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Comment by dontwearitout | Hacker News Reader

Strongly disagree. Mathematician Gregory Chaitin said "compression is comprehension", and I think this is exactly correct: something is "more understood" if it can be internally represented or externally reproduced with less information.

Based on how transformers are trained to predict the next token, you can view them as powerful compression algorithms [1]. They truly must - and do - understand complex concepts to generate text as they do.

Understanding isn't the full picture for intelligence. Marcus Hutter has argued that AGI requires both compression/comprehension, and being goal directed/having agency. I'd say chatGPT has advanced understanding, but zero agency.

[1] https://ml.berkeley.edu/blog/posts/dalle2/

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There's the hutter prize which is related to that. I feel like it sort of missed the mark in terms of scale though: the compression techniques needed to compress a relatively "small" and specific dataset like Wikipedia are completely different from the techniques needed to "compress" the entire internet. It's only with the latter that we're seeing something interesting start to happen. The other constraint the hutter prize has is lossless compression, which isn't really conducive to general "learning"

Fair enough. I can see how for a limited domain of understanding, it can be a reasonable analogue for compression. I still don't see the benefit of reducing the domain of a word just to be able to put a new technology on arbitrary scale of "intelligence". I think we should be able to appreciate the power of ChatGPT-like models without lossy compressing of our vocabulary in the process.

Well the prompt is there to provide agency isn't it?

Not in any real sense - it just reacts to the prompt, and has no choice in how.

However, if you ductaped a simple goal directed reinforcement learning embodied agent onto a large language model, I think you'd approach something resembling animal-like intelligence. It would have motives, and the means to try to achieve them and adapt to it's environment. There's research in using LLMs for "internal dialog" in robots which is super interesting (https://innermonologue.github.io/)

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