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Wayne Radinsky
18,644 followers -
Software Design Engineer
Software Design Engineer

18,644 followers
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"How artificial intelligence could increase the risk of nuclear war." "It's not the killer robots of Hollywood blockbusters that we need to worry about; it's how computers might challenge the basic rules of nuclear deterrence and lead humans into making devastating decisions. That's the premise behind a new paper from RAND Corporation, How Might Artificial Intelligence Affect the Risk of Nuclear War?"

"To understand how intelligent computers could raise the risk of nuclear war, you have to understand a little about why the Cold War never went nuclear hot." "If both sides have weapons that can survive a first strike and hit back, then the situation is stable. Neither side will risk throwing that first punch. The situation gets more dangerous and uncertain if one side loses its ability to strike back or even just thinks it might lose that ability."

"Computers can already scan thousands of surveillance photos, looking for patterns that a human eye would never see. It doesn't take much imagination to envision a more advanced system taking in drone feeds, satellite data, and even social media posts to develop a complete picture of an adversary's weapons and defenses."
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A deep-learning system that can automatically spot face-swap videos has been developed. "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 team began by creating a large data set of face-swap videos and their originals."
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The future evolution of mathematically chaotic systems have been predicted by machine learning out to stunningly distant horizons. "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."

"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."

"The algorithm knows nothing about the Kuramoto-Sivashinsky equation itself; it only sees data recorded about the evolving solution to the equation."
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"Amazon has a top-secret plan to build home robots." Um... hate to break it to you, but it's not top secret if it's on the home page of Bloomberg.

So what will it do? My laundry? Set the table? Wash the dishes? Vacuum? Clean the bathtub? Organize my book collection? Hmm, no mention of any of those things. Wait, they do mention vacuuming -- to mention the fact that the Roomba already does that.

"Prototypes of the robots have advanced cameras and computer vision software and can navigate through homes like a self-driving car."
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"We recently published an article announcing five papers on deep neuroevolution, including the discovery that genetic algorithms can solve deep reinforcement learning problems as well as popular alternatives, such as deep Q-learning and policy gradients. That work follows on Salimans et al. 2017, which showed the same for evolution strategies (ES), another neuroevolution algorithm. We further described how ES can be improved by adding exploration in the form of a pressure for agents to be novel, and how ES relates to gradient descent. All of that research was computationally expensive: It was conducted on between 720 and 3000 CPUs distributed across a large, high-performance computing cluster, seemingly putting deep neuroevolution out of reach for most researchers, students, companies, and hobbyists."

"Today, we are releasing open source code that makes it possible to conduct such research much faster and cheaper. With this code, the time it takes to train deep neural networks to play Atari, which takes ~1 hour on 720 CPUs, now takes ~4 hours on a single modern desktop."
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CoBlox is a programming language for robots inspired by Scratch, a visual programming language for kids.
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"SkyKnit: When knitters teamed up with a neural network." "The knitters helped me crowdsource a dataset of 500 knitting patterns, ranging from hats to squids to unmentionables. JC Briar exported another 4728 patterns from the site stitch-maps.com."

"I gave the knitting patterns to a couple of neural networks that I collectively named 'SkyKnit'. Then, not knowing if they had produced anything remotely knittable, I started posting the patterns." "MrsNoddyNoddy wrote, 'it's difficult to explain why 6395, 71, 70, 77 is so asthma-inducingly funny.' (It seems that a 6000-plus stitch count is, as Gloria Hanlon put it, 'optimism')."

"The knitters didn't follow SkyKnit's directions exactly, as it turns out. For most of its patterns, doing them exactly as written would result in the pattern immediately unraveling (due to many dropped stitches), or turning into long whiplike tentacles (due to lots of leftover stitches)."
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"A face is still a face, even when it's squished."

"Beyond 80%, performance starts to fall off, but even at a distortion level of 90% -- in which the face is reduced to a mere 'sliver' -- volunteers were still able to recognize about half of the celebrities."
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What's the next number in this sequence: 1, 2, 4, 6, 16, 12, 64, 24, 36, 48, 1024, ___ ?
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"Google is testing solar-powered drones at Spaceport America in New Mexico to explore ways to deliver high-speed internet from the air." "High frequency millimetre waves can theoretically transmit gigabits of data every second, up to 40 times more than today's 4G LTE systems. Google ultimately envisages thousands of high altitude 'self-flying aircraft' delivering internet access around the world."

"The huge advantage of millimetre wave is access to new spectrum because the existing cellphone spectrum is overcrowded. It's packed and there's nowhere else to go." "However, millimetre wave transmissions have a much shorter range than mobile phone signals. A broadcast at 28GHz, the frequency Google is testing at Spaceport America, would fade out in around a tenth the distance of a 4G phone signal. To get millimetre wave working from a high-flying drone, Google needs to experiment with focused transmissions from a so-called phased array." "This is very difficult, very complex and burns a lot of power."
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