111–120 of 242 posts
Don't want to jump into the fight between DL and Genetic algorithms, but can somebody explain their experience with the music paper, demo, and work? I personally am not impressed...Are you?
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Genetic algorithms yes but genetic programming is actually able to invent new things. Possibly optimizing the set of weight of a neural network to solve a given problem is more similar to genetic programming, if you see the NN as a computational unit.
Genetic algorithms 'invent new things' by doing exactly the same thing as other optimization methods - tweaking the values of particular parameters. The only difference in is how they do so.
Genetic algorithm: Shift parameters in a batch of random ways, evaluate using fitness function, keep the best.
There is nothing more fundamentally 'creative' about one than the other; these are both just attacks on the problem "here is this function, find parameter settings that get higher scores in it".
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Optimizing genes leading to an intelligent neural network is what biology did basically. If your optimization problem changes the behavior of something able to compute in order to maximize the ability to solve a task, then that's inventing things IMHO.
Respectfully, We've had "learning-to-learn" algorithms for a few years now. Including LSTMs that can be learn the gradients for other LSTMs, and deep-RL algorithms that can optimize neural networks. Its hard to say its inventing new things... there is still a clear goal - a loss - and we are optimizing it; poorly in the case of genetic algorithms. I would say genetic methods are simply poorly described reinforcement…
Also note that in the 90s things produced by genetic programming went patented, because they were novel algorithms.
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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…
> 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…
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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…
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 th…
We understand many small and big things about life, yes.
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…
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…
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> 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…
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.
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.
If only wishing made it so... Evolutionary approaches have always had one big feature in their favour: they are far more fun to work with. They produce all these fascinating oddities, like the one that learnt to outsmart it’s opponents at infinite tic-tac-toe by playing coordinates 10 2312 and 4 7875 and watching them run out of memory trying to build a data structure for the board. They are also far easier to combin…
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 formed a similar impression in my PhD research.
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…
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…
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.