Chinese scientists introduced a new brain-inspired network model on Friday, designed to address challenges in traditional artificial intelligence (AI), such as high computing resource consumption, according to a research published in the journal Nature Computational Science.

The Institute of Automation under the Chinese Academy of Sciences introduced this innovative approach, which focuses on "internal complexity" rather than the current method of scaling up neural networks to achieve general intelligence.

Researcher Li Guoqi explained that the prevalent approach of building larger and more complex neural networks, known as "external complexity," consumes vast amounts of energy and computational power while lacking interpretability. In contrast, the human brain, with its 100 billion neurons and 1,000 trillion synaptic connections, operates efficiently on just 20 watts of power.

Inspired by the brain's internal dynamics, scientists from the Institute of Automation, in collaboration with Tsinghua University and Peking University, applied an "internal complexity" approach to AI. Their experiments confirmed the model's effectiveness in managing complex tasks, offering a new method for integrating neuroscience into AI development and optimizing AI performance.

(With input from Xinhua)