It's not just statistics: GPT-4 does reason (opens in new tab)

"A computer will never beat a chess master", etc.

Here are the facts for you: our reasoning is done by our brain. Our brain is just a bunch of processes. Those processes can be replicated in a computer. The number of cells and the speed can be improved. And there you have it, a superior reasoning machine.

These "only humans can do X" mostly comes from religion or other superiority bullshit, but in the end humans are not that special, although we seem to like to think so.

Biologically, we're clearly an increment over the next smartest thing - we have the same kind of hardware, doing the same things, built by the same process. But that increment carried us through the threshold where our brains became powerful enough to break our species free of biological evolution, and subjecting us to much faster process of technological evolution. This is why chimpanzees live in zoos built by humans, and not the other way around.

If anything, biological history of humanity tells us LLMs may just as well be thinking and reasoning in the same sense we are. That's because evolution by natural selection is a dumb, greedy, local optimization process that cannot fixate anything that doesn't provide incremental benefits along the way. In other words, whatever makes our brains tick, it's something that must 1) start with simple structures, 2) be easy to just randomly stumble on, 3) scale far, and 4) be scalable along a path that delivers capability improvements at every step. Transformer models fit all four of the points.

> with enough input (in the form of LLM) it can predict what a human’s reasoning may look like, but philosophically, that’s a different thing

By what school of philosophy? The one I subscribe to (whatever it's name) says it's absolutely the same thing. It's in agreement with science on this one.

so it's great that it can reason better than humans on small-medium probems already well trained for, but so far Transformers are not reasoning (not doing causal graph analysis, or not even doing zero order logic), they are eerily well writing text that has the right keywords. and of course it's very powerful and probably will be useful for many applications.

I claim I can juggle. I pick up three tennis balls and juggle them. You hand me three basketballs. I try and fail. My original claim, that I can juggle, still stands.

The model outputs what it predicts a statistically normal output would fit in the context given.

Truly “llm” and these gpt tools are very much large scale “soundex” models.

Fantastic and great.

But not ai or even agi.

This is why we see improvement in GPT when chain of thought/tree of thought is used with reasoning for each step. That can't correct every failure mode, but it increases the likelihood you'll receive a more correct answer.

When that is combined with the fact that transformers provably can implement proper deterministic sorting algorithms, it seems that the benefit of the doubt should go to the transformer having learned a sorting algorithm?

LLMs aren't plastic in the sense that they don't learn anything when they aren't being trained. But they can be trained to execute different programs depending on the contents of the context window, like if it contains "wrong, try again:" so maybe they can learn from their mistakes in that sense.

But if you could teach an LLM to sort by explaining it in the context window, the network would already have necessarily learned and stored a sorting algorithm somewhere; the text "here is how sorting is done: [...]" would just be serving as the trigger for that function call.

That's an interesting take, because I wouldn't call quicksort itself to be "reasoning". It's a step-by-step algorithm. Once a human learns it, accepts it as correct, and then runs it in their thought-space in order to transform some thought-space structure by sorting, only then I'd call it an exercise of reasoning. Note here that for humans, running quicksort is generally a slow, bug-prone, step-by-step Turing machine emulation in the conscious layer. Maaaaaybe after doing this enough, your subconscious layer will get a feel for it and start executing it for you faster.

The reason I'm saying it is that:

> I suppose something I'm interested in is whether an LLM that can't sort numbers could be instructed how as a prompt and then do so.

I think if you could describe a quicksort-equivalent algorithm to an LLM, one that does things LLM can't tackle directly, and it proceeded to execute that algorithm - I'd give it the same badge of "exercise reasoning" as I'd give to a human.

I think GPT-4 is very much capable of this for simple enough algorithms, but the way it looks like is, you need to get it to spell out individual steps (yes, this is the "chain of thought" "trick"). In my eyes, GPT-4 is playing part of our inner voice - the language-using process bridging subconscious and conscious levels. So if you want it to do equivalent of conscious reasoning, you need to let it "talk it out loud" and have it "hear" itself, the same way a human stepping an algorithm in their head will verbalize, or otherwise keep conscious awareness off, the algorithm description, and the last few steps they've executed.

With this set up, LLMs will still make mistakes. But so do humans! We call this "losing focus", "brain farts", "forgetting to carry one" or "forgetting to carry over the minus sign", etc. Humans can also cheat, off-loading parts of the process to their subconscious, if it fits some pattern they've learned. And so can LLMs - apparently, GPT-4 has a quite good feel for Python, so it can do larger "single steps" if those steps are expressed in code.

The main difference in the above comparison is, indeed, plasticity. Do the exercise enough times, and humans will get better at it, by learning new patterns that subconscious level can execute in one step. LLMs currently can't do that - but that's more of an interface limitation. OpenAI could let GPT-4 self-drive its fine-tuneing based on frequently seen problems, but at this point in time, it would likely cost a lot and wouldn't be particularly effective. But we can only interact with a static, stateless version of the model. But hey, maybe one of the weaker, cheaper, fine-tuneable model is already good enough someone could test this "plasticity by self-guided fine-tuning" approach.

FWIW, I agree with GP/author on:

> the embeddings generated by an LLM go beyond the normal word2vec picture that most people have of embeddings, and I believe are closer to whatever "understanding" means if it could be formally defined.

In fact, my pet hypothesis is that the absurd number of dimensions LLM latent spaces allow to encode any kind of semantic similarity we could think of between tokens, or groups of tokens, as spatial proximity along some subset of dimensions - and secondly, that this is exactly how "understanding" and "abstract reasoning" works for humans.

Here, as you say, a cake is a cake, and an intelligence is an intelligence, regardless of how it came to be. We can revisit the relevance of causal history once we reach the point we can assemble organisms from from cells, and/or create cells out of dead matter - at which point the only difference between "born" and "made" will be the Colour of its cells.

For example, if I have an LLM that had your copyrighted works in its training data, then any of its output is causally deriving from those copyrighted works of yours - it comes out painted in colour of "causally derived from ${kelseyfrog's works present in the training set}" - but whether or not it also carries the colour of "derivative of ${kelseyfrog's works...} in copyright law sense", depends on... the copyright law, and may change over time based on how that set of laws evolve.