The deep learning boom caught almost everyone by surprise

I think neural nets are just a subset of machine learning techniques.

I wonder what would have happened if we poured the same amount of money, talent and hardware into SVMs, random forests, KNN, etc.

I don't say that transformers, LLMs, deep learning and other great things that happened in the neural network space aren't very valuable, because they are.

But I think in the future we should also study other options which might be better suited than neural networks for some classes of problems.

Can a very large and expensive LLM do sentiment analysis or classification? Yes, it can. But so can simple SVMs and KNN and sometimes even better.

I saw some YouTube coders doing calls to OpenAI's o1 model for some very simple classification tasks. That isn't the best tool for the job.

KANs (Kolmogorov-Arnold Networks) are one example of a promising exploration pathway to real AGI, with the advantage of full explain-ability.

"Explainable" is a strong word.

As a simple example, if you ask a question and part of the answer is directly quoted from a book from memory, that text is not computed/reasoned by the AI and so doesn't have an "explanation".

But I also suspect that any AGI would necessarily produce answers it can't explain. That's called intuition.

Why? If I ask you what the height of the Empire State Building is, then a reference is a great, explainable answer.

It wouldn't be a reference; "explanation" for an LLM means it tells you which of its neurons were used to create the answer, ie what internal computations it did and which parts of the input it read. Their architecture isn't capable of referencing things.

What you'd get is an explanation saying "it quoted this verbatim", or possibly "the top neuron is used to output the word 'State' after the word 'Empire'".

You can try out a system here: https://monitor.transluce.org/dashboard/chat

Of course the AI could incorporate web search, but then what if the explanation is just "it did a web search and that was the first result"? It seems pretty difficult to recursively make every external tool also explainable…

Then you should have a stronger notion of "explanation". Why were these specific neurons activated?

Simplest example: OCR. A network identifying digits can often be explained as recognizing lines, curves, numbers of segments etc.. That is an explanation, not "computer says it looks like an 8"

But can humans do that? If you show someone a picture of a cat, can they "explain" why is it a cat and not a dog or a pumpkin?

And is that explanation the way how they obtained the "cat-nes" of the picture, or do they just see that it is a cat immediately and obviously and when you ask them for an explanation they come up with some explaining noises until you are satisfied?