Hard disagree. We only discovered the role that glial cells play in processing around 2014. We're still uncertain how patterns of activation consolidate through long term potentiation, let alone how signaling encodes information. We understand quite a bit about the role of the hippocampus and subiculum in encoding memories; but we don't understand the structural layout of engram complexes - which were themselves mapped for the first time only in 2022!

Taking effective results in machine learning, and somehow assuming that they apply to cognition - simply because neural nets were inspired by our limited knowledge of neural signaling and structure - is like trying to apply aircraft engineering to studying ornithology. For a better articulation of this point (from the reverse direction) check out the paper 'Could a Neuroscientist Understand a Microprocessor?' from 2017 - https://journals.plos.org/ploscompbiol/article?id=10.1371/jo...

> I highly recommend the philosophers read some neuroscience Philosophers do read neuroscience and technical reports on AI.

> The whole "model weights" thing in AI is modeled after the synaptic connections and between actual neurons In reality, it is a really poor and basic model of what is actually happening in a real brain

Brains and modern AI systems (LLMs for example) are structurally different. (Don't get confused by topology. A structure is more than topology: it is also what the structure is made of, thus the properties of the material contribute and define what can emerge atop the structure)

> There are also differences between discrete neuron firing and weights as signals, but there is enough similarity to make artificial neural nets useful and do things similar to what real one do.

There is barely a surface-level similarity. The best example I can come up with is this…

Imagine the most intricate and beautiful tall building that you can think of. Think like an older skyscraper in Chicago or a palace. There are water features and moving parts everywhere but also tiny little handmade carvings and materials throughout.

Now imagine we have no reference designs and no blueprints - we hire an architect to attempt to study the building by looking at it from a distance and understand everything they possibly can about it. She can go into the building to check but every time she does, it stops functioning normally.

That architect is a neuroscientist.

Then the ML researcher is like a graphic designer who sees the work that the architect is doing and makes a napkin sketch of the building the architect has been studying, to use for a project later. Sure the designer has some of her representations. But the difference in complexity between the designer’s napkin sketch and the architect’s analysis is massive. Several orders of magnitude.

Then another many orders of magnitude is the level of detail that the architect can understand about this strange building without being able to fully interact with it, versus the actual complexity of the building.

So yeah, an AI is modeled after neurons in the sense that they represent a couple of surface level features of neurons. But the difference in complexity is about as much as a napkin drawing of a grand building represents the actual structure and details of the building, no matter the level of skill that the graphic designer has