[davmre]

Neural nets require a couple of FLOPs per synapse. The processing power required is a direct function of the number of synapses. Each neuron is essentially applying a particular logical op, and counting the neurons and their inputs gives you the number of ops. I don't get why this seems so objectionable.

Sure, real neurons in the brain might be doing something a couple of orders of magnitude more complicated than the nodes in an ANN, so you could tack on another 10^2 factor to those estimates if you like. But fundamentally, counting synapses is a reasonable way to get a Fermi estimate of the brain's processing power, and Bostrom's estimates are not significantly different from those others have arrived at by similar methods.

[ctl]

You’re right. I didn’t read the paper very carefully, and was myopically focused on the emulating-a-real-brain AI strategy. As in, let’s slice up a real human brain, map the neurons and synapses, and then simulate them as faithfully as possible.

To do that you need a great deal of fidelity in your simulations of neurons, which are enormously complex. But there is an argument to be made that neuronal complexity is incidental to the brain’s overall “computational capacity”; that you could replace the neurons in a human brain with much simpler nodes and still end up with a functional intelligence, after sufficient rewiring.

I don’t think that claim is obvious, but it’s definitely possible. And if it’s true, you can have human-level intelligence for 10^19 ops, given suitable software.

So I apologize for my post. It was over the top and unfair.

All that said, I still disagree with Bostrom’s conclusions. I think he enormously understates the difficulty of creating intelligent software, if we’re not just copying an existing brain.