IRVINE, Calif. — The Rubik’s Cube has been frustrating would-be solvers for decades, and whenever someone is able to conquer it, it’s an achievement worthy of recognition. Now, researchers from the University of California have created an extraordinary artificial intelligence system capable of solving the Rubik’s Cube — in a fraction of a second.
The AI system is a deep reinforcement learning algorithm named DeepCubeA, and — perhaps even more astonishing — it doesn’t need to be provided with any specific information on the Rubik’s cube or in-game coaching to beat the puzzle. It learns all on its own.
For reference on just how impressive beating a Rubik’s Cube in under a second really is, consider that each cube features billions of possible moves and completion paths.
According to researchers, the AI solved the Rubik’s Cube in 100% of all test runs, and found the absolute shortest path to victory 60% of the time. DeepCubeA doesn’t just play with Rubik’s Cubes either, its creators say it can also play games such as Lights Out and Sokoban.
A team of Swiss researchers with bugs on the brain has created an army of simple robotic “ants” capable of some impressive feats. The takeaway from these 10 gram bots, which are inexpensive to make and surprisingly simple in design? Teamwork makes the dream work.
As described in a new paper in the journal Nature, the ants can communicate with each other, assign roles among themselves, and complete complex tasks and overcome obstacles together. That means that while simple compared to much more complex autonomous agents, these origami-inspired robots can solve complex challenges, such navigating uneven surfaces or, yes, moving comparatively huge objects.
The robots, which are T-shaped and called Tribots by researchers at the École polytechnique fédérale de Lausanne, a Swiss research institute, have infrared and proximity sensors for detection and communication. Made of foldable thin materials, they’re also easy to manufacture. The actuated robots can jump and crawl to explore uneven surfaces.
“Their movements are modeled on those of Odontomachus ants,” says Zhenishbek Zhakypov, the first author of the Nature article. “These insects normally crawl, but to escape a predator, they snap their powerful jaws together to jump from leaf to leaf.”