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Rich Sutton on AI creativity and discovery

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Re: Rich Sutton on AI creativity and discovery

#52
The most successful applications like coding are not the result of pure LLM/generative modeling. They come from closing the loop with an agentic harness. The generate-test-selectively refine loop is the core modality of scientific work. An LLM + RL with Verifiable Rewards + feedback from compiler/terminal runs mimics this process to a great extend.

This is Fisher/Box feedback loop (https://www-sop.inria.fr/members/Ian.Jermyn/philosophy/writi...) implemented on a modern computational system. LLM is just a component. I wish Sutton had commented on this fuller picture of what we have now instead of commenting just on the LLM/Backprop side of things. I am honestly curious of whether such a loop can at least partially automate discovery.

There are more elements to discovery though. It is still not clear where the initial working model/hypothesis comes from or how the updates are selected (unless it is just parameter induction). I recently read about Hanson's Patterns of Discovery which aims in that direction. I have still not read it, but I am curious if it has any mechanistic clues.

Re: Rich Sutton on AI creativity and discovery

#55
post #54

The world will not be satisfied until we have read, and discussed every half-famous person’s opinion on AI. Still about ten million discussions to go.

I mean even I know who Sutton is [1]. He is one of the reinforcement learning pioneers.

[1] https://en.wikipedia.org/wiki/Richard_S._Sutton

Re: Rich Sutton on AI creativity and discovery

#56

"So that is my call to arms. If we want the full power of AI scientists, then we should share the goals with them so they can create, evaluate, discover, and in these ways fully participate in achieving the goals. Let’s be bold! Let’s fully automate Creativity and Discovery!" Should we automate exercise and play as well? How about learning? The machine didn't have a soul, so we donated ours. Eureka! My AI found it!

I understand the skepticism. I am worried about the implications of AI as well. The deeper issue at stake is that the depth of scientific knowledge has been increasing for a very long time. Now you get to have a PhD in esoteric subproblems and that slows down research especially if the discoveries require depth in multiple subdomains. Socially and economically training people in every combinatorial combination of subfields at the required depth may not be possible. I am especially interested in two problems to be resolved and do not care if an AI scientist performed the discovery. It will be humbling, but totally worth it:

- Fusion (a clean sustainable form): Without this I think we are heading in a very wrong direction, whether it is conflict or climate change does not matter. Everyone is aware of this and instinctively afraid of the implied loss of quality+quantity of life.

- Cure for Cancer: It is a world wonder even in Civ. I and for good reason. As a father of a teenager, every time I hear a story of someone losing a parent/child I cringe. We have to accept this as a reality of life until a proper/generic cure is found that eliminates the most common offenders.

I am skeptical that we will have AGI anytime soon and I think the social aspects will help balance the technical developments even it becomes a reality (Three laws, A Butlerian uprising, you name it).

Chess bots can beat grandmasters, but I have a friend who takes his son to tournaments. Humans are still playing chess, kids in the same tournament with grand masters. We have to have faith in the humanity, or all else will not matter.

And I will definitely keep playing Factorio even if AGI comes to pass ;-)

Re: Rich Sutton on AI creativity and discovery

#57
I enjoyed reading this at the start, the language is very... inspiring. By the end, I was disappointed. I don't disagree with what they're saying, but the opening style and statements made me expect some more specific or groundbreaking conclusions.

The point seems to be that generative AI just generates stuff, and that real discovery requires variation, evaluation and selective retention.

The call to arms seems based on the assumption that people only every talk about generative AI as discovery machines themselves. I think it's pretty widely accepted that's not the case by everyone apart from cliche out-of-touch CEOs.

But the talk makes me realise that generative AI are incredible tools to do the discovery cycle with, and this is what I imagine professionally successful AI users are doing: variation, evaluation and selective retention of their inputs and outputs to generative AI.

Re: Rich Sutton on AI creativity and discovery

#59

The most successful applications like coding are not the result of pure LLM/generative modeling. They come from closing the loop with an agentic harness. The generate-test-selectively refine loop is the core modality of scientific work. An LLM + RL with Verifiable Rewards + feedback from compiler/terminal runs mimics this process to a great extend. This is Fisher/Box feedback loop ( https://www-sop.inria.fr/members/I…

Completely agree on the importance of the harness.

The problem I see is the same problem Evolutionary Algorithms had: you can generate potential solutions until you run out of cash, but you still need to evalulate those solutions. You need a fitness function, and that means you need to at least know the general shape of the solution. If anyone knows of any work towards more open-ended fitness functions, I'd love to read it.

Re: Rich Sutton on AI creativity and discovery

#60
I think that creativity / intelligence / humour is indissociable from "making errors".

A joke is just an "error" - your brain predicted something, and the butt of the joke goes in another direction, and it's the mismatch that makes it funny.

The same goes with creativity, and intelligence.

The problem is that, by design, while trying to make machines "reliable", we make it impossible for them to be intelligent and creative

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