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Comment by stephc_int13 | Hacker News Reader

The problem is the naming.

Even weak/incomplete AI results tends to fuel the imagination, we can easily fill the gaps with anything from fiction or our own dreams.

On the contrary, when AI works and is strong (like with chess) we stop calling it AI and start seeing it for what it is.

Stockfish is called a chess engine. Not an AI.

And the algorithm behind current chess engines is not super smart, it is mostly brute force.

The trick is to avoid doing brute force at every move, but instead doing the grunt work once a huge computer and store everything in a compact form.

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I agree on the general thrust of your comment. Though it's worth pointing out that the just the neural net behind AlphaZero is still pro-level on its own, without any search algorithm attached (the "engine", so to speak). It's not super-human without the engine, but still "super smart" for some definition of the words.

That said, Stockfish NNUE took the heart of AlphaZero (the evaluation neural net), ripped it out, refined and shrunk it, and stuck it back into Stockfish, and now Stockfish is clearly back on top and is still clearly an "engine" and not an "AI" as you say.

It seems like this will be the case for AI vs. Engine in a lot of fields going forward. A bio-inspired "AI" approach will do well. Then that thing will get stripped down and refined to the bare minimum to do some particular task, and it will then be more of an "engine".

It will be very interesting to see what this looks like for "general intelligence". That is, how many components of intelligence are separable and distillable into engines? E.g. reasoning, creativity, knowledge, foresight, etc.

In my opinion, the process you describe when transforming the AI approach into an engine is simply related to our understanding of why it works and how.

And this can clearly be done to cover a wide range of useful things.

But I would make two observations, first, neural nets are much more closely related to applied statistics than biology mimicry. The historical name should not matter much but it does because humans are driven by emotions, even in research.

Second, AGI is still too badly defined to be seriously discussed.

Every time people can observe some progress in the AI field, they are tempted to make predictions that, in my opinion, are impossible to make.

We're getting closer. That's all.

> first, neural nets are much more closely related to applied statistics than biology mimicry

Well, aren't biological systems really really refined statistical systems?

Maybe, but real neurons are not closer to artificial neurons than a digestive system is to a furnace or a barrel of acid.

There is a very loose relation between them, because neural nets were historically inspired by the way real neurons are connected.

This is not saying anything about the capabilities of neural nets, I think they can be extremely potent, up to AGI level, but not because of biological mimicry but thanks to Computational Equivalence.

https://worldscienceu.com/lessons/2-6-principle-of-computati...

I have a theory that the fact that the net on its own is a decent player is precisely its greatest weakness. Obviously the deep NN must be doing some type of search, but this search is maybe slower than the engine search, because it can't interact with the various engine cache tables, and feed into the statistics used to heuristically order moves.

Stockfish NNUE is a much simpler neural net that's just a really strong, static evaluation function, not doing its own search and therefore the engine can search further and get the most out of the data(transposition table, continuation tables etc) generated by that search.

I think the AlphaGo architecture makes more sense for Go than the chess family of games, but that's a different discussion altogether.

Yes, it does seem plausible (or even likely) that a deep net (AlphaZero is 80 layers deep) is doing some kind of implicit search from layer to layer.

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