Amazon isn't synonymous with high fashion yet, but the company may be poised to lead the way when it comes to replacing stylists and designers with ever-so-chic AI algorithms. Researchers at the e-commerce juggernaut are currently working on several machine-learning systems that could help provide an edge when it comes to spotting, reacting to, and perhaps even shaping the latest fashion trends. The effort points to ways in which Amazon and other companies could try to improve the tracking of trends in other areas of retail--making recommendations based on products popping up in social-media posts, for instance. And it could help the company expand its clothing business or even dominate the area. "There's been a whole move from companies like Amazon trying to understand how fashion develops in the world," says Kavita Bala, a professor at Cornell University who took part in a workshop on machine learning and fashion organized by Amazon last week.

A child reacts as she tries out a robotic Padbot T2 at an exhibitor booth during the World Robot Conference at the Yichuang International Conference and Exhibition Centre in Beijing. The annual conference is a showcase of China's burgeoning robot industry ranging from companion robots to those deployed on manufacturing assembly line and entertainment.

In this paper, we introduce a new sparsity-promoting prior, namely, the "normal product" prior, and develop an efficient algorithm for sparse signal recovery under the Bayesian framework. The normal product distribution is the distribution of a product of two normally distributed variables with zero means and possibly different variances. Like other sparsity-encouraging distributions such as the Student's $t$-distribution, the normal product distribution has a sharp peak at origin, which makes it a suitable prior to encourage sparse solutions. A two-stage normal product-based hierarchical model is proposed. We resort to the variational Bayesian (VB) method to perform the inference. Simulations are conducted to illustrate the effectiveness of our proposed algorithm as compared with other state-of-the-art compressed sensing algorithms.

In modern large-scale machine learning applications, the training data are often partitioned and stored on multiple machines. It is customary to employ the "data parallelism" approach, where the aggregated training loss is minimized without moving data across machines. In this paper, we introduce a novel distributed dual formulation for regularized loss minimization problems that can directly handle data parallelism in the distributed setting. This formulation allows us to systematically derive dual coordinate optimization procedures, which we refer to as Distributed Alternating Dual Maximization (DADM). The framework extends earlier studies described in (Boyd et al., 2011; Ma et al., 2017; Jaggi et al., 2014; Yang, 2013) and has rigorous theoretical analyses. Moreover with the help of the new formulation, we develop the accelerated version of DADM (Acc-DADM) by generalizing the acceleration technique from (Shalev-Shwartz and Zhang, 2014) to the distributed setting. We also provide theoretical results for the proposed accelerated version and the new result improves previous ones (Yang, 2013; Ma et al., 2017) whose iteration complexities grow linearly on the condition number. Our empirical studies validate our theory and show that our accelerated approach significantly improves the previous state-of-the-art distributed dual coordinate optimization algorithms.

Ruiner might be one of the most eye-catching titles showcased at Gamescom -- something that's hard to achieve when every company is bombarding you with posters, flags and bags at every turn. The aggressive, manga-styled protagonist and angry catch copy are difficult to ignore. It's also the first title to come out of Reikon studio, an indie Polish team founded by veteran gamesmakers that had previously worked on The Witcher, Shadow Warrior and many more. Cofounder Magdalena Tomkowicz explained how she had grown tired of big gaming projects, and wanted to recover the passion of making a game: this top-down shooter / slasher is the result of that. The Hotline Miami comparisons might be fair at the simplest level, but Ruiner seems to take that top-down gameplay mechanic in a very different direction.

Sydney-based startup, Hyper Anna, has secured $16 million in funding to fuel a global rollout of'Anna', a virtual data scientist that taps into business intelligence and delivers real-time insights based on natural language requests. The Series A round of funding was led by Sequoia China, with significant investments from Airtree Ventures, Westpac Reinventure and IAG Firemark Ventures. Both Westpac and general insurer, IAG, are early adopters of Hyper Anna and use the platform internally to answer questions about business performance. Hyper Anna co-founder and CEO, Natalie Nguyen, told CMO customers have largely been SSI and in the financial and insurance sectors, but said the product could easily apply to other sectors such as media and technology. "The fact that some of our investors are also our customers gives us great confidence in the company, in ourselves, and the product," she said.

Washed up on the beach, you, young boy/girl/robot, are the hero that will save the island of Ditto. Or you'll fail, and plunge the land into a hundred years of darkness until another hero is born. OneBitBeyond's The Swords Of Ditto puts you control one tiny adventurer at a time, and if when you die, when the Big Evil (some sorceress of some kind) fries you on the spot, you won't live to fight another day. However, someone else will claim your hero's sword and continue the struggle. That's the crux, but it's how OneBitBeyond have executed it, in a top-down action RPG that leans heavily on SNES-era Zelda (and some Secret Of Mana), with a punchy cartoon style that belies the small team behind it all.

Not content to simply blame millennials for killing practically everything, baby boomers are now expecting the younger generations to care for them in their agedness. Indeed, some 13 percent of the American population is now 65 or older, though a recent report from the Pew Research Center suggests that figure will nearly double by midcentury. Given that the current annual median price of a nursing-home room is around $92,000 (and rising), and because we can't just up and dump a quarter of America at the Springfield Retirement Castle, robots will have to start lending elderly folks a hand. Because if there's anybody who inherently trusts new and confusing technologies, it's the olds. The problem of caring for a rapidly aging population is especially pronounced in Japan, where a full 20 percent of current residents are eligible for discounts at Golden Corral.

The field of artificial intelligence has made great strides recently, as in AlphaGo's victories in the game of Go over world champion South Korean Lee Sedol in March 2016 and top-ranked Chinese Go player Ke Jie in May 2017, leading to great optimism for the field. But are we really moving toward smarter machines, or are these successes restricted to certain classes of problems, leaving others untouched? In 2015, the Allen Institute for Artificial Intelligence (AI2) ran its first Allen AI Science Challenge, a competition to test machines on an ostensibly difficult task--answering eighth-grade science questions. Our motivations were to encourage the field to set its sights more broadly by exploring a problem that appears to require modeling, reasoning, language understanding, and commonsense knowledge in order to probe the state of the art while sowing the seeds for possible future breakthroughs. Challenge problems have historically played an important role in motivating and driving progress in research.

When the crew of an $80-million super-yacht in the Ionian Sea checked its computer, they realized they were drifting slightly off course, likely as a result of strong currents buffeting their ship. The crew made adjustments and went back to work--without realizing they were now taking directions from a hacker. In the bowels of the ship, Todd Humphreys, an associate professor in the Department of Aerospace Engineering and Engineering Mechanics at the University of Texas at Austin, worked with his team to feed the super-yacht's crew false navigation data using a few thousand dollars worth of hardware and software. The crew was completely unaware they were now piloting in a direction of Humphreys' choosing. Thankfully, it was all an experiment that took place with the yacht owner's blessing.