Now, though, an algorithm developed by researchers at MIT to find predictive patterns in unfamiliar data has performed better than two-thirds of human teams.

However, a group of MIT researchers have developed an algorithm that, while not completely optimized, allows robots to formulate on-the-fly calculations and work together with amazing speeds.

Researchers at MIT are digging into this issue and have created an algorithm that can help optimize how fresh the data is on a simple wireless network.

Swarms of drones flying in terrifyingly perfect formation could be one step closer, thanks to a control algorithm being developed at MIT. The complexities involved in controlling teams of moving ...

The team fed its algorithm tens of thousands of images from several different datasets, including LaMem and the scene-oriented SUN and Places (all of which were developed at CSAIL).

The algorithm, using a technique developed by MIT’s Daniel Zoran and Yair Weiss of Hebrew University of Jerusalem, separates images into 8-by-8-pixel squares. It uses statistics related to the ...

3. In tests, the algorithm decreased “categorical bias” by more than 60 percent compared to existing state-of-the-art facial-detection models, according to the article.

MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) thinks it has a couple of killer algorithms that could help. One is an algorithm that helps automated drones detect and ...

A team from MIT's Electrical Engineering and Computer Science (EECS) is using large language models (LLMs) to give robots the "common sense knowledge" they need to be helpful around the house.

Unfortunately, traditional algorithms would take tens of minutes to plan out the sequence of actions to shift its grip using support from a wall or table. That's what the new research from MIT ...

Bias in algorithms is more common than you might think. An academic paper in 2012 showed that facial recognition systems from vendor Cognitec performed 5 to 10 percent worse on African Americans ...