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But, more importantly, who is going on in there?

I have a bachelor's and master's in computer science, specialised in data manipulation and ML.

The problem with AI is that you don't really need to understand the math behind it to work with it, even with training. Who cares how the distribution of the net affects results and information retention? who cares how stochastic gradient descent really works? You get a network crafted by professionals that gets X input parameters, which modify the network's capacity in a way that's given to you, explained, and you just press play in the script that trains stuff.

It's the fact that you only need to care about input data quality and quantity and some input parameters that freaking anyone can work with AI.

All the thinking on the NN is given to you, all the tools to work with training the NN are given to you.

I even worked with darknet and Yolo and did my due diligence to learn Yolov4, how it condensed info and all that, but I really didn't need to for the given use case. Most of the work was labelling private data and cleaning it thoroughly. Then, playing with some Params to see how the final results worked, how the model over fitted...

That's the issue with people building AI models, their work is more technical that that of "prompt engineers" (😫), but not much.

Il'll give you the point regarding LLMs.. but conventional neural networks? Nah. They've been used for a reason, and generally been very successful where other methods have failed. And there very much are investments into stuff with real brains or analog brain-like structures.. it's just that it's far more difficult, especially as have very little idea on how real brains work.

A big issue regarding digitally emulating real brain structures is that it's very computationally expensive. Real brains work using chemistry, after all. Not something that's easy to simulate. Though there is research in this are, but that research is mostly to understand brains more, not for any practical purpose, from what I know. But also, this won't solve the black box problem.

Neural networks are great at what they do, being a sort of universal statistics optimization process (to a degree, no free lunch etc.). They solved problems that failed to be solved before, that now are considered mundane. Like, would anyone really think it would be possible to have your phone be able to detect what it was you took a picture of 15 years ago? That was considered to be practically impossible. Take this xkcd from a decade ago, for example https://xkcd.com/1425/

In addition, there are avenues that are being explored such as "Explainable AI" and so on. The field is more varied and interesting than most people realize. And, yes, genuinely useful. And not every neural network is a massive large scale one, many are small-scale and specialized.

Way back in the 90s when Neural Networks were at their very beginning and starting to be used in things like postal code recognition for automated mail sorting, it was already the case that the experts did not know why it worked, including why certain topologies worked better than others at certain things, and we're talking about networks with less than a thousand neurons.

No wonder that "add shit and see what happens" is still the way the area "advances".

Objectively, per Ed Zitron.

adept

I think you're giving them a little too much credit there

... 1957

Perceptrons. The math dates back to the 40s, but '57 marks the first artificial neural network.

Also 35 years is infancy in science, or at least teenage, as we see from deep learning's growing pains right now. Visualizations of neural network responses and reverse engineering neural networks to understand how they tick predate 2010 at least. Deep Dream was actually built off an idea of network inversion visualizations, and that's ten years old now.