Researchers at MIT have built an artificial intelligence system that is designed to evolve into ultimate cyber defense. AI2, a joint effort between machine learning startup PatternEx and Massachusetts Institute of Technology, merges AI with analyst intuition to predict future attacks. It builds predictive models of what might happen in the near future, allowing business to come up with ways to bolster security. The system employs three different machine-learning algorithms to catch shady events. Like any artificial intelligence system, it requires some feedback from a human to correctly identify if those events are actually mistrustful or not.

According to experts, 2016 may finally be the year that artificial intelligence comes into its own -- not in the science fiction "robots will take over humanity" sense, but in a much more practical and useful way. AI is already excellent at problem-solving -- when it comes to finding patterns, it can usually solve a problem much faster than its human counterpart. For the most part, though, AI still very limited in scope, and the dreams of a general intelligence are still far off. To some, AI being able to execute nearly any task that humans can perform today may sound like a worst-case scenario. But, in actuality, this future will bring about a new era of creativity and innovation.

Scientists at Massachusetts Institute of Technology (MIT) claim they have created an AI that can detect 85% of cyber attacks -- albeit with the help of humans. The "AI2" algorithm, developed by MIT's Computer Science and Artificial Intelligence Lab (CSAIL) and machine learning startup PatternEx, can reportedly detect cyber attacks three times more effectively than today's current systems. AI2 has been tested on 3.6 billion pieces of data, known as "log lines," which were created over a three month period by millions of people. In order to predict attacks, AI2 scans sets of data and identifies suspicious activity. It does this by clustering the data into meaningful patterns using unsupervised machine-learning, according to MIT.

Computer scientists from the Michigan Institute of Technology (MIT) and a machine learning startup, PatternEx, have reportedly developed a new system that can correctly detect 85% of cyberattacks using artificial intelligence merged with input from human experts. At the moment, security systems are closely monitored by humans and programmed to pick up on cyberattacks that only follow very specific rules, as such missing any attacks that do not follow those rules. But, there are also systems autonomously run by computers that practice anomaly detection – i.e. the identification of items, events or observations – that do not conform to an expected pattern or other items in a dataset. This method often leads to false positives, meaning that humans doubt the reliability of the system and are forced to go back and check all the results anyway. To improve this, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), in collaboration with PatternEx, have developed the AI2 artificial intelligent platform, which merges three different machine learning methods that enable computers to learn unsupervised.

MIT researchers have announced that they've concocted a new artificial intelligence system capable of successfully detecting 85% of cyber-attacks. The AI2 platform, produced by the MIT's Computer Science and Artificial Intelligence Laboratory (in conjunction with PatternEx, a machine learning startup), has notched up a much better record than previous systems. The 85% accuracy rating is three times better than previous benchmarks which have been recorded, and it also produced far less false positives, in fact a reduction of a factor of five was observed. MIT notes that AI2's initial testing ran over a period of three months and involved combing through some 3.6 billion log lines looking for suspicious activity, using machine learning to make the initial detections and then putting those in front of a human security analyst who confirmed whether or not a detection was an actual cyber-attack. AI2 then learned from that feedback, improving its routines for the next round of detection. Essentially, the system utilises the best in artificial intelligence smarts combined with human error correction which feeds the machine learning process, and AI2 is apparently capable of honing itself rapidly indeed.

Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have collaborated alongside the machine learning startup PatternEx to demonstrate how an artificial intelligence platform that makes use of continuous input from human experts would be able to predict cyber attacks better than the systems that exist today. CSAIL and PatternEx are calling their new AI platform AI² due to how it combines the intuition of analysts with artificial intelligence. Previously'analyst-driven solutions' relied on the rules created by security experts which resulted in attacks that did not correspond to their rules slipping through the cracks. The machine learning approach to cyber-security relies on'anomaly detection' to discover possible attacks but often results in false positives. CSAIL and PatternEx created AI² by combining machine learning's ability to detect anomalies with the knowledge of analysts to flag possible threats.

Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), and a machine-learning startup, PatternEx, have developed a virtual artificial intelligence analyst that can predict 85% of cyber-attacks using input from humans. As a way to address some of the challenges security analysts face, researchers from CSAIL and PatternEx presented a paper on a new artificial intelligence platform called AI2 They found that their system is able to detect cyberattacks more frequently because it is continuously incorporating input from human analysts. There are several problems that currently exist in today's state of cybersecurity. One major problem includes the lack of qualified security analysts in the market. It is critical to increase analyst efficiency, but existing tools generate too many false positives that create distrust and need to be investigated by humans in the end, said Ignacio Arnaldo Lucas, one of the researchers at PatternEx.

Researchers with MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have announced a breakthrough in cyber threat detection that promises to dramatically lower the amount of time human analysts spend sorting through information in search of evidence of cyber attacks. IT analysts spend countless hours scouring through mountains of data in order to find evidence of compromised systems. Often, the search for anomalies in data is so extensive, that overwhelmed human teams are forced to overlook potentially critical information for lack of time. That's where MIT's researchers believe they can help. In a world where most companies are under constant threat of cyber attack, having a system that never sleeps or takes a coffee break is a big help.

When it comes to cybersecurity, it would appear that the best offense is a good defense, and by and large we have neither. Following a damning report of the U.S. government's capabilities when it comes to online security and the emergence of yet another dangerous piece of malware that has already stolen some 4 million from dozens of banks and financial institutions, our digital defenses look to be down and out. But a new solution from the Massachusetts Institute of Technology may be our saving grace. In a new paper, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) unveil an artificial-intelligence platform called "AI squared" that claims to predict cyberattacks "significantly better than existing systems by continuously incorporating input from human experts." Combining the capabilities of humans and machines, AI-squared was shown to detect an impressive 85 percent of attacks, which represents a threefold improvement over previous benchmarks.

Artificial intelligence and wearables thrive across healthcare sectors, says Frost & Sullivan MOUNTAIN VIEW, Calif. While pharma companies have been seeking tools for tracking the effectiveness of therapeutics, imaging companies have been augmenting the interpretability of scans through cognitive pattern interpretation. For complimentary access to more information on this research, please visit: http://frost.ly/2b. Approximately 24 percent of consumers currently use mobile apps to track health and wellness, 16 percent use wearable sensors and 29 percent use electronic personal health records. Significantly, 47 percent of consumers reported they would consider using wearables in the near future, prompting technology developers to design wearables with advanced sensing, capture and analytical functionalities.