[resu_nimda]

1) One of the biggest reasons they fell out of favor for more "mathematical" approaches was that no one could really explain why exactly they worked.

Kind of like how nobody can really explain how the brain works, or life in general. My gut feeling is that it is hubris to think that we are going to "figure out" intelligence with increasingly sophisticated mathematical models anytime soon. We are not giving proper credit to how complex it is, and the multi-billion year developmental process that it took. We think we can just short-circuit that with some fancy math because we've had success with planetary orbits and other comparatively rudimentary phenomena.

The current industry approaches are great for extracting certain kinds of value out of large data sets, but in terms of producing a result that could even begin to be considered as interesting as life (i.e. AGI or "strong AI"), I believe we will have to rely on creating a system whose inner workings are too complex for us to understand.

In other words, going off of Arthur C Clarke's definition, life is magic. And we're trying to create something equally magical. Almost by definition, if we can analytically understand it, it's not going to be interesting enough.

[pas]

> We are not giving proper credit to how complex it is, and the multi-billion year developmental process that it took.

Or we are simply not ready to accept that it's simply a big book of heuristics fine-tuned over biological eons.

It's just big. We have too many interwoven, interdependent, synergistic faculties. Input, output, and a lot of mental stuff for making the right connections between the ins and the outs. Theory of mind, basic reasoning, the whole limbic system (emotions, basic behavior, dopaminergic motivaton), the executive functions in the prefrontal cortex, all are very specialized things, and we have a laundry list of those, all fine-tuned for each other.

And there's no big magic. Nothing to "understand", no closed formula for consciousness. It's simply a faculty that makes the "all's good, you're conscious" light go green, and it's easy to do that after all the other stuff are working well that does the heavy lifting to make sense of reality.

[jcora]

I'd call this pulling a Dennett: trivializing complexity to something that cannot or just doesn't have to be explained. Being unable to conceive consciousness at this moment doesn't mean there's nothing to conceive of: even if we never get to the final satisfactory answer, there is undoubtedly much more room left for useful concepts we don't have yet, around or inside this idea

[clicea]

I'd call bullshit. Dennett's argument not that the brain is complex but that it's not obvious it is not reductible.

[sgt101]

That's a lot of nots. So you are saying that Dennett says that the brain might be reducible[1]?

I don't think that's a strong claim or that it even qualifies as a claim at all. Lots of things might decompose into simple components if subjected to the right analysis, very few things definitely won't - for example many clever people have spent a great deal of time attempting to reduce quantum and cosmic scale physics to simple intuitively founded laws... If Dennett's claim is that the human brain is the same order of object as the universe I can accept it only if we agree that all objects share the same order. Where does that get us?

[1] Apologies, I don't know what reductible means, but guessed typo - I'm open to education though and unworried by typos!

[resu_nimda]

Your last paragraph seems to contain the kind of overconfidence that I'm talking about. I don't understand how you can say "consciousness is simply X" or "it's easy to do that [if you handwave away the hard parts]." Clearly it's not that simple or easy, or we would have done it.

We can't even create life from non-life. How can we begin to understand all the stuff you're talking about that's been layered on top? We don't understand this stuff well enough to just handwave it away as unimportant or trivial.

[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.

[0xfeba]

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

We can't really define life in the first place.

But this is not necessarily to your favour. I think it's more of an indication of how the world doesn't fit into our... anthropomorphic way of thinking. That is, everything follows the laws of physics, no magic involved. We aren't special.

[Retra]

We certainly can define life, it's just that people don't generally agree on a definition. Some people get offended if you don't include their favorite things in your definition.

Even so, we understand it just the same no matter how you define it, because what we understand is not a function of word choice or definition. It's a function of capability.

[cr0sh]

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

Have you not been following the work of Craig Venter? Depending on your point of view, he's already done it. Even if you don't agree, you have to admit that he's probably one of the few closest to actually doing it.

[hndamien]

Craig Venter has not created life from non-life. He has synthesised code that can reproduce and grow into a synthetic life form once implanted into an already living cell. So no, not life from non-life.

[resource0x]

200 years ago there was nothing to understand in electricity: it was just a liquid :) https://en.wikipedia.org/wiki/Fluid_theory_of_electricity

[pas]

There were experiments that were not explained by the liquid theory.

Now we have data and people for some reason want to claim that a theory with magical super complex and not-even-yet-describable and very-very-irreducible element(s) is a better fit than a good old box full of tiny yet specialized parts fine-tuned to work together over millions of years.

[resource0x]

Can you think of any experiment that cannot be explained by your theory?

[pas]

You mean, is this a falsifiable/testable theory? Yes, it's testable, we see very specific neuropathologies, almost like on-off switches affecting very specific functions/faculties of the mind, and they usually correspond well to brain damage locations.

So in that sense the experiment is to enumerate the basic (built-in) functional components of the mind and corresponding implementational level machinery, and the of course the reverse (try to enumerate the implementation components and match them with functions) can generate important data (is there a function that has no implementation?).

That said, since the claim is that there's no magical component in the mind, and that's kind of hard to prove, but easily falsifiable, just find a/the magical component.

The problem is the same as with the soul, and the self, and so on.

[ozy]

https://psyarxiv.com/387h9

[padthai]

There is plenty of magic going on. Today we cannot replicate or understand how emergent properties born of biological structures. Not even in "simple" systems as the metabolic pathways.

[pas]

We mapped the whole genome and connectome of C. elegans, no? And most of that is understood. For example, it seems to be a good model for substance addiction (especially for nicotine). That seems a pretty complex emergent behavior to me.

If you mean bigger biology, yes, sure, we don't have a full map of functional genomics for humans, but we're getting there.

Or maybe not, maybe it's so exponentially more complex, that it'd take as much time to understand it as it took for evolution to work it out. (Especially considering that evolution played with every individual, whereas we like to constrain our data gathering to non-aggressive methods.)

[ronjobber]

We have the connectome of C. elegans, yes, but we are still pretty far from understanding how it 'works'. The functional connections are still an active area of research, and it is greatly complicated by connections not explicit in the connectome (neuromodulator effects), as well as the internal dynamics of neurons and non-linear network dynamics.

The connectome is necessary, but far from sufficient, to 'understand' a brain, even one made from only 302 neurons as in C. elegans.

[ozy]

Same way waves emerge in water.

The rules that govern a system can create patterns, which themselves behave according to rules, but with a set of rules that was "hard to predict" from the underlying system.

[padthai]

That is exactly my point. We can use fluid dynamics and PDEs in waves. We understand some properties and processes. We are nowhere as close in biological system.

I put the example of the metabolic pathways because last time checked (~2015) the most advanced things in the field were extremely simple and without any predictive power. Things like calculating the kernel of a stoichiometric matrix or the centrality of a node in the interactomic graph.

[ozy]

But you've now shifted the "how" question. (Or I misunderstood the original.)

It is not a question of how there is emergence, why there is magic. The answer to that is is because systematic interactions at a low level can create higher level playing fields.

So the "how" is now a technical question, what is this system, how complex is it, and at which levels can we understand it. And since this system has been learning how to avoid erasure by entropy or by competition for 3.5 billion years, it has searched quite a possibility space, namely 2^1277500000000, if we assume making a copy every day.

[pas]

There's probably no need to go that low-level for modeling a mind, but of course the aggregate effects of biochemistry has to be taken into account (and it's full of non-linearities).

None of that means we don't understand the principles. I'd say it's pretty much like fusion. Yes, we know how the Sun works, but putting it into a bottle is a bit of a pickle, similarly with brains. (Except brains have a lot more complexity.)

[mrfusion]

Keep in mind the genome really isn’t big enough to store enough hueristics to make a functioning human.

[pas]

Yes, of course, the problem of bootstrapping consciousness from blueprints of a human mind is that we depend on our parents' whole epigenetic and other extra informational make up, plus their support for years while our mind finishes setting up.

[TaylorAlexander]

While I can’t predict that we can solve intelligence completely any time soon, I would argue that machine intelligence allows us to examine the phenomena of intelligence more thoroughly than anything else in the past.

I would expect this to yield new insights. So at a minimum, I’d think we can learn more now than we have been able to before. Maybe that will lead to a great increase in understanding of intelligence or a slight one, but it will lead to an increase in our understanding of the phenomena.

[oldandtired]

The only insights will be related to what insights we get about the programming we are doing with machines. It is an entirely different order to providing insights into even the simplest of organic neurological systems.

Computer simulations can give insight into simple systems like water flows, etc. Simulating more complex systems like a single living cells or any system built on livings cells would require systems that would not really be worth building. It would be simpler to use cells directly.

[jamesrcole]

> going off of Arthur C Clarke's definition, life is magic. And we're trying to create something equally magical.

I assume you're referring to his "any sufficiently advanced technology is indistinguishable from magic"? If so, you're misrepresenting it, because he's clearly saying it's not magic, it just appears that way to the unadvanced. And there's a big difference between "appears to be" and "is".

[ramblerman]

But we are the unadvanced on this matter, so it's magic.

Also 'appears to be' != 'indistinguishable', the latter is far closer to 'is' imo.

[jamesrcole]

The quote is about something seeming a certain way when we don't understand it, not about it actually being that way. It is not saying the thing itself is literally indistinguishable from magic. Anyone who understood the thing (eg an advanced alien race who created a technology far beyond our current capabilities) would not see it as magic, and would know that it isn't magic.

[JunkDNA]

Taking your point a step further, I often wonder if mathematics is a local minimum for humans. It has been so damn effective in so many ways that we can’t imagine that there might be some other mechanism for solving hard problems. In cases where the mathematics gets really complex, I wonder if this is a hint that there’s some other way to represent the situation.

[theparanoid]

Computational processes have picked up where mathematics stops.

[hnuser1234]

>The current industry approaches are great for extracting certain kinds of value out of large data sets, but in terms of producing a result that could even begin to be considered as interesting as life (i.e. AGI or "strong AI"), I believe we will have to rely on creating a system whose inner workings are too complex for us to understand.

Speak for yourself.

[dorgo]

> My gut feeling is that it is hubris to think that we are going to "figure out" intelligence with increasingly sophisticated mathematical models anytime soon.

We did it already. Compter understand language, translate it, react to it. They can recognize items on a picture. Is there a task left which can't be done by computers better and faster than by humans?

>Almost by definition, if we can analytically understand it, it's not going to be interesting enough.

I think current ML is magic. I understand the math behind it. But still, I'm amazed every time when the training is over and it actually works like intended. Everything which is big enough is more than the sum of it's parts.

[flavio81]

>I'm amazed every time when the training is over and it actually works like intended. Everything which is big enough is more than the sum of it's parts.

What about when it doesn't work as intended and fails ridiculously, even though it usually works perfectly well?

http://www.labsix.org/physical-objects-that-fool-neural-nets...

[photojosh]

Humans do the same thing; not working as intended some of the time. It's just that the failure modes for ML are different, and so we see them as ridiculous.

[boomlinde]

Well, if the result of the different failure modes means that a machine can't tell the difference between what obviously resembles a turtle and a rifle, or a cat and guacamole, then that's something that anyone who watched the video is better at. You can call it a different failure mode, but these are things no human would misclassify unless seriously ill, and being able to classify simple objects is important to our day to day lives.

Imagine some sort of Robocop deciding to neutralize someone for holding a turtle toy.

[tree_of_item]

> Is there a task left which can't be done by computers better and faster than by humans?

Are you serious? You think we're done?

[oldandtired]

Probably better if you gave a task example like being a mother or a father or a grandparent or an uncle or an aunt or a mentor or a friend or anything to do with human interaction.

[dorgo]

There is no market for fathers and uncles. Nobody wants a roboter as father. Mentor of friend sounds more interesting though.

[hueving]

Yep, we're definitely done: http://rocknrollnerd.github.io/ml/2015/05/27/leopard-sofa.ht...

[dorgo]

Show this leopard sofa to 100 humans for 1 second each and see how many will make the same mistake. We are 99% there and you point to the 1% to prove how bad we failed. Sure, the error rate will go down even further in the next years. But what we have is more than good enough to be used in commercial products. It's not like we are trying to win a contest man vs. machine (which we will or already have).

[goatlover]

> Is there a task left which can't be done by computers better and faster than by humans?

All the tasks humans still earn money doing. And given that we're nowhere near full automation, I'd say it's quite a few tasks.

[dorgo]

Like the collector in the supermarket who scans the products and takes my cash? Surely it must be impossible to automate such a complex task.

Industry/Economy lacks behind state-of-the-art technology by decades.

[goatlover]

That's a poor example, since self-checkout scanners have existed for a while now. But notice how they aren't used exclusively. The bigger orders still require the manned scanners, and the self-checkout always has someone on duty.

A better example is plumbing. How would you go about automating a human plumber who handles all sorts of piping and crawl spaces in a large variety of settings?

[jononor]

Self-checkout is not automated, it is just the customer who does the work instead of an employee.

[dorgo]

It was an example of a job which can be automated (and as you pointed out, is already automated), but which is still dominated by human workers. You suggested that all the jobs done by humans can't be automated. My response is: most (or at least some) can, but aren't.

Sure, plumbing is a much more challenging example. But it is an economic problem. The cost to automate plumbing is much higher than the utility of it.

[jcora]

You should study ML a bit more, to see just how wrong you are about it

[dorgo]

I said two things. Which am I wrong about?

[jcora]

1. We haven't 'figured out' intelligence, far from it, we don't even know what we don't know.

Your comment sounds like Lord Kelvin proclaiming that physics is "over" a couple years before people figured out there were huge holes in the theory which eventually led to quantum mechanics. Our understanding of intelligence is probably less complete than our understanding of physics _back then_.

2. ML is really underwhelming if you measure it against actually intelligent behavior. Figuring out cool regression mechanisms is neat and all, but that's what it is, and it has nothing to do with intelligence in the general sense, much like expert systems had nothing to do with actual domain knowledge, they were just one of the most primitive models, low-hanging fruit that we could exploit.

[ramblerman]

We definitely have not 'figured' out intelligence