The U.K.'s Brexit vote was something few prognosticators saw coming prior to the June 23 referendum. But once the results were in, it was clear the vote to leave the European Union would have a major impact on financial markets. The pound sterling fell in value by 11% two days after the vote, and both the Dow Jones Industrial Average and the London Stock Exchange's FTSE 100 index lost more than 2% of their total value. This left millions of traders all over the world scrambling to find safer investment positions. But at least one group of investors was relatively calm, according to Omer Cedar, CEO of Omega Point Research Inc., a New York-based software company that sells analytics tools to help investment managers review their portfolios for risks.

Before the end of this year, Darktrace plans to release its Antigena technology. Antigena is designed to replicate the function of human antibodies, which identify and neutralise bacteria and viruses, by neutralising cyber threats automatically without human intervention. Darktrace is researching how information security teams respond to situations with a view to enabling the system not only to learn what they do, but also to predict what they will do and then use that information to offer better support information. "This is the kind of thing that really interests us, and is the kind of envelope-pushing, self-learning, machine-learning, AI-type stuff that we really want to get into," said Palmer. "An entirely AI security operations centre is not an unreasonable objective for us to have as researchers, and is certainly one of our goals, especially considering how quickly technology is moving in areas such as self-driving cars, which not long ago were considered to be pure fiction."

By beating humans at games of Go and Jeopardy, artificial intelligence engines like Google's DeepMind and IBM's Watson have captured attention for their promise of solving bigger human problems. Watson, for example, is being enlisted to help doctors predict cancer in patients. The American internet pioneer Douglas Engelbart suggests that AI's grandest promise is the amplification of human ability. Whether it's automating rote cognitive tasks like tagging people in photos or assisting in complex work flows like cancer treatment, the human-augmentation promise feels almost inevitable in every product and domain. Self-driving cars rely on massive amounts of data collected over several years from efforts like Google's people-powered street canvassing, which provides the ability to "see" roads.

NEW YORK (Reuters) – IBM is in an unusual fix in telling big U.S. banks they can use its Watson software of Jeopardy-winning fame as a cost-saving solution: bankers say they like it, but cannot afford it. IBM is in good company. Banks are in the fifth year of their belt-tightening campaigns that began in 2011, chasing billions of dollars' worth of savings, and vendors that offer everything from technology to janitorial services are getting squeezed. With persistently low interest rates hurting revenue and businesses like bond trading hemmed in by new regulations, few on Wall Street expect the austerity to end any time soon. For IBM the irony lies in the fact that senior bank executives say they believe its artificial intelligence software could help them achieve cost-cutting goals in coming years, but are not ready to pay for Watson today.

At an artificial intelligence conference in New York City last week, Professor Alexi Samsonovich from the Moscow-based National Research Nuclear University (MEPhl) Cybernetics Department told Sputnik News, "We are on the verge of a major breakthrough" in AI. In the past six months, we've seen AI master the board game Go, write a short film script, and infiltrate Snapchat filters. Each of these achievements is impressive in its own right. Together, they show just how quickly AI is advancing. But what was this breakthrough Samsonovich hinted at in NYC? Digital Trends reached out to him to find out.

Ford Motor Co. became the world's first automaker to announce hard plans to deliver driverless cars. "We'll have mass-produced, fully autonomous cars on the road in five years," said Raj Nair, head of global product development, at an event Tuesday in Palo Alto, Calif. Ford said the cars would be first used for ride hailing and ride sharing, but no companies were named. A few years after that, the company plans to roll out driverless cars to consumers. All the carmakers, plus Google and maybe Apple, are working on driverless cars, but none has announced firm dates.

Ford is developing a fully-autonomous vehicle without a steering wheel or gas and brake pedals, the auto industry giant announced on Tuesday. The car maker expects the vehicle to be ready by 2021, and says it will be made specifically for ride-hailing and ride-sharing services. Google's prototype self-driving cars don't have pedals or a steering wheel either, since the company says it "lets the software and sensors handle the driving." Ford will be doubling its Silicon Valley team and more than doubling its campus in Palo Alto, Calif. to help it reach this goal. The company has also acquired and invested in a few startups specializing in technologies that can help advance the development of its autonomous vehicle.

Ford will make a driverless car for ride-sharing purposes by 2021, using its Ford Fusion Hybrids (shown here) as technology test mules. CEO Mark Fields set the target at Ford's Research and Innovation facility here, which will double its staff to 300 and grow its footprint by 150,000 square feet by year's end to respond to the challenge. "This is one example of how we're thinking about expanding our business into mobility more broadly," Fields told USA TODAY. "Taking the driver out of the equation improves the economics for us as well as consumers." Currently, Ford is testing around a dozen self-driving Ford Fusion Hybrids on California, Michigan and Arizona roads.

Expedia has grown far beyond a search engine for flights -- it's now the parent company of a dozen travel brands including Trivago and Hotels.com The business of delivering quality flight search results is tough, and Fleischman describes it as an "unbounded computer science problem". The reason for this is because flight itineraries and schedules are constantly changing, and Expedia's proprietary'best fare search' (BFS) has to'learn' and adapt all the time. The extent of the problem can be summed up by one statistic. In those three seconds you will see, on average, 16,000 flight options, in order of convenience or price or time.

AI and Machine Learning grew up and matured in the world of gaming -- pitting computational power and algorithmic mastery against the best human players up for the challenge of taking on a machine. It may have taken decades, but machines eventually asserted their dominance. In 1997 IBM's Big Blue defeated chess master Garry Gasparov; In 2011 IBM Watson defeated two of Jeopardy's greatest champions; and most recently, Google's AlphaGo bested reigning champ Lee Sedol in the game of GO, a 2,500-year-old game that's exponentially more complex than chess and, form the human perspective, requires intuition as well as calculation to execute a winning strategy. Machine Learning owes its current prowess to gaming in a literal sense. The holy grail of computer science has always been to create intelligent machines that can perceive the world as we do, understand our language, and learn from examples.