That's no moon.

AI is not a shoulder. It's a make-believe sentence generator, which has encoded a large collection of sentences discussing homework (*). That's actually counterproductive to stand on, because it wastes time and builds weakness into the foundation of science.

(*) On the Internet. Badly.

It is likely that the next algorithmic improvement will happen in 30 years time at the very earliest. Until then we are likely going to be stuck in the dark ages of transformers and Markov chains calling themselves LLMs and pretending to be intelligent.

Research operates on cycles of research careers. Paradigm shifts can happen when most of the current population of researchers stop working in AI and thereby stop flooding the world with variations on the same brute force trick. This will give a new generation of students breathing room to be actually creative and innovative. But before any of that happens, people like Musk and Zuckerberg need to run out of money or die. When the money and interest dries up, AI research will no longer be attractive. That's when the dedicated, methodical kids with a real interest in the field will get their chance to shine.

There's theory and there's reality. I shouldn't be required to look left, then right, then left again before crossing the street. But actually, I prefer to do it anyway:)

Part of it is learning to be diplomatic with ignorant people such as those you mention. Don't say: you're wrong, the book you are looking for doesn't exist. Say instead: sorry, the library computer can't find it rigght now, maybe it was misfiled, come back another day. You will seem helpful and mildly incompetent to them, and then they will go way.

Sometimes you have to break a few eggs to make an omelette. The wider issue is: what is the acceptable tradeoff between false positives and false negatives that keeps the slop in check for everyone? It is clearly not to err on the side of no false positives at all. That's merely sweeping the problem under the rug.

The people you mention who didn't use AI are essentially victims of the AI cheaters whose behaviour causes predictable countermeasures. Just like the wider journal readership are victims, who are being hoodwinked with fake papers and fraudulent datasets.

Do not assume that the maggots are fully autonomous idiots.

In other parts of the world, there are fledgling maggot movements too. What is particularly interesting and relevant about those is that they often quote some ideas and misconceptions that simply do not apply where those movements are forming. This is due to cultural and legal differences in the other countries.

You can see this by observing marches and protests and interviews in other countries. The slogans and demands just don't make sense locally most of the time, yet are carbon copies of American ideas.

This tells you two things: 1) the maggots in America and abroad are being paid to propagate the conservative hate speech in their own countries. 2) the groups who are paying them are Americans, because the talking points are American conservative talking points even in the rest of the world where it makes no sense. The local maggot movements are simply paid to propagate the American talking points in their local cultures, and nobody bothers to adapt them or see if they make sense at all.

The last thing this tells you is this: if you follow the money to the source, then you will know who needs to be stopped for the good of the world. When the payola stops, the movements will stop. The ball is in Americans' court (For now. Don't sit on your ass too long).

First, who actually goes to banks in person any more? Second, I can think of half a dozen business local to me that currently don't have the same closing times already. So your argument falls flat.

I never said anything about either ratio or geography. Are you replying to the right person? Regardless, again, the alternative solution is to adjust hours, not clocks. If you arrive at and leave work or school an hour later in the summer, it has the same effect as DST.

First, I'm not promoting standard time. Personally, I'd prefer DST all year. But that's irrelevant except to point out that you shouldn't make assumptions. Second, your "problem" can be solved easily by having "core hours" for businesses that need to deal with others. But there are plenty of business that could set their hours however they please.

The best solution I heard was to switch to permanent standard time, but businesses and local governments would be (and always were) free to have "summer hours" (where hours of operation shift back one hour, the opposite of the way we currently shift clocks) and "winter hours." So instead of forcibly making everyone change their clocks, give them the choice to change their hours.

How's the tariffs going, old chap? Is Amazon finally allowed to pass the costs on to the consumers or is Bezos still supposed to take a hit because Trump told him so for the last 6 months?

Excellent! Now go forth and multiply! (but apply the exponent first)

Why did you never use awk?

The deep learning revolution did not solve the problem you claim. What deep learning does is allow more complex piecewise linear functions to be modelled efficiently (if you use relu that is, which is the most popular activation (*)). That's both a blessing and a curse.

What actually happened in the deep learning revolution is that humans solved the problem of designing basic features over many generations of papers and progressively simplified the solution, discovering what is important and what isn't. The algorithms were weeded out until the point we are now, which is that data input is matched to algorithm, in this case the algorithm of choice is of deep learning type. It only looks like deep learning is good for every dataset, but it's not true.

For example, in vision problems, try training a deep network on input that is not in the form of pixels and not in the form of multiple color planes. It will fail miserably, the quality of recognition will be abysmal. That's why data design is so important, you have to know what the strengths of the AI model actually are. In this case, the statistical regularities between neighbouring pixels are what is enabling the CNN layers to extract information. These regularities are an artefact of choosing to stack pixels and colour planes into a rectangular grid. That's solving most of the problem.

Now pixels didn't always exist, they were invented by people quite recently. Try looking up TV technologies of the 1930s and you'll find that it's all about deflecting electron beams. There's really nothing natural about pixels, it's just what our current technologies are based on. And so there's nothing natural about what a deep network does either, it's just a system that has been selected for fitness against our current tech stack, for a handful of high value problem domains. It doesn't imply anything about other problem domains that haven't been studied so intensively.

(*) if you don't use relu but some other smooth activation family for your deep network, then there will always be a close piecewise linear approximation, as these functions are dense. So it's not a big loss of generality to assume relu everywhere.