Yoshinori Nomura felt like weeping. It was the morning of June 24, Brexit day, and markets were moving against him. It was the hedge fund manager's self-learning computer program that had placed the bet, selling Japanese stock-index futures before a sizable market advance. Nomura had anticipated a rally, but decided not to interfere, and his fund was paying the price. Then, in an instant, everything changed.

By luck or design, Nomura's Simplex Equity Futures Strategy Fund ended the day with a 3.4 percent gain, one of its best results in three months of trading. The tumult has been rough on hedge funds, with a gauge of Japan-focused managers tracked by Eurekahedge Pte dropping 3.5 percent this year. It's often difficult to distinguish AI funds from their more ubiquitous "quantitative" investing precursors, according to Motoyuki Sato, a general manager and researcher at Man Group Japan Ltd., a unit of the world's largest publicly traded hedge fund manager. Simplex Asset Management is one of Japan's fastest-growing money managers, overseeing 560 billion for clients.

While the oil and gas industry has had its share of ups and downs over the past decade, many financial institutions are banking on a very slow growth of oil prices in 2017. Though some believe that the efficiency gains that the oil industry can capture are quickly coming to an end, this sentiment is only capturing hard technology specifically related to oil and gas. To help bring the O&G industry to the 21st century, technology from other industries needs to be incorporated, using many hard-earned years of expertise and different lines of thinking. Oilprice previously mentioned incorporating food industry technology to increase safety standards when fracking, but incorporating technology from the IT industry is something that the O&G industry as a whole can benefit from. Whether its neural networks, machine learning, fuzzy logic, case-based reasoning or expert systems, AI has the potential to transform the industry.

Germany's Interior Minister says he wants to introduce facial recognition software at train stations and airports to help identify suspects following two attacks in the country last month. In a report published on Sunday in the German newspaper Bild am Sonntag, Thomas de Maiziere said internet software was able to determine whether persons shown in photographs were celebrities or politicians. "I would like to use this kind of facial recognition technology in video cameras at airports and train stations. Then, if a suspect appears and is recognised, it will show up in the system," he told the paper. Germany's Thomas de Maiziere takes aim at face veils He said a similar system was already being tested for unattended luggage, which the camera reports after a certain number of minutes.

While walking around the larger industry shows, those hosting say more than 140 vendors, it doesn't take long to realise that artificial intelligence and machine-learning are the current'it' girls of the cyber-security industry. In an effort to define what'artificial intelligence' actually is, Luger & Stubblefield described in their 2004 book on artificial intelligence, that an ideal "intelligent" machine is a flexible rational agent that perceives its environment and takes actions that maximise its chance of success at some goal based on a complex set of calculations. As notifications from UBA, SIEM and threat intelligence systems continue to grow, artificially intelligent systems are being touted as the solution to the fatigue experienced by SOC teams who have to try and figure out what to do with each threat, and whether or not they should investigate it further. Research from security company Hexadite, a security automation company, claimed that 37 percent of cyber-security professionals face 10,000 alerts per month" with 52 percent of alerts turning out to be false positive. He responded: "Highly repetitive and intricate tasks may be well suited for a machine rather than a human.

This new map of the world is very enlightening to scientists, in that it helps them see what areas of the world are hardest hit by poverty -- and that could be key in helping us eliminate poverty worldwide. This data is helping authorities track crop conditions, deforestation and, now, where poverty can be found across the globe where data collection by governments isn't very good. This data is potentially invaluable to humanitarian organizations and policymakers, and a team of researchers from Stanford University believes that they have created a deep-learning algorithm that can spot signs of poverty just based on the satellite images alone. It can check the conditions of roads, for example, to see how good the infrastructure in a particular area. "We have a limited number of surveys conducted in scattered villages across the African continent, but otherwise we have very little local-level information on poverty," study coauthor Marshall Burke, an assistant professor of Earth system science at Stanford and a fellow at the Center on Food Security and the Environment, said in a statement.

Delta Airlines experienced a major computer outage recently, one that caused canceled flights and massive delays. Many passengers were stranded in airports, and the company now has to look back and find the cause. Most of the work will involve a close look at the IT systems Delta is using and efforts to create better redundancy, but there's also the "soft" issue of dealing with passengers who need to be rebooked on a flight or at least reassured that everything is OK. That's where A.I., machine learning, and chatbots could help, says Dave O'Flanagan, the CEO of Boxever. His company, based in Dublin, with offices in the U.S. and elsewhere, makes a cloud platform that uses A.I.-assisted analytics and machine learning.

Machine learning security offers many advantages over signature-based detection, but the technology can be as difficult to explain as it is for malware to beat. During an interview with SearchSecurity, Brett Hansen, executive director of data security solutions at Dell, offered insight into his company's investment in machine learning security and its partnership with advanced threat protection startup Cylance Inc. In part one of the interview, Hansen discussed the problems with traditional antivirus and antimalware programs relying on signature-based detection methods. In part of two of the interview, Hansen talks about the advantages of machine learning for smaller businesses, why it's a struggle to discuss the technology behind it, and how machine learning security serves as a better defense against ransomware attacks and other emerging threats. Here are excerpts from the conversation with Hansen. Is the move to machine learning security more about the shortcomings with signature-based detection and the frustrations people have had with it, or the benefits and value of machine learning?

In 2014, Google acquired the artificial intelligence startup DeepMind, but it wasn't cheap. With a price tag of 500M, there must have been something special that Google saw in DeepMind that was worth acquiring. While the company hasn't produced any actual products for commercial use, they have focused on machine learning. Machine learning is a type of artificial intelligence that aims to provide computers with the ability to learn new information without being directed to do so. Machine learning involves the development of computer programs that have the ability to teach themselves to grow and alter themselves when presented with new data.

A fascinating new simulation finds that self-driving cars will terraform cities: 90% of cars will be eliminated, acres of land will open up, and commute times will drop 10%. A team of transportation scientists at the Organization for Cooperation and Development took data on actual trips in Lisbon, Portugal and looked at how a fleet of self-driving, shared "taxibots" would change city landscape [PDF]. These "taxibots", the researchers imagine, are a marriage of mass carpooling and UPS delivery intelligence: they constantly roam throughout cities and match carpool routes with mathematical elegance. Ultimately, they estimate, 9 out of 10 cars would be completely unnecessary -- as would public transit. "Nearly the same mobility can be delivered with 10% of the cars TaxiBots combined with high-capacity public transport could remove 9 out of every 10 cars in a mid-sized European city," the paper concludes.