Yep, any increase in demand is going to affect prices even if it's not in your location as costs for supply will increase. This increases for everyone not just the people next to datacentres.

Not that the OP didn't also have a valid point.

So reading comprehension was never something that you learned as a kid? Damn, the educational system failed you completely. Home schooled?

No doubt you're right, but it would be lovely to see the people who squawk about subsidies for renewable energy held to the same standard they demand of everybody else, even just for a little while.

Lately it's been the first one, I haven't run into the other claims for a while.

True enough, which is one of the reasons why I'm using Google less and less. It just irritates me that the company thinks it knows what I want better than I do, especially when I know that their 'summary' is artificially slanted towards whatever the company WANTS that I should believe rather than what's actually real.

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.