**machine_learning**13089

You probably don't need AI/ML. You can make do with well written SQL scripts

6 hours ago by dmartinez

From https://news.ycombinator.com/item?id=16898827:

What ends up happening is effectively the Data Science engagement becomes 90% data cleaning, a handful of SQL statements that should have existed beforehand but never did because the data infrastructure wasn't there, and possibly a veneer of ML/AI just to say it was used. Clients come out happy (sometimes), despite overpaying for what was a much more basic engagement than they think it was, and they go on preaching to their business exec friends the virtue of ML/AI and the cycle continues.

sql
machine_learning
What ends up happening is effectively the Data Science engagement becomes 90% data cleaning, a handful of SQL statements that should have existed beforehand but never did because the data infrastructure wasn't there, and possibly a veneer of ML/AI just to say it was used. Clients come out happy (sometimes), despite overpaying for what was a much more basic engagement than they think it was, and they go on preaching to their business exec friends the virtue of ML/AI and the cycle continues.

6 hours ago by dmartinez

Machine Learning’s ‘Amazing’ Ability to Predict Chaos | Quanta Magazine

11 hours ago by gmisra

In new computer experiments, artificial-intelligence algorithms can tell the future of chaotic systems.

articles
complex-systems
machine_learning
weather
predictions
11 hours ago by gmisra

For years, this popular test measured anyone’s racial bias. But it might not work after all. - Vox

yesterday by rrraul

But here’s the thing: It turns out the IAT might not tell individuals much about their individual bias. According to a growing body of research and the researchers who created the test and maintain it at the Project Implicit website, the IAT is not good for predicting individual biases based on just one test. It requires a collection — an aggregate — of tests before it can really make any sort of conclusions.

racism
politics
postmodernism
machine_learning
yesterday by rrraul

Phys. Rev. Lett. 120, 024102 (2018) - Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach

2 days ago by rvenkat

We demonstrate the effectiveness of using machine learning for model-free prediction of spatiotemporally chaotic systems of arbitrarily large spatial extent and attractor dimension purely from observations of the system’s past evolution. We present a parallel scheme with an example implementation based on the reservoir computing paradigm and demonstrate the scalability of our scheme using the Kuramoto-Sivashinsky equation as an example of a spatiotemporally chaotic system.

-- first, deep learning for detecting phase transitions; now, reservoir computing for predictions of spatiotemporal chaos. Did the physicists of the yore miss something or there is something not right with these works?

chaos
nonlinear_dynamics
prediction
machine_learning
-- first, deep learning for detecting phase transitions; now, reservoir computing for predictions of spatiotemporal chaos. Did the physicists of the yore miss something or there is something not right with these works?

2 days ago by rvenkat

[1710.07313] Using Machine Learning to Replicate Chaotic Attractors and Calculate Lyapunov Exponents from Data

2 days ago by rvenkat

We use recent advances in the machine learning area known as 'reservoir computing' to formulate a method for model-free estimation from data of the Lyapunov exponents of a chaotic process. The technique uses a limited time series of measurements as input to a high-dimensional dynamical system called a 'reservoir'. After the reservoir's response to the data is recorded, linear regression is used to learn a large set of parameters, called the 'output weights'. The learned output weights are then used to form a modified autonomous reservoir designed to be capable of producing arbitrarily long time series whose ergodic properties approximate those of the input signal. When successful, we say that the autonomous reservoir reproduces the attractor's 'climate'. Since the reservoir equations and output weights are known, we can compute derivatives needed to determine the Lyapunov exponents of the autonomous reservoir, which we then use as estimates of the Lyapunov exponents for the original input generating system. We illustrate the effectiveness of our technique with two examples, the Lorenz system, and the Kuramoto-Sivashinsky (KS) equation. In particular, we use the Lorenz system to show that achieving climate reproduction may require tuning of the reservoir parameters. For the case of the KS equation, we note that as the system's spatial size is increased, the number of Lyapunov exponents increases, thus yielding a challenging test of our method, which we find the method successfully passes.

chaos
nonlinear_dynamics
prediction
machine_learning
2 days ago by rvenkat

Kensho

2 days ago by amy

Kensho deploys scalable machine learning and analytics systems across the most critical government and commercial institutions in the world to solve some of the hardest analytical problems of our time.

analytics
machine_learning
2 days ago by amy

Machine Learning’s ‘Amazing’ Ability to Predict Chaos | Quanta Magazine

2 days ago by amy

In new computer experiments, artificial-intelligence algorithms can tell the future of chaotic systems.

..Researchers have used machine learning to predict the chaotic evolution of a model flame front.

machine_learning
physics
..Researchers have used machine learning to predict the chaotic evolution of a model flame front.

2 days ago by amy

Machine Learning’s ‘Amazing’ Ability to Predict Chaos | Quanta Magazine

3 days ago by shannon_mattern

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
“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

3 days ago by shannon_mattern

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