Evolution Is the New Deep Learning

I pretty much agree with what you said, but DL (and even more deep reinforcement learning) is really computationally expensive

No free lunch theorem!

EAs optimise black-box objective functions. NFL prevents them from doing well on all possible objective functions. But rather weak assumptions are enough to evade NFL, for example the assumption that maximal steepness in the objective space is not achieved (there are many others also). It's particularly easy to make such assumptions when the problems arise in our universe (that's the subset of problems we're interested in), because our universe has structure -- see eg Lin and Tegmark.

Yes, indeed, but that's philosophy at its best. Thinking about nothing or everything. Zero and/or infinite complexity.

No predictive power whatsoever.

Also, I'm amazed by consciousness, by reasoning, by our cognition, by intelligence, how we apply it, day-to-day, from pure math to messy, but useful engineering, through the ugliness of realpolitik, and the beautiful and dreadful human tangle that our civilization is. The contrasts, the why-s. (Consider the so stark difference between US and Mexico especially the border towns, which is of course deceiving, as the problems don't stop at the border, the cities, states, nations are connected. The warring cartels, the corruption, the hopeless have-nots, the dealers, the addicts, the war on drugs/terror/smuggling/slavery/yaddayadda, the DEA, ATF, their foreign counterparts, the policy going against the market, the hard on crime ideology, the big data vs gerrymandering case just on the Supreme Court's plate, the pure math and reasoning behind all that again, are all connected, just harder to frame them in a "deep" picture.)

But so far, none involves any actual irreducible complexity. No magical formula, just layers upon layers of complexity and fine-tuning.

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.