A new paradigm will shake up the IP landscape, as cognitive activities once performed by humans will now increasingly be performed by machines. Artificial intelligence and machine learning (collectively AI) are hot topics in almost every industry, affecting everything from robotics, autonomous vehicles, and consumer devices to health and pharmaceutical technologies. AI is being used to solve complex problems and improve decision making, as well as to develop new products and processes. AI systems utilize algorithms that enable those systems to learn and develop through the analysis of information they are provided, oftentimes without human intervention or instruction. U.S. Patent Law defines the inventor as "the individual or, if a joint invention, the individuals collectively who invented or discovered the subject matter of the invention."

The Web app, which is powered partly by artificial intelligence, analyzes posts on social media as well as police radio chatter and feeds of the local airspace in virtually any region. The software, which is linked to IBM's Watson artificial intelligence, combs through tweets and images, specific hashtags and phrases, or posts from or about a particular geographic area and then uses computer algorithms to gauge the mood of that swirling digital conversation. The AI aspects of the iAWACS app only monitor the social media posts -- they don't analyze the audio from police scanners nor from the airspace maps. The result, which the Jester said was still a work in progress, was built from the ground up for law enforcement and intelligence officials with real-time information needs.

In this talk, I will introduce the audience to the emerging area of computational social science, focusing on how machine learning for social science differs from machine learning in other contexts. I will present two related models -- both based on Bayesian Poisson tensor decomposition -- for uncovering latent structure from count data. The first is for uncovering topics in previously classified government documents, while the second is for uncovering multilateral relations from country-to-country interaction data. Finally, I will talk briefly about the broader ethical implications of analyzing social data. Hanna Wallach is a Senior Researcher at Microsoft Research New York City and an Adjunct Associate Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst.

Video has taken the world by storm with a myriad of intelligent devices continuously capturing vast amounts of data about how people live and what they do. At the end of 2014, IHS Technology estimated over 245 million operational cameras were active globally. London alone has 500,000 cameras dotted throughout the city, which works out at about one camera for every 16 people. Thanks to smart cameras, CCTV devices, and even drones mounted with intelligent cameras, users are able to record videos at an unprecedented scale and pace. This vast store of data-rich content is used for a range of purposes – from gaming and law enforcement to crowd management at large events.

Dr George Beaton is a partner in beaton and a senior fellow in Melbourne Law School, Australia. His published works include NewLaw New Rules – A Conversation About the Future of the Legal Services Industry (2013) and Remaking Law Firms: Why & How (2016). You have been a pioneer in research into NewLaw, what place does technology have in NewLaw? Is it central to its development? Just 18 months ago when I wrote Fresh thinking on the evolving BigLaw–NewLaw taxonomy little mention was made of the role of technology in NewLaw or BigLaw business model firms.

At least, that's what Google and so many business and tech journalists said when the search giant first faced antitrust complaints in Europe six years ago. And indeed, Microsoft had filed one of those complaints. It was also the money-weilding mastermind behind the Initiative for a Competitive Online Marketplace, a group that lobbied the European Union and helped others bring complaints against arch-rival Google. But all these years later, Microsoft has removed itself from the fight, reaching an agreement with Google that says both companies will drop all regulatory complaints against each other. And yet, Google's antitrust problems are only getting worse. Yesterday, the European Union announced a new round of antitrust charges against Google, this time pointing the finger at the company's advertising service, some of the most important tech in Google's moneymaking arsenal.

The other day, a search for "artificial intelligence in law" produced 86,400 results from just the News section of Google's vast index. From the Web as a whole, 32,800,000 results and from Videos – 261,000, beginning with Jude Law's role as Gigolo Joe in the movie A.I. (thank you, RankBrain). The first News story was "Law firm bosses envision Watson-type computers replacing young lawyers," reporting on the answers to one question in the recent Altman & Weil survey of law firm leaders (page 82). As wittily argued by Ryan McClead, "the question is flawed on many levels [and] … it's time to cut the hysteria surrounding artificial intelligence in law." But we need to parse the pile a bit.

Lawyers using artificial intelligence technology have an ethical obligation to spot mistakes and recognize anomalies. But how can the public be protected when using the technology for legal services? The answer is regulation of artificial intelligence in legal services, according to an op-ed by Hinshaw Culbertson partner Wendy Wen Yun Chang, a member of the ABA's Standing Committee on Ethics and Professional Responsibility. Chang is expressing her own views in the column for Bloomberg Big Law Business. Artificial-intelligence technology processes and analyzes large amounts of data to reach reasoned conclusions, providing immense potential benefits, she writes.

The most important fields are currently machine learning including deep learning and predictive analytics, natural language processing (NLP), comprising translation, classification & clustering and information extraction. Strong AI would match or exceed human intelligence which is often defined as the ability "to reason, represent knowledge, plan, learn, communicate in natural language and integrate all these skills toward a common goal." Regardless of whether this growth will continue and whether the growth of computational power means that the abilities of AI systems will grow exponentially as well, people have the tendency to underestimate the potential of tomorrow's applications by evaluating them in terms of today's enabling technologies. Current techniques used in legal technology tools are called machine learning (including deep learning and predictive analysis) and natural language processing (NLP).

Artificial intelligence is on the rise and has already become a buzzword in the legal industry. So far, the discussion around the use of technology in the legal industry focuses on the battle between humans (lawyers) and machines (robots) – and the possibility of the latter taking over the jobs of lawyers. This short article focuses on the underlying technologies behind the paradigm. Artificial Intelligence (AI) was famously defined by John McCarthy as "the science and engineering of making intelligent machines." AI could also be defined as "cognitive technologies."