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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

#41

Post by mkempe »

internetman55 wrote: post #26
greeneggs wrote: post #19

Earlier quoted context omitted.

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

Why do you feel sad about the fact that an individual who values science is "privately funding" a great publication? surely you don't object to private wealth and/or activity?

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

#42

Post by diehunde »

heavenlyblue wrote: post #20
diehunde wrote: post #15

Earlier 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.

I was pretty sure someone was going to focus on the example I gave instead of the idea :), very predictable. Chaotic systems are not just functions, that's why there's an entire discipline that studies them. There are many ways to approximate functions that have been developed during a long time and all have had trouble approximating chaotic functions.

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

#43

Post by scottie_m »

heavenlyblue wrote: post #20
diehunde wrote: post #15

Earlier 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.

If you’re going to be that pedantic, I’ll see your pedantry and raise you. Time travel is not only possible, but routine, just in the forward direction only. It’s only travel into the past which is probably impossible, and to be even more pedantic and abstract, impossible only in our observable spacetime geometry.

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

#44

Post by ekun »

chestervonwinch wrote: post #37
heavenlyblue wrote: post #20

Earlier quoted context omitted.

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.

Especially, if you have complex systems where discretization and linearization aren't computationally achievable and/or numerically accurate ... like predicting global weather patterns or even very small experiments. I think about the phrase: All models are wrong; some models are useful.

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

#45

Post by curuinor »

downandout wrote: post #31

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…

You can card count on apps already. The repeated linear operator + nonlinearity of the ESN (the reservoir) could implement a card-counting memory but, you know, so could a card counting memory in a normal program.

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

#46

Post by intended »

mkempe wrote: post #41
internetman55 wrote: post #26

Earlier quoted context omitted.

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

Why do you feel sad about the fact that an individual who values science is "privately funding" a great publication? surely you don't object to private wealth and/or activity?

Being dependent on the whim of a wealthy individual is saddening.

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

#47

Post by mmirate »

intended wrote: post #46
mkempe wrote: post #41

Earlier quoted context omitted.

Why do you feel sad about the fact that an individual who values science is "privately funding" a great publication? surely you don't object to private wealth and/or activity?

Being dependent on the whim of a wealthy individual is saddening.

Would it be any less saddening to depend on the whim of an uneducated, innumerate and manipulable mob?

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

#48

Post by downandout »

curuinor wrote: post #45
downandout wrote: post #31

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…

You can card count on apps already. The repeated linear operator + nonlinearity of the ESN (the reservoir) could implement a card-counting memory but, you know, so could a card counting memory in a normal program.

Oh I wasn't talking about card counting. I was talking about being able to determine, knowing the order of the cards going into the shuffle, whether or not the next shoe (after the shuffle) would have an overall positive or negative expectation. The theoretical expectation accounting for every single possible order of cards is slightly negative. However, if a massive percentage of the possible orders that the cards can be in is eliminated, the expectation may either be much more positive or negative than the overall theoretical return.

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

#49

Post by joe_the_user »

contravariant wrote: post #38
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,…

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?

Yeah, they're predicting the outcome of a chaotic but deterministic process based on the output data given.

Surely they're discovering hidden (or obvious) regularities based specifically on the process being deterministic.

As a counter example, the Smale Horseshoe [1] is an example of a inherently unpredictable map - it's approximately equivalent to choosing a real number "at random" and attempting to "predict" each digit as you find it. Sure, if some implementation has chosen a number with repeating digits, you can "predict" those digits but this would have nothing to do with being an oracle for algorithm itself.

More broadly, I think it's pretty established that any realistic model of weather is kind of an extension of this concept - it depends unstably on it's initial conditions (small changes in initial conditions result in large changes in final result after some period).

[1] https://en.wikipedia.org/wiki/Horseshoe_map

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

#50

Post by hnuser1234 »

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,…

Check this out: https://www.wunderground.com/wundermap?lat=40&lon=-105

Just start breaking the problem down. We have modestly accurate ideas of air pressure features across most of the world. Not enough to predict every breeze, but enough to predict what days it'll be stormy in a given location about a week in advance, plus or minus a few days, and plus or minus a bit of severity. With continued training, the models can only get more accurate. Personal weather stations are a thing now, and only growing in numbers.

Combine that with radar and satellite data for an even more accurate picture.

Now, if only we could get a log of every (5min? 1min?) reading of every station...

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