61–70 of 242 posts
the music generation demo isn't really impressive to me. the outputs were pretty random.
This is funny. I did my master thesis back in 2003 using "genetic" algorithms. Maybe I should try to get funding for an AI startup
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.…
http://users.sussex.ac.uk/~lionelb/downloads/EASy/publicatio...
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…
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.
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 agree that when there's a fast, perfect solution, it doesn't make sense to use genetic algorithms. But when finding solutions to a non-general problem (optimize CNC tooling to produce a list of orders, each of which has a series of operations that require a certain amount of time, on certain machines, and require being moved from machine to machine, such that you produce the most on-time orders for high-priority clients), genetic algorithms can work very well.
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.
Thank you, saved it for later. Do you have any other links to offer?
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/userspage/
Risto Miikkulainen's Evolving Deep Neural Networks paper: https://arxiv.org/abs/1703.00548
Ken Stanley & team's work at Uber, with links to some recent papers: https://eng.uber.com/deep-neuroevolution/
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…
That is the first time I have heard that claim, and since we have a large body of knowledge describing how evolution works (that sampo description is one the clearest I've seen) and how it can be optimized, I imagine you are talking about some other problem.
Is it about predicting the causes of some learned trait? Is there some interesting research on that?
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…
So I was surprised to see them make a return about a decade later. Hopefully there is a little more rigor this time around.
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…
> no one could really explain why exactly they worked That is the first time I have heard that claim, and since we have a large body of knowledge describing how evolution works (that sampo description is one the clearest I've seen) and how it can be optimized, I imagine you are talking about some other problem. Is it about predicting the causes of some learned trait? Is there some interesting research on that?