|
> Approximating continuous functions is likely quite the same as what people do too. In a very broad sense, if you just mean "the human brain also just approximates some class of functions", sure. However, human brains can surely represent many classes of non-continuous functions as well (tan, lots of piece wise functions, etc). And, crucially, some of these are necessary for our physical models of the world. So, if neural networks are limited to only representing continuous functions, that is a strong indication that they are fundamentally unable to mimic the human mind. > You think there isn’t some mathematical model under the hood of how the brain works too? Of course it does. I do believe that the mind is simply a program running on the physical computer that is our brain. And I am sure that some day we will be able to create an AI that is human-like, and probably much better at it, running on silicone. That doesn't mean that we should believe every program running on silicone, despite somewhat obvious fundamental limitations, is going to be the next AGI any day now. That's all I'm trying to point out: neural networks are not a great model for AGI, and backpropagation/gradient descent as a training algorithm even less so. |