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Machine Learning’s ‘Amazing’ Ability to Predict Chaos

quantamagazine.org

51–60 of 77 posts

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

#51
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.

Could one be, instead, appreciative of and grateful to the considered, generous decision of that individual? how do you think he would respond to someone expressing one kind of feeling or the other?

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

#53
post #41

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?

Please don't put opinions in other people's mouths. Not everyone believes that extreme levels of wealth controlled by one individual is good.

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

#54
post #47

Earlier quoted context omitted.

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?

Better would be the considered preferences of an educated numerated mobs.

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

#55
post #51

Earlier quoted context omitted.

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

Could one be, instead, appreciative of and grateful to the considered, generous decision of that individual? how do you think he would respond to someone expressing one kind of feeling or the other?

I assume that he became a multibillionaire in part by not troubling himself with the opinions of nobodies.

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

#56

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 sounds like the next step, but even if weather were reducible to machine-discoverable patterns, you first must face the need to collect an immense amount of high-resolution condition data from around the globe on an ongoing basis.

There has been some success using Ensemble Kalman Filters (EnKF) to predict hurricanes [1]. I think that these filters sit somewhere between machine learning and deterministic models. The filters are based on a semi-realistic mathematical model but are updated with statistics every few hours so that they can improve their predictions.

As you said, going full on machine learning "sounds like the next step". However, I would disagree that the ideas at the end of the article are unrealistic. If the new technique can beat the Kalman filters it is already useful.

[1] http://hfip.psu.edu/realtime/AL2016/forecast_track.html

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

#57

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,…

Why did they choose such a specific task to make a general statement? Wouldn't it make more sense to say, predict the real world complex motion of a double pendulum?

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

#58

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,…

What's a good middle ground?

A "chaotic system" that is (a) real (b) a system and (c) condition data is a crossable hurdle. Ideas?

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

#59

I want to know how this does with traffic. Pretty remarkable.

I'd like to apply it to the stock market.

Ergodicity becomes a problem here. The nonlinear systems typically studied by complexity and chaos theorists have strong fixed rules that do not change in time. Markets have systematic and structural changes which can make prior observations completely irrelevant.

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

#60

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,…

I wouldn't say that natural behavior is non-algorithmic. Its more large scale hidden information algorithmic.
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