Don’t be evil.

AI is clearly no match for little Bobby Tables.

Yep my sentiment entirely.

I had actually written a couple more paragraphs using weather models as an analogy akin to your quartz crystal example but deleted them to shorten my wall of text…

We have built up models which can predict what might happen to particular weather patterns over the next few days to a fair degree of accuracy. However, to get a 100% conclusive model we’d have to have information about every molecule in the atmosphere, which is just not practical when we have a good enough models to have an idea what is going on.

The same is true for any system of sufficient complexity.

This article, along with others covering the topic, seem to foster an air of mystery about machine learning which I find quite offputting.

Known as generalization, this is one of the most fundamental ideas in machine learning—and its greatest puzzle. Models learn to do a task—spot faces, translate sentences, avoid pedestrians—by training with a specific set of examples. Yet they can generalize, learning to do that task with examples they have not seen before.

Sounds a lot like Category Theory to me which is all about abstracting rules as far as possible to form associations between concepts. This would explain other phenomena discussed in the article.

Like, why can they learn language? I think this is very mysterious.

Potentially because language structures can be encoded as categories. Any possible concept including the whole of mathematics can be encoded as relationships between objects in Category Theory. For more info see this excellent video.

He thinks there could be a hidden mathematical pattern in language that large language models somehow come to exploit: “Pure speculation but why not?”

Sound familiar?

models could seemingly fail to learn a task and then all of a sudden just get it, as if a lightbulb had switched on.

Maybe there is a threshold probability of a positied association being correct and after enough iterations, the model flipped it to “true”.

I’d prefer articles to discuss the underlying workings, even if speculative like the above, rather than perpetuating the “It’s magic, no one knows.” narrative. Too many people (especially here on Lemmy it has to be said) pick that up and run with it rather than thinking critically about the topic and formulating their own hypotheses.

I question the value of this type of research altogether which is why I stopped following it as closely as yourself. I generally see them as an exercise in assigning labels to subsets of a complex system. However, I do see how the COT paper adds some value in designing more advanced LLMs.

You keep quoting research ad-verbum as if it’s gospel so miss my point (and forms part of the apeal to authority I mentioned previously). It is entirely expected that neural networks would form connections outside of the training data (emergent capabilities). How else would they be of use? This article dresses up the research as some kind of groundbreaking discovery, which is what people take issue with.

If this article was entitled “Researchers find patterns in neural networks that might help make more effective ones” no one would have a problem with it, but also it would not be newsworthy.

I posit that Category Theory offers an explanation for these phenomena without having to delve into poorly defined terms like “understanding”, “skills”, “emergence” or Monty Python’s Dead Parrot. I do so with no hot research topics at all or papers to hide behind, just decades old mathematics. Do you have an opinion on that?

I’ve read the article and it’s just clickbait which offers no new insights.

What was of interest in it to yourself specifically?

No I’m not.

You’re nearly there… The word “understanding” is the core premise of what the article claims to have found. If not for that, then the “research” doesn’t really amount to much.

As has been mentioned, this then becomes a semantic/philosophical debate about what “understanding” actually means and a short Wikipedia or dictionary definition does not capture that discussion.

Understanding as most people know it implies some kind of consciousness or sentience as others have alluded to here.

It’s the whole point of your post.

You’re being downvoted because you provide no tangible evidence for your opinion that human consciousness can be reduced to a graph that can be modelled by a neural network.

Addidtionally, you don’t seem to respond to any of the replies you receive in good faith and reach for anecdotal evidence wherever possible.

I also personally don’t like the appeal to authority permeating your posts. Just because someone who wants to secure more funding for their research has put out a blog post, it doesn’t make it true in any scientific sense.