In the Era of AI Large Models, the Demand for Talents Has Changed

In the era of large AI models, the demand for talent has changed. The actions of global technology giants show that talent is the primary driving force for AI development. For example, the core team of Google’s NotebookLM left to start their own businesses, and the three authors of Vision Transformer joined OpenAI. The implementation of AI technology faces the “last mile” problem. Generative AI has entered the stage of large-scale application, but the Killer App has not yet appeared. There is a “looking for nails with a hammer” problem in AI product research and development. There is a cognitive gap between developers and practitioners, and both technical paths for AI to enter enterprises have their difficulties, resulting in products that are difficult to meet actual needs and low willingness of customers to cooperate. In the face of this dilemma, should AI developers learn industry knowledge or should industry practitioners learn AI technology? A two-way effort is a better choice. It is more reasonable to let industry experts who understand AI help large models “establish values”. Nowadays, the threshold for using AI technology is lowered, and many new AI application cases have emerged in various industries, such as in the fields of healthcare, chemical industry, and smart ports. However, this path faces practical challenges, including “no one to teach”, lacking “dual-qualified” teachers, systematic textbooks, and practical cases; “lacking computing power”, as universities lack computing resources; “lacking data”, as industry data is rarely publicly circulated. To solve these problems, academia, industry, and educational institutions need to cooperate to build a talent cultivation system. For example, Northeastern University and China Medical University, in cooperation with Huawei, launched an innovation incubation camp for industry AI applications to cultivate compound talents in the healthcare field. Huawei also launched an artificial intelligence application innovation competition in the chemical industry and conducted “chemical industry + AI” practical training courses. Huawei has formed the core principle of “four based on”. This talent cultivation model is of great significance. The implementation of large model technology puts forward more demands for AI talents. China has technological driving forces and scenario advantages in terms of AI talents, but more efforts are needed to solve the problem of talent shortage.

Posted on December 25, 2024 at 5:33:16 PM, Wednesday.