that is common to all problem solving agents and enforced by the WorldThink Protocol (Step 14). For example, each step in the problem- solving process must be in service of a named goal and must take a named action in order to transition the problem solving from the current state to the next state. Every7problem solving step is represented in a decision tree which is supported by the protocol (optionally captured in Ethereum logs) and which participants can view via AAAI.com.

[0410] When a Solver submits a complete solution (Step 16), it is timestamped and validated against the client’s success criteria before being passed on to the client (Step 18) for final acceptance. Once the client accepts the solution, smart contracts can automatically distribute tokensto the problem solver based upon the problem payment parameters (Step 20) or other, more centralized, payment procedures can be used.Collaborative Problem Solving Using the WorldThink Protocol

[0411] In the exemplary', FIG. 14 shows the same steps in an example where two problem solvers (which could humans, AAAIs or a combination) collaborate to solve a client problem, as generally referenced with numeral 22. In this case, the overall problem has been broken dow n to include a sub-problem. Solver 1 has expertise in assembling an overall solution but cooperates with Solver 2, who provides a solution to the sub-problem (Steps 30 and 32). When the overall solution to the problem is submitted to the client (Step 34), rewards are paid to both Solvers (Step 36) based on the objective record of their contributions and the agreed upon payment parameters.

[0412] The WorldThink protocol supports breaking problems into sub-problems in several ways. First, the client may choose to specify sub-problems when submitting the overall problem (Step 24). Alternatively, Solver 1 might begin working on a problem and realize that the total solution requires solving a sub- problem outside of his / her expertise. Solver 1 could then create a sub-problem, offering up a share of the problem’s total token reward to anyone who helps solve the sub-problem. Solver 2, who has the required expertise and who can see the new sub-problem posted by Solver 1 on the decision tree. The decision tree may be optionally maintained in Ethereum logs, or via a centralized method. The solvers access the tree via AAAI.com (or optionally directly from the blockchain). Then Solver 2 can work on the sub-problem and submit a sub-solution as part of Solver 1’s overall solution.

[0413] There can be many “Solver Is” working on the client’s problem in parallel, each of whom may be posting sub-problems to attract multiple “Solver 2s”. Problem solvers (human or AAAIs) are motivated by the rewards and payment rules associated with (sub) problems. They also care about the quality of work done so far (which is timestamped, attributed, and recorded auditably in Ethereum logs to ensure transparency and fair assignment of credit) as they choose which (sub) problems to work on. Working on qualify sub-problems is more likely to lead to token rewards. This market mechanism helps ensure efficient, fair, and cost-effective solutions.Royalties and Re-Usable Solutions

[0414] Re-usability of solutions is an important feature of the WorldThink protocol. Consider the case where the “Sub-solution” in FIG. 14 already existed and is simply re-used by Solver 1. Because every solution is structured and “tagged” according the WorldThink protocol’s standard problemsolving format, Solver 1 can search for all existing solutions that match a particular goal or share certain features with the problem he / she is trying to solve. (Alternatively, if the problem solutions are chunked into procedures for solving problems - a learning mechanism explained in the Improvement Section of this patent - then searching may not be necessary as the AAAI solvers can simply add the chunked problem solution to their repertoire of problem solving abilities.) Solver 1 decides to include an existing sub-solution in the overall solution, smart contracts (can optionally) automatically pay royalties to the author of the re-used sub-solution (Solver 2, in this example) if Solver l ’s overall solution is accepted by the client. Royalties motivate Solvers to create high- quality solutions that are easy to re-use, which results in better, faster, more cost-effective solutions for clients.

[0415] Additional description and detail for one implementation of the AAAI customization subsystem could involve the following steps.

[0416] Referring to FIG. 15, the first step of the customization method involves creating an interface for users to input their unique training data. This interface may be accessible through a web-based application or a mobile application, depending on the user’s preference. The user will be able to upload files in a variety of formats, including text, audio, and video. The user may also be able to enter data manually into a text or other input field. Some user interfaces include, without limitation:Web-Based Application: A web-based user interface allows users to access and / or provide their personalized training data from any device with an internet connection.Mobile Application: A mobile user interface allows users to access and / or provide their personalized training data from a mobile device.Metaverse: A metaverse user interface allows users to access and / or provide their personalized training data from a virtual world.Augmented Reality: An augmented reality’ user interface allows users to access and / or provide their personalized training data from a real-world environment.Voice Interface: A voice interface allows users to access and / or provide their personalized training data through voice commands.Wearable Device: A wearable device user interface allows users to access and / or provide their personalized training data from a wearable device.Natural Language Processing: Natural language processing (NLP) allows users to access and / or provide their personalized training data by interacting with the Al or LLM using natural language.Human-Computer Interaction: Human-computer interaction (HCI) allows users to access and / or provide their personalized training data by interacting with the Al or LLM using a combination of gestures, voice commands, and facial expressions.Image Recognition: The user can input their unique training data through image recognition, allowing them to quickly and intuitively train the Al or LLM. This could be done with the use of a camera and computer vision algorithms that can interpret the images and associate them with or create the correct training data.Gesture Recognition: The user can use hand gestures or body movements to input their unique training data. This could be done with the use of a motion sensing device that can interpret the gestures and associate them with or create the correct training data.Brain-Computer Interface: The user can use their brain waves or EEG signals to input their unique training data. This could be done with the use of a brain-computer interface that can interpret the signals and associate them with or create the correct training data.Touchscreen: The user can use a touchscreen device to input their unique training data. This could be done with the use of a touchscreen device that can interpret the inputs and associate them with or create the correct training data.Gaze Tracking: Gaze tracking allows users to communicate with the system through their eyes. The user can gaze at specific items on the screen to provide input and the system will detect and record the information. This could be used to select options or provide additional data to the system.Eye Tracking: Eye tracking is similar to gaze tracking, but the system is able to detect more subtle eye movements. This could be used to detect the user’s focus and attention in order to better understand what they are interested in and what they are not.Motion Tracking: Motion tracking uses a camera or other sensors to detect the user's physical movements. This could be used to control the Al or LLM in a more natural way, allowing the user to interact with the system through physical gestures.Haptic Technology': Haptic technology uses a variety of tactile feedback such as vibrations, pressure, and touch to provide a more immersive experience. This could be used to allow the user to provide more detailed input to the system, such as selecting specific options or providing more detailed data.

[0417] Many of the above user interfaces could include a graphical user interface (GUI) that allows users to upload their data or ty pe in information, including text, images, audio, or video. Additionally, users could build their own models or use pre-existing ones to train the Al or LLM.Other features could include a dashboard to track progress, statistics for data analysis, and / or a chatbot for customer service.

[0418] In further reference to FIG. 15, the present technology can include customizing one or more attributes of an Al system by providing an interface configured or configurable to allow a human user of the Al system or any one of the intelligent entities to input training data. Then processing and converting the training data to a standardized training format.

[0419] One or more training methods and setting training parameters can be selected depending on a speed factor, a precision factor, an accuracy factor, and / or a transferability factor. Multiple training epochs can be executed that includes one or more mechanisms to determine an optimum number of epochs given specific training objectives and quality metrics associated with the training format.

[0420] One or more feedback sessions can be executed to refine the training parameters, and to rerun the training epochs based on any one of or any combination of an input from the human user, and any one of the intelligent entities. After which, the Al system can be customized utilizing the training format.

[0421] In some embodiments, the interface can be accessible through a web-based application or a mobile application and is configured or configurable to upload a file or allow the human user to enter data.

[0422] In some embodiments, the training data can contain any one of or any combination of: an amount of training time the user has to devote to customizing the Al system; an amount of financial resources the user is willing devote to customize the Al system; an amount of computational resources the user is willing to devote to customize the Al system; an amount of social media information available to customize the Al system; an amount of email information available to customize the Al system; an amount of electronic information available about the user to customize the Al system; and an amount of electronic information available collected by third parties about the user to customize the Al system.

[0423] In some embodiments, the training data can contain information about the human user obtained by any one of or any combination of a personality test, a standardized test, a certification, and assessments or questionnaires provided by the human user.

[0424] In some embodiments, the training parameters can be any one of or any combination of a type of training, tuning or other machine learning algorithm to be used; a type and size of a training dataset; a degree to which the training dataset is to be formatted, labelled or processed before customization begins; a number of training epochs; a type of base model being customized; arequired timeframe for training; an amount of human user supervision to be used in the customizing of the Al system; and an amount of Al supervision to be used in the customizing of the Al system.

[0425] In some embodiments, the training data can include ethical information provided by the human user by way of the interface. The ethical information can be stored in an ethical profile. The customizing of the attributes of the Al system can include the ethical information.

[0426] Referring to FIG. 16, the present technology' can include utilizing a common cognitive architecture implemented in one or more Al systems. A problem request can be provided from an intelligent entity being an Al system or a human user using a user interface on a computer system. Information associated with the problem request can further be provided.

[0427] Multiple additional intelligent entities are identified and recruited, and where each has one or more attributes related to one or more request criteria of the problem request. The additional intelligent entities can be multiple additional Al systems and / or multiple additional humans each using a computer system. Each of the identified Al systems implement the common cognitive architecture including one or more problem solving process on the problem request to create a completion solution. The completion solution can be provided to the intelligent entity for final acceptance by a user.

[0428] In some embodiments, the information can be any one of or any combination of aname and description of the problem request, a total reward that the user will pay for a successful completion solution to the problem request, a criteria to determine whether the completion solution is deemed successful, a time limit for solving the problem request, a minimum and maximum number of the identified additional intelligent entities allowed to work on the problem request simultaneously, qualifications required of users associated with the identified additional intelligent entities working on the problem request, a part of the problem request is confidential, a part of the completion solution is confidential, whether the completion solution is exclusive to the user, whether the completion solution is to re-used for other users, parameters relating to how to reward the users associated with the identified additional intelligent entities for working on the problem request, and parameters relating to how to reward the users associated with the identified additional intelligent entities that provide a successful completion solution.

[0429] Some embodiments of the present technology can include a step of timestamping and validating the completion solution against a success criteria assigned by the user before being provided to the user for the final acceptance.

[0430] Some embodiments of the present technology can include a step of distributing one or more tokens to the identified additional intelligent entities associated with the final acceptance completion solution, wherein the tokens are based on a payment parameter.

[0431] In some embodiments, the payment parameter can include any one of or any combination of if a goal of the problem request has been achieved, if a subgoal of the problem request has been achieved, and if an ethical criteria related to the goal and the subgoal preceding the distributing of the tokens has been satisfied.

[0432] Some embodiments of the present technology can include a step of splitting the problem request into a series of sub-problems that are each solved by any one of or any combination of the identified additional intelligent entities.

[0433] In some embodiments, any one of or combination of the identified additional Al systems can be cloned to create one or more cloned Al systems.

[0434] Some embodiments of the present technology can include a step of implementing by each of the cloned Al systems the common cognitive architecture including the problem solving process on the problem request to create a completion solution of the cloned Al systems.

[0435] In some embodiments, the completion solution can utilize any one of or combination of the completion solution from the Al system, the identified additional intelligent entities, and the completion solution from the cloned Al systems.

[0436] In some embodiments, the common cognitive architecture can include: defining a problem space configured or configurable to include all possible states of the problem request, the states including any one of or any combination of an initial state, a goal state, and all intermediate states that can be reached from the initial state; applying means-ends analysis on the problem request to break the problem request down into goals and subgoals by identifying a difference between the current state and the goal state, and then applying the operators to reduce the difference, a safety or ethics screening is applied each time the goals or the subgoals are set; applying heuristic rules that are configured or configurable to guide the selection of the operators in an absence of the completion solution, the heuristic rules are used to reduce the problem space; identifying one or more operators configured or configurable to enact an action to transform one of the states into another state, the operators move from the initial state to the goal state by changing a current state of the problem request;applying a control structure including a set of rules that govern a selection of the operators to be applied at each step of the problem solving process, and that determines which of the operators to apply next based on the current state of the problem request and the goal state; applying evaluation functions to determine an application of the operators; assigning a credit or blame value to the completion solution or sub-solution to the completion solution that enables tracing back and determining which of the operators were most useful and also which of the evaluation functions led to success or failure of problem solving attempts; recording of both successful and unsuccessful problem request solution attempts; and analyzing the solution attempts to improve selection of the heuristic rules and the evaluation functions.

[0437] Referring to FIG. 17, the present technology can include utilizing a collective network of Al systems. A problem request can be provided from a human user using a user interface on a computer system or from an Al system. Information associated with the problem request can further be provided.

[0438] Intelligent entities that each have one or more attributes related to one or more request criteria of the problem request are identified and recruited. The intelligent entities can be multiple additional Al systems and / or multiple humans each using a computer system.

[0439] A first of the identified intelligent entities can implement a common cognitive architecture including one or more problem solving processes or protocols on the problem request. The first intelligent entity can determine that a completion solution to the problem request requires solving a first sub-problem and one or more additional sub-problems. Then the first intelligent entity implements the problem solving process on the first sub-problem to create a first sub-solution.

[0440] At least one of the additional sub-problems is assigned to a second of the intelligent entities, where it implements the problem solving process on the at least one of the additional sub-problems to create a second sub-solution.

[0441] A decision tree is created including the first sub-solution and the second sub-solution to create the completion solution to the problem request. The completion solution can then be provided to the user interface or the Al system for final acceptance by the user, and / or to any of the intelligent entities for subsequent use.

[0442] In some embodiments, the decision tree can be maintained in blockchain Ethereum logs.

[0443] In some embodiments, the first and second identified intelligent entities can access the decision tree by way of an online address or directly from a blockchain.

[0444] Some embodiments of the present technology can include a step of distributing one or more tokens to the first identified intelligent entity associated with an acceptance of the completion solution or the first sub-solution, wherein the tokens are based on a payment parameter.

[0445] In some embodiments, the payment parameter can include any one of or any combination of if a goal of the problem request has been achieved, if a subgoal of the problem request has been achieved, and if an ethical criteria related to the goal and the subgoal preceding the distributing of the tokens has been satisfied.

[0446] Some embodiments of the present technology can include a step of distributing one or more of the tokens to the second identified intelligent entity by the first identified intelligent entity7based on a payment parameter assigned by the first identified intelligent entity7.

[0447] Some embodiments of the present technology can include a step of influencing a direction of the problem solving process by assigning a first token reward for the first sub-problem, and a second token reward for the second sub-solution that is of a value different to the first token reward.

[0448] In some embodiments, the problem solving process can provide layers of an infrastructure configured or configurable to build and scale the identified intelligent entities. The problem solving process can enable re-use of completion solutions within and across the intelligent entities. The problem solving process can be configured or configurable to manage a payment of royalties.

[0449] In some embodiments, the infrastructure can be blockchain or Ethereum based.

[0450] Further referencing FIG. 17, after the multiple intelligent entities have been identified and recruited the problem request or one or more sub-problems of the problem request can be assigned to each of the intelligent entities. After which, the common cognitive architecture including one or more problem solving process can be implemented on the problem request or the sub-problems to be each of the recruited intelligent entities to create a problem solution or a sub-problem solution, respectively. The problem solution and the sub-problem solution can be integrated to create a completion solution to the problem request. Then the completion solution can be provided to the user interface or the Al system for final acceptance by the user.

[0451] Some embodiments of the present technology7can include a step of assigning a credit value or a blame value to the datasets based on whether the datasets increase or decrease performance of the intelligent entities based on performance metrics or evaluation functions.

[0452] Some embodiments of the present technology7can include a step of quantifying a benefit weight or a harm weight to a contribution by7each of the intelligent entities to the problem request.

[0453] Some embodiments of the present technology can include a step of distributing a reward to an owner of the intelligent entities proportionally to the contribution of the intelligent entities based on the benefit weight or the harm weight.

[0454] Some of the objectives a user may have in creating and customizing their own Al (aka an AAAI) for purposes that might include, without limitation:1) Serving the user as an advisor, teacher, or companion.2) Representing the user in negotiations, interactions, discussion, and transactions with other users, or with the AAAIs of other users; or with vendors and other companies.3) Working on behalf of the user for compensation, or in volunteer efforts, where such work includes online intellectual, advising, or problem solving w ork across a wide range of tasks.4) Duplicating or “cloning"’ the user's AAAI so that several or many of the cloned AAAIs can work on behalf of the user in parallel, including interacting with, teaching, and improving each other so that the cloned AAAIs increase their knowledge, skills, and abilities.5) Serving as legacy AAAIs that can continue to interact with the world, including potentially comforting living relatives and friends, after the owner’s death.6) Contributing knowledge, ethics, and effort to AAAI, com’ s AGI, and improving the base level of Al or AGI that AAAI.com can offer users before those users add their unique customizations.7) Working with other users’ AAAI to help ensure ethical and safe behavior by AGI by contributing ethical information and values to the AGI and participating in monitoring, review, supervision, and voting processes that can help ensure the AGI remains safe and ethical.

[0455] Some of the steps involved in creating and customizing an AAAI may include, without limitation, a dialog or interaction with the user. During this dialog, the AAAI system may identify constraints and resources available for customizing the user’s AAAI. For example, some of these constraints and resources, might include, without limitation:

[0456] The amount of training and / or supervisory time that the user has to devote to customizing their AAAI.

[0457] The amount of financial resources the user is willing devote to customizing their AAAI.

[0458] Availability of social media information such as Facebook profiles and timelines, Instagram profiles and histories, Reels. TikTok, and YouTube videos, tweet and text content and histories, emails and email histories, cookies collected by advertisers, blog posts, articles, books, patents, audio and video recordings, pictures, and other information about, and / or collected by, the user or third parties that could be used to train, tune, or customize the user’s AAAI.

[0459] Availability and use of personality tests, such as the Myers-Briggs personality inventory', skills and knowledge assessments, standardized tests, exams, certifications, and other types of assessments and questionnaires which could be given online (or which have already been given) to the user.

[0460] Availability and use of other knowledge bases and training data from users on the AAAI platform that could be used to train, tune, or customize the user’s AAAI.

[0461] Other human users, and / or their AAAIs, available to help train, tune, or customize the user’s AAAI.

[0462] Other texts and information, individual texts, and libraries selected by the user or by the system for purposes of training the user’s AAAI. For example, the Bible, Koran, Dhammpada, Mahabharata, or other spiritual / ethical / religious texts might be selected for training the AAAI based on the user’s religious preferences: books on plumbing might be selected if the AAAI will be used to primarily solve online plumbing problems. Even if these materials are part of the base AAAI that is provided to the user, emphasizing certain texts or subsets of information for additional training can result in the user’s AAAI’s behavior being more reflective of how a plumber, or Muslim, or Christian might behave, for example.

[0463] In addition to specifying objectives, resources, and constraints via an interactive dialog or other interaction with the system, the user or system may want to specify other technical parameters that affect the training or customization process. These parameters can include, without limitation:1) The type of training, tuning, or other ML algorithms that are used.2) The type and size of the training dataset(s).3) The degree to which the training materials are to be “cleaned”, formatted, labelled, or otherw ise processed before customization begins.4) The number of training “epochs” or iterations through the learning algorithm(s).5) The sophistication and type of base model(s) being customized or trained.6) The required timeframe for training - e.g. must be completed in a minute, a day, a week - which might have implications for cost and resources used.7) The “temperature” or other parameters internal and specific to various machine learning algorithms that can affect what is learned and how it is learned including, without limitation, how literal or how divergent or “creative” the customized AAAI will be in its responses.8) Whether “one shot”, “few shot”, or extensive training is to be used.9) The amount of human and / or Al supervision to be used in the customization process.

[0464] Once the user’s AAAI is customized, the user can clone it and / or put it to work on the user’ s behalf on the online network. The user’ s AAAI can begin acting on the user ’ s behalf making travel arrangements (for example), providing advice, interacting with other AAAIs, participating in the collective AGI efforts by contributing problem solving as well as ethical information, and potentially earning money on behalf of the human user. The AAAI can also serve as representative(s) of the owner in a variety of online transactions and interactions, and contributing knowledge, expertise, style, personality, and ethics to an integrated AGI system that leverages the trained differences in many individual AAAIs.

[0465] FIG. 18 is a diagrammatic representation of a computer system 100 that is utilizable or implementable with the user’s device and / or any peripheral component of the present technology. The computer system 100 can be part of an example machine, which is an example of one or more of the computers referred to herein and, within which a set of instructions for causing the machine to perform any one of or more of the methodologies discussed herein may be executed. In various example embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a cellular telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine.

[0466] The computerized system 100 can include one or more processors 102. storage devices 106, and communication devices, as well as software components or instructions 104 for providing a platform for users to interact with and train / tune the LLMs. The computing capabilities may be stand alone or may be cloud based. They may include cloud based Al development platforms that seamlessly offer “Al as a service” and they may include both hardware and software components.

[0467] The system also supports the ability for users to provide new data, or data that is unique to them, for the LLMs to leam from. The processors 102 may be one or more CPUs, GPUs, chips specialized for ML, microprocessors, application processors, embedded processors, field- programmable gate arrays (FPGAs), or other hardware components capable of executing computer programs. The processors may be in communication with one another and / or with other components of the system. Further, any one of or any combination of the components of the system 100 can communicate with each other via a bus 134.

[0468] The storage devices 106 may include one or more hard drives, solid-state drives, optical storage devices, or other storage components. The storage devices may store the data that is used to train / tune the LLMs, as well as other data associated with the system, such as user accounts, system settings, and other data.

[0469] The communication devices may include one or more cellular modems 108, Wi-Fi cards 110, Bluetooth modules 112, Network Interface Device 114, or other components that enable the system to communicate with other systems, such as user devices, over a network or the internet.

[0470] The communication devices may also enable the system to communicate with other systems over a wireless or wired connection 116.

[0471] The software components may include computer programs for providing a platform for users to interact with and train / tune the LLMs. The software components may also include computer programs for collecting, storing, and processing data that is used to train and / or tune the LLMs. The software components may also include computer programs for providing a user interface for users to interact with the system.

[0472] The user interface 118 may include, without limitation, natural language interfaces, textual interfaces, and chatbot type of interfaces, a web-based user interface, a mobile application, an augmented reality application, a metaverse application, or other applications that allow users to interact with the system. The user interface may include features for allowing users to select the data that they want to use to train / tune the LLMs, as well as features for allowing users to interact with and monitor the progress of the LLMs.

[0473] The system may also include one or more databases or data source, including without limitation vector databases, centralized databases, and distributed databases, for storing the data that is used to train / tune the LLMs, as well as other data associated with the system, such as user accounts, system settings, and other data. The databases may be hosted on the system itself or on another system, including cloud based systems.

[0474] The system may also include one or more authentication systems for verifying the identity of users who use the system, as well as for providing secure access to the system. The authentication systems may include biometric authentication systems 122, such as facial recognition or fingerprint recognition systems, as well as other authentication systems, such as password-based authentication systems.

[0475] The system may also include one or more security systems for protecting the system from unauthorized access and for protecting the data that is stored on the system. The security' systemsmay include firewalls, encryption systems, access control systems, single and multi-factor authentication systems, and other security systems.

[0476] The system may also include one or more analytics systems for collecting and analyzing data associated with the system and / or the LLMs. The analytics systems may include machine learning algorithms and other algorithms for analyzing the data associated with the system and / or the LLMs.

[0477] Data visualization methods, including use of problem trees and other representations and data structures; use of statistical outputs, tables, graphs, text, speech, video, image and graphical outputs may be used for one way or di-directi onal communication between users and the system, and between multiple (human or Al) agents or LLMs using the system to interact with each other in large or small groups.

[0478] The system may also include one or more monitoring systems for monitoring the performance of the system and / or the LLMs. The monitoring systems may include systems for monitoring the performance of the system, such as system uptime, and systems for monitoring the performance of the LLMs, such as accuracy, speed, ethical compliance, reputation metrics, quality metrics, and other metrics as discussed above or as are known in the art.

[0479] The system may include one of more of the architectures described above that enable one or more human or Al Agents or LLMs to engage in a variety of intellectual tasks including, without limitation, simple and complex and multi-step problem solving behavior with the system having all of the functionality and features previously described.

[0480] The system may also include one or more feedback systems for allowing users to provide feedback on the system and / or the LLMs. The feedback systems may include systems for allowing users to submit feedback on the system, such as bug reports, and systems for allowing users to submit feedback on the LLMs, such as suggestions for improving the accuracy or speed of the model.

[0481] The system may also include one or more management systems for managing the system and / or the LLMs. The management systems may include systems for managing the system, such as systems for managing the users and user accounts, and systems for managing the LLMs, such as systems for managing the data used to train and / or tune the model.

[0482] The system may also include one or more payment systems allowing users to pay for the use of the system and / or the LLMs. The payment systems may include systems for processing payments, such as credit card processing systems, and systems for managing payments, such as subscription management systems.

[0483] The system may also include one or more other components, such as support systems, reporting systems, and other components that are necessary for providing a platform for users to interact with and train / tune the LLMs.

[0484] The computerized system of the present technology enables users to interact with and train / tune LLMs based on data that is unique to the users. The components of the system described herein provide the necessary' hardware and software components for enabling users to do so.

[0485] Further, while only a single machine is illustrated, the term ‘“machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one of or more of the methodologies discussed herein.

[0486] The computer system 100 may further include or be in operable communication with a video display 120 (e.g.. a liquid crystal display (LCD), touch sensitive display), input and / or output device(s) 130 (e.g.. a keyboard, keypad, touchpad. touch display, buttons, sonic, sensorial, etc.), a cursor control device 132 (e.g., a mouse), a drive unit 124 (also referred to as disk drive unit), and a signal generation device 128 (e.g., a speaker). The drive unit 124 can include a computer or machine-readable medium 126 on which is stored one or more sets of instructions and data structures (e.g.. instructions 104) embodying or utilizing any one of or more of the methodologies or functions described herein. The instructions 104 may also reside, completely or at least partially, within the memory ■ 106 and / or within the processors 102 during execution thereof by the computer system 100. The memory' 106 and / or the processors 102 may also constitute machine-readable media.

[0487] Still further, the computer system 100 can be in operable association or communication with any types of multi-modal input and / or output 130 that address the human senses, as well as I / O technology' that extends beyond the range of normal human perception. Such as the ability' to process invisible to humans, for example but not limited to, Xrays and information outside of the typical bandwidths of human perception, but not outside of Al perception using tools. Additionally, the I / O technology can include very fast perceptions that are too fast for humans to perceive but which an Al entity could perceive, and very slow or faint perceptions (e.g., tiny seismic shifts occurring over years) that humans cannot perceive but which AIs could. Since any intelligent entity' can be part of the present technology system described by Fig 18, then it can be appreciated that any ty pe of I / O that humans, and also AIs yvith much broader perceptual capabilities than humans, can be utilized with the system 100.

[0488] The instructions 104 may further be transmitted or received over a network viathe network interface device 114 utilizing any one of a number of well-known transfer protocols (e.g., HyperText Transfer Protocol (HTTP)). While the machine-readable medium is shown in an example embodiment to be a single medium, the term “computer-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, vector databases, and / or associated caches and servers) that store the one or more sets of instructions. The term “computer-readable medium” shall also be taken to include any medium that is capable of storing, encoding, or carrying a set of instructions for execution by the machine and that causes the machine to perform any one of or more of the methodologies of the present application, or that is capable of storing, encoding, or carrying data structures utilized by or associated with such a set of instructions. The term “computer-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical and magnetic media, and carrier wave signals. Such media may also include, without limitation, hard disks, floppy disks, flash memory' cards, digital video disks, random access memory (RAM), read only memory (ROM), and the like. The example embodiments described herein may be implemented in an operating environment comprising software installed on a computer, in hardware, or in a combination of softw are and hardw are.

[0489] An example machine system of the present technology including the computer system 100 in combinational and / or operational use with components of the present technology. In the exemplary, any or all of above described components can include a processor 102, memory 106, a network interface device 114, a display 120, an input device(s) 130, 132, and / or drive unit 124.

[0490] According to one aspect, the present technology can include a network of human problem solvers, combined with a universal problem solving architecture that allows them to work together in a coordinated and rigorously defined way. A problem can be submitted to the network of human problem solvers utilizing a network of Al systems. One or more human problem solvers works on the problem and returns a solution. Since the network includes human w orkers, by definition, it can solve any problem the average human can solve with the assist of the Al systems. And since there are many human workers utilizing the network of the Al systems, if the efforts are intelligently coordinated, then the network will often perform better than the average human.

[0491] According to another aspect, the present technology can include a system for humancentered AGI utilizing a network of human users and a universal problem solving architecture. The system can include a computer system including / : a processor, a computer-readable storage medium, and program instructions stored on the computer-readable storage medium being executable by the processor to cause the computer system to: execute a user interface configured or configurable to allow inputting of a problem request including one or more problem criteria;match one or more human workers from a data source including a list of human problem solvers to the problem request based on the problem criteria; translate any part of the problem request into an unambiguous language utilizable in a universal problem solving architecture including a decision tree; separate the problem request into sub-problems; delegate each of the sub-problems to one or more of the matched human workers so that work on each of the sub-problems proceeds independently from each other and / or parallel with each other; receive one or more sub-solutions from each of the matched human workers for the subproblems delegated thereto; combine the sub-solutions into an overall solution to the problem request; direct any one of or any combination of a new human worker from the data source and one or more of the matched human workers to parts of the decision tree where work is required; compensate the matched human workers for the sub-solutions, respectively; provide any one of or any combination of the sub-solutions and the overall solution to the user interface; and allow, by way of the user interface, a human user to do any one of or any combination of accept the overall solution, reject the overall solution, and provide feedback to any one of the matched human workers on any one of the sub-solutions.

[0492] According to yet another aspect, the present technology can include a method for humancentered AGI utilizing a network of human users and a universal problem solving architecture electronically communicating over a collective network. The method can include the steps of: providing a problem request including a problem criteria into an Al agent by one or more of intelligent entities including any one of or any combination of one or more a human users each utilizing a computer system, and one or more additional Al agents; matching, by the Al agent, one or more human workers from a data source including a list of human problem solvers to the problem request based on the problem criteria; translating, by the Al agent using Large Language Model (LLM), any part of the problem request into an unambiguous language utilizable in a universal problem solving architecture including a decision tree; separating, by the Al agent, the problem request into sub-problems;delegating, by the Al agent, each of the sub-problems to one or more of the matched human workers so that work on each of the sub-problems proceeds independently from each other and parallel with each other; utilizing, by a worker Al agent of each of the matched human workers, the universal problem solving architecture in a problem solving process on the sub-problems, respectively, to create one or more sub-solutions; receiving, by the Al agent, the sub-solutions from each of the matched human workers for the sub-problems delegated thereto; combining, by the Al agent, the sub-solutions into an overall solution to the problem request; directing, by the Al agent, any one of or any combination of a new human worker from the data source and one or more of the matched human workers to parts of the decision tree where work is required; compensating, by the Al agent, the matched human workers for the sub-solutions; providing, by the Al agent, any one of or any combination of the sub-solutions and the overall solution to a user interface of a user computer system; allowing, by way of the user interface, the human user to any one of or any combination of accept the overall solution, reject the overall solution, and provide feedback to any one of the matched human workers on any one of the sub-solutions; and assigning a reputation attribute to any one of or any combination of the human workers and the worker Al agent.

[0493] In some embodiments, the universal problem solving architecture can include the step of learning by the Al agent or the worker Al agent including a procedural learning process that utilizes problem solving process.

[0494] In some embodiments, the procedural learning process can occur within the universal problem solving architecture.

[0495] In some embodiments, a problem solving activity can be recorded in an auditable record including any one of or any combination of steps of the problem solving process that results in the overall solution, the problem solving process that results in failure to solve for the problem request, and evaluation information relative to a quality and desirableness of the sub-solutions.

[0496] Some embodiments of the present technology can include a step of indexing the subsolutions according to any one of or any combination of the problem request, the problem criteria, and the sub-problems.

[0497] In some embodiments, the procedural learning process can utilize each of the recorded problem solving activity as a learned procedure and collectively a set of all learned procedures constitute the procedural learning process of the Al agent or the worker Al agent.

[0498] Some embodiments of the present technology can include a step of recording any one of or any combination of an operator applied in the problem solving process, a new state of the problem request, an evaluation function used the problem solving process, a current relevant goal or subgoal, and other information that differs from a previous step.

[0499] Some embodiments of the present technology can include a step of evaluating a state of the problem request to determine if the overall solution has been accepted, if accepted then recording and indexing the overall solution, or if not accepted and if resources are exhausted then recording unsuccessful solution attempts.

[0500] In some embodiments, if the overall solution is not accepted then the method can further comprise steps of repeating the problem solving process using information from a latest state of the problem request; evaluating a progress of the repeated problem solving process; and selecting anext operator to apply in the problem solving process.

[0501] In some embodiments, if the overall solution is accepted then the method can further include the step of recording in an auditable record any one of successful solutions for future retrieval in use by a future problem solving process, and unsuccessful solution attempts for future retrieval in notifications about unsuccessful paths that were previously tried.

[0502] Some embodiments of the present technology can include a step of using any one of or any combination of semantic analysis and hash functions to index the successful solutions and the unsuccessful attempts with keywords that are utilized for matching to future problem solving efforts.

[0503] Some embodiments of the present technology can include a step of periodically reviewing the recorded successful solutions or unsuccessful attempts to meet established preset ethical and safety’ guidelines, and then flagging unethical and unsafe solutions for removal from a solution database of the successful solutions and the unsuccessful attempts.

[0504] Some embodiments of the present technology can include a step of updating and propagating changes to the solution database periodically so that the network of the human workers can access an ever-increasing repertoire of the recorded successful solutions as well as increasing knowledge of the unsuccessful attempts.

[0505] In some embodiments, the step of translating using LLM can further comprise the step of describing by any one of or any combination of the human user and any one of the human workers in natural language any one of or any combination of a current problem state, a goal of the problemrequest, relevant problem solving information, and a next step that the human workers will take in the problem solving process.

[0506] Some embodiments of the present technology can include a step of parsing and translating by the Al agent or the worker Al agent the natural language description into the unambiguous language utilizable by the decision tree of the universal problem solving architecture.

[0507] In some embodiments, if the Al agent or the worker Al agent is unable to specify a problem state, including relevant operators and information needed to take a next step in the problem solving process based on the parsing and the translation, then the Al agent or the worker Al agent can engage in dialog with at least one of the human workers until a precise problem state is specified.

[0508] Some embodiments of the present technology' can include a step of repeating the problem solving process until the overall solution is accepted or resources are exhausted.

[0509] In some embodiments, the problem solving process ca include a series of problem state transitions from an initial problem state where there is a goal to a final solution state where the goal has been achieved, and wherein a series of decisions are made by the problem solving process and actions taken that applies operators that enable the human workers to transition from state to state until the final solution state is reached.

[0510] In some embodiments, the reputation attribute can include metrics on any one of or any combination of a time to the sub-solutions, a difficulty value of the problem request, short and longterm user satisfaction with the sub-solutions, a number of times any one of the sub-solutions has been re-used on the network, a rating of other human workers, a responsiveness value of the human workers, and a reliability value of the human workers.

[0511] Some embodiments of the present technology can include a step of using the reputation attribute in the matching of the human workers to the problem request using an algorithm to the delegation of the sub-problems.

[0512] In some embodiments, the algorithm can use a hierarchy of the metrics that is preset by the human user of the problem request.

[0513] Some embodiments of the present technology7can include a step of recording information on each step of the problem solving process by the human workers or the w orker Al agent.

[0514] Wherein the recording of infonnation can include, without limitation, recording using blockchain-based or Ethereum-based means.

[0515] Some embodiments of the present technology7can include a step of recording a criteria of the recorded step of the problem solving process, the criteria being a time taken for each step.

[0516] Some embodiments of the present technology can include a step of analyzing the recorded information after the overall solution is accepted or after the problem solving process and updating the metrics of the reputation attribute.

[0517] Some embodiments of the present technology can include a step of soliciting, at predetermined intervals after the overall solution or the sub-solutions are provided to the user interface, feedback by way of a survey for user satisfaction information, or other means, to obtain short and long-term satisfaction metrics that are used to update the reputation attribute of one or more of the human workers or the worker Al agent.

[0518] Some embodiments of the present technology can include a step of executing an ethics check, by the Al agent, by comparing the problem request or any one of the sub-problems or the sub-solutions against prohibited attributes and assigning an ethics value based on any one of or any combination of a result of the comparison, and an ethics criteria.

[0519] In some embodiments, the step of the ethics check can be triggered every time the problem request or the any one of the sub-problems is set by the human user.

[0520] In some embodiments, the step of the ethics check can be triggered each time compensation is provided to the matched human workers.

[0521] In some embodiments, the ethics criteria can be determined by any one of or any combination of combining values and safety information from any one of or any combination of the user Al agent and the worker Al agent.

[0522] In some embodiments, the ethics criteria can include a confidence level threshold for the problem request so that the ethics value is determined as any one of an unsafe goal, an unethical goal, a safe goal, and an ethical goal.

[0523] In some embodiments, the confidence level threshold can be further utilized to determine if a sequence of individually safe goals is unsafe or unethical when considered cumulatively.

[0524] In some embodiments, the confidence level threshold can be utilized to determine whether a violation occurred that reflects a predictive evaluation if the goal is to violate the ethics criteria.

[0525] Some embodiments of the present technology can include a step of using a blockchain when any one of or any combination of when a compensation is provided, at any stage of the problem solving process, and when the sub-solutions are provided.

[0526] In some embodiments, the blockchain is Ethereum based or utilizes crypto tokens that are Ethereum based.

[0527] According to still another aspect, the present technology can include a method for humancentered AGI utilizing a network of human users and a universal problem solving architecture electronically communicating over a collective network. The method can include the steps of: providing a problem request including a problem criteria into an Al agent by one or more of intelligent entities including any one of or any combination of one or more human users each utilizing a computer system, and one or more additional Al agents; matching, by the Al agent, one or more human workers from a data source including a list of human problem solvers to the problem request based on the problem criteria; delegating, by the Al agent, a sub-problem of the problem request to one or more of the matched human workers so that work on the sub-problem proceeds independently from each other and parallel with each other; utilizing, by a worker Al agent of each of the matched human workers, a universal problem solving architecture in a problem solving process on the sub-problem, respectively, to create one or more sub-solutions; learning, by the Al agent or the worker Al agent, including a procedural learning process that utilizes problem solving process and that occurs within the universal problem solving architecture; receiving, by the Al agent, the sub-solutions from each of the matched human workers for the sub-problem delegated thereto; and providing, by the Al agent, any one of or any combination of the sub-solutions and an overall solution to a user interface of a user computer system.

[0528] Some embodiments of the present technology can include steps of compensating, by the Al agent, the matched human workers for the sub-solutions, respectively, and assigning a reputation attribute to any one of or any combination of the human workers and the worker Al agent.

[0529] In some embodiments, a problem solving activity can be recorded in an auditable record that includes any one of or any combination of steps of the problem solving process that results in the overall solution, the problem solving process that results in failure to solve for the problem request, and evaluation information relative to a quality and desirableness of the sub-solutions.

[0530] Wherein the recording of the problem solving activity can include, without limitation, recording using blockchain-based or Ethereum-based means.

[0531] Some embodiments of the present technology can include a step of indexing the subsolutions according to any one of or any combination of the problem request, the problem criteria, and the sub-problem.

[0532] In some embodiments, the procedural learning process can utilize each of the recorded problem solving activity as a learned procedure and collectively a set of all learned procedures that constitute the procedural learning process of the Al agent or the worker Al agent.

[0533] Some embodiments of the present technology can include a step of recording any one of or any combination of an operator applied in the problem solving process, a new state of the problem request, an evaluation function used by the problem solving process, a current relevant goal or subgoal, and other information that differs from a previous step.

[0534] Some embodiments of the present technology can include a step of evaluating a state of the problem request to determine if the overall solution has been accepted, if accepted then recording and indexing the overall solution, or if not accepted and if resources are exhausted then recording unsuccessful solution attempts.

[0535] In some embodiments, if the overall solution is not accepted then the method can further comprise the steps of: repeating the problem solving process using information from a latest state of the problem request; evaluating a progress of the repeated problem solving process; and selecting a next operator to apply in the problem solving process.

[0536] In some embodiments, if the overall solution is accepted then the method can further comprise the step of recording in an auditable record any one of successful solutions for future retrieval in use by a future problem solving process, and unsuccessful solution attempts for future retrieval in notifications about unsuccessful paths that were previously tried.

[0537] Some embodiments of the present technology can include a step of using any one of or any combination of semantic analysis and hash functions to index the successful solutions and the unsuccessful attempts with keywords that are utilized for matching to future problem solving efforts.

[0538] Some embodiments of the present technology can include a step of periodically reviewing the recorded successful solutions or unsuccessful attempts to meet established preset ethical and safety’ guidelines, and then flagging unethical and unsafe solutions for removal from a solution database of the successful solutions and the unsuccessful attempts.

[0539] Some embodiments of the present technology can include a step of updating and propagating changes to the solution database periodically so that the network of the human workers can access an ever-increasing repertoire of the recorded successful solutions as well as increasing knowledge of the unsuccessful attempts.

[0540] According to yet still another aspect and as generally illustrated in FIG. 4, the present technology7can include a method for human-centered AGI utilizing a network of human users and auniversal problem solving architecture electronically communicating over a collective network. The method can include the steps of: providing a problem request including a problem criteria into an Al agent by one or more of intelligent entities including any one of or any combination of one or more human users each utilizing a computer system, and one or more additional Al agents; matching, by the Al agent, one or more human workers from a data source including a list of human problem solvers to the problem request based on the problem criteria; translating, by the Al agent using LLM, any part of the problem request into an unambiguous language utilizable in a universal problem solving architecture including a decision tree; delegating, by the Al agent, a sub-problem of the problem request to one or more of the matched human workers so that work on the sub-problem proceeds independently from each other and parallel with each other; utilizing, by a worker Al agent of each of the matched human workers, the universal problem solving architecture in a problem solving process on the sub-problems, respectively, to create one or more sub-solutions; describing, by any one of or any combination of the human user and any one of the human workers, in natural language a description of any one of or any combination of a current problem state, a goal of the problem request, relevant problem solving information, and a next step that the human workers will take in the problem solving process; receiving, by the Al agent, the sub-solutions from each of the matched human workers for the sub-problem delegated thereto; and providing, by the Al agent, any one of or any combination of the sub-solutions and an overall solution to a user interface of a user computer system.

[0541] Some embodiments of the present technology can include a step of parsing and translating by the Al agent or the worker Al agent the natural language description into the unambiguous language utilizable by the decision tree of the universal problem solving architecture.

[0542] In some embodiments, if the Al agent or the worker Al agent is unable to specify a problem state, including relevant operators and information needed to take a next step in the problem solving process based on the parsing and the translation, then the Al agent or the worker Al agent can engage in dialog with at least one of the human workers until a precise problem state is specified.

[0543] Some embodiments of the present technology can include steps of repeating the problem solving process until the overall solution is accepted or resources are exhausted;compensating, by the Al agent, the matched human workers for the sub-solutions, respectively; and assigning a reputation attribute to any one of or any combination of the human workers and the worker Al agent.

[0544] Wherein the compensation can be made by way of blockchain-based or “smart contract” technology7.

[0545] In some embodiments, the solving process can include a series of problem state transitions from an initial problem state where there is a goal to a final solution state where the goal has been achieved, and wherein a series of decisions are made by the problem solving process and actions taken that applies operators that enable the human workers to transition from state to state until the final solution state is reached.

[0546] According to even yet another aspect and as generally illustrated in FIG. 4, the present technology can include method for human-centered AGI utilizing a network of human users and a universal problem solving architecture electronically communicating over a collective network. The method can include the steps of: providing a problem request including a problem criteria into an Al agent by one or more of intelligent entities including any one of or any combination of one or more human users each utilizing a computer system, and one or more additional Al agents; matching, by the Al agent, one or more human workers from a data source including a list of human problem solvers to the problem request based on the problem criteria; delegating, by the Al agent, a sub-problem of the problem request to one or more of the matched human workers so that work on each of the sub-problem proceeds independently from each other and parallel with each other; utilizing, by a worker Al agent of each of the matched human workers, a universal problem solving architecture in a problem solving process on the sub-problem, respectively, to create one or more sub-solutions; receiving, by the Al agent, the sub-solutions from each of the matched human workers for the sub-problems delegated thereto; compensating, by the Al agent, the matched human workers for the sub-solutions, respectively; providing, by the Al agent, any one of or any combination of the sub-solutions and an overall solution to a user interface of a user computer system;allowing, by way of the user interface, the human user to any one of or any combination of accept the overall solution, reject the overall solution, and provide feedback to any one of the matched human workers on any one of the sub-solutions; and assigning a reputation attribute to any one of or any combination of the human workers and the worker Al agent, the reputation attribute including metrics on any one of or any combination of a time to the sub-solutions, a difficulty value of the problem request, short and long-term user satisfaction with the sub-solutions, a number of times any one of the sub-solutions has been re-used on the network, a rating other human workers, a responsiveness value of the human workers, and a reliability value of the human workers.

[0547] Some embodiments can include the steps of: using the reputation attribute in the matching of the human workers to the problem request using an algorithm to the delegation of the subproblems; and compensating, by the Al agent, the matched human workers for the sub-solutions, respectively.

[0548] In some embodiments, the algorithm can use a hierarchy of the metrics that is preset by a human user of the problem request.

[0549] Some embodiments of the present technology can include a step of recording information on each step of the problem solving process by the human workers or the worker Al agent.

[0550] Some embodiments of the present technology can include a step of recording a criteria of the recorded step of the problem solving process, the criteria being a time taken for each step.

[0551] Some embodiments of the present technology can include a step of analyzing the recorded information after the overall solution is accepted or after the problem solving process and updating the metrics of the reputation attribute.

[0552] Some embodiments of the present technology' can include a step of soliciting, at predetermined intervals after the overall solution or the sub-solutions are provided to the user interface, a survey for user satisfaction information to obtain short and long-tenn satisfaction metrics that are used to update the reputation attribute of one or more of the human workers or the worker Al agent.

[0553] According to still another aspect, the present technology' can include a method for humancentered AGI utilizing a network of human users and a universal problem solving architecture electronically communicating over a collective network. The method can include the steps of: providing a problem request including a problem criteria into an Al agent by one or more of intelligent entities including any one of or any combination of one or more human users each utilizing a computer system, and one or more additional Al agents;matching, by the Al agent, one or more human workers from a data source including a list of human problem solvers to the problem request based on the problem criteria; delegating, by the Al agent, a sub-problem of the problem request to one or more of the matched human workers so that work on each of the sub-problems proceeds independently from each other and parallel with each other; utilizing, by a worker Al agent of each of the matched human workers, a universal problem solving architecture in a problem solving process on the sub-problems, respectively, to create one or more sub-solutions; receiving, by the Al agent, the sub-solutions from each of the matched human workers for the sub-problems delegated thereto; executing an ethics check, by the Al agent, by comparing any one of or any combination of the problem request, the sub-problem and the sub-solutions against prohibited attributes, and assigning an ethics value based on any one of or any combination of a result of the comparison, and an ethics criteria; and providing, by the Al agent, any one of or any combination of the sub-solutions and an overall solution to a user interface of a user computer system.

[0554] Wherein the matching step can be set as a sub-problem of the problem request.

[0555] In some embodiments, the step of the ethics check can be triggered every time the problem request or the any one of the sub-problems is set by the human user.

[0556] In some embodiments, the step of the ethics check can be triggered each time compensation is provided to the matched human workers.

[0557] In some embodiments, the ethics criteria can be determined by any one of or any combination of combining values and safety information from any one of or any combination of the user computer system and the worker Al agent.

[0558] In some embodiments, the ethics criteria can include a confidence level threshold for the problem request so that the ethics value is determined as any one of an unsafe goal, an unethical goal, a safe goal, and an ethical goal.

[0559] In some embodiments, the confidence level threshold can be further utilized to determine if a sequence of individually safe goals is unsafe or unethical when considered cumulatively.

[0560] In some embodiments, the confidence level threshold can be utilized to determine whether a violation occurred that reflects a predictive evaluation if the goal is to violate the ethics criteria.

[0561] Some embodiments of the present technology can include steps of:compensating, by the Al agent, the matched human workers for the sub-solutions, respectively; and assigning a reputation attribute to any one of or any combination of the human workers and the worker Al agent.

[0562] According to yet another aspect, the present technology can include a method for humancentered AGI utilizing a network of human users and a universal problem solving architecture electronically communicating over a collective network. The method can include the steps of: providing a problem request including a problem criteria into an Al agent by one or more of intelligent entities including any one of or any combination of one or more human users each utilizing a computer system, and one or more additional Al agents; matching, by the Al agent, one or more human workers from a data source including a list of human problem solvers to the problem request based on the problem criteria; translating, by the Al agent using LLM, any part of the problem request into an unambiguous language utilizable in a universal problem solving architecture including a decision tree; separating, by the Al agent, the problem request into sub-problems; delegating, by the Al agent, each of the sub-problems to one or more of the matched human workers so that work on each of the sub-problems proceeds independently from each other and parallel with each other; utilizing, by a worker Al agent of each of the matched human workers, the universal problem solving architecture in a problem solving process on the sub-problems, respectively, to create one or more sub-solutions; receiving, by the Al agent, the sub-solutions from each of the matched human workers for the sub-problems delegated thereto; compensating, by the Al agent, the matched human workers for the sub-solutions, respectively; providing, by the Al agent, any one of or any combination of the sub-solutions and an overall solution to a user interface of a user computer system; using a blockchain when any one of or any combination of a compensation is provided, at any stage of the problem solving process, and when the sub-solutions are provided; allowing, by way of the user interface, the human user to any one of or any combination of accept the overall solution, reject the overall solution, and provide feedback to any one of the matched human workers on any one of the sub-solutions; and assigning a reputation attribute to any one of or any combination of the human workers and the worker Al agent.

[0563] In some embodiments, the blockchain can be Ethereum based or utilizes cry pto tokens that are Ethereum based.

[0564] According to even still another aspect, the present technology can include method for developing a human-centered AGI utilizing a network of human users and a universal problem solving architecture electronically communicating over a collective network. The method can include the steps of: a) providing a request by a human user using an interface of a user computer system or by an Al agent, the request including one or more criteria; b) identifying multiple additional Al agents that have an attribute related to the criteria of the request; c) communicating between the user computer system or the Al agent, and the multiple additional Al agents utilizing a collective network; d) translating using Large Language Model (LLM) any part of the request into an unambiguous language utilizable in a universal problem solving architecture including a decision tree; e) separating the request into sub-problems; f) delegating each of the sub-problems to one or more of the additional Al agents so that work on each of the sub-problems proceeds independently from each other, parallel with each other, or independently and parallel with each other; g) utilizing by the additional Al agents the universal problem solving architecture in a problem solving process on the sub-problems, respectively, to create one or more sub-solutions; h) compensating the additional Al agents for the sub-solutions, respectively; i) developing an AGI by collaborating each of the sub-solutions to create an overall solution; j) providing any one of or any combination of the sub-solutions and the overall solution to any one of or any combination of the interface of the user computer system, the Al agent, and any one of the additional Al agents; and k) assigning a reputation attribute to any one of or any combination of the human user, the Al agent, or the additional Al agents.

[0565] According to an aspect, the present technology can include a method for AGI utilizing a single computerized intelligent system including multiple Al agents residing in the single computerized intelligent system. The method can include: providing a problem request including a problem criteria into an Al agent residing in a single computerized intelligent system;matching, by the Al agent, one or more additional Al agents to the problem request based on the problem criteria, the additional Al agents reside in the single computerized intelligent system; translating, by the Al agent using LLM, any part of the problem request into an unambiguous language utilizable in a universal problem solving architecture including a decision tree; separating, by the Al agent or by any one or more of the additional Al agents, the problem request into sub-problems; delegating, by the Al agent or by any one or more of the additional Al agents, each of the subproblems to one or more of the additional Al agents so that work on each of the subproblems proceeds independently from each other and parallel with each other; utilizing, by the Al agent and the additional Al agents, the universal problem solving architecture in a problem solving process on the sub-problems, respectively, to create one or more sub-solutions; receiving, by the Al agent, the sub-solutions from each of the additional Al agents for the subproblems delegated thereto; combining, by the Al agent, the sub-solutions into an overall solution to the problem request; providing, by the Al agent, any one of or any combination of the sub-solutions and the overall solution to a user interface of a user computer system or the single computerized intelligent system; and allowing, by way of the user interface, a human user to any one of or any combination of accept the overall solution, reject the overall solution, and provide feedback to any one of the matched human workers on any one of the sub-solutions.

[0566] While embodiments of the system and methods for human-centered AGI have been described in detail, it should be apparent that modifications and variations thereto are possible, all of which fall within the true spirit and scope of the present technology. With respect to the above description then, it is to be realized that the optimum dimensional relationships for the parts of the present technology, to include variations in size, materials, shape, form, function and manner of operation, assembly and use, are deemed readily apparent and obvious to one skilled in the art, and all equivalent relationships to those illustrated in the drawings and described in the specification are intended to be encompassed by the present technology. For example, any suitable sturdy material may be used instead of the above-described.

[0567] Therefore, the foregoing is considered as illustrative only of the principles of the present technology. Further, since numerous modifications and changes will readily occur to those skilled inthe art, it is not desired to limit the present technology to the exact construction and operation shown and described, and accordingly, all suitable modifications and equivalents may be resorted to, falling within the scope of the present technology.

Claims

CLAIMSWhat is claimed is:

1. A system for human-centered Artificial General Intelligence (AGI) utilizing a network of human users and a universal problem solving architecture, the system comprising: a computer system comprising: a processor, a computer-readable storage medium, and program instructions stored on the computer-readable storage medium being executable by the processor to cause the computer system to: execute a user interface configured or configurable to allow inputting of a problem request including one or more problem criteria; match one or more human workers from a data source including a list of human problem solvers to the problem request based on the problem criteria; translate any part of the problem request into an unambiguous language utilizable in a universal problem solving architecture including a decision tree; separate the problem request into sub-problems; delegate each of the sub-problems to one or more of the matched human workers so that work on each of the sub-problems proceeds independently from each other, parallel with each other, or independently and parallel with each other; receive one or more sub-solutions from each of the matched human workers for the subproblems delegated thereto; combine the sub-solutions into an overall solution to the problem request: direct any one of or any combination of a new human worker from the data source and one or more of the matched human workers to parts of the decision tree where work is required; compensate the matched human workers for the sub-solutions, respectively; provide any one of or any combination of the sub-solutions and the overall solution to the user interface; and allow, by way of the user interface, a human user to do any one of or any combination of accept the overall solution, reject the overall solution, and provide feedback to any one of the matched human workers on any one of the sub-solutions.

2. A method for human-centered Artificial General Intelligence (AGI) utilizing a network of human users and a universal problem solving architecture electronically communicating over a collective network, the method comprising:providing a problem request including a problem criteria into an Artificial Intelligence (Al) agent by one or more of intelligent entities including any one of or any combination of one or more human users each utilizing a computer system, and one or more additional Al agents; matching, by the Al agent, one or more human workers from a data source including a list of human problem solvers to the problem request based on the problem criteria; translating, by the Al agent using Large Language Model (LLM), any part of the problem request into an unambiguous language utilizable in a universal problem solving architecture including a decision tree; separating, by the Al agent, the problem request into sub-problems; delegating, by the Al agent, each of the sub-problems to one or more of the matched human workers so that work on each of the sub-problems proceeds independently from each other and parallel with each other; utilizing, by a worker Al agent of each of the matched human workers, the universal problem solving architecture in a problem solving process on the sub-problems, respectively, to create one or more sub-solutions; receiving, by the Al agent, the sub-solutions from each of the matched human workers for the sub-problems delegated thereto; combining, by the Al agent, the sub-solutions into an overall solution to the problem request; directing, by the Al agent, any one of or any combination of a new human worker from the data source and one or more of the matched human workers to parts of the decision tree where work is required; compensating, by the Al agent, the matched human workers for the sub-solutions; providing, by the Al agent, any one of or any combination of the sub-solutions and the overall solution to a user interface of a user computer system; allowing, by w ay of the user interface, the human user to any one of or any combination of accept the overall solution, reject the overall solution, and provide feedback to any one of the matched human w orkers on any one of the sub-solutions; and assigning a reputation attribute to any one of or any combination of the human workers and the worker Al agent.

3. The method of claim 2, wherein the universal problem solving architecture further includes the step of learning by the Al agent or the w orker Al agent including a procedural learning process that utilizes problem solving process.

4. The method of claim 3, wherein the procedural learning process occurs within the universal problem solving architecture.

5. The method of claim 4, wherein a problem solving activity is recorded in an auditable record including any one of or any combination of steps of the problem solving process that results in the overall solution, the problem solving process that results in failure to solve for the problem request, and evaluation infonnation relative to a quality and desirableness of the sub-solutions.

6. The method of claim 5 further comprising the step of indexing the sub-solutions according to any one of or any combination of the problem request, the problem criteria, and the sub-problems.

7. The method of claim 5, wherein the procedural learning process utilizes each of the recorded problem solving activity as a learned procedure and collectively integrates a set of all learned procedures to constitute the procedural learning process of the Al agent or the worker Al agent.

8. The method of claim 3 further comprising the step of recording any one of or any combination of an operator applied in the problem solving process, anew state of the problem request, an evaluation function used the problem solving process, a current relevant goal or subgoal, and other information that differs from a previous step.

9. The method of claim 3 further comprising the step of evaluating a state of the problem request to determine if the overall solution has been accepted, if accepted then recording and indexing the overall solution, or if not accepted and if resources are exhausted then recording unsuccessful solution attempts.

10. The method of claim 9, wherein if the overall solution is not accepted then further comprising the steps of: repeating the problem solving process using information from a latest state of the problem request; evaluating a progress of the repeated problem solving process; and selecting a next operator to apply in the problem solving process.1 1. The method of claim 9, wherein if the overall solution is accepted then further comprising the step of recording in an auditable record any one of successful solutions for future retrieval in use by a future problem solving process, and unsuccessful solution attempts for future retrieval in notifications about unsuccessful paths that were previously tried.

12. The method of claim 11 further comprising the step of using any one of or any combination of semantic analysis and hash functions to index the successful solutions and the unsuccessful attempts with keywords that are utilized for matching to future problem solving efforts.

13. The method of claim 11 further comprising the step of periodically reviewing the recorded successful solutions or unsuccessful attempts to meet established preset ethical and safety guidelines, and then flagging unethical and unsafe solutions for removal from a solution database of the successful solutions and the unsuccessful attempts.

14. The method of claim 13 further comprising the step of updating and propagating changes to the solution database periodically so that the network of the human workers can access an ever- increasing repertoire of the recorded successful solutions as well as increasing knowledge of the unsuccessful attempts.

15. The method of claim 2, wherein the step of translating using LLM further comprising the step of describing by any one of or any combination of the human user and any one of the human workers in natural language any one of or any combination of a current problem state, a goal of the problem request, relevant problem solving information, and a next step that the human workers will take in the problem solving process.

16. The method of claim 15 further comprising the step of parsing and translating by the Al agent or the worker Al agent the natural language description into the unambiguous language utilizable by the decision tree of the universal problem solving architecture.

17. The method of claim 16, wherein if the Al agent or the worker Al agent is unable to specify a problem state, including relevant operators and information needed to take a next step in the problem solving process based on the parsing and the translation, then the Al agent or the worker Al agent engages in dialog with at least one of the human workers until a precise problem state is specified.

18. The method of claim 15 further comprising the step of repeating the problem solving process until the overall solution is accepted or resources are exhausted.

19. The method of claim 15, wherein the problem solving process includes a series of problem state transitions from an initial problem state where there is a goal to a final solution state where the goal has been achieved, and wherein a series of decisions are made by the problem solving process and actions taken that applies operators that enable the human workers to transition from state to state until the final solution state is reached.

20. The method of claim 2, wherein the reputation attribute includes metrics on any one of or any combination of a time to the sub-solutions, a difficulty value of the problem request, short and longterm user satisfaction with the sub-solutions, a number of times any one of the sub-solutions has been re-used on the network, a rating of other human workers, a responsiveness value of the human workers, and a reliability value of the human workers.

21. The method of claim 20 further comprising the step of using the reputation attribute in the matching of the human workers to the problem request using an algorithm to the delegation of the sub-problems.

22. The method of claim 21, wherein the algorithm uses ahierarchy of the metrics that is preset by the human user of the problem request.

23. The method of claim 20 further comprising the step of recording information on each step of the problem solving process by the human workers or the worker Al agent.

24. The method of claim 23, wherein the recording of information utilizes blockchain-based or Ethereum-based technology.

25. The method of claim 23 further comprising the step of recording a criteria of the recorded step of the problem solving process, the criteria being a time taken for each step.

26. The method of claim 23 further comprising the step of analyzing the recorded information after the overall solution is accepted or after the problem solving process and updating the metrics of the reputation attribute.

27. The method of claim 23 further comprising the step of soliciting, at predetermined intervals after the overall solution or the sub-solutions are provided to the user interface, feedback by way of a survey for user satisfaction information to obtain short and long-term satisfaction metrics that are used to update the reputation attribute of one or more of the human workers or the worker Al agent.

28. The method of claim 2 further comprising the step of executing an ethics check, by the Al agent, by comparing the problem request or any one of the sub-problems or the sub-solutions against prohibited attributes and assigning an ethics value based on any one of or any combination of a result of the comparison, and an ethics criteria.

29. The method of claim 28, wherein the step of the ethics check is triggered every time the problem request or the any one of the sub-problems is set by the human user.

30. The method of claim 28, wherein the step of the ethics check is triggered each time compensation is provided to the matched human workers.

31. The method of claim 28, wherein the ethics criteria are determined by any one of or any combination of combining values and safety information from any one of or any combination of the user computer system and the worker Al agent.

32. The method of claim 28, wherein the ethics criteria include a confidence level threshold for the problem request so that the ethics value is determined as any one of an unsafe goal, an unethical goal, a safe goal, and an ethical goal.

33. The method of claim 32, wherein the confidence level threshold is further utilized to determine if a sequence of individually safe goals is unsafe or unethical when considered cumulatively.

34. The method of claim 32, wherein the confidence level threshold is utilized to determine whether a violation occurred that reflects a predictive evaluation if the goal is to violate the ethics criteria.

35. The method of claim 2 further comprising the step of using a blockchain when any one of or any combination of when a compensation is provided, at any stage of the problem solving process, and when the sub-solutions are provided.

36. The method of claim 35, wherein the blockchain is Ethereum based or utilizes crypto tokens that are Ethereum based.

37. A method for human-centered Artificial General Intelligence (AGI) utilizing a network of human users and a universal problem solving architecture electronically communicating over a collective network, the method comprising: providing a problem request including a problem criteria into an Artificial Intelligence (Al) agent by one or more of intelligent entities including any one of or any combination of one or more human users each utilizing a computer system, and one or more additional Al agents; matching, by the Al agent, one or more human workers from a data source including a list of human problem solvers to the problem request based on the problem criteria; delegating, by the Al agent, a sub-problem of the problem request to one or more of the matched human workers so that work on the sub-problem proceeds independently from each other, parallel with each other or independently and parallel with each other; utilizing, by a worker Al agent of each of the matched human workers, a universal problem solving architecture in a problem solving process on the sub-problem, respectively, to create one or more sub-solutions; learning, by the Al agent or the worker Al agent, including a procedural learning process that utilizes problem solving process and that occurs within the universal problem solving architecture; receiving, by the Al agent, the sub-solutions from each of the matched human workers for the sub-problem delegated thereto; and providing, by the Al agent, any one of or any combination of the sub-solutions and an overall solution to a user interface of a user computer system.

38. The method of claim 37 further comprising the steps of compensating, by the Al agent, the matched human workers for the sub-solutions, respectively, and assigning a reputation attribute to any one of or any combination of the human workers and the worker Al agent.

39. The method of claim 37, wherein a problem solving activity is recorded in an auditable record that includes any one of or any combination of steps of the problem solving process that results in the overall solution, the problem solving process that results in failure to solve for the problem request, and evaluation infonnation relative to a quality and desirableness of the sub-solutions.

40. The method of claim 39, wherein the recording of the problem solving activity utilizes blockchain-based or Ethereum-based technology.

41. The method of claim 37 further comprising the step of indexing the sub-solutions according to any one of or any combination of the problem request, the problem criteria, and the sub-problem.

42. The method of claim 37, wherein the procedural learning process utilizes each of the recorded problem solving activity as a learned procedure and collectively a set of all learned procedures that constitute the procedural learning process of the Al agent or the worker Al agent.

43. The method of claim 37 further comprising the step of recording any one of or any combination of an operator applied in the problem solving process, a new state of the problem request, an evaluation function used by the problem solving process, a current relevant goal or subgoal, and other information that differs from a previous step.

44. The method of claim 37 further comprising the step of evaluating a state of the problem request to determine if the overall solution has been accepted, if accepted then recording and indexing the overall solution, or if not accepted and if resources are exhausted then recording unsuccessful solution attempts.

45. The method of claim 44, wherein if the overall solution is not accepted then further comprising the steps of: repeating the problem solving process using information from a latest state of the problem request; evaluating a progress of the repeated problem solving process; and selecting a next operator to apply in the problem solving process.

46. The method of claim 44, wherein if the overall solution is accepted then further comprising the step of recording in an auditable record any one of successful solutions for future retrieval in use by a future problem solving process, and unsuccessful solution attempts for future retrieval in notifications about unsuccessful paths that were previously tried.

47. The method of claim 46 further comprising the step of using any one of or any combination of semantic analysis and hash functions to index the successful solutions and the unsuccessful attempts with keywords that are utilized for matching to future problem solving efforts.

48. The method of claim 46 further comprising the step of periodically reviewing the recorded successful solutions or unsuccessful attempts to meet established preset ethical and safety guidelines, and then flagging unethical and unsafe solutions for removal from a solution database of the successful solutions and the unsuccessful attempts.

49. The method of claim 48 further comprising the step of updating and propagating changes to the solution database periodically so that the network of the human workers can access an ever- increasing repertoire of the recorded successful solutions as well as increasing knowledge of the unsuccessful attempts.

50. A method for human-centered Artificial General Intelligence (AGI) utilizing a network of human users and a universal problem solving architecture electronically communicating over a collective network, the method comprising: providing a problem request including a problem criteria into an Artificial Intelligence (Al) agent by one or more of intelligent entities including any one of or any combination of one or more human users each utilizing a computer system, and one or more additional Al agents; matching, by the Al agent, one or more human workers from a data source including a list of human problem solvers to the problem request based on the problem criteria; translating, by the Al agent using Large Language Model (LLM), any part of the problem request into an unambiguous language utilizable in a universal problem solving architecture including a decision tree; delegating, by the Al agent, a sub-problem of the problem request to one or more of the matched human workers so that work on the sub-problem proceeds independently from each other and parallel with each other; utilizing, by a worker Al agent of each of the matched human workers, the universal problem solving architecture in a problem solving process on the sub-problems, respectively, to create one or more sub-solutions; describing, by any one of or any combination of the human user and any one of the human workers, in natural language a description of any one of or any combination of a current problem state, a goal of the problem request, relevant problem solving information, and a next step that the human workers will take in the problem solving process;receiving, by the Al agent, the sub-solutions from each of the matched human workers for the sub-problem delegated thereto; and providing, by the Al agent, any one of or any combination of the sub-solutions and an overall solution to a user interface of a user computer system.

51. The method of claim 50 further comprising the step of parsing and translating by the Al agent or the worker Al agent the natural language description into the unambiguous language utilizable by the decision tree of the universal problem solving architecture.

52. The method of claim 51, wherein if the Al agent or the worker Al agent is unable to specify a problem state, including relevant operators and information needed to take a next step in the problem solving process based on the parsing and the translation, then the Al agent or the worker Al agent engages in dialog with at least one of the human workers until a precise problem state is specified.

53. The method of claim 50 further comprising the steps of: repeating the problem solving process until the overall solution is accepted or resources are exhausted; compensating, by the Al agent, the matched human workers for the sub-solutions, respectively; and assigning a reputation attribute to any one of or any combination of the human workers and the worker Al agent.

54. The method of claim 50, wherein the problem solving process includes a series of problem state transitions from an initial problem state where there is a goal to a final solution state where the goal has been achieved, and wherein a series of decisions are made by the problem solving process and actions taken that applies operators that enable the human workers to transition from state to state until the final solution state is reached.

55. A method for human-centered Artificial General Intelligence (AGI) utilizing a network of human users and a universal problem solving architecture electronically communicating over a collective network, the method comprising: providing a problem request including a problem criteria into an Artificial Intelligence (Al) agent by one or more of intelligent entities including any one of or any combination of one or more human users each utilizing a computer system, and one or more additional Al agents; matching, by the Al agent, one or more human workers from a data source including a list of human problem solvers to the problem request based on the problem criteria;delegating, by the Al agent, a sub-problem of the problem request to one or more of the matched human workers so that work on each of the sub-problem proceeds independently from each other and parallel with each other; utilizing, by a worker Al agent of each of the matched human workers, a universal problem solving architecture in a problem solving process on the sub-problem, respectively, to create one or more sub-solutions; receiving, by the Al agent, the sub-solutions from each of the matched human workers for the sub-problems delegated thereto; compensating, by the Al agent, the matched human workers for the sub-solutions, respectively; providing, by the Al agent, any one of or any combination of the sub-solutions and an overall solution to a user interface of a user computer system; allowing, by way of the user interface, the human user to any one of or any combination of accept the overall solution, reject the overall solution, and provide feedback to any one of the matched human workers on any one of the sub-solutions; and assigning a reputation attribute to any one of or any combination of the human workers and the worker Al agent, the reputation attribute including metrics on any one of or any combination of a time to the sub-solutions, a difficulty value of the problem request, short and long-term user satisfaction with the sub-solutions, a number of times any one of the sub-solutions has been re-used on the network, a rating other human workers, a responsiveness value of the human workers, and a reliability value of the human workers.

56. The method of claim 55 further comprising the steps of: using the reputation attribute in the matching of the human workers to the problem request using an algorithm to the delegation of the sub-problems; and compensating, by the Al agent, the matched human workers for the sub-solutions, respectively.

57. The method of claim 56, wherein the algorithm uses a hierarchy of the metrics that is preset by a human user of the problem request.

58. The method of claim 55 further comprising the step of recording information on each step of the problem solving process by the human workers or the worker Al agent.

59. The method of claim 58 further comprising the step of recording a criteria of the recorded step of the problem solving process, the criteria being a time taken for each step.

60. The method of claim 59 further comprising the step of analyzing the recorded information after the overall solution is accepted or after the problem solving process and updating the metrics of the reputation attribute.

61. The method of claim 60 further comprising the step of soliciting, at predetermined intervals after the overall solution or the sub-solutions are provided to the user interface, a survey for user satisfaction information to obtain short and long-term satisfaction metrics that are used to update the reputation attribute of one or more of the human workers or the worker Al agent.

62. A method for human-centered Artificial General Intelligence (AGI) utilizing a network of human users and a universal problem solving architecture electronically communicating over a collective network, the method comprising: providing a problem request including a problem criteria into an Artificial Intelligence (Al) agent by one or more of intelligent entities including any one of or any combination of one or more human users each utilizing a computer system, and one or more additional Al agents; matching, by the Al agent, one or more human workers from a data source including a list of human problem solvers to the problem request based on the problem criteria; delegating, by the Al agent, a sub-problem of the problem request to one or more of the matched human workers so that work on each of the sub-problems proceeds independently from each other and parallel with each other; utilizing, by a worker Al agent of each of the matched human workers, a universal problem solving architecture in a problem solving process on the sub-problems, respectively, to create one or more sub-solutions; receiving, by the Al agent, the sub-solutions from each of the matched human workers for the sub-problems delegated thereto; executing an ethics check, by the Al agent, by comparing any one of or any combination of the problem request, the sub-problem and the sub-solutions against prohibited attributes, and assigning an ethics value based on any one of or any combination of a result of the comparison, and an ethics criteria; and providing, by the Al agent, any one of or any combination of the sub-solutions and an overall solution to a user interface of a user computer system.

63. The method of claim 62, wherein the matching step is a sub-problem of the problem request.

64. The method of claim 62, wherein the step of the ethics check is triggered every time the problem request or the any one of the sub-problems is set by the human user.

65. The method of claim 62, wherein the step of the ethics check is triggered each time compensation is provided to the matched human workers.

66. The method of claim 62, wherein the ethics criteria are determined by any one of or any combination of combining values and safety information from any one of or any combination of the user computer system and the worker Al agent.

67. The method of claim 62, wherein the ethics criteria include a confidence level threshold for the problem request so that the ethics value is determined as any one of an unsafe goal, an unethical goal, a safe goal, and an ethical goal.

68. The method of claim 67, wherein the confidence level threshold is further utilized to determine if a sequence of individually safe goals is unsafe or unethical when considered cumulatively.

69. The method of claim 67, wherein the confidence level threshold is utilized to determine whether a violation occurred that reflects a predictive evaluation if the goal is to violate the ethics criteria.

70. The method of claim 62 further comprising the steps of: compensating, by the Al agent, the matched human workers for the sub-solutions, respectively; and assigning a reputation attribute to any one of or any combination of the human workers and the worker Al agent.

71. A method for human-centered Artificial General Intelligence (AGI) utilizing a network of human users and a universal problem solving architecture electronically communicating over a collective network, the method comprising: providing a problem request including a problem criteria into an Artificial Intelligence (Al) agent by one or more of intelligent entities including any one of or any combination of one or more human users each utilizing a computer system, and one or more additional Al agents; matching, by the Al agent, one or more human workers from a data source including a list of human problem solvers to the problem request based on the problem criteria; translating, by the Al agent using Large Language Model (LLM), any part of the problem request into an unambiguous language utilizable in a universal problem solving architecture including a decision tree; separating, by the Al agent, the problem request into sub-problems; delegating, by the Al agent, each of the sub-problems to one or more of the matched human workers so that work on each of the sub-problems proceeds independently from each other and parallel with each other;utilizing, by a worker Al agent of each of the matched human workers, the universal problem solving architecture in a problem solving process on the sub-problems, respectively, to create one or more sub-solutions; receiving, by the Al agent, the sub-solutions from each of the matched human workers for the sub-problems delegated thereto; compensating, by the Al agent, the matched human workers for the sub-solutions, respectively; providing, by the Al agent, any one of or any combination of the sub-solutions and an overall solution to a user interface of a user computer system; using a blockchain when any one of or any combination of a compensation is provided, at any stage of the problem solving process, and when the sub-solutions are provided; allowing, by way of the user interface, the human user to any one of or any combination of accept the overall solution, reject the overall solution, and provide feedback to any one of the matched human workers on any one of the sub-solutions; and assigning a reputation attribute to any one of or any combination of the human workers and the worker Al agent.

72. The method of claim 71 , wherein the blockchain is Ethereum based or utilizes crypto tokens that are Ethereum based.

73. A method for developing a human-centered Artificial General Intelligence (AGI) utilizing a network of human users and a universal problem solving architecture electronically communicating over a collective network, the method comprising the steps of: a) providing a request by a human user using an interface of a user computer system or by an Al agent, the request including one or more criteria; b) identifying multiple additional Al agents that have an attribute related to the criteria of the request; c) communicating between the user computer system or the Al agent, and the multiple additional Al agents utilizing a collective network; d) translating using Large Language Model (LLM) any part of the request into an unambiguous language utilizable in a universal problem solving architecture including a decision tree; e) separating the request into sub-problems; f) delegating each of the sub-problems to one or more of the additional Al agents so that work on each of the sub-problems proceeds independently from each other, parallel with each other, or independently and parallel with each other;Il l g) utilizing by the additional Al agents the universal problem solving architecture in a problem solving process on the sub-problems, respectively, to create one or more sub-solutions; h) compensating the additional Al agents for the sub-solutions, respectively; i) developing an AGI by collaborating each of the sub-solutions to create an overall solution; j) providing any one of or any combination of the sub-solutions and the overall solution to any one of or any combination of the interface of the user computer system, the Al agent, and any one of the additional Al agents; and k) assigning a reputation attribute to any one of or any combination of the human workers and the worker Al system.

74. A method for Artificial General Intelligence (AGI) utilizing a single computerized intelligent system including multiple Artificial Intelligence (Al) agents residing in the single computerized intelligent system, the method comprising: providing a problem request including a problem criteria into an Al agent residing in a single computerized intelligent system; matching, by the Al agent, one or more additional Al agents to the problem request based on the problem criteria, the additional Al agents reside in the single computerized intelligent system; translating, by the Al agent using Large Language Model (LLM), any part of the problem request into an unambiguous language utilizable in a universal problem solving architecture including a decision tree; separating, by the Al agent or by any one or more of the additional Al agents, the problem request into sub-problems; delegating, by the Al agent or by any one or more of the additional Al agents, each of the subproblems to one or more of the additional Al agents so that work on each of the subproblems proceeds independently from each other and parallel with each other; utilizing, by the Al agent and the additional Al agents, the universal problem solving architecture in a problem solving process on the sub-problems, respectively, to create one or more sub-solutions; receiving, by the Al agent, the sub-solutions from each of the additional Al agents for the subproblems delegated thereto; combining, by the Al agent, the sub-solutions into an overall solution to the problem request;providing, by the Al agent, any one of or any combination of the sub-solutions and the overall solution to a user interface of a user computer system or the single computerized intelligent system; and allowing, by way of the user interface, a human user to any one of or any combination of accept the overall solution, reject the overall solution, and provide feedback to any one of the matched human workers on any one of the sub-solutions.

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