"I've been waiting for you," Sophia tells 60 Minutes correspondent Charlie Rose. They're mid-interview, and Rose reacts with surprise. "But it makes a good pickup line." Sophia managed to get a laugh out of Charlie Rose. Rose interviewed the human-like machine for this week's two-part 60 Minutes piece on artificial intelligence, or A.I.

A new study from Germany's Federal Highway Research Institute (BASt) found that the autopilot feature of the Tesla Model S constitutes a "considerable traffic hazard," according to a report in Der Spiegel. Unsurprisingly, Tesla CEO Elon Musk doesn't agree and today said in a tweet that those reports were "not actually based on science," and repeated that "Autopilot is safer than manually driven cars." Tesla reports that its vehicles drove more than 130 million miles with Autopilot engaged before one was involved in a fatal crash. Statistically, that beats the safety record for manually driven cars which are involved in a fatal crash every 100 million miles in the U.S., according to data from the Insurance Institute for Highway Safety. It's worth noting that Der Spiegel reports that the study was an internal one, and did not represent a final evaluation.

When last I looked at Intel (NASDAQ:INTC), I introduced an unconventional valuation method; and with the stock in the 24 area, set a 35 target, which has since been met. The thinking is, R&D and Capex define what the company is doing to increase EPS and share price. And the dividend represents the benefit to shareholders from these activities. The cap rates or multiples are arbitrary, but provide useful indications. If Capex is money well spent, it will be validated by acceptable ROE.

A new artificial intelligence-powered virtual assistant that helps people on the Autism spectrum organize their lives and stay employed will be available in iOS and Android app stores next month, according to Identifor CEO Cuong Do. Do was one of more than a dozen speakers at the Intelligent Assistants Conference, a two-day event held Sept. 12-13 at the Palace Hotel in San Francisco. Called Companion, the app includes a virtual assistant named Abby. Abby is designed to identify an individual's interests and needs and to support a person on the Autism spectrum throughout the day. The virtual assistant uses artificial intelligence to learn the routines of users and keep their work, school, and social life on track.

As Google Home gets closer to launch, the AI assistant is not only getting smarter, but also a little more friendly and -- hopefully -- a whole lot funnier. As the Wall Street Journal's Christopher Mims notes in a piece about friendly AI like Siri, Alexa and Google Assistant, Alphabet's big play for the space includes hiring up comedy writing alumni of Pixar and The Onion. While none of the major AI assistants on the market today or coming soon are truly "artificial intelligence" (that is: the device itself doesn't actually understand the conversation) people have a natural tendency to form an emotional connection with the little robot voice in the kitchen speaker. So, subtle improvements like a witty joke or unexpected bit of humor can go a long way to improving the user experience, especially as voice and conversation becomes the interface itself. Although Siri and Alexa already have a few jokes in their repertoire, they tend to be pretty bad and definitely don't have quite the same punch as the topical humor of "America's Finest News Source."

Artificial intelligence can be just as biased as human beings, which is why experts are trying to prevent discrimination in machine learning. In a new paper, three Google researchers note that there is no existing way to ensure--as the White House calls it--"equal opportunity by design," but they have an idea. "Despite the need, a vetted methodology in machine learning for preventing this kind of discrimination based on sensitive attributes has been lacking," wrote Moritz Hardt, a research scientist with the Google Brain Team and co-author of the paper, in a blog post. Hardt throws out two seemingly intuitive approaches, "fairness through unawareness" and "demographic parity," but dismisses them for their respective loopholes. By learning from the shortcomings of the aforementioned methods, the team came up with a new approach.

Securing your digital assets is a clear need for any business and individual, whether you are looking to protect your personal photos, company's intellectual property, customers' sensitive data or any other aspect that can harm your reputation or business continuity. This need will continue to grow massively over the next few years as the amount of generated and aggregated data is exploding (IDC predicts that by 2020, the volume of digital data will reach 44 Zettabytes, 1,000,000,000,000 GB 1ZB). The greatest challenge, in all disciplines of Cybersecurity, is to be able to recognize new threats efficiently without relying on any signatures or easy to bypass heuristics, which rely on known, previously-seen malicious activities. Supporting this trend, although billions of dollars are spent on cybersecurity (the latest estimate by Garter, worldwide information security spending will reach 81.6 billion in 2016), we keep seeing the growing number of reported cyber-attacks and the higher magnitude of breaches every day, for example the recently published high-magnitude cyber-breaches -- Yahoo 500M accounts data breach is among the biggest in the history, Dropbox confirmed 68M accounts details leaked. There are many Cybersecurity frontiers where harnessing the predictive power of AI might bring the upper hand to security vendors and to us all, individuals and businesses.

It seems like there isn't a week in which deep learning doesn't come up as achieving some kind of remarkable task. I understand that one of the powers of deep learning is that it is capable of learning the features. This capacity seems totally decoupled from the underlaying problem. So basically I read this as "no matter what problem you have... You can use deep learning".

Experimental tests have shown that the new system, which is based on the artificial intelligence algorithm known as "reservoir computing," not only performs better at solving difficult computing tasks than experimental reservoir computers that do not use the new algorithm, but it can also tackle tasks that are so challenging that they are considered beyond the reach of traditional reservoir computing. The results highlight the potential advantages of self-learning hardware for performing complex tasks, and also support the possibility that self-learning systems--with their potential for high energy-efficiency and ultrafast speeds--may provide an extension to the anticipated end of Moore's law. The researchers, Michiel Hermans, Piotr Antonik, Marc Haelterman, and Serge Massar at the Université Libre de Bruxelles in Brussels, Belgium, have published a paper on the self-learning hardware in a recent issue of Physical Review Letters. "On the one hand, over the past decade there has been ...