> it all can't be deep learning unfortunately, because that's not how we learn
If you could humor me, why can't it all be deep learning? It seems like a robust model for how we learn: we start with some priors (random weights), experience something that either confirms or negates those priors (sampling), and then adjust our priors based on that experience.
Of course there are a wide variety of architectures and the human brain is more sophisticated than one conv net. Our brains have on the order of 100 billion neurons and I believe the largest neural networks are on the order of 10's of millions. I would argue that a combination of better architectures and scale could simulate human intelligence.
i dont think we start with random priors. We start with a set of priors that are geared for certain things - facial recognition, language etc. The environment then fine tunes them.
There is no way that the genome encodes synaptic layouts to such a detail as to specify an algorithmic prior. At best it specifies, loosely, the generic architecture of how many neurons, how many layers, how tightly folded, and where the inputs (senses) connect. We develop the same algorithms because we all start with the same priors in largely the same gestational and early infant environments.
No, we don't "learn" how to balance, how to identify objects and navigate 3d environments. All of that is highly codified in our DNA from hundreds of millions of years of evolution. Human babies, similar to kangaroos, are just born too early for those systems to have developed.
Part of it could be hardware related, we may need special chips just geared for this, with special coded sensors.
Also, not everything we do is in our brain -- there's also muscle memory, and even tissue inside the gut stores some memories and 'knowledge'. Technically every cell in our body has some purpose/goal and can contribute to our entire being/feeling/mood/etc.
There's a lot we don't know about conscience that makes developing an artificial one, very tough. That's why it's not estimated to happen till after 2050. I don't doubt it will happen, I don't doubt deep learning as is will be crucial to the discovery, I just think there's possibilities that undiscovered and better/alternate techniques might be discovered that could potentially trump dl as we now know it.
There's a lot of things we do not know to get us to general ai, and to get there we may need to jump outside some 'boxes' that ai researches have put themselves into.
If you could humor me, why can't it all be deep learning? It seems like a robust model for how we learn: we start with some priors (random weights), experience something that either confirms or negates those priors (sampling), and then adjust our priors based on that experience.
Of course there are a wide variety of architectures and the human brain is more sophisticated than one conv net. Our brains have on the order of 100 billion neurons and I believe the largest neural networks are on the order of 10's of millions. I would argue that a combination of better architectures and scale could simulate human intelligence.