u/Effective-Guest1601

Is it a problem to consider that artists are equal to everyone else? Not better, not more important. Their jobs are no more or less important than a grocery store clerk, or a gardner, or a doctor, or anyone else in my opinion.

I feel often that if I don't prioritize artists above all else, it is perceived as being against them, when I literally don't care about them more or less than anyone else. It's odd. But it seems that there is a lot of pushback if you don't think of artists differently.

Do artists think they are better or more important than those other people?

https://preview.redd.it/gqlps3fd1coh1.png?width=1200&format=png&auto=webp&s=183247c7cb3bb2f1882ceb4ca2ee02abdde59a01

>We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics.

>The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra.

>The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.

https://x.com/OpenAI/status/2097374640582668336

https://openai.com/index/navier-stokes-solution/

I'm one of over a billion AI users, and have very special insights into how AI should and shouldn't be used. I'm something of an expert, because even though I don't understand how any of it works, I've been using it for a few months so pretty sure I know best particularly when it comes to telling other people what to do.

I strongly feel that AI should only be used the ways I want to use it, and it should be illegal to use in ways I don't want to use it.

You're welcome.

No. The answer is always no, stop asking.

https://www.reddit.com/r/aiwars/comments/1dcg4kn/how_to_scrape_cara/

Looks like all the vulnerabilities noted in those comments from years ago were never addressed, the site is still wide open.

VibeMathed is a record of the mathematical problems that AI models have solved, in full or in part, kept by a community of mathematicians and enthusiasts. It spans everything from famous conjectures to the long tail of specialist questions and the numbered Erdős problems.

Any positive anti news out there? Haven't read any for a while, antis, tell us what good is happening in your world!

Rich Sutton

March 13, 2019

The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin. The ultimate reason for this is Moore's law, or rather its generalization of continued exponentially falling cost per unit of computation. Most AI research has been conducted as if the computation available to the agent were constant (in which case leveraging human knowledge would be one of the only ways to improve performance) but, over a slightly longer time than a typical research project, massively more computation inevitably becomes available. Seeking an improvement that makes a difference in the shorter term, researchers seek to leverage their human knowledge of the domain, but the only thing that matters in the long run is the leveraging of computation. These two need not run counter to each other, but in practice they tend to. Time spent on one is time not spent on the other. There are psychological commitments to investment in one approach or the other. And the human-knowledge approach tends to complicate methods in ways that make them less suited to taking advantage of general methods leveraging computation.  There were many examples of AI researchers' belated learning of this bitter lesson, and it is instructive to review some of the most prominent.

In computer chess, the methods that defeated the world champion, Kasparov, in 1997, were based on massive, deep search. At the time, this was looked upon with dismay by the majority of computer-chess researchers who had pursued methods that leveraged human understanding of the special structure of chess. When a simpler, search-based approach with special hardware and software proved vastly more effective, these human-knowledge-based chess researchers were not good losers. They said that ``brute force" search may have won this time, but it was not a general strategy, and anyway it was not how people played chess. These researchers wanted methods based on human input to win and were disappointed when they did not.

A similar pattern of research progress was seen in computer Go, only delayed by a further 20 years. Enormous initial efforts went into avoiding search by taking advantage of human knowledge, or of the special features of the game, but all those efforts proved irrelevant, or worse, once search was applied effectively at scale. Also important was the use of learning by self play to learn a value function (as it was in many other games and even in chess, although learning did not play a big role in the 1997 program that first beat a world champion). Learning by self play, and learning in general, is like search in that it enables massive computation to be brought to bear. Search and learning are the two most important classes of techniques for utilizing massive amounts of computation in AI research. In computer Go, as in computer chess, researchers' initial effort was directed towards utilizing human understanding (so that less search was needed) and only much later was much greater success had by embracing search and learning.

In speech recognition, there was an early competition, sponsored by DARPA, in the 1970s. Entrants included a host of special methods that took advantage of human knowledge---knowledge of words, of phonemes, of the human vocal tract, etc. On the other side were newer methods that were more statistical in nature and did much more computation, based on hidden Markov models (HMMs). Again, the statistical methods won out over the human-knowledge-based methods. This led to a major change in all of natural language processing, gradually over decades, where statistics and computation came to dominate the field. The recent rise of deep learning in speech recognition is the most recent step in this consistent direction. Deep learning methods rely even less on human knowledge, and use even more computation, together with learning on huge training sets, to produce dramatically better speech recognition systems. As in the games, researchers always tried to make systems that worked the way the researchers thought their own minds worked---they tried to put that knowledge in their systems---but it proved ultimately counterproductive, and a colossal waste of researcher's time, when, through Moore's law, massive computation became available and a means was found to put it to good use.

In computer vision, there has been a similar pattern. Early methods conceived of vision as searching for edges, or generalized cylinders, or in terms of SIFT features. But today all this is discarded. Modern deep-learning neural networks use only the notions of convolution and certain kinds of invariances, and perform much better.

This is a big lesson. As a field, we still have not thoroughly learned it, as we are continuing to make the same kind of mistakes. To see this, and to effectively resist it, we have to understand the appeal of these mistakes. We have to learn the bitter lesson that building in how we think we think does not work in the long run. The bitter lesson is based on the historical observations that 1) AI researchers have often tried to build knowledge into their agents, 2) this always helps in the short term, and is personally satisfying to the researcher, but 3) in the long run it plateaus and even inhibits further progress, and 4) breakthrough progress eventually arrives by an opposing approach based on scaling computation by search and learning. The eventual success is tinged with bitterness, and often incompletely digested, because it is success over a favored, human-centric approach.

One thing that should be learned from the bitter lesson is the great power of general purpose methods, of methods that continue to scale with increased computation even as the available computation becomes very great. The two methods that seem to scale arbitrarily in this way are search and learning.

The second general point to be learned from the bitter lesson is that the actual contents of minds are tremendously, irredeemably complex; we should stop trying to find simple ways to think about the contents of minds, such as simple ways to think about space, objects, multiple agents, or symmetries. All these are part of the arbitrary, intrinsically-complex, outside world. They are not what should be built in, as their complexity is endless; instead we should build in only the meta-methods that can find and capture this arbitrary complexity. Essential to these methods is that they can find good approximations, but the search for them should be by our methods, not by us. We want AI agents that can discover like we can, not which contain what we have discovered. Building in our discoveries only makes it harder to see how the discovering process can be done.

tldr; no real surprise AI has advanced to where it is, iykyk

>To distinguish digitally altered images from unaltered ones, a committee on photographic standards at New York University has proposed that manipulated images be accompanied by a "not-a-lens" symbol - a circle inside a square with a diagonal slash and a description of what in the picture was changed. The idea has received substantial attention, but it's unlikely to work. The symbol's absence might signify that a photograph is legitimate, or it might simply mean that its publisher declines to display the icon. After all, wouldn't a publication that wished to deceive its readers be unlikely to denote its deceptions with a symbol? A better approach might be to remind readers to view photographs with the same healthy skepticism they apply to the written word.

pastebin of the doc text: https://pastebin.com/KTnbYncW

https://www.whitehouse.gov/releases/2026/07/white-house-launches-gold-eagle-initiative-for-unprecedented-cybersecurity-vulnerability-coordination/

It looks like now the Trump administration has stepped in to control who has access to certain AI models - although they don't specify who does or what criteria they use.

So good news I suppose for the people who are eager for government regulations to step in for potentially dangerous models, now the Trump admin has stepped into that role.

Full message here -

> I consider that, today, there's no ethical justification for the use of
> generative AI in FOSS development.

So this is where the discussion ends.

As I mentioned, Linux has never been a social warrior project.

If you don't have technical reasons, you don't have reasons.

You can choose not to use AI, but that's your PERSONAL choice.

It has absolutely no impact on anybody else, and you should not expect
it to have any.

Put another way: if you are vegetarian because you think meat is
murder, that's perfectly fine. I know for a fact we have several vegan
kernel developers, and I'm sure they have varied reasons for it. Maybe
they just don't like the taste, maybe they have social or religious
reasons for it. Lots of perfectly valid reasons, possibly driven by
ethics.

But they don't expect the rest of the kernel community to become
vegetarian because of their personal ethical standpoint, do they?

This is absolutely no different.

And yes, I feel very strongly about this, not because I feel strongly
about AI per se, but because we have a long history interacting with
the FSF.

They have their "ethical" reasons too, and use them as a weapon, and
as a way to drive away sane people.

It's why Linux is *not* GNU/Linux, and why we call things "open
source" instead of "Free Software".

So keep your ethics where they belong - in your personal life. Don't
try to enforce your ethics on others.

                Linus

“Artificial intelligence means it’s much easier for us to make movies,” Lucas said. “It’s very much like sitting here saying, ‘Well, I believe the horse and the buggy is really where it’s at. These cars, they break down, they need gas, there’s all kinds of problems with them and pretty soon they’ll be making them into tanks, and then they’ll be killing people. It’s terrible.’ There’s nothing you can do about it. That’s progress, it’s the future.”

https://preview.redd.it/4uiixndusich1.png?width=1080&format=png&auto=webp&s=f5685734f7ac1e2d5f2a24cb2b49e6d2a6342468

https://x.com/__eknight__/status/2075643583491965248?s=46