[superkuh]

All that, more, and it seems like every computational biology analogy just completely forgets about the most common cell type in the brain: astrocytes. And then there are things like axo-axonal transmission that totally blow up the simple models, https://www.cell.com/neuron/fulltext/S0896-6273(22)00656-0

[bismuthcrystal]

Biologists just won't allows us to have any fun. It is always this kind of rhetoric: "what, are you modeling the brain without considering the influence of <insert obscure type of cell> on the hormone regulated blood flow around ion pump circuits during chinese new year neuron firing patterns? you are obviously bounded to fail..."

This whole AI field keeps on failing because people like to overthink things. Did Michelangelo need to know molecular chemistry to make sculptures? Why do people pretend there is no artistic component to building AI? Rant finished.

[IIAOPSW]

While I agree there is a large contingent of people who seem to believe every detail of real neurons are necessary to cognition (up to and including the atoms), and while I tend to agree those people are way off the mark about which details are actually relevant to the algorithms of cognition versus which are specific to natures implementation thereof, I also think the existing AI field goes too far in the other direction and oversimplifies more than it can get away with.

So far we've been able to gloss over our mistake through the raw brute force of voluminous training data and GPU power. It works in the same way using a hammer to drive a screw into a wall works. Sure, we can do it to an extent, but there's a much better way. We need to figure out how to use a screwdriver. And by screwdriver I mean slightly more sophisticated artificial neuron.

[bee_rider]

I don't think this is the case; the whole field of AI seems pretty healthy at the moment, not failing, and not all that worried about the inaccuracy of their model (It's only a model /Patsy -- but it can still host the whole song-and-dance).

[ad404b8a372f2b9]

Can you elaborate on how the axo-axonal transmissions blow up our simple models? I couldn't understand anything from that summary.

Would it be the equivalent of edges communicating between each other in artifical neural networks?