How To Prepare AI For Uses In Science

The speaker discusses the challenges AI faces in outperforming humans in scientific endeavors. They emphasize that while AI excels in areas with 'shallow computation,' it struggles with tasks that require complex, irreducible computations. AI's success in language, attributed to the regularity and simplicity of language, contrasts with its poor performance in predicting patterns like sine waves. The speaker also touches on AI's potential in analyzing large text data, its struggle with originality and creativity, and the importance of the observer's or creator's transformation in the creative process.

This paragraph delves into the concept of data compression in scientific discovery, where complex phenomena are simplified into understandable laws or equations. The speaker also introduces the idea that creativity in both art and science involves a transformative journey for the creator. They argue that while AI can generate original outputs, the challenge lies in aligning these with human interests and narratives. The discussion concludes with the speaker's work on computational language, aiming to formalize the world's elements computationally, and the potential of AI to assist in this exploration.

The speaker recounts their efforts in developing a computational language, emphasizing its importance in representing the world computationally. They compare this to historical advancements in language, logic, and mathematics. The paragraph highlights the evolution of computational language, its applications in various fields, and the efficiency it brings to computational work. The speaker also discusses the integration of AI, particularly LLMs, into this computational framework, suggesting that AI can be a valuable tool for generating and refining computational code, thus aiding in the exploration of computational possibilities.

In this paragraph, the speaker explores the potential of training large language models (LLMs) for complex tasks such as mathematical proofs and the challenges therein. They express skepticism about the current capabilities of LLMs in performing tasks that require precise logical reasoning. The speaker also discusses the emerging workflow where LLMs are used to generate computational code, which is then refined by humans for accuracy. The conversation touches on the limitations of LLMs in educational contexts, particularly in solving problems at the edge of human knowledge.

The final paragraph contemplates the future of AI, questioning whether the goal should be to create a general intelligence that surpasses human capabilities or to build systems that extend human intelligence. The speaker argues for the latter, suggesting that creating systems that augment human abilities is more aligned with human needs and interests. They also reflect on the complexity of understanding and predicting machine learning's success and the importance of aligning AI's capabilities with what humans find valuable.