The investigation into Sunday's death of "Star Trek" actor Anton Yelchin, 27, in a 2015 Jeep Grand Cherokee rolling accident continues. But signs point to a problem that Fiat Chrysler Automobiles has had for years with its electronic automatic gear shifters -- namely that some of its cars have shifters that can leave drivers believing they've been put safely into park. Instead, the cars can roll away, causing accidents, injuries and even death. And while the problem vexing more than 800,000 recent Jeep Grand Cherokee SUVs, Dodge Charger muscle cars and Chrysler 300 luxury sedans isn't widespread across the industry, the increasing use of electronics for crucial vehicle functions is posing new challenges for automakers, and new risks for drivers. "There's this rush to electrify every aspect of a vehicle," said Byron Bloch, an auto safety expert with 40 years of experience inspecting vehicles and testifying on accidents.

Tiny cartoonish characters mill around a cartoon village on a player's phone screen, building cute little armies that they let loose on enemy camps. Often, just when things are going really great for the clan, resources run out; then players have to wait a few hours while the game slowly regenerates gold and elixir, or they can spend four dollars and ninety-nine cents to buy in-game currency and keep playing right away. Since most of us are impatient, Supercell, the company that makes Clash of Clans, has done quite nicely. Those real-money-for-virtual-stuff purchases, or micro-transactions, contributed to the company's 2.3 billion dollars in sales in 2015. This week, the China-based company Tencent Holdings paid 8.6 billion dollars for a controlling stake in Supercell, and therefore a stake in our need for instant gratification.

How important are skills in computational thinking for computing app constructors and for computing users in general? If we can teach our children early on to smile, talk, write, read, and count through frequent and repetitive use of patterns in well-chosen examples, is it also possible for us, assuming we have the skills, to teach our children to construct computing applications? Do we need to first teach them anything about computational thinking before we look to teach how to construct computing apps? If not, how important will computational skills be for us all, as Jeannette Wing suggests in her blog@cacm "Computational Thinking, 10 Years Later" (Mar. Many competent and successful computing app constructors and users never hear a word about computational thinking but still manage to acquire sufficient construction and user skills through frequent and repetitive use of patterns in well-chosen examples.

Workers insert a new CMS Beam Pipe during maintenance on the Large Hadron Collider. Everything about the Large Hadron Collider (LHC), the particle accelerator most famous for the Nobel Prize-winning discovery of the elusive Higgs boson, is massive--from its sheer size to the grandeur of its ambition to unlock some of the most fundamental secrets of the universe. At 27 kilometers (17 miles) in circumference, the accelerator is easily the largest machine in the world. This size enables the LHC, housed deep beneath the ground at CERN (the European Organization for Nuclear Research) near Geneva, to accelerate protons to speeds infinitesimally close to the speed of light, thus creating proton-on-proton collisions powerful enough to recreate miniature Big Bangs. The data about the output of these collisions, which is processed and analyzed by a worldwide network of computing centers and thousands of scientists, is measured in petabytes: for example, one of the LHC's main pixel detectors, the ultra-durable high-precision cameras that capture information about these collisions, records an astounding 40 million pictures per second--far too much to store in its entirety.

Back in 1979, two scientists wrote a seminal textbook on computational complexity theory, describing how some problems are hard to solve. The known algorithms for handling them grow in complexity so fast that no computer can be guaranteed to solve even moderately sized problems in the lifetime of the universe. While most problems could be deemed either relatively easy or hard for a computer to solve, a few fell into a strange nether region where they could not be classified as either. The authors, Michael Garey and David S. Johnson, helpfully provided an appendix listing a dozen problems not known to fit into one category or the other. "The very first one that's listed is graph isomorphism," says Lance Fortnow, chair of computer science at the Georgia Institute of Technology.

Bots (short for software robots) have been around since the early days of computers. One compelling example of bots is chatbots, algorithms designed to hold a conversation with a human, as envisioned by Alan Turing in the 1950s.33 The dream of designing a computer algorithm that passes the Turing test has driven artificial intelligence research for decades, as witnessed by initiatives like the Loebner Prize, awarding progress in natural language processing.a Many things have changed since the early days of AI, when bots like Joseph Weizenbaum's ELIZA,39 mimicking a Rogerian psychotherapist, were developed as demonstrations or for delight. Today, social media ecosystems populated by hundreds of millions of individuals present real incentives--including economic and political ones--to design algorithms that exhibit human-like behavior. Such ecosystems also raise the bar of the challenge, as they introduce new dimensions to emulate in addition to content, including the social network, temporal activity, diffusion patterns, and sentiment expression. A social bot is a computer algorithm that automatically produces content and interacts with humans on social media, trying to emulate and possibly alter their behavior. Social bots have inhabited social media platforms for the past few years.7,24

That is what I said when I was asked whether we would ever see computer science taught in K–12. It was 2009, and I was addressing a gathering of attendees to a workshop on computational thinking (http://bit.ly/1NjmcRJ) It has been 10 years since I published my three-page "Computational Thinking" Viewpoint (http://bit.ly/1W73ekv) in the March 2006 issue of Communications. To celebrate its anniversary, let us consider how far we've come. Since the dotcom bust, there had been a steep and steady decline in undergraduate enrollments in computer science, with no end in sight.

The 19th-century U.K. Locomotive Act, also known as the Red Flag Act, required motorized vehicles to be preceded by a person waving a red flag to signal the oncoming danger. Movies can be a good place to see what the future looks like. According to Robert Wallace, a retired director of the CIA's Office of Technical Service: "... When a new James Bond movie was released, we always got calls asking, 'Do you have one of those?' If I answered'no', the next question was, 'How long will it take you to make it?' Folks didn't care about the laws of physics or that Q was an actor in a fictional series--his character and inventiveness pushed our imagination ..."3 As an example, the CIA successfully copied the shoe-mounted spring-loaded and poison-tipped knife in From Russia With Love. It's interesting to speculate on what else Bond movies may have led to being invented. For this reason, I have been considering what movies predict about the future of artificial intelligence (AI). One theme that emerges in several science fiction movies is that of an AI mistaken for human.

Machine learning and artificial intelligence (AI) may sound intimidating, but Dean said enterprises don't need the technical resources of a company like Google to get started. There are now lots of options that let businesses bring their own data to machine learning platforms that contain pretrained models or algorithms that organizations can train themselves. Google offers such a service, and the Spark data processing engine contains a library of machine learning algorithms. Such offerings lower the bar to entry. Other speakers at the Spark conference agreed the time is ripe for machine learning applications across various vertical markets.

A team from the University of Oregon have developed a system that can read people's thoughts via brain scans, and rebuild the faces they were visualising in their heads. The study, led by Brice Kuhl and Hongmi Lee from the University of Oregon, used artificial intelligence (AI) that analysed brain activity in an attempt to reconstruct one of a series of faces that participants were seeing. It's not an exact science, but the AI did get close. "We can take someone's memory – which is typically something internal and private – and we can pull it out from their brains," Kuhl told Vox. "Some people use different definitions of mind reading, but certainly, that's getting close," Kuhl told Vox. Kuhl and his colleague Lee recently published a paper in The Journal of Neuroscience with a conclusion straight out of science fiction: Kuhl and Lee created images directly from memories using an MRI, some machine learning software, and a few hapless human guinea pigs.