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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 algorithm

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, 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. I'm guessing our current mesh of satellites and weather stations, as impressive as it is, comes nowhere close to enough raw data to sufficiently feed the beast in this case. And even that assumes that we _know about_ and can measure all of the factors that go into weather patterns. I sincerely doubt that's the case.

IAmEveryone 9y ago
(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.
- heavenlyblue 9y ago
  Well, but then this is just a fancy name for combinatorial optimisation.
contravariant 9y ago
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?
- joe_the_user 9y ago
  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 https://en.wikipedia.org/wiki/Horseshoe_map
  - evrydayhustling 9y ago
    This interpretation sounds right to me. I found it cool that the article characterizes success in terms of prediction accuracy out to N "Lyapunov times", a concept that is supposed to incorporate the unpredictability of the system. So perhaps discovering these regularities proves that in this implementation, the true Lyapunov time is longer.
- isthatart 9y ago
  I believe (but I might be wrong) that are compared the results of two different methods: (a) "algorithmic" where solution(t+1) = algo(solution(t)) and errors accumulate with time steps, vs. (b) based on sufficiently many available pairs (initial_1, solution_1), ...,(initial_N,solution_N), each solution_i a function of time t \in [0,T], a NN (or another gadget) builds a function ALGO(initial)=solution so that the errors in time of solution(t) wrt real_solution(t) are uniform, where real_solution is the real solution for the initial condition initial.
chiefalchemist 9y ago
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.
hnuser1234 9y ago
Check this out: https://www.wunderground.com/wundermap?lat=40&lon=-105 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...
papaf 9y ago
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 http://hfip.psu.edu/realtime/AL2016/forecast_track.html
- aeorgnoieang 9y ago
  > machine learning and deterministic models This is a confusing distinction. Aren't lots of machine learning algorithms deterministic? Sure, some include stochastic elements or components, but even those can be deterministic, e.g. by reusing the same seed for the random number generators.
k_lander 9y ago
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?
dalbasal 9y ago
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?
guardian5x 9y ago
I wouldn't say that natural behavior is non-algorithmic. Its more large scale hidden information algorithmic.
- posterboy 9y ago
  I agree but the point was to derogitate the computer simulation, which is literally algorithmic.

IAmEveryone 9y ago

(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.

heavenlyblue 9y ago
Well, but then this is just a fancy name for combinatorial optimisation.

contravariant 9y ago

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?

joe_the_user 9y ago
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 https://en.wikipedia.org/wiki/Horseshoe_map
- evrydayhustling 9y ago
  This interpretation sounds right to me. I found it cool that the article characterizes success in terms of prediction accuracy out to N "Lyapunov times", a concept that is supposed to incorporate the unpredictability of the system. So perhaps discovering these regularities proves that in this implementation, the true Lyapunov time is longer.
isthatart 9y ago
I believe (but I might be wrong) that are compared the results of two different methods: (a) "algorithmic" where solution(t+1) = algo(solution(t)) and errors accumulate with time steps, vs. (b) based on sufficiently many available pairs (initial_1, solution_1), ...,(initial_N,solution_N), each solution_i a function of time t \in [0,T], a NN (or another gadget) builds a function ALGO(initial)=solution so that the errors in time of solution(t) wrt real_solution(t) are uniform, where real_solution is the real solution for the initial condition initial.

chiefalchemist 9y ago

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.

hnuser1234 9y ago

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

papaf 9y ago

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 http://hfip.psu.edu/realtime/AL2016/forecast_track.html

aeorgnoieang 9y ago
> machine learning and deterministic models This is a confusing distinction. Aren't lots of machine learning algorithms deterministic? Sure, some include stochastic elements or components, but even those can be deterministic, e.g. by reusing the same seed for the random number generators.

k_lander 9y ago

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?

dalbasal 9y ago

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?

guardian5x 9y ago

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

posterboy 9y ago
I agree but the point was to derogitate the computer simulation, which is literally algorithmic.