However, wetware still ages, gets diseases, needs life support, and is fairly non-serviceable.

If I could pick what platform I run on, it'd be hardware (and I hope such hardware eventually comes along).

Sure. But if they're cheap to grow, just make more.

"Cattle, not pets."

(I do realize the pretty horrific undertones of this discussion.)

While dead brains might be cheaper than electronic, I doubt that growing neurons in a lattice and doing what you want will ever be.

There is some research going on in using spintronics for neural network, and I think that both density and power usage would be a fraction of a biological system.

There is probably a long way until we can emulate human brains, but for generic neutral networks, I think electronics or spintronics is the probable route.

We first need the algorithms and datasets. Deep learning needs to learn general reasoning skills.

Brains are not general purpose computers though. You could probably train lumps of brain tissue do to certain tasks, but putting any kinds of FLOP/s numbers on the label is like stating horse power for levers.

That's why I said "OPS" instead of FLOPS. The kind of operations that brains are good at are orthogonal to the kinds that computers are good at, so a hybrid approach is probably a good idea. Again, I'm primarily thinking about this for AI type applications, not like fluid dynamics or other applications requiring high precision.

EDIT:And technically brains are general computing devices. Just really inefficient ones. Emulating logic in wetware is highly inefficient (and usually requires an external memory device, like pen and paper, though pure wetware memory also works in some especially skilled individuals, although still with severe capacity limitations). Of course, emulating wetware in logic isn't terribly efficient, either.