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nyeah

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Yeah I don't understand the kvetching about someone making a movie. What's the downside? * The movie might suck. * Millions of people might watch a movie I don't like * Family or friends might force me to watch it.

Two words: why should I care?

Exception: If you personally are Homer, I'd like to hear your thoughts.

TFA gives an example where kids are at risk and an old person is misusing a phone. It is clearly not advocating for this scenario to occur more often. Can you show anything else to indicate that the conclusion is "toughen them up"?

I agree that there are several different issues at work. Can you show an example of how TFA conflates some of them?

You may disagree with the author's conclusions, but that doesn't make the article nonsense.

In fact LLMs are trained to predict the next token in the training set. Of course sometimes a new text input doesn't match the training set, or it matches two or more places in the training set. LLMs use a neural network to interpolate, so that's fine. Please look this up if you have any doubts.

Ok. Now. I think you're adding something to the description above. Maybe what you're describing is something "emergent," or maybe it's basically just word vectors that were built in on purpose. You may be adding something correct, or something incorrect. Fine.

But it's not reasonable to say that the "reductive" description above is a "lie". It's not. It's more like a recipe. If you look at correct instructions for making steak, and you call the author a "liar" then you are missing something important.

Balancing parentheses requires semantic understanding?

Look, some folks are more impassioned about this stuff than I am. Maybe that's a good thing. But LLMs do in fact just try to predict the next token, using a very big training set. They're very impressive (at tasks the training set prepares them for). But that's how they work.

No, they were correct. In fact an LLM stitches together stuff it observed in its training data. That scales up way better than a lot of us expected, but it's still correct.

If you train it on lots of working code, then it's useful for coding. If you trained it primarily on non-working code it would produce nonsense.

What's really thin is the oxide layer on the surface

https://en.wikipedia.org/wiki/Anodizing

When exposed to air at room temperature, or any other gas containing oxygen, pure aluminium self-passivates by forming a surface layer of amorphous aluminium oxide 2 to 3 nm thick,[4] which provides very effective protection against corrosion. Aluminium alloys typically form a thicker oxide layer, 5–15 nm thick, but tend to be more susceptible to corrosion.