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.

Over the course of the last month, two astounding videos surfaced showing the extent to which artificial intelligence and robotics have developed. If you've been keeping tabs on either of these projects, you would agree that both respective developments represent incredible breakthroughs. But perhaps the most interesting aspect of Atlas and DeepMind lies in the idea that such systems are now able to directly interact with people. Add to this the exponential rise of self-driving cars and the internet of things, and we begin to realise that the next decade will present legal challenges we never before thought possible. We've spoken at length here at Technolegem about the challenges facing the legal industry, tracking things such as the regulation of Uber, anti-piracy schemes and the saga that was Dallas Buyers Club.

Shares of Intel (NASDAQ:INTC) have been trading along the 100-day moving average after bouncing off the low in February, as analysts raised concerns about PC and notebook sales during the first-quarter 2016. Intel will report its first-quarter 2016 earnings after the market close on April 19. Investors will be closely watching the results from the Client Computing Group, or CCG, Intel's mobile and PC business, and the Data Center Group, or DCG, as about 88.9% of their revenues last year came from these two groups. Less than a week ahead of the earnings report, Pacific Crest warned it expects Intel to report first-quarter earnings below the midpoint of guidance, and to lower its guidance for the full-year as well. Wall Street opinions about Intel's data center outlook are mixed, according to Barron's.

Rapid advancements in artificial intelligence (AI) has led many to ask questions about what if these intelligent machines and robots start acting unethically? This has led many to go to the extreme demanding for an outright ban on robotics and AI research while others are calling for more research to understand how AI might be constrained. But how can robots learn ethical behavior if there is no "user manual" for being human? Researchers Mark Riedl and Brent Harrison from the School of Interactive Computing at the Georgia Institute of Technology believe the answer lies in "Quixote" -- to be unveiled at the AAAI-16 Conference in Phoenix, Ariz. Quixote teaches "value alignment" to robots by training them to read stories, learn acceptable sequences of events and understand successful ways to behave in human societies.

John Williams is well-known for his melodies in his music for films and concerts. In today's episode of Film Cue Review, we find out that his gift to support the film goes much deeper than we had anticipated. We take a look at his harmonic and orchestrational choices on the cue "Hide & Seek" from Steven Spielberg's 2001 film, Artificial Intelligence.

The green tractor trundling across a Manitoba field with an empty cab looks like it's on a collision course with Matt Reimer's combine--until it neatly turns to pull alongside so he can pour freshly harvested wheat into its trailer. It's an eight-year-old John Deere that the 30-year-old Mr. Reimer modified with drone parts, open-source software and a Microsoft Corp. MSFT 1.52 % tablet. All told, those items cost him around 8,000. He said that's about how much he saved on wages for drivers helping with last year's harvest. Mr. Reimer's alterations, which he hopes to replicate for other farmers this year, are part of a technology revolution sweeping North America's breadbasket.