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Machine Learning’s ‘Amazing’ Ability to Predict Chaos

quantamagazine.org

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Re: Machine Learning’s ‘Amazing’ Ability to Predict Chaos

#21

Post by jessriedel »

IAmEveryone wrote: post #14
shas3 wrote: post #12

Novel 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").

First, sensitivity to initial conditions is a necessary but not sufficient property for chaos. Some sort of folding/mixing is also necessary, which can be gauranteed by a bounded state space.

Second, it's definitely true that information processing systems, if they are to be reliable enough to be useful, are not going to be chaotic throughout their state space. They need to return the same output given a certain input. But I'd imagine there are also at least a few noisy regions.

Re: Machine Learning’s ‘Amazing’ Ability to Predict Chaos

#22

Post by heavenlyblue »

IAmEveryone wrote: post #17
skywhopper wrote: post #6

Don'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,…

(Part of) the idea seems to be synthesizing missing data from later observations of the known data, i. e. "to arrive at this state (A_t,C_t,E_t), the initial state must have been...(A_t-1,B_t-1,C_t-1,E_t-1)..." I have my doubts that this can just overcome the fundamental problem of chaos, but it doesn't sound impossible.

Well, but then this is just a fancy name for combinatorial optimisation.

Re: Machine Learning’s ‘Amazing’ Ability to Predict Chaos

#23

Post by wybiral »

greeneggs wrote: post #19
jimmytidey wrote: post #8

Can 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.

> But the other Renaissance magnate, Bob Mercer, as his own media hobby funded Breitbart News.

And was one of the major sources of funds behind Cambridge Analytica.

Re: Machine Learning’s ‘Amazing’ Ability to Predict Chaos

#25

Post by perl4ever »

IAmEveryone wrote: post #14
shas3 wrote: post #12

Novel 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").

I always thought that a perception like "it's a tree" that remains stable could possibly be an attractor in a chaotic system. If you look at the trajectory of a particle around an attractor, its position is very unpredictable after a while, but which attractor it is orbiting is not so random or unstable. Thinking of the visual of the Lorenz attractor.

Re: Machine Learning’s ‘Amazing’ Ability to Predict Chaos

#26

Post by internetman55 »

greeneggs wrote: post #19
jimmytidey wrote: post #8

Can 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.

Yeah it's kind of sad to me. Quanta is amazing but I'm not sure if I could reasonably expect to exist without Simons or someone like him privately funding it

Re: Machine Learning’s ‘Amazing’ Ability to Predict Chaos

#27

Post by fmihaila »

nolite wrote: post #4
dangerlibrary wrote: post #2

I was really hoping this was about weather forecasting.

It can be!

From the article:

“This paper suggests that one day we might be able perhaps to predict weather by machine-learning algorithms and not by sophisticated models of the atmosphere,” Kantz said.

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