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Machine Learning’s ‘Amazing’ Ability to Predict Chaos | Quanta Magazine
In a series of results reported in the journals Physical Review Letters and Chaos, scientists have used machine learning — the same computational technique behind recent successes in artificial intelligence — to predict the future evolution of chaotic systems out to stunningly distant horizons. The approach is being lauded by outside experts as groundbreaking and likely to find wide application.

“I find it really amazing how far into the future they predict” a system’s chaotic evolution, said Herbert Jaeger, a professor of computational science at Jacobs University in Bremen, Germany.

The findings come from veteran chaos theorist Edward Ott and four collaborators at the University of Maryland. They employed a machine-learning algorithm called reservoir computing to “learn” the dynamics of an archetypal chaotic system called the Kuramoto-Sivashinsky equation. The evolving solution to this equation behaves like a flame front, flickering as it advances through a combustible medium. The equation also describes drift waves in plasmas and other phenomena, and serves as “a test bed for studying turbulence and spatiotemporal chaos,” said Jaideep Pathak, Ott’s graduate student and the lead author of the new papers....

The algorithm knows nothing about the Kuramoto-Sivashinsky equation itself; it only sees data recorded about the evolving solution to the equation. This makes the machine-learning approach powerful; in many cases, the equations describing a chaotic system aren’t known, crippling dynamicists’ efforts to model and predict them. Ott and company’s results suggest you don’t need the equations — only data....

“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.

Besides weather forecasting, experts say the machine-learning technique could help with monitoring cardiac arrhythmias for signs of impending heart attacks and monitoring neuronal firing patterns in the brain for signs of neuron spikes. More speculatively, it might also help with predicting rogue waves, which endanger ships, and possibly even earthquakes.

Ott particularly hopes the new tools will prove useful for giving advance warning of solar storms
machine_learning  complexity  chaos  prediction 
7 hours ago by shannon_mattern
The Artificial Intelligentsia
My life and times as an acolyte in the mystical cult of predictive data.
data  monitoring  prediction  context  fraud  intelligence  analysis  mentions  web  harvesting  collection 
yesterday by markhgn
Steve Blank Why Entrepreneurs Start Companies Rather Than Join Them
If you asked me why I gravitated to startups rather than work in a large company I would have answered at various times: “I want to be my own boss.” “I love risk.” “I want flexible work hours.” “I want to work on tough problems that matter.” “I have a vision and want to see…
prediction  asymmetric_information  social_science  entrepreneurship  signaling  business  inefficiency  structure 
6 days ago by jbkcc
Congress wants to 'inflict pain’ on Mark Zuckerberg. Is he ready for it? | Technology | The Guardian
Anticipating Zuckerberg’s testimony to Congress. Selling members’ personal data to advertisers is the key money engine of @facebook. Moreover, the data enables prediction of highly private things about you
facebook  zuckerberg  congress  analysis  prediction  Q2  2018  news  abuse  data  russia  2016  election  scandal 
9 days ago by csrollyson
Welcome to Facebook's Censorship and Surveillance Platform - Global Guerrillas
How #Facebook is becoming a global #surveillance, #censor & control platform, goaded by governments trying to combat terrorism
facebook  google  global  surveillance  platform  prediction  analysis  terrorism  government  face  recognition  censorship  artificial  intelligence  identification 
13 days ago by csrollyson

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