The American transportation industry has been calling for national rules governing self-driving cars, and it looks like it might get its wish. Senators Bill Nelson, Gary Peters and John Thune have unveiled the principles they'll use to craft legislation that greenlights autonomous vehicles. Safety will be the top priority, they say, but they also want make sure the law is "tech neutral," clears up the roles of federal and state governments and improves cars' online security. And importantly, they want to "reduce existing roadblocks" in the law -- after all, many laws assume that someone needs to take the wheel. Don't get your hopes up for legislation in the immediate future.

Bike Sharing System (BSS) is a green mode of transportation that is employed extensively for short distance travels in major cities of the world. Unfortunately, the users behaviour driven by their personal needs can often result in empty or full base stations, thereby resulting in loss of customer demand. To counter this loss in customer demand, BSS operators typically utilize a fleet of carrier vehicles for repositioning the bikes between stations. However, this fuel burning mode of repositioning incurs a significant amount of routing, labor cost and further increases carbon emissions. Therefore, we propose a potentially self-sustaining and environment friendly system of dynamic repositioning, that moves idle bikes during the day with the help of bike trailers. A bike trailer is an add-on to a bike that can help with carrying 3-5 bikes at once. Specifically, we make the following key contributions: (i) We provide an optimization formulation that generates “repositioning” tasks so as to minimize the expected lost demand over past demand scenarios; (ii) Within the budget constraints of the operator, we then design a mechanism to crowdsource the tasks among potential users who intend to execute repositioning tasks; (iii) Finally, we provide extensive results on a wide range of demand scenarios from a real-world data set to demonstrate that our approach is highly competitive to the existing fuel burning mode of repositioning while being green.

Robots acting in human-scale environments must plan under uncertainty in large state-action spaces and face constantly changing reward functions as requirements and goals change. Planning under uncertainty in large state-action spaces requires hierarchical abstraction for efficient computation. We introduce a new hierarchical planning framework called Abstract Markov Decision Processes (AMDPs) that can plan in a fraction of the time needed for complex decision making in ordinary MDPs. AMDPs provide abstract states, actions, and transition dynamics in multiple layers above a base-level "flat" MDP . AMDPs decompose problems into a series of subtasks with both local reward and local transition functions used to create policies for subtasks. The resulting hierarchical planning method is independently optimal at each level of abstraction, and is recursively optimal when the local reward and transition functions are correct. We present empirical results showing significantly improved planning speed, while maintaining solution quality, in the Taxi domain and in a mobile-manipulation robotics problem. Furthermore, our approach allows specification of a decision-making model for a mobile-manipulation problem on a Turtlebot, spanning from low-level control actions operating on continuous variables all the way up through high-level object manipulation tasks.

We investigate solvable-unsolvable phase transitions in the single-machine scheduling (SMS) problem. SMS is at the core of practical problems such as telescope and satellite scheduling and manufacturing. To study the solvability phase transition, we construct a variety of instance families param-eterized by the set of the processing times, the window size (deadline minus release time), and the horizon. We empirically establish the phase transition and look for an easy-hard- easy pattern for this family using several common solvers. While in many combinatorial problems a phase transition coincides with typically hard instances, whether or not that is the case with SMS remains an open question, and merits further study.

When minimizing makespan during off-line planning, the fastest action sequence to reach a particular state is, by definition, preferred. When trying to reach a goal quickly in on-line planning, previous work has inherited that assumption: the faster of two paths that both reach the same state is usually considered to dominate the slower one. In this short paper, we point out that, when planning happens concurrently with execution, selecting a slower action can allow additional time for planning, leading to better plans. We present Slo'RTS, a metareasoning planning algorithm that estimates whether the expected improvement in future decision-making from this increased planning time is enough to make up for the increased duration of the selected action. Using simple benchmarks, we show that Slo'RTS can yield shorter time-to-goal than a conventional planner. This generalizes previous work on metareasoning in on-line planning and highlights the inherent uncertainty present in an on-line setting.

The United States appears poised to heighten scrutiny of Chinese investment in Silicon Valley to better shield sensitive technologies seen as vital to U.S. national security, current and former U.S. officials tell Reuters. Of particular concern is China's interest in fields such as artificial intelligence and machine learning, which have increasingly attracted Chinese capital in recent years. The worry is that cutting-edge technologies developed in the United States could be used by China to bolster its military capabilities and perhaps even push it ahead in strategic industries. Of particular concern is China's interest in fields such as artificial intelligence and machine learning, which have increasingly attracted Chinese capital in recent years. The U.S. government is now looking to strengthen the role of the Committee on Foreign Investment in the United States (CFIUS), the inter-agency committee that reviews foreign acquisitions of U.S. companies on national security grounds. An unreleased Pentagon report, viewed by Reuters, warns that China is skirting U.S. oversight and gaining access to sensitive technology through transactions that currently don't trigger CFIUS review.

Uber CEO Travis Kalanick announced that he would take an indefinite leave of absence on Tuesday as the embattled company released a damning report on its workplace culture that called on the company to "review and reallocate" Kalanick's responsibilities. "I need to take some time off of the day-to-day to grieve my mother, whom I buried on Friday, to reflect, to work on myself, and to focus on building out a world-class leadership team," Kalanick wrote in an email to staff that referenced the death of his mother last month in a boating accident. "If we are going to work on Uber 2.0, I also need to work on Travis 2.0 to become the leader that this company needs and that you deserve." Kalanick's leave comes at a time of considerable turmoil for the ride-hail app. On Sunday, the board of directors voted unanimously to adopt the recommendations of a workplace review led by the law firm of former US attorney general Eric Holder.

The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar. Japan's On-Art Corp's CEO Kazuya Kanemaru poses with his company's eight metre tall dinosaur-shaped mechanical suit robot'TRX03' and other robots during a demonstration in Tokyo, Japan Japan's On-Art ...

The US Defense Advanced Research Projects Agency (DARPA) will officially begin collaborating with Intel on a computing platform that uses artificial intelligence (AI) and machine learning to make it easier for businesses to utilize graph analytics in big data, the pair announced on Monday. The partnership is an expansion of DARPA's Hierarchical Identify Verify & Exploit (HIVE) program. The HIVE program is working to "develop new technologies to realize 1,000x performance-per-watt gains in the ability to handle graph analytics," according to a press release. Graph analytics focus on the many-to-many relationships in data, giving insight into multi-layered, indirect relationships in those datasets, the release said. Graph analytics are also helpful in gleaning insights from large and complex datasets.

Google's latest effort to thaw relations with China involves an artificial intelligence pow-wow -- and a few games of Go. Google's latest effort to thaw relations with China involves an artificial intelligence pow-wow -- and a few games of Go. Years after Beijing locked out virtually every Alphabet Inc. service, executive chairman Eric Schmidt and a cadre of mid-level Chinese government officials kicked off a summit in the historic canal-laced town of Wuzhen Tuesday: a rare instance of the search leader working in tandem with the country's bureaucrats at a high-profile public event. Google experts and prominent local academics will exchange notes and host discussions but the centerpiece will be a contest between DeepMind's so-far undefeated AlphaGo system and Ke Jie, local champion of the 2,500-year-old strategy board game Go.