Scientists and engineers normally create AI that can learn via deep learning, and training them usually takes up weeks. However, it seems Microsoft and scientists from the Swiss National Computing Centre have taken this one step further and trained AI via deep learning within minutes! This is an astonishing feat in the realm of robotics and artificial intelligence, one that could easily generate results within hours or minutes. This, coupled with the introduction of supercomputing technology, customers will now have the ability to solve problems such as image, video and speech recognition, as well as natural language processing. This can enable future researchers to be able to make technologies that could only previously been seen in science fiction.

Hearing that Bob Dylan just won the Nobel Prize for Literature, how could I not begin this talk with his famous line, "Something is happening here, but you don't know what it is, do you, Mr. Jones?" The future is full of amazing things. On my way here, I spoke out loud to a $200 device in my kitchen, and asked it to call a Lyft to take me to the airport. And in a few years, that car might well be driving itself. Someone seeing this for the first time would have every excuse to say "WTF?"

Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode I spoke with Ion Stoica, cofounder and chairman of Databricks. Stoica is also a professor of computer science at UC Berkeley, where he serves as director of the new RISE Lab (the successor to AMPLab). Fresh off the incredible success of AMPLab, RISE seeks to build tools and platforms that enable sophisticated real-time applications on live data, while maintaining strong security.

The business world is in the midst of a digital transformation that is quickly separating the wheat from the chaff. The following article unveils the technology trends that will allow you to successfully and confidently navigate the digital era in the coming year, and considers how conversational systems, humanised big data, and augmented information will benefit everyday business. Recommender systems or so-called chatbots that carry on a dialogue with the user in order to guide him through a business process are no longer a rarity. However, the recommendations are usually so vague and the intelligence of the chatbots so limited that the effectiveness of the process suffers. This is about to change.

Data driven recruitment has a significant, positive impact on talent management strategies and business performance. As technology becomes more sophisticated, AI is playing an increasingly essential role in decisions made around hiring and is used by brands such as Facebook as an integral part of the screening and assessment of candidates. This article examines its ongoing effect on the jobs market and the ways in which HR can harness its advantages to better understand, improve and predict hiring needs and potential problems. AI is broadly defined as'machines which perform tasks which humans are capable of performing'. It has been traditionally been regarded as a threat to jobs, with the most drastic predictions suggesting that unemployment rates will reach 50% within 30 years, but perceptions and predictions are changing.

Microsoft is positioning itself as the world's platform for artificial intelligence, and that's a smart move. In 2014 I wrote that Microsoft's Cortana would be the next big thing. Redmond's vision for its johnny-come-lately AI is that it, like the GUI before it, will be pivotal in the evolution of the personal computing user interface. Microsoft's ambitions for Cortana were evident in 2014. Microsoft envisions an unbounded AI that developers and partners will incorporate into a range of everyday and innovative devices enabled by the Cortana SDK.

Job automation predictions from an individual expert typically draw from years of academic research experience, or time "in the trenches" of industry. With growing interest and speculation on the job market of the next decade, we set out to garner a perspective as to what Silicon Valley thinks about the possibilities of automations in various business tasks. In the infographics and article below, we explore the survey responses from nearly 80 Bay Area investors, founders, and tech folks – on which business functions have the greatest potential for automation today, and in the coming five years ahead. Together with San Fransisco-based venture firm BootstrapLabs, we designed a simple survey that was handed out during their "Autonomous Corporation" event in November 2016. It is interesting to note all three groups of respondents considered business intelligence to be the business function with the most current automation potential.

NAGOYA – Major auto parts maker Denso Corp. will team up with NEC Corp. on developing self-driving automobile technology that makes use of artificial intelligence, sources at the two companies said. With NEC boasting expertise in developing artificial intelligence and software, Denso plans to use its information technology expertise to accelerate research into automotive parts for next-generation vehicles. Denso hopes to marry its camera and sensor technology with NEC's "deep-learning" AI technology to develop systems that will enable cars to sense danger and avoid people and obstacles in their way, the sources said Saturday. The deal will also work toward enhancing cybersecurity due to the surge in data leaks and other dangers expected as automakers keep pushing to link cars and trucks to the internet. With alliances between the automotive and IT industries playing important roles in developing next-generation vehicles, Denso has moved to tie up with Toshiba Corp. and Sony Corp. to compete with overseas.

On Tuesday, the White House released a chilling report on AI and the economy. It began by positing that "it is to be expected that machines will continue to reach and exceed human performance on more and more tasks," and it warned of massive job losses. Yet to counter this threat, the government makes a recommendation that may sound absurd: we have to increase investment in AI. The risk to productivity and the US's competitive advantage is too high to do anything but double down on it. This approach not only makes sense, but also is the only approach that makes sense.

Regression is a modelling technique for predicting the values of an outcome variable from one or more explanatory variables. Logistic Regression is a specific approach for describing a binary outcome variable (for example yes/no). Let's assume you are own a new boutique shop. You have a list of potential clients you are thinking of inviting to a special event with the aim of maximizing the number of sales – who should you invite? Data on previous events you have run is a great starting point here, allowing you to predict an individual's likelihood of buying given the information you have on them.