Today was a day for the history books. The UK has voted to leave the European Union and opened a deep crack in the heart of Europe. As a consequence of this result, Prime Minister David Cameron will step down by October urging for a fresh leadership. At this point nobody knows the repercussions of these results. Will the Brexit hurt the economy of the UK and ignite a new recession?

It sounds like an idea for a science fiction film, but here in the UK scientists and engineers are spending time and money to see if they can do exactly that. British warplanes are already flying with parts made from a 3D printer. Researchers are already using that same technology to build drones. The military advantage is obvious - building equipment quickly and close to the battlefield - without long waits and long supply chains - gives you an enormous advantage over any enemy. But the latest innovation being developed by Prof Lee Cronin at Glasgow University takes 3D printing to another level.

The result of the UK's recent referendum to leave the EU has raised question marks over the fate of the European Union. Many people are wondering whether the Brexit decision will trigger another recession, pave the way for Scottish independence, or begin the demise of the EU as a whole. With so much uncertainty surrounding the possible outcomes, Federico Pascual from MonkeyLearn published a machine learning analysis of the Brexit result. The analysis is based on what people are saying about Brexit in more than 450,000 tweets using the hashtag #Brexit on Twitter. They filtered out the non-English tweets, leaving around 250,000, then ran a MonkeyLearn analysis using ready-to-use machine learning models and sentiment analysis to identify whether the tone was positive, negative or neutral.

As the automation of physical and knowledge work advances, many jobs will be redefined rather than eliminated--at least in the short term. The potential of artificial intelligence and advanced robotics to perform tasks once reserved for humans is no longer reserved for spectacular demonstrations by the likes of IBM's Watson, Rethink Robotics' Baxter, DeepMind, or Google's driverless car. Just head to an airport: automated check-in kiosks now dominate many airlines' ticketing areas. Pilots actively steer aircraft for just three to seven minutes of many flights, with autopilot guiding the rest of the journey. Passport-control processes at some airports can place more emphasis on scanning document bar codes than on observing incoming passengers.

JUDY WOODRUFF: Today, the Obama administration revealed new information that sheds light on the reality of modern warfare, the number of civilians accidentally killed in U.S. airstrikes. JOHN YANG: Today's release is the first time the White House has said how many terrorists and innocent civilians it believes have been killed by airstrikes, including by drones. Between 2009 and 2015, the administration says it launched 473 airstrikes in Pakistan, Yemen and Africa. It estimates that as many as 2,581 combatants, and as many as 116 noncombatants were killed. Now, these numbers do not include airstrikes in Iraq, Afghanistan or Syria, what the administration calls areas of active hostilities. A new executive order has also been issued, with the aim of decreasing the number of civilian deaths.

Trader Navinder Singh Sarao, who is resisting market manipulation charges, at Westminster Magistrates' Court In Robert Harris's 2011 novel The Fear Index a secretive hedge fund builds a computer capable of making its own trading decisions. Gobbling up information, the machine starts to confuse its human creators by building huge stakes and making a handsome profit from a market panic. As they assess the outcome, one of the protagonists notes: "The beauty of it is that it was but 0.4 per cent of total market volatility. No one will ever notice, except us." As markets increasingly rely on computer algorithms, reality is imitating fiction: artificial intelligence is becoming a bigger part of investing and it is also helping regulators ensure that traders do not get away with bad behaviour.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

The artificial intelligence, dubbed ALPHA, was the victor in that simulated scenario, and according to Lee, is "the most aggressive, responsive, dynamic and credible AI I've seen to date." Details on ALPHA -- a significant breakthrough in the application of what's called genetic-fuzzy systems are published in the most-recent issue of the Journal of Defense Management, as this application is specifically designed for use with Unmanned Combat Aerial Vehicles (UCAVs) in simulated air-combat missions for research purposes. The tools used to create ALPHA as well as the ALPHA project have been developed by Psibernetix, Inc., recently founded by UC College of Engineering and Applied Science 2015 doctoral graduate Nick Ernest, now president and CEO of the firm; as well as David Carroll, programming lead, Psibernetix, Inc.; with supporting technologies and research from Gene Lee; Kelly Cohen, UC aerospace professor; Tim Arnett, UC aerospace doctoral student; and Air Force Research Laboratory sponsors. ALPHA is currently viewed as a research tool for manned and unmanned teaming in a simulation environment. In its earliest iterations, ALPHA consistently outperformed a baseline computer program previously used by the Air Force Research Lab for research.

Financial institutions have a regulatory requirement to monitor account activity for anti-money laundering (AML). Regulators take the monitoring and reporting requirements very seriously as evidenced by a recent set of FinCEN fines. One challenge with AML is that it rarely manifests as the activity of a single person, business, account, or a transaction. Therefore detection requires behavioral pattern analysis of transactions occurring over time and involving a set of (not obviously) related real-world entities. For large transactions, banks file Currency Transaction Reports (CTR) that are used by FinCEN for processing and analysis.