As part of yesterday's WorldWide Developer Conference (WWDC) Google launched their foray into home connectivity with the HomePad called by Apple CEO Tim Cook, "breakthrough home speaker with amazing sound and incredible intelligence that will reinvent home audio." Don't overlook the last word, in his statement, despite the desire some will have to group HomePad with voice activatedAmazon Echo and Google Home, the product really belongs in a separate category, and here's why. The HomePod is a 7-inch tall smart speaker covered in a "seamless 3D mesh" fabric It contains a four-inch subwoofer According to Apple it " uses an advanced algorithm that continuously analyzes the music and dynamically tunes the low frequencies for smooth, distortion‑free sound." This includes "seven beamforming tweeters" that possess spatial awareness and direct the sound beams throughout the room. It automatically analyzes the acoustics, adjusting the sound based on the speaker's location, and steers the music in the optimal direction. According to Tim Cook: "Just like with portable music, we want to reinvent home music."

For anyone who's getting their first introduction to Einstein AI features, now rapidly being deployed in the Salesforce platform, two questions may arise. Why is this different from past cycles of AI excitement and disappointment? Why is an integrated layer of AI capability so crucial to a modern CRM? Six decades of research, development, and sci-fi hype have followed the first famous "Dartmouth memo" – which naïvely proposed "a 2 month, 10 man study" as being enough to make "a significant advance" in "how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves." The challenge was clearly much greater than first realized: most observers agree that people had vastly underestimated the portion of our intelligence that comes from what we know, rather than how cleverly we think. In laboratory settings and research universities, the focus was on making software smart.

For the first time on Broadway human and drone performances fuse to create a new form of artistic expression. The magic happened in Cirque du Soleil's first musical on Broadway: 'Paramour' at the Lyric Theatre. The show is themed on the Golden Age of Hollywood and follows the life of a poet who is forced to choose between love and art. The contributions of the technology firm Verity Studios include the choreography of the drone show segment, the frame and lighting design of the drone costumes, and all underlying drone technologies. The system was operated by the show's automation team, with Verity Studios providing maintenance services twice per year.

A screen shows information on 5G during a keynote address by CEO of Huawei Consumer Business Group Richard Yu at CES 2017 at The Venetian Las Vegas on January 5, 2017 in Las Vegas, Nevada. CES, the world's largest annual consumer technology trade show, runs through January 8 and features 3,800 exhibitors showing off their latest products and services to more than 165,000 attendees. Every product in today's social media-driven world is, in a sense, overhyped. But even taking that into consideration, consumer electronics probably take the cake for most shameless overpromising, and none more than the brouhaha surrounding AI and VR the past couple of years. Every commercial for VR headsets, whether it's by LG or Samsung or Sony, want you to believe that putting on the device is to step into a truly immersive experience, except no, the pixels are clearly visible, there's a bit of lag, and the headsets are not comfortable to wear more than 15 to 20 minutes at a time.

MUELHEIM-KAERLICH, GERMANY – As head of the Muelheim-Kaerlich nuclear reactor, Thomas Volmar spends his days plotting how to tear down his workplace. The best way to do that, he says, is to cut out humans. About 200 nuclear reactors around the world will be shut down over the next quarter century, mostly in Europe, according to the International Energy Agency. That means a lot of work for the half a dozen companies that specialize in the massively complex and dangerous job of dismantling plants. Those firms -- including Areva, Rosatom's Nukem Technologies Engineering Services, and Toshiba's Westinghouse -- are increasingly turning away from humans to do this work and instead deploying robots and other new technologies.

The experts go on to predict a 50 percent chance that AI will be better than humans at more or less everything in about 45 years. Artificial intelligence is changing the world and doing it at breakneck speed. The promise is that intelligent machines will be able to do every task better and more cheaply than humans. Rightly or wrongly, one industry after another is falling under its spell, even though few have benefited significantly so far. And that raises an interesting question: when will artificial intelligence exceed human performance?

Multi-agent forward search (MAFS) is a state-of-the-art privacy-preserving planning algorithm. We describe a new variant of MAFS, called multi-agent forward-backward search (MAFBS) that uses both forward and backward messages to reduce the number of messages sent and obtain new privacy properties. While MAFS requires agents to send a state s produced by an action a to all agents that can apply any action in s, MAFBS sends such messages forward only to agents that have an action that requires one of the effects of a. To achieve completeness, it sends messages backward to agents that can supply a missing precondition. This more focused message passing scheme reduces states exchanged, and requires that agents be aware only of other agents that they directly interact with, leading to agent privacy.

Stability of the policy update is a major issue for these methods, rendering them hard to apply for highly nonlinear systems. Recent approaches combine classical Stochastic Optimal Control methods with information-theoretic bounds to control the step-size of the policy update and could even be used to train nonlinear deep control policies. These methods bound the relative entropy between the new and the old policy to ensure a stable policy update. However, despite the bound in policy space, the state distributions of two consecutive policies can still differ significantly, rendering the used local approximate models invalid. To alleviate this issue we propose enforcing a relative entropy constraint not only on the policy update, but also on the update of the state distribution, around which the dynamics and cost are being approximated. We present a derivation of the closed-form policy update and show that our approach outperforms related methods on two nonlinear and highly dynamic simulated systems.

The problem of finding conflict-free trajectories for multiple agents of identical circular shape, operating in shared 2D workspace, is addressed in the paper and decoupled, e.g., prioritized, approach is used to solve this problem. Agents' workspace is tessellated into the square grid on which any-angle moves are allowed, e.g. each agent can move into an arbitrary direction as long as this move follows the straight line segment whose endpoints are tied to the distinct grid elements. A novel any-angle planner based on Safe Interval Path Planning (SIPP) algorithm is proposed to find trajectories for an agent moving amidst dynamic obstacles (other agents) on a grid. This algorithm is then used as part of a prioritized multi-agent planner AA-SIPP(m). On the theoretical side, we show that AA-SIPP(m) is complete under well-defined conditions. On the experimental side, in simulation tests with up to 250 agents involved, we show that our planner finds much better solutions in terms of cost (up to 20%) compared to the planners relying on cardinal moves only.

Collaboration between humans and robots to accomplish different kinds of tasks has been recently studied as a planning problem and several techniques have been developed to define and generate shared plans where humans and robots collaborate to achieve a common goal. However, current methods require the knowledge of the human about the plan under execution and an agreement between users and robots about their roles before the execution of the plan. In this paper, we propose an extension to the Hierarchical Agent-based Task Planner (HA TP) that enables humans and robots to negotiate some aspects of the collaboration online during the execution of the plan. The proposed method is based on the automatic generation of a conditional plan in which missing information is acquired at execution time by means of sensing actions. The proposed method has been fully implemented and tested on a real robot performing collaborative tasks in an office-like environment.