Google, Uber and Tesla may be giddy about the prospect of self-driving cars, but U.S. consumers aren't convinced that the technology is safe or that they need it. Only 10 percent of people surveyed by the University of Michigan said they would have no concerns at all about riding in fully self-driving cars, while two thirds said they would be moderately or very concerned about it. The level of confidence goes up a little for cars that would be only partially self-driving. In that case, 16 percent would have no concern and 50 percent would be moderately or very concerned. The research shows that while coverage of self-driving cars and the technology behind them has increased in the media, consumers are still unsure that the cars are safe.

Can machines ever produce creative works to rival our own creations? Google is hoping to find out in its latest project aimed at teaching artificial intelligent how to generate music and art. Dubbed Magenta, the project was created in Google's Brain AI group, which is also responsible for Google Translate, Photos and Inbox. Google wants to use Tensortflow software to teach machines how to compose music and create works of art. DeepDream, Google's visual AI that could transform photos into psychedelic art (such as Salvador Dali's Persistence of Memory, pictured), worked on a similar principle Google currently uses TensorFlow for speech recognition in the Google app, Smart Reply in Inbox and to search in Google Photos.

HARI SREENIVASAN: The man who led the Afghan Taliban for the past year was killed in a U.S. operation over the weekend. The group had been gaining ground and waging a bloody war against the Afghan government. So, what's next for the Taliban, and the countries who fight it? Chief foreign affairs correspondent Margaret Warner begins our coverage. MARGARET WARNER: Smoldering wreckage on a Pakistani roadside was all that remained of the Taliban commander's vehicle hours after he died in it Saturday.

On a spring afternoon in 2014, Brisha Borden was running late to pick up her god-sister from school when she spotted an unlocked kid's blue Huffy bicycle and a silver Razor scooter. Borden and a friend grabbed the bike and scooter and tried to ride them down the street in the Fort Lauderdale suburb of Coral Springs. Just as the 18-year-old girls were realizing they were too big for the tiny conveyances--which belonged to a 6-year-old boy--a woman came running after them saying, "That's my kid's stuff." Borden and her friend immediately dropped the bike and scooter and walked away. But it was too late--a neighbor who witnessed the heist had already called the police. Borden and her friend were arrested and charged with burglary and petty theft for the items, which were valued at a total of 80. Compare their crime with a similar one: The previous summer, 41-year-old Vernon Prater was picked up for shoplifting 86.35 worth of tools from a nearby Home Depot store. Prater was the more seasoned criminal. He had already been convicted of armed robbery and attempted armed robbery, for which he served five years in prison, in addition to another armed robbery charge. Borden had a record, too, but it was for misdemeanors committed when she was a juvenile.

Next up in our series on top Kagglers is the #1: Owen Zhang (Zhonghua Zhang). Owen comes from an engineering background and currently works as the Chief Product Officer at DataRobot. Back in 2011 I had just switched to analytics as a full time job (after several years working in IT), and was eager to improve my skills and to "prove myself". So it was fortuitous that Kaggle came along with the first Allstate competition. Being in the same industry I felt I had some advantage as well.

Machine learning is accomplishing Facebook's mission of connecting the environment throughout language obstacles. Fb is now serving 2 billion textual content translations for every working day. Fb can translate throughout 40 different languages in 1800 instructions like French to English. And 800 million people, pretty much 50 % of all Fb people, see translations just about every month. Alan Packer, Facebook's Director of Engineering for language technological innovation, discovered this progress nowadays at MIT's Emtech Electronic convention in San Francisco.

Each year Google hosts a huge gathering of developers and press, and traditionally they've converged on San Francisco in May. The only exceptions are the first two years back in 2006 and 2007, but for the 10th anniversary of Google I/O, Google is bringing the party back home. This isn't just in Mountainview either, it's just across the street from Google headquarters at the Shoreline Amphitheater, and it's the first time I/O has been at an outdoor only venue too. This also meant that Google's traditionally small list of attendees could grow larger, and this year we saw a whopping 7,000 people on the grounds for the 3 day affaire. Among the many firsts at this year's I/O was the fact that this was my personal first time going to Google's get together, although we as a site have been going for quite a long time now.

Taking inspiration from the way humans seem to learn, scientists have created AI software capable of picking up new knowledge in a far more efficient and sophisticated way. The new AI program can recognize a handwritten character about as accurately as a human can, after seeing just a single example. The best existing machine-learning algorithms, which employ a technique called deep learning, need to see many thousands of examples of a handwritten character in order to learn the difference between an A and a Z. The software was developed by Brenden Lake, a researcher at New York University, together with Ruslan Salakhutdinov, an assistant professor of computer science at the University of Toronto, and Joshua Tenenbaum, a professor in the Department of Brain and Cognitive Sciences at MIT. Details of the program, and the ideas behind it, are published today in the journal Science.

This is what has happened with Bitcoin mining (mostly in Tibet/China) since cost-differentiation between miners is no longer about who is using specialized hardware, but about who uses them most cheaply.

The scenarios sound like they belong to the plot of a science fiction film. From'enslaving mankind' to'destroying the universe', a checklist of worst case outcomes for AI has been created to help safeguard humanity against disaster. The list was drawn up by computer scientist Roman Yampolskiy and entrepreneur Federico Pistono – and they say, as far-fetched as they may sound, we need to take the threats seriously. The list of how AI could turn evil was drawn up by computer scientist Roman Yampolskiy and entrepreneur Federico Pistono – and they say, as far-fetched as they may sound, we need to take the threats seriously. Pictured is a scene from the 2015 film, 'Terminator: Genisys' This can be accomplished through forced cryonics or concentration camps.