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.

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.

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.

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.

A NASA-developed air traffic control system for drones could take a major step forward this week when up to 24 drones take to the skies from locations across the U.S. in the agency's first coordinated test. Called UTM, for unmanned aircraft system traffic management, the platform is seen as a key safety system that would allow greater numbers of drones to fly in the sky and avoid mid-air collisions with piloted aircraft and other drones. It allows drone operators to enter a flight plan and request clearance for the flight. The system checks the plan for conflicts with other planned drone flights and accepts or rejects it. For example, a farmer could program a drone flight above her land, effectively reserving it for a certain period of time, and then carry out the flight in the knowledge that a delivery drone scheduled to arrive at the same time will automatically calculate a path to avoid a collision.

Deep learning machines could help decode ISIS as a network and develop strategy for defeat. The use of deep learning machines could help the Pentagon decode the structure of ISIS as a network and allow for a more precisely, developed strategy for its defeat, according to Pentagon Deputy Secretary Robert Work. He was making the case for using artificial intelligence (A.I.) for open-source data crunching, Inverse.com "We are absolutely certain that the use of deep-learning machines is going to allow us to have a better understanding of ISIL as a network and better understanding about how to target it precisely and lead to its defeat," said Secretary Work, according to DoD's website. Speaking at an event organized by the Washington Post, Work said he had his epiphany while watching a Silicon Valley tech company demonstrate "a machine that took in data from Twitter, Instagram, and many other public sources to show the July 2014 Malaysia Airlines Flight 17 shoot-down in real time."

Have you heard the one about how our jobs are about to be snatched away by machines? Or how artificial intelligence will ultimately rise up against us? AI is a field full of tropes, many of which come from places of truth: AI is evolving at an incredible speed, and humans are teaching some AI to learn using the same basic model found in our own craniums. But for a more realistic take on the future of AI, look no further than the many software engineers and companies that have struggled to create an intelligent system that can identify cyber attacks. "We were trying to figure out what is the foundational problem--why do we have so many cyber attacks and data breaches that are going undetected?" says Kalyan Veeramachaneni, a research scientist at MIT's Computer Science and Artificial Intelligence Lab and the author of a paper released today titled "Training A Big Data Machine To Defend."

As Artificial Intelligence (AI) is fast moving beyond the realms of science fiction and entering the workplace, and this will have implications for the corporate IT infrastructure of the future. Facebook's announcement that it will enable businesses to deliver automated customer support, online shopping guidance, content and interactive experiences to its users through ChatBots is just the beginning. Whilst some Bots, like the Microsoft's recent Tay experiment, aren't foolproof yet, Messenger Bots represent the new and acceptable face of Artificial Intelligence. ChatBots use deep learning and neural networks to allow them to learn from data sets in the same way a human brain does. Whilst still in development phase, they are fast becoming increasingly popular with developers and platforms.

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 AIs that simply look 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.