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Evolution Is the New Deep Learning

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Re: Evolution Is the New Deep Learning

#221

Post by yazr »

ofrancon wrote: post #67
vanderZwan wrote: post #4

Earlier quoted context omitted.

Thank you, saved it for later. Do you have any other links to offer?

A few links you can look at if you're interested in neuroevolution, from the same group of researchers: Ken Stanley and Risto Miikkulainen original NEAT (NeuroEvolution of Augmenting Topologies) paper: http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf Ken Stanley's novelty search page, and a link to his book, "Why Greatness Cannot Be Planned: The Myth of the Objective": http://eplex.cs.ucf.edu/noveltysearch/u…

Are these evolutionary techniques considered more or less sample-efficient (and or cpu-efficient) compared to DL with GD ??

Re: Evolution Is the New Deep Learning

#222

Post by breuleux »

Houshalter wrote: post #187
breuleux wrote: post #178

Earlier quoted context omitted.

Not exactly. The required bandwidth for the evolutionary strategy is actually very small: if every node knows each other's random seed, they can reconstruct the best model themselves, using the seed of whichever node declares the best result. There is no need to transfer any weights. Of course, this trick only works if it's cheap to compute the weights other nodes are using, which is not the case for SGD. So evolutio…

Well that would work for simple hillclimbing. In a full genetic algorithm, every member of the population is the product of thousands of previous individuals.

I mean, we were talking about simple hill climbing.

Still, even more complex systems still do not require particularly high bandwidth. You only need to broadcast the fitness of the individuals, and from there each node can independently recalculate and combine the best ones.

Re: Evolution Is the New Deep Learning

#223

Post by jamesrcole »

ramblerman wrote: post #212
jamesrcole wrote: post #181

Earlier quoted context omitted.

> 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".

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.

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.

Re: Evolution Is the New Deep Learning

#224

Post by make3 »

cshenton wrote: post #130
make3 wrote: post #52

it's just not. Evolution learns more sloly than reinforcement learning, which itself learns much more slowly than supervised deep learning. Evolution doesn't have gradiants, and needs a huge amount of samples to learn anything.

Actually, evolutionary strategies make use of either empirical or naturalised gradients on the search space.

I guess sexual reproduction is kind of like sharing gradients. Is that what you mean?

Re: Evolution Is the New Deep Learning

#225

Post by pas »

resource0x wrote: post #214
pas wrote: post #204

Earlier quoted context omitted.

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.

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

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.

Re: Evolution Is the New Deep Learning

#226

Post by [deleted] »

randomsearch wrote: post #203
ewjordan wrote: post #19

Earlier quoted context omitted.

Wait, I'm confused...you're saying genetic approaches fell out of favor because they're basically just stochastic gradient descent? Most of modern DL relies heavily on SGD at various points during training. My impression is that they fell out of favor precisely because don't actually use any gradients, and end up converging on good maxima slower than you could if you used the gradients from the net. Am I off base her…

GAs are not "basically just stochastic gradient descent." That's the confusion. Why did they fall out of favour? Fashion, perhaps a natural break in progress - hit a wall and couldn't get further. But there is plenty of GA research going on.

[deleted]

Re: Evolution Is the New Deep Learning

#227

Post by dorgo »

goatlover wrote: post #217
dorgo wrote: post #191

Earlier quoted context omitted.

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.

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?

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.

Re: Evolution Is the New Deep Learning

#228

Post by jononor »

goatlover wrote: post #217
dorgo wrote: post #191

Earlier quoted context omitted.

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.

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?

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

Re: Evolution Is the New Deep Learning

#229

Post by ozy »

padthai wrote: post #163
ozy wrote: post #117

Earlier quoted context omitted.

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.

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 n…

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.

Re: Evolution Is the New Deep Learning

#230

Post by vinn124 »

Houshalter wrote: post #151
inverse_pi wrote: post #103

Earlier quoted context omitted.

My thesis was on Generic Algorithm. I stopped and started working on Deep Learning mainly because like you said, GAs don't really have a strong mathematical foundation. Ironically, no one could really explain why CNNs work mathematically either. I've heard a lot of hand-wavy arguments about local search, local sensitivity, etc. However, no one could really prove anything meaningful. There are some papers around certa…

Why should we expect there to be any mathematical foundation to this stuff? It's quite possible to imagine an alternate universe where GAs and neural nets don't work. Because they have different datasets that don't fit the structure of NNs well. Or problems that happen to not be solvable by the search strategies of GAs. In fact we have many such problems in our own universe. I can give many examples of things NNs and…

> Why should we expect there to be any mathematical foundation to this stuff?

i would be surprised that "this stuff" would be exception to the unreasonable effectiveness of mathematics. mathematics underpins virtually every observed phenomenon, including theoretical physics, computer science, economics. in fact, the mathematical structure of any physical theory often points the way to further advances in that theory and even to empirical predictions.

to not expect that "this stuff" should not have any mathematical foundation is a fantastically naive view.

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