The Chinese researchers are developing an autonomous bicycle which navigates with the help of a neuromorphic chip, modelled after human brain. It highlights the effort to achieve new levels of AI with novel kinds of chips. China is investing heavily in the idea of ‘ AI chip .’

Existing robots can learn to open a door or toss a ping-pong ball into a plastic bin, but the training takes hours to days of trial and error. Even then, the skills 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.

AI is being developed through neural networks. These are complex mathematical systems that can learn tasks by analysing vast amounts of data. A neural network can learn to recognise a cat by metabolising thousands of cat photos. This technology is Face recognition technology. Many smart phones use this. It facilitates the development of autonomous robots and self-driving cars.

A neural network does not learn on the fly. Engineers train a neural network for a particular task before deploying it in the real world. It learns after absorbing enormous number of examples.

Researchers are developing the neuromorphic processore including chips which imitate the network of neurons in the brain. These systems include faux neurons. Instead of confining to processing of 0s and Os, these neurons operate by trading tiny bursts of electrical signals. It is called firing or spiking when input signals reach critical thresholds. This is what biological neurons do. It is unifying computer science and neuro science.

By mimicing the brain, AI systems are helped to learn skills and executive tasks more efficiently.

Faux neurons fire on demand rather than continuously. Neuromorphic chips thus consume lesser energy than traditional processors. As information is processed in short bursts, it could lead to systems that learn on the fly, from much smaller amounts of data.