Google and Tesla agree autonomous vehicles will make streets safer, and both are racing toward a driverless future. But when Google tested its self-driving car prototype on employees a few years ago, it noticed something that would take it down a different path from Tesla. Once behind the wheel of the modified Lexus SUVs, the drivers quickly started rummaging through their bags, fiddling with their phones and taking their hands off the wheel -- all while traveling on a freeway at 60 mph. "Within about five minutes, everybody thought the car worked well, and after that, they just trusted it to work," Chris Urmson, the head of Google's self-driving car program, said on a panel this year. "It got to the point where people were doing ridiculous things in the car."

The first death has been reported in self-driving cars. Our worst fears about artificial intelligence are already beginning to come true. A test man working with Tesla motors was killed after the autopilot feature failed to see a bright-white 18-wheeler which was poorly contrast against the bright lit sky. It is unfortunate, and spooky, since white is the most common color for tractor trailers, and the big rigs account for the most deadly accidents. One wonders how a tiny, smart car making decisions of a computer would stand up to such a vehicle. Not "sophisticated enough to overcome blindness from bright or low contrast light."

When I read the headlines Friday about the fatal crash of a Tesla vehicle while in self-driving mode, I immediately thought about Three Mile Island. It's not that Tesla's autopilot mode is the vehicular equivalent of a nuclear meltdown. As the company would very much like you to note, self-driving cars are doing better, statistically speaking, than human drivers. Tesla says autopilot was used for 130 million miles worth of driving before this fatal crash. Human-driven cars in the U.S. have 1.08 fatal crashes for every 100 million miles.

In January 2016, the Obama administration set aside four billion dollars to fast-forward the development and implementation of self-driving vehicles through real-world pilot projects. Without a doubt, self-driving vehicles will be safer than any cars driven by humans. In fact, it's estimated that autonomous vehicles will reduce traffic accidents by 94 percent. So, whether you're for or against self-driving cars, there's no turning back -- the future is here. But before we get too ahead of ourselves, there are still some kinks we need to work out.

Trying to think of a practical, real world use of Machine Learning? Bank and financial institutions have regulatory requirements to monitor account activity for money laundering activities. Regulators around the world take these monitoring and reporting requirements very seriously. However, the big challenge facing anti-money laundering (AML) efforts is that money laundering rarely appears in the activity of a single person, business, account, or a transaction. Money launderers have gotten quite sophisticated.

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.

The crash that killed a Tesla driver in Florida when his car struck a tractor-trailer may mark the world's first fatal accident in which a computer was at the wheel. The crash occurred when the truck turned left across the 2015 Model S Tesla's path and the car's autopilot failed to slow down. The deadly accident, which took the life of 40-year-old Joshua David Brown of Ohio and is the subject of a federal safety investigation that Tesla disclosed Thursday, is bound to raise a lot of questions about vehicle automation and the future of car travel. It may be tempting to describe this as a driverless car crash, but don't give in. There's a big difference between assisted driving technologies and full automation, and what we have here is the former. We'll get into that below, but let's start first with the nuts and bolts of the autopilot technology at the center of the crash.

WASHINGTON/SAN FRANCISCO – Tesla Motors Inc. says the self-driving feature suspected of being involved in a fatal crash on May 7 is experimental, yet it's been installed on all 70,000 of its cars since October 2014. For groups that have lobbied for stronger safety rules, that's precisely what's wrong with U.S. regulators' increasingly anything-goes approach. "Allowing automakers to do their own testing, with no specific guidelines, means consumers are going to be the guinea pigs in this experiment," said Jackie Gillan, president for Advocates for Highway and Auto Safety, a longtime Washington consumer lobbyist who has helped shape numerous auto-technology mandates. "This is going to happen again and again and again." Tesla's use of a technology still in development, while common in its Silicon Valley home, contrasts with the cautious method of General Motors Co. and other automakers that have restricted their semi-autonomous cars to test tracks and professional drivers.

The problem of how to defend a country changes when your attacker isn't acting rationally. Terrorists put their causes above their home country and don't necessarily fear death or retaliation. So shortly after 9/11, Milind Tambe, a professor of computer science and engineering at USC, proposed a radical new style of protection: Why not use artificial intelligence to make your own targets harder to attack? By matching predictive algorithms with machine learning and some massive processing power, you could create a computer program capable of figuring out how to deploy limited security forces around sensitive places most effectively. The trick would be for those schedules or formations to remain unpredictable.