"Today's security systems usually fall into one of two categories: man or machine," Adam Conner-Simon from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) wrote in a post on the MIT News site. "So-called'analyst-driven solutions' rely on rules created by human experts and therefore miss any attacks that don't match the rules," he said. "Meanwhile, today's machine-learning approaches rely on'anomaly detection,' which tends to trigger false positives that both create distrust of the system and end up having to be investigated by humans, anyway." The MIT and PatternEx platform attempts to merge those two approaches. AI2 predicts attacks by combing through data and detecting suspicious activity by clustering it into meaningful patterns using unsupervised machine learning, according to researchers at MIT.

A team of MIT researchers created an artificial intelligence system called AI2 that can help stop cyberattacks. The AI is designed to review data from tens of millions of log lines each day and look for anything suspicious. When it finds something out of the ordinary, it hands off the information to a human that checks for any signs of a breach. "You can think about the system as a virtual analyst," said research lead Kalyan Veeramachaneni. "It continuously generates new models that it can refine in as little as a few hours, meaning it can improve its detection rates significantly and rapidly."

In summary, I had a blast at my first GTC. The only downside was that I wasn't on-site long enough to totally absorb everything, certainly not even a fraction of all the great talks on Deep Learning and AI. But no worries, I treated my attendance as a learning experience and I fully intend to drill down on many areas of interest after-the-fact (starting with this field report). As I sat in the conference press room watching the frenetic activity of the attendees passing by, I anticipated hours of fun digesting all that I saw. Look for many future articles here on insideBIGDATA that cover GPU technology, NVIDIA, the vendors I met, as well as leading-edge research taking place in this space. I'm excited, and I hope you are too!

Developing and experimenting with machine learning models in Python is easy and well supported by robust and agile libraries such as scikit-learn, although efficiently deploying multi-model systems at scale is still a challenge in the data science field. This talk will focus on the main issues related to deploying machine learning models and how to make scikit-learn production-ready with minimal operational efforts, by means of Cloud Computing services, in particular Amazon Web Services.

Weird Of The Week: This is part of a series of articles that looks at some of the most bizarre and niche business ideas we see here at Springwise. Advertising and media are often at the forefront of new technology, and we have already seen augmented reality platforms showing content in the real world and a virtual reality advertising network for brands. Now an artificial intelligence robot, AI-CD?, developed by Japanese advertising and marketing agency McCann Japan, is set to work on providing new creative direction for commercials. The AI will give input on projects, mining and analyzing creative databases of adverts to find the best commercials for products and messages. But the robot is also being treated as somewhat part of the team at McCann, taking the title of "creative director" and attending the opening ceremony for new company employees.

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.