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Machine Learning’s ‘Amazing’ Ability to Predict Chaos
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.

After training itself on data from the past evolution of the Kuramoto-Sivashinsky equation, the researchers’ reservoir computer could then closely predict how the flamelike system would continue to evolve out to eight “Lyapunov times” into the future, eight times further ahead than previous methods allowed, loosely speaking. The Lyapunov time represents how long it takes for two almost-identical states of a chaotic system to exponentially diverge. As such, it typically sets the horizon of predictability.

“This is really very good,” Holger Kantz, a chaos theorist at the Max Planck Institute for the Physics of Complex Systems in Dresden, Germany, said of the eight-Lyapunov-time prediction. “The machine-learning technique is almost as good as knowing the truth, so to say.”

AI  MachineLearning  AlgoCulture  nct  ncpin  Tech  ChaosTheory  Science 
1 hour ago by walt74
This algorithm automatically spots “face swaps” in videos
Andreas Rossler at the Technical University of Munich in Germany and colleagues […] developed a deep-learning system that can automatically spot face-swap videos. The new technique could help identify forged videos as they are posted to the web.

But the work also has sting in the tail. The same deep-learning technique that can spot face-swap videos can also be used to improve the quality of face swaps in the first place—and that could make them harder to detect.

The new technique relies on a deep-learning algorithm that Rossler and co have trained to spot face swaps. These algorithms can only learn from huge annotated data sets of good examples, which simply have not existed until now.

So the team began by creating a large data set of face-swap videos and their originals. They use two types of face swaps that can be easily made using software called Face2Face. (This software was created by some members of this team.)

The first type of face swap superimposes one person’s face on another’s body so that it takes on their expressions. The second takes the expressions from one face and modifies a second face to show them.

The team have done this with over 1,000 videos, creating a database of about half a million images in which the faces have been manipulated with state-of-the-art face-editing software. They called this the FaceForensics database.

ncpin  nct  Tech  AlgoCulture  Face2Face  Facetracking  ComputerVision  AI 
1 hour ago by walt74
Silicon Valley’s Sixty-Year Love Affair with the Word “Tool”
Tool talk encodes an entire attitude to politics—namely, a rejection of politics in favor of tinkering. In the sixties, Brand and the “Whole Earth Catalog” presented tools as an alternative to activism. Unlike his contemporaries in the antiwar, civil-rights, and women’s movements, Brand was not interested in gender, race, class, or imperialism. The transformations that he sought were personal, not political. In defining the purpose of the “Catalog,” he wrote, “a realm of intimate, personal power is developing—power of the individual to conduct his own education, find his own inspiration, shape his own environment, and share his adventure with whoever is interested.” Like Zuckerberg, Brand saw tools as a neutral means to engage any and every user. “Whole Earth eschewed politics and pushed grassroots direct power—tools and skills,” he later wrote. If people got good enough tools to build the communities they wanted, politics would take care of itself.
tech  language 
4 hours ago by elrob
VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop
We present a new neural text to speech (TTS) method that is able to transform text to speech in voices that are sampled in the wild. Unlike other systems, our solution is able to deal with unconstrained voice samples and without requiring aligned phonemes or linguistic features. The network architecture is simpler than those in the existing literature and is based on a novel shifting buffer working memory. The same buffer is used for estimating the attention, computing the output audio, and for updating the buffer itself. The input sentence is encoded using a context-free lookup table that contains one entry per character or phoneme. The speakers are similarly represented by a short vector that can also be fitted to new identities, even with only a few samples. Variability in the generated speech is achieved by priming the buffer prior to generating the audio. Experimental results on several datasets demonstrate convincing capabilities, making TTS accessible to a wider range of applications. In order to promote reproducibility, we release our source code and models.


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Audio  AI  Tech  Papers  ncpin  ncp  Sounds 
5 hours ago by walt74

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