IBM Research (NYSE: IBM) today announced a multi-year collaboration with the Department of Brain & Cognitive Sciences at MIT to advance the scientific field of machine vision, a core aspect of artificial intelligence. The new IBM-MIT Laboratory for Brain-inspired Multimedia Machine Comprehension's (BM3C) goal will be to develop cognitive computing systems that emulate the human ability to understand and integrate inputs from multiple sources of audio and visual information into a detailed computer representation of the world that can be used in a variety of computer applications in industries such as healthcare, education, and entertainment. The BM3C will address technical challenges around both pattern recognition and prediction methods in the field of machine vision that are currently impossible for machines alone to accomplish. For instance, humans watching a short video of a real-world event can easily recognize and produce a verbal description of what happened in the clip as well as assess and predict the likelihood of a variety of subsequent events, but for a machine, this ability is currently impossible. Beginning in September 2016 in Cambridge, the BM3C collaboration will bring together leading brain, cognitive, and computer scientists to conduct research in the field of unsupervised machine understanding of audio-visual streams of data, using insights from next-generation models of the brain to inform advances in machine vision.

In 1950, Alan Turing asked a provocative question: Can machines think? Now, the answer is clear. Evidence of machine learning is all around us. From simply performing a Google search which learns from previous searches, to services like Netflix which provide the viewer with relevant film recommendations based on earlier inputs. Speculation around whether machines will "take our jobs" has been prevalent when discussing the technology. In the marketing industry, the increasing use of tech in campaign delivery is leading many to question how machine learning will impact the marketer and whether it will negatively impact their role.

Webroot has acquired the assets of San Diego-based CyberFlow Analytics in a move that beefs up the company's machine learning capabilities. CyberFlow's FlowScape network behavioral analytics solution applies data science to network anomaly detection. Thus, the acquisition extends Webroot's machine learning-based cybersecurity to the network layer, to address the explosion of internet-connected devices and an increasingly complex threat landscape. SaaS-based FlowScape adversarial analytics and unsupervised machine can identify polymorphic malware and advanced persistent threats (APTs) that mask their activities within everyday network noise, the company said, identifying network anomalies in both IPv4 and IPv6 traffic. Security analysts can view alerts via a SIEM solution or the automated FlowScape visualization console, which creates self-forming behavioral clusters that provide an early warning system of high-risk activity that forms over time.

This is a follow up to an article I wrote last year, Machine Learning in a Week, on how I kickstarted my way into machine learning (ml) by devoting five days to the subject. After this highly effective introduction, I continued learning on my spare time and almost exactly one year later I did my first ml project at work, which involved using various ml and natural language processing (nlp) techniques to qualify sales leads at Xeneta. This felt like a blessing: getting paid to do something I normally did for fun! It also ripped me out of the delusion that only people with masters degrees or Ph.D's work with ml professionally. The truth is you don't need much maths to get started with machine learning, and you don't need a degree to use it professionally.

MIT has used machine learning to create video from still images, and the results are pretty impressive. As you can see from the above image, there's a lot of natural form to the movement in the videos. The system "learns" types of videos (beach, baby, golf swing...) and, starting from still images, replicates the movements that are most commonly seen in those videos. So the beach video looks like it has crashing waves, for instance. But like other machine-generated images, these have limitations. The first is size: what you see above is the extent to which the program can render its video.

Dean Nelson has joined transportation company Uber as head of Uber Compute, as the company continues to expand its data storage and processing capabilities. Nelson previously worked at companies including Sun Microsystems, Allegro Networks, and eBay. In March, he left his position as the man leading eBay's data center strategy to establish a data center industry group called Infrastructure Masons. "My pivotal moment is today. I'm starting the most exciting job of my career as the Head of Uber Compute," Nelson said in a LinkedIn post.

Machine learning, autonomy, and artificial intelligence are being explored as key new areas for cybersecurity. What is their history and likely future? As we rely more on autonomous machines for our security, what are the benefits? How can policymakers keep pace? A panel of leading experts will explore the issues from the perspectives of computer science, political science, ethics and law.

But deciding to build an app in the first place was driven by a broader purpose. As one doctor put it, "If you want to do world-class sociology research, build Facebook."

When you see or hear something happen, you can instantly describe it: "a girl in a blue shirt caught a ball thrown by a baseball player," or "a dog runs along the beach." It's a simple task for us, but an immensely hard one for computers -- fortunately, IBM and MIT are partnering up to see what they can do about making it a little easier. The new IBM-MIT Laboratory for Brain-inspired Multimedia Machine Comprehension -- we'll just call it BM3C -- is a multi-year collaboration between the two organizations that will be looking specifically at the problem of computer vision and audition. The problem of computer vision spans multiple disciplines, so it has to be attacked from multiple directions. Say your camera is good enough to track objects minutely -- what good is it if you don't know how to separate objects from their background?

Spending on cognitive systems in Asia Pacific excluding Japan (APeJ) will reach over US 1.9 billion in 2019 at a compounded annual growth rate (CAGR) of 65.36 percent from 2014 - 2019. According to IDC findings, more than 40 percent of all cognitive systems spending throughout the forecast will go to software, which includes both cognitive applications and cognitive software platforms, which facilitate the development of intelligent, advisory, and cognitively enabled solutions. "The barriers of artificial intelligence, machine and deep learning are rapidly dropping," says Chwee Chua, AVP, Big Data and Analytics and Cognitive Computing, IDC Asia/Pacific. "As such, cognitive systems will soon be powering data-driven applications across a wide spectrum of solutions. "This new generation of tools and capabilities is capable of offering intelligent assistance, advice, and recommendations to end users; thus enhancing their competitive edge or supplementing information for better decision making." Cognitive applications is the largest and fastest-growing category in cognitive systems in APeJ, with spending expected to reach US 909.33 million by 2019. Cognitive-related services (e.g. business services and IT consulting) represent the second largest spending category while hardware spending, which is mostly on servers and storage, will grow nearly as fast as software spending. "Cognitively enabled solutions are the next evolution in analysing structured and unstructured data," Chua adds. "Cognitive platforms are offering innovators the ability to build new products and services that would have been previously impossible without large resources." The manufacturing industry currently spends the most on cognitive systems, representing nearly 32 percent of the total APeJ spend throughout the forecast - leading uses in manufacturing include quality management, recommendation systems, and research. Other verticals actively leveraging on cognitive systems are the retail and healthcare industries with an estimated combined spending on cognitive systems over US 675 million in 2019. The leading use cases in retail are automated customer service agents and merchandising for omni-channel operations while the leading use case in healthcare is diagnosis and treatment systems. "We are already seeing use cases for cognitive systems being implemented in Asia/Pacific to address problems across verticals," adds Qiao Li, Senior Market Analyst, Asia/Pacific Big Data and Analytics, IDC Asia/Pacific. "For instance, banks are using cognitive applications to improve customer experiences with recommendations based on customer profile and changing market conditions.