> As I've said in previous statements: most of human and animal learning is unsupervised learning.
I don't think that's true. When baby is learning to use muscles of its hands to wave them around there's no teacher to tell it what should its goal be. But physics and pain teaches it fairly efficiently which moves are bad idea.
It has built in face detection engine and the orienting and attempting to move and reach towards it is clear goal. Reward circuit in the brain do the supervision.
The difference between supervised and unsupervised is that the inputs are paired with known outputs for supervised. In unsupervised the agent has (initially) no knowledge of what the outputs will be, given the inputs.
The baby does not know (initially) that something will cause pain, or the extremities of its joints. It must learn this over time and experience. The baby must also learn how to use the built in components, as it has no idea what outputs will occur given the inputs.
As you allude to, there are built in mechanisms/configurations in the brain which provide various forms of feedback, as well as built in behaviours and responses. If there was no basic structure to the brain, I think it would be almost impossible for an unsupervised agent to develop and learn to the complexity and level of a human brain. These basic behaviours significantly speed the initial development process up.
> The difference between supervised and unsupervised is that the inputs are paired with known outputs for supervised. In unsupervised the agent has (initially) no knowledge of what the outputs will be, given the inputs.
I'd still call learning to move, supervised (or reinforced) then. You're feeding the world some input (muscle contractions), and the world immediately gives you the output in terms of pain. You are using it to adjust your internal function. After a while you have pretty good function that maps you muscle contractions to whether it valid move or not and you can generalize it to when your position is different and get to some other stuff like trying which moves can alter what you see and feel (apart from your hands that you already know).
> If there was no basic structure to the brain, I think it would be almost impossible for an unsupervised agent to develop and learn to the complexity and level of a human brain.
I agree that there's some stuff built in, but I think it's surprisingly little of it. How little I think we can see when we learn about people blind from birth or with deformities. They still learn to operate their bodies as well as it's physically possible.
Whatever person can relearn after physical brain damage I think can't be built-in. I think the structure we see in the brain is result of built-ins + various structural optimizations that make some stuff faster (or more energy efficient) than if the structure was different.
For me the real trick in neural networks is to find out how exactly natural neurons learn because it's not back-propagation and it's important. Do we know that? In detail? How scratching yourself on the face as a baby translates to chemical changes in synapses of neurons that fired recently?
Sorry but what you describe is not what supervised learning means. It has a specific meaning in AI. At best, what you describe is reinforcement learning. And I don't think this is how we learn to recognize people's faces.
I'm not sure if supervised learning is such a narrow term but I'll take your word for it. As for recognizing faces
I think we have that pretty much baked in the hardware. Faces are recognized immediately and not just by humans, also animals. I think people who lost ability to see faces, can't re-learn it.
I don't think that's true. When baby is learning to use muscles of its hands to wave them around there's no teacher to tell it what should its goal be. But physics and pain teaches it fairly efficiently which moves are bad idea.
It has built in face detection engine and the orienting and attempting to move and reach towards it is clear goal. Reward circuit in the brain do the supervision.