How To Prepare AI For Uses In Science

Wow, Joscha Bach and Stephen Wolfram on one stage. Is there more of this discussion?

By YouSum Live 00:00:00 Science and AI limitations in predicting complex systems. 00:00:30 AI struggles with extrapolation beyond trained data. 00:01:41 Language simplicity aids AI success in text analysis. 00:02:07 AI's limitations in creativity and originality. 00:06:53 Computational exploration of vast possibilities by humans. 00:09:23 Computational language as a tool for formalizing the world. 00:14:52 The importance of computational thinking and automation in work. 00:15:20 Leveraging AI as an interface for computational tasks. 00:16:48 Training AI models for specific computational tasks. 00:17:45 Weak form of computation in llms. 00:17:50 Challenges in guiding proofs using llms. 00:18:00 Limitations of llms in mathematical proofs. 00:18:43 Llms excel in making homework but struggle at edge of human knowledge. 00:19:02 Llms prone to errors in math without guidance. 00:19:37 OpenAI's focus on long-form reasoning surpassing human capabilities. 00:20:20 Building systems to extend human capabilities. 00:20:36 Exploring the fundamental workings of machine learning. 00:21:26 Balancing computational capabilities with human needs. 00:22:11 Challenges in developing effective AI tutoring systems. 00:22:28 Goal for llms to understand and assist human learning. 00:23:00 Conceptualizing beyond human intelligence and AI capabilities. By YouSum Live

My AI predicted the text “so to speak”. Only joking, I love Stephen’s videos. He’s a true genius.

thanks

Wolfram is our modern day genius.

Anyone know where to find the full talk? Thank you

So, there is a plugin for ChatGPT so it can access Wolfram resources. How about an interface to Wolfram resources that can be used by any language model?

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Also it's pretty discrediting to LLMs to say they are only good because language has (easy) grammar. A lot of tests on LLMs show that they have a (though limited, incomplete) world model. It understands basic mathematics, and some basic things about our world.

Thank you Stephen for being a scientist and a man of truth!

I liked the part where he said "computational"

Computationally speaking of course.

Semantic space has a shape. It's a model, so of course it has a similar shape to what is being modeled. I like the idea that only that which is simple or computationally reducible can be modeled sufficiently in current scale foundation models. Rigorous agentic behavior is necessary to deal with computationally difficult activation pathways.

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It’s encoding not compression. The difference is subtle but important for technical rigour and to explain the decoding which holds the generative capacity. Decompression wouldn’t be considered correct either it’s called decoding.

Dream : Wolphram and Tegmark takking to each other.

Before we can expect an AI to accurately predict meaningful events, it probably needs to be able to accurately describe the present, and prior events. A graph structure is probably a good way to represent the present and the past.

There is no specificity regarding the metrics of measuring computational intelligence and representing it.

Would be helpful if he could produce a simple example in which LLM plus his calculation engine is better than LLM alone.