Recently, the federal office of Science and Technology Policy issued a request for public feedback on "overarching questions in [Artificial Intelligence], including AI research and the tools, technologies, and training that are needed to answer these questions." OSTP is in the process of co-hosting four public workshops in 2016 on topics in AI in order to spur public dialogue on these topics and to identify challenges and opportunities related to this emerging technology. These topics include the legal and governance issues for AI, AI for public good, safety and control for AI, and the social and economic implications of AI. The Request for Information lists 10 specific topics on which the government would appreciate feedback, including "the use of AI for public good" and "the most pressing, fundamental questions in AI research, common to most or all scientific fields." One of the academics who answered the request for information is Shannon Vallor, who is the William J. Rewak Professor at Santa Clara University, and one of the Markkula Center for Applied Ethics' faculty scholars.

The same hacking group that took over Mark Zuckerberg's Twitter account has now found a way to break into accounts connected to the hit game Minecraft. The group, OurMine, made the claim on Tuesday in a video demonstrating its hack. The attack is aimed at the user login page run by Minecraft's developer, Mojang. OurMine isn't revealing all the details behind the hack. The group said it works by stealing the Internet cookies from the site, which can be used to hijack any account.

Companies working on self-driving cars need to focus on safety -- "we want people who start a trip to finish it," Transportation Secretary Anthony Foxx announced Tuesday, saying his department will issue new guidelines on the vehicles this summer. "Autonomous doesn't mean perfect," he told attendees at an industry conference in San Francisco. "We need industry to take the safety aspects of this very seriously." Foxx's remarks come in the wake of May's fatal crash involving a Tesla Model S sedan being used in semi-autonomous "autopilot" mode. The car crashed into a truck that the autopilot feature did not sense, killing the car's driver. The Transportation Department has been working with Google, BMW, General Motors and other companies developing driverless and partly autonomous cars to adapt existing safety rules to the new technologies.

Tesla's Autopilot uses a combination of sensors and cameras to monitor the car's environment. The recent crash of Tesla Model S under Autopilot control has raised some serious concerns about the safety of autonomous driving features on Teslas, in particular, and all cars in general. The US National Highway Traffic Safety Administration (NHTSA)--the organization that offers the 5-star safety rating systems for new cars--is investigating the details of the unfortunate incident and may come up with more guidelines in this area, which many people believe is severely lacking in any real oversight. Much has already been written on the issue, but everything I've seen has ignored the key question that this incident has brought to our attention. Is it really reasonable or safe to offer a semi-autonomous driving mode, where a driver temporarily gives over complete control of an auto to computer-controlled systems within the car, but then needs to take it back under certain situations (such as a potential safety hazard)? To put it in the language of NHTSA and their guidelines for the development of autonomous driving technology, should there really be a Level 3 for autonomous driving?

The Great Convergence continues in compressive sensing hardware and machine learning: DeepBinaryMask: Learning a Binary Mask for Video Compressive Sensing by Michael Iliadis, Leonidas Spinoulas, Aggelos K. Katsaggelos In this paper, we propose a novel encoder-decoder neural network model referred to as DeepBinaryMask for video compressive sensing. In video compressive sensing one frame is acquired using a set of coded masks (sensing matrix) from which a number of video frames is reconstructed, equal to the number of coded masks. The proposed framework is an end-to-end model where the sensing matrix is trained along with the video reconstruction. The encoder learns the binary elements of the sensing matrix and the decoder is trained to recover the unknown video sequence. The reconstruction performance is found to improve when using the trained sensing mask from the network as compared to other mask designs such as random, across a wide variety of compressive sensing reconstruction algorithms.

A Land Rover team of engineers is helping the Land Rover BAR team make its America's Cup entry faster. America's Cup is the pinnacle of sailing competitiveness. Teams spend upwards of 100 million just to sniff success and rumor has it Oracle Team USA owner Larry Ellison blew past that by nearly double when his team won it in 2013. However, in an attempt to bring the Cup to Great Britain for the first time ever, one team is looking not to outspend its opponents, but to outthink them. The Land Rover BAR team is using the brains from one of its sponsors – Land Rover – to build a faster boat … faster because it's smarter.

Learn how to use Machine Learning in your projects using actual production-ready python frameworks, namely Python Scikit-Learn (for most code) and TensorFlow (for neural nets). This book favors a practical approach using real-life production-ready tools, and it builds up instincts quickly using concrete examples and minimal theory (avoiding spending too much time on excessive theory and unnecessary details of every algorithm).

In the past couple of years, artificial intelligence, or AI, has come to the fore as an emerging technology that will disrupt the way business is done. This has happened before, several times, since the 1980s, with AI becoming the hot new thing, only to cool off when it's realized there is a lack of business cases. This time may or may not be different, but it's instructional to take a look at what its impact could be on insurance operations. In a new report, NTT DATA Consulting did just that, examining the potential impact on agents and underwriters in the new age of digital personal assistants and robots and bots. AI may not be a perfect fit for insurance companies – at least not yet.

Last Friday, at NYU's Skirball Center, the White House hosted a symposium on Artificial Intelligence, ethics, health, and machine learning. Led by Kate Crawford, a prinicipal researcher at Microsoft Research, and Meredith Whittaker, lead for Google Open Source Research Group. The day time events (invitation only) consisted of lightening talks from researchers at IBM Watson, Microsoft, policy makers, lawyers, artists and data visualizers such as Jer Thorp (blprnt). It was an incredibly diverse crowd, from careers to gender to race, and was something that the organizers had intended and carefully curated for the event itself. To create and germinate better discussions around AI, and to make better artificial intelligence, the group better be diverse, and AINow beyond succeeded with that.

According to a new release, "Zendesk, Inc. today announced the launch of Automatic Answers, a feature powered by machine learning within Zendesk that helps customers solve their inquiries faster and enables businesses to have more efficient support teams. Zendesk is one of the first customer service platforms implementing machine learning to natively auto-respond to customer tickets with relevant knowledge base articles, helping solve and deflect customer inquiries before they ever reach an agent. Automatic Answers was developed in Australia by Zendesk's Melbourne-based development team, who previously brought Satisfaction Prediction to market and was awarded the 2016 Victorian iAward for Big Data Innovation of the Year."