Recent advances in artificial intelligence have made it clear that our computers need to have a moral code. Consider this: A car is driving down the road when a child on a bicycle suddenly swerves in front of it. Does the car swerve into an oncoming lane, hitting another car that is already there? Does the car swerve off the road and hit a tree? Does it continue forward and hit the child?

After escalating one of the most lethal covert operations in U.S. history, President Obama finally made a public estimate of the civilian cost of the nation's secret drone program, which has targeted Islamic militants in remote corners of the globe. Human rights groups immediately challenged the estimate and the amount of transparency from the administration, saying both were too limited. The White House said that 64 to 116 civilians had been wrongly killed in 473 strikes launched by the U.S. government from the time Obama was inaugurated and the end of last year. The vast majority of the attacks were launched by drones, officials said, but the estimate also covers some strikes using manned aircraft. Monitoring organizations estimate the number of civilians killed in U.S. strikes ranges from 200 to more than 1,000.

BMW, a company that prides itself on building "the ultimate driving machine," plans to start producing fully autonomous vehicles by 2021 for ridesharing programs. Think of it as Uber for people who don't like people. This is a surprising move, given that the company has said essentially nothing about technology that everyone from Google to General Motors to Tesla is racing to develop. And it marks a radical departure from the slow-and-steady approach of the mainstream automakers, who see the technology rolling out slowly over the next two decades. Still, ze Germans see themselves surging ahead by relying upon help from Intel and Mobileye, an Israeli firm that dominates the market for the cameras that are key to active safety features like collision warning and lane-keeping.

On 7 May, a Tesla Model S was involved in a fatal accident in Florida. At the time of the accident, the vehicle was driving itself, using its Autopilot system. The system didn't stop for a tractor-trailer attempting to turn across a divided highway, and the Tesla collided with the trailer. In a statement, Tesla Motors said this is the "first known fatality in just over 130 million miles [210 million km] where Autopilot was activated" and suggested that this ratio makes the Autopilot safer than an average vehicle. Early this year, Tesla CEO Elon Musk told reporters that the Autopilot system in the Model S was "probably better than a person right now."

Louis Columbus recently wrote in Forbes, "Machine learning's core technologies align well with the complex problems manufacturers face daily. From striving to keep supply chains operating efficiently to producing customized, built- to-order products on time, machine learning algorithms have the potential to bring greater predictive accuracy to every phase of production. Many of the algorithms being developed are iterative, designed to learn continually and seek optimized outcomes. These algorithms iterate in milliseconds, enabling manufacturers to seek optimized outcomes in minutes versus months. The ten ways machine learning is revolutionizing manufacturing include the following."

VR Infinite Gesture is a plugin for developing VR games in the Unity Game Engine. Watch the Getting Started tutorial video here: https://youtu.be/NfHi7ZnA8EE Use the included VR user interface to Record, Edit, and Train your gestures into the Neural Network. After you've trained it, it can detect your gestures. We've included Playmaker extensions so you can create a VR gesture tracking game without code!

PyData Seattle 2015 Machine learning models often result in actions: search results are reordered, fraudulent transactions are blocked, etc. But how do you evaluate model performance when you are altering the distribution of outcomes? I'll describe how injecting randomness in production allows you to evaluate current models correctly and generate unbiased training data for new models. Stripe processes billions of dollars in payments a year and uses machine learning to detect and stop fraudulent transactions. Like models used for ad and search ranking, Stripe's models don't just score---they dictate actions that directly change outcomes.

The advent of the Industrial Internet has raised the bar for security analysts and data scientists, a workforce whose number is quickly being dwarfed by the amount of connected machines. Now, machine learning and artificial intelligence professionals are teaming with traditional embedded vendors to help suppress the rising tide of cyber threats and Big Data. On their website, cyber security firm Norse Corporation generates a detailed, real-time map of cyber attacks occurring around the world, including the attack origin, attack type, and attack target (Figure 1). Hundreds of attacks are registered in any given minute, which, while disconcerting, pales in comparison with the number of systems being connected to the Industrial Internet. This brief exercise demonstrates a couple of things: 1) the quantity and speed of cyber threats that can be used to attack vulnerable, safety-critical industrial systems; and 2) the growing need for data scientists and security analysts1, as well as tools and technologies to support them as the Internet of Things (IoT) expands.

Called the structure-mapping engine (SME), the new model is capable of analogical problem solving, including capturing the way humans spontaneously use analogies between situations to solve moral dilemmas. Previous models of analogy, including prior versions of SME, have not been able to scale to the size of representations that people tend to use. Forbus's new version of SME can handle the size and complexity of relational representations that are needed for visual reasoning, cracking textbook problems, and solving moral dilemmas. To encourage research on analogy, Forbus's team is releasing the SME source code and a 5,000-example corpus, which includes comparisons drawn from visual problem solving, textbook problem solving, and moral decision making.

Northwestern Engineering's Ken Forbus is closing the gap between humans and machines. Using cognitive science theories, Forbus and his collaborators have developed a model that could give computers the ability to reason more like humans and even make moral decisions. Called the structure-mapping engine (SME), the new model is capable of analogical problem solving, including capturing the way humans spontaneously use analogies between situations to solve moral dilemmas. "In terms of thinking like humans, analogies are where it's at," said Forbus, Walter P. Murphy Professor of Electrical Engineering and Computer Science in Northwestern's McCormick School of Engineering. "Humans use relational statements fluidly to describe things, solve problems, indicate causality, and weigh moral dilemmas."