In the near future, artificial intelligence may well disrupt the way Frequently Asked Questions (FAQ) software is conceived. Naturally we are accustomed to traditional forms of FAQ with a simple user interface that reveals questions and answers. Not so long ago, IBM revealed the idea behind Watson, a system that is capable of processing natural language and machine learning in order to uncover insights and "help connect the dots." These types of systems are able to analyze huge amounts of data and extract meaning for future reuse and consultation. This natural form of "assembling" questions and answers was not possible decades ago.

Neither humans nor A.I. has proven overwhelmingly successful at maintaining cybersecurity on their own, so why not see what happens when you combine the two? That's exactly the premise of a new project from MIT, and it's achieved some impressive results. Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and machine-learning startup PatternEx have developed a new platform called A.I.2 that can detect 85 percent of attacks. It also reduces the number of "false positives" -- nonthreats mistakenly identified as threats -- by a factor of five, the researchers said. The system was tested on 3.6 billion pieces of data generated by millions of users over a period of three months.

Neither humans nor AI has proven overwhelmingly successful at maintaining cybersecurity on their own, so why not see what happens when you combine the two? That's exactly the premise of a new project from MIT, and it's achieved some pretty impressive results. Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and machine-learning startup PatternEx have developed a new platform called AI2 that can detect 85 percent of attacks. It also reduces the number of "false positives" -- nonthreats mistakenly identified as threats -- by a factor of five, the researchers said. The system was tested on 3.6 billion pieces of data generated by millions of users over a period of three months.

There are really two decisions that must be made regarding the hidden layers: how many hidden layers to actually have in the neural network and how many neurons will be in each of these layers. We will first examine how to determine the number of hidden layers to use with the neural network. Problems that require two hidden layers are rarely encountered. However, neural networks with two hidden layers can represent functions with any kind of shape. There is currently no theoretical reason to use neural networks with any more than two hidden layers.

Using a system that MIT is calling AI2, which was developed by the institute's Computer Science and Artificial Intelligence Laboratory, researchers have made it easier for humans to detect network breaches. Finding the evidence of a compromised network is a daunting take for security experts, at least for humans. The system MIT has developed doesn't sleep and can sift through millions of log lines looking for abnormalities before bringing them to an analyst's attention. After AI2 has found an anomaly following a review of data, it points out abnormalities to a human who takes over and has a thorough look at AI2's findings. According to the researchers, this human/AI team identified just shy of 90% of attacks while saving the human component hours and hours of time by not chasing after false leads.

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.