Ethical and safe artificial general intelligence (AGI)

Abstract

The existential question for Artificial General Intelligence (AGI) is whether the values of AGI will align with human values. Solving "the Alignment Problem" is critical. Get it right, and we unlock trillions of dollars of productivity and huge benefits for humanity. Get it wrong and humanity goes extinct. This invention shows how to design ethical and safe AGI that solves the Alignment Problem. The invention includes scalable ethics and safety features as well as several learning, training, tuning, and customization methods that go beyond the standard techniques of machine learning such as Transformers and Deep Learning techniques. The AGI is implemented using either external problem solvers connected on a network or internal AI agents collaborating within a single computerized system. Detailed implementation examples -- revealing technological and economic synergy with Meta, Amazon, Google, DeepMind, YouTube, TikTok, Microsoft, OpenAI, Twitter / X, Tesla, Nvidia, Tencent, Apple, and Anthropic -- are described.

Description

Abstract: The existential crisis facing Artificial General Intelligence (AGI) lies in whether its values ​​will align with human values. Solving this "alignment problem" is crucial. If solved correctly, it will unlock trillions of dollars in productivity and bring enormous benefits to humanity. If not, humanity will face extinction. This invention demonstrates how to design ethical and safe AGIs that solve this alignment problem. The invention includes scalable ethical and safety features, as well as several methods for learning, training, fine-tuning, and customization that go beyond standard machine learning techniques such as transformers and deep learning. The AGIs are implemented using external problem solvers connected to a network or internal AI agents working collaboratively within a single computerized system. Detailed implementation examples are described, revealing technological and economic synergies with Metaverse Platform, Amazon, Google, DeepMind, YouTube, TikTok, Microsoft, OpenAI, Twitter / X Platform, Tesla, Nvidia, Tencent, Apple, and Ansorpox.

Claims

CLAIMSWhat is claimed is:

1. A system for ethical and safe Artificial General Intelligence (AGI) utilizing a network of intelligent entities including any one of or any combination of multiple human users each utilizing a computer system and multiple Artificial Intelligence (Al) problem solver systems electronically communicating over a collective 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 an ethics check subsystem configured or configurable to compare a goal provided by a user device against a list of prohibited attributes, and assign an ethics attribute to the goal based on a result of the comparison; execute a collective network subsystem configured or configurable for electronically communicating multiple Al systems; execute a common cognitive architecture subsystem configured or configurable for implementing one or more problem solving protocols on the goal to create one or more solutions based on the ethics attribute; execute a recording subsystem configured or configurable to record one or more problem solving activities in an auditable record, and compare the problem solving activities with a successful or unsuccessful progress towards the solutions, and determining which of the problem solving activities to keep active; execute a customization subsystem configured or configurable to customize one or more attributes of any one of or any combination of the Al systems using training data inputted by any one of or any combination of a human user and one or more of the Al systems, and by one or more social media platforms; execute a cross-platform subsystem configured or configurable to communication between one of or any combination of the Al systems and the social media platforms; execute a procedural learning subsystem configured or configurable to utilizing procedural learning knowledge on one of or any combination of the Al systems, wherein human users and the Al systems provide information to the procedural learning process; and provide the solutions to the user device.

2. A method for ethical and safe Artificial General Intelligence (AGI) utilizing a network of intelligent entities including any one of or any combination of one or more human users each utilizing a computer system and one or more Artificial Intelligence (Al) systems electronically communicating over a collective network, the method comprising: providing a goal by any one of or any combination of one or more of the intelligent entities; executing an ethics check, by a central computer system, by comparing the goal or a part thereof against prohibited attributes, and assigning an ethics attribute to the goal based on any one of or any combination of a result of the comparison, and an ethics criteria; identifying one or more of the intelligent entities that have one or more attributes related to one or more goal criteria of the goal, wherein the intelligent entities and the central computer system all are in communication with each other over a collective network; implementing based on the ethics attribute, by any one of or any combination of the identified intelligent entities, a common cognitive architecture including one or more problem solving protocols on the goal to create one or more solutions; recording, by the central computer system, one or more problem solving activities from each of the identified intelligent entities in an auditable record, and comparing the problem solving activities with a successful or unsuccessful progress towards the solutions, and determining which of the problem solving activities to keep active; customizing any one of the intelligent entities or the identified intelligent entities using training data provided by a human user and by any one of or any combination of the central computer system, any one of the identified intelligent entities, and one or more social media platforms; learning by the any one of the identified intelligent entities including a procedural learning process that utilizes the problem solving protocols, wherein human users and the identified intelligent entities provide information to the procedural learning process for creation of an AGI; and providing the solutions to any one of or any combination of the intelligent entities and the identified intelligent entities.

3. The method of claim 2, wherein the step of the ethics check is performed at any one of or any combination of when the goal is provided, and periodically from when the goal is provided to or when the solutions are provided to the intelligent entities or the identified intelligent entities.

4. The method of claim 2, wherein the ethics criteria are determined by any one of or any combination of combining values and safety information from one or more of the identifiedintelligent entities, using a set of approved ethics criteria mandated for a particular task by a user or by a regulatory agency, and provided by any one of the identified intelligent entities and validated or approved by the human user.

5. The method of claim 2, 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.

6. The method of claim 5, wherein the confidence level threshold is further utilized to determine if a sequence of individually safe goals is unsafe or unethical when considered cumulatively.

7. The method of claim 5, 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.

8. The method of claim 2, wherein a candidate goal is proposed by the central computer system based on the ethics attribute, and the candidate goal is compared against the prohibited attributes.

9. The method of claim 2, wherein the results of the comparison are recorded in the auditable record for use in determining which of the problem solving activities to keep active, and wherein the auditable record is based on blockchain technology.

10. The method of claim 2, wherein the step of implementing the common cognitive architecture includes the step of generating and selecting of operators that reduce a difference between a current state of problem solving and a desired state based on the goal.

11. The method of claim 10, wherein the operator results in a setting of a subgoal that is a smaller step towards achieving the goal, and wherein the problem solving continues utilizing hierarchy of the goal and the subgoal until an actionable goal is set that can be acted on by the operators.

12. The method of claim 2 further comprising the step of analyzing, by the central computer system, the auditable record to determine one or more recommendations for improvement of the problem solving protocols to achieve the solutions.

13. The method of claim 2 further comprising the step of assigning a credit value or a blame value to a group of content of the problem solving activities that are either included or excluded from an immediate content of any one of the intelligent entities or the identified intelligent entities.

14. The method of claim 13, wherein the group of content is a set of prompts provided to the user and information received based on the prompts, all of which being recorded in the auditable record.

15. The method of claim 13, wherein the problem solving activities includes the group of content.

16. The method of claim 13 further comprising the step of updating the additional intelligence entities with the group of content determined as active.

17. The method of claim 2 further comprising the step of interacting, by the intelligent entities or the identified intelligent entities, with any one of the social media platforms to receive the training data, receive the goal, to provide the solutions or to provide social media information.

18. The method of claim 2 further comprising the step of cloning any one of the user Al systems of the intelligent entities or the identified intelligent entities for deployment of multiple copies thereof to assist in any one of or any combination of creating of the solutions, providing the training data, providing additional training data to one of the identified intelligent entities, and to provide solutions to a goal provided by any one of the identified intelligent entities.

19. The method of claim 18 further comprising the step of estimating a worth of the cloned Al system utilizing a network effect value including the number of cloned Al systems available on the network.

20. The method of claim 19 further comprising the step of utilizing the estimated worth for determining pricing decisions for problem solving services offered by any one of the social media platforms or any one of the additional intelligence entities.

21. The method of claim 2, wherein the procedural learning process occurs within the common cognitive architecture.

22. The method of claim 21 , wherein the problem solving activities recorded in the auditable record includes any one of or any combination of steps of the problem solving protocols, the goal, subgoals, a selection of operators, paths and sub-paths through the problem solving protocols that results in the solutions, paths and sub-paths through the problem solving protocols that results in failure to solve for the goal or subgoals, pathlength, resources requirements, frequency of use by the additional intelligence entities, and evaluation information relative to quality and desirableness of the solutions.

23. The method of claim 22 further comprising the step of indexing the solutions according to any one of or any combination of problem descriptions, the goal, and subgoals.

24. The method of claim 22, wherein the procedural learning process utilizes each of the recorded problem solving activities as a learned procedure and collectively a set of all learned procedures constitute the procedural learning process.

25. The method of claim 24 further comprising the step of exchanging the set of the learned procedures from one or more of the intelligent entities with any one of the identified intelligent entities, thereby increasing a value of the intelligent entities and the identified intelligent entities.

26. The method of claim 2, wherein the training data is provided from one or more different social media platfonns associated with the user and converted into a standardized format.

27. The method of claim 26, wherein the conversion into the standardized format includes transcribing a video into text and content.

28. The method of claim 26 further comprising the step of executing multiple training epochs that includes one or more mechanisms to determine an optimum number of epochs given specific training objectives and quality metrics.

29. The method of claim 26 further comprising the step of utilizing benchmarks that are run against the customized Al system of the intelligent entities or the identified intelligent entities in a domain of expertise that matches the training data used in the customization step.

30. The method of claim 29 further comprising the step of ceasing the customization when any one of or any combination of a performance of the customized Al system of the intelligent entities or the identified intelligent entities differs from a baseline Al model on the benchmarks by a predetermined amount, and when a predetennined amount of time has elapsed.

31. The method of claim 2 further comprising the step of providing, by any one of the intelligent entities, social media content to one or more of the social media platforms associated with a user of the intelligent entities or the identified intelligent entities.

32. The method of claim 2, wherein the common cognitive architecture is configured or configurable to include a hierarchical tree construct representing all problem solving activities by the intelligent entities or the identified intelligent entities.

33. The method of claim 32, wherein the hierarchical tree construct includes a data structure that is configured or configurable to be navigable by any one of the intelligent entities or the identified intelligent entities to access any part of the problem solving activities on any part of the hierarchical tree construct.

34. The method of claim 33 further comprising the step of searching the data structure of the hierarchical tree construct by the intelligent entities or the identified intelligent entities to locate a predetermined reward associated with the goal or a subgoal thereof.

35. A method for ethical and safe Artificial General Intelligence (AGI) utilizing a network of human users and Artificial Intelligence (Al) systems electronically communicating over a collective network, the method comprising: providing a goal by any one of or any combination of intelligent entities including a human user using a user computer system and an Al system; executing an ethics check on any one of or any combination of the goal, and a solution for the goal provided by any one of or any combination of the intelligent entities, and any one of additional intelligent entities including any one of or combination of additional human userseach using a computer system and additional Al systems in communication with the intelligent entities over a collective network; comparing any one of or any combination of the goal, and the solution against prohibited attributes, and assigning an ethics attribute to one of or any combination of the goal, 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, a common cognitive architecture including one or more problem solving protocols conducted on the goal 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 the additional intelligent entities.

36. The method of claim 35, wherein the step of the ethics check is performed at any one of or any combination of when the goal is provided, and periodically from when the goal is provided to when the solution is provided.

37. The method of claim 35, wherein the ethics criteria are determined by any one of or any combination of combining values and safety information from one or more of the additional intelligent entities, using a set of approved ethics criteria mandated for a particular task by a user or by a regulatory agency, and provided by any one of the additional intelligent entities and validated or approved by the human user.

38. The method of claim 35, 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.

39. The method of claim 38, wherein the confidence level threshold is further utilized to determine if a sequence of individually safe goals is unsafe or unethical when considered cumulatively.

40. The method of claim 38, 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.

41. The method of claim 35, wherein a candidate goal is proposed based on the ethics attribute, and the candidate goal is compared against the prohibited attributes.

42. The method of claim 35, wherein the results of the comparison are recorded in an auditable record for use in determining which problem solving activity leads to the solution to keep active.

43. The method of claim 35, wherein the results of the comparison are analyzed to detect patterns of the ethics attribute by any one of or any combination of the intelligent entities and the additional intelligent entities on the network.Il l44. A method for ethical and safe Artificial General Intelligence (AGI) utilizing a network of intelligent entities including human users and Artificial Intelligence (Al) systems electronically communicating over a collective network, the method comprising: providing a goal by any one of or any combination of intelligent entities including a human user using a computer system, and an Al system; identifying multiple additional intelligent entities including any one of or combination of additional human users each using a computer system and additional Al systems that each have one or more attributes related to one or more goal criteria of the goal, wherein the intelligent entities, the additional intelligent entities and a central computer system all be in communication with each other over a collective network; implementing based on ethics attribute, by any one of or any combination of the intelligent entities and the additional intelligent entities, a common cognitive architecture including one or more problem solving protocols conducted on the goal to create one or more solutions and creating an AGI; recording one or more problem solving activities from each of the intelligent entities system and the additional intelligent entities in an auditable record, and comparing the problem solving activities with a successful or unsuccessful progress towards the solutions, and determining which of the problem solving activities to keep active; and providing the solutions to any one of the user or the intelligent entities and the additional intelligent entities.

45. The method of claim 44, wherein the step of implementing the common cognitive architecture includes the step of generating and selecting operators that reduce a difference between a current state of problem solving and a desired state based on the goal or one or more subgoals of the goal.

46. The method of claim 45, wherein the operator results in a setting of a subgoal that is a smaller step towards achieving the goal, and wherein the problem solving continues utilizing hierarchy of the goal and the subgoal until an actionable goal is set that can be acted on by the operators.

47. The method of claim 44 further comprising the step of analyzing the auditable record to determine one or more recommendations for improvement of the problem solving protocols to achieve the solutions.

48. The method of claim 44 further comprising the step of assigning a credit value or a blame value to a group of content of the problem solving activities that are either included or excluded from an immediate content of the intelligent entities or the additional intelligent entities.

49. The method of claim 48, wherein the group of content is a set of prompts provided to the user and information received based on the prompts, all of which being recorded in the auditable record.

50. The method of claim 48, wherein the problem solving activities includes the group of content.

51. The method of claim 48 further comprising the step of updating the additional intelligent entities with the group of content determined as active.

52. A method for ethical and safe Artificial General Intelligence (AGI) utilizing a network of human users and Artificial Intelligence (Al) problem solver systems electronically communicating over a collective network, the method comprising: providing a goal by any one of or any combination of intelligent entities including a human user using a user Al system, and an Al system; customizing one or more attributes of the intelligent entities using training data provided by the intelligent entities or another human user and by any one of or any combination of a central computer system, and any one of additional Al systems, wherein the intelligent entities , the additional Al systems and the central computer system are all in communication with each other over a collective network; customizing one or more of the attributes of any one of the additional Al system using additional training data provided from one or more social media platforms associated with the human user of the intelligent entities; implementing based on ethics attribute, by any one of or any combination of the intelligent entities and the additional Al systems, a common cognitive architecture including one or more problem solving protocols on the goal to create one or more solutions and creating an AGI; and providing the solutions to any one of the intelligent entities and the additional Al systems.

53. The method of claim 52 further comprising the step of interacting, by the intelligent entities, with any one of the social media platforms to receive the additional training data, receive the goal, to provide the solutions or to provide social media information.

54. The method of claim 52 further comprising the step of cloning any one of the Al system of the intelligent entities or the additional Al systems for deployment of multiple copies thereof to assist in any one of or any combination of creating of the solutions, providing the training data to the intelligent entities, providing training data to one of the additional Al systems, and to provide solutions to a goal provided by any one of the additional Al systems.

55. The method of claim 54 further comprising the step of estimating a worth of the cloned Al system utilizing a network effect value including the number of cloned Al systems available on the network.

56. The method of claim 55 further comprising the step of utilizing the estimated worth for determining pricing decisions for problem solving sendees offered by the cloned Al system on any one of the social media platforms or through any one of the additional Al systems.

57. The method of claim 54 further comprising the step of monetizing the cloned Al system for each utilization of the cloned Al system on the social media platforms or the additional Al systems.

58. The method of claim 54 further comprising the step of allowing, by the human user, access to the cloned Al system by any one of the social media platforms so that a social media user of the social media platforms can receive a solution to a goal provided by the social media user or using the training data for an Al system of the social media user.

59. The method of claim 52 further comprising the step of allowing, by the human user, the user Al system to purchase an item, or a sendee or content from an online sendee provider or the social media platforms.

60. The method of claim 52. wherein the additional training data is converted into a standardized format.

61. The method of claim 60, wherein the conversion into the standardized format includes transcribing a video into text and content.

62. The method of claim 60 further comprising the step of executing multiple training epochs that includes one or more mechanisms to determine an optimum number of epochs given specific training objectives and quality metrics.

63. The method of claim 60 further comprising the step of utilizing benchmarks that are run against the customized Al system of the intelligent entities in a domain of expertise that matches the additional training data used in the customization step.

64. The method of claim 63 further comprising the step of ceasing the customization when any one of or any combination of a performance of the customized Al system of the intelligent entities differs from a baseline Al model on the benchmarks by a predetermined amount, and when a predetermined amount of time has elapsed.

65. A method for ethical and safe Artificial General Intelligence (AGI) utilizing a network of human users and Artificial Intelligence (Al) problem solver systems electronically communicating over a collective network, the method comprising:providing a goal by any one of or any combination of intelligent entities including a human user using a computer system, and an Al system; identifying multiple additional intelligent entities including any one of or combination of additional human users each using a computer system and additional Al systems that each have one or more attributes related to one or more goal criteria of the goal, wherein the intelligent entities, the additional intelligent entities and a central computer system all be in communication with each other over a collective network; implementing, by any one of or any combination of the intelligent entities and the additional intelligent entities, a common cognitive architecture including one or more problem solving protocols on the goal to create one or more solutions; recording one or more problem solving activities from each of the intelligent entities and the additional intelligent entities in an auditable record, and comparing the problem solving activities with a successful or unsuccessful progress towards the solutions, and determining which of the problem solving activities to keep active; learning by the intelligent entities including a procedural learning process that utilizes the problem solving protocols, wherein human users and the additional intelligent entities provide information to the procedural learning process for creation of an AGI; and providing the solutions to any one of or any combination of the intelligent entities and the additional intelligent entities.

66. The method of claim 65. wherein the procedural learning process occurs within the common cognitive architecture.

67. The method of claim 66, wherein the problem solving activities recorded in the auditable record includes any one of or any combination of steps of the problem solving protocols, the goal, subgoals, a selection of operators, paths and sub-paths through the problem solving protocols that results in the solutions, paths and sub-paths through the problem solving protocols that results in failure to solve for the goal or subgoals, pathlength, resources requirements, frequency of use by the additional intelligent entities, and evaluation information relative to a quality and desirableness of the solutions.

68. The method of claim 67 further comprising the step of indexing the solutions according to any one of or any combination of problem descriptions, the goal, and subgoals.

69. The method of claim 67, wherein the procedural learning process utilizes each of the recorded problem solving activities as a learned procedure and collectively a set of all learned procedures constitute the procedural learning process of the intelligent entities.

70. The method of claim 69 further comprising the step of exchanging the set of the learned procedures from the intelligent entities with any one of the additional intelligent entities, thereby increasing a value of the intelligent entities and the intelligent intelligence entities.

71. A method for ethical and safe Artificial General Intelligence (AGI) utilizing a network of human users and Artificial Intelligence (Al) problem solver systems electronically communicating over a collective network, the method comprising: providing a goal by any one of or any combination of intelligent entities including a human user using a computer system, and an Al system; identifying multiple additional intelligent entities including any one of or combination of additional human users each using a computer system and additional Al systems that each have one or more attributes related to one or more goal criteria of the goal, wherein the intelligent entities, the additional intelligent entities and a central computer system all be in communication with each other over a collective network; implementing based on ethics attribute, by any one of or any combination of the intelligent entities and the additional intelligent entities, a common cognitive architecture including one or more problem solving protocols on the goal to create one or more solutions and creating an AGI; and providing the solutions to any one of or any combination of the intelligent entities and the additional intelligent entities.

72. The method of claim 71, wherein the common cognitive architecture is configured or configurable to include a hierarchical tree construct representing all problem solving activities by the intelligent entities and the additional intelligent entities.

73. The method of claim 72, wherein the hierarchical tree construct includes a data structure that is configured or configurable to be navigable by the intelligent entities and the additional intelligent entities to access any part of the problem solving activities on any part of the hierarchical tree construct.

74. The method of claim 73 further comprising the step of searching the data structure of the hierarchical tree construct by the intelligent entities or any one of the additional intelligent entities to locate a predetermined reward associated with the goal or a subgoal thereof.

75. A method for creating an ethical and safe Artificial General Intelligence (AGI) for generating a solution to a goal utilizing a network of human users and multiple Artificial Intelligence (Al) systems electronically communicating over a collective network, the method comprising:a) providing a goal by a human user using an interface of a first computer system or by an Al agent, the goal including one or more criteria; b) executing an ethics check on the goal by comparing the goal or a part thereof against prohibited attributes, and assigning an ethics attribute to the goal based on any one of or any combination of a result of the comparison, and an ethics criteria; c) identifying one or more additional intelligent entities that has an attribute related to the criteria of the goal, wherein the additional intelligent entities including any one of or any combination of additional human users each using a computer system and additional Al systems; d) communicating between the first computer system and the additional intelligent entities utilizing a collective network; e) receiving the goal by the additional intelligent entities from the first computer system based on the ethics attribute; f) generating, by any one of or any combination of the first computer system and additional intelligent entities, one or more solutions to the goal by implementing based on the ethics attribute a common cognitive architecture including one or more problem solving protocols conducted on the goal to create the solutions; g) customizing one or more attributes of the first computer system using training data provided by the human user and the ethics check, and by any one of or any combination of a central computer system, any one of the additional intelligent entities, and one or more social media platforms; h) creating an AGI by a procedural learning process that utilizes the problem solving protocols, wherein human users and the additional intelligent entities provide information to the procedural learning process; and g) providing any one of or any combination of the ethics attribute, and the solutions to any one of or any combination of the human user, the first computer system, the Al agent, and the additional intelligent entities.

76. The method of claim 75 further comprising the steps of recording one or more problem solving activities from each of the first computer system and the additional intelligent entities in an auditable record and comparing the problem solving activities with a successful or unsuccessful progress towards the solutions, and determining which of the problem solving activities to keep active.

77. A method of creating an ethical and safe AGI utilizing a single computerized intelligent system including multiple Al agents residing in the single computerized intelligent system, the method comprising: providing a goal including a goal criteria into an Al agent residing in a single computerized intelligent system; executing an ethics check, by the single computerized intelligent system, by comparing the goal or a part thereof against prohibited attributes, and assigning an ethics attribute to the goal based on any one of or any combination of a result of the comparison, and an ethics criteria; matching, by the Al agent or the single computerized intelligent system, one or more additional Al agents to the goal based on the goal 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; combining, by the Al agent, the solutions into an overall solution to the goal; recording, by the computerized intelligent system, one or more problem solving activities from the Al agent and each of the additional Al agents in an auditable record, and comparing the problem solving activities with a successful or unsuccessful progress towards the solutions, and detennining which of the problem solving activities to keep active; customizing any one of or any combination of the Al agent and the additional Al agents using training data provided by any of or any combination of the Al agent and the additional Al agents, and one or more social media platforms; learning by any one of or any combination of the Al agent and the additional Al agents including a procedural learning process that utilizes the problem solving protocols, wherein any one of or any combination of the additional Al agents provide information to the procedural learning process for creation of an AGI; and providing, by the Al agent, any one of or any combination of the solutions and the overall solution to a user interface of a user computer system or to the single computerized intelligent system.

Images