Tag: AI

Google’s and Nvidia’s AI Chips

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Google

Google will soon launch a cloud computing service that provides exclusive access to a new kind of artificial intelligence chip designed by its own engineers. CEO Sundar Pichai revealed the new chip and service this morning in Silicon Valley during his keynote at Google I/O, the company’s annual developer conference.

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This new processor is a unique creation designed to both train and execute deep neural networks—machine learning systems behind the rapid evolution of everything from image and speech recognition to automated translation to robotics. Google says it will not sell the chip directly to others. Instead, through its new cloud service, set to arrive sometime before the end of the year, any business or developer can build and operate software via the internet that taps into hundreds and perhaps thousands of these processors, all packed into Google data centers.

According to Dean, Google’s new “TPU device,” which spans four chips, can handle 180 trillion floating point operations per second, or 180 teraflops, and the company uses a new form of computer networking to connect several of these chips together, creating a “TPU pod” that provides about 11,500 teraflops of computing power. In the past, Dean said, the company’s machine translation model took about a day to train on 32 state-of-the-art CPU boards. Now, it can train in about six hours using only a portion of a pod.

Nvidia

Nvidia has released a new state-of-the-art chip that pushes the limits of machine learning, the Tesla P100 GPU. It can perform deep learning neural network tasks 12 times faster than the company’s previous top-end system (The TitanX). The P100 was a huge commitment for Nvidia, costing over $2 billion in research and development, and it sports a whopping 150 billion transistors on a single chip, making the P100 the world’s largest chip, Nvidia claims. In addition to machine learning, the P100 will work for all sorts of high-performance computing tasks — Nvidia just wants you to know it’s really good at machine learning.

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To top off the P100’s introduction, Nvidia has packed eight of them into a crazy-powerful $129,000 supercomputer called the DGX-1. This show-horse of a machine comes ready to run, with deep-learning software preinstalled. It’s shipping first to AI researchers at MIT, Stanford, UC Berkeley, and others in June. On stage, Huang called the DGX-1 “one beast of a machine.”

The competition between these upcoming AI chips and Nvidia all points to an emerging need for simply more processing power in deep learning computing. A few years ago, GPUs took off because they cut the training time for a deep learning network from months to days. Deep learning, which had been around since at least the 1950s, suddenly had real potential with GPU power behind it. But as more companies try to integrate deep learning into their products and services, they’re only going to need faster and faster chips.

 

(via Wired, Forbes, Nvidia, The Verge)

 

Neuralink

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The rise of A.I. and over-powering humans will prove to be a catastrophic situation. Elon Musk is attempting to combat the rise of A.I. with the launch of his latest venture, brain-computer interface company Neuralink.  Detailed information about Neuralink on Wait But Why. (Highly Recommended to Read!)

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Musk seems to want to achieve a communications leap equivalent in impact to when humans came up with language – this proved an incredibly efficient way to convey thoughts socially at the time, but what Neuralink aims to do is increase that efficiency by multiple factors of magnitude. Person-to-person, Musk’s vision would enable direct “uncompressed” communication of concepts between people, instead of having to effectively “compress” your original thought by translating it into language and then having the other party “decompress” the package you send them linguistically, which is always a lossy process.

Another thing in favor of Musk’s proposal is that symbiosis between brains and computers isn’t fiction. Remember that person who types with brain signals? Or the paralyzed people who move robot arms? These systems work better when the computer completes people’s thoughts. The subject only needs to type “bulls …” and the computer does the rest. Similarly, a robotic arm has its own smarts. It knows how to move; you just have to tell it to. So even partial signals coming out of the brain can be transformed into more complete ones. Musk’s idea is that our brains could integrate with an AI in ways that we wouldn’t even notice: imagine a sort of thought-completion tool.

So it’s not crazy to believe there could be some very interesting brain-computer interfaces in the future. But that future is not as close at hand as Musk would have you believe. One reason is that opening a person’s skull is not a trivial procedure. Another is that technology for safely recording from more than a hundred neurons at once—neural dust, neural lace, optical arrays that thread through your blood vessels—remains mostly at the blueprint stage.

 

( via Wired, TechCrunch )

Is A.I. Mysterious?

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Contemplating on the book I recently began, Superintelligence: Paths, Dangers, Strategies by Nick Bostrom, the author clearly describes the future of AI as tremendously frightening and tells us in what ways AI will overpower human beings, until a point in the future where the humans will no longer be needed. Such kind of intelligence will be superior to human beings, hence coined the term Superintelligence. Nick, further mentioned in the initial pages of the book that such kind of invention would be the last invention humans would ever make, and the AI would then invent new things itself, without feeling the need of a human being.

Does AI possess a darker side? (Enter article by MitTechReview)

Last year, a strange self-driving car was released onto the quiet roads of Monmouth County, New Jersey. This experimental vehicle was developed by researchers at Nvidia, didn’t look different from other autonomous cars, but it was unlike anything demonstrated by Google, Tesla, or General Motors, and it showed the rising power of artificial intelligence. The car didn’t follow a single instruction provided by an engineer or programmer. Instead, it relied entirely on an algorithm that had taught itself to drive by watching a human do it. Getting a car to drive this way was an impressive feat. But it’s also a bit unsettling since it isn’t completely clear how the car makes its decisions. Information from the vehicle’s sensors goes straight into a huge network of artificial neurons that process the data and then deliver the commands required to operate the steering wheel, the brakes, and other systems. The result seems to match the responses you’d expect from a human driver. But what if one day it did something unexpected—crashed into a tree, or sat at a green light? As things stand now, it might be difficult to find out why. The system is so complicated that even the engineers who designed it may struggle to isolate the reason for any single action. And you can’t ask it: there is no obvious way to design such a system so that it could always explain why it did what it did.

Now, once again, such kind of experiments and research may seem obscure at the moment or may even be neglected by some people, but what if an entire fleet of vehicles start working in the manner they learned and ignoring the commands by a human.

Enter OpenAI: Discovering an enacting the path to safe artificial general intelligence.

OpenAI is a non-profit artificial intelligence (AI) research company, associated with business magnate Elon Musk, that aims to carefully promote and develop friendly AI in such a way as to benefit humanity as a whole.

Hopefully, OpenAI will help us have friendly versions of AI.

 

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The artist Adam Ferriss created this image, and the one below, using Google Deep Dream, a program that adjusts an image to stimulate the pattern recognition capabilities of a deep neural network. The pictures were produced using a mid-level layer of the neural network.

This image sure seems kind of spooky. But who knows, there might be some hidden sarcasm in the AI that we are yet to discover.

Meanwhile, you can give it a try https://deepdreamgenerator.com/ .

 

 

Sources (MitTechReview, OpenAI, Wiki)

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The Poker Playing AI

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As we know that the game of Poker involves dealing with imperfect information, which makes the game very complex, and more like many real-world situations. At the Rivers Casino in Pittsburgh this week, a computer program called Libratus (A latin word meaning balanced), an AI system that may finally prove that computers can do this better than any human card player. Libratus was created by Tuomas Sandholm, a professor in the computer science department at CMU, and his graduate student Noam Brown.

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The AI Poker play against the world’s best poker players. Kim is a high-stakes poker player who specializes in no-limit Texas Hold ‘Em. Jason Les and Daniel McAulay, two of the other top poker players challenging the machine, describe its play in much the same way. It does a little bit of everything,” Kim says. It doesn’t always play the same type of hand in the same way. It may bluff with a bad hand or not. It may bet high with a good hand—or not. That means Kim has trouble finding holes in its game. And if he does find a hole, it disappears the next day.

“The bot gets better and better every day. It’s like a tougher version of us,” said Jimmy Chou, one of the four pros battling Libratus. “The first couple of days, we had high hopes. But every time we find a weakness, it learns from us and the weakness disappears the next day.”

Libratus is playing thousands of games of heads-up, or two-player, no-limit Texas hold’em against several expert professional poker players. Now a little more than halfway through the 20-day contest, Libratus is up by almost $800,000 against its human opponents. So a victory, while far from guaranteed, may well be in the cards.

Regardless of the pure ability of the humans and the AI, it seems clear that the pros will be less effective as the tournament goes on. Ten hours of poker a day for 20 days straight against an emotionless computer was exhausting and demoralizing, even for pros like Doug Polk. And while the humans sleep at night, Libratus takes the supercomputer powering its in-game decision making and applies it to refining its overall strategy.

A win for Libratus would be a huge achievement in artificial intelligence. Poker requires reasoning and intelligence that has proven difficult for machines to imitate. It is fundamentally different from checkers, chess, or Go because an opponent’s hand remains hidden from view during play. In games of “imperfect information,” it is enormously complicated to figure out the ideal strategy given every possible approach your opponent may be taking. And no-limit Texas hold’em is especially challenging because an opponent could essentially bet any amount.

“Poker has been one of the hardest games for AI to crack,” says Andrew Ng, chief scientist at Baidu. “There is no single optimal move, but instead an AI player has to randomize its actions so as to make opponents uncertain when it is bluffing.”

(Sources: MitTechReview, The Verge, Wired)