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Curious. How can AI/ML perform on a problem that is, as far as I understand, inherently chaotic / unpredictable ? It sounds like a fundamental contradiction to
by pyb 3y ago
Curious. How can AI/ML perform on a problem that is, as far as I understand, inherently chaotic / unpredictable ? It sounds like a fundamental contradiction to me.
- vosper 3y agoWeather isn’t fundamentally unpredictable. We predict weather with a fairly high degree of accuracy (for most practical uses), and the accuracy getting better all the time. https://scijinks.gov/forecast-reliability https://scijinks.gov/forecast-reliability
- sosodev 3y agoI'm kinda surprised that this government science website doesn't seem to link sources. I'd like to read the research to understand how they're measuring the accuracy.
- keule 3y agoIMO a chaotic system will not allow for long-term forecast, but if there is any type of pattern to recognize (and I would assume there are plenty), an AI/ML model should be able to create short-term prediction with high accuracy.
- pyb 3y agoNot an expert, but "Up to 10 days in advance" sounds like long-term to me ?
- joaogui1 3y agoI think 10 days is basically the normal term for weather, in that we can get decent predictions for that span using "classical"/non-ML methods.
- pyb 3y agoIDK, I wouldn't plan a hike in the mountains based on 10-day predictions.
- keule 3y agoTo be clear: With short-term I meant the mentioned 6 hours of the article. They use those 6 hours to create forecasts for up to 10 days. I would think that the initial predictors for a phenomenon (like a hurricane) are well inside that timespan. With long-term, I meant way beyond a 14-day window.
- kouru225 3y agoBut AI/ML models require good data and the issue with chaotic systems like weather is that we don’t have good enough data.
- joaogui1 3y agoThe issue with chaotic systems is not data, is that the error grows superlinearly with time, and since you always start with some kind of error (normally due to measurement limitations) this means that after a certain time horizon the error becomes to significant to trust the prediction. That hasn't a lot to do with data quality for ML models
- kouru225 3y agoThat’s an issue with data: If your initial conditions are wrong (Aka your data collection has any error or isn’t thorough enough) then you get a completely different result.
- nl 3y agoEvery measurement has inherent errors in it - and those errors are large if the task is to measure the location and velocity of every molecule in the atmosphere. You also need to measure the exact amount of solar radiation before it hits these molecules (which is impossible, so we assume this is constant depending on latitude and time) These errors compound (the butterfly effect) which is why we can't get perfect predictions. This is a limit inherent in physical systems because of physics, not really a data problem.
- kouru225 3y agoYes. Very accurate as long as you don’t need to predict the unpredictable. So it’s useless. Edit: I do see a benefit to the idea if you compare it to the Chaos Theorists “gaining intuition” about systems.
- pyb 3y agoIDK if it's useless, but it's counter-intuitive to me.
- crazygringo 3y agoBecause there are tons of parts of weather where chaos isn't the limiting factor currently. There are a limited number of weather stations producing measurements, and a limited "cell size" for being able to calculate forecasts quickly enough, and geographical factors that aren't perfectly accounted for in models. AI is able to help substantially with all of these -- from interpolation to computational complexity to geography effects.