Artificial intelligence has been on humanity's radar for quite some time. But recent developments such as AlphaGo's triumph over Lee Sedol and Watson winning the quiz show Jeopardy have given us an even greater and discomfiting awareness of the vast strides that have been made in this field. On the other hand, Microsoft's famously bungled attempt to create an AI Twitter account, which started spouting racist and genocidal vitriol within a day, reminds us that an AI-orchestrated apocalypse is still far off. Indeed, the simple fact of the matter that all iPhone users realize is that Siri hasn't even quite mastered the nuances of human colloquialisms and syntax. This is precisely the point that the Winograd Schema Challenge seeks to prove.

Progressive is one of the largest auto insurers in the United States with over 13 million policies in force. Progressive is a pioneer in data analytics with more than 14 billion miles of driving data collected through its telematics offering, Snapshot. Hear from Pawan Divakarla, Data and Analytics Business Leader, on how H2O is helping build models and derive insights in just seconds using open source Machine Learning. Highlights from video: "Predictive analytics is making a very positive culture shift and I see it growing exponentially. It's fostering a lot more creativity…giving that empowerment to our data scientists is key for us. H2O is an enabler in how people are thinking about data."

Japan-based IT products and services provider Fujitsu has collaborated with Jorudan which is a public transport and route navigation website. Both will be adding a train delay time prediction function, using Artificial Intelligence (AI) machine learning technology, to Jorudan's "Norikae Annai," a service that provides public transportation route-planning information. With this collaboration, Fujitsu aims to deliver, predicted train delay times using AI technology for the Norikae Annai service and verify prediction effectiveness. This process is provided as the cloud service Fujitsu Intelligent Society Solution SPATIOWL, a service that studies previous railway operations data and data submitted by the users and with this, it forecasts the changing delay times built on fresh submitted data and operational information. These predictions are displayed in the route search results in Jorudan's Norikae Annai app, supporting users' route selection when trains are delayed.

Twelve months ago, in my one-bedroom apartment in NYC, my founding team at Wade & Wendy got together on a mission to make the recruiting process human again. The four of us had experienced the many pains of the recruiting process from all angles. Our CTO, Josh Brandoff, a highly-sought after engineer bombarded by pushy recruiters every time he joins a new social network. Our Head of Growth, Ian Jaffrey, a former chief of staff at American Express with a deep appreciation of the intimate mentorship it takes for career advancement. Our Head of Product Design, Adrian Von Der Osten, who was trained and educated in the field of architecture but found himself in the wonderful world of startups doing user experience design, a radical career jump many can only dream of.

When I woke up this morning, I asked my assistant a simple question: "Siri, is it going to rain today?" Siri understood my intent, pulled the local weather data via an API and answered me in less than two seconds: "There's no rain in the forecast for today." In the not-too-distant past, this kind of human-computer interaction would have blown away technologists and delighted consumers -- but in 2016, it's nothing special. Conversations with Siri are commonplace, just like they are with Microsoft's Cortana and Amazon's Alexa. Machine learning (ML) and narrow forms of artificial intelligence (AI) have officially reached the mainstream.

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.