4 ms·

Your humility is as inspiring as your many arguments by analogy.

• logicallee 9y ago
  my sibling post lists some of the numbers involved, but you should especially look the number of neurons, their connectedness, firing speed, and above all realize how incredibly slow this analog biochemical signal propagation is. You can look at how many parts make it up. (About a hundred billion.) Finally you can know for sure that the algorithm can be encoded in well under 700 MB, since that is literally the entire blue print of the human genome (bit-perfect, at 2 bits per base pair), which (obviously) encodes human intelligence as just a small part of it. (Since it includes everything else too, such as your entire rest-of-your-body.) Look at the number of "mechanical" (biochemical) parts making up the human brain, their blueprint, the size they're contained in. Finally look at the results that analogous machine learning algorithms are making with (albeit much simpler) neural net topology models. The idea that nobody will ever match it is like looking at a mechanical watch and saying no digital model of it could ever come up with similar emulated results. What. When you look at the size of the datasets involved you will see that it is exactly the sort of thing we can expect to be emulating now.