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As we approach the celebration of the 4th of July, I thought it might be interesting to share a request for information made to the US Federal Register and a post on the Whitehouse blog. The US government is interested in what Artificial Intelligence might mean to the people of the United States, and how we could learn about it more. To find out, they are asking for comments by July 22, 2016. Ed Felton, Deputy U.S. Chief Technology Officer wrote the following blog post about what the government would like to learn: How to Prepare for the Future of Artificial Intelligence. Broadly, OSTP is interested in developing a view of AI across all sectors for the purpose of recommending directions for research and determining challenges and opportunities in this field.

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. On July 1, the US National Highway Traffic Safety Administration (NHTSA) said US traffic deaths rose by 7.7% to 35,200 in 2015 – the highest annual tally since 2008 and biggest single-year jump since 1966. In March, 20 automakers agreed with regulators to make automatic emergency braking standard on nearly all US vehicles by 2022, a move that could prevent thousands of rear-end crashes annually. Hours before the crash became public knowledge on June 30, US National Transportation Safety Board Chairman Christopher Hart said driverless cars will not be perfect.

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.

Happy almost Independence Day, America, now prepare to witness the impending doom stalking humanity in the form of drones that are "grown" from chemical compounds. SEE ALSO: The FAA's new rules for drones are bad news for Amazon Evoking images from the liquid metal and nearly unstoppable Terminator from the movies, BAE Systems has posted concept video footage of a process to create drones of the future. "A radical new machine called a Chemputer could enable advanced chemical processes to grow aircraft and some of their complex electronic systems, conceivably from a molecular level upwards," reads the description on the company's website. The video shows a drone being produced in a large vat of chemicals and then moved to a staging area where a robotic arm inserts what are, presumably, the drone's engine components. Sure this is just a concept, but BAE Systems has a track record of turning concepts into reality, so this isn't just another concept video promising unlikely inventions.

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.

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.

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.

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.