
An authority on artificial intelligence introduces a theory that explores the workings of the human mind and the mysteries of thought.
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.
My favorite in this regard is Minsky's "Society of Mind" where essentially every chapter/mini-essay is one page, a form which he argued is a good fit for our mental processes.
He's a smart man with well-reasoned arguments, but I think he's also a bit poisoned by working at such a huge org, with all the constraints that comes with. Like, this:
You can’t just tell them something and they’ll remember it.
It might take a decade to work through this issue if you just want to put a single LLM in a single computer and have it be a fully-fledged human, sure. And since he works at a company making some of the most advanced LLMs in the world, that perspective makes sense! But of course that's not how it's actually going to be (/already is).LLMs are a necessary part of AGI(/"agents") due to their ability to avoid the Frame Problem[1], but they're far from the only needed thing. We're pretty dang good at "remembering things" with computers already, and connecting that with LLM ensembles isn't going to take anywhere close to 10 years. Arguably, we're already doing it pretty darn well in unified systems[2]...
If anyone's unfamiliar and finds my comment interesting, I highly recommend Minsky's work on the Society of Mind, which handled this topic definitively over 20 years ago. Namely;
A short summary of "Connectionism and Society of Mind" for laypeople at DARPA: https://apps.dtic.mil/sti/tr/pdf/ADA200313.pdf
A description of the book itself, available via Amazon in 48h or via PDF: https://en.wikipedia.org/wiki/Society_of_Mind
By far my favorite paper on the topic of connectionist+symbolist syncreticism, though a tad long: https://www.mit.edu/~dxh/marvin/web.media.mit.edu/~minsky/pa...
[1] https://plato.stanford.edu/entries/frame-problem/
[2] https://github.com/modelcontextprotocol/servers/tree/main/sr...
