Zhou Zhihua's Advice: Correct the "Big Models Solve Everything" Misconception and Establish Interdisciplinary Special Zones

At a time when artificial intelligence is sweeping across the globe, big models have become the "magic pill" for the research community. However, a recent statement by Academician Zhou Zhihua of the Chinese Academy of Sciences has provided a sobering reminder. He clearly pointed out that there is a serious misconception in the current research field of blindly following the idea that "big models solve everything," and it is necessary to further optimize the overall layout of artificial intelligence research.

Academician Zhou Zhihua keenly observed that many so-called "AI-powered research" are just playing with gimmicks. Many studies merely involve the simple application of tools, and some even imagine relying on training a general "scientific big model" to handle all scientific problems. This "force over finesse" mindset leads to excessive resources being directed towards application layers that consume a lot of computing power, while neglecting the most fundamental algorithmic research.

In addition to the deviation in research directions, the lack of data and the chaos in standards are also major obstacles in AI research. Zhou Zhihua pointed out that scientific data not only has high acquisition costs but also lacks uniformity and willingness to share, leading to low efficiency and poor reliability in model training. This situation causes serious duplication of efforts and resource waste, and significantly reduces the potential of AI in scientific discovery.

To address these pain points, Academician Zhou Zhihua proposed two solutions: first, return to the fundamentals and increase support for algorithm innovation targeting specific issues; second, reform the talent training model. He suggested establishing "interdisciplinary special zones," breaking through traditional constraints in areas such as degrees, titles, and assessments, so that interdisciplinary talents no longer face the dilemma of being "in the middle of nowhere" in evaluation systems.

This "rectification" of AI research is not only an examination of technical paths but also a reshaping of the research ecosystem. After all, the path to truth never relies on blind accumulation, but on the wisdom of deepening foundational work.