Awesome read and absolutely loved the interactivity. A lot of effort was put into this.
Compression is prediction
141–150 of 322 posts
Re: Compression is prediction
#142[deleted]
Re: Compression is prediction
#143Ted Chiang made a similar point in his article "ChatGPT is a blurry JPEG of the web" a few years ago: https://www.newyorker.com/tech/annals-of-technology/chatgpt-...
It's a great line, but that's obviously not all it is. You don't get new results in e.g. mathematics by looking carefully at the pixels of a JPEG.
A few years ago it might have been. You have to judge assertions in the context of when they were made. Obviously some things have changed.
Re: Compression is prediction
#144The comparison can be carried on to another even crazier level: Evolution is compression. All the complexity of biology is executed at the highest possible efficiency.
I don't think this is true. Evolution is lazy and suboptimal, not characterized by the "highest possible efficiency".
Re: Compression is prediction
#145Earlier quoted context omitted.
It's a great line, but that's obviously not all it is. You don't get new results in e.g. mathematics by looking carefully at the pixels of a JPEG.
A few years ago it might have been. You have to judge assertions in the context of when they were made. Obviously some things have changed.
It's a good line! It's even sort of useful. It's just not the whole story.
Re: Compression is prediction
#146Earlier quoted context omitted.
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.
Shorter description isn't understanding, let alone of it is lossy. When you shorten a description in a lossy way, you are deciding a priori that some differences in the object don't matter, and it's not because you understand the object, but because it serves your goal of shortening the description.
I'm not aware of a better definition of "understanding" that would allow me to tell whether some system "understands" some other system. Do you happen to know one?
Re: Compression is prediction
#147[dead]
Re: Compression is prediction
#148Earlier quoted context omitted.
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…
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.
Re: Compression is prediction
#149This perspective is a useful source of intuition against the “LLMs can’t have new ideas, they’re just next-token-predictors” style arguments. What if you shift your perspective to thinking of training as optimization over a vast parametrized family of compression algorithms? Well, it suddenly looks a lot more plausible that “new” “ideas” can emerge from that process!
> it suddenly looks a lot more plausible that “new” “ideas” can emerge from that process This is not intuitive to me. It seems like a "new idea" is something that (almost by definition) isn't in the training set. Can you elaborate a bit? Edit: but perhaps a good model could arise from training, which would be a good idea in the sense that parsimonious ideas are good scientific ideas.
Therefore Einstein couldn't have had the idea of relativity because it wasn't in his training data.
Joking aside, the reality is that a pre-Einstein in 1850 couldn't have had the idea of relativity because Maxwell wasn't in his training data.
And a pre-Maxwell couldn't have had the idea of electromagnetism in 1820 because Faraday wasn't in his training data.
What we call "new ideas" are actually just deduced from the available training data, which includes all the previous ideas and all experimental results. AI can do the exact same thing (I assert). Perhaps not today's AI, but some AI, probably not too far in the future.
Re: Compression is prediction
#150This 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 all compression on this level assumes that you have a bit-based X, and you compress it.
However, in deep neural networks, you are dealing with compression on different levels of abstraction. 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?
When you start looking at what it would mean to compress feature representations, you immediately get to the question of data. You realize that Shannon simply was given a form of a very low abstraction data and that information theory came out to handle data at this level, but it's not suited for the data representations of many higher level modalities.
If you read Society of Mind by Marvin Minsky, which has aged well to about 80%, you can get the hint of the kind of abstractions that humans make and what would be needed to represent them, this is not representable in bits, you need to go to higher level shared features, and then you open all of the questions above as well as credit assignment, mutual information approximation, Fischer information between bayesians, etc.