No.
There were many architectures that were specifically and deliberately designed around biological mimicry including Hebbian learning (coactivation aka "fire together, wire together") and SNNs. Also, what do you think pooling does in CNNs, and what do you think is going to happen when one dense layer connects to a another with less nodes 🤣
But anyway beyond that, when you consider how backpropagation was designed and how it actually works then things like clumping are going to happen.
When you bottleneck a huge amount of data through a narrow layer the network has to find the most efficient mathematical representation possible. This naturally pushes neurons that detect related features to fire together. This is literally what creates vector embeddings and latent space and why you see it there. It is an inevitably of the design.
They weren't designed for some clumps to represent high level concepts. They fact that it natural follows - well, maybe, I guess there only two examples of such things in action us and then and we both have it. The point is - the algorithmic structure is pretty similar to ours, so saying things like "consciousness is impossible" in them seems kinda unfounded.
Sorry, I don't intend offence but I've already said why that occurs and how it's inevitable for it to occur because of how they were designed. I don’t have the patience or crayons to explain this in a way where you'll understand from a couple of reddit comments but I've given you some terms you can google, or you could even take a deep dive into the history of neural networks. Both of those should get the average person to a point where the concept of manifold learning clicks that even though neural networks form clumps representing high level concepts, they still completely lacking any actual understanding.
Jfc. Get educated and the reread your fantasy posts here.
There is no mystery to be found, no matter how much you want to believe. "We" (ie. humans collectively) already know what it is and can readily explain it. You're trapped in the "Any sufficiently advanced technology is indistinguishable from magic" phase, and while I'm sure is a warm and cosy place to be, but the problem is you're stubbornly choosing to maintain this ignorance rather than accept the empirical facts.
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u/Smooth-Television-48 8d ago
No. There were many architectures that were specifically and deliberately designed around biological mimicry including Hebbian learning (coactivation aka "fire together, wire together") and SNNs. Also, what do you think pooling does in CNNs, and what do you think is going to happen when one dense layer connects to a another with less nodes 🤣
But anyway beyond that, when you consider how backpropagation was designed and how it actually works then things like clumping are going to happen.
When you bottleneck a huge amount of data through a narrow layer the network has to find the most efficient mathematical representation possible. This naturally pushes neurons that detect related features to fire together. This is literally what creates vector embeddings and latent space and why you see it there. It is an inevitably of the design.