The law is pretty firm when it comes to drunk driving -- the consequences are obvious. Now, officials are turning their attention to drunk droning. New Jersey has just approved a bill that, if signed by Governor Chris Christie, would make it illegal to fly one under the influence of drugs or alcohol. Operating a drone with a blood alcohol concentration of 0.8 percent of more (the same legal limit for driving a vehicle) would be considered a disorderly offence under the new rules, and would carry a $1,000 fine and up to six months in prison. There's been no shortage of drone crash stories in the news in recent times -- one of the most famous being the 3AM crash land on White House grounds in 2015, thanks to a drunken mishap.

Artificial Intelligence and Robotics have already become part of our daily lives. AI is becoming highly autonomous by day and acquiring more and more cognitive abilities. Moreover, some futurists such as Elon Musk believe that AI may acquire such high powers that it may even impose a threat on mankind. Well, it cannot be denied: AI has already become a part of society. Since ethics and legal rules are the underlying ingredients of a functioning society; where does AI fit in to all of this?

Being able to tell when a person is lying is an important part of everyday life, but it's even more crucial in a courtroom. People may vow under oath that they will tell the truth, but they don't always adhere to that promise, and the ability to spot those lies can literally be the difference between a verdict of innocent or guilty. To address this issue, researchers from the University of Maryland (UMD) developed the Deception Analysis and Reasoning Engine (DARE), a system that uses artificial intelligence (AI) to autonomously detect deception in courtroom trial videos. The team of UMD computer science researchers led by Center for Automation Research (CfAR) chair Larry Davis describe their AI that detects deception in a study that's still to be peer-reviewed. DARE was taught to look for and classify human micro-expressions, such as "lips protruded" or "eyebrows frown," as well as analyze audio frequency for revealing vocal patterns that indicate whether a person is lying or not.

TORONTO, Jan. 9, 2018 /PRNewswire/ – TD Bank Group (TD) (TSX and NYSE: TD) today announced the acquisition of Layer 6 Inc. ("Layer 6"), a world-renowned artificial intelligence (AI) company based in Toronto, Ontario. Layer 6 has emerged as a global thought-leader and pioneer in the delivery of responsive, personalized and insight-driven experiences for the financial services industry. Layer 6 founders Tomi Poutanen and Jordan Jacobs are also co-founders of the Vector Institute, a world leader in AI research and education that TD also supports. "Anticipating and meeting customer needs are at the heart of our promise, and we are excited to further accelerate our innovation agenda to deliver well into the future. As we deploy new solutions, we will extend our deep relationship with customers across all of our platforms and offer personalized, connected and legendary experiences for our customers in the digital age."

Technology innovators intensified efforts over the last year to secure intellectual property rights on transformative technologies ranging from AI to blockchain platforms. While IBM maintained its overwhelming lead in U.S. patent awards, Chinese manufacturers continue to make steady progress gaining control of IP while the growth rate for patent awards in machine learning soared. An annual patent survey released by IFI Claims Patent Services found that machine-learning patent applications are among the fastest growing categories. Larry Cady, a senior analyst with IFI, estimates the patent category that includes machine learning and neural networks grew at a 34 percent annual rate over the last five years. "I don't see that trend slowing," Cady added in an interview.

The field of Multi-Agent System (MAS) is an active area of research within Artificial Intelligence, with an increasingly important impact in industrial and other real-world applications. Within a MAS, autonomous agents interact to pursue personal interests and/or to achieve common objectives. Distributed Constraint Optimization Problems (DCOPs) have emerged as one of the prominent agent architectures to govern the agents' autonomous behavior, where both algorithms and communication models are driven by the structure of the specific problem. During the last decade, several extensions to the DCOP model have enabled them to support MAS in complex, real-time, and uncertain environments. This survey aims at providing an overview of the DCOP model, giving a classification of its multiple extensions and addressing both resolution methods and applications that find a natural mapping within each class of DCOPs. The proposed classification suggests several future perspectives for DCOP extensions, and identifies challenges in the design of efficient resolution algorithms, possibly through the adaptation of strategies from different areas.

He previously held directorships in both public libraries and corporate libraries and earned joint master's degrees in Library and Information Sciences and Medieval European History from Catholic University of America. Self-aware robots, androids or call-them-what-you-will have been part of science fiction almost from its beginnings. But let's take a step back and consider what we know already, and then move on to what may soon be coming. Think about programs like Google's DeepMind, or the natural language generation program Wordsmith. Because the programs themselves involve no specific human interference or guidance, the results might be best construed as the intellectual property of those who "worked the machine," i.e. the users.

As 2018 begins Supply Management rounds up some predictions for the year ahead. Brexit's questions will start to be answered: While 2017 was the year of Brexit uncertainty, 2018 will be the year where things start to change. Procurement teams will need to start proactively helping their businesses deal with that change and minimise exposure to contract and legal risk, says Sam De Silva, partner at law firm CMS Cameron McKenna Nabarro Olswang. Procurement can no longer be a discrete and siloed function, says Shivani Govil, VP solutions management at SAP Ariba. "In the year ahead, companies will move away from niche offerings that address pieces of the procurement puzzle towards integrated platforms." Automation, machine learning and artificial intelligence will continue to be buzzwords for the year ahead.

Technology innovators intensified efforts over the last year to secure intellectual property rights on transformative technologies ranging from AI to blockchain platforms. While IBM maintained its overwhelming lead in U.S. patent awards, Chinese manufacturers continue to make steady progress gaining control of IP while the growth rate for patent awards in machine learning soared. An annual patent survey released by IFI Claims Patent Services found that machine-learning patent applications are among the fastest growing categories. Larry Cady, a senior analyst with IFI, estimates the patent category that includes machine learning and neural networks grew at a 34 percent annual rate over the last five years. "I don't see that trend slowing," Cady added in an interview.

The rise of big data policing rests in part on the belief that data- based decisions can be more objective, fair, and accurate than traditional policing. Data is data and thus, the thinking goes, not subject to the same subjective errors as human decision making. As David Vladeck, the former director of the Bureau of Consumer Protection at the Federal Trade Commission (who was, thus, in charge of much of the law surrounding big data consumer protection), once warned, "Algorithms may also be imperfect decisional tools. Algorithms themselves are designed by humans, leaving open the possibility that unrecognized human bias may taint the process. And algorithms are no better than the data they process, and we know that much of that data may be unreliable, outdated, or reflect bias."