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.

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 .

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.

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.

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.

Google is doubling down in Canada's artificial intelligence scene. The search giant said Monday that it's creating a new AI research group in its Montreal office and will invest $4.5 million over three years in the Montreal Institute for Learning Algorithms, an AI research lab part of the University of Montreal. Google's goog new Montreal AI research outpost will be part of Google's Brain team, the search giant's company wide-AI research group headquartered in Mountain View, wrote Google Montreal head of engineering Shibl Mourad in a blog post. Google hired Hugo Larochelle, who was recently a top research scientist at Twitter twtr, to lead the new Montreal unit. Part of Google's investment will involve funding renowned AI expert Yoshua Bengio's research projects as head of the Montreal Institute for Learning Algorithms.

Artificial Intelligence (AI) can help us in many ways: it can perform hard, dangerous or boring work for us, can help us to save lives and cope with disasters, can entertain us and make our daily life more comfortable. Advances in AI are occurring at high speed. The potential risks and problems of AI technology are filling newspapers (e.g. However, rather than being a threat to our existence or plotting to take over the rule of the world, AI is already changing our daily lives, almost entirely in ways that improve human health, safety, and productivity. In the coming years we can expect AI systems to be used increasingly in domains such as transportation, service robots, healthcare, education, low-resource communities, public safety and security, employment and workplace, and entertainment (100 Year AI report).

As the presence of artificial intelligence continues to grow in the world, industry leaders and scholars are starting to explore the ethics surrounding the science. As the presence of artificial intelligence continues to grow in the world, industry leaders and scholars are starting to explore the ethics surrounding the science. In 1941, science-fiction writer Isaac Asimov stated "The Three Laws of Robotics," in his short story "Runaround." Law One: A robot may not injure a human being or, through inaction, allow a human being to come to harm. Law Two: A robot must obey orders given it by human beings except where such orders would conflict with the First Law.