"Machine learning is fascinating as it will revolutionize how we conduct simple tasks like translating content, but it also has major security and health consequences that can improve societies around the world," said Paul Sallomi, vice chairman and global TMT industry leader, Deloitte LLP and U.S. technology sector leader. "For example, mobile machine learning is a strong entry point to improve responses to disaster relief, help save lives with autonomous vehicles, and even turn the tide against the growing wave of cyberattacks." "Our predictions for 2017 showcase the enormous influence that machine learning and the Internet of Things are having on the current technology marketplace," said Sandy Shirai, principal, Deloitte Consulting LLP and U.S. technology, media and telecommunications leader. "With many technologies coming into their own as their power and speed increases and the cost of delivering them goes down, we'll continue to see these platforms grow exponentially and expand their role across industries, creating a whole new value proposition and opportunities." Another innovation with the power to transform the world is autonomous braking.

Your child is already a creative genius by virtue of being human. Humans are far more creative than any other species. Sure, chimpanzees have come up with ideas like termite fishing (using a stick to get tasty termites out of a hole), but most of us would contend that inventions such as space travel and the Large Hadron Collider are slightly more impressive. Expert reveals five tips that parents can use to make their kids creative geniuses. Yet humans vary in creative ability – some of us are simply better at thinking outside the box than others.

January 11, 2017 - Artificial Intelligence in all its forms is being rapidly adopted and incorporated in many new applications. Of those developers working with some form of AI, 34% are involved with Deep Learning techniques, and the industry most likely to be targeted is the Financial/Insurance industry with 16.4% targeting it, according to Evans Data's recently released Artificial Intelligence and Big Data Survey. The survey, conducted with 440 professional developers involved with Artificial Intelligence and/or Big Data also showed that targeting of Internet of Things (14.9%) and non-computer manufacturing (12.5%) are also among the top industries for Deep Learning implementations. Further survey results focused on Deep Learning showed that for almost a third of developers working with Deep Learning, the most common type of data being used is numerical inputs. The most popular types of methods being used are Markov Chain Monte Carlo and Contrastive Divergences.

What historian has time to read tens of millions of news articles from more than a century of British history? So computer scientists and historians have taught computers how to do the job instead, analysing billions of words of news reports to take a new look at the 19th and early 20th centuries. The study, published in the journal PNAS, marks the early steps of the emerging field of "culturomics". Computers analysed a total of 28.6 billion words from 35 million British regional news stories published between 1800 and 1950, which made up about 14% of the total output of the regional press in that period. For comparison, the average adult has a reading speed of about 300 words per minute. At that rate, it would take someone about 180 solid years to do all that reading, not including a lunch break.

Google parent company Alphabet (NASDAQ:GOOG) (NASDAQ:GOOGL) is set to report results for its fourth quarter of 2016 on Thursday, Jan. 26. Before the numbers for the quarter are released, here's a broad overview of some areas to watch. The pressure is certainly on for Alphabet's fourth quarter, especially after the company's expectation-beating quarter before it. For Alphabet's third quarter, both the company's revenue and earnings per share (EPS) exceeded expectations. Revenue and non-GAAP (non-generally accepted accounting principles) EPS was $22.5 billion and $9.06, respectively, up significantly from $18.7 billion and $7.35, respectively, in the year-ago quarter.

The overeager adoption of big data is likely to result in catastrophes of analysis comparable to a national epidemic of collapsing bridges. Hardware designers creating chips based on the human brain are engaged in a faith-based undertaking likely to prove a fool's errand. Despite recent claims to the contrary, we are no further along with computer vision than we were with physics when Isaac Newton sat under his apple tree. Those may sound like the Luddite ravings of a crackpot who breached security at an IEEE conference. In fact, the opinions belong to IEEE Fellow Michael I. Jordan, Pehong Chen Distinguished Professor at the University of California, Berkeley. Jordan is one of the world's most respected authorities on machine learning and an astute observer of the field. His CV would require its own massive database, and his standing in the field is such that he was chosen to write the introduction to the 2013 National Research Council report "Frontiers in Massive Data Analysis." San Francisco writer Lee Gomes interviewed him for IEEE Spectrum on 3 October 2014. IEEE Spectrum: I infer from your writing that you believe there's a lot of misinformation out there about deep learning, big data, computer vision, and the like. Michael Jordan: Well, on all academic topics there is a lot of misinformation. The media is trying to do its best to find topics that people are going to read about. Sometimes those go beyond where the achievements actually are.

The rules of the game called psychological science.

Artificial Intelligence in all its forms is being rapidly adopted and incorporated in many new applications. SANTA CRUZ, CA, January 11, 2017 /24-7PressRelease/ -- Artificial Intelligence in all its forms is being rapidly adopted and incorporated in many new applications. Of those developers working with some form of AI, 34% are involved with Deep Learning techniques, and the industry most likely to be targeted is the Financial/Insurance industry with 16.4% targeting it, according to Evans Data's recently released Artificial Intelligence and Big Data Survey. The survey, conducted with 440 professional developers involved with Artificial Intelligence and/or Big Data also showed that targeting of Internet of Things (14.9%) and non-computer manufacturing (12.5%) are also among the top industries for Deep Learning implementations. Further survey results focused on Deep Learning showed that for almost a third of developers working with Deep Learning, the most common type of data being used is numerical inputs.

Four of the world's best professional poker players will compete against artificial intelligence developed by Carnegie Mellon University in an epic rematch to determine whether a computer can beat humans playing one of the world's toughest poker games. Artificial Intelligence: Upping the Ante," beginning Jan. 11 at Rivers Casino, poker pros will play a collective 120,000 hands of Heads-Up No-Limit Texas Hold'em over 20 days against a CMU computer program called Libratus. The pros--Jason Les, Dong Kim, Daniel McAulay and Jimmy Chou--are vying for shares of a $200,000 prize purse. The ultimate goal for CMU computer scientists, as it was in the first Brains Vs. AI contest at Rivers Casino in 2015, is to set a new benchmark for artificial intelligence. "Since the earliest days of AI research, beating top human players has been a powerful measure of progress in the field," said Tuomas Sandholm, professor of computer science. "That was achieved with chess in 1997, with Jeopardy! in 2009 and with the board game Go just last year.

Deep learning has been all over the news lately. In a presentation I gave at Boston Data Festival 2013 and at a recent PyData Boston meetup I provided some history of the method and a sense of what it is being used for presently. This post aims to cover the first half of that presentation, focusing on the question of why we have been hearing so much about deep learning lately. The content is aimed at data scientists who might have heard a little about deep learning and are interested in a bit more context. Regardless of your background, hopefully you will see how deep learning might be relevant for you.