AI helped health professionals to better detect pre-cancerous growths in the colon, but when the assistance was removed,
AI improved doctors’ ability to spot cancer.
The problem was not exercising a skill for several months, and then taking away the tool which was better than that skill.
It sounds like this is about when they stopped using AI.
If they do better with it than without it, why optimize how good they are without it? Like, I know how to do math, by hand. But I also own a calculator. If the speed and accuracy of my multiplication is life-and-death for worried families, maybe I should use the calculator.
Haha, hadn’t even considered that one. I just mean specifying it’s an animated premier, and adult animation, aaand only in the last five years. So in 2020 some specifically-adult specifically-animated show must have done better. But saying so doesn’t let them use the superlative, “most-viewed.”
I feel the need to point out, that’s a lot of qualifiers.
Shit, I’ll take sequels over as-a-service any day. Let continuing revenue be the result of making new things and games being good.
Microsoft is in way too deep with the US government for ‘it’s us or Israel’ to ever favor ‘us.’
There’s a nonzero chance they close the studio for this. They’ve been on a streak, lately.
And the power their employees need to drive to work?
And the power for all the computers they used growing up?
And the power to make those computers?
And the power Intel used inventing the microprocessor?!
And the power the entire telephone grid used while AT&T developed the transistor?!?!
It’s highlighting hypocrisy. It’s asking: do you take this problem seriously, or are you just complaining?
Having LLMs shoved into everything is a serious problem. But it’s a problem the way that forced updates and invasion of privacy were already a problem. Fixating on energy use is pretense. It’s working backwards to point at the negative externalities of something you’ve already made conclusions about, as if those factors were relevant to your conclusion. Using that as rhetoric is the nature of bad faith.
The shipping industry emits a billion tons of CO2e per year. Training a model emits… maybe a thousand? An impact that could be offset by reducing Chinese imports by 0.0001%. Or arbitrarily limited by strong-arming the very few companies involved. DeepSeek knocked off a few orders of magnitude and R1 seems to work, as well as any of these things work.
But some people don’t really give a shit about the electricity involved - it’s just a negative for them to latch onto.
Now, it is a problem locally, where datacenters turn power straight into heat, as fast as they can manage. Anything like a tiny sliver of the global shipping industry becomes noticeable when it’s concentrated in one building within range of a commute.
Please don’t mistake vindication for a lack of ambiguity. When this took off, we had no goddamn idea what the limit was. The fact it works half this well is still absurd.
Simple examples like addition were routinely wrong, but they were wrong in a way that indicated - the model might actually infer the rules of addition. That’s a compact way to predict a lot of arbitrary symbols. Seeing that abstraction emerge would be huge, even if it was limited to cases with a zillion examples. And it was basically impossible to reason about whether that was pessimistic or optimistic.
A consensus for “that doesn’t happen” required all of this scholarship. If we had not reached this point, the question would still be open. Remove all the hype from grifters insisting AGI is gonna happen now, oops I mean now, oops nnnow, and you’re still left with a series of advances previously thought impossible. Backpropagation doesn’t work… okay now it does. Training only plateaus… okay it gets better. Diffusion’s cute, avocado chairs and all, but… okay that’s photoreal video. It really took people asking weird questions on high-end models to distinguish actual reasoning capability from extremely similar sentence construction.
And if we’re there, can we please have models ask a question besides ‘what’s the next word?’
All-nighters!
But the morning after…
Ass-pull nonsense metric.
Someone already told you ‘you have to know how to use the tool, to use the tool,’ and it didn’t fucking help. Excuse me for trying to politely guide you toward what should be obvious.
And AI also isn’t particularly new.
Synecdoche is not multiple-choice. The kind we’re talking about didn’t really exist four years ago.
Seems like loss of context. By the end it’s seeing a list of US states, alphabetically, and it’d usually be weird to skip one.
The question is not kept at the forefront for each state named.
Charles Babbage was once asked, ‘But if someone puts in the numbers wrong, how will your calculator get the right answer?’
Using a chatbot to code is useful if you don’t know how to code. You still need to know how to chatbot. You can’t grunt at the machine and expect it to read your mind.
Have you never edited a Google search, because the first try didn’t work?
This kind of assertion wildly overestimates how well we understand intelligence.
Higher levels of bullshitting require more abstraction and self-reference. Meaning must be inferred from observation, to make certain decisions, even when picking words from a list.
Current models are abstract enough to see a chessboard in an Atari screenshot, figure out which pieces each jumble of pixels represents, and provide a valid move. Scoffing because it’s not actually good at chess is a bizarre line to draw, to say there’s zero understanding involved.
Current models might be abstract enough to teach them a new game by explaining the rules.
Current models are not abstract enough to explain why they’re bad at a game and expect them to improve.
“I can do math by hand.”
“But what if you can’t?”
Incorrect.