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I know that people constantly underestimated the required computing power, as more and more finer details of the brain and cognition are unraveling. That doesn't make my argument invalid. I don't think we need to do a full brain emulation. That's the worst case scenario. We're getting pretty good at computer vision, what's lacking is the backend for reasoning, for generating the distributions for object segmentation and scene interpretation. Basically the supervisor. (As unsupervised learning is of course just means that the supervision and goal/utility functions are external/exogenous to the ML system, such as natural selection in case of evolution.) My example illustrates that yes, we can give an upper bound on molecule by molecule climate modeling, but that's just a large exponential number, not interesting, what we're interested in is useful approximations, which are polynomial, but they being models, they need a lot of special treatment for the edge cases. (Literally the edges of homogeneous structures, like ice-water-air, water-air, water-land, air-land [mountains, big flats, etc] interfaces. And the second order induced effects, like currents, and so on.) That means precise measurements of these effects, and modelling them. (Which would be needed anyway, even if we were to do a back to the basics N-S hydrodynamics model, as there are a lot of parameters to fine-tune.) For the brain we know the number of neurons, the firing activity, the bandwidth of signals, etc. We can estimate the upper limit in information terms, no biggie, but that doesn't get us [much] closer for the requirements of a realistic implementation. > Yet we have failed to realistically emulate a worm. http://openworm.org/getting_started.html#goal seems to be matter of time, not lack of understanding. ( https://github.com/openworm/OpenWorm#quickstart ) But maybe I'm not up to date on the issues. > it's high time to give up on the delusion that more power, naturally coming in the future, will somehow enable a creation of predictive brain model, for this would truly be magic. a) people are saying exactly this for years, that we have enough data already, we need better theories/models b) they fail to accept that more computing power and data is the way to test and generate theories. > The brain is not a Rube Goldberg machine that manages to produce any sort of work simply due to its excessive complexity A Rube Goldberg machine is simple, just has a lot of simple failure modes. (A trigger fails to trigger the next part, either because the part itself fails, or the interface between parts failed.) > Its discrete elements aren't really small by today's standards, If you mean cells, or cortices, agreed. If you mean functional cognitive constituents, I also agree, but a bit disagree, as they are small parts of a big mind, all interwoven, influencing, inhibiting, motivating, restricting, reinforcing, calibrating, guiding, enhancing each other to certain degrees. So in that sense consciousness is a big matrix which gives the coefficients for the coupling "constants" between parts. A magical formula if you will. But not more magical, than the SM of physics. |