Do you know that many of the artificially-intelligent things we use in our everyday, quotidian lives "learned" how to "think" to varying degrees by studying the emails of some of the most craven and degraded capitalists in our deeply weird corporate history? Brooklyn's Sam Lavigne and Tega Brain have a new piece of internet art out called The Good Life (Enron Simulator). We first told you about it back in August, right after it won a Rhizome Net Art Microgrant. You input your email into a very Windows 95-looking website and the site sends you each of the 500,000 publicly-available emails from the Enron archives in the order they were sent. You can choose to receive these emails over the course of seven days, 30 days, one year or seven years. Depending on your choice, you'll receive somewhere between 100,000 and 196 emails per day.

Pokeman Go has been a raging success. But its launch was marred by frequent downtimes and dropped connections. In a recent chat at the Google Cloud Platform Next Conference, Niantic CTO Phil Keslin talked about the "hair on fire" experience where the team had to firefight and upgrade key components on the live production system in order to handle the unexpected surge in new users joining in. Among the various upgrades made to the system, Niantic had to replace the network load balancer with a much more sophisticated HTTP/S load balancing system that could handle a larger overall throughput and offer faster connections. Keslin says that this timely upgrade made it possible for his team to launch in Japan without an incident although the number of new user signups at this point was triple what it was during their earlier US launch.

We consider the problem of learning in single-player and multiplayer multiarmed bandit models. Bandit problems are classes of online learning problems that capture exploration versus exploitation tradeoffs. In a multiarmed bandit model, players can pick among many arms, and each play of an arm generates an i.i.d. reward from an unknown distribution. The objective is to design a policy that maximizes the expected reward over a time horizon for a single player setting and the sum of expected rewards for the multiplayer setting. In the multiplayer setting, arms may give different rewards to different players. There is no separate channel for coordination among the players. Any attempt at communication is costly and adds to regret. We propose two decentralizable policies, $\tt E^3$ ($\tt E$-$\tt cubed$) and $\tt E^3$-$\tt TS$, that can be used in both single player and multiplayer settings. These policies are shown to yield expected regret that grows at most as O($\log^{1+\epsilon} T$). It is well known that $\log T$ is the lower bound on the rate of growth of regret even in a centralized case. The proposed algorithms improve on prior work where regret grew at O($\log^2 T$). More fundamentally, these policies address the question of additional cost incurred in decentralized online learning, suggesting that there is at most an $\epsilon$-factor cost in terms of order of regret. This solves a problem of relevance in many domains and had been open for a while.

Not a day goes by when I don't hear another artificial intelligence horror case. If evoking more of a modern and less of a killer machine image is desired, the protagonist in Ex Machina (although no less scary) is selected. The audience is really interested now. Even more critical -- the end of the human race is beckoning! Going back to work is less motivating when you know you'll be replaced by your Roomba in a few years' time.

Japan plans to build a super-efficient computer that could vault it to the top of the world's supercomputer rankings by the end of next year. With a processing capacity of 130 petaflops, the planned computer would outperform the current world leader, China's Sunway TaihuLight, which delivers 93 petaflops. One petaflop is one million billion floating-point operations per second. Japan's National Institute of Advanced Industrial Science and Technology (AIST) isn't just aiming to build the world's fastest supercomputers, it also wants to make one of the most efficient. It is aiming for a power consumption of under 3 megawatts -- a staggering figure, given that Japan's current highest entry in the Top500 supercomputer list, Oakforest-PACS, delivers one-tenth the performance (13.6 petaflops) for the same power.

Active deep learning classification of hyperspectral images is considered in this paper. Deep learning has achieved success in many applications, but good-quality labeled samples are needed to construct a deep learning network. It is expensive getting good labeled samples in hyperspectral images for remote sensing applications. An active learning algorithm based on a weighted incremental dictionary learning is proposed for such applications. The proposed algorithm selects training samples that maximize two selection criteria, namely representative and uncertainty. This algorithm trains a deep network efficiently by actively selecting training samples at each iteration. The proposed algorithm is applied for the classification of hyperspectral images, and compared with other classification algorithms employing active learning. It is shown that the proposed algorithm is efficient and effective in classifying hyperspectral images.

Automated translation, including translating one programming language into another one (for instance, SQL to Python - the converse is not possible) Spell checks, especially for people writing in multiple languages - lot's of progress to be made here, including automatically recognizing the language when you type, and stop trying to correct the same word every single time (some browsers have tried to change Ning to Nong hundreds of times, and I have no idea why after 50 failures they continue to try - I call this machine unlearning) Detection of earth-like planets - focus on planetary systems with many planets to increase odds of finding inhabitable planets, rather than stars and planets matching our Sun and Earth Distinguishing between noise and signal on millions of NASA pictures or videos, to identify patterns Customized, patient-specific medications and diets Predicting oil demand, oil reserves, oil price, impact of coal usage Predicting chances that a container in a port contains ...

Automation and artificial intelligence are no longer hype, but reality – the Fourth Industrial Revolution has begun! Over the next 3-5 years, combining advances in simple, easily configurable RPA technology with cognitive capabilities will lead to the cost reductions, improved performance and enhanced real-time decision making that all adds up to massive competitive advantage. RPA and Artificial Intelligence for Enterprise unites the needs of the 250,000-strong SSON and PEX Network communities to bring together those furthest along the maturity curve in automated and intelligent service innovation, like Vodafone, Barclays, ENGIE and even NHS Wales Shared Services, with those who are just starting out, like SAB Miller, LV and National Grid, for a frank and open discussion surrounding the best ways to compete in the digital business era. Combining scores of practical end-user case studies, multiple conference streams surrounding human workforce augmentation across the front and back-offices and over 15 hours of interactive sessions and networking, this is your one-stop shop for ensuring you build the value-adding, scalable, intelligent processes that meet the business needs of the future.

Not a day goes by when I don't hear another artificial intelligence horror case. There's something called artificial intelligence coming up. This big unknown is personified with a picture of the Terminator. If evoking more of a modern and less of a killer machine image is desired, the protagonist in Ex Machina (although no less scary) is selected. The audience is really interested now.

More than fifty years of research in molecular biology have demonstrated that the ability of small and large molecules to interact with one another and propagate the cellular processes in the living cell lies in the ability of these molecules to assume and switch between specific structures under physiological conditions. Elucidating biomolecular structure and dynamics at equilibrium is therefore fundamental to furthering our understanding of biological function, molecular mechanisms in the cell, our own biology, disease, and disease treatments. By now, there is a wealth of methods designed to elucidate biomolecular structure and dynamics contributed from diverse scientific communities. In this survey, we focus on recent methods contributed from the Robotics community that promise to address outstanding challenges regarding the disparate length and time scales that characterize dynamic molecular processes in the cell. In particular, we survey robotics-inspired methods designed to obtain efficient representations of structure spaces of molecules in isolation or in assemblies for the purpose of characterizing equilibrium structure and dynamics. While an exhaustive review is an impossible endeavor, this survey balances the description of important algorithmic contributions with a critical discussion of outstanding computational challenges. The objective is to spur further research to address outstanding challenges in modeling equilibrium biomolecular structure and dynamics.