Carnegie Mellon's No-Limit Texas Hold'em software made short work of four of the world's best professional poker players in Pittsburgh at the grueling "Brains vs. Artificial Intelligence" poker tournament. Poker now joins chess, Jeopardy, go, and many other games at which programs outplay people. But poker is different from all the others in one big way: players have to guess based on partial, or "imperfect" information. One of the most annoying things about Android has long been the custom skins that manufacturers would slap on top of the operating system. Things have gotten better in recent years, but plenty of users would be happier using Android as Google intended.

The latest KPMG analysis revealed that China saw Venture Capital (VC) investments reach US$31 billion in 2016, a 19% tear-on-year growth and a new record high, despite a global slowdown. The consultant predicts that Artificial Intelligence (AI) will be an additional focus for investors. However, deal volumes declined 42% to 300 from 513 a year earlier, according to Venture Pulse, KPMG's quarterly global report on VC trends. The strong performance is attributed to a number of mega-deals recorded early in the year. The report highlights that artificial intelligence and cognitive learning are poised to transform almost every aspect of people's lives.

Any video gamer knows how boring NPCs (non-playable characters) in digital worlds are. Their behavior is simple and predictable and their words entirely scripted by a staff of writers. This makes them uninteresting opponents and unsatisfying companions. We're far more likely to emotionally attach to lifelike characters, like the emo robot sidekicks in the Star Wars franchise, but crafting believable, autonomous entities you can actually interact with is no easy feat. Character models built by artificial intelligence aim to break out of the uncanny valley and imbue inanimate objects and digital characters with an aura of realism and life.

Regularized discriminant analysis (RDA), proposed by Friedman (1989), is a widely popular classifier that lacks interpretability and is impractical for high-dimensional data sets. Here, we present an interpretable and computationally efficient classifier called high-dimensional RDA (HDRDA), designed for the small-sample, high-dimensional setting. For HDRDA, we show that each training observation, regardless of class, contributes to the class covariance matrix, resulting in an interpretable estimator that borrows from the pooled sample covariance matrix. Moreover, we show that HDRDA is equivalent to a classifier in a reduced-feature space with dimension approximately equal to the training sample size. As a result, the matrix operations employed by HDRDA are computationally linear in the number of features, making the classifier well-suited for high-dimensional classification in practice. We demonstrate that HDRDA is often superior to several sparse and regularized classifiers in terms of classification accuracy with three artificial and six real high-dimensional data sets. Also, timing comparisons between our HDRDA implementation in the sparsediscrim R package and the standard RDA formulation in the klaR R package demonstrate that as the number of features increases, the computational runtime of HDRDA is drastically smaller than that of RDA.

Could AI-powered bots fight hate speech by flooding the internet with love? As social media platforms have become ever more intrinsic to how we live our lives and begun to evolve into the primary medium through which we communicate and listen to the rest of the world, their rise has handed a megaphone to the world's hate and vitriol. In fact, it was Twitter who initially stepped forward to staunchly defend the rights of terrorists and their sympathizers to communicate via its platform before abruptly reversing itself in the face of fierce public criticism. Yet, despite myriad programs and policies designed on paper to fight abuse, in reality the platforms have done very little to curb the spread of hate speech, harassment and violent threats. This raises the question of whether the rise of deep learning-powered "bots" could offer a powerful solution to online hate speech, by deploying them en masse to report, counter and overwhelm hateful posts in realtime. Over the last few years deep learning algorithms have made enormous advances in their ability to process human text and imagery at levels of sophistication and accuracy that approach human levels at times, while even simple ELIZA bots have managed to carry on fairly convincing chats for more than half a century.

Robots are taking increasingly bigger roles in life and business – moving well beyond manufacturing and into transportation, education, medicine and care for the elderly. But ethics and law haven't caught up. Dr. Kate Darling, a pioneer in the fields, is helping quicken the pace. A leading expert in robot ethics, she is a researcher at the Massachusetts Institute of Technology (MIT) Media Lab where she investigates social robotics and conducts experimental studies on human-robot interaction. Darling explores the emotional connection between people and life-like inventions, seeking to influence technology design and policy direction.

Twelve days into the strangest poker tournament of their lives, Jason Les and his companions returned to their hotel, browbeaten and exhausted. Huddled over a pile of tacos, they strategized, as they had done every night. With about 60,000 hands played -- and 60,000 to go -- they were losing badly to an unusual opponent: a computer program called Libratus, which was up nearly $800,000 in chips. That wasn't supposed to happen. In 2015, Les and a crew of poker pros had beaten a similar computer program, winning about $700,000.

The legal profession is not far away from being exposed to the disruptive potential of AI. In India, a leading full service law firm, Cyril Amarchand Mangaldas has plans to deploy AI technology. This will help the firm to enhance its delivery model, in an attempt to provide certain legal services more efficiently, swiftly, and more accurately to its clients. The firm recently signed an agreement with a leading Canada-based machine learning software provider, called Kira Systems. Cyril Shroff, Managing Partner, Cyril Amarchand Mangaldas, remarks "The firm has been working on several interconnected initiatives since last year. The collaboration with Kira will help us bring these technologies for our clients in a proactive manner."

With the U.S. election finally in the rear-view mirror, the marketing community can get back to normal, or at least to debating what is normal now. After a period of rapid technological changes--connecting cars, homes, and humans--insiders agree 2017 will be a year of adapting to many of the recent trends. "We are between two worlds of legacy thinking and the new reality. Those two worlds don't mesh," said David Cohen, president of Magna North America. Part of that middle ground we predicted here--such as the mainstreaming of virtual reality and debates about transparency, fraud, and ad blocking--have come true even faster than expected. Others, such as "social shopping" and marketers having to pay consumers for their data, have met resistance.

Steve Irvine is the founder and CEO of Toronto-based Integrate.AI I recently made the decision to leave my executive role at Facebook Inc. in Silicon Valley to start a company, Integrate.AI, focused on applied artificial intelligence. Normally, this news would not raise an eyebrow in the valley. In fact, people in positions like mine often leave the comfortable confines of large tech companies to build their own startups in hot new areas, such as AI. However, there was a notable difference in my case – I chose to start my company in Toronto. A lot of people immediately assumed I had made the move strictly for personal reasons, as my wife and I are both Canadian.