The parameter count equivalent of a human brain is not yet known, but if it was one per synapse then a full human brain replica would need about 1.5e14.

We also don't yet know how to be as efficient with training examples as any living creatures' brain, and we only partially make up for this by training on so many examples it would take you a million or so years to do the same, so we'd still stuggle with something proportionally smaller-brained such as a cat.

That said, remote controlled androids are going to be economically disruptive, as they make every (unlicensed) job open to outsourcing from an office in a low wage country.

Real neurons are orders of magnitude more complex than their artificial pseudo-approximation (it is all based on the century-old understanding of how neurons work). You can think of _individual_ biological neuron as an analog of the small artificial neural network. You can see this simple visual explanation on YouTube[1]. So we aren't even close. It doesn't mean the AI is impossible, it just means people underestimate the "computing power" of real brains, as well as that AI, even the future one might be totally different in how it works from the natural intelligence.

[1] https://www.youtube.com/watch?v=hmtQPrH-gC4

But for biological neurons to do something that can't be efficiently approximated on a digital computer (but conductive to useful information processing) they need to have unknown unknowns (well, partial unknowns like an unknown quantum algorithm will do too).

We don't know the violations of the physical Church-Turing thesis that are conductive for machine learning. We don't have evidence for their existence in the brain (although, the brain would be the prime candidate for finding them as evolution works directly with the true physical laws).

BTW, large ANNs don't try to model how the brain does things. They are trying to mimic what the brain does. So, using "how many transistors/artificial neurons it takes to model a biological neuron" is not a good approach.

We have no evidence. We even have no solid theories how this can work (Penrose's OrchOR is "OrchOR somehow taps into mathematical knowledge somehow encoded into the structure of spacetime"). But people, for some reason, insist that there should be something there. I can't attribute it to anything else but to deeply entrenched feeling of human exceptionalism.

You're talking about a philosophical debate whether the brain is computable, the other commenters are pointing out that even conservative estimates point to a brain-like NN requiring over a quadrillion parameters.

...assuming that modelling the physical structure of the brain is the only way to model its functions.

Building a "NN with similar capability as the brain" is not modelling its physical structure. The assumption is not made.

Let's define the terms. What does it mean "with similar capacity"? As far as I understand xvilka was taking about the number of artificial neurons required to model a biological neuron times the number of biological neurons in the human brain. It is modelling its physical structure (on a neuronal level).

You vastly overestimate how much we know about how our brains work.

Look up the neural correlates replication crisis, and e. g. the "dead salmon" study by Bennett et al.

That is we have unknowns that might increase or decrease estimates of computational demands of a functionally equivalent ANN. Not everything that happens in the brain contributes to information processing.

Brain's information flow also isn't just through direct neuron-to-neuron connections. Firing also releases neuromodulators into the extracellular space which affects how other neurons operate. Furhermore neuron connection architecture is very different between brain and feedforward ANNs, with the former exhibiting a lot of recurrent connections.

Deep learning doesn't try to mimic all the intricacies of biological processes. It tries to approximate the end result (information processing).

We certainly don’t need to replicate humans or overly anthropomorphise robots. Just like cars didn’t need to imitate mechanical horses.