Unlike tennis balls and musketeers, AI ventures don't typically come in threes. The former AI leader at both Google and Baidu has been on an entrepreneurial spree, making his third major announcement in recent months to launch Landing.ai. The startup will help make large manufacturing companies more efficient by using artificial intelligence, according Ng's blog post today (Dec. He also serves as chairman of Coursera, the online education company he cofounded in 2012. Ng writes that his startup and Foxconn will "jointly develop and deploy AI solutions and training globally."

Gargantuan Taiwanese manufacturer Foxconn employs more than 1 million people and tens of thousands of robots making iPhones and other electronics. It has a reputation for cost cutting, including at the expense of its workers. Now, it's teaming up with an artificial-intelligence researcher who helped trigger Google's reorientation around machine learning in order to make its own factories more efficient. Andrew Ng was a Stanford professor when he joined Google in 2011 to work on a project that created software able to recognize cats--and a new corporate emphasis on AI at Google. He later led AI research at Chinese search engine Baidu.

AI is coming for your job. AI is taking over the world. If we compiled all the headlines about artificial intelligence from the last year, we'd have a picture of a dystopian world where jobs are scarce and AI and automation rule everything we do. In this scenario, millions of people are impacted by AI and autonomous systems created with little regard for their consequences: They are deployed in unethical ways, riddled with errors and bias, and discriminatory. The obscurity of how AI works and where it's used result in fear and confusion.

"Deep Learning" computer systems, based on artificial neural networks that mimic the way the brain learns from an accumulation of examples, have become a hot topic in computer science. In addition to enabling technologies such as face- and voice-recognition software, these systems could scour vast amounts of medical data to find patterns that could be useful diagnostically, or scan chemical formulas for possible new pharmaceuticals. But the computations these systems must carry out are highly complex and demanding, even for the most powerful computers. Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. Their results appear today in the journal Nature Photonics in a paper by MIT postdoc Yichen Shen, graduate student Nicholas Harris, professors Marin Soljacic and Dirk Englund, and eight others.

Artificial Intelligence is transforming the landscape of human evolution, with one of the biggest landmarks for the technology being the declaration of an AI robot - Sophia - as a "national citizen" of the Kingdom of Saudi Arabia. Despite the fact that the earliest footprints of Artificial Intelligence can be traced back to 1956, its impact had been behind the curtains, until recently. During the last decade, AI has sparked polar reactions from observers, with some crowning it as the "face of the future", while others demarking it as the "beginning of the end". Elon Musk, the CEO of Tesla Motors, went as far as saying that AI would largely be attributable for the outburst of World War-III. On the other side of the fence, however, we have the likes of Mark Zuckerberg and Bill Gates, who could not have expressed more hope and faith in the revelation.

If there's any emerging technology that's gripped the public consciousness in recent years it's AI and machine learning (ML). Autonomous vehicles, shopping recommendations, Siri and Alexa, these are just a few of the day to day examples of the rapid evolution of ML applications. The fervour around AI and ML's development is only fuelling these advancements. As public interest grows we're already seeing more students attracted to ML and AI courses. Just look at the popularity of Professor Andrew Ng's Coursera course on machine learning or the record number of Stanford students who enrolled in the machine learning class this semester.

We develop an approach to risk minimization and stochastic optimization that provides a convex surrogate for variance, allowing near-optimal and computationally efficient trading between approximation and estimation error. Our approach builds off of techniques for distributionally robust optimization and Owen's empirical likelihood, and we provide a number of finite-sample and asymptotic results characterizing the theoretical performance of the estimator. In particular, we show that our procedure comes with certificates of optimality, achieving (in some scenarios) faster rates of convergence than empirical risk minimization by virtue of automatically balancing bias and variance. We give corroborating empirical evidence showing that in practice, the estimator indeed trades between variance and absolute performance on a training sample, improving out-of-sample (test) performance over standard empirical risk minimization for a number of classification problems.

An undercover sex sting in Ohio picked up a suspect this week, as a Youngstown State student reportedly wanted to have sex with a 15-year-old boy who was, in reality, an undercover officer. Albert Maruna IV, 22, was arrested and charged Tuesday for unlawful sexual conduct with a minor, among other things. Police say Maruna started talking to the fictitious teen boy in early December on an online dating app. According to the report, Maruna told the undercover officer that he "didn't believe in age." The conversations became explicitly sexual in nature, with Maruna sending nude pictures and suggesting the two should get married someday.

The most dramatic cybersecurity story of 2016 came to a quiet conclusion Friday in an Anchorage courtroom, as three young American computer savants pleaded guilty to masterminding an unprecedented botnet--powered by unsecured internet-of-things devices like security cameras and wireless routers--that unleashed sweeping attacks on key internet services around the globe last fall. What drove them wasn't anarchist politics or shadowy ties to a nation-state. It was a hard story to miss last year: In France last September, the telecom provider OVH was hit by a distributed denial-of-service (DDoS) attack a hundred times larger than most of its kind. Then, on a Friday afternoon in October 2016, the internet slowed or stopped for nearly the entire eastern United States, as the tech company Dyn, a key part of the internet's backbone, came under a crippling assault. As the 2016 US presidential election drew near, fears began to mount that the so-called Mirai botnet might be the work of a nation-state practicing for an attack that would cripple the country as voters went to the polls.

Interest in machine learning has exploded over the past decade. You see machine learning in computer science programs, industry conferences, and the Wall Street Journal almost daily. For all the talk about machine learning, many conflate what it can do with what they wish it could do. Fundamentally, machine learning is using algorithms to extract information from raw data and represent it in some type of model. We use this model to infer things about other data we have not yet modeled.