The recent emergence of a powerful machine-learning technique known as deep learning has made computing giants such as Google, Facebook, and Microsoft even hungrier for data. It's what lets software learn to do things like recognize images or understand language. Yet many problems where deep learning could be most valuable involve data that is hard to come by or is held by organizations that are unwilling to share it. And as Apple CEO Tim Cook puts it, some consumers are already concerned about companies "gobbling up" their personal information. "A lot of people who hold sensitive data sets like medical images are just not going to share them for legal and regulatory concerns," says Vitaly Shmatikov, a professor at Cornell Tech who studies privacy.

ReplyYes has seen success with its automated messaging system. Chatbots already have a little bit of a bad name. Early reviews for the ones on Facebook Messenger have been rough due to apparent malfunctions, and Microsoft's Tay has been an utter disaster, at least on a couple of occasions. But a startup called ReplyYes, which offers a text-to-buy system for retailers, provides a glimpse into the potential of automated messaging. Interestingly, the company has a pair of e-commerce ventures: One sells vinyl records, the other graphic novels.

Last week, Facebook launched the messenger chatbot platform, which like Slack, Telegram and others, presents a big market opportunity for startups to innovate. In addition, the companies and products that determine how to most efficiently distribute their product on these new platforms will benefit from user curiosity and less competition, both of which result in lower cost-of-customer acquisition. These are the five questions I'm wondering about in this new world: New user experience and distribution platforms come around perhaps once every ten years. Bots have the opportunity to create a discontinuity in the market. It's going to be enthralling to see all the innovation that founders and startups will bring to this market.

Mobileye, the Israeli car automation company that came onto the self-driving car scene as sort of an anti-Google, is now looking at the future in terms that seem a bit closer to Google's than used to be the case. Speaking Friday at a conference organized by Goldman Sachs (which owned a chunk of Mobileye's shares when the company first became publicly traded in 2014), Amnon Shashua, Mobileye's founder and chief technical officer, placed a lot of emphasis on mapping, something Google has done all along. And now Shashua is predicting utterly hands-free driving--if only on the highway--by 2021. Mobileye had always emphasized incremental steps, such as active cruise control and emergency braking, collectively called advanced driver assistance systems (ADAS). It was Google that proposed to skip all half measures and get right to full-bore self-driving cars.

Humankind shuddered once again as machines seemed to score yet another triumph in what had been an exclusively human arena. In case you missed it, last month an artificial intelligence (AI)-generated novel almost won a Japanese literary competition, inspiring awe, intrigue, and eventually skepticism. The new novel's plot "is essentially told from the subjective of an AI that becomes aware of its budding talents as a writer, and abandons its primary task of serving humanity," according to the "Motherboard" channel at Vice.com, and the Los Angeles Times, citing a report in The Japan News, even provided a translation of the novel's final thrilling sentence. "I writhed with joy, which I experienced for the first time, and kept writing with excitement. The day a computer wrote a novel. The computer, placing priority on the pursuit of its own joy, stopped working for humans."

Facebook's AI hardware is now, like its software, open source, joining a broad movement towards outsourcing the world's artificial intelligence intelligence. Facebook also stated it hoped independent AI technicians would develop deep learning tech superior to what the company currently uses, and that it would buy this technology. The tech giant has developed deep learning technology, which it uses for Facebook-related functions like identifying faces in pictures and curating news feeds, but can also apply to a wide range of computing tasks. Through the Open Compute Project, Facebook's custom hardware designs -- a GPU-based server called "Big Sur" -- will join Google's and others' open source deep learning designs. The hope is that more workers will devote themselves to these projects and become familiar with using the technology.

That's the first day of Apple's 2016 Worldwide Developers Conference in San Francisco, when the company is expected to reveal the latest version of iOS, a bump to the Apple TV, and maybe even a renamed release of OS X – or "MacOS", as it hinted at last week. The event was announced, bizarrely, through Siri, which started giving out a more precise answer to the question "when is WWDC?" than previously. Until Monday evening, the digital assistant had answered with "WWDC is not yet announced", but now it correctly says that "the Worldwide Developers Conference (WWDC) will be held June 13 through June 17 in San Francisco. Although WWDC is more developer-focused than most other Apple events, it usually involves the first look at major software updates coming later in the year. If past events are any indication, this June will see the launch of iOS 10, as well as updates to WatchOS and tvOS (the software that runs the Apple TV). It will also be the first chance for developers (and Mac users) to find out information about the next version of OS X.

People platform to discuss and share, research, ideas, #deeplearning methods and applications.

Once the stuff of science fiction novels, machine learning--where computers improve automatically through experience--is now attracting the attention of a wide range of industries. As with many recent advances in tech, machine learning's growth has been largely fueled by the development of new learning algorithms and theory, and by the ongoing explosion in the availability of online data and low-cost computation. Machines are better equipped than ever to capture and analyze large quantities of multisourced, ever-changing data. But analyzing data is not the same thing as using it to make decisions--and here is where humans come in. Humans are still needed to innovate, to put ideas in an appropriate context, and to suss out an action's wide-ranging implications.

Spoiler alert: If you are yet to watch the Season 5 finale of "Game of Thrones" and have also managed to avoid a year of headlines about the fate of one particular character, read on at your own peril. For the rest of us, we've been subject to months of speculation that Jon Snow may not be completely dead, despite being run through with cold, hard steel by most of the Night's Watch when we saw him last. Now, a machine learning algorithm designed by a team at the Technical University of Munich has analyzed data on all the characters in Westeros, both dead and alive, and concluded that it is very likely that Snow is actually a survivor. The project, dubbed "A Song of Ice and Data", basically scrapes info from the online Wiki of Ice and Fire encyclopedia, which focuses largely on the series of books by George R.R. Martin, but also covers the HBO show they inspired. Using this data source, two dozen features of each character are statistically compared to try and figure out which features make a character most likely to die.