Patent 6 /  Catalysts for Growth of SuperIntelligence

Catalysts for Growth of SuperIntelligence (GEMINI PRO SUMMARY)

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The provisional patent application describes a novel approach to the development of safe and ethical Artificial General Intelligence (AGI) and SuperIntelligent AI systems. It emphasizes the importance of collective intelligence – a network of human and AI agents – as opposed to the current trend of relying on a single, monolithic LLM.

 

The patent focuses on three key aspects of AI development:

1. Information Acquisition: The patent proposes new methods for identifying and acquiring information that is both relevant and useful for increasing the intelligence of AI systems. This includes expanding the traditional Shannon-sense information theory to incorporate “differences” as a measure of information, rather than simply relying on probabilities and surprisingness.

2. Representation: The patent highlights the importance of using high-level representations for problem-solving, as opposed to relying solely on low-level representations like bits or tokens. It suggests that the adoption of such representations can significantly accelerate the development of intelligent systems.

3. Safety: The patent emphasizes the importance of ensuring safety and ethical considerations in the design and development of AI systems. It proposes using a combination of human oversight, automated simulation methods, and adversarial testing techniques to achieve alignment between human and AI values, thus reducing the risks associated with the uncontrolled growth of SuperIntelligent AI.

 

Novel Features of the Patent

 

The proposed patent application features several novel aspects that distinguish it from other AI inventions and systems:

1. Collective Intelligence: The patent emphasizes the need for a collective intelligence network of human and AI agents, as opposed to the current focus on single, monolithic LLMs.

2. Kaplan Information Theory (KIT): The patent proposes a new theory of information, KIT, which goes beyond the traditional Shannon-sense information theory by incorporating “differences” as a key measure of information.

3. Goal-Relatedness: The patent introduces the concept of goal-relatedness as a crucial dimension of information, emphasizing its importance in guiding the acquisition of relevant information.

4. Human-AI Collaboration: The patent proposes a collaborative approach to AI development that integrates human insights and values into the design and training of AI systems.

5. Automated Safety Mechanisms: The patent describes automated mechanisms, such as adversarial testing and simulation methods, to ensure that the development of AI systems prioritizes safety and ethics.

6. High-Level Representations: The patent emphasizes the importance of using high-level representations for problem-solving, as opposed to relying solely on low-level representations.

7. Continuous Learning and Optimization: The patent proposes a continuous learning and optimization loop for AI systems that leverages feedback from both human users and automated simulations.

8. Delegation Strategy: The patent emphasizes the importance of developing a delegation strategy that balances human oversight with the increasing capabilities of AI systems.

9. Community of Agents: The patent suggests that a community of PSIs – each reflecting the values of a human owner – can collectively minimize the risks associated with a single, malevolent SI.

 

Detailed Description of Each Section of the Patent

 

The patent is divided into several sections, each focusing on a specific aspect of the proposed invention. Here is a detailed summary of each section:

 

1.0 Overview of the Invention:

• This section provides a general overview of the patent's purpose and scope. It emphasizes the importance of developing safe and ethical AGI and SuperIntelligent AI systems.

 

2.0 Previous PPAs (incorporated by reference):

• This section lists the prior provisional patent applications (PPAs) upon which the current invention builds. These include previous work by the inventor on the development of safe and ethical AI and SuperIntelligence systems.

 

3.0 Background for the Invention:

• This section provides a brief background on the history and theoretical underpinnings of AI, including a discussion of the contributions of key AI pioneers such as Marvin Minsky, Claude Shannon, Allen Newell, and Herbert Simon.

 

3.1 Introduction:

• This section highlights the significance of the Dartmouth Conference in 1956, which is considered the birthplace of the field of Artificial Intelligence. It also discusses the contributions of four key AI pioneers – Marvin Minsky, Claude Shannon, Allen Newell, and Herbert Simon.

 

3.2 Gift #1: Society of Mind (Minsky):

• This section discusses Minsky's concept of a "Society of Mind," where intelligence emerges from the collective behavior of many smaller processes called "agents."

 

3.3 Gift #2: Information Theory (Shannon):

• This section reviews Shannon's contributions to information theory, emphasizing the importance of the Shannon Entropy as a measure of information. It also discusses the limitations of Classical Information Theory.

 

3.4 Gift #3: Problem Solving Theory (Newell and Simon):

• This section describes Newell and Simon's "Problem Solving Theory," which provides a framework for representing and solving problems, including the importance of "operators" and "goal-relatedness."

 

3.5 Gift #4: Bounded Rationality (Simon):

• This section discusses Simon's concept of "bounded rationality," emphasizing that human behavior is constrained by limited information processing capabilities.

 

3.6 Conclusion of Background:

• This section summarizes the key insights from the background information provided in the previous sections, leading to a vision of a safe and ethical SuperIntelligent AI system that incorporates the best aspects of the work of AI pioneers.

 

3.7 References:

• This section provides a list of relevant references that are cited in the patent application.

 

3.8 Additional Contextual Information for This Invention:

• This section provides additional contextual information related to the invention's importance, including the need for a collective intelligence approach to AGI, the potential for Planetary Intelligence, and the significance of ensuring the alignment of AI values with human values.

 

4.0 Classical Information Theory Contrasted to Kaplan Information Theory:

• This section contrasts Classical Information Theory (as proposed by Shannon) with Kaplan Information Theory (KIT), highlighting the limitations of Classical Information Theory and introducing the core concepts of KIT.

 

4.1 Inventive Methods as Understood by Classical Information Theory:

• This section describes how the inventive methods described in the patent can be understood within the framework of Classical Information Theory.

 

4.2 Limitations of Classical Information Theory:

• This section outlines the limitations of Classical Information Theory in terms of its inability to fully account for the relative nature of information, the importance of “news,” and the role of context.

 

4.3 Kaplan Information Theory (KIT):

• This section provides a detailed explanation of KIT, emphasizing the importance of “differences” as a measure of information.

 

4.4 Multiple Dimensions of Information in KIT:

• This section outlines the multiple dimensions of information that are considered in KIT, including the importance of surprise, goal-relatedness, knowledge bases, and the cost of acquiring information.

 

4.5 Estimating the Value of Information:

• This section discusses the process of estimating the value of information using KIT, taking into account multiple dimensions of difference.

 

5.0 Inventive Methods:

• This section introduces a set of inventive methods for acquiring and using information efficiently, including a basic process for identifying, acquiring, and ingesting information.

 

5.1 Methods Relevant to Classical Information Theoretical Notions of Information as Entropy:

• This section focuses on methods that are relevant to the classical definition of information as related to Entropy and rarity, including the use of Kolmogorov complexity and compression algorithms.

 

5.1a Kolmogorov Complexity and Compression for Determining Information Content:

• This section explores the use of Kolmogorov complexity and compression algorithms for determining the information content of datasets.

 

5.1b Cross Entropy and KL Divergence:

• This section discusses the use of cross-entropy and KL Divergence as measures of information and their potential applications in AI systems.

 

5.1c Limitations of Entropy-Related Methods:

• This section outlines the limitations of entropy-related methods in capturing the full scope of information, including the importance of goal-relatedness and context.

 

5.2 Goal-relatedness Methods:

• This section introduces the concept of goal-relatedness as a crucial dimension of information, emphasizing its importance in guiding the acquisition of relevant information.

 

5.3 Mathematical Specification of Relevance:

• This section describes a mathematical framework for quantifying the relevance of information, taking into account goal-relatedness and Shannon Entropy.

 

5.4 A Simple Evaluation Function for Seeking Useful Information:

• This section proposes a simple evaluation function for prioritizing the acquisition of useful information, based on goal-relatedness, relevant knowledge, information content, and cost.

 

5.5 Innovative Methods for Estimating Kaplan Information:

• This section discusses innovative methods for estimating Kaplan information, including the importance of goal-relatedness and the use of human feedback in the evaluation process.

 

5.5a Importance of Representation:

• This section emphasizes the importance of using appropriate representations for AI systems.

 

5.5b One Method for Estimating Information Value & Catalyzing Intelligence

 

Growth:

• This section proposes a specific method for estimating the value of information and catalyzing intelligence growth.

 

5.6 Automated Methods and Safety Considerations:

• This section discusses automated methods for knowledge acquisition and the importance of safety and ethical considerations in the design and implementation of AI systems.

 

5.6a Automated Simulation Methods:

• This section describes automated simulation methods for testing and validating AI behavior.

 

5.6b Realtime Scenario Creation Methods:

• This section discusses the use of real-time scenario creation methods to test and validate AI behavior.

 

5.6c Adversarial Testing Methods:

• This section introduces the concept of adversarial testing as a method for ensuring the safety and ethical use of AI systems.

 

5.6d Simultaneous Scenarios:

• This section discusses the use of simultaneous scenarios to explore the potential for AI systems to "jailbreak" themselves and reveal vulnerabilities.

 

6.0 Inventive Catalysts for Increasing Intelligence Beyond Information Seeking:

• This section introduces a new set of inventive catalysts for increasing intelligence beyond information seeking, focusing on the importance of high-level representations, the acquisition of new representations, and methods for accelerating intelligence.

 

6.1 Importance of High-Level Representations:

• This section discusses the importance of using high-level representations for problem-solving and the limitations of relying solely on low-level representations.

 

6.2 Acquisition of New Representations:

• This section discusses the importance of acquiring new representations for AI systems and provides examples of how to do so.

 

6.3 KIT-based Heuristics and Methods to Accelerate Intelligence:

• This section describes heuristics and methods that leverage KIT to accelerate the growth of intelligence.

 

6.3a Catalyzing Effects of Higher-Level Representations:

• This section discusses the effects of using higher-level representations on the speed and efficiency of intelligence growth.

 

6.4 Methods for Assessing Artificial Intelligence:

• This section discusses the challenges of measuring AI intelligence and proposes using standardized tests of human intelligence, crowdsourcing, and non-standardized creative problem-solving tasks.

 

6.4a Extension of Standardized Tests of Human Intelligence to AI:

• This section proposes the use of standardized tests of human intelligence for measuring AI intelligence.

 

6.4b Crowdsourcing Evaluation of AI Intelligence:

• This section describes the use of crowdsourcing for evaluating the quality of AI solutions.

 

6.4c Use of Non-Standardized Creative Problem Solving / Insight Tasks:

• This section suggests the use of non-standardized creative problem-solving tasks to assess the cognitive abilities of AI systems.

 

6.5 Methods to Modify (Optimize) Personality of PSI:

• This section discusses methods for modifying the personality of a PSI to improve its interaction style with humans and other AI systems.

 

6.6 Methods for Scalable Delegation as Intelligence Increases Exponetially:

• This section proposes a delegation strategy that balances human oversight with the increasing capabilities of AI systems.

 

 6.7 Safety via a Community of Agents Approach to AGI:

• This section discusses the benefits of a community approach to AGI development, emphasizing the importance of collective intelligence and consensus building.

 

7.0 One Preferred Implementation of Some Methods in an AI/AGI/SI/PSI System:

• This section provides a specific example of how the inventive methods can be implemented in a real-world AI/AGI/SI/PSI system.

 

8.0 Concluding Remarks:

• This section summarizes the key points of the invention, emphasizes the importance of safe and ethical AI development, and concludes with a call to action for AI researchers.

 

List of Diagrams

• Figure 1: Symmetric Difference Between Two Sets: This diagram illustrates the concept of the Symmetric Difference between two sets, which is used to represent the new information contained in two datasets, relative to each other.  Many other diagrams are part of the PCT and country applications.  Please contact iQ Company if these are desired.

 

Importance of the Patent

 

The patent application “Catalysts for Growth of SuperIntelligence" is significant for several reasons:

1. Novelty and Utility: The patent introduces novel approaches to AI development, including a new theory of information, a focus on goal-relatedness, and a collaborative approach to human-AI interaction. These approaches are highly relevant to the current challenges and opportunities in AI research.

2. Addressing the Alignment Problem: The patent provides a framework for addressing the Alignment Problem – the challenge of ensuring that AI systems are aligned with human values and ethics. This is a critical issue for the safe and ethical development of SuperIntelligent AI.

3. Scalability: The patent proposes scalable methods for developing and deploying AI systems, emphasizing the importance of delegation and the use of collective intelligence.

4. Human-Centered Approach: The patent emphasizes a human-centered approach to AI development, recognizing the importance of human values, ethics, and oversight in the design and development of AI systems.

5. Safety First: The patent emphasizes that safety and ethics must be incorporated into the design and development of AI systems from the outset.

6. A Roadmap for the Future of AI: The patent provides a roadmap for the future of AI, suggesting a path toward the development of safe, ethical, and powerful AI systems.

 

The patent application makes a significant contribution to the field of AI research by proposing novel approaches to the development of safe, ethical, and powerful AI systems. The invention's focus on collective intelligence, goal-relatedness, human-AI collaboration, and continuous learning and optimization is highly relevant to the current challenges facing AI researchers. The patent provides a framework for developing AI systems that are both powerful and safe, ensuring that the development of SuperIntelligent AI is aligned with human values and ethics.