On October 17, at the Guanghua Thought and Power Forum, E Weinan, an academician of the Chinese Academy of Sciences and a chair professor at Peking University, said that artificial intelligence is essentially “mathematics + computer engineering”. This will significantly lower the barriers to scientific research and promote “equality in scientific research”. “In the future, amateur scientists will be the main force in scientific research,” he predicted. Through open laboratories and cloud services, “every team and individual with ideas and capabilities can participate in technological innovation.”
Ewen Nan pointed out that when enterprises actually engage in production, they lack original innovation and can only compete in areas such as management and pricing. “The fundamental reason for this internal competition is the lack of innovation ability.” He believes that high-end industrial software and high-end machine tool CNC systems are currently controlled by humans. However, “now it is not only possible to achieve independent control in a relatively short period of time, but even to ‘overtake on a different track’.” An AI-native industrial software system can be developed, “in principle, a system that can not only compare with Siemens but even surpass Siemens. It can be completed within one or two years.”
He distinguished between AI for Science and AI for Industry, pointing out that the latter is more difficult to start, as industrial software is more closed, and most industrial data is source-closed. However, modeling industrial production lines is easier on a macro level, and the core task in the future is to achieve excellence in modeling industrial production lines. He believes this process can be achieved “in three years, at most five years”.
Ewen Tan said that the current research system in the era of artificial intelligence will face huge challenges. Taking the mathematical community as an example, he said, “If the number of submissions to top journals increases by a hundred times, we actually don’t know how to respond.” He emphasized that disruptive innovation “is largely achieved through free exploration, rather than being driven by task definitions,” and criticized “equating free exploration with writing papers as a mistake.”
He believes that the form of scientific research results will shift from papers to intellectual property rights, and the industrial structure will also be redefined. Universities must establish a innovation-driven integrated talent training system that deeply understands needs.