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In my perception, human brains are neural networks that evolved to create a simulation of the physical environment that the organism inhabits. Human brains are a result of tuning over billions of years of natural selection.
Operating on an internal representation of the world is inherently cheaper than parsing out the data from the senses. This approach also allows the brain to create simulations of events that happened in the past or may happen in the future allowing for learning and planning. There's a lot more that can be said about this, but I think these are the key features that make complex brains valuable from natural selection perspective. I generally agree with the ideas outlined in this book.
i see ๐ค
i'm still not totally convinced that there's a fundamental division/difference between the criteria that constitute a brain/consciousness (many of them you mentioned) and just artifacts of learning algorithms at a scale we can't model/execute on a computer
I don't think there's anything magical about consciousness that can't be modelled and executed on a computer. I'm just saying that current approaches to AI are inherently limited because they're not based on symbolic logic.
A neural network gets tuned based on some input data, and it has no understanding what that data represents. It's just a bunch of numbers without any context. All it can do is to say that a particular numeric input matches one of the inputs its been trained on previously within a certain confidence interval.
On the other hand, the neural network in the brain evolved to represent the physical environment, and that's the shared context we have when we interact with one another. Our language relies on a lot of shared context based on this.
And I think that in order to make AI that has human style intelligence we have to train it within the context of a physical environment that it learns to interact with to create this share context that we can relate to.