A group of international tech firms and venture capitalists injected almost £1 billion ($1.4 billion) into the U.K.'s artificial intelligence (AI) industry on Thursday. The deal was backed by Japanese venture capital firm Global Brain, Canadian venture capital firm Chrysalix, U.K. automaker Rolls-Royce, the British government and other investors. The investment will be used to fund new specialist AI qualifications and computer science teachers, to tackle a skills shortage in the sector. Theresa May's government has been driving an industrial strategy to tap into the growing industry, which Gartner estimates will be worth more than $3.9 trillion in business value by 2022. "Artificial intelligence provides limitless opportunities to develop new, efficient and accessible products and services which transform the way we live and work," Greg Clark, U.K. business and energy secretary, said in a statement.

Machine learning is now able to analyze court decisions and predict how judges may respond in certain situations, based on an analysis of existing caselaw. Artificial intelligence technology is currently available to analyze tax and employment caselaw, but it's easy to see how this could be relevant to the insurance space as well – particularly in the claims area. Potential uses could include analysis of decisions around tort liability, for example, or accident benefits cases. A tax law expert recently provided an illustration of how the technology works in a webinar Wednesday. Benjamin Alarie, CEO and co-founder of Blue J Legal, uses the artificial intelligence suite to predict how the courts may decide issues about things such as tax deductions.

Everybody knows that artificial intelligence is an exceptionally weaponizable technology. So it's no mystery why militaries everywhere are racing to exploit AI to its maximum potential. Autonomous vehicles, for example, will become the most formidable weapon systems humanity has ever developed. AI gives them the ability to see, hear, sense, and adjust real-time strategies far better and faster than most humans. It will almost certainly produce casualty counts in future battles that are staggering and lopsided, especially when one side is almost entirely composed of AI-powered intelligent weapons systems equipped with phalanxes of 3-D camera, millimeter-wave radar, biochemical detectors and other ambient sensors. There's no point in dancing around a huge and growing controversy in the AI industry: the appropriateness of tech vendors such as Google Inc., Microsoft Corp. and Amazon Web Services Inc. assisting the U.S. Department of Defense in developing AI technologies not just for offensive purposes, but also for defensive and back-office applications that can sustain this country's war-making bureaucracy.

Rotman School of Management professor Ajay Agrawal explains how AI changes the cost of prediction and what this means for business. With so many perspectives on the impact of artificial intelligence (AI) flooding the business press, it's becoming increasingly rare to find one that's truly original. So when strategy professor Ajay Agrawal shared his brilliantly simple view on AI, we stood up and took notice. Agrawal, who teaches at the University of Toronto's Rotman School of Management and works with AI start-ups at the Creative Destruction Lab (which he founded), posits that AI serves a single, but potentially transformative, economic purpose: it significantly lowers the cost of prediction. In his new book, Prediction Machines: The Simple Economics of Artificial Intelligence, coauthored with professors Joshua Gans and Avi Goldfarb, Agrawal explains how business leaders can use this premise to figure out the most valuable ways to apply AI in their organization.

Every year, thousands of people receive consumer product related injuries. Research indicates that online customer reviews can be processed to autonomously identify product safety issues. Early identification of safety issues can lead to earlier recalls, and thus fewer injuries and deaths. A dataset of product reviews from Amazon.com was compiled, along with \emph{SaferProducts.gov} complaints and recall descriptions from the Consumer Product Safety Commission (CPSC) and European Commission Rapid Alert system. A system was built to clean the collected text and to extract relevant features. Dimensionality reduction was performed by computing feature relevance through a Random Forest and discarding features with low information gain. Various classifiers were analyzed, including Logistic Regression, SVMs, Na{\"i}ve-Bayes, Random Forests, and an Ensemble classifier. Experimentation with various features and classifier combinations resulted in a logistic regression model with 70.2\% precision in the top 50 reviews surfaced. This classifier outperforms all benchmarks set by related literature and consumer product safety professionals.

Deep ocean robotics is not generally an area where we expect to see much in the way of significant innovation. When we do write about submersible robots, they're usually confined to very near-surface operations. This isn't a total surprise: It seems like the only people who really worry about what's going on in the deep ocean (meaning hundreds or thousands of meters beneath the surface) are the military, the occasional scientist, and the oil and gas industry. Robots are important to these folks, even critical in some cases, but the technology has been more or less stagnant for decades, which is why we don't write about it very frequently. To be fair, there are some very good reasons why it's hard to innovate when it comes to submersible robotics.

Who will win the race to adopt artificial intelligence for cyber warfare--the defenders of vulnerable corporate networks or the cyber criminals constantly inventing new ways to attack them? The promise--or unrealistic hope--that AI will "transform the world," has given rise to a number of significant races. Most prominent is the race among nations for AI superiority, primarily the U.S. and China, with several European countries (e.g., France) and the European Union attempting to position themselves not too far behind. This reminds one of the nuclear arms-race and the use of the same technology for both beneficial and destructive purposes. In a recent paper warning of the potential of AI to "upend the foundations of nuclear deterrence," RAND researchers wrote: "The dual-use nature of many AI algorithms will mean AI research focused on one sector of society can be rapidly modified for use in the security sector as well."

The applications of artificial intelligence to enhance humans' life open a vast array of opportunities available to those who have the foresight to begin to venture down that path. Looking at the aging population trends in European countries, North America, Japan or China, we can easily conclude that one of the biggest challenges governments are facing is dealing with an increasing population over the age of 65 together with a declining birth rate. As The Independent published about grave concerns in Japan on this issue a few months ago, "Prime Minister Shinzo Abe called for a "national movement" to address Japan's demographic challenges. The government has taken steps to keep older workers in their jobs longer, and to encourage companies to invest in automation". Aging-in-place may be the key to reducing the side-effects of the phenomenon where the number of rooms in nursing homes cannot keep up with the string increase in senior population.

Cloud-based, AI-powered location technology is creating the highly accurate and always up-to-date maps that can revolutionize everything from autonomous cars to connected cities. To learn more about the application of data-enriched mapping to industries from retail to automotive, manufacturing, transportation and city planning, don't miss this VB Live event! Location is at the heart of everything: it's the nexus between a device or an individual and the environment they interact with, and it can become the foundation of a smarter society. Location data is powered by cloud capabilities: global maps, traffic information and hundreds of millions of connected devices brought together to create the most up-to-date maps and power the "The Location of Things." You can call it the fourth industrial revolution of modern history -- and this time the catalyst of major change is the cloud.

With 400 hours of video being uploaded to YouTube every minute and fleets of self-driving cars mapping high definition 3D maps of roads all over the world, data is being created, stored and processed at a rate never seen before. Ninety percent of the world's data has been created in the last two years, according to IBM and other industry sources, and with new data hungry applications on the rise (autonomous driving, the Internet of Things, artificial intelligence), there's no sign of this trend abating. Organizing, storing and processing all that data comes with not only business, but also environmental challenges. In fact, networking and telecom equipment maker Huawei has estimated that global computing power could consume as much as 20% of global electricity in 2025 and account for 3.5% of global emissions.[1] All this data processing also requires large amounts of water to keep servers from overheating - roughly 1.8 liters for every kWh consumed - according to the U.S. Department of Energy (DOE).