Consider that every single cell in the body is essentially a teeny tiny distributed system with on the order of 10 million moving parts only counting proteins (RNA is probably as important for storing and encoding the state of various cellular processes) with a bare minimum of 23k distinct types not counting splice variants. We have barely begun to understand the computations that occur within a single cell for tuning, learning, and transmission of information and metabolic load balancing.
I suspect that there is some additional computational overhead needed to run the requisite biological processes in neurons but ANNs are astoundingly far from even a single neuron let alone a network of neurons (perceptrons are sometimes compared to dendritic branches and even that is a stretch).
The real question to me is whether we actually need to replicate all the biology underneath to get some of the higher level abstractions that we recognize as intelligence. I also have to point out that it took nature on the order of 2 billion years to develop the set of rules that are used to run cells and coordinate multicellular systems and it may very well be the case the some of them are purely empirical. I know the AI guys gave up on rule based systems long ago, but even if you aren't going to fill them all in by hand you need a way to find the rules that work and the search space is monstrously large (keeping in mind that even a hyper intelligent being remains bound by the laws of physics, it would still have to do a whole bunch of experiments in order to develop a model that might let it predict what rules it would need to operate more effective).
edit: The assumptions that go into the calculation of the computational complexity are gross simplifications. His average firing rates are also about an order of magnitude too high (though this apparently is Kurzweil's fault).
Comments
Consider that every single cell in the body is essentially a teeny tiny distributed system with on the order of 10 million moving parts only counting proteins (RNA is probably as important for storing and encoding the state of various cellular processes) with a bare minimum of 23k distinct types not counting splice variants. We have barely begun to understand the computations that occur within a single cell for tuning, learning, and transmission of information and metabolic load balancing.
I suspect that there is some additional computational overhead needed to run the requisite biological processes in neurons but ANNs are astoundingly far from even a single neuron let alone a network of neurons (perceptrons are sometimes compared to dendritic branches and even that is a stretch).
The real question to me is whether we actually need to replicate all the biology underneath to get some of the higher level abstractions that we recognize as intelligence. I also have to point out that it took nature on the order of 2 billion years to develop the set of rules that are used to run cells and coordinate multicellular systems and it may very well be the case the some of them are purely empirical. I know the AI guys gave up on rule based systems long ago, but even if you aren't going to fill them all in by hand you need a way to find the rules that work and the search space is monstrously large (keeping in mind that even a hyper intelligent being remains bound by the laws of physics, it would still have to do a whole bunch of experiments in order to develop a model that might let it predict what rules it would need to operate more effective).
edit: The assumptions that go into the calculation of the computational complexity are gross simplifications. His average firing rates are also about an order of magnitude too high (though this apparently is Kurzweil's fault).