Google’s AlphaGo defeated the South Korean Go grandmaster Lee Sedol four games to one Artificial intelligence had suddenly reached a new and unexpected height.

But AlphaGo is no longer the best Go “player” in the world Google’s artificial intelligence group, DeepMind. has created the next generation of its Go-playing program, called AlphaGo Zero The new A1 program is unique in the way it learned to play Go Instead of learning from human matches, as its predecessor (前任) did, AlphaGo Zero mastered Go in just two days without any human knowledge of the game and defeated AlphaGo by day three, reported The Guardian It then went on to defeat AlphaGo 100 games to zero.

To learn how to play Go, AlphaGo Zero played millions of matches against itself using only the basic rules of the game to rapidly create its own knowledge of it tike the previous version, it used “reinforcement (增强) learning to become its own teacher, ”ccording to DeepMind’s website.

“It’s more powerful than previous approaches. ’’ David Silver,   AlphaGo’s lead researcher, told The Guardian, “because by not using human data, or human expertise in any fashion, we’ve removed the constraints (约束) of human knowledge and it is able to create knowledge itself . ”

AlphaGo Zero’s approach to selt-learning is a significant advancement in AI could be applied to help solve some of the world s biggest problems, according a recent research report published in the journal Nature. For example, DeepMind co-founder DemisHassabis argues that AlphaGo Zero could probably find cures for a number of serious diseases within weeks, according to The Telegraph Indeed, the AI is now being used to study protein folding, which is connected to diseases such as Parkinson’s and Alzheimer’s.

So now that AI has surpassed human beings in some ways, perhaps the question is not about what AI can learn from humans, but what humans can learn from AI We can only wait and see.

Roboticists at the University of California San Diego have developed an affordable, easy to use system to track the location of flexible surgical robots inside the human body. The system performs as well as current state of the art methods, but the whole system, including the robot, magnets and magnet localization setup, costs around $100. Many current methods also require exposure to radiation, while this system does not.

The system was developed by Tania Morimoto, a professor of mechanical engineering at the Jacobs School of Engineering at UC San Diego, and mechanical engineering Ph. D. student Connor Watson.

“Continuum medical robots work really well in highly constrained (受限的) environments inside the body,” Morimoto said. “But it becomes a lot harder to track their location and their shape inside the body.” The researchers used existing magnet localization methods, which work very much like GPS, to develop a computer model that predicts the robot’s location.

GPS satellites ping (发送) smartphones and based on how long it takes for the signal to arrive, the GPS receiver in the smartphone can determine where the cell phone is. Similarly, researchers know how strong the magnetic field should be around the magnet placed in the robot. They rely on four sensors that are carefully spaced around the area where the robot operates to measure the magnetic field strength. Based on how strong the field is, they are able to determine where the tip of the robot is.

Morimoto and Watson went a step further. They then trained a neural network to learn the difference between what the sensors were reading and what the model said the sensors should be reading. As a result, they improved localization accuracy to track the tip of the robot. “Ideally we are hoping that our localization tools can help improve these kinds of growing robot technologies. We do want to push this research forward so that we can test our system in a clinical setting and eventually translate it into clinical use,” Morimoto said.