NEW YORK, NY--(Marketwired - Jun 15, 2016) - Datorama, a global leader in marketing analytics innovation, today announced the company has added an office in Europe. The latest addition to Datorama's global footprint is located in Hamburg, Germany and marks a critical milestone as the company expands into the German, Austrian and Swiss (DACH) region. Datorama's Hamburg office further strengthens a robust EMEA presence, which includes: Amsterdam, Barcelona, London and Paris. Designed for marketers, Datorama's Marketing Integration Engine helps leading enterprises, agencies and publishers centralize all of their marketing data across silos for cross-channel visualization, analysis and data-driven insight generation. By analyzing inputs from unlimited data sources, including online and offline marketing channels, and first- and third-party applications across CRM, billing, call centers, and more, the company's patent-pending artificial intelligence (AI)-based software delivers a single source of truth at the data layer to drive tactical and strategic marketing performance optimization.

A tinny voice asks, "Would you like fries with that?" A flashing screen says, "That colour really brings out your eyes." Welcome to our automated future, where more than 40 per cent of Canadian jobs have been replaced by technology. "Our findings show that a significant percentage of Canadian jobs are at a high risk of being replaced by automation over the next 10 to 20 years," Brookfield Institute for Innovation Entrepreneurship executive director Sean Mullin said in a written statement. In a report released Wednesday, the Ryerson University-based institute says that nearly 42 per cent of Canada's workforce is at a high risk of being affected by automation over the next two decades. You can see what the probability of automation in your field is via this interactive chart.

SugarCRM has put A.I. at the core of its product plans and is working on a new intelligence service along with a Siri-like agent named Candace. Tapping the company's recent acquisitions of Stitch and Contastic, the new technology will be designed to help businesses spend less time entering data into their customer relationship management (CRM) software, and more time learning from and acting upon it. SugarCRM is scheduled to demonstrate the new capabilities Wednesday at its SugarCon conference in San Francisco. "In the CRM space, we want people to focus on what they're good at: Relating to others, such as customers and partners," Rich Green, SugarCRM's chief product officer, said in an interview last week. "As data becomes more and more available, it typically has required quite a bit of labor to ensure that your CRM system stays up to date," Green explained.

The Earth isn't getting any bigger, so we need to start finding more efficient ways to feed the projected 10 billion people by 2050 using the same amount of land. Researchers from EPFL in Switzerland and Penn State University used the Caffe deep learning framework and Tesla K40 GPUs to train a model that identifies crop diseases. For now, the researchers created a website, Plant Village, an open access database of 50,000 images of healthy and diseased crops. The goal is to launch a mobile app to help farmers around the world by providing them with the ability to snap a photo of their diseased plant and the app would automatically diagnose it. Silicon Valley-based Blue River Technology has developed a deep learning solution called LettuceBot that rolls through a field photographing 5,000 young plants a minute, using algorithms and machine vision to identify each sprout as lettuce or a weed.

As what was once mere data evolves into actionable intelligence, the context that binds that data becomes ever more essential. With no context around those four letters, you might not understand the reference or make any sort of connection. But if you add just one word to "java," such as "development," "island," or "coffee," the reference changes completely--and that's with just a single word of context. This is the type of active context and connection that the Brainspace engine provides. "Context is a very important part of what we do. When we analyze documents, we take the context into consideration," says Ravi Sathyanna, vice president of technology and product management at Brainspace.

Fleets of autonomous vehicles are coming to roads near you sooner than ever, thanks to strong US and Chinese interest, and the 2020 Tokyo Olympics.

At its annual Worldwide Developers Conference (WWDC) in San Francisco this week, Apple suggested that it was more than willing to compete toe-to-toe against Google, Amazon, and Microsoft in the artificial intelligence (A.I.) arena. As those rivals build increasingly sophisticated bots that can respond to users' natural-language commands, Apple has opened its own digital assistant, Siri, to third-party developers. If things pan out as the company expects, Siri will rapidly add more functionality, essentially becoming a butler for a growing collection of tasks. And that's just the first of what will surely be many A.I.-related initiatives on Apple's part. But how can Apple reconcile its need for personal data--the fuel of an effective A.I. platform--with its rigorous user privacy stance?

If you've been out on the streets of Silicon Valley or New York City in the past nine months, there's a good chance that your bad driving habits have already been profiled by Nexar. This U.S.-Israeli startup is aiming to build what it calls "an air traffic control system" for driving, and has just raised an extra 10.5 million in venture capital financing. Since Nexar launched its dashcam app last year, smartphones running it have captured, analyzed, and recorded over 5 million miles of driving in San Francisco, New York, and Tel Aviv. The company's algorithms have now automatically profiled the driving behavior of over 7 million cars, including more than 45 percent of all registered vehicles in the Bay Area, and over 30 percent of those in Manhattan. Using the smartphone's camera, machine vision, and AI algorithms, Nexar recognizes the license plates of the vehicles around it, and tracks their location, velocity, and trajectory.

Data science, and knowledge discovery, are among the most "brain-like" operations that a company does, and its practitioners have a unique vantage point into the utility of artificial intelligence. With the emergence of deep learning now upending AI, it is worth exploring how this powerful class of techniques relates to knowledge and understanding, using our own brain as a gold standard for how information is stored for synthesis and insight. Is there a general "process" by which data can be turned into knowledge, or a "rule" for learning rules? Most neuroscientists think so, and so do deep learning researchers. They comprise two search parties, looking for the self-organizing logic that is the magic key for turning data into knowledge.

What if a simple camera capturing data for machine learning could tell you the threat level of an individual approaching a fence? What if the same combination of camera and computer could classify the behavior of shoppers in a grocery store isle and judge things like intent to purchase, presence of decision paralysis, and ease of identifying desired products? Fueled by advances in image recognition and processing power, smart-cameras that can classify human behavior rather than simply observe it may be the next step for IoT. Nanit is one of the first companies in this space. Dr. Assaf Glazer, a parent himself, and his team are working to take the pain out of one of the most strenuous tasks of any parent, making sure their baby gets a good night's sleep.