Not to mention Amiga had visualizers before people even knew what Windows was..

In fact, music visualizers existed even before people knew what windows were!

Isn't that the train driver? I thought the engineer is the one shovelling coal into the furnace and making sure that the tank doesn't explode?

Mod parent up. I use gen AI all the time when I search for man pages and python docs. And by use, I mean I scroll past the results text as far as I need to until there's something resembling a link that's not an advertisement. Then I click the link.

That's not in the job description. Companies need to dial back the expectations on employees. Especially if they treat their employees as disposable contractors.

Maybe I'm old or out of touch (or both) but for the life of me I cannot think of a reason for that number to be more than maybe 1% outside of some lazy programmers.

I'm disappointed, I expected to see the quack with his copy/pasta claiming that EVs cause more air pollution than gas cars by now.

I did not need that image...

Wow, the zip-bomb link is interesting. This is why I keep coming to /.

I haven't been paying close attention, thus my first question: did they manage to factor an arbitrary number larger than 21?

"Secret trick destroys AI" is bullshit. What is not bullshit is that for less common tokens, the conditional distributions of their occurrence in language depend on a relatively small number of examples. This is not an LLM property, it's a property of the language data itself. Also known as the hapax problem. Any language generator, including LLMs, is constrained by this fact. It has nothing to do with the architecture.

In practical terms, this means if you have a learning machine that tries to predict a less common token from some context (either directly like an LLM, or as an explicit intermediate step), then the local output will be strongly affected if it sees a single new context in the training data, such as when someone is poisoning a topic.

There are no solutions for this in the current ML learning paradigm. The system designers can make the system less sensitive to the tokens in the training data, but this comes at a price of being less relevant, due to deliberately discounting newly encountered facts against a implicit or explicit prior. Your example falls in this category.

It is fundamentally impossible to recognise the truth and value of a newly encountered datum without using a semantic model of reality. Statistical language models do not do this.

Even small amounts of garbage poison the AI models. This has already been shown to work. The argument that this guy's impact is immaterial to Meta is unproven.

Agreed. $150K does not qualify even remotely as a monster payout. With that kind of money, you can't even buy an inground swimming pool large enough just to hope to swim in your total winnings.

Female AIs like the Bad Datas...

Not really. Plastics require hydrocarbons that can be sourced from anything, including coal or wood. Oil is just the most convenient source, but it's certainly not the only one.

And anyway, only 6% of oil is used for plastic production. Even increasing the demand for plastics won't materially affect oil consumption. Fossil hydrocarbons are also used as a feedstock for other industrial processes (fertilizer production mainly), but adding up all these uses accounts for just about 15% of global production.

No. Economy is movement of value, not of money. That's just speculation.

As little kids in a socialist country, we got fed the following tale:
Heating fuel in a mining town was very expensive. So the protagonist took a cart and a friend, went to the other side of a forest to buy wood cheaply, and with much effort, hauled it home. When asked why he won't haul another load and sell it at home, the guy answered: "I'm a worker not a speculant".
Abstracting from the nonsense of the price difference of wood being so high on two sides of a forest, the commies who produced this piece of propaganda inadvertently produced a nice example why trade is good despite not producing something by itself: the value it creates comes from moving goods to where they're appreciated (ie, needed).

Thus: economy is the movement of value (goods, services) between different owners who value those goods and services differently.
On the other hand, movement of money or similar tokens (eg. a wash trade) isn't valuable by itself.