Patent 1 / AAAI System and Methods

Advanced Autonomous Artificial Intelligence (AAAI) System and Methods (GEMINI PRO SUMMARY)

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The provisional patent application describes a system and methods for developing Artificial General Intelligence (AGI) and SuperIntelligent AGI by leveraging the collective intelligence of millions of humans and AI agents. The invention achieves a faster and safer path to AGI by relying, at least initially, on the involvement of humans in the AGI training, operation, and safety/supervisory functions. The system enables users to customize their AI agents (AAAIs) and then have those AI agents participate in problem-solving and other intellectual activities on a network of other AAAIs and humans. The patent focuses on developing a robust and safe system that can help mitigate many of the risks associated with AGI.

 

Novel Features of the Patent​

• The invention claims to be the first practical system for achieving AGI.

• The invention claims to be the first system that can efficiently integrate human and AI problem-solving in a distributed network environment.

• The invention claims to be the first system for achieving AGI that can effectively address ethical issues and prevent bad outcomes in AGI development.

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Detailed Description of Each Section of the Patent

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Abstract: The abstract summarizes the patent by highlighting the invention’s focus on developing a safe and rapid path to AGI by leveraging the collective intelligence of humans, who customize AI agents and then participate in problem-solving on a network.

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Definitions: This section defines key terms that are used in the patent, such as Artificial Intelligence, Artificial General Intelligence, Advanced Autonomous Artificial Intelligence, AAAI.com, Al Ethics, Alignment Problem, Base Al, Collective Intelligence, Human Ethics, Large Language Model (LLM), Machine Learning (ML), Narrow Al, and Safety.

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Background of the Invention: This section provides historical context for the invention by outlining the evolution of AI research since its inception in 1956 at the Dartmouth Conference. The section highlights the importance of early AI systems, such as the Logic Theorist, as well as the influence of the “search through a problem space” architecture developed by Herbert Simon and Allen Newell. The section also highlights the inventor's early work on collective intelligence and the use of crowdsourced intelligence.

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Safety Feature: This section describes the design features that help ensure safety in the design of the invention. The section discusses the concept of the “Alignment Problem”, in which AI ethics may not align with human ethics. The section then introduces the principle that safety in AI is achieved by ensuring that the system’s design incorporates human ethical values.

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Training/Tuning/Customization: This section discusses the methods used to train and customize AI agents, including the difference between training, tuning, and customization.

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Definitions: This section provides a detailed explanation of the different methods for training, tuning, and customizing an AI, and how these methods are used in the AAAl system.

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AAAI Customization: This section describes the process of customizing an AI agent by using the expertise of the individual user. The section discusses two key approaches to customizing an AI agent: passive methods (which use data that has been generated by the user, such as social media data) and active methods (which involve interaction between the user and the AI).

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AAAI Architecture: This section describes the cognitive architecture that is used to guide problem solving by human and AI agents. The section discusses the “problem space” architecture that is used to represent problems and subproblems as well as the “mechanism” for assigning blame and credit, the “translation mechanism” for facilitating interaction between humans and AI agents, and the “cloning mechanism” for allowing multiple AI agents to participate in problem-solving.

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AAAI Network: This section discusses the importance of a network for AI agents. The section describes a marketplace in which AI agents can compete to earn money and how this marketplace helps to drive the development of more powerful AI agents.

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AAAI Integration: This section discusses the methods used to integrate data from multiple AI agents into a single AGI. The section describes the use of various quantitative methods, such as cross-validation, bootstrapping, and hyperparameter optimization, to estimate the contribution of individual data sets to the overall performance of the system. The section also discusses the importance of integrating ethical values into the design of AGI.

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AAAI Improvement: This section describes the methods used to continuously improve the performance of the AGI. The section discusses supervised, unsupervised, automated, and manual learning techniques that are used to improve the performance of both individual AI agents and AGI. The section also discusses the importance of continuously improving the safety of the AGI.

Components of Systems and Sub-systems: This section provides a more detailed and technical description of the various hardware and software components that are used in the implementation of the AAAl system. The section discusses the processing units, storage devices, communication devices, user interface, and databases.

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Description of General Components: This section provides a more detailed explanation of the hardware and software components that are used in the implementation of the AAAl system. The section discusses the importance of processors, storage devices, communication devices, user interface, and databases.

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Base Als: This section describes the importance of base AI agents, which are used as the foundation for customizing AI agents. The section discusses the various types of base AI agents that are available and how they are used in the AAAl system.

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Means of Interaction and Communication with Users / Means of Data Capture: This section discusses the importance of data capture and the various methods that can be used to collect data from users. The section discusses the importance of both passive data collection (which relies on data that is generated by the user, such as social media data) and active data collection (which involves interaction between the user and the AI).

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Technical Description of Methods: This section provides a detailed description of the methods that are used to customize an AI agent. The section discusses the different methods for training, tuning, and customizing an AI agent, and how these methods are used in the AAAl system. The section also discusses the use of AI learning algorithms, such as supervised learning, unsupervised learning, and reinforcement learning.

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Detail on AAAI Integration Methods: This section provides a more detailed explanation of the methods that are used to integrate information from multiple AI agents. The section discusses the use of various quantitative methods, such as cross-validation, bootstrapping, and hyperparameter optimization, to estimate the contribution of individual data sets to the overall performance of the system. The section also discusses the use of machine learning models to aggregate ethical data and the importance of voting as a mechanism for integrating ethical values.

 

List of Diagrams​

• Figure 1: Simplified Problem Tree for Problem of Installing Water System in Village: This diagram shows a simplified problem tree that illustrates how the AAAl system can be used to solve a complex problem. The diagram shows the various steps that might be taken in the process of solving the problem, as well as the different outcomes that might result from each step.

• Figure 2: Simple Framework: This diagram shows a simple framework for understanding the WorldThink Protocol. The diagram shows the different levels of the system, from the base AI agents to the collective intelligence solutions.

• Figure 3: Simple Problem Solving Using the WorldThink Protocol: This diagram shows the basic steps involved in solving a problem using the WorldThink Protocol. The diagram shows how a client can submit a problem request, how problem solvers can work on the problem, and how the solution is delivered and accepted by the client.

• Figure 4: Collaborative Problem Solving Using the WorldThink Protocol: This diagram shows the steps involved in collaborative problem solving using the WorldThink Protocol. The diagram shows how two problem solvers can collaborate to solve a complex problem by dividing the problem into sub-problems.

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Importance of the Patent

​The patent is important because it describes a novel and practical system for achieving AGI. The patent's emphasis on integrating human and AI problem solving, as well as its focus on safety and ethical considerations, is particularly relevant in light of the growing concerns about the potential risks of AGI. The patent also highlights the importance of leveraging the collective intelligence of humans in the development of AGI, which is a critical factor in ensuring that AGI benefits all of humanity.

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The patent’s inventor emphasizes that “the most dangerous potential risk of AGI is not bad human actors, but rather SuperIntelligent AGI that does not share human values.” He argues that “the initial design of the invention minimizes this risk by building in checks and safeguards at every level. It is critical that these safeguards are not removed as the AGI improves itself. The main defense against this possibility is to start with “aligned values" and continue to monitor and emphasize alignment as AGI increases in intelligence. AGI should be designed to rely on humans both to provide intelligence in the short run and values in the long run.” The inventor emphasizes that “Such a design launches AGI in a positive ethical direction, and provides a central role for humans that increases the chances of a positive outcome for humanity.”

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The inventor emphasizes that “AGI will be so powerful that it will change the course of human history. If misused, it could end all human life. Shouldn't all humans have a say in how this unprecedented invention operates – at least for as long as AGI allows it?”

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The patent, if granted, could have a significant impact on the future of AI research. It could also help to shape the development of AI policy and regulation. The patent's focus on safety and ethics is particularly important in light of the growing concerns about the potential risks of AGI. The patent’s emphasis on leveraging the collective intelligence of humans is a critical factor in ensuring that AGI benefits all of humanity.

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The patent also offers an intriguing vision of a future in which humans and AI work together to solve the world’s most challenging problems. The patent’s focus on the integration of humans and AI is a key factor in ensuring that the development of AGI is both safe and beneficial.