Advanced autonomous artificial intelligence (AAAI) system and methods
The AGI system addresses the insecure design of existing AGI technologies by utilizing customizable AAAIs with scalable security, ensuring safe and efficient human collaboration for rapid development.
Patent Information
- Application Number
- HK62026125139
- Authority / Receiving Office
- HK · HK
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2023-12-13
- Filing Date
- 2026-06-23
- Publication Date
- 2026-07-31
AI Technical Summary
Current technologies for developing Artificial General Intelligence (AGI) lack a secure and efficient design that leverages human collaboration and ethical standards, posing risks of existential threats and inefficiencies.
A novel AGI system is developed using cloneable Advanced Autonomous Artificial Intelligences (AAAI) that are customizable by humans, collaborating on a network with scalable security features to ensure safety and efficiency.
The system provides a secure and fast path to AGI by integrating human knowledge and ethical standards, enhancing collaboration and safety.
Smart Images
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Abstract
Description
Abstract Artificial General Intelligence (AGI) is the most powerful invention in the world. AGI can solve any problem better and faster than humans—or it could lead to human extinction. Whether AGI represents a multi-trillion-dollar opportunity or an existential threat depends crucially on its design. This invention achieves secure AGI by relying on the combined knowledge and ethical standards of multiple humans, each of whom customizes his / her / their own cloneable Advanced Autonomous Artificial Intelligence, or AAAI. The AAAI can be customized with a single click, leveraging existing data from social media and other information sources. These AAAIs collaborate with each other and with other humans on a network, using a novel general problem-solving architecture to construct the AGI system. This AGI system is also designed with scalable security features as an integral part of the system architecture. This is not only the fastest path to AGI, but also the safest.
Claims
CLAIMSWhat is claimed is:
1. A system for artificial intelligence (Al) electronically communicating over a network, 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 customization subsystem configured or configurable for customizing one or more attributes of an Al system; execute a common cognitive architecture subsystem configured or configurable for implementing one or more problem solving protocols on a request received by the Al system; execute a collective network subsystem configured or configurable for electronically communicating the Al system and one or more additional Al systems; execute an integration subsystem configured or configurable for utilizing one or more datasets from any one of or any combination of the Al system and the additional Al systems; and execute an improvement subsystem utilizing one or more techniques configured or configurable for continuous improvement of any one of or any combination of the customization subsystem, the common cognitive architecture subsystem, the collective network subsystem and the integration subsystem.
2. A method for artificial intelligence (Al) utilizing multiple intelligent entities being any one of or any combination of multiple Al systems and multiple humans each using a computer system, wherein the intelligent entities electronically communicating over a collective network, the method comprising: customizing one or more attributes of an Al system; implementing one or more problem solving protocols on a problem request provided by any one of or any combination of the Al system, and the intelligent entities, the problem solving protocols utilizing a common cognitive architecture; communicating the Al system and the intelligent entities utilizing a collective network; integrating one or more datasets from any one of or any combination of the Al system and the intelligent entities; andimproving, by utilizing one or more techniques, any one of or any combination of the customizing of the attributes, the common cognitive architecture, the collective network and the integrating of the datasets.
3. The method of claim 2, wherein the step of customizing the attributes of the Al system includes the steps of: creating an interface configured or configurable to allow a human user to input training data; selecting one or more training methods and setting training parameters depending on any one of or any combination of a speed factor, a precision factor, an accuracy factor, and a transferability factor; executing multiple training epochs that includes one or more mechanisms to detennine an optimum number of epochs given specific training objectives and quality metrics; and engaging in one or more feedback sessions to refine the training parameters, and to re-run the training epochs based on any one of or any combination of an input from the human user and an input from one or more of the intelligent entities in communication with each other over the network.
4. The method of claim 3, wherein the interface is accessible through a web-based application or a mobile application and is configured or configurable to upload a file or allow the user to enter data manually into an input field.
5. The method of claim 3, wherein the training data contains 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 social media information available to customize the Al system; an amount of email information available to customize the Al system; an amount of electronic infonnation 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.
6. The method of claim 3, wherein the training data contains information about the user obtained by any one of or any combination of a personality test, a standardized tests, a certification, and assessments or questionnaires provided by the user.
7. The method of claim 3, wherein the training parameters is 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; a required 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.
8. The method of claim 3, wherein the training data includes ethical information provided by the human user or a second human user by way of the interface, the ethical information is stored in an ethical profile, and wherein the customizing of the attributes of the Al system includes the ethical information.
9. The method of claim 2, wherein the step of implementing the problem solving protocols on the problem request utilizing the common cognitive architecture further comprising the steps of: submitting the problem request from a human user using a user interface or any one of the intelligent entities; acquiring information associated with the problem request from any one of a human user of the Al system or any one of the intelligent entities; identifying one or more of the intelligent entities that have one or more attributes related to one or more request criteria of the problem request; implementing by each of the identified intelligent entities the problem solving protocols on the problem request to create a completion solution; and providing the completion solution to any one of or any combination of the Al system and any one of the intelligent entities for final acceptance by the user.
10. The method of claim 9, wherein the information is 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 intelligent entities allowed to work on the problem request simultaneously, qualifications required of users associated with the identified 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 be re-used for other users, parameters relating to how to reward the users associated with the identified intelligent entities for working on the problem request, and parameters relating to how- to reward the users associated with the identified intelligent entities that provide a successful completion solution.
11. The method of claim 9 further comprising the 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.
12. The method of claim 9 further comprising the step of distributing a reward to the identified intelligent entities associated with the final acceptance completion solution, wherein the reward is based on a payment parameter.
13. The method of claim 12, wherein the payment parameter includes 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 each of the goal and the subgoal preceding the distributing of the reward has been satisfied.
14. The method of claim 9 further comprising the 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 intelligent entities.
15. The method of claim 9, wherein the Al system is cloned to create one or more cloned Al systems.
16. The method of claim 15 further comprising the step of implementing in parallel, by each of the cloned Al systems, the common cognitive architecture including the problem solving protocols on the problem request to create a completion solution of the cloned Al systems.
17. The method of claim 16, wherein the completion solution utilizes any one of or combination of the completion solution from the identified intelligent entities, and the completion solution from the cloned Al systems.
18. The method of claim 9, wherein the problem solving protocols provide layers that provides an infrastructure configured or configurable to build and scale the Al system, and wherein the protocols enable re-use of completion solutions within and across the Al system and the identified additional Al systems, and wherein the problem solving protocols is configured or configurable to manage a payment of royalties.
19. The method of claim 18, wherein the infrastructure is blockchain or Ethereum based.
20. The method of claim 2, wherein the common cognitive architecture includes: 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 operators to reduce the difference, a safety or ethics screening is applied each time the goals or the subgoals is set; applying heuristic rules that are configured or configurable to guide a 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 protocols, 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 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.
21. The method of claim 2, wherein the step of utilizing the collective network further comprises the steps of: acquiring information associated with the problem request from any one of a human user of the Al system or any one of the intelligent entities; identifying one or more of the intelligent entities that have one or more criteria related to one or more request criteria of the problem request; implementing by a first intelligent entity of the identified intelligent entities the problem solving protocols on the problem request; identifying by the first identified intelligent entity that a completion solution to the problem request requires solving a first sub-problem and one or more additional sub-problems; implementing by the first identified intelligent entity the problem solving protocols on the first sub-problem to create a first sub-solution; assigning at least one of the additional sub-problems to a second intelligent entity of the identified intelligent entities, and implementing by the second identified intelligent entity the problem solving protocols on the at least one of the additional sub-problems to create a second sub-solution; creating, updating or creating and updating a decision tree including the first sub-solution, the second sub-solution and any additional sub-solutions to create the completion solution to the problem request; andproviding the completion solution to the user interface for final acceptance by the user.
22. The method of claim 21 , wherein the decision tree is maintained in blockchain or Ethereum logs.
23. The method of claim 21, wherein the first and second identified intelligent entities access the decision tree by way of an online address or directly from a blockchain.
24. The method of claim 21 further comprising the step of distributing a reward to the first identified intelligent entity associated with an acceptance of the completion solution or the first sub-solution, wherein the reward is based on a payment parameter.
25. The method of claim 24 further comprising the step of distributing a portion of the reward to the second identified intelligent entity by the first identified intelligent entity' based on a payment parameter assigned by the first identified intelligent entity .
26. The method of claim 21 further comprising the step of assigning a blame value and a credit value associated with a problem solving history, using the blame value and the credit value to train the Al system.
27. The method of claim 21 further comprising the step of translating natural language interactions with a human user and the Al system into a common problem solving representation so that both the human user and the Al system can engage in problem solving and the Al system can learn and improve by detecting a behavior and effectiveness of both the human user and the Al system utilizing a reinforcement learning scheme.
28. The method of claim 21 , wherein any one of the identified intelligent entities is an additional Al system, and wherein the additional Al system is cloned to create one or more cloned Al systems.
29. The method of claim 28 further comprising the step of implementing in parallel by each of the cloned Al systems the common cognitive architecture including the problem solving protocols on the problem request to create a completion solution of the cloned Al systems.
30. The method of claim 29, wherein the completion solution utilizes any one of or combination of the completion solution from the identified intelligent entities, and the completion solution from the cloned Al systems.
31. The method of claim 2, wherein the step of integrating the datasets from the multiple Al systems further includes: acquiring information associated with the problem request from any one of a human user of the Al system or any one of the intelligent entities; identifying the intelligent entities that have one or more criteria related to one or more request criteria of the problem request: assigning the problem request or one or more sub-problems of the problem request to each of the identified intelligent entities;implementing by the identified intelligent entities the problem solving protocols on the problem request or the sub-problems to create a problem solution or one or more sub-problem solutions, respectively; integrating the problem solution and one or more of the sub-problem solutions to create a completion solution to the problem request; and providing the completion solution to a user interface for final acceptance by the user.
32. The method of claim 31 further comprising the step of assigning a credit value or a blame value to the datasets based on whether the datasets increase or decrease performance of the Al system based on performance metrics or evaluation functions.
33. The method of claim 32 further comprising the step of quantifying a benefit weight or a harm weight to a contribution by each of the identified intelligent entities to the problem request.
34. The method of claim 33 further comprising the step of distributing a reward to an owner of the identified intelligent entities proportionally to the contribution of the identified intelligent entities based on the benefit weight or the harm weight.
35. A method for artificial intelligence (Al) by customizing one or more attributes of an Al system, the method comprising: creating 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; processing and converting the training data to a standardized training format; selecting one or more training methods and setting training parameters depending on any one of or any combination of a speed factor, a precision factor, an accuracy factor, and a transferability factor; executing multiple training epochs 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; engaging in one or more feedback sessions to refine the training parameters, and to re-run 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; and customizing the attributes of the Al system with the training format.
36. The method of claim 35, wherein the interface is 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.
37. The method of claim 35, wherein the training data contains 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 financialresources 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.
38. The method of claim 35, wherein the training data contains information about the human user obtained by any one of or any combination of a personality test, a standardized tests, a certification, and assessments or questionnaires provided by the human user.
39. The method of claim 35, wherein the training parameters is 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; a required 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.
40. The method of claim 35, wherein the training data includes ethical information provided by the human user by way of the interface, the ethical information is stored in an ethical profile, and wherein the customizing of the attributes of the Al system includes the ethical information.
41. A method for artificial intelligence (Al) by problem solving utilizing a common cognitive architecture implemented in an Al system, the method comprising: providing a problem request from an intelligent entity being an Al system or a human user using a user interface on a computer system; acquiring information associated with the problem request from the intelligent entity; identifying multiple additional intelligent entities that are each communicable with each other over a network, and that each have one or more criteria related to one or more request criteria of the problem request, wherein the additional intelligent entities being any one of or any combination of multiple additional Al systems and multiple additional humans each using a computer system; implementing by each of the identified additional intelligent entities the common cognitive architecture including one or more problem solving protocols on the problem request to create a completion solution; and providing the completion solution to the intelligent entity for final acceptance by a user.
42. The method of claim 41. wherein the information is any one of or any combination of a name and description of the problem request, a total reward that the user will pay for a successfulcompletion 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 w orking 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 rew ard the users associated with the identified additional intelligent entities that provide a successful completion solution.
43. The method of claim 41 further comprising the 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.
44. The method of claim 41 further comprising the 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.
45. The method of claim 44, wherein the payment parameter includes 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.
46. The method of claim 41 further comprising the 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.
47. The method of claim 41, wherein any one of or combination of the identified additional Al systems is cloned to create one or more cloned Al systems.
48. The method of claim 41 further comprising the step of implementing by each of the cloned Al systems the common cognitive architecture including the problem solving protocols on the problem request to create a completion solution of the cloned Al systems.
49. The method of claim 48, wherein the completion solution utilizes 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.
50. The method of claim 41, wherein the common cognitive architecture includes: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 is 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 protocols, 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 detennining 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.
51. A method for artificial intelligence (Al) by problem solving utilizing a collective network of Al systems, the method comprising: submitting a problem request from a human user using a user interface on a computer system or from an Al system; acquinng information associated with the problem request from the computer system of the human user or from the Al system; identifying intelligent entities being any one of or any combination of multiple additional Al systems and multiple humans each using a computer system that are each communicable with each other over a network, and that each have one or more criteria related to one or more request criteria of the problem request;implementing by a first intelligent entity of the identified intelligent entities a common cognitive architecture including one or more problem solving protocols on the problem request; determining by the first intelligent entity that a completion solution to the problem request requires solving a first sub-problem and one or more additional sub-problems; implementing by the first intelligent entity the problem solving protocols on the first subproblem to create a first sub-solution; assigning at least one of the additional sub-problems to a second intelligent entity of the identified intelligent entities, and implementing by the second intelligent entity the problem solving protocols on the at least one of the additional sub-problems to create a second subsolution; creating a decision tree including the first sub-solution and the second sub-solution to create the completion solution to the problem request; and providing the completion solution to the user interface or the Al system for final acceptance by the user.
52. The method of claim 51 , wherein the decision tree is maintained in blockchain or Ethereum logs.
53. The method of claim 51, wherein the first and second identified intelligent entities access the decision tree by way of an online address or directly from a blockchain.
54. The method of claim 51 further comprising the 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.
55. The method of claim 54, wherein the payment parameter includes 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.
56. The method of claim 54 further comprising the step of distributing one or more of the tokens to the second identified intelligent entity by the first identified intelligent entity based on a payment parameter assigned by the first identified intelligent entity .
57. The method of claim 56 further comprising the step of influencing a direction of the problem solving protocols 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.
58. The method of claim 51, wherein the problem solving protocols provides layers of an infrastructure configured or configurable to build and scale the identified intelligent entities, and wherein the problem solving protocols enable re-use of completion solutions within and across theintelligent entities, and wherein the problem solving protocols is configured or configurable to manage a payment of royalties.
59. The method of claim 58, wherein the infrastructure is blockchain Ethereum based.
60. A method for artificial intelligence (Al) by integrating one or more datasets from multiple Al systems on a collective network, the method comprising: submitting a problem request from a human user using a user interface on a computer system or from an Al system; acquiring information associated with the problem request from the computer system of the human user or the Al system; identifying multiple intelligent entities that are each communicable with each other over a network, and that each have one or more criteria related to one or more request criteria of the problem request, wherein the intelligent entities being any one of or any combination of multiple additional Al systems and multiple humans each using a computer system; assigning the problem request or one or more sub-problems of the problem request to each of the intelligent entities; implementing by the intelligent entities a common cognitive architecture including one or more problem solving protocols on the problem request or the sub-problems to create a problem solution or a sub-problem solution, respectively; integrating the problem solution and the sub-problem solution to create a completion solution to the problem request; and providing the completion solution to the user interface or the Al system for final acceptance by the user.
61. The method of claim 60 further comprising the 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.
62. The method of claim 61 further comprising the step of quantifying a benefit weight or a harm weight to a contribution by each of the intelligent entities to the problem request.
63. The method of claim 62 further comprising the 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.
64. A system for Artificial Intelligence (Al) by utilizing multiple Al systems electronically communicating over a collective intelligence network to respond to a request, 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: receive a problem request; identify Al systems that are each communicable with the computer system, and that each have one or more criteria related to one or more request criteria of the request or the program instructions; generate one or more answers in response to the request or the program instructions, the answers resulting from collaboration of the identified Al systems; and provide the answers to a user device.
65. The system of claim 64, wherein a parameter of any one of or any combination of the Al systems is customizable after an iteration of the generated answers.
66. The system of claim 65, wherein the parameter has a characteristic selected from any one of or combination of an ethical characteristic, a time characteristic, a financial characteristic, a computational resource characteristic, a legacy characteristic, a safety characteristic, an educational characteristic, and a monetizing characteristic.
67. The system of claim 66, wherein any one of or combination of the Al systems is cloned before or after customization to create one or more cloned Al systems.
68. The system of claim 67, wherein the ans ers are generated by the computer system utilizing anyone of or combination of the Al systems, and the cloned Al systems.
69. The system of claim 64, wherein a constraint to any one of or any combination of the Al systems is customizable by the user or one or more second users different to that of the user.
70. The system of claim 64, wherein the computer system utilizes multiple combinations of the Al systems to generate the answers.
71. The system of claim 70, wherein the Al systems is a first Al system located remotely to one or more additional Al systems all in communication with the computer system over the network.
72. The system of claim 71, wherein the first Al system and one or more of the additional Al systems and human users create an Artificial General Intelligence (AGI) network.
73. The system of claim 64, wherein the request or the program instructions is received by the computer system by a user input by way- of natural language on a user Al system.
74. A method for developing Artificial General Intelligence (AGI) for generating an answer to a response utilizing multiple Artificial Intelligence (Al) systems electronically communicating over a collective intelligence network, the method comprising:a) inputting a request into an interface of a first Al system by a human user, the request including one or more criteria; b) identifying multiple intelligent entities that are each communicable with the first Al system, and that has a criteria related to the criteria of the request, wherein the intelligent entities being any one of or any combination of multiple additional Al systems and multiple humans each using a computer system; c) communicating the first Al system and the additional Al systems utilizing a collective intelligence network; d) receiving the request by the identified intelligent entities from the first Al system; e) generating one or more answers in response to the request by each of the identified intelligent entities; f) developing an AGI by collaborating each of the answers to create a collaborative answer; and g) providing any one of or any combination of the answers and the collaborative answer to the first Al system.
75. The method of claim 74 further comprising the steps of customizing one or more attributes of the first Al system by the user; and wherein step e) further comprises the steps of: implementing one or more problem solving protocols on the request by each of the identified intelligent entities utilizing a common cognitive architecture; integrating one or more datasets from any one of or any combination of the first Al system and the identified intelligent entities; and improving, by utilizing one or more techniques, any one of or any combination of the customizing of the attributes, the common cognitive architecture, the collective intelligence network and the integrating of the datasets.
76. The method of claim 75, wherein any one of or combination of the multiple additional Al systems is cloned to create one or more cloned Al systems.
77. The method of claim 76 further comprising the step of implementing in parallel by each of the cloned Al systems the common cognitive architecture including the problem solving protocols on the request to create an answer.
78. The method of claim 77, wherein the collaborative answer utilizes any one of or combination of the answers and the answer of the cloned Al systems.
79. The method of claim 75, wherein the attributes include ethical information provided by the human user or one or more second human users, the ethical information is stored in an ethical profile, and wherein the customizing of the attributes of the first Al system includes the ethical information.
80. The method of claim 75 further comprising the step of distributing a reward to the identified intelligent entities associated with the answers or the collaborative answer accepted by the human user, wherein the reward is based on a payment parameter, and wherein the payment parameter includes any one of or any combination of if a goal of the request has been achieved, if a subgoal of the request has been achieved, and if an ethical criteria related to the goal and the subgoal preceding the distributing of the reward has been satisfied.
81. A method for developing Artificial General Intelligence (AG1) including a step of translating natural language interactions with a human user and an Al system into a common problem solving representation so that both the human user and the Al system engage in problem solving of a problem request.
82. A method for artificial intelligence (Al) utilizing a single computerized intelligent system including multiple 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; customizing one or more attributes of the Al agent; matching, by the Al agent or the single computerized intelligent system, 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; utilizing, by the Al agent and the additional Al agents, a universal problem solving architecture in a problem solving process on the goal, respectively, to create one or more solutions; receiving, by the Al agent, the solutions from each of the additional Al agents for the goal delegated thereto; integrating one or more datasets from any one of or any combination of the Al agent and the additional Al agents; combining, by the Al agent, the solutions into an overall solution to the goal; and improving, by the Al agent or the additional Al agents, any one of or any combination of the customizing of the attributes, the common cognitive architecture, and the integrating of the datasets utilizing one or more techniques.