Here’s what’s really going on inside an LLM’s neural network

Anthropic's conceptual mapping helps explain why LLMs behave the way they do.

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[wirrbeltier]

Most of neurobiology looks like this, too: "Find the relevant location for $Behavior, artificially tune its activity up or down, watch what happens".
I find it strangely amusing that we've arrived at a conceptually similar procedure for artificial neural nets.

Spoiler: Cool neuroscience article as example

See for example this neat study, which pinpointed the neurons that control how pregnant female mice build nests for their pups. After finding the neurons, they made them artificially more excitable (by making them sensitive to light) or less excitable (by knocking in an engineered receptor for a specific chemical), and then saw that nests were more or less elaborate. >5 years of work, building on 120 years of neuroscience, neuroanatomy and behaviour studies.

Edinger-Westphal peptidergic neurons enable maternal preparatory nesting
Topilko et al., Neuron 2022
https://doi.org/10.1016/j.neuron.2022.01.012

Graphical abstract:

 

[wirrbeltier]

This is what I always think after I read a comment from a computer person along the lines of "this is nothing like a human brain, this is just an extremely complicated network of connections reacting to input by searching for patterns in that network." Before I decide whether or not LLMs can ever become human-like, I'll need to hear the opinion of someone who's an expert in computers AND neuroscience.

I'm only a neurobiologist with some Python knowledge, but the following quote from Sci-Fi writer Charles Stross feels plausible enough to me: "What we're getting, instead, is self-optimizing tools that defy human comprehension but are not, in fact, any more like our kind of intelligence than a Boeing 737 is like a seagull." (check out the entire keynote, it's amazingly prescient for being 6 years old).

More to your point of expertise: If you are into podcasts, I'd very much recommend the Brain Inspired podcast. It features long-form, in-depth discussions on the edge of experimental neuroscience, theoretical neuroscience, and AI research, with actual researchers in those fields: https://braininspired.co/podcast/