From self-driving cars to the internet of things, artificial intelligence (AI) has reached new levels of sophistication in recent years. With that in mind, this week MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) launched an industry collaboration focused on using machine learning to create functional human-like systems.
In the latest issue of the journal Autonomous Robots, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory and their colleagues present a new technique for preventing malicious hackers from commandeering robot teams’ communication networks. The technique could provide an added layer of security in systems that encrypt communications, or an alternative in circumstances in which encryption is impractical.
For robots to do what we want, they need to understand us. Too often, this means having to meet them halfway: teaching them the intricacies of human language, for example, or giving them explicit commands for very specific tasks. But what if we could develop robots that were a more natural extension of us and that could actually do whatever we are thinking?
The age of big data has seen a host of newtechniques for analyzing large data sets. But before any of those techniques can be applied, the target data has to be aggregated, organized, and cleaned up.
That turns out to be a shockingly time-consuming task. In a 2016 survey, 80 data scientists told the company CrowdFlower that, on average, they spent 80 percent of their time collecting and organizing data and only 20 percent analyzing it.
This fall’s new FAA regulations have made drone flight easier than ever for both companies and consumers. But what if the drones out on the market aren’t exactly what you want?
A new system from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) is the first to allow users to design, simulate and build their own custom drone. Users can change the size, shape and structure of their drone based on the specific needs they have for payload, cost, flight time, battery usage and other factors.
At the annual meeting of the Association for the Advancement of Artificial Intelligence last weekend, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) presented a new way of modeling robot collaboration that reduces the need for communication by 60 percent. They believe that their model could make it easier to design systems that enable humans and robots to work together — in, for example, emergency-response teams.
Getting drones to fly around without hitting things is no small task. Obstacle-detection and motion-planning are two of computer science’s trickiest challenges because of the complexity involved in creating real-time flight plans that avoid obstacles and handle surprises like wind and weather. In a pair of projects announced this week, CSAIL researchers demonstrated software that allow drones to stop on a dime to make hairpin movements over, under, and around some 26 distinct obstacles in a simulated “forest.”