Organizations implementing AI applications have several considerations to ponder in choosing the proper infrastructure. But one critical consideration is making a distinction between the training portion of AI and inferencing. This is the view of Michael Lang, solutions architecture manager at NVIDIA, speaking on a panel discussion on implementing AI at the recent NexGen Connectivity Forum. The forum comprised both industry participants and solution providers. The training and learning piece of AI, said Lang, is very different and often requires a different infrastructure environment to the one used for inferencing with AI. "The training and learning piece is about HPC and data-intensive needs," said Lang. "That means big data centers and infrastructure and big capability."

Unmeasured or latent variables are often the cause of correlations between multivariate measurements and are studied in a variety of fields such as psychology, ecology, and medicine. For Gaussian measurements, there are classical tools such as factor analysis or principal component analysis with a well-established theory and fast algorithms. Generalized Linear Latent Variable models (GLLVM) generalize such factor models to non-Gaussian responses. However, current algorithms for estimating model parameters in GLLVMs require intensive computation and do not scale to large datasets with thousands of observational units or responses. In this article, we propose a new approach for fitting GLLVMs to such high-volume, high-dimensional datasets. We approximate the likelihood using penalized quasi-likelihood and use a Newton method and Fisher scoring to learn the model parameters. Our method greatly reduces the computation time and can be easily parallelized, enabling factorization at unprecedented scale using commodity hardware. We illustrate application of our method on a dataset of 48,000 observational units with over 2,000 observed species in each unit, finding that most of the variability can be explained with a handful of factors.

Fan Hui, the European Go champion, needed some fresh air. "I don't understand myself anymore." Hui was the first professional Go player to face AlphaGo, Google's artificial intelligence system and the title of a new documentary by Greg Kohs that debuted last week at the Tribeca Film Festival in New York. When Hui was invited to visit Google's London office housing the DeepMind research group that developed AlphaGo, he was feeling confident. After all, as Hui puts it, "it is just a program."

Fan Hui, the European Go champion, needed some fresh air. "I don't understand myself anymore." Hui was the first professional Go player to face AlphaGo, Google's artificial intelligence system and the title of a new documentary by Greg Kohs that debuted last week at the Tribeca Film Festival in New York. When Hui was invited to visit Google's London office housing the DeepMind research group that developed AlphaGo, he was feeling confident. After all, as Hui puts it, "it is just a program."

Fan Hui is speaking to a chatty audience at the 2016 European Go Congress in St. Petersburg, Russia, gushing over a game of Go played by one of his mentors. Hui's enthusiasm is infectious--the room's chatter subsides as he pulls up slides of the complex Chinese game, whose players battle to dominate a 19 19 board with black and white tiles called stones. Hui's mentor, AlphaGo, has studied strategies built over the game's 4,000-year history, and has played thousands of practice matches. But a training regiment that took Hui years to perfect, AlphaGo did in about four weeks. The game Hui showed to the Saint Petersburg audience is one that the machine played against itself, and to Hui, is an example of the beauty of AlphaGo's strategic mind Even the smartest humans miss patterns that computers see instantly. Problems like file-compression, translation, and custom drug fabrication have millions of variables and data points whose limits exceed human understanding dozens of times over.

Artificial intelligence researchers at DeepMind, a research unit that operates under the umbrella, just surpassed a very significant benchmark in machine learning. It was first reported on Wednesday in Nature that the artificial intelligence system dubbed AlphaGo successfully beat a professional Go player, Fan Hui, in series of five matches. Go, a complex Chinese board game played by two players with black and white stones, has traditionally been deemed an excellent test for AI. According to a Google press release, previously computers have only been ale to play the game as successfully as amateur Go players, making this win all that much more significant. AlphaGo also won all but one of 500 matches against other top AI Go programs (a 99.8% success rate).

In a major win for artificial intelligence, Google DeepMind's AlphaGo has beat European Go champion Fan Hui in the complex 2,500-year-old Chinese game of Go, touted the official Google blog. A victory in a Go game against a human champion has long been coveted among AI researchers, because the possible moves that a player can take can reach into the quadrillions and beyond. As a result, Go has proven a formidable challenge for artificial intelligence researchers. Microsoft and Facebook, for example, have been working on ways to win in the game over a human champion, but have had no luck to date, according to a BBC news report. Last October, Google DeepMind held a private, closed-door Go match in its London office between its AlphaGo system and Hui.

Billionaire CEO Yueting Jia told employees in a letter last week that the company's rapid growth was leading to stagnation and management problems at LeEco, and announced that he would invest $10 million into the company and cut his salary to 15 cents in order to keep LeEco steady as it expands into the United States and develops a self-driving car. But North American operations lead Brian Hui told the audience at TechCrunch China today that things aren't as dire as the letter made them seem. "If you read the letter, it's not about whether it's sustainable or not sustainable," Hui said of LeEco's growth. It's how you can spend your money wisely… You go through a very aggressive user base growth period before you enter a financially sustainable period. I think this is pretty normal, applying to any kind of startup." Although the letter led to rumors that LeEco would abandon the development of a self-driving vehicle, Hui said that work on the car was a "highest priority" and would ...

When Grace Hui moved to Los Angeles from China in 2014 and Googled the Chinese characters for "Los Angeles immigrant," the first result was Chineseinla.com. The Chino Hills-based website, a disorganized Yelp-meets-Craigslist hybrid, was a throwback, and Hui, 29, thought some of the posts were phishing scams. But with more than 680,000 listings, more than 350,000 registered users, 2 million monthly visits and sister sites in 15 cities, Chineseinla.com It's one of the only ways that Hui could connect to a country she couldn't understand. "American Internet is useless to me," said Hui, who used Chineseinla.com

In a major win for artificial intelligence, Google DeepMind's AlphaGo has beat European Go champion Fan Hui in the complex 2,500-year-old Chinese game of Go, touted the official Google blog. A victory in a Go game against a human champion has long been coveted among AI researchers, because the possible moves that a player can take can reach into the quadrillions and beyond. As a result, Go has proven a formidable challenge for artificial intelligence researchers. Microsoft and Facebook, for example, have been working on ways to win in the game over a human champion, but have had no luck to date, according to a BBC news report. Last October, Google DeepMind held a private, closed-door Go match in its London office between its AlphaGo system and Hui.