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Evolution Is the New Deep Learning

sentient.ai

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Re: Evolution Is the New Deep Learning

#131

Post by rpm13 »

peterstoziek wrote: post #2

As expected, the article seems to be a typical content marketing piece. If you're looking for real insights into evolutionary algorithms, specifically "neuroevolution", I highly recommend to read this article: https://www.oreilly.com/ideas/neuroevolution-a-different-kin... I enjoyed it much more than - what feels like - a quickly thrown together marketing piece with no real value for the reader.

Note that this blog post is not an article per se, but an overview of a research website (https://sentient.ai/sentient-labs/ea) built around five new research papers. The website offers demos that illustrate neuroevolution and evolutionary computation concepts at a much more concrete level than the papers can.

Re: Evolution Is the New Deep Learning

#132

Post by guest12268 »

vanderZwan wrote: post #38
mav3r1ck wrote: post #15

Having studied this extensively back when they were called Genetic Algorithms, I would like to offer a few insights. 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. It makes sense on the surface that "survival of the fittest" and doing something akin to multiple stochastic gradient descents would work, but no one has…

Regarding 3), do you know of any work on genetic programming as a method of doing research into evolvability itself? So basically, as a form of simulation? Tierra obviously counts, but I was thinking of more specific examples. Say, something like this paper, which showed that adding a tiny cost-function to a network spontaneously makes it more modular: [0] http://rspb.royalsocietypublishing.org/content/280/1755/2012.…

Yes there has been some very interesting recent work. In particular, how evolvability emerges and is harnessed in evolutionary computation. A few papers come to mind:

1. Evolvability is Inevitable: http://journals.plos.org/plosone/article?id=10.1371/journal....

2. Extinction Events can Accelerate Evolution (2015): http://journals.plos.org/plosone/article?id=10.1371/journal....

3. Evolvability Search: Directly selecting for evolvability in order to study and produce it (2016): http://www.evolvingai.org/mengistu-lehman-clune-2016-evolvab...

Re: Evolution Is the New Deep Learning

#133

Post by diyseguy »

mav3r1ck wrote: post #15

Having studied this extensively back when they were called Genetic Algorithms, I would like to offer a few insights. 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. It makes sense on the surface that "survival of the fittest" and doing something akin to multiple stochastic gradient descents would work, but no one has…

I don't understand why A.I. can't be explainable. Can't they just add logging every time it makes a decision and then trace through the trail of decisions to the final result?

Re: Evolution Is the New Deep Learning

#134

Post by Retra »

0xfeba wrote: post #94
resu_nimda wrote: post #81

Earlier quoted context omitted.

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 do…

> 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.

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.

Re: Evolution Is the New Deep Learning

#135

Post by cr0sh »

resu_nimda wrote: post #81
pas wrote: post #73

Earlier quoted context omitted.

> 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. Th…

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 do…

> 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.

Re: Evolution Is the New Deep Learning

#136

Post by flavio81 »

dorgo wrote: post #120
resu_nimda wrote: post #64

Earlier quoted context omitted.

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 cre…

> 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 i…

>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...

Re: Evolution Is the New Deep Learning

#137

Post by abhgh »

diyseguy wrote: post #133
mav3r1ck wrote: post #15

Having studied this extensively back when they were called Genetic Algorithms, I would like to offer a few insights. 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. It makes sense on the surface that "survival of the fittest" and doing something akin to multiple stochastic gradient descents would work, but no one has…

I don't understand why A.I. can't be explainable. Can't they just add logging every time it makes a decision and then trace through the trail of decisions to the final result?

The AI that is not explainable is not because it cannot log things, its because the semantic interpretation of what it can log is hard. Starting with the real world input (which we understand) a lot of algorithms progressively apply mathematical transformations till reaching the output. It is the real world "meanings"of these transformations, or what is eventually learned: the stack of these transformations - that is hard to grasp.

Re: Evolution Is the New Deep Learning

#138

Post by guest12268 »

dorgo wrote: post #125
sampo wrote: post #110

Earlier quoted context omitted.

Good notion, pointing the Kolmogorov complexity. Yeah. You have a function, so basically a long array of numbers, and you want to find the maximum. If the data in the array has some structure, like it's sampled from a sine wave or something, you can use some strategies to find the maximum. Like gradient descent, or binary search. Something. But if the array is filled with random numbers, looking at other arrays eleme…

>So there doesn't exist any more efficient strategies to find the maximum number, than linear or random search. For classic old school computers yes. I'm not so sure about quantum computers. Consider: https://en.wikipedia.org/wiki/Grover%27s_algorithm

I don't think Grover ("the other GA") helps us here. https://qbnets.wordpress.com/2010/01/06/grovers-algorithm-fo...

Re: Evolution Is the New Deep Learning

#139

Post by abhgh »

avilay wrote: post #116
evolutioner wrote: post #72

Earlier quoted context omitted.

Sentient employee here. I'll give an example on a problem for which we use evolutionary algorithms: website optimization. Say you want to try many various changes like the title of your page, the color of the background, the position of your buy button etc. We solve this problem by trying out random variations of these websites - like A/B testing with more candidates - and by crossing the best performing ones to crea…

What is the advantage of using evolutionary algos in this case over using something like Thompson Sampling or Contextual Multi-Armed Bandit? ( http://www.kdd.org/kdd2017/papers/view/an-efficient-bandit-a... )

I think, not much. This is an optimization problem to which you can apply any algorithm that "fits" --- no closed-form functional form, interactive feedback --- so bayesian optimization (with GPs), some form of RL, evolutionary algorithms et al.

Re: Evolution Is the New Deep Learning

#140

Post by hndamien »

cr0sh wrote: post #135
resu_nimda wrote: post #81

Earlier quoted context omitted.

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 do…

> 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.

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.
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