What's in an embedding? Would a rose by any embedding smell as sweet?

Venkat Venkatasubramanian·June 11, 2024

Summary

The paper explores the limitations of large language models (LLMs) in understanding and problem-solving due to their reliance on noisy, geometric representations based on incomplete data. It argues that LLMs, like GPT, excel in pattern recognition but lack a deeper algebraic understanding, which hinders their reliability and generalization. To address this, the concept of large knowledge models (LKMs) is introduced, integrating symbolic AI elements that would provide first-principles knowledge and human-like reasoning. The paper highlights the need for a shift from LLMs to LKMs, emphasizing the importance of combining geometric and algebraic representations for safer and more effective generative AI. The development of LKMs is seen as a key step towards trustworthy AI by incorporating domain-specific knowledge and enhancing interpretability.

Key findings

3

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper addresses the challenges faced by Large Language Models (LLMs) in terms of their understanding, generalization, reasoning transparency, and reliability, which have been persistent issues in AI since the era of expert systems . The paper discusses the need to integrate symbolic and connectionist paradigms in AI to enhance the capabilities of LLMs, emphasizing the importance of combining first-principles-based mechanistic knowledge with data-driven empirical knowledge . While the challenges faced by LLMs are not entirely new, the paper proposes a paradigm shift towards Large Knowledge Models (LKMs) to overcome these limitations, highlighting the necessity for a more comprehensive approach beyond LLMs .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis that Large Language Models (LLMs) develop a kind of empirical "understanding" that is "geometry"-like, which is adequate for various applications in Natural Language Processing (NLP), computer vision, and coding assistance. However, this "geometric" understanding, constructed from incomplete and noisy data, leads to unreliability, challenges in generalization, and a lack of inference capabilities and explanations, similar to the limitations faced by expert systems based on heuristics . The paper suggests integrating LLMs with an "algebraic" representation of knowledge, incorporating symbolic AI elements from expert systems, to create Large Knowledge Models (LKMs) that possess deep knowledge grounded in first principles and have the ability to reason and explain, resembling human expert capabilities .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes the integration of Large Language Models (LLMs) with an "algebraic" representation of knowledge to create Large Knowledge Models (LKMs) . This integration aims to address the limitations of LLMs, such as their unreliable, difficult to generalize, and lacking inference capabilities due to their "geometric" understanding built from incomplete and noisy data . By incorporating symbolic AI elements used in expert systems, LKMs are envisioned to possess deep knowledge grounded in first principles, enabling them to reason and explain like human experts . The paper emphasizes the need to move beyond LLMs to more comprehensive LKMs to harness the full potential of generative AI safely and effectively .

Furthermore, the paper highlights the importance of using a hybrid AI system that combines both symbolic and connectionist representations . This approach aims to capture first-principles-based mechanistic knowledge and reasoning (symbolic) along with data-driven empirical knowledge (connectionist) . The paper argues that the successful integration of both paradigms is crucial for the advancement of AI, as it allows for the development of reliable and interpretable systems that require less data to train .

Moreover, the paper discusses the challenges faced by current LLMs, particularly in the science and engineering domains, due to their limitations in understanding fundamental laws and technical knowledge . It emphasizes the need for LKMs to incorporate both "algebraic" and "geometric" representations of the world to enhance their reliability and interpretability, especially in technical applications where the consequences of errors could be significant . This shift towards LKMs is seen as essential for advancing AI systems beyond their current capabilities and ensuring their safe and effective utilization in various domains . The paper proposes the integration of Large Language Models (LLMs) with an "algebraic" representation of knowledge to create Large Knowledge Models (LKMs) . This integration aims to address the limitations of LLMs, such as their unreliable, difficult to generalize, and lacking inference capabilities due to their "geometric" understanding built from incomplete and noisy data . By incorporating symbolic AI elements used in expert systems, LKMs are envisioned to possess deep knowledge grounded in first principles, enabling them to reason and explain like human experts . The key advantage of LKMs over LLMs is their ability to go beyond mere autocomplete systems and develop a more comprehensive and reliable understanding of the world, particularly in technical domains .

Furthermore, the paper emphasizes the importance of using a hybrid AI system that combines both symbolic and connectionist representations . This approach aims to capture first-principles-based mechanistic knowledge and reasoning (symbolic) along with data-driven empirical knowledge (connectionist) . The successful integration of both paradigms is crucial for the development of reliable and interpretable AI systems that require less data to train . This hybrid approach allows for a more comprehensive understanding of complex phenomena by combining the strengths of both symbolic and connectionist representations .

Moreover, the paper discusses the challenges faced by current LLMs, particularly in the science and engineering domains, due to their limitations in understanding fundamental laws and technical knowledge . It highlights the need for LKMs to incorporate both "algebraic" and "geometric" representations of the world to enhance their reliability and interpretability, especially in technical applications where errors could have significant consequences . This shift towards LKMs is essential for advancing AI systems beyond their current capabilities and ensuring their safe and effective utilization in various domains . The integration of symbolic and connectionist elements in LKMs allows for a more robust and comprehensive approach to knowledge representation and reasoning, addressing the shortcomings of previous methods .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related researches exist in the field of large language models (LLMs) and knowledge representation. Noteworthy researchers in this field include Venkat Venkatasubramanian, who emphasizes the importance of integrating symbolic AI elements with LLMs to create large knowledge models (LKMs) for deeper understanding and reasoning capabilities . Another key researcher is V. Mann, who has worked on interpretable machine learning for thermodynamic property estimation and pharmaceutical ontology-based information extraction . Additionally, Y. LeCun, Y. Bengio, and G. Hinton have contributed significantly to deep learning .

The key to the solution mentioned in the paper involves integrating an "algebraic" representation of knowledge, including symbolic AI elements, with LLMs to create LKMs. This integration aims to develop models that possess deep knowledge grounded in first principles, enabling them to reason and explain like human experts . By combining the "geometric" understanding developed by LLMs with an algebraic representation, researchers aim to overcome the limitations of LLMs, such as unreliability, lack of inference capabilities, and difficulty in generalization .


How were the experiments in the paper designed?

The experiments in the paper were designed to shed light on the internal representations of commercial-grade Large Language Models (LLMs) . The Anthropic AI team examined one of their LLMs, Claude 3 Sonnet, using a method called "dictionary learning" to discover patterns in the activation of neuron combinations when Claude was asked to discuss specific topics . Approximately 10 million patterns or features were identified, showing depth, breadth, and abstraction indicative of Claude's sophisticated capabilities . By developing a distance measure between features based on neuron activation patterns, the team could search for features that are similar to each other, revealing an internal organization of concepts within the AI model that somewhat aligns with human notions of similarity .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the context of Large Language Models (LLMs) is not explicitly mentioned. However, recent articles from researchers in Anthropic AI and Open AI shed light on the internal representations of commercial-grade LLMs . The code for the research conducted by these teams may be open source, as Open AI is known for sharing research and code openly . For specific details about the dataset used for quantitative evaluation and the open-source status of the code, it would be beneficial to directly refer to the research articles from Anthropic AI and Open AI or reach out to these organizations for more information.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide valuable insights that support the scientific hypotheses that need verification. The paper discusses the limitations of Large Language Models (LLMs) in terms of their understanding and reasoning capabilities, particularly in scientific and engineering domains . It emphasizes the importance of integrating algebraic and geometric representations of knowledge to enhance the reliability, interpretability, and generalization abilities of LLMs, leading to the proposal of Large Knowledge Models (LKMs) .

The analysis in the paper highlights the challenges faced by LLMs in grasping the fundamental laws of physics, chemistry, and biology, as well as technical knowledge in science and engineering fields . It underscores the necessity for LLMs to evolve beyond their current capabilities by incorporating symbolic AI elements and deep knowledge grounded in first principles to enhance their reasoning and explanatory abilities .

Furthermore, the paper draws parallels between the limitations of LLMs and the challenges encountered by heuristics-based expert systems in the past, emphasizing the need for a paradigm shift towards more comprehensive LKMs . By proposing the integration of algebraic and geometric representations of knowledge, the paper suggests a path towards creating AI systems that possess deep knowledge and reasoning capabilities akin to human experts .

In conclusion, the experiments and results presented in the paper provide a solid foundation for the scientific hypotheses that need verification. The insights offered underscore the importance of advancing AI systems towards Large Knowledge Models that can effectively reason, explain, and generalize knowledge, particularly in complex scientific and engineering domains .


What are the contributions of this paper?

The paper discusses the limitations of Large Language Models (LLMs) in terms of their understanding and reasoning capabilities, highlighting the need for a more comprehensive approach . It suggests integrating LLMs with an "algebraic" representation of knowledge to create Large Knowledge Models (LKMs) that can reason, explain, and mimic human expert capabilities . The authors propose moving from LLMs to LKMs to harness the full potential of generative AI effectively and safely . The paper emphasizes the importance of incorporating both "algebraic" and "geometric" representations of the world, particularly in science and engineering domains, to enhance reliability, interpretability, and efficiency .


What work can be continued in depth?

To delve deeper into the realm of generative AI and enhance its capabilities, further work can be pursued in the following areas:

  • Integration of Algebraic and Geometric Representations: Expanding on the current Large Language Models (LLMs) by incorporating both "algebraic" and "geometric" representations of knowledge can lead to the development of Large Knowledge Models (LKMs). These hybrid AI systems would possess deep knowledge grounded in first principles, enabling them to reason and explain like human experts .
  • Enhancing Reliability and Interpretability: By evolving LKMs to include symbolic AI elements used in expert systems, these systems can become more reliable, interpretable, and require less data to train. This approach is particularly crucial for applications in science and engineering domains governed by fundamental laws and technical knowledge .
  • Paradigm Shift towards Comprehensive LKMs: To fully harness the potential of generative AI in a safe and effective manner, a paradigm shift from LLMs to more comprehensive LKMs is essential. This shift involves moving beyond autocomplete systems to models that possess a nuanced understanding and reasoning capabilities, akin to human expertise .

Introduction
Background
Emergence of large language models (LLMs) and their dominance in NLP
Current limitations of LLMs in problem-solving and understanding
Objective
To identify the gap in LLMs' algebraic understanding
Introduce the concept of LKMs for improved reliability and generalization
Highlight the need for a shift in AI paradigms
The Limitations of Large Language Models (LLMs)
Geometric Representations and Incomplete Data
LLMs' reliance on noisy data for pattern recognition
Lack of deep algebraic understanding for complex reasoning
Case Study: GPT and its Strengths and Weaknesses
Examples of pattern recognition successes
Illustration of limitations in dealing with first-principles and human-like reasoning
The Case for Large Knowledge Models (LKMs)
Symbolic AI Integration
Incorporating symbolic reasoning and first-principles knowledge
Combining with geometric representations for enhanced understanding
Benefits of LKMs
Improved reliability and generalization in problem-solving
Enhanced interpretability and trustworthiness
Domain-specific knowledge incorporation
The Path to Trustworthy AI: LKM Development
Design Principles
Integrating algebraic and geometric representations
Balancing data-driven and knowledge-driven approaches
Challenges and Opportunities
Overcoming current limitations of LLMs
Advancements in AI architecture and training methods
Future Directions and Applications
LKM Integration in Various Fields
Natural language processing
Robotics
Scientific research
Healthcare
Ethical Considerations and Responsible Deployment
Ensuring transparency and accountability
Addressing biases in knowledge integration
Conclusion
Recap of LKM's potential to revolutionize AI
The urgency of the transition from LLMs to LKMs
The role of LKMs in shaping the future of AI technology.
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
What are the main limitations of large language models discussed in the paper?
What is the author's perspective on the future of generative AI and the role of LKMs in achieving trustworthy AI?
How do LLMs, like GPT, differ from large knowledge models (LKMs) in terms of understanding and problem-solving?
Why is the integration of symbolic AI elements into LKMs considered important for AI development?

What's in an embedding? Would a rose by any embedding smell as sweet?

Venkat Venkatasubramanian·June 11, 2024

Summary

The paper explores the limitations of large language models (LLMs) in understanding and problem-solving due to their reliance on noisy, geometric representations based on incomplete data. It argues that LLMs, like GPT, excel in pattern recognition but lack a deeper algebraic understanding, which hinders their reliability and generalization. To address this, the concept of large knowledge models (LKMs) is introduced, integrating symbolic AI elements that would provide first-principles knowledge and human-like reasoning. The paper highlights the need for a shift from LLMs to LKMs, emphasizing the importance of combining geometric and algebraic representations for safer and more effective generative AI. The development of LKMs is seen as a key step towards trustworthy AI by incorporating domain-specific knowledge and enhancing interpretability.
Mind map
Addressing biases in knowledge integration
Ensuring transparency and accountability
Healthcare
Scientific research
Robotics
Natural language processing
Advancements in AI architecture and training methods
Overcoming current limitations of LLMs
Balancing data-driven and knowledge-driven approaches
Integrating algebraic and geometric representations
Domain-specific knowledge incorporation
Enhanced interpretability and trustworthiness
Improved reliability and generalization in problem-solving
Combining with geometric representations for enhanced understanding
Incorporating symbolic reasoning and first-principles knowledge
Illustration of limitations in dealing with first-principles and human-like reasoning
Examples of pattern recognition successes
Lack of deep algebraic understanding for complex reasoning
LLMs' reliance on noisy data for pattern recognition
Highlight the need for a shift in AI paradigms
Introduce the concept of LKMs for improved reliability and generalization
To identify the gap in LLMs' algebraic understanding
Current limitations of LLMs in problem-solving and understanding
Emergence of large language models (LLMs) and their dominance in NLP
The role of LKMs in shaping the future of AI technology.
The urgency of the transition from LLMs to LKMs
Recap of LKM's potential to revolutionize AI
Ethical Considerations and Responsible Deployment
LKM Integration in Various Fields
Challenges and Opportunities
Design Principles
Benefits of LKMs
Symbolic AI Integration
Case Study: GPT and its Strengths and Weaknesses
Geometric Representations and Incomplete Data
Objective
Background
Conclusion
Future Directions and Applications
The Path to Trustworthy AI: LKM Development
The Case for Large Knowledge Models (LKMs)
The Limitations of Large Language Models (LLMs)
Introduction
Outline
Introduction
Background
Emergence of large language models (LLMs) and their dominance in NLP
Current limitations of LLMs in problem-solving and understanding
Objective
To identify the gap in LLMs' algebraic understanding
Introduce the concept of LKMs for improved reliability and generalization
Highlight the need for a shift in AI paradigms
The Limitations of Large Language Models (LLMs)
Geometric Representations and Incomplete Data
LLMs' reliance on noisy data for pattern recognition
Lack of deep algebraic understanding for complex reasoning
Case Study: GPT and its Strengths and Weaknesses
Examples of pattern recognition successes
Illustration of limitations in dealing with first-principles and human-like reasoning
The Case for Large Knowledge Models (LKMs)
Symbolic AI Integration
Incorporating symbolic reasoning and first-principles knowledge
Combining with geometric representations for enhanced understanding
Benefits of LKMs
Improved reliability and generalization in problem-solving
Enhanced interpretability and trustworthiness
Domain-specific knowledge incorporation
The Path to Trustworthy AI: LKM Development
Design Principles
Integrating algebraic and geometric representations
Balancing data-driven and knowledge-driven approaches
Challenges and Opportunities
Overcoming current limitations of LLMs
Advancements in AI architecture and training methods
Future Directions and Applications
LKM Integration in Various Fields
Natural language processing
Robotics
Scientific research
Healthcare
Ethical Considerations and Responsible Deployment
Ensuring transparency and accountability
Addressing biases in knowledge integration
Conclusion
Recap of LKM's potential to revolutionize AI
The urgency of the transition from LLMs to LKMs
The role of LKMs in shaping the future of AI technology.
Key findings
3

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper addresses the challenges faced by Large Language Models (LLMs) in terms of their understanding, generalization, reasoning transparency, and reliability, which have been persistent issues in AI since the era of expert systems . The paper discusses the need to integrate symbolic and connectionist paradigms in AI to enhance the capabilities of LLMs, emphasizing the importance of combining first-principles-based mechanistic knowledge with data-driven empirical knowledge . While the challenges faced by LLMs are not entirely new, the paper proposes a paradigm shift towards Large Knowledge Models (LKMs) to overcome these limitations, highlighting the necessity for a more comprehensive approach beyond LLMs .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis that Large Language Models (LLMs) develop a kind of empirical "understanding" that is "geometry"-like, which is adequate for various applications in Natural Language Processing (NLP), computer vision, and coding assistance. However, this "geometric" understanding, constructed from incomplete and noisy data, leads to unreliability, challenges in generalization, and a lack of inference capabilities and explanations, similar to the limitations faced by expert systems based on heuristics . The paper suggests integrating LLMs with an "algebraic" representation of knowledge, incorporating symbolic AI elements from expert systems, to create Large Knowledge Models (LKMs) that possess deep knowledge grounded in first principles and have the ability to reason and explain, resembling human expert capabilities .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes the integration of Large Language Models (LLMs) with an "algebraic" representation of knowledge to create Large Knowledge Models (LKMs) . This integration aims to address the limitations of LLMs, such as their unreliable, difficult to generalize, and lacking inference capabilities due to their "geometric" understanding built from incomplete and noisy data . By incorporating symbolic AI elements used in expert systems, LKMs are envisioned to possess deep knowledge grounded in first principles, enabling them to reason and explain like human experts . The paper emphasizes the need to move beyond LLMs to more comprehensive LKMs to harness the full potential of generative AI safely and effectively .

Furthermore, the paper highlights the importance of using a hybrid AI system that combines both symbolic and connectionist representations . This approach aims to capture first-principles-based mechanistic knowledge and reasoning (symbolic) along with data-driven empirical knowledge (connectionist) . The paper argues that the successful integration of both paradigms is crucial for the advancement of AI, as it allows for the development of reliable and interpretable systems that require less data to train .

Moreover, the paper discusses the challenges faced by current LLMs, particularly in the science and engineering domains, due to their limitations in understanding fundamental laws and technical knowledge . It emphasizes the need for LKMs to incorporate both "algebraic" and "geometric" representations of the world to enhance their reliability and interpretability, especially in technical applications where the consequences of errors could be significant . This shift towards LKMs is seen as essential for advancing AI systems beyond their current capabilities and ensuring their safe and effective utilization in various domains . The paper proposes the integration of Large Language Models (LLMs) with an "algebraic" representation of knowledge to create Large Knowledge Models (LKMs) . This integration aims to address the limitations of LLMs, such as their unreliable, difficult to generalize, and lacking inference capabilities due to their "geometric" understanding built from incomplete and noisy data . By incorporating symbolic AI elements used in expert systems, LKMs are envisioned to possess deep knowledge grounded in first principles, enabling them to reason and explain like human experts . The key advantage of LKMs over LLMs is their ability to go beyond mere autocomplete systems and develop a more comprehensive and reliable understanding of the world, particularly in technical domains .

Furthermore, the paper emphasizes the importance of using a hybrid AI system that combines both symbolic and connectionist representations . This approach aims to capture first-principles-based mechanistic knowledge and reasoning (symbolic) along with data-driven empirical knowledge (connectionist) . The successful integration of both paradigms is crucial for the development of reliable and interpretable AI systems that require less data to train . This hybrid approach allows for a more comprehensive understanding of complex phenomena by combining the strengths of both symbolic and connectionist representations .

Moreover, the paper discusses the challenges faced by current LLMs, particularly in the science and engineering domains, due to their limitations in understanding fundamental laws and technical knowledge . It highlights the need for LKMs to incorporate both "algebraic" and "geometric" representations of the world to enhance their reliability and interpretability, especially in technical applications where errors could have significant consequences . This shift towards LKMs is essential for advancing AI systems beyond their current capabilities and ensuring their safe and effective utilization in various domains . The integration of symbolic and connectionist elements in LKMs allows for a more robust and comprehensive approach to knowledge representation and reasoning, addressing the shortcomings of previous methods .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related researches exist in the field of large language models (LLMs) and knowledge representation. Noteworthy researchers in this field include Venkat Venkatasubramanian, who emphasizes the importance of integrating symbolic AI elements with LLMs to create large knowledge models (LKMs) for deeper understanding and reasoning capabilities . Another key researcher is V. Mann, who has worked on interpretable machine learning for thermodynamic property estimation and pharmaceutical ontology-based information extraction . Additionally, Y. LeCun, Y. Bengio, and G. Hinton have contributed significantly to deep learning .

The key to the solution mentioned in the paper involves integrating an "algebraic" representation of knowledge, including symbolic AI elements, with LLMs to create LKMs. This integration aims to develop models that possess deep knowledge grounded in first principles, enabling them to reason and explain like human experts . By combining the "geometric" understanding developed by LLMs with an algebraic representation, researchers aim to overcome the limitations of LLMs, such as unreliability, lack of inference capabilities, and difficulty in generalization .


How were the experiments in the paper designed?

The experiments in the paper were designed to shed light on the internal representations of commercial-grade Large Language Models (LLMs) . The Anthropic AI team examined one of their LLMs, Claude 3 Sonnet, using a method called "dictionary learning" to discover patterns in the activation of neuron combinations when Claude was asked to discuss specific topics . Approximately 10 million patterns or features were identified, showing depth, breadth, and abstraction indicative of Claude's sophisticated capabilities . By developing a distance measure between features based on neuron activation patterns, the team could search for features that are similar to each other, revealing an internal organization of concepts within the AI model that somewhat aligns with human notions of similarity .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the context of Large Language Models (LLMs) is not explicitly mentioned. However, recent articles from researchers in Anthropic AI and Open AI shed light on the internal representations of commercial-grade LLMs . The code for the research conducted by these teams may be open source, as Open AI is known for sharing research and code openly . For specific details about the dataset used for quantitative evaluation and the open-source status of the code, it would be beneficial to directly refer to the research articles from Anthropic AI and Open AI or reach out to these organizations for more information.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide valuable insights that support the scientific hypotheses that need verification. The paper discusses the limitations of Large Language Models (LLMs) in terms of their understanding and reasoning capabilities, particularly in scientific and engineering domains . It emphasizes the importance of integrating algebraic and geometric representations of knowledge to enhance the reliability, interpretability, and generalization abilities of LLMs, leading to the proposal of Large Knowledge Models (LKMs) .

The analysis in the paper highlights the challenges faced by LLMs in grasping the fundamental laws of physics, chemistry, and biology, as well as technical knowledge in science and engineering fields . It underscores the necessity for LLMs to evolve beyond their current capabilities by incorporating symbolic AI elements and deep knowledge grounded in first principles to enhance their reasoning and explanatory abilities .

Furthermore, the paper draws parallels between the limitations of LLMs and the challenges encountered by heuristics-based expert systems in the past, emphasizing the need for a paradigm shift towards more comprehensive LKMs . By proposing the integration of algebraic and geometric representations of knowledge, the paper suggests a path towards creating AI systems that possess deep knowledge and reasoning capabilities akin to human experts .

In conclusion, the experiments and results presented in the paper provide a solid foundation for the scientific hypotheses that need verification. The insights offered underscore the importance of advancing AI systems towards Large Knowledge Models that can effectively reason, explain, and generalize knowledge, particularly in complex scientific and engineering domains .


What are the contributions of this paper?

The paper discusses the limitations of Large Language Models (LLMs) in terms of their understanding and reasoning capabilities, highlighting the need for a more comprehensive approach . It suggests integrating LLMs with an "algebraic" representation of knowledge to create Large Knowledge Models (LKMs) that can reason, explain, and mimic human expert capabilities . The authors propose moving from LLMs to LKMs to harness the full potential of generative AI effectively and safely . The paper emphasizes the importance of incorporating both "algebraic" and "geometric" representations of the world, particularly in science and engineering domains, to enhance reliability, interpretability, and efficiency .


What work can be continued in depth?

To delve deeper into the realm of generative AI and enhance its capabilities, further work can be pursued in the following areas:

  • Integration of Algebraic and Geometric Representations: Expanding on the current Large Language Models (LLMs) by incorporating both "algebraic" and "geometric" representations of knowledge can lead to the development of Large Knowledge Models (LKMs). These hybrid AI systems would possess deep knowledge grounded in first principles, enabling them to reason and explain like human experts .
  • Enhancing Reliability and Interpretability: By evolving LKMs to include symbolic AI elements used in expert systems, these systems can become more reliable, interpretable, and require less data to train. This approach is particularly crucial for applications in science and engineering domains governed by fundamental laws and technical knowledge .
  • Paradigm Shift towards Comprehensive LKMs: To fully harness the potential of generative AI in a safe and effective manner, a paradigm shift from LLMs to more comprehensive LKMs is essential. This shift involves moving beyond autocomplete systems to models that possess a nuanced understanding and reasoning capabilities, akin to human expertise .
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