[pas]

I'm assuming a simpler model, no need for magic, because so far I don't see what behavior/data this simple model cannot explain.

> Clearly it's not that simple or easy, or we would have done it.

We don't have the computational power yet. Not to mention the vast amount of development required. Think of the climate models, that are huge (millions of lines of code), but they're still nowhere near complete enough, and they only have to model sunlight (Earth's rotation, orbital position, albedo), clouds, flows (winds and currents), some topography (big mountains, big flats), ice (melting, freezing), some chemistry (CO2, salts). And they only have to match a simple graph, not the behavior of a human mind (eg Turing test).

So, it's not easy, even if simple.

> We can't even create life from non-life.

We understand life. Cells, RNA, DNA, proteins, mitochondria, actins, etc. It's big, it's a lot of moving parts, and we understand it, but we can't just pop a big chunk of matter into an atomic assembler and make a cell.

And I think intelligence/sentience is similar. It's big, not magic.

[textor]

> We don't have the computational power yet

Certainly you do realise that this has been a moving goalpost for half a century? It seems that lately people started to avoid giving a concrete estimate of the power required, though. It was so easy in the 90-s! «Human visual/verbal system processes gygabytes per second/has n flops to the xth» or the like. Well, now we have that and more; how come a deeper modeling, a finer processing, a more complicated network has come to be needed?

And your examples are incorrect, for example Navier-Stokes equations plus some general physics knowledge always have allowed us to estimate how much data we need for a certain fidelity of a finite-term weather forecast. Certainly we need more for a complete climate model, but we know what we need. No such thing about the brain.

It's an easy way to score some rationality points by voicing rejection of "magic", but it's a strawman. Nobody will bother arguing for a mythical homunculus in the seat of the soul, nor even for a concise formula summing up the workings of the mind. Pick harder targets. "It's just big" or "it's just a bunch of heuristics cobbled together" is a non-explanation. The brain is not a Rube Goldberg machine that manages to produce any sort of work simply due to its excessive complexity – it is energetically economical, taking into account that neurons are living cells that need to sustain their metabolism and not merely "compute" when provided with energy. Its discrete elements aren't really small by today's standards, nor are they fast. The number of synapses is ridiculous, but since they aren't independent, at a glance it doesn't add that much complexity too (unless we abandon reason and emulate everything close to the physical level).

Yet we have failed to realistically emulate a worm. By all accounts we have enough power for 302 neurons already. There's no workload to give to overwhelm available supercomputers. It's knowledge and understanding that we lack, and 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.

[pas]

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.

[VVyattPrentice]

If intelligence/sentience isn't magic for you, then consider existence. How strange it is to be anything at all.

[pas]

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.

[ahartmetz]

We don't fully understand life. We don't even understand all the proteins. We sure don't understand a single neuron.

We understand many small and big things about life, yes.

[oldandtired]

In part I agree with you. The big difference is that we don't understand the fundamental difference between what is alive and what isn't. We have many different ideas about the quality that is called "life" or living. We have no clue about what it is.

We have little or no understanding of the complex protocols that occur within a cell. If we did, our standard manufacturing techniques would be vastly different.

We can modify DNA and RNA in interesting ways, but they are not living. It is not until we put them into an already existing living cell that we can reprogram some characteristics of that cell.

[pas]

It's a spectrum. A rock is non-alive, and a human talking to another human is rather alive. A virus is closer to a rock than a cockroach is to a human newborn in terms of life, but a brain dead patient is probably closer to a tree than to a butterfly, and so on.

We have pretty fine understanding of cells, but our materials science and manufacturing technology is not "vastly parallel incremental molecular", but "big precise drastic pure chunk" based compared to cellular manufacturing. Not to mention protein folding and self-assembling biomachines and so on. We're getting there.