Machine Learning helps make complex systems more efficient. By applying advanced Machine Learning techniques such as Cognitive Fingerprinting, wind project operators can utilize these tools to learn from collected data, detect regular patterns, and optimize their own operations. In his session at 18th Cloud Expo, Stuart Gillen, Director of Business Development at SparkCognition, will discuss how research has demonstrated the value of Machine Learning in delivering next generation analytics to improve safety, performance, and reliability in today's modern wind turbines. Speaker Bio Stuart Gillen is the Director of Business Development at SparkCognition. In this role, he is responsible for driving business engagements, partner development, marketing activities, and go-to market strategy.

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Researchers at MIT's Computer Science and Artificial Intelligence Lab (CSAIL) have developed a cybersecurity system that combines human and machine-learning approaches to reduce cyber attacks and false positives. Named AI2 to signify that it merges artificial intelligence with "analyst intuition," the system was developed by Kalyan Veeramachaneni, a research scientist at CSAIL, and Ignacio Arnaldo, a former postdoctoral researcher at CSAIL who is now a chief data scientist at PatternEx. In tests, the researchers demonstrated that "AI2 can detect 85 percent of attacks, which is roughly three times better than previous benchmarks, while also reducing the number of false positives by a factor of five," according to a news release from CSAIL. Most modern cybersecurity systems use either analyst-driven solutions or machine-learning approaches. Analyst-driven systems rely on rules created by people and consequently can't detect attacks that don't adhere to those rules, whereas machine-learning systems rely on anomaly detection, which tends to generate false positives that have to be investigated by people.

The revolution in GPU computing started with games, and spread to the HPC centers of the world eight years ago with the first "Fermi" Tesla accelerators from Nvidia. But hyperscalers and their deep learning algorithms are driving the architecture of the "Pascal" GPUs and the Tesla accelerators that Nvidia unveiled today at the GPU Technical Conference in its hometown of San Jose. Not only did the hyperscalers and their AI efforts help drive the Pascal architecture, but they will be the first companies to get their hands on all of the Tesla P100 accelerators based on the Pascal GP100 GPU that Nvidia can manufacture, long before they become generally available in early 2017 through server partners who make hybrid CPU-GPU systems. As was the case with the prior generations of GPU compute engines, Nvidia will eventually offer multiple versions of the Pascal GPU for specific workloads and use cases, but Nvidia has made the big bet and created its high-end GP100 variant of Pascal and making other big bets at the same time, such as moving to a 16 nanometer FinFET process from chip fab partner Taiwan Semiconductor Manufacturing Corp and adding in High Bandwidth Memory from memory partner Samsung at the same time. Jen-Hsun Huang, co-founder and CEO at Nvidia, said during his opening keynote that Nvidia has a rule about how many big bets it can make.

As cool as those handheld 3D printing pens are, you have to have some amount of talent (or at least practice) in order to make anything that's much more recognizable than a mangled three-dimensional squiggle. A proper 3D printer is basically one of those 3D printing pens stapled to a robot that can move it in three axes and do a much better job making things that look nice and function well, but it doesn't allow for much artistic participation from you. For some people, that's the point, but if you'd like to be more directly involved, Yeliz Karadayi's thesis project, called "Guided Hand," is a 3D printing pen with a haptic interface that helps keep you from screwing things up too badly. These haptic interfaces are basically little robot arms, although you can produce the same effect with robot arms of any size). It's hard to explain how it feels to use one of these things, and the experience doesn't come through very well on video, but basically, the end of the arm (being a robot) knows exactly where it is in 3D space, which means it can tell whether it is about to intersect a virtual 3D object or not.

Finding evidence that someone compromised your cyber defenses is a grind. Sifting through all of the data to find abnormalities takes a lot of time and effort, and analysts can only work so many hours a day. But an AI never gets tired, and can work with humans to deliver far better results. A system called AI2, developed at MIT's Computer Science and Artificial Intelligence Laboratory, reviews data from tens of millions of log lines each day and pinpoints anything suspicious. A human takes it from there, checking for signs of a breach.

Researchers with MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) believe that can offer the security world a huge boost in incident response and preparation with a new artificial-intelligence platform it believes can eventually become a secret weapon in squeezing the most productivity from security analyst teams. Dubbed AI2, the technology has shown the capability to offer three times more predictive capabilities and drastically fewer false positive than todays analytics methods. CSAIL gave a sneak peek into AI2 in a presentation to the academic community last week at the IEEE International Conference on Big Data Security, which detailed the specifics of a paper released to the public this morning. The driving force behind AI2 is its blending of artificial intelligence with what researchers at CSAIL call "analyst intuition," essentially finding an effective way to continuously model data with unsupervised machine learning while layering in periodic human feedback from skilled analysts to inform a supervised learning model. "You can think about the system as a virtual analyst," says CSAIL research scientist Kalyan Veeramachaneni, who developed AI2 with former CSAIL postdoc Ignacio Arnaldo, who is now a chief data scientist at PatternEx.

CkBot is a reconfigurable modular robot that is able to reassemble itself after being kicked apart - the Self-Assembly After Explosion problem. Mark Yim, a professor of mechanical engineering at the University of Pennsylvania Modular Robotics Lab (ModLab), describes the ideas behind CkBot.

Knowing a cyberattack's going to occur before it actually happens is very useful--but it's tricky to achieve in practice. Now MIT's built an artificial intelligence system that can predict attacks 85 percent of the time. Cyberattack spotters work in two main ways. Some are AI that simply looks out for anomalies in internet traffic. They work, but often throw up false positives--warnings about a threat when actually nothing's wrong. Other software systems are built on rules developed by humans, but it's hard to create systems like that which catches every attack.

Which are the cancerous cells in this image? With a new technique that combines a microscope and deep learning software, it might be easier than ever to tell the difference. Identifying cancer based on blood samples can be surprisingly challenging. Often, doctors add chemicals to a sample that can make the cancerous cells visible, but that makes the sample impossible to use in other tests. Other techniques identify cancerous cells based on their abnormal structure, but those take more time (those cells are often rare in a given sample) and can misidentify healthy misshapen cells as cancerous.