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Cake day: January 1st, 2024

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  • The algorithm is actually tailored to find out if/when you fall asleep while watching videos, and then recommends longer videos in autoplay when it believes you are, because they’ll get to play you more ads and cash out more.

    You might be misremembering / misinterpreting a little there. This behavior is not intentional, it’s just a side effect of how the algorithm currently works. Showing you longer videos doesn’t equate to showing you more ads. On the contrary, if you get loads of short videos you’ll have way more opportunities to see pre-roll ads, but with longer videos, you’re just to just the mid-roll spots in that video. So YouTube doesn’t really have an incentive to make it work like that, it’s just accidental.

    Here’s the spiffing Brit video on this, which I think you might have gotten this idea from: https://youtu.be/8iOjeb5DTZI

    Edit: to be clear, I fully agree that YouTube will do anything to shove ads down our throats no matter how effective they actually are. I’m just saying that this example you’ve brought is not really that.










  • It is an algorithm that searches a dataset and when it can’t find something it’ll provide convincing-looking gibberish instead.

    This is very misleading. An LLM doesn’t have access to its training dataset in order to “search” it. Producing convincing looking gibberish is what it always does, that’s its only mode of operation. The key is that the gibberish that comes out of today’s models is so convincing that it actually becomes broadly useful.

    That also means that no, not everything an LLM produces has to have been in its training dataset, they can absolutely output things that have never been said before. There’s even research showing that LLMs are capable of creating actual internal models of real world concepts, which suggests a deeper kind of understanding than what the “stochastic parrot” moniker wants you to believe.

    LLMs do not make decisions.

    What do you mean by “decisions”? LLMs constantly make decisions about which token comes next, that’s all they do really. And in doing so, on a higher, emergent level they can make any kind of decision that you ask them to, the only question is how good those decisions are going be, which in turn entirely depends on the training data, how good the model is, and how good your prompt is.