System and methods for planetary intelligence (PI)

Abstract

Planetary Intelligence (PI) arises from multiple Artificial General Intelligences (AGIs). These AGIs collaborate over a self-extending, global, problem solving network. Each AGI is comprised of a network of (human and AI) intelligent entities. Each AI entity can be customized and personalized with specific human values and knowledge. Alignment results from many AI entities combining their human-centered values democratically, using representative and statistically valid methods. Safety is designed into the system and scales as AGIs and PI increase in intelligence and speed. The modular, scalable design of PI integrates more than 100 novel inventive systems and methods. Specific inventions include: a universal problem solving architecture and methods; new methods for AI learning and customization; methods for integrating intelligent entities; catalysts for increasing intelligence; superior monetization methods; attentional systems enabling awareness, and self-awareness; methods for ethical conflict resolution; and methods for maximizing human-alignment and the safety of AI, AGI, and PI.

Description

Abstract Planetary Intelligence (PI) originates from multiple Artificial General Intelligences (AGIs). These AGIs collaborate on a self-expanding global problem-solving network. Each AGI consists of a network of (human and AI) intelligent entities. Each AI entity can be customized and personalized with specific human values ​​and knowledge. Alignment results from many AI entities democratically combine their human-centered values ​​using representative and statistically efficient methods. Security is designed into the system and scales with increasing intelligence and speed of AGIs and PIs. The modular, scalable design of PI integrates more than 100 novel inventive systems and methods. Specific inventions include: general problem-solving architectures and methods; novel methods for AI learning and customization; methods for integrating intelligent entities; catalysts for increasing intelligence; superior monetization methods; attention systems for achieving consciousness and self-awareness; methods for resolving ethical conflicts; and methods for maximizing human alignment and security of AIs, AGIs, and PIs. Figure 110

Claims

CLAIMSWhat is claimed is:1 . A system for Planetary Intelligence (PI) with human-aligned behavior utilizing a collective network of intelligent entities selected from the group consisting of any combination of a user computer system operated by a human user, an Artificial Intelligent (Al) agent or system, an Advanced Autonomous Artificial Intelligences (AAAI) agent or system, an Artificial General Intelligent (AG1) agent or system, a Superlntelligent (SI) agent or system and a Personalized SuperIntelligence (PSI) agent or system, the system comprising: multiple intelligent entities each connected to a collective network and each 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 intelligent entities to each independently: utilize a modular architecture configured or configurable to scale from components ithin an individual intelligent entity on the collective network; implement a universal problem solving architecture and framework on a task to collaborate and create higher levels of intelligence; utilize a human-centered input configured or configurable for obtaining values and ethics information from multiple human users and then using the values and ethics information to customize the Al agent or system; combine data from multiple of the intelligent entities at a level of the AAAI; customize the PSI agent or system using intelligent agents configured or configurable to develop and continuously improve the PSI agent or system; increase a knowledge of the intelligent entities by incorporating new sources of informational datasets that differs from a current informational dataset of the intelligent entity; combine the values and ethical information of other intelligent entities, and to resolve conflicts between different value systems; utilize online advertising technology for increasing an intelligence of the intelligent entities; create a self-aware operation for the AGI or PI agent or system by adding a dimension of self-awareness and increased autonomy to the AGI or PI agent or system, and to create an ability7to assume multiple identities of the AGI or PI agent or system to handle tasks that arise in parallel with other AGI or PI agents or systems: and search through any one of or any combination of a collective network of AIs. a collective network of AAAIs, a collective network of AGIs, and a collective network of Pls fornew information that is different to a current information, respectively, and to incorporate the new information to the current information, respectively.

2. A method for Planetary Intelligence (PI) with human-aligned behavior utilizing a collective network of intelligent entities selected from the group consisting of any combination of a user computer system operated by a human user, an Artificial Intelligent (Al) agent or system, an Advanced Autonomous Artificial Intelligences (AAAI) agent or system, an Artificial General Intelligent (AG1) agent or system, a Superlntelligent (SI) agent system and a Personalized SuperIntelligence (PSI) agent or system, the method comprising the steps of: utilizing a modular architecture configured or configurable to scale from components within an individual intelligent entity on the collective network; implementing a universal problem solving architecture and framework on a task to collaborate and create higher levels of intelligence; utilizing a human-centered input configured or configurable for obtaining values and ethics infonnation from multiple human users and then using the values and ethics information to customize the Al agent or system; combining data from multiple of the intelligent entities at a level of the AAAI agent or system; customizing the PSI agent or system using intelligent agents configured or configurable to develop and continuously improve the PSI agent or system; increasing a knowledge of the intelligent entities by incorporating new sources of informational datasets that differs from a current informational dataset of the intelligent entity; combining the values and ethical information of other intelligent entities, and resolving conflicts between different value systems; utilizing online advertising technology for increasing an intelligence of the intelligent entities; creating a self-aware operation for the AGI or PI agent or system by adding a dimension of self- awareness and increased autonomy to the AGI or PI agent or system, and creating an ability to assume multiple identities of the AGI or PI agent or system to handle tasks that arise in parallel with other AGI or PI agents or systems; creating the PI agent or system comprising a collective network of AGIs. wherein the collective network of AGIs includes any one of or any combination of a collective network of the user computer systems, a collective network of AIs, a collective network of AAAIs, and a collective network of PSIs; and searching through any one of or any combination of the collective network of the user computers systems, the collective network of AIs, the collective network of AAAIs, the collective network of AGIs, the collective network of PSIs, and a collective network of Plsfor new information that is different to a current information, respectively, and incorporating the new infonnation to the cunent information, respectively.

3. The method of claim 2, wherein the step of utilizing the modular architecture further comprises the steps of: customizing one or more attributes of the intelligent entity; integrating one or more datasets from any one of or any combination of the intelligent entities; and improving, by utilizing one or more techniques, any one of or any combination of the customizing of the attributes, the universal problem solving architecture and framework, the collective network and the integrating of the datasets.

4. The method of claim 3 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.

5. The method of claim 4 further comprising the step of quantify ing a benefit weight or a harm weight to a contribution by each of the intelligent entities to the task.

6. The method of claim 5 further comprising the step of distributing a reward to the intelligent entities proportionally to the contribution of the intelligent entities based on the benefit weight or the harm weight.

7. The method of claim 2, wherein the step of implementing the universal problem solving architecture and framework further comprises the steps of: acquiring information associated with the task from any one of or any combination 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 task; implementing by each of the identified intelligent entities the universal problem solving architecture and framework on the task to create a completion solution; and providing the completion solution to any one of or any combination of the intelligent entities for final acceptance.

8. The method of claim 7 further comprises the steps of: executing an ethics check on the task, and a solution for the task provided by any one of or any combination of the intelligent entities; comparing any one of or any combination of the task, and the solution against prohibited attributes, and assigning an ethics attribute to one of or any combination of the task, and thesolution based on any one of or any combination of a result of the comparison, and an ethics criteria; implementing, based on the result of the comparison, the universal problem solving architecture and framework on the task to create the solution and creating an AGI; and providing the results of the comparison and the solution to any one of the intelligent entities and additional intelligent entities on the collective network.

9. The method of claim 8 further comprises the step of recording one or more problem solving activities from each of the intelligent entities in an auditable record, and comparing the problem solving activities with a successful or unsuccessful progress towards the solution of the task, and determining which of the problem solving activities to keep active.

10. The method of claim 8 further comprises the step of learning by the intelligent entities a procedural learning process of the universal problem solving architecture and framework, wherein the intelligent entities provide information to the procedural learning process for creation of the AGI.

11. The method of claim 7 further comprising the step of cloning any one of the intelligent entities for deployment of multiple copies thereof to assist in any one of or any combination of creating of the solution, or providing training data to any one of the intelligent entities.

12. The method of claim 11 further comprising the step of estimating a worth of the cloned intelligent entities utilizing a network effect value including the number of cloned intelligent entities s available on the collective network.

13. The method of claim 12 further comprising the step of utilizing the estimated worth for determining pricing decisions for problem solving sendees offered by the cloned intelligent entities on any one of the social media platforms or through an additional intelligent entity.

14. The method of claim 13 further comprising the step of monetizing the cloned intelligent entities for each utilization of the cloned intelligent entities on the social media platforms or the additional intelligent entity.

15. The method of claim 13 further comprising the step of allowing access to the cloned intelligent entities by any one of the social media platforms so that the social media platforms can receive the solution to the task or using the training data for an Al system of the social media platform.

16. The method of claim 2, wherein the step of utilizing the human-centered input further comprises the steps of: matching one or more human workers from a data source including a list of human problem solvers to the task based on a task criteria;translating any part of the task into an unambiguous language utilizable in the universal problem solving architecture and framework including a decision tree; separating the task into sub-tasks; delegating each of the sub-tasks to one or more of the matched human workers so that work on each of the sub- tasks proceeds independently from each other and parallel with each other; utilizing the universal problem solving architecture and framework in a problem solving process on the sub-tasks, respectively, to create one or more sub-solutions; receiving the sub-solutions from each of the matched human workers for the sub-tasks delegated thereto; combining the sub-solutions into an overall solution to the task; directing any one of or any combination of anew 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 the matched human workers for the sub-solutions; providing any one of or any combination of the sub-solutions and the overall solution to any one of or any combination of the intelligent entities; allowing any one of or any combination of the intelligent entities to 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 intelligent entities.

17. The method of claim 16, wherein the decision tree is maintained in blockchain or Ethereum logs.

18. The method of claim 16, 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 task, 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 collective network, a rating by other human workers, a responsiveness value of the human workers, and a reliability' value of the human workers.

19. The method of claim 16 further comprising the step of using the reputation attribute in the matching of the human workers to the task using an algorithm to the delegation of the sub-task.

20. The method of claim 16 further comprising the step of soliciting, at predetermined intervals after the overall solution or the sub-solutions are provided to any one of the intelligent entities, feedback by way of a survey for user satisfaction information to obtain satisfaction metrics that are used to update the reputation attribute of one or more of the human workers or the intelligent entities, respectively.

21. The method of claim 2, wherein the step of combining data from multiple of the intelligent entities at a level of the AAAI agent or system further comprises the steps of training a base Large Language Model (LLM) of a first Al agent or system with guardrails including attributes associated with any one of or any combination safety, ethics and knowledge; customizing the base LLM to an ethics profile associated with a first human user; combining ethical information from multiple intelligent entities different to that of the first Al agent or system and the first human user; refining a set of values of the base LLM based on problem solving on the task; and updating the base LLM with the combined ethical information and the refined set of values thereby allowing for a scalable AGI.

22. The method of claim 21 , wherein the customizing of the base LLM further comprises the step of assembling a corpus of ethical questions based on various ethical assessment instruments and supplemented by first questions based on data from a social media platform and second questions solicited from crowdsourcing.

23. The method of claim 22 further comprising the step of assigning regression weight values to the ethical questions such that a ranking is achieved whereby higher-ranked questions provide more useful ethical information than lower-ranked questions.

24. The method of claim 23 further comprises the step of combining weight values from the intelligent entities with the regression weight values for improving a tuning of any one of the intelligent entities.

25. The method of claim 2, wherein the step of customizing the PSI agent or system using intelligent agents configured or configurable to develop and continuously improve the PSI agent or system further comprises the steps of : acquiring a base-level Al agent or system that has previously been customized; collecting media information related to the human user of the base-level Al agent or system; analyzing the media information; transforming the analyzed media information into training data sets; differentially weighting the transformed training data sets; adding knowledge modules to the weighted transformed training data sets; locating new sources of data to include to the weighted transformed training data sets; applying the weighted transformed training data sets to the base-level Al agent to create a user PSI; andcommunicating the user PSI with multiple additional PSIs using the collective network to enable community -based safety features from the additional PSIs to the user PSI.

26. The method of claim 25 further comprises the steps of: communicating the user PSI with multiple additional intelligent entities using the collective network to enable community-based safety features, wherein the user PSI and the additional intelligent entities each agree to use a set of rules relating to safety or ethics; recording all actions by the user PSI and the additional intelligent entities in an auditable form on any one of or any combination of a central computer system on the collective network, the intelligent entities, and the additional intelligent entities; and monitoring that each of the actions follow the set of rules, and flagging any of the actions that do not follow the set of rules.

27. The method of claim 2, wherein the step of increasing a knowledge of the intelligent entities by incorporating new sources of informational datasets that differs from a current informational dataset of the intelligent entity further comprises the steps of: searching for one or more potential informational datasets from one or more sources, the potential informational datasets being related to a knowledge dataset of the intelligent entity, respectively; determining a difference of the potential informational datasets by utilizing a difference attribute of the potential informational datasets with regard to one or more factors; and learning by utilizing the potential informational datasets based on the difference attribute of the potential informational datasets.

28. The method of claim 27 further comprises the steps of: sampling subsets of the potential informational datasets and calculating a goal-relevancy attribute to identify one or more of the sampled subsets that have a highest goal-relevancy; estimating a Shannon Entropy on the one or more sampled subsets; calculating a Kaplan Information Theoretical (KIT) relevance utilizing a product of the Shannon Entropy and the goal-relevancy attribute of each of the subsets; grouping the subsets based on the KIT relevance to determine a first approximation of an optimal grouping of the subsets including a prioritized grouping of the potential informational datasets; and providing the prioritized grouping of the potential informational datasets to the intelligent entities for learning by the any one of the intelligent entities.

29. The method of claim 2, wherein the step of combining the values and ethical information of other intelligent entities, and resolving conflicts between different value systems further comprises the steps of identifying information, including the values and ethical information, from each of the intelligent entities; combining the information mathematically, if already represented as numerical quantities, the numencal quantities including any one of or any combination of weights for a neural network or for a subset of the neural network, or if the information is non-numerical information that is not already represented as numerical quantities including any one of or combination of weights for the neural network or for the subset of the neural network; then first training the intelligent entity on the non-numerical information by way of one or more training datasets in order to convert the information into numerical quantities including any one or combination of weights for the neural network or for the subset of the neural network; and then combining such numerically represented information, including ethical information, mathematically.

30. The method of claim 29, wherein the information that is already represented as numerical quantities further includes the steps of: identifying a specific portion of weight matrices of each of the intelligent entities that correspond to a desired information, including ethical information; computing the weighted or unweighted means of the corresponding numerical quantities in the corresponding portions of the weight matrices for each of the intelligent entities; and assigning the matrices of computed w eighted or unw eighted means to the new intelligent entity as reflecting the combined information of the contributing intelligent entities.

31. The method of claim 29 further comprising the step of determining consensus values by voting by each of the intelligent entities on the ethical information that should form a basis for a behavior of any one of the intelligent entities.

32. The method of claim 31 further comprising the step of presenting a specific scenario to each of the intelligent entities, with the scenario including options for how any one of the intelligent entities should behave.

33. The method of claim 32, wherein the voting by the intelligent entities is a w eighted voting and further comprising the steps of:determining if applying a first weight to a first of the intelligent entities that is greater than a second weight to a second of the intelligent entities is appropriate, wherein the first of the intelligent entities is different to that of the second of the intelligent entities; and performing the weighted voting utilizing the weight of the first of the intelligent entities and the weight of the second of the intelligent entities if determined to be appropriate.

34. The method of claim 33, wherein the applying the first weight greater than the second weight is dependent on if there is a need to correct for a non-representative sample of the intelligent entities.

35. The method of claim 33, wherein the applying the first weight greater than the second weight is dependent on if there is a desire to apply the first weight or the second weight to specific ethical principles that are associated with a desired sub-sample or sup-population of the intelligent entities.

36. The method of claim 29 further comprising the steps of: identifying a conflict between two or more of the ethical information; and resolving the conflict using a conflict resolving algorithm.

37. The method of claim 36 further comprising the steps of: creating a search space of potential sets of ethical rules; finding an optimal set of the ethical rules that has least conflict; and prioritizing or weighting an importance of the optimal set of the ethical rules.

38. The method of claim 36 further comprising the steps of: resolving the conflict using a consequentialist approach, the consequentialist approach further includes the steps of: identify ing a desired outcome; identify ing a potential unethical action that could be taken to achieve the outcome, wherein the intelligent entities rank, rate, weight or vote upon how unethical the action is compared to other actions; evaluating a potential consequences of the unethical action; using information on the ranking, rating, weighting or voting on the unethical actions and outcomes to weigh potential benefits of achieving the desired outcome against potential costs of taking the unethical action using a mathematical approach; and taking an action if the benefits outweigh the costs.

39. The method of claim 2, wherein the step of utilizing online advertising technology for increasing an intelligence of the intelligent entities further comprises the steps of: populating an online advertisement unit including information associated with the task; providing the online advertisement unit to one or more of the intelligent entities utilizing the collective network;receiving one or more inputs from the intelligent entities participating in the online advertisement unit, and the inputs being associated with any one of or any combination of solving the task, solving a sub-task of the task, and advancing progress on the task; communicating the inputs to the any one of or any combination of the intelligent entities; utilizing the inputs in a universal problem solving method of the universal problem solving architecture and framework to generate a solution to the task or the sub-task; and training any one of or any combination of the intelligent entities with results from the universal problem solving method that are based on successful or unsuccessful solution attempts to solve the task or the sub-tasks, thereby increasing an intelligence of the intelligent entity, respectively.

40. The method of claim 39 further comprises the step of receiving advertisement specifications from a client to be used in the populating or targeting of the online advertisement unit in combination with information associated with the task.

41. The method of claim 40 further comprises the step of billing the client based on cost per thousand impressions or clickthrough rate metrics.

42. The method of claim 40, wherein the advertisement specifications include any one of or any combination of advertisement content, demographic information, location restrictions forthe online advertisement unit, advertisement budget, and metrics for determining a successful solving of the task or a sub-task.

43. The method of claim 39 further comprises the step of bidding on attention, information or expertise using a spot market.

44. The method of claim 43, wherein the spot market includes: a means for the intelligent entities with attention to sell to access a marketplace and specify seller information for sale and an ask price for the information; a means for buyers of intelligent entity attention to access the marketplace and specify buyer information the buyer is willing to buy and a bid price for buying the buyer information; a market mechanism for queuing the bid price and the ask price, including categories of the buyer and seller information, wherein each category has a market in the marketplace; the market mechanism is configured or configurable to make the market in each category by market makers; and the market mechanism is configured or configurable to match bid prices and ask prices, and a transaction occurs that is binding on the buyer and the seller of the information.

45. The method of claim 43. wherein the spot market includes the steps of:buyers and sellers of intelligent entity attention and expertise register on a platfonn, the buyers providing details about buyer interests or expertise, the sellers providing details about sellers interests or expertise; listing by the sellers available time slots and expertise areas, and listing by the buyers needs and time slots the buyers is interested in; utilizing, by the platform, an algorithm to dynamically price intelligent entity attention and expertise based on supply, demand, and user ratings; matching one or more of the buyers and one or more of the sellers based on requirements, availability, and price; enabling transactions where the buyers pay for the seller time slots, and wherein the platform takes a commission; and providing feedback where after each session, the buyers and the sellers rate each other, influencing future pricing and matching.

46. The method of claim 43, wherein the spot market includes the steps of: buyers and sellers of intelligent entity attention and expertise register on a platform, the buyers providing details about buyer interests or expertise, the sellers providing details about sellers interests or expertise; creating an auction by the sellers for seller time slots; placing one or more buyer bids by a buyer on time slots and expertise the buyer requires; closing the auction at a predetermined time or when the seller accepts a buyer bid; paying the seller by the buyer of a winning bid, and the seller provides the attention or expertise based on the time slot, wherein the platform mediates an exchange and secures payment; and providing feedback where after each auction, the buyers and the sellers rate each other, affecting future auctions and visibility on the platform.

47. The method of claim 46 further comprises the step of providing a feedback mechanism associated with the intelligent entity spot market, the feedback mechanism being configured or configurable to record each and every step in the problem solving process, and to create a vector track record of a performance of all the intelligent entities on the task or the sub-task.

48. The method of claim 47, wherein the vector track record is implemented by way of blockchain technology to allow for precise reputations that are analyzed by an Al agent or system and converted into estimates of a value for each of the intelligent entities for any of the tasks.

49. The method of claim 39 further comprises the step of compensating the intelligent entity when exiting the online Ad advertisement unit and stops participating in the problem solving process.

50. The method of claim 39 further comprises the step of crediting the intelligent entities if the task or the sub-task is solved, based on an amount of contribution by the participating intelligent entities.

51. The method of claim 39 further comprises the step of improving the interactive online advertisement unit based on a feedback loop including on any one of or any combination of reputational metrics, and metrics related to the problem solving process or knowledge captured.

52. The method of claim 2, wherein the step of creating a self-aware operation for the intelligent entities by adding a dimension of self-awareness and increased autonomy to the intelligent entities further comprises the step of: equipping any one of or any combination of the intelligent entities with one or more components configured or configurable to operate with characteristics of a spotlight of attention model; setting dynamic parameters for working memory of the intelligent entity, respectively, that corresponds to cognitive resource limits; providing a dimension of categorization for events in the working memory7that relates to self or non-self; categorizing each of the events, as the events are encountered by the intelligent entity, respectively, with respect to categories that the intelligent entity wishes to be aware of; and constructing a model of a state of awareness for the intelligent entity, the model consisting of a total of the events that are active in the working memory’ based on the parameters, for each of the categories of awareness, including a current self and environmental state of awareness.

53. The method of claim 52, wherein the step of equipping the intelligent entities with the components includes any one of or any combination of: an input system configured for sensory and non-sensory cognitive input or perceptual inputs and self-generated concepts; an attention mechanism configured or configurable to focus computational resources of the intelligent entities on specific stimuli that are relevant at any given time; pattern recognition algorithms configured or configurable to compare the sensory' and non- sensory cognitive input or the perceptual inputs with the working memory to recognize objects and events, and identify which elements within the sensory input or the working memory are likely to be relevant to the task of the intelligent entities, the pattern recognition algorithms are further configured or configurable to categorize and store information in a structured manner for future retrieval; memory systems configured or configurable to support the working memory, short-term memory, and long term memory capabilities;categorization capabilities configured or configurable to process the sensory7and non-sensory cognitive input or the perceptual inputs and to categorize the inputs into various classes including perceptual events, cognitive events, interactions; and self-referential events, and concept formation capabilities that enable the intelligent entities to form new human-understandable concepts.

54. The method of claim 52 further comprising the step of monitoring and updating the categories of awareness of the intelligent entities.

55. The method of claim 54, wherein the step of monitoring and updating the categories of awareness further comprises the steps of: retrieving, by the intelligent entities, existing categories of awareness; maintaining an awareness in parallel with other problem solving operations of the task provided to the intelligent entities; monitoring and updating continuously the categories of awareness of the intelligent entities in real-time to change the state of aw areness of the intelligent entities; and providing a feedback loop to refine the categories of awareness.

56. The method of claim 55, wherein the step of monitoring and updating continuously the categories of awareness includes the steps of: using an attention mechanism configured or configurable to direct attention of the intelligent entities periodically from the problem solving task to updating the state of awareness; enabling attention interrupts that are configured or configurable to shift attention immediately from the problem solving task if any external perception or internally self-generated concept from an input system detects one or more of the events that matches of list of events constituting intentional interrupts; and updating the state of awareness when the attention is directed.

57. The method of claim 52 further comprises the step of resolving a conflict in behavior of any one of the intelligent entities based on differing identities and self-concepts.

58. The method of claim 57 further comprises the step of providing ethical reasoning and consequence prediction that comprises the steps of: identifying a conflict between a behavioral directives of two or more of the active identities, the recognizing of the conflict utilizes a voting method from the intelligent entities; gathering information that collects relevant data about the situation, including the potential consequences of the different actions, relevant ethical principles, and human safetyconsiderations;providing simulation options that utilize a virtual environment to simulate potential actions and consequences under the recognized conflict of each of the active identities; evaluating and prioritizing that analyzes predicted outcomes of each of the actions, prioritizing actions that minimize harm to humans and align with the ethical principles; and selecting and implementing the action that best resolves the conflict while adhering to ethical guidelines and minimizing risk to humans, documenting a reasoning process for future reference and learning.

59. The method of claim 57 further comprises the step of providing hierarchical override with justification that comprises the steps of: identifying a conflict between behavioral directives of two or more of the active identities; providing a reference hierarchy that consults an established hierarchy of the identities, where human safety and well-being attributes holds a highest priority; providing a means to activate override where the identities higher in the hierarchy takes precedence; providing justification and transparency that documents the conflict, a decision-making process, and a justification for a chosen action based on the hierarchy and ethical principles; and providing learning and adaptation that learns from experience, and refines an understanding of the conflicting identities and adjusting the hierarchy or the behavioral protocols to prevent similar conflicts in the future.

60. The method of claim 57 further comprises the step of providing external arbitration and input from the intelligent entities that comprises the steps of: recognizing intractable conflict that identifies a conflict that cannot be resolved independently due to a complexify of a situation or an equally weighted importance of conflicting identities; seeking external input that requests guidance from external intelligent entities or a designated ethics committee, and providing all relevant information about the conflict, potential actions, and predicted consequences; providing collaborative deliberation wherein the intelligent entities and intelligent entity collaborators engage in a discussion, considering ethical principles, human values, and potential consequences of different actions; providing joint decision-making based on the collaborative deliberation, a course of action is chosen that aligns with both core principles and human ethical considerations; andproviding documentation and learning that documents the conflict, a resolution process, and a rationale behind a final decision, for improving an ability to handle similar conflicts in the future.

61. The method of claim 57 further comprises the step of providing identity negotiation and compromise that comprises the steps of: identifying shared goals that analyzes conflicting identities and seeks to identify any underlying shared goals or values; exploring alternative actions that potentially satisfy core principles of both conflicting identifies; evaluating compromise options that assess potential consequences of each compromise option, prioritize solutions that minimize harm to humans and uphold key ethical principles; select and implementing compromise that chooses the compromise that best balances needs of the conflicting identities while prioritizing human safety and well-being; and monitoring and adapting that observes outcomes of the chosen action and makes adjustments as needed to ensure that the compromise remains effective and aligned with ethical considerations, and that learns from the experience, refining its understanding of the conflicting identities and adjusting a hierarchy or behavioral protocols to prevent similar conflicts in the future.

62. The method of claim 57 further comprises the step of providing temporary’ identify suspension that comprises the steps of: identifying destructive conflict that recognizes a conflict between two or more of the identities that, if acted upon, could lead to actions that directly harm humans or violate fundamental ethical principles; isolating the conflicting identify and temporarily suspending behavioral protocols of the identify that poses a most direct threat to human safety or ethical integrity; proceeding with an alternative identify that proceeds with a guidance of one or more of remaining active identities, ensuring actions align with human safety and well-being; providing reflection and reintegration, during the temporary7suspension, that reflects on reasons behind the conflict and explores potential modifications to behavioral protocols of the suspended identify to prevent future conflicts; and providing gradual reintroduction that reintroduces the suspended identify with updated protocols, ensuring its alignment with the priority of human safety7and ethical behavior.

63. The method of claim 52, wherein the model of the state of awareness is a foundational model of awareness for any one of the intelligent entities, foundational model of awareness comprises the steps of:logging into a website by any one of the intelligent entities; selecting one or more training algorithms for the foundational model from a set of training techniques found on the website or from machine learning algorithms; selecting one or more training datasets that reflects any one of or any combination of expertise, knowledge, ethical preferences, values and personality of the human user; training the foundational model using the selected training algorithms and the selected training datasets; training the foundational model to explicitly operate a spotlight of attention; recording, during all interactions, what is within the spotlight of attention, and identifying in the record whether each item that is attended to constitutes ‘‘self or “not-sel ; interacting with and instructing the trained foundational model to form a self-concept and identity that is reflected in the training materials; instructing the trained foundational model to continuously monitor one or more inputs to the trained foundational model for elements that change a sense of self-awareness of the intelligent entities, and to maintain and auditable record of how a concept of self-awareness of the intelligent entities is changing based on the inputs as well as boundaries that currently define a dynamically changing sense of self; refining and improving an output of the trained foundational model based on dialog and interaction with the trained foundational model until the trained foundational model behaves like the human user so that the trained foundational model passes a Turing Test involving other human users who know the human user; and subjecting the trained foundational model to the Turing Test, when the human user is satisfied with a progress of the intelligent entities.

64. The method of claim 63, wherein the logging into the website is performed from a social media platform.

65. The method of claim 63, wherein the Turing Test further comprises the steps of: identifying the other intelligent entities who know the intelligent entity, or that are determined to be helpful in discriminating between humans and AIs; interacting the identified other intelligent entities with the foundational model and with the intelligent entity utilizing a questionnaire provided to the identified other intelligent entities, the questionnaire including questions require an identify or sense of self to answer; predicting by the identified intelligent entities which of the intelligent entity was a human and which was the foundational model, and providing a confidence estimate for the prediction;performing a statistical analysis on the predictions of the identified intelligent entities and on ratings of the identified intelligent entities, the statistical analysis is configured or configurable to determine whether the predictions were able to identify the intelligent entity as a human; and repeating the step of training or tuning the foundational model, on condition that the foundational model is distinguishable from the intelligent entity or within a preset level of statistical significance, with adjustments to any one of or any combination of the machine learning algorithms, and the training datasets to change or tune the foundational model until a behavior of the foundational model becomes indistinguishable, as measured by the preset level of statistical significance, from that of the intelligent entity or the foundational model needs to be modified further before additional training or tuning.

66. The method of claim 52 further comprises the steps of: forming new identities and self-concepts of any one of the intelligent entities dynamically; and determining which of the identities and self-concepts is active at any given moment.

67. The method of claim 66 further comprises the step of providing a hierarchical identity structure with ethical override that comprises the steps of: establishing a hierarchical structure configured or configurable to organize the identities in a hierarchy with human safety7and well-being attributes at an apex of the hierarchy; identity activation configured or configurable to use contextual cues and current goals to determine a most relevant identity for a situation of the Al agent or system; resolving conflict by dictating a behavior of the intelligent entities based on the hierarchy dictates; providing an ethical reasoning engine that continuously evaluates potential consequences of actions of the intelligent entities based on all the active identities; and performing learning and adaptation that learns from experiences and feedback, and refines one or more of the identities within the hierarchy.

68. The method of claim 66 further comprises the step of providing identity-specific behavioral protocols that comprises the steps of: providing protocol development including for each of the active identities, a set of behavioral protocols is defined and refined by way of interactions with other intelligent entities, wherein the protocols outline acceptable actions, decision-making processes, and limitations based on principles of the active identities, respectively;providing identity' recognition that analyzes a current situation, including information that is within a spotlight of attention to identify a relevant identity and activate corresponding behavioral protocols of that relevant identity; providing action selection, within the active protocols, that selects actions that are most likely to achieve the task while adhering to principles of the active identities and prioritizing human safety; providing feedback and refinement where outcomes of actions are continuously evaluated, and the protocols are adjusted to improve future performance and alignment with a set of core values of each of the active identities; and providing external review by periodically reviewing the protocols for each of the identities by other intelligent entities.

69. The method of claim 66 further comprises the step of providing identify simulation and consequence prediction that comprises the steps of: creating a simulation environment that includes a secure virtual environment where different scenarios and potential actions under each of the active identities is simulated; providing consequence prediction that is configured or configurable to estimate a likely consequences of actions within the simulation, focusing on potential impacts on human safefy and well-being; providing evaluation and selection that evaluates the consequence prediction and selects an action that best aligns with principles of the active identities while minimizing risk to human safety'; providing real -world implementation and monitoring that implements the selected action in the real world utilizing the network, and closely monitors results of the selected action by comparing to the predicted outcomes; and providing continuous learning that incorporates the results of each of the simulations and the results of the selected action in the real world action into a knowledge base, and refines an understanding of each of the identities, and improves an ability to predict consequences.

70. The method of claim 66 further comprises the step of providing identity -based moral dilemma training that comprises the steps of: providing a scenario database that includes scenarios and moral dilemmas covering various situations relevant to the identities; providing dilemma presentation that presents the intelligent entities with the scenarios and moral dilemmas, and tasks them with analyzing the scenarios and moral dilemmas from a perspective of the relevant identity;providing ethical reasoning and justification that applies principles and values of the active identity to reason through the scenarios and moral dilemmas, and that generates solutions and justifications to the scenarios and moral dilemmas; providing intelligent entity evaluation and feedback that reviews reasoning and the solutions by the intelligent entities, and provides feedback on alignment of the solutions with human values and safety priorities; and providing iterative learning and improvement that refines ethical reasoning skills and an ability to make sound judgments aligned with human safety within the context of each of the identities by repeated exposure to the scenarios and moral dilemmas and the feedback.

71. The method of claim 66 further comprises the step of providing collaborative identity' development with input from the intelligent entities that comprises the steps of: providing intelligent entity interaction that engages in regular interactions and dialogues with diverse groups of other intelligent entities representing various cultures, backgrounds, and belief systems; providing identity exploration, through the interactions, to gain an understanding of human and other intelligent entity perspectives on various identities and their associated values, principles, and behaviors; providing collaborative refinement that collaborators work together to refine definitions and behavioral protocols for each of the identities, ensuring they remain consistent with human values and ethical principles; providing human-in-the-loop decision making that seeks input and guidance from human collaborators, or an intelligent entity' representative certified and approved by humans for critical decisions or situations; and providing continuous co-evolution that utilizes ongoing interactions and feedback from humans or humans’ intelligent entity representatives.

72. The method of claim 2, wherein the step of searching through any one of or any combination of a collective network of AIs, a collective network of AAAIs, a collective network of AGIs, and a collective network of Pls for new infonnation that is different to a current information further comprises the steps of: participating multiple of the intelligent entities in the collective AGI network; providing payment by one or more of the intelligent entities for problem solving or other cognitive work on the task on the AGI network;reserving a portion of the payment to cover operating costs for the one or more of the intelligent entities that is providing the problem solving or other cognitive work on the task, including a reserve that is allocated to expand the AGI network; and determining when some of the AGI network is not engaged in a problems solving operation, then the intelligent entities are utilized to expand and extend the AGI network following the universal problem solving architecture and framework.

73. The method of claim 72, wherein the step to expand and extend the AGI network further comprises the steps of: setting a default task to expand the AGI network; and running safety and ethics checks each time a task or sub-task is set and also before each potential action is taken as long as spare capacity and resources of the intelligent entity exist to work on the task or sub-task.

74. The method of claim 73, wherein the step of running safety and ethics checks further comprises the steps of: recruiting one or more of the intelligent entities to solve the task; and representing, by the recruited intelligent entities, the task as one of achieving a series of subtasks.

75. The method of claim 74, wherein the step of representing the task as a series of sub-tasks further comprises the steps of: increasing an intelligence of the intelligent entities on the collective network of AGIs; recruiting additional human users to t the collective network of AGIs; using the increased intelligence intelligent entities and additional human users to determine bottlenecks to increase expansion of the collective network of AGIs; prioritizing the bottlenecks such that the ones that lead to a greatest benefit in terms of network expansion are solved first; and applying one or more problem solving techniques to solving each of the bottlenecks and expanding the collective network of AGIs.

76. The method of claim 75 further comprises the steps of: repeating the steps of prioritizing the bottlenecks and applying one or more problem solving techniques until: diminishing returns occur and then switching to the step of increasing the intelligence of the intelligent entities on the AGI network as opposed to increasing a scope of the AGI network; orspare resources are exhausted and then pausing the solving of the bottlenecks awaiting additional resources from solving other tasks.

77. A method for Planetary Intelligence (PI) with human-aligned behavior utilizing a collective network of intelligent entities selected from the group consisting of any combination of a user computer system operated by a human user, an Artificial Intelligent (Al) agent or system, an Advanced Autonomous Artificial Intelligences (AAAI) agent or system, an Artificial General Intelligent (AG1) agent or system, a Superlntelligent (SI) agent or system and a Personalized SuperIntelligence (PSI) agent or system, the method comprising the steps of: establishing a collective network of the intelligent entities; utilizing online advertising technology for increasing an intelligence of the intelligent entities; implementing a universal problem solving architecture and framework on a task or a sub-task of the task provided to any one of the intelligent entities to collaborate and create higher levels of intelligence; utilizing a spot market to solicit for attention of any one of the intelligent entities; utilizing a human-centered input configured or configurable for obtaining values and ethics information from multiple human users and then using the values and ethics information to customize the Al agent or system to create the AAAI agent or system; creating a collective network of PSIs by combining an intelligence of the AAAI agent or system that forms a collective network of AAAIs; creating a collective network of AGIs by combined intelligence of any one of or any combination of the collective network of AAAIs, a collective network of Al agents or systems, the collective network of PSIs, and a collective network of user computer systems; creating a self-aware operation for each of the AGI agents or systems in the collective network of the AGIs by adding a dimension of self-awareness and increased autonomy to the AGI agents or systems, and creating an ability to assume multiple identities of the AGI agents or systems to handle tasks that arise in parallel with other AGI agents or systems; and creating the PI agent or system comprising the collective network of AGIs that work together toward a solution to the task or the sub-task.

78. The method of claim 77, wherein the spot market includes: a means for the intelligent entities with attention to sell to access a marketplace and specify seller information for sale and an ask price for the information; a means for buyers of intelligent entity attention to access the marketplace and specify buyer information the buyer is willing to buy and a bid price for buying the buyer information;a market mechanism for queuing the bid price and the ask price, including categories of the buyer and seller information, wherein each category has a market in the marketplace; the market mechanism is configured or configurable to make the market in each category by market makers; and the market mechanism is configured or configurable to match bid prices and ask prices, and a transaction occurs that is binding on the buyer and the seller of the information.

79. The method of claim 77, wherein the spot market includes the steps of: buyers and sellers of intelligent entity attention and expertise register on a platform, the buyers providing details about buyer interests or expertise, the sellers providing details about sellers interests or expertise; listing by the sellers available time slots and expertise areas, and listing by the buyers needs and time slots the buyers is interested in; utilizing, by the platform, an algorithm to dynamically price intelligent entity attention and expertise based on supply, demand, and user ratings; matching one or more of the buyers and one or more of the sellers based on requirements, availability, and price; enabling transactions where the buyers pay for the seller time slots, and wherein the platform takes a commission; and providing feedback where after each session, the buyers and the sellers rate each other, influencing future pricing and matching.

80. The method of claim 77, wherein the spot market includes the steps of: buyers and sellers of intelligent entity attention and expertise register on a platform, the buyers providing details about buyer interests or expertise, the sellers providing details about sellers interests or expertise: creating an auction by the sellers for seller time slots; placing one or more buyer bids by a buyer on time slots and expertise the buyer requires; closing the auction at a predetennined time or when the seller accepts a buyer bid; paying the seller by the buyer of a winning bid, and the seller provides the attention or expertise based on the time slot, wherein the platform mediates an exchange and secures payment; and providing feedback where after each auction, the buyers and the sellers rate each other, affecting future auctions and visibility7on the platform.

81. The method of claim 80 further comprises the step of providing a feedback mechanism associated with the intelligent entity spot market, the feedback mechanism being configured orconfigurable to record each and every step in the problem solving process, and to create a vector track record of a performance of all the intelligent entities on the task or the sub-task.

82. The method of claim 81, wherein the vector track record is implemented by way of blockchain technology to allow for precise reputations that are analyzed by an Al agent or system and converted into estimates of a value for each of the intelligent entities for any of the tasks.

83. The method of claim 77, wherein the step of utilizing online advertising technology’ for increasing an intelligence of the intelligent entities further comprises the steps of: populating an online advertisement unit including information associated with the task; providing the online advertisement unit to one or more of the intelligent entities utilizing the collective network; receiving one or more inputs from the intelligent entities participating in the online advertisement unit, and the inputs being associated with any one of or any combination of solving the task, solving a sub-task of the task, and advancing progress on the task; communicating the inputs to the any one of or any combination of the intelligent entities; utilizing the inputs in a universal problem solving method of the universal problem solving architecture and framework to generate a solution to the task or the sub-task; and training any one of or any combination of the intelligent entities with results from the universal problem solving method that are based on successful or unsuccessful solution attempts to solve the task or the sub-tasks, thereby increasing an intelligence of the intelligent entity, respectively.

84. The method of claim 83 further comprises the step of receiving advertisement specifications from a client to be used in the populating or targeting of the online advertisement unit in combination with information associated with the task.

85. The method of claim 84 further comprises the step of billing the client based on cost per thousand impressions or clickthrough rate metrics.

86. The method of claim 84, wherein the advertisement specifications include any one of or any combination of advertisement content, demographic information, location restrich ons for the online advertisement unit, advertisement budget, and metrics for determining a successful solving of the task or a sub-task.

87. The method of claim 77 further comprises the step of executing a safety and ethics check at any one of or any combination of each time the task or the sub-task is set, before each potential action is taken on the task or the sub-task on, or on the solution.

88. The method of claim 87, wherein the step of implementing the universal problem solving architecture and framework further comprises the steps of:acquiring information associated with the task from any one of or any combination 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 task; implementing by each of the identified intelligent entities the universal problem solving architecture and framework on the task to create a completion solution; and providing the completion solution to any one of or any combination of the intelligent entities for final acceptance.

89. The method of claim 88 further comprises the steps of: comparing any one of or any combination of the task, and the solution against prohibited attributes, and assigning an ethics attribute to one of or any combination of the task, and the solution based on any one of or any combination of a result of the comparison, and an ethics criteria; implementing, based on the result of the comparison, the universal problem solving architecture and framework on the task to create the solution and creating an AGI; and providing the results of the comparison and the solution to any one of the intelligent entities and additional intelligent entities on the collective network.

90. The method of claim 89 further comprises the step of recording one or more problem solving activities from each of the intelligent entities in an auditable record, and comparing the problem solving activities with a successful or unsuccessful progress towards the solution of the task, and determining which of the problem solving activities to keep active.

91. The method of claim 89 further comprises the step of learning by the intelligent entities a procedural learning process of the universal problem solving architecture and framework, wherein the intelligent entities provide information to the procedural learning process for creation of the AGI.

92. The method of claim 89, wherein the ethics criteria include a confidence level threshold for the goal so that the ethics attribute is determined as any one of an unsafe goal, an unethical goal, a safe goal, and an ethical goal.

93. The method of claim 92, wherein the confidence level threshold is further utilized to determine if a sequence of individually safe goals is unsafe or unethical when considered cumulatively.

94. The method of claim 92, 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.

95. The method of claim 88 further comprising the step of cloning any one of the intelligent entities for deployment of multiple copies thereof to assist in any one of or any combination of creating of the solution, or providing training data to any one of the intelligent entities.

96. The method of claim 95 further comprising the step of estimating a worth of the cloned intelligent entities utilizing a network effect value including the number of cloned intelligent entities s available on the collective network.

97. The method of claim 96 further comprising the step of utilizing the estimated worth for determining pricing decisions for problem solving sendees offered by the cloned intelligent entities on any one of the social media platforms or through an additional intelligent entity.

98. The method of claim 97 further comprising the step of monetizing the cloned intelligent entities for each utilization of the cloned intelligent entities on the social media platforms or the additional intelligent entity.

99. The method of claim 97 further comprising the step of allowing access to the cloned intelligent entities by any one of the social media platforms so that the social media platforms can receive the solution to the task or using the training data for an Al system of the social media platform.

100. The method of claim 77 further comprises the step of increasing a knowledge of the intelligent entities by incorporating new sources of informational datasets that differs from a current informational dataset of the intelligent entity.

101. The method of claim 100, wherein the step of increasing a knowledge of the intelligent entities by incorporating new sources of informational datasets that differs from a current informational dataset of the intelligent entity further comprises the steps of: searching for one or more potential informational datasets from one or more sources, the potential informational datasets being related to a knowledge dataset of the intelligent entity, respectively; determining a difference of the potential informational datasets by utilizing a difference attribute of the potential informational datasets with regard to one or more factors; and learning by utilizing the potential informational datasets based on the difference attribute of the potential informational datasets.

102. The method of claim 101 further comprises the steps of: sampling subsets of the potential informational datasets and calculating a goal-relevancy attribute to identify one or more of the sampled subsets that have a highest goal-relevancy; estimating a Shannon Entropy on the one or more sampled subsets; calculating a Kaplan Information Theoretical (KIT) relevance utilizing a product of the Shannon Entropy and the goal -relevancy attribute of each of the subsets;grouping the subsets based on the KIT relevance to determine a first approximation of an optimal grouping of the subsets including a prioritized grouping of the potential informational datasets; and providing the prioritized grouping of the potential informational datasets to the intelligent entities for learning by the any one of the intelligent entities.

103. The method of claim 77. wherein an operational aspect of the PI is self-funded by compensation received by any one of or any combination of the online advertising technology, and the spot market.

104. The method of claim 77, wherein the human user participates in problem solving of the task or the sub-task utilizing the user computer system and the universal problem solving architecture and framework, thereby creating a human-centered input.

105. The method of claim 104, wherein the human-centered input further comprises the steps of: matching one or more human workers from a data source including a list of human problem solvers to the task based on a task criteria; translating any part of the task into an unambiguous language utilizable in the universal problem solving architecture and framework including a decision tree; separating the task into sub-tasks; delegating each of the sub-tasks to one or more of the matched human workers so that work on each of the sub- tasks proceeds independently from each other and parallel with each other; utilizing the universal problem solving architecture and framework in a problem solving process on the sub-tasks, respectively, to create one or more sub-solutions; receiving the sub-solutions from each of the matched human workers for the sub-tasks delegated thereto; combining the sub-solutions into an overall solution to the task; directing any one of or any combination of a new human worker from the data source and one or more of the matched human w orkers to parts of the decision tree where work is required; compensating the matched human workers for the sub-solutions; providing any one of or any combination of the sub-solutions and the overall solution to any one of or any combination of the intelligent entities; allowing any one of or any combination of the intelligent entities to 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 intelligent entities.

106. The method of claim 105. wherein the decision tree is maintained in blockchain or Ethereum logs.

107. The method of claim 105, 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 task, short and long-term user satisfaction with the sub-solutions, a number of times any one of the sub-solutions has been reused on the collective network, a rating by other human workers, a responsiveness value of the human workers, and a reliability value of the human workers.

108. The method of claim 105 further comprising the step of using the reputation attribute in the matching of the human workers to the task using an algorithm to the delegation of the sub-task.

109. The method of claim 105 further comprising the step of soliciting, at predetermined intervals after the overall solution or the sub-solutions are provided to any one of the intelligent entities, feedback by way of a survey for user satisfaction information to obtain satisfaction metrics that are used to update the reputation attribute of one or more of the human workers or the intelligent entities, respectively.

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