OpenAI shows 'Strawberry' to feds, races to launch it

When you do this with "give me some html that looks like some image" or whatever, it so happens that a lot of text on the Internet is html that actually does what you asked for. As far as I'm aware, no text describing an AI takeover is something that actually happened or likely could happen. It's not the kind of thing you can learn to do by reading about it.

My theory: reasoning is the application of analogies. Here `analogy = memorization + pattern matching`. Pattern matching is just an associative memory query; remembering an example with fuzzy enough details to apply generally. Analogies are self-similar/recursive and sometimes transcend contexts -- they're useful for thinking, for thinking about thinking, etc. Analogies pervade our language, our thoughts, our approach to novel problems, and the cached solutions to trivial/practiced problems.

There is no qualitative distinction between System 1 & System 2 thinking. It's a spectrum, quantitative. The more analogy steps required, the more thinking done, and the more System 2-ish that thoughts feel. This (probably wrong) theory has many consequences:

1. LLMs are truly intelligent, albeit barely. They rely heavily on memorization rather than much analogy matching. Their depth of memorized knowledge can somewhat substitute/mislead us to misjudge that intelligence.

2. The validation set grokking phenomenon occurs when a new analogy is internalized.

3. Scale really is enough. The scaling laws will continue holding with astonishing accuracy.

4. MCTS heuristics are useful inductive biases to vastly lower compute at the expense of model flexibility. Eventually, The Bitter Lesson will come for MCTS too.

I have much more to say about this bonkers theory, but I fear already sounding like a ranting madman. Anyway, just my 2c.

I have a favorite question to a LLM that can be (and is) learned from papers from 2010-2012 (well before advent of LLMs) and I am keeping asking it for two years now.

LLMs are able to cite relevant papers with "word-by-word" accuracy, they remember them quite well. Every paper on the subject has all relevant definitions in them. Yet, LLMs cannot combine adjacent definitions to come up with the ultimate solution to my question.

The question: "implement blocked clause decomposition in Haskell."

Google "blocked clause decomposition" for papers on subject.

Have fun.

Over time, LLM's seem to lose the ability to even approach the solution to this question in a first answer. They need more and more attention and correction nowadays.

I see it as a knowledge collapse mentioned in paper I provided a link to. Instead of an answer I get a gamified pretense of a helping hand and we all do.

This kind of benefit is what sets AlphaZero apart from earlier engines in the first place. It was better able to evaluate a position just from the pattern on the board, and needed to search a much smaller part of the search space than the older chess engines did.

An engine that at a glance would be quite good at predicting what move AlphaZero would do, given enough time, would take this principle to the next level.

You see the same in humans like Magnus Carlsen. Even if he doesn't "calculate" a single move (meaning traversing part of the search space in chess lingo), he can beat most good amateurs by just going for the move that looks "obvious" to him.

Anyway, the point isn't chess. The point is that the search part of the Alpha family of models (which tend to be specialized) seems to be making its way to multi-modal models.

And that this makes them slower and more expensive to run. That's fine for some applications, but since their output is not really just regurgitating the input, their output MAY be more useful as training data for other models than other available training data.

Now there is another difference between AlphaZero and traditional LLM's, and that is in the RL-through-self-play. I don't really know if something like Strawberry would also require RL-training to actually outperform the original training data.