Two fundamental problems in computational game theory are computing a Nash equilibrium and learning to exploit opponents given observations of their play (opponent exploitation). The latter is perhaps even more important than the former: Nash equilibrium does not have a compelling theoretical justification in game classes other than two-player zero-sum, and for all games one can potentially do better by exploiting perceived weaknesses of the opponent than by following a static equilibrium strategy throughout the match. The natural setting for opponent exploitation is the Bayesian setting where we have a prior model that is integrated with observations to create a posterior opponent model that we respond to. The most natural, and a well-studied prior distribution is the Dirichlet distribution. An exact polynomial-time algorithm is known for best-responding to the posterior distribution for an opponent assuming a Dirichlet prior with multinomial sampling in normal-form games; however, for imperfect-information games the best known algorithm is based on approximating an infinite integral without theoretical guarantees. We present the first exact algorithm for a natural class of imperfect-information games. We demonstrate that our algorithm runs quickly in practice and outperforms the best prior approaches. We also present an algorithm for the uniform prior setting.

When our robot overlords arrive, will they decide to kill us or cooperate with us? New research from DeepMind, Alphabet Inc.'s London-based artificial intelligence unit, could ultimately shed light on this fundamental question. They have been investigating the conditions in which reward-optimizing beings, whether human or robot, would choose to cooperate, rather than compete. The answer could have implications for how computer intelligence may eventually be deployed to manage complex systems such as an economy, city traffic flows, or environmental policy. Joel Leibo, the lead author of a paper DeepMind published online Thursday, said in an e-mail that his team's research indicates that whether agents learn to cooperate or compete depends strongly on the environment in which they operate.

NAO is an endearing, interactive robot companion that can be personalized and used for lots of different tasks. The idea is to customize the bot, and it's been a big hit, with over 7000 sold. We are often reassured that even though robots are automating many jobs, humans possess emotional intelligence and unique decision-making skills that can't be replicated. But new research proves that robots can actually be quite good at making tough choices, even when many factors complicate the matter. A team of researchers at MIT's Computer Science and Artificial Intelligence Lab developed a technique called "apprenticeship scheduling" to enable robots to help with scheduling in various workplaces.

When Japanese mobile phone company SoftBank offered 1000 of its emotionally intelligent Pepper robots for the consumer market last summer, the entire run sold out in under a minute. At CES this year, SoftBank announced that IBM would be bringing Watson's artificial intelligence to Pepper, a bid to ready the robot for broad adoption in the home. Now SoftBank is planning to branch into the U.S. At Google IO today, the company announced that it's opening up a new developer portal and adding SDK Android Studio to enable the development of custom applications for Pepper, continuing to evolve it's capabilities ahead of its U.S. launch, which it's planning later this year. "We'll also be announcing the opening of SoftBank's U.S. office, headquartered in San Francisco, which will be driving the efforts surrounding the launch of Pepper in the U.S.," a company spokesman told me. Today's announcement came along with a demonstration of Pepper's functionality and features for developers.

Google announced today it has acquired machine learning startup Moodstocks in an effort to bolster its work around smartphone image and item recognition. Terms of the deal were not disclosed. See how the cloud is disrupting traditional operating models for IT departments and entire organizations. Moodstocks began developing image recognition technology in 2012 and more recently shifted into object recognition technology. The Paris-based startup said on its website that its "dream has been to give eyes to machines by turning cameras into smart sensors able to make sense of their surroundings."

Microsoft's intelligent assistant, Cortana, is coming to iOS and Android devices, according to a Reuters report published today. But don't expect Siri to step aside right away. The Reuters report, based on interviews with Eric Horvitz, managing director of Microsoft Research, and unnamed sources, is frustratingly vague about the timeframe for the appearance of Cortana in standalone apps on non-Windows platforms. Horvitz told Reuters that technology from an artificial intelligence project codenamed Einstein, "will play a central role in the next roll out of Cortana, which we are working on now for the fall time frame." The standalone apps for iOS and Android are due "later," according to Reuters.

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Intensix raises $8.3M for machine-learning tech to head off ICU complications Intensix, which is harnessing machine learning in the ICU, reeled in an $8.3 million Series A. The funds are pegged for the expansion of its sales and marketing ops in North America as well as further development of its predictive analytics platform. The Intensix platform applies machine learning to the early detection of life-threatening complications in intensive care. A proliferation of structured and unstructured ICU data--from vital signs to historical and demographic data--goes into the system, is run through a set of models and results in predictions, CEO Gal Salomon said. ICU staff and management could use these predictions to head off deterioration before it happens. Studies have shown that the tech could potentially save lives, reduce the length of hospital stays and lower costs, the company said in a statement.

Ford Motor Co. says it's investing $1 billion over the next five years in a Pittsburgh startup called Argo AI to develop the virtual-driver system for Ford's autonomous vehicles. Argo AI was founded only a few weeks ago by CEO Bryan Salesky, who directed hardware development for Google's self-driving cars; and chief operating officer Peter Rander, who led Uber's program to develop self-driving cars. Salesky and Rander, as well as other Argo AI executives, have worked on robotics and AI at Carnegie Mellon University in Pittsburgh, which helps explain the placement of the startup's headquarters. The technology coming out of the collaboration could be licensed to other companies, Ford President and CEO Mark Fields said today in a statement announcing the deal. "We believe that investing in Argo AI will create significant value for our shareholders by strengthening Ford's leadership in bringing self-driving vehicles to market in the near term and by creating technology that could be licensed to others in the future," Fields said.

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