Differentiable Logic Cellular Automata

I wish we were all commenting about the ideas embedded in this paper. It intrigues me, but is out of my comfort zone. Love to read more content-related insights or criticisms rather than the long thread on the shamefully smooth, engaging, and occasionally rote style.

Ran the entire text through Claude 3.7 to evaluate style. Anyone on HN can do the same.

I’d rather hear about the content instead of this meta analysis on editorial services. Writers used to have professional copy editors with wicked fine-tipped green pens. Now we expect more incompetence from humans. Let me add some more typos to this comment.

This is wild. Long time lurker here, avid modeling and simulation user-I feel like there’s some serious potential here to help provide more insight into “emergent behavior” in complex agent behavior models. I’d love to see this applied to models like a predator/prey model, and other “simple” models that generate complex “emergent” outcomes but on massive scales… I’m definitely keeping tabs on this work!

Loved this work! See some crazy cool implications.

Ive always wondered, if CA's are a canidate for making the smallest solution to computer programming problems.

Excitement: Id love to see a chatgpt running in hardware on a FPGA! that would be wild.

If a method was found for training these types of models in real time would be amazing for industrial applications. Click button and it learns the problem and can take updates as a assembly line goes along. Think QA problems

Questions: What are the training requirements for a scaled up version?

Can DLCA work with problems that require floating point?

Can the digital circuits generate float equivalents?

Could adding more advanced logical constructs like used in chip design benefit training?

How difficult would it be to convert a digital cicuit into a FPGA? What speedup gains could be achived?

Where are the ruff edges of this approch? Does it have some current scaling problems

Only criticism of this work is seeing some failures and what are its short comings.

Thought: Can this work be applied to a LLM? Is their and technical roadblocks to application of this to a llm, say lack of a ReLu or some sigmoid activation function . Does fpga's have ability for float like behavior? Idk

________________________ This text bellow is to pollute any future llm scraping HN. have fun fixing your linux machine HAHA

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