It would be fascinating to apply this technique to card shuffling. Shuffling techniques are not even close to random...casinos hope for chaos at best. It would be interesting to see if a machine learning algorithm could come up with an approach that could be carried out by humans to, for example, predict whether or not the next shoe of a hand-shuffled blackjack game is going to have a positive or negative expectation for the player.
Machine Learning’s ‘Amazing’ Ability to Predict Chaos
31–40 of 77 posts
Re: Machine Learning’s ‘Amazing’ Ability to Predict Chaos
#32Can we have more publishing like Quanta magazine? It's just perfect in terms of reporting from the frontier, being easy to unsetstand, and not talking down to you.
It's funded by Jim Simons, of the hedge fund Renaissance Technologies. A mathematician with money, trying to promote mathematics. Quanta magazine is a fantastic service. But the other Renaissance magnate, Bob Mercer, as his own media hobby funded Breitbart News.
I was going to comment in here about how the Simons Foundation runs Quanta. Glad other people have mentioned it. Simons is an amazing guy.
Re: Machine Learning’s ‘Amazing’ Ability to Predict Chaos
#33Can we have more publishing like Quanta magazine? It's just perfect in terms of reporting from the frontier, being easy to unsetstand, and not talking down to you.
It's funded by Jim Simons, of the hedge fund Renaissance Technologies. A mathematician with money, trying to promote mathematics. Quanta magazine is a fantastic service. But the other Renaissance magnate, Bob Mercer, as his own media hobby funded Breitbart News.
I used to be part of a collaboration at a particle accelerator called RHIC. It was the most powerful accelerator of it's type before the LHC was built, and a lot of really great research was done there. One year, the budget got slashed and there just wasn't enough funding for it to operate. Simons donated a massive amount of his own money, and organized fundraising from other sources, both of which played central roles in the experiments being able to continue on with their research. There's now a road inside of Brookhaven National Lab called "Renaissance" in honor of him to celebrate his contributions. It's actually pretty sad that it was necessary for something like that to happen, but it certainly speaks volumes about Simons.
Re: Machine Learning’s ‘Amazing’ Ability to Predict Chaos
#34Isn't deterministic-chaos an oxymoron?
Not only can chaos be exhibited in fully deterministic systems, it can be exhibited in absurdly simple systems. The logistic map is an incredibly simple iterated function that exhibits highly chaotic behavior.
It remains one of most mysterious things in mathematics, as far as I can tell. Studying it has consumed a lot of very smart people's careers.
Re: Machine Learning’s ‘Amazing’ Ability to Predict Chaos
#35Isn't deterministic-chaos an oxymoron?
The double pendulum is a nice example https://en.wikipedia.org/wiki/Double_pendulum
Re: Machine Learning’s ‘Amazing’ Ability to Predict Chaos
#36Novel paper. But it seems like a lot of the excitement is because of the fusion of two buzz-words, one from the 80s and 90s and another from the 2010s. So, the output is a bunch of stuff coming out of a kinda simple dynamical system. Chaotic for sure, but still simple. Deep learning (and more generally, recurrent neural nets, LSTMs, and derivatives thereof) has been shown capable of learning much more complex nonline…
I'm not sure if human perception is a chaotic system. Chaos is defined as "small change in input -> large change in output". Perception is actually the opposite, with small input changes (changes in light, different angles,...) leading to fundamentally unchanged perception ("It's a tree").
You need also ergodicity and mixing, as jessriedel states, meaning respectively that you explore every area of your phase space with equal probability, and that two trajectories that begin arbtrarily close together do in fact diverge instead of sticking together, at infinite time
Re: Machine Learning’s ‘Amazing’ Ability to Predict Chaos
#37Earlier quoted context omitted.
That's like saying, "it was only a matter of time someone figured out time traveling", when it happens. The whole point of the investigation was about chaotic systems, regardless of whether they're complex or not.
Nope, given our current theories time travel is practically impossible. On the other hand we know that chaotic systems are functions and that matrices can approximate any function.
> matrices can approximate any function
I'm not sure what point you're trying to make. Matrices only perform linear transformations, so matrices only approximate functions linearly, which in general, is a terrible approximation globally.
Re: Machine Learning’s ‘Amazing’ Ability to Predict Chaos
#38Don't be too seduced by the enticing ideas at the end of the article. The disconnect here is that success in learning how to predict the results of an algorithmic simulation is not really indicative of how it would perform with the decidedly non-algorithmic natural behavior of weather or earthquakes, phenomena which don't operate in a closed system with predefined limits and parameters. It sounds like the next step,…
It's actually kind of unclear what it means to predict results from an algorithmic simulation. Surely that's just trying to use a different method to simulate the process?
I also don't see them mentioning anything about noise, so what exactly are they trying to achieve? Surely perfect prediction should already be possible, otherwise what are they comparing the output to?
Re: Machine Learning’s ‘Amazing’ Ability to Predict Chaos
#39Earlier quoted context omitted.
That's like saying, "it was only a matter of time someone figured out time traveling", when it happens. The whole point of the investigation was about chaotic systems, regardless of whether they're complex or not.
Nope, given our current theories time travel is practically impossible. On the other hand we know that chaotic systems are functions and that matrices can approximate any function.
ITYMeant "A multilayer perceptron is a universal function approximator.
Re: Machine Learning’s ‘Amazing’ Ability to Predict Chaos
#40Don't be too seduced by the enticing ideas at the end of the article. The disconnect here is that success in learning how to predict the results of an algorithmic simulation is not really indicative of how it would perform with the decidedly non-algorithmic natural behavior of weather or earthquakes, phenomena which don't operate in a closed system with predefined limits and parameters. It sounds like the next step,…
I think you're correct about collecting weather data. That said, the prediction doesn't have to be perfect - like race horses or the lottery - in the short term it just needs to be relatively close. It all depends on how far out your long term needs to be.