Brain like intelligence has become a hot topic in the field of artificial intelligence. Recently, the "Wentian I" class of brain computer technology achievements were released in Nanjing, Jiangsu. This computer simulates the operation of brain neural networks and is currently the leading and largest class of brain computers in China. Brain like intelligence, also known as neuromorphic computing, enables computer software and hardware to efficiently process information by imitating the operation of the human brain. Compared to traditional artificial intelligence, it has the characteristics of low power consumption and high computing power. The human brain is currently the most complex information processing system discovered, and its simplicity and efficiency are unparalleled. Therefore, experts in the field of artificial intelligence envision the possibility of developing more powerful artificial intelligence based on the brain as a prototype. "Speaking of brain like intelligence, Professor Wu Jingzhu from the School of Computer and Artificial Intelligence at Beijing Business University told Science and Technology Daily. Model driven artificial intelligence technology has limitations. This year, multiple large language models have been introduced, sparking waves of artificial intelligence craze around the world. At present, the mainstream applications of artificial intelligence represented by large models are actually model driven. The program developer sets the behavior and structure for the software, and on this basis, the software can be continuously trained through data to form artificial intelligence that can interact with people. The limitations of this technological route are obvious. Large models require high-quality annotated data, and we have to put in a lot of manpower for this. The more prominent problem is that the computational resources required for training large models are very large, requiring the support of supercomputing power. In addition, this type of artificial intelligence has weak self-learning and adaptive abilities, and relatively lacks logical analysis and reasoning abilities. "Wu Jingzhu introduced. In 1956, at the Dartmouth Conference, where computer science experts gathered, scientists proposed that a multidisciplinary collaborative working mechanism could be established based on the two fundamental fields of brain neuroscience and cognitive science to develop artificial intelligence that could reach or even surpass human level. However, for the technical level at that time, these ideas were too advanced to be implemented, and it was not until recent years that they were put on the agenda. Wu Jingzhu emphasized that brain science and cognitive science are the most important fundamental disciplines for developing brain like intelligence. In recent years, with the development of imaging technologies such as functional magnetic resonance imaging, human cognitive level of the brain has greatly improved, which provides necessary conditions for designing computer software and hardware to imitate the brain. Soft brain and hard brain are the two main paths to achieve brain like intelligence. According to Han Liqun, a professor at Beijing Business University and an academician of the Academy of Engineering and Technology in the Developing World, the implementation paths of brain like intelligence can be roughly divided into two categories: soft brain and hard brain. Wu Jingzhu explained that the main difference between the two lies in their focus, with the former emphasizing algorithms and the latter emphasizing hardware. Although the paths are different, overall, the two complement each other. The soft brain mainly focuses on enabling algorithms and models to simulate the working patterns of the brain. Although there are no substances such as nerve cells or proteins, computers can mimic the information processing mechanisms of the brain, formalize real-world substances, and simulate the brain in software. The hard brain mainly focuses on seeking breakthroughs in hardware materials, by developing neuromorphic chips (such as brain like chips) and other media to enhance bioelectricity
Edit：Ling Xiaotong Responsible editor：Li Yi
Source：TechWorld
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