Fujitsu Laboratories Ltd. and Fujitsu Research and Development Center Co., Ltd. are developing new technology to analyze traffic video in order to provide real-time information on congestion, accidents and crime violations. Using machine learning and image processing, the technology analyzes the images from surveillance cameras installed along highways and streets, and groups characteristics that can lead to recognition errors, such as changes in lighting or environmental factors like nighttime and fog. The technology also analyzes moving objects, such as vehicles, bicycles and people, to identify accidents. A comparison of the previous traffic camera technology (left) and a sample of the application Fujitsu is testing (right). Source: Fujitsu The goal is to improve the way surveillance cameras can be used to improve traffic safety, reduce pollution and reduce congestion.

Whether it's Google's headline-grabbing DeepMind AlphaGo victory, or Apple's weaving of "using deep neural network technology" into iOS 10, deep learning and artificial intelligence are all the rage these days, promising to take applications to new heights in how they interact with us mere mortals. To go deeper (yes, I went there) on the subject, I reached out to the team at the deep learning-focused company Skymind, creators of Deep Learning For Java (DL4J), and authors of the recently released O'Reilly book Deep Learning: A Practitioner's Approach, Josh Patterson and Adam Gibson. Josh and Adam offer us a gentle introduction to the subject in this interview, as well as insight into how they are building an open source-based business around deep learning. Adam Gibson (AG): Deep learning is just another term for neural networks, a set of algorithms that have been around for decades. For a long time people were skeptical about them, but as chips got more powerful and as we gathered more data to train them on, deep neural nets started breaking records.

Getting ready to step into the self-driving Ford Fusion in Dearborn, MI. These technological advances, which are dependent on the machine learning branch of AI, rely on the data collected by car companies--data from real miles driven, such as with Tesla's Autopilot, data from simulations of autonomous driving, and data from test situations, such as Uber's driverless fleet in Pittsburgh. Big data, said Michael Cavaretta, director of analytics infrastructure at Ford Motor Company, means data that is "too big to easily handle within your computational resources." It's about looking at datasets with "high velocity, high volume and high variety," he said. And as computers have become more powerful and storage is cheaper, grappling with this data has become more difficult.

Gartner, Inc. today highlighted the top technology trends that will be strategic for most organizations in 2017. Analysts presented their findings during the sold-out Gartner Symposium/ITxpo, which is taking place here through Thursday. Gartner defines a strategic technology trend as one with substantial disruptive potential that is just beginning to break out of an emerging state into broader impact and use or which are rapidly growing trends with a high degree of volatility reaching tipping points over the next five years. "Gartner's top 10 strategic technology trends for 2017 set the stage for the Intelligent Digital Mesh," said David Cearley, vice president and Gartner Fellow. "The first three embrace'Intelligence Everywhere,' how data science technologies and approaches are evolving to include advanced machine learning and artificial intelligence allowing the creation of intelligent physical and software-based systems that are programmed to learn and adapt. The next three trends focus on the digital world and how the physical and digital worlds are becoming more intertwined. The last four trends focus on the mesh of platforms and services needed to deliver the intelligent digital mesh."

Pixar founder George Lucas might have given the world the word "Droid" before he sold the company to Apple's Steve Jobs but now Apple CEO Tim Cook is making no secret of the fact that he wants the company to play its part in bringing Artificial Intelligence to the world. This week he's been spilling at least some of the beans on Apple's plans for AI, although his vision seems more connected to machine learning rather than the AI technologies that would help to bring droids like C3PO and R2D2. "We see AI as being horizontal in nature and running across all our platforms and products," said Cook, following on to say, "we see it being used in ways that most people don't even think about." That sounds grandoise – and, at first sight, it sounds like they have something amazing up their sleeves. So just what kind of unexpected use cases was he talking about?

Sketch a picture of a cat, or a castle, or a crab and most people will get what you're trying to convey -- but unless you have a little talent, the drawing probably doesn't look a lot like the real thing. That's not a problem for this system created by Belgian computer scientists. Their system can recognize toddler-level sketches of 250 categories of objects. This has been done a couple of times before, but one interesting aspect of this approach is that the machine learning system is exposed to the drawing as it's created, seeing it at various fractions of completeness. Turns out that can help identify the object; after all, you ever see anyone draw the chimney on the house first?

Part of the thrill of buying vintage design is in hunting down great finds. That process loses its appeal online, where buyers face an overwhelming quantity of listings, a lack of consistency in how pieces are identified, and, occasionally, outright mislabeling. This week, eBay announced Collective, a new section of the site that uses artificial intelligence along with in-house curation to make it easier for shoppers to sift through the company's 1 billion-plus listings and get their design fix. EBay describes Collective as a "bespoke experience" and features furniture, antiques, contemporary design, and fine art from 21 hand-picked dealers; product collections assembled with an editorial eye; and straightforward Pinterest-like pages of tables, seating, lamps, case goods, and more. But the potential gamechanger is the use of artificial intelligence in a "Get the Look" feature that scans listings to find products that have similar traits to objects in room vignettes.

NEW YORK: In a major breakthrough in the field of speech recognition, Microsoft researchers have created a technology that accurately recognises the words in a conversation like humans do. The team from Microsoft Artificial Intelligence and Research reported a speech recognition system that makes the same or fewer errors than professional transcriptionists. The researchers reported a word error rate (WER) of 5.9 percent, down from the 6.3 percent WER the team reported just last month. The 5.9 percent error rate is about equal to that of people who were asked to transcribe the same conversation, and it's the lowest ever recorded against the industry standard "Switchboard" speech recognition task. This is an historic achievement," said Xuedong Huang, the company's chief speech scientist in a Microsoft blog post. The milestone means that, for the first time, a computer can recognise the words in a conversation as well as a person would. In doing so, the team has beat a goal they set less than a year ago - and greatly exceeded everyone else's expectations as well. "Even five years ago, I wouldn't have thought we could have achieved this.

Microsoft's speech-recognition AI could eventually be used to enhance Cortana's accessibility features, say, for deaf people. Microsoft researchers have developed a system that recognizes speech as accurately as a professional human transcriptionist. Researchers and engineers from Microsoft's Artificial Intelligence and Research group have set a new record in speech recognition, achieving a word error rate of 5.9 percent, down from the 6.3 percent reported a month ago. The word error rate is the percentage of times in a conversation that a system, in this case a combination of neural networks, mishears different words. Microsoft's system performed as well as humans who were asked to listen to the same conversations.

It's a red-letter day at Microsoft Research: a team working on speech recognition has hit a serious symbolic goal with a system that's as good as you at hearing what people are saying. Specifically, the system has a "word error rate" of 5.9 percent, on par with professional human transcribers. Even they don't hear things perfectly, of course, but 94 percent accuracy is more than good enough for conversation. "This accomplishment is the culmination of over twenty years of effort," said Geoffrey Zweig, one of the researchers, in a Microsoft blog post. Indeed, speech recognition is one of those tasks that's been pursued for decades by pretty much every major tech business and research outfit.