I mean I don't know for sure but I think they often just code program logic in to filter for some requests that they do not want.

My evidence for that is that I can trigger some "I cannot help you with that" responses by asking completely normal things that just use the wrong word.

I made a cat detector using Octave. Just ‘detected’ cats in small monochrome bitmaps, but hey, I felt like Neo for a while!

Objectively, per Ed Zitron.

adept

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

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".

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.

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

This method is definitely a great way to achieve some degree of explainability for images, but it is based on the assumption that nearby pixels will have correllated meanings. When AI is making connections between far-away features, or worse, in a feature space that cannot be readily visualized like images can, it can be very hard to decouple the nonlinear outputs into singular linear features. While AI explainability has come a long way in the last few years, the decision-making processes of AI are so different from human thought that even when it can "show its work" by showing which neurons contributed to the final result, it doesn't necessarily make any intuitive sense to us.

For example, an image-identification AI might identify subtle lens blur data to determine the brand of camera that took a photograph, and then use that data to make an educated guess about which country the image was taken in. It's a valid path of reasoning. But it would take a lot of effort for a human analyst to notice that the AI is using this process to slightly improve its chances of getting the image identification correct, and there are millions of such derived features that combine in unexpected ways, some logical and some irrationally overfitting to the training data.

... 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.

A bot might, but this post is pointing out how common it is for people who consider themselves AI experts to not recognize this diagram that is basically part of AI 101

They're not saying that the bots are asking what the image is, but users (may be bots or not) that sell themselves as AI/ML experts.

Would you recognize if someone made a block diagram of your brain?

Silk Road and other black market vendors existed well before the scams started

Silk Road was launched in 2011, the same year of the first big Mt. Gox crypto heist (now largely recognized as an inside job).

Crypto scams are as old as Bitcoin itself.

Theil is buying now but he's a late arrival.

Anthony Di Iorio wrote: "Ethereum was founded by Vitalik Buterin, Myself, Charles Hoskinson, Mihai Alisie & Amir Chetrit (the initial 5) in December 2013. Joseph Lubin, Gavin Wood, & Jeffrey Wilcke were added in early 2014 as founders."

You actually do "file:mp3", this is how found most of my course literature without ending up in a bunch off spam sites.

SEO optimization

Man they should really incorporate optimization into the initialism

It is random, at least while it is learning. It would have most likely tried 5 layers, or even 50.

But the point is to simplify it enough while still working the way it should. And when maximizing the efficiency, you generally get only a handful of efficient ways your problem can be solved.

In practice it's very systematic for small networks. You perform a search over a range of values until you find what works. We know the optimisation gets harder the deeper a network is so you probably won't go over 3 hidden layers on tabular data (although if you really care about performance on tabular data you would use something that wasn't a neural network).

But yes, fundamentally, it's arbitrary. For each dataset a different architecture might work better, and no one has a good strategy for picking it.