[burnished]

You might be interested by neuromorphic hardware. The basic observation is that animal computation and silicon computation operate in very different ways. Animals use lots of neurons that perform comparatively poorly (slow, not deterministic) that are sparsely connected, but have a high degree of parallelism. Compared to say a computer chip, which uses relatively few components that all operate at very high speeds with a high degree of determinism, are very thoroughly connected, and do not operate at nearly the same degree of parallelism. So if we want to explore AI maybe we should try making hardware that is more similar to the goop in our heads.

[q1w2]

Neurons in your brain have tens of thousands of connections each, and are not limited to the current AI design where all connections are laid out in a neat linear layers for matrix operations.

Squishy human brains connect in all directions - there's no "layer" to every thought. It creates feedback loops, intricate pathways, as well as direct connections.

Modern AI tech is fundamentally dumbs down intelligence by this notion of layered matrix operations.

It is done for scalability because matrices can be computed easily on a GPU, but it's not the same architecture.

[burnished]

There are pretty recognizable layers actually, and groupings of neurons that resemble 'cells' in the sense that they have recognizable inputs and recognizable outputs, and a large degree of interconnectivity.

What you are talking about sounds like deep learning. What I'm talking about is the hardware. Your tone makes it sound like you think you are correcting me, I'd like to inform you that you are not.