As work continues on self-driving cars, a team of researchers in China is rethinking autonomous transportation using a souped-up bicycle.

The motorized bike needs no rider. It can roll over a bump on its own, staying perfectly upright. It follows directions: When a person walking behind it says “left,” it turns left. It also has eyes — it can follow someone jogging several yards ahead, turning each time the runner turns. And if it encounters an obstacle, it can swerve to the side, keeping its balance and returning to its original direction once it’s past the hindrance.

It’s probably not the future of transportation, although it could find a niche in a future world swarming with package-delivery vehicles, drones and robots. And the Chinese are not the only ones experimenting with the idea of self-driving bikes; Cornell University also has a project underway.

What’s drawing worldwide attention to this bicycle is the belief that it demonstrates the future of computer hardware. It navigates with help from what is called a neuromorphic chip, modeled after the human brain.

In a paper published in the science journal Nature, the researchers described how such a chip could help machines respond to voice commands, recognize the surrounding world, avoid obstacles and maintain balance. The researchers also provided a video showing these skills at work on the bicycle.

The short video did not show the limitations of the bicycle, which presumably tips over occasionally. But in handling all these skills with a neuromorphic processor, the project highlighted the wider effort to achieve new levels of artificial intelligence with novel kinds of chips.

This effort spans myriad startup companies and academic labs, as well as big-name tech companies like Google, Intel and IBM. And as the Nature paper demonstrates, the movement is gaining significant momentum in China, a country with little experience designing its own computer processors but which has invested heavily in the idea of an “AI chip.”

The hope is that such chips eventually will allow machines to operate with an autonomy not possible today. Existing robots require hours to days of trial and error to learn even basic skills that are viable only in very particular situations. With help from neuromorphic chips and other new processors, machines could learn more complex tasks more efficiently and be more adaptable in executing them.

“That is where we see the big promise,” said Mike Davies, who oversees Intel’s efforts to build neuromorphic chips.

Over the past decade, the development of artificial intelligence has accelerated thanks to what are called neural networks: complex mathematical systems that can learn tasks by analyzing vast amounts of data. By metabolizing thousands of cat photos, for instance, a neural network can learn to recognize a cat.

But a neural network must be trained for a particular task, and it can’t learn without enormous numbers of examples. Researchers aim to build systems that can learn skills in a manner similar to the way people do.

Mimicking a brain

Dozens of companies and academic labs are developing chips specifically for training and operating AI systems.

The most ambitious projects are the neuromorphic processors, including the Tianjic chip under development at Tsinghua University in China. Such chips are designed to imitate the network of neurons in the brain. Neuromorphic chips typically include hundreds of thousands of faux neurons, and rather than just processing ones and zeros, these neurons operate by trading tiny bursts of electrical signals, as biological neurons do.

“This is about trying to bridge and unify computer science and neuroscience,” said Gordon Wilson, chief executive of Rain Neuromorphics, a startup company that is developing a neuromorphic chip.

Neuromorphic chips are by no means a re-creation of the brain. But the hope is that by operating a bit more like the brain, they can help AI systems learn skills and execute tasks more efficiently. Some researchers believe they could lead to systems that learn on the fly using much smaller amounts of data.

The rub is that building the right hardware might require at least several more years of research. “We are still in the trial and error stage,” said Georgios Dimou, who previously worked on Intel’s neuromorphic project.

The Chinese researchers believe that time will bring far more than just autonomous bicycles. Their paper paints the Tianjic chip as a step toward “artificial general intelligence,” a machine that can do anything you and your brain can do.