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Comment by tim333 | Hacker News Reader

I'll give you algorithmic breakthroughs have been quite slow to come about - I think backpropagation in 1986 and then transformers in 2017. Still the fact that LLMs can do well in things like the maths olympiad have me thinking there must be some way to tweak this to be more brain like. I recently read how LLMs work and was surprised how text focused it is, making word vectors and not physical understanding.

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> Still the fact that LLMs can do well in things like the maths olympiad have me thinking there must be some way to tweak this to be more brain like

That's because you, as you admit in the next sentence, have almost no understanding of how they work.

Your reasoning is on the same level as someone in the 1950s thinking ubiquitous flying cars are just a few years away. Or fusion power, for that matter.

In your defense, that seems to be about the average level of engagement with this technology, even on this website.

Maybe but the flying cars and fusion ran into fundamental barriers of the physics being hard. With human level intelligence though we have evidence it's possible from our brains which seem to use less compute than the LLMs going by power usage so I don't see a fundamental barrier to it just needing some different code.

You could say there is no fundamental barrier to humans doing anything that is allowed by the laws of Physics, but that is not a very useful statement, and doesn't indicate how long it may take.

Since nobody has yet figured out how to build an artificial brain, having that as a proof it's possible doesn't much help. It will be decades or more before we figure out how the brain works and are able to copy that, although no doubt people will attempt to build animal intelligence before fully knowing how nature did it.

Saying that AGI "just needs some different code" than an LLM is like saying that building an interstellar spaceship "just needs some different parts than a wheelbarrow". Both are true, and both are useless statements offering zero insight into the timeline involved.

> I don't see a fundamental barrier to it

Neither did the people expecting fusion power and flying cars to come quickly.

We have just as much evidence that fusion power is possible as we do that human level intelligence is possible. Same with small vehicle flight for that matter.

None of that makes any of these things feasible.

> Still the fact that LLMs can do well in things like the maths olympiad have me thinking there must be some way to tweak this to be more brain like.

That's like saying, well, given how fast bicycles make us, so much closer to horse speed, I wonder if we can tweak this a little to move faster than any animal can run. But cars needed more technological breakthroughs, even though some aspects of them used insights gained from tweaking bicycles.

Yes, it's a bit shocking to realize that all LLMs are doing is predicting next word (token) from samples in the training data, but the Transformer is powerful enough to do a fantastic job of prediction (which you can think of as selecting which training sample(s) to copy from), which is why the LLM - just a dumb function - appears as smart as the human training data it is copying.

The Math Olympiad results are impressive, but at the end of the day is just this same next word prediction, but in this case fine tuned by additional LLM training on solutions to math problems, teaching the LLM which next word predictions (i.e. output) will add up to solution steps that lead to correct problem solutions in the training data. Due to the logical nature of math, the reasoning/solution steps that worked for training data problems will often work for new problems it is then tested on (Math Olympiad), but most reasoning outside of logical domains like math and programming isn't so clear cut, so this approach of training on reasoning examples isn't necessarily going to help LLMs get better at reasoning on more useful real-world problems.

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