The fatal crash of a Tesla Motors Inc Model S in Autopilot mode has turned up pressure on auto industry executives and regulators to ensure that automated driving technology is deployed safely. The first such known accident, which occurred in Florida in May, has highlighted tensions surrounding efforts to turn over responsibility for braking, steering and driving judgements to machines. It may delay the US government's plan to outline guidelines for self-driving cars this month. The cause of the Model S crash is still under investigation by federal and Florida state authorities, which are looking into whether the driver was distracted before his 2015 Model S went under a truck trailer. Shares of Tesla and Mobileye NV, the maker of the camera vision system used in the Model S, rose on July 1 as analysts said the accident was likely a short-term setback.

ISLAMABAD – A U.S. Senate delegation paid a rare visit Sunday to a tribal region along the Afghanistan border that has long been considered a stronghold of al-Qaida, the Taliban and other insurgents. The delegation led by Sen. John McCain, R-Ariz. McCain posted pictures on his Twitter account of the delegation visiting Pakistani helicopter pilots at an air base in Miram Shah. Foreigners are largely banned from the tribal region, where Pakistan has been waging a military offensive to root out insurgents for two years. The U.S. frequently carries out drone strikes in the region targeting Taliban and al-Qaida leaders.

Recent years have seen significant technical progress on robot planning, enabling robots to compute actions and motions to accomplish challenging tasks involving driving, flying, walking, or manipulating objects. However, robots that have been commercially deployed in the real world typically have no or minimal planning capability. These robots are often manually programmed, teleoperated, or programmed to follow simple rules. Although these robots are highly successful in their respective niches, a lack of planning capabilities limits the range of tasks for which currently deployed robots can be used. In this article, we highlight key conclusions from a workshop sponsored by the National Science Foundation in October 2013 that summarize opportunities and key challenges in robot planning and include challenge problems identified in the workshop that can help guide future research towards making robot planning more deployable in the real world.

The Association for the Advancement of Artificial Intelligence presented the 2015 Fall Symposium Series, on Thursday through Saturday, November 12-14, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the six symposia were as follows: AI for Human-Robot Interaction, Cognitive Assistance in Government and Public Sector Applications, Deceptive and Counter-Deceptive Machines, Embedded Machine Learning, Self-Confidence in Autonomous Systems, and Sequential Decision Making for Intelligent Agents. This article contains the reports from four of the symposia.

Civil unrest events (protests, strikes, and “occupy” events) are common occurrences in both democracies and authoritarian regimes. The study of civil unrest is a key topic for political scientists as it helps capture an important mechanism by which citizenry express themselves. In countries where civil unrest is lawful, qualitative analysis has revealed that more than 75 percent of the protests are planned, organized, or announced in advance; therefore detecting references to future planned events in relevant news and social media is a direct way to develop a protest forecasting system. We report on a system for doing that in this article. It uses a combination of keyphrase learning to identify what to look for, probabilistic soft logic to reason about location occurrences in extracted results, and time normalization to resolve future time mentions. We illustrate the application of our system to 10 countries in Latin America: Argentina, Brazil, Chile, Colombia, Ecuador, El Salvador, Mexico, Paraguay, Uruguay, and Venezuela. Results demonstrate our successes in capturing significant societal unrest in these countries with an average lead time of 4.08 days. We also study the selective superiorities of news media versus social media (Twitter, Facebook) to identify relevant trade-offs.

The 2015 conference continued the tradition with a selection of 6 deployed applications describing systems in use by their intended end users, 13 emerging applications describing works in progress, and three papers in a new category for challenge problems. In the first article, Activity Planning for a Lunar Orbital Mission, John Bresina describes a deployed application of current planning technology in the context of a NASA mission called LADEE (Lunar Atmospheric and Dust Environment Explorer). Bresina presents an approach taken to reduce the complexity of the activity-planning task in order to perform it effectively under the time pressures imposed by the mission requirements. One key aspect of this approach is the design of the activity-planning process based on principles of problem decomposition and planning abstraction levels. The second key aspect is the mixed-initiative system developed for this task, the LADEE activity scheduling system (LASS). The primary challenge for LASS was representing and managing the science constraints that were tied to key points in the spacecraft's orbit, given their dynamic nature due to the continually updated orbit determination solution. In our second article, Helping Novices Avoid the Hazards of Data: Leveraging Ontologies to Improve Model Generalization Automatically with Online Data Source, Sasin Janpuangtong and Dylan Shell describe an emerging application of an endto-end learning framework for large-scale data analytics that allows a novice to create models from data easily by helping structure the model-building process.

This article describes a challenging, real-world planning problem within the context of a NASA mission called LADEE (Lunar Atmospheric and Dust Environment Explorer). I present the approach taken to reduce the complexity of the activity-planning task in order to perform it effectively under the time pressures imposed by the mission requirements. One key aspect of this approach is the design of the activity planning process based on principles of problem decomposition and planning abstraction levels. The second key aspect is the mixed-initiative system developed for this task, called LASS (LADEE Activity Scheduling System). The primary challenge for LASS was representing and managing the science constraints that were tied to key points in the spacecraft’s orbit, given their dynamic nature due to the continually updated orbit determination solution.

During his 15 years as president of the Service Employees International Union, Andy Stern was a controversial figure. He suffered his share of criticism from inside and outside the union. There was, however, no disputing his success in making SEIU the largest and fastest growing union in the country and a powerful political machine that was instrumental in electing President Obama and getting the Affordable Care Act passed. During Stern's tenure as national organizing director and president, he introduced and implemented strategies of industry-wide organizing and bargaining to counter the changing reality of employers who were becoming large and international. He took SEIU out of the AFL-CIO and formed a new labor federation called Change to Win, because he felt the mainstream labor movement was too conservative about organizing and limited its power by refusing to consolidate smaller unions into bigger and more powerful ones.

While everyone's eyes were on the Brexit vote and staggering economic implications this week, my mind was drifting to another crucial development within the EU. A draft European Parliament motion was recently put forward to begin to classify robots as "electronic persons." In a response to the proliferation of robotics and AI in the workplace, policy makers are attempting to stay ahead of the curve and preempt challenges they foresee around bureaucratic issues such as taxation and legal liability. As corporations rely more and more on automation and less and less on human workers, a new taxation structure will be required to ensure that governments still have funding flowing into their coffers. Moreover, as robotics become more sophisticated and we hand them increasingly critical tasks, issues around actor liability and insurance become very complicated.

Stealthy MedyMatch emerged in February with plans to improve emergency room care using cognitive analysis and artificial intelligence. Now, in its first collaboration with a U.S. hospital, the company is developing its first real-time decision-support tool using data from New Jersey-based Capital Health. Under the agreement, Capital Health will supply Israel-based MedyMatch with anonymized data to help it develop the tool, which will target stroke patients. It will analyze medical images and provide the ER radiologist with information to help him or her determine the course of treatment. It combines "deep vision, advanced cognitive analytics and artificial intelligence" to analyze images and identify anomalies that may be invisible to the human eye.