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Comment by hacker_9 | Hacker News Reader

Behind every successful neural network is a human brain. Neural networks are a tool, an advanced tool for sure, but still just a tool. If we are looking for AGI, and assuming the brain is an AGI, then there are still many differences to resolve. For example, back propagation has not been observed in nature. Nor has gradient descent. So the core mechanisms for learning in nature have still to reveal their secrets.

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> Behind every successful neural network is a human brain.

I've spent a lot of time trying to explain this to people, that there is a confluence between the human brain and the machine, people tend to look at the machine separately, which is a mistake. When I say unequivocally, 'there is no such thing as machine intelligence', I just get blank stares.

Arguably, there are successful brains behind every successful brain, too. Every great innovator and thinker was building off the backs of numerous other thinkers and teachers in their life. Should we be surprised that it's much easier for a tool+human(s) to do better than a tool alone, given we also expect a single human + human(s) as colleagues to do much better? Never mind the whole learning/development process, during which 22+ years of dedicated effort by adults to shape/craft a functional human worker.

Overall, I'd agree that really powerful tools for specific tasks is going to be the majority of "AI" in the coming years.

Sure, I'd agree. But this brings up the idea of autopoiesis, and then I think things get really murky.

One question that interests me is this: Does intelligence have as a prerequisite a living system, such as a cell? If so, what is our definition of the living system and why is that important? If not, what abstract qualities of intelligence are we really trying to capture?

I think self replication and a vast, rich environment are missing ingredients in current RL agents. The human brain doesn't just do intelligent behaviour, it also builds itself up from a single cell. Neural nets don't grow like that, they are lesser, from a point of view. They lack the constraints of self replication - survival and procreation. The richness of the environment and the presence of specific constraints are essential for the development of intelligence. And lots of time to try things out.

I mean it's difficult to 'observe' gradient descent, there are no characteristic properties that you can identify without specifying the relative objective function. But most of the process theories from computational neuroscience are based on some form of gradient descent. Even if it's only implicit, you'll be able to describe the variables of the system as moving against the gradient of some function.

But yes, it's extremely unlikely that nature implements backpropagation directly, as it relies on non-local gradients.

Your reasoning does not follow. To see why, take something humans already clearly created: Flight. Kerosene-type jet fuel propulsion has not been observed in nature. It is flight nonetheless.

Human flight is not as agile or energy-effective as a dragonfly, but it is faster and stronger. Just like artificial learning may not be as sample-effecient as the human brain. It is a learning intelligence nonetheless and we are already working with the core mechanisms of reasoning and deduction.

Behind every successful brain is a little strand of DNA and some environmental inputs. Somehow a brain might be more than the DNA however.

That's called an emergent property.

Behind every brain is a successful neural network. Or at least that's the promise of connectionism.

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