Last year, artificial intelligence research made great strides. In the third of three matches in China, an AI program beat the world champion at Go – a fearsomely more complex game than chess while, last March, Carnegie Mellon University's Libratus AI program beat humans at poker for the first time.

T.J.M. Bench-Capon, P.H. Leng, G. StanifordA computer supported environment for the teaching of legal argument.

If you're placing any Golden Globes bets, then maybe you might want to consider what the latest A.I. predictions are saying. In both Golden Globes Best Picture categories -- comedy and drama -- Unanimous A.I. and applied their unique systems to forecast possible winners. And it looks like this year, you can place a lot of faith in a certain sea monster cleaning up. The Shape of Water is looking good in the categories of Best Actress, but also Best Picture, too. On Friday, in order to forecast possible outcomes in a variety of categories at the Golden Globes, Unanimous A.I. used what's known as "swarm intelligence."

When the engineers had at last finished their work, Eugenia Kuyda opened a console on her laptop and began to type. "This is your digital monument." It had been three months since Roman Mazurenko, Kuyda's closest friend, had died. Kuyda had spent that time gathering up his old text messages, setting aside the ones that felt too personal, and feeding the rest into a neural network built by developers at her artificial intelligence startup. She had struggled with whether she was doing the right thing by bringing him back this way. At times it had even given her nightmares. But ever since Mazurenko's death, Kuyda had wanted one more chance to speak with him. A message blinked onto the screen. "You have one of the most interesting puzzles in the world in your hands," it said. Kuyda promised herself that she would. Born in Belarus in 1981, Roman Mazurenko was the only child of Sergei, an engineer, and Victoria, a landscape architect. They remember him as an unusually serious child; when he was 8 he wrote a letter to his descendents declaring his most cherished values: wisdom and justice. In family photos, Mazurenko roller-skates, sails a boat, and climbs trees.

When Black Mirror first hit the air in 2011, it drew invariable comparisons to The Twilight Zone. Understandably so: Both shows dealt with elements of science fiction and psychological horror, and both functioned as anthology shows, with episodes so distinct from one another that an uninitiated viewer could plunge in at random and be as familiar with a given episode's premise as a seasoned fan. It was a selling point; it made the show easy to recommend to people who might be wary of committing to a complex, serialized narrative. But since its purchase by Netflix in 2015, Black Mirror has begun to chip away at its episodic edges. Technologies introduced in one installation reappear in another; news tickers on characters' TV screens chronicle events from previous episodes; musical cues repeat again and again.

The woman in the photo seems familiar. She looks like Jennifer Aniston, the "Friends" actress, or Selena Gomez, the child star turned pop singer. She appears to be a celebrity, one of the beautiful people photographed outside a movie premiere or an awards show. That's because she's not real. She was created by a machine.

It also fit well with Fyodorov topology detrvialization theory (High-dimensional landscapes and random matrices).

Millions of fans all over the world are eagerly anticipating this week's release of Star Wars: The Last Jedi, the eighth in the series. At last we will get some answers to questions that have been vexing us since 2015's The Force Awakens. Throughout the franchise, the core characters have been accompanied by a number of much-loved robots, including C-3PO, R2-D2 and more recently, BB-8 and K2-SO. While often fulfilling the role of wise-cracking sidekicks, these and other robots also play an integral role in events. Interestingly, they can also tell us useful things about automation, such as whether it poses dangers to us and whether robots will ever replace human workers entirely.

I've been reading ever more about Google's Tensor Processing Units (TPUs), as a majority of my ML is already done on Google Cloud GPUs, and paid public access to them aside from the Tensorflow Research Cloud doesn't seem all that far off. I would like to learn more about what to expect from working with a gen 2 TPU, or more specifically it's like working on the Tensorflow Research Cloud. I get the impression there's some sort of non-disclosure agreement with researchers, for I assume commercial/competitive reasons, as I see very little discussion about using them let alone editorials. I know there was the benchmark paper they released for the previous gen, but it would be refreshing to at least see some current practical benchmarks on MNIST or Imagenet/Inception considering they already have sample code available for lucky TPU Alpha customers. I'm also wondering why that magic 180 TFLOP/s number quoted by Google purposely omits which kind of floating point ops they're talking about

New this year are dedicated areas and programming focusing on sports, smart cities, artificial intelligence, high tech retailing, digital money and more. One of the fastest growing areas at CES, Eureka Park, will feature more than 900 startups from 42 countries, up 50 percent over last year. More than 170 government officials will attend CES to use the show and the Innovation Policy Summit conference track to examine hot-button policy topics that drive innovation and advance the industry. Last but not least, C Space returns for its fourth year and brings together the world's innovators, content creators, marketers and creatives in one venue including Amazon, eBay, Facebook, Google, Hulu, NBCU, Spotify and more. "CES is constantly evolving to capture the technologies and innovation of the future, it changes as our industry morphs and showcases the most cutting-edge work the tech world has to offer," said Gary Shapiro, president and CEO, CTA.