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points by mwernolagoo 4 days ago | 0 comments

Further than that, aren't "weights" just another sequence of tokens? We see them as vectors, pass to the API or as strings into a chatbox interface. Is it so surprising that the high-dimensionality linear algebra machine crunches them and gives us back useful vectors that can be mapped to text?

Isn't that what this whole technology paradigm is about? How is it surprising?

Granted, it is cool. But I see so much of "look at this cool thing I did with LLMs" and honestly... we need more structured text, more explicit documentation, more ergonomic and intuitive CRUD applications.

The amount of "look at cool things" is growing way faster than before and they are all sort of low effort now because the "agentic" LLM-VM looping apparatus can do a lot of heavy lifting and run with our prompts.

And I mean, if one has jargon, knowledge, and intention, they can also leverage that into great structured text, ergonomic and intuitive CRUD apps etc, all of which add great value to our mundane pursuits of applied research, software products and services and so on.

Honestly, I begin to think the problem is people dazzling themselves instead of doing the housekeeping that is the bulk of life. Technology choice is entirely secondary.

[–] fennecfoxy 4 days ago

Idk. Yes of course it can interpret weights as a bunch of comparatively low dimensionality tokens but the way the weights operate as the model gives them incredibly high dimensionality, for very large models potentially a downright absurd number of pathways and influence on the overall result.

But frontier AI can work out math and of course this is all just...math. So I imagine given enough time + testing and probing that it could work out what each weight does/influences and trace all the pathways.

And presumably this means that it would be possible to train a model to interpret weights as current models interpret human language although our language remains pretty constant, even different quants of the same model will experience enough change to mean no real basis for training a model to do that.

But I suppose it would surely be possible to train a model to interpret the weights in a generic "these are weights for an MLP" way? But then if I give a partial sentence from the middle of a book, you can pretty effectively guess what's going on locally but perhaps not globally. If I give you a chunk of loosely correlated weights from the center of a model...I feel like that's much, much more difficult but that the same applies?