Perhaps the most photographed individual at the AI for Global Good Summit in Geneva, Switzerland, last week was not a human but a humanoid called Sophia. Cyber protection: Have you received the data call? "As I get smarter, I hope to understand people better -- help you, work with you as a friend, to imagine and build a better future for us all," Sophia, an uncannily human-like robot, said in a Facebook Live interview. In that interview and onstage at the summit, her eyebrows lifted, she smiled gently, and her eyes lit up as she answered questions from the audience, with moments where only the glimpse of cords behind her face revealed that she is a machine. Hanson Robotics developed Sophia as part of its mission to "create genius machines that live and love," and work together with humans to build a "a smarter and better future."

WASHINGTON – 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 have told 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. 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 reviews. Such deals would include joint ventures, minority stakes and early-stage investments in start-ups.

Massive breaches in corporate and government security in the last few years mean that most organizations are now aware of the importance of cybersecurity training. However, there are some challenges in training cybersecurity professionals. Mike Moniz, president, founder and CEO of Circadence Corp., identifies two additional challenges. First, there's a shortage of qualified cybersecurity instructors. Second, it's difficult to truly reproduce real-life cybersecurity threats with the same sophistication and passion that hackers or nation states have when they commit a cybercrime.

Lyft casts itself as the softer, kinder alternative to Uber. But there's nothing gentle about the way the country's second-largest ride-hailing company is capitalizing on its chief rival's missteps. While Uber has been engulfed in months of turmoil, capped off Tuesday by the release of the findings of an investigation into workplace culture and news of founder Travis Kalanick's hiatus, Lyft has raised an additional $600 million in funding to fuel its expansion. It has announced a deal with Jaguar Land Rover to unveil a fleet of luxury vehicles, once Uber's forte, and announced self-driving car partnerships with General Motors and Google's parent company, Alphabet, which is suing Uber, accusing it of stealing trade secrets. And don't think for a second that Lyft wasn't giddy about the #DeleteUber campaign in January, when social media users lashed out at Uber for appearing to continue to offer service at John F. Kennedy Airport in New York during a taxi strike over President Trump's ban on travel from seven majority Muslim countries.

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.