Shibl Mourad, the head of engineering for Google's Montreal office, says the company hopes to help turn the city into a "super-cluster" of AI knowledge that will attract corporate investors, burgeoning startups and researchers. He said much of the credit goes to Bengio and his colleagues, whose research over the last decade has put the city ahead of its competitors. Had these researchers not invested that decade of their lives, "we would not be where we are," Mourad said. The lab Bengio leads is one of the largest in the world dedicated to studying Deep Learning, one of the underpinnings of AI. Over the past decade, they learned that by layering several "neural networks" that mimic how the brain works, computer programs could "learn" to solve complex problems on their own instead of needing to be programmed step-by-step.

Montreal's burgeoning AI and machine learning ecosystem just got a boost. Today, the Canadian Press reported that the Montreal Institute for Learning Algorithms (MILA) at the Université de Montréal will receive $4.5 million over three years from Google to support its research. Google will also open an AI research group at its Montreal office. "(AI) will affect pretty much every economic sector; right now is just the tip of the iceberg," Yoshua Bengio, head of MILA, told The Canadian Press. "One of the things we are going to see more of is how these technologies affect how we interact with computers."

John Charles Thomas was born in Akron, Ohio and attended Ellet High School. He graduated from Case Western Reserve University majoring in psychology and minoring in mathematics and drama. He received a Ph.D. in experimental psychology from the University of Michigan. His dissertation compared human performance in a problem solving task to that of an early AI system called "The General Problem Solver." After graduate school, Dr. Thomas managed a research project on the psychology of aging at Harvard Medical School.

AI technologies have clear value paths in marketing, including sales and enhancing customer experience. When most companies think about artificial intelligence (AI)--and its many sub-categories, such as machine learning, natural language processing (NLP), or cognitive computing--they think about applications such as IBM's Watson technology being used to diagnose patients. What they often miss, according to Fern Halper, VP and senior research director of advanced analytics at TDWI, is that "marketing is often one of the first departments in an organization to use advanced analytics." In a recent webinar, Halper discussed the current potential and future pathways for marketing analytics with David Stodder, senior research director of business intelligence at TDWI, and Wilson Raj, the global director of customer intelligence with SAS. They all agreed that among all the different business departments, applying AI to marketing could provide the most immediate, recognizable return on investment .

In its Worldwide Semiannual Cognitive/Artificial Intelligence Systems Spending Guide IDC predicted that AI revenues will grow from $8.0 billion this year to $47 billion in 2020, for a compound annual growth rate (CAGR) of 55.1 percent. "Software developers and end user organizations have already begun the process of embedding and deploying cognitive/artificial intelligence into almost every kind of enterprise application or process," said David Schubmehl, research director, Cognitive Systems and Content Analytics at IDC. "Recent announcements by several large technology vendors and the booming venture capital market for AI startups illustrate the need for organizations to be planning and undertaking strategies that incorporate these wide-ranging technologies." But AI is a wide and diverse field. Which areas should organizations be paying attention to? This slideshow highlights ten artificial intelligence trends that seem likely to continue into 2017, and they all could be significant for enterprise IT teams. Many of these AI trends are already well underway but, like the AI field as a whole, will probably become more important as time goes on.

Google has been one of the biggest names in AI for a very long time, and most of their expertise happens to be focused on a single venture that provides benefits over their entire range of AI products. Called "Google Brain", it's almost exactly what it sounds like; an absolutely massive data center, staffed only by the best AI technicians that Google can find, full of networked computers whose sole focus is artificial intelligence. Google's special brain team has near-full autonomy, and near-exclusive access to the privilege of being in the physical presence of the Google Brain structure. Now, it seems that Google is organizing an arm of that team in the AI talent hotbed that is Montreal, and is also throwing the local Institute for Learning Algorithms some cash. Programming for AI is completely different from almost any other type of programming and coding; it's based on algorithms and theoretical logic.

SAN FRANCISCO – November 15, 2016 – Today at Minds Machines, GE (NYSE: GE) announced new products, acquisitions and partner programs to enable further adoption of Predix, the operating system for the Industrial Internet. The platform enhancements, acquisitions and new ISV partner program further complement the Predix technology stack and make it easier for industrial companies to execute a strategic digital transformation to drive internal productivity. In 2016, orders from GE's portfolio of software solutions are on track to climb 25% to more than $7 billion – making GE the fastest growing digital industrial company in the world. Demonstrating the strength of Predix within GE, digital thread productivity will exceed $600 million, accelerating into 2017. "The opportunity for industry is now," said Bill Ruh, Chief Digital Officer of GE and CEO, GE Digital.

Want to put all those years of scribbling in the corners of your school notebooks to good use? You might not think that doodling could be part of an A.I. experiment, but with the new site from Google, Quick, Draw!, you're scribbles are key to machine learning. What is Google Quick, Draw!, exactly? If you have ever wanted to play Pictionary with a computer, now is your chance! The new game, which launched in mid-November, prompts players to speedily draw six different objects while an algorithm attempts to guess what it is that they are drawing.

Deep neural networks have evolved to be the state-of-the-art technique for machine learning tasks ranging from computer vision and speech recognition to natural language processing. However, deep learning algorithms are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, deep compression significantly reduces the computation and storage required by neural networks. For example, for a convolutional neural network with fully connected layers, such as Alexnet and VGGnet, it can reduce the model size by 35x-49x. Even for fully convolutional neural networks such as GoogleNet and SqueezeNet, deep compression can still reduce the model size by 10x.

Users are looking for more than the humble SMS text message to communicate with friends and family. Our communication requirements now demand group messaging capabilities with the ability to seamlessly share an image or video on the move. Apple's iMessage, WhatsApp and Facebook's Messenger are leading the way, but the recent release of Google Allo suggests that messaging has become the new tech battleground. Our love affair with mobile apps is changing because we have so many, often over 50, yet on average we only actually use five of them on a regular basis. Searching for an app that is hidden in a folder of apps on page 3 of our phones is no longer deemed productive in an age of instant gratification.