151–160 of 322 posts
I mean, save for the part where the founders recognised the threat it posed to society and acted responsibly rather than unleashing it on the public and sucking down billions in VC money.
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I had a long ranting comment I deleted. I just don't like this trend of people presenting work in a way that makes you think some combo of 1) they discovered from scratch themselves 2) it's new 3) they didn't try to cite or acknowledge where they learned it/point to good sources 4) they don't really care about trying to teach something deeply, they want shiny stuff that makes them seem deep. This post references spec…
I'm of two minds here. The pro is that the "you could have invented this" walkthrough from first principles is more engaging than "and then so and so introduced this term in 1972 and the definition is such and such". This style is a reaction to that boring and dry teaching style and tries to push towards what eg Feynman pointed at in the Brazil critique. The con is that you don't get to understand and see any of the…
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And yet to this day, in AI threads, so many people act shocked and surprised if you dare follow the obvious implication and claim that understanding is a form of lossy compression.
>understanding is a form of lossy compression.. No. Teaching is compression. Understanding and application is decompression.
You don't memorize the specific details, you memorize the flow of systems. But you get back to the details to put things into practice.
In Silicon Valley, Richard Hendricks creates a revolutionary lossless data compression algorithm for his startup, Pied Piper.
2) if 1) then "prediction" isn't the word I'm looking at.
This is the thesis behind the "Information Theory, Inference, and Learning Algorithms" course that was taught at Cambridge University. > Why unify information theory and machine learning? Because they are two sides of the same coin. In the 1960s, a single field, cybernetics, was populated by information theorists, computer scientists, and neuroscientists, all studying common problems. Information theory and machine l…
As much as I want to, I sadly don't think Information Theory makes sense in this setting, and I really wanted to believe this. When Shannon made his theory of information, he was always dealing with informational representations on the abstraction level of bits. At Bell Labs, a lot of the work was on the compression of data for transfer over telephone wires. Entropy coding, later codexes like algorithmic coding, and…
The whole point is that the representation is learned. When you talk about various levels of abstraction, you're missing that all of these levels are representable with words and the relationships between them. That is verbatim what LLMs are optimized for. Interestingly, when you take an embedding, you do see that some transformations in embedding space actually hold which is quite interesting (e.g. tree + many ~ forest)
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As much as I want to, I sadly don't think Information Theory makes sense in this setting, and I really wanted to believe this. When Shannon made his theory of information, he was always dealing with informational representations on the abstraction level of bits. At Bell Labs, a lot of the work was on the compression of data for transfer over telephone wires. Entropy coding, later codexes like algorithmic coding, and…
> How do you decide what shared features a peacock and a palm tree have? At what scale should they be represented? How do you deal with invariance under affine transforms? Do you want to open the box of invariance under non-affine transforms? The whole point is that the representation is learned. When you talk about various levels of abstraction, you're missing that all of these levels are representable with words an…
2) Language is already a higher-order lossy compressed abstraction made by humans to communicate fast and fill out the left out information with a learned prior. If you train a model on language only, it will not have the opportunity to have a non-compressed representation to make its own abstraction from.
3) If you are LLM-pilled and believe that we will be able to reach arbitrary levels of precise informational representation using language only, and that all abstractions that we may ever want can live on every single embedding layer in an LLM, your argument is fair.
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I don't think this is a fair critique. The author of the post uses standard terminology like entropy coding and arithmetic coding, and cited a paper "in 2023, Google DeepMind released a paper arguing that language modeling and compression are two views of the same thing" which discusses it further. This blog post is great. Well explained, and clearly took a lot of effort. I don't interpret it as them claiming to have…
Citing 2023 makes it seem like this is newer than it is. Compression, prediction and intelligence have long been known to be deeply connected.
It’s a blog post. If it was, say, a peer reviewed paper by Hinton or LeCunn that fails to cite Schmidhuber, that would be reasonable criticism in my opinion. (Spoiler: they fail to cite him)
This is the thesis behind the "Information Theory, Inference, and Learning Algorithms" course that was taught at Cambridge University. > Why unify information theory and machine learning? Because they are two sides of the same coin. In the 1960s, a single field, cybernetics, was populated by information theorists, computer scientists, and neuroscientists, all studying common problems. Information theory and machine l…