AlphaGo's AI upgrade gets round the need for human input

New Scientist

NOT so long ago, mastering the ancient Chinese game of Go was beyond the reach of artificial intelligence. But then AlphaGo, Google DeepMind's AI player, started to leave even the best human opponents in the dust. Yet even this world-beating AI needed humans to learn from. AlphaGo Zero has surpassed its predecessor's abilities, bypassing AI's traditional method of learning games, which involves watching thousands of hours of human play. Instead, it simply starts playing at random, honing its skills by repeatedly playing against itself.


Computer Learns To Play Go At Superhuman Levels 'Without Human Knowledge'

NPR

According to the researchers, there are 10 to the power of 170 possible board configurations in Go -- more than the number of atoms in the known universe. According to the researchers, there are 10 to the power of 170 possible board configurations in Go -- more than the number of atoms in the known universe. A year after a computer beat a human world champion in the ancient strategy game of Go, researchers say they have constructed an even stronger version of the program -- one that can teach itself without the benefit of human knowledge. The program, known as AlphaGo Zero, became a Go master in just three days by playing 4.9 million games against itself in quick succession. "In a short space of time, AlphaGo Zero has understood all of the Go knowledge that has been accumulated by humans over thousands of years of playing," lead researcher David Silver of Google's DeepMind lab said in remarks on YouTube.


Ethics by numbers: how to build machine learning that cares

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You may have heard that algorithms will take over the world. But how are they operating right now? We take a look in our series on Algorithms at Work. Machine learning algorithms work blindly towards the mathematical objective set by their designers. It is vital that this task include the need to behave ethically.


How AI finds big value in big data 7wData

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Bots can augment human interaction, create greater business efficiencies, and remove friction from customer interactions. It's a market that has already rolled up $24 billion in funding for companies at every stage, from startup to multinational. Industry leaders from IBM to Facebook are making big efforts to take advantage, spending significant resources to encourage developers to create new bots that enable more personalized customer interactions. In March 2016, Cisco announced the Spark Innovation Fund, a $150 million investment in bots and developers who want to make new products for Cisco endpoints in offices around the world. Some of the most obvious uses for bots revolve around communication, customer service, and ecommerce.


Just own the damn robots.

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Paul unlocked the box containing the tape recording that controlled them all. The tape was a small loop that fed continuously between magnetic pickups. On it were recorded the movements of a master machinist turning out a shaft for a fractional horsepower motor. He'd been in on the making of the tape, the master from which this one had been made. He had been sent to one of the machine shops to make the recording.


Amazon's Alexa gets a board game: When in Rome

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In what appears to be a first, Amazon's Alexa will act as a guide for a board game called When in Rome, according to the startup Sensible Object. Due out in March 2018, When in Rome will be the first of six voice-augmented games Sensible Object plans to release next year. Each game in the series called Voice Originals will cost $24.99, CEO Alex Fleetwood told VentureBeat in a phone interview. When in Rome serves up trivia questions from locals in 20 cities around the world.


Apache Kafka and the four challenges of production machine learning systems

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Machine learning has become mainstream, and suddenly businesses everywhere are looking to build systems that use it to optimize aspects of their product, processes or customer experience. The cartoon version of machine learning sounds quite easy: you feed in training data made up of examples of good and bad outcomes, and the computer automatically learns from these and spits out a model that can make similar predictions on new data not seen before. What could be easier, right? Those with real experience building and deploying production systems built around machine learning know that, in fact, these systems are shockingly hard to build. This difficulty is not, for the most part, the algorithmic or mathematical complexities of machine learning algorithms.


AlphaGo Zero , the machine learns by itself Exciting step forward, the origin...

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AlphaGo Zero, the machine learns by itself Exciting step forward, the original Alpha Go was fed with a lot human knowledge, played games etcetera. In this version, Alpha Go Zero, learns only by playing by itself and quickly surpasses human level of play. The BIG question is ofcourse, can this be applied to other fields besides the gameworld of Go, for example medicine and other sectors.


Google's Deepmind AI unit releases new version of AlphaGo that can learn on its own

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Deepmind, the artificial intelligence research organization owned by Google, announced some stunning results Wednesday from research into the next generation of its AlphaGo system: the machines are getting smarter. AlphaGo Zero, the new version of the AlphaGo system that defeated the world's best Go players in competitions over the past few years, was able to teach itself how to play the ancient board game as well as its predecessors in a matter of days with no other input than the basic rules of the game, Deepmind said in a blog post Wednesday. Previous versions of AlphaGo built to compete against human masters of the game required hours and hours of training on Go gameplay, but AlphaGo Zero was able to teach itself to play using a technique called reinforcement learning. Reinforcement learning involves training a system to figure out the best reward outcome from a series of actions, unlike supervised learning, in which the system is taught which outcomes are desired and trained over and over to recognize the factors that lead to those outcomes. Deepmind set up a neural network that played games of Go against itself until it learned how to formulate a winning strategy for a game in which capturing as many stones as possible can be satisfying in early stages, but can lead to big problems as the game plays out.


Adobe says it wants AI to amplify human creativity and intelligence

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About a year ago, Adobe announced its Sensei AI platform. Unlike other companies, Adobe says that it has no interest in building a general artificial intelligence platform -- instead, it wants to build a platform squarely focused on helping its customers be more creative. This week, at its Max conference, Adobe provided both more insight into what this means and showed off a number of prototypes for how it plans to integrate Sensei into its flagship tools. "We are not building a general purpose AI platform like some others in the industry are -- and it's great that they are building it," Adobe CTO Abhay Parasnis noted in a press conference after today's keynote. "We have a very deep understanding of how creative professionals work in imagining, in photography, in video, in design and illustration.