2024-08-19
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On January 24, 2024, the "2024 Report on Generative AI Applications in the Financial Industry" was released and symposium co-sponsored by Tsinghua University School of Economics and Management, Du Xiaoman, MIT Technology Review China, and the Center for Dynamic Competition and Innovation Strategy Research of Tsinghua University School of Economics and Management was successfully held at Tsinghua University School of Economics and Management. The press conference was hosted by Li Jizhen, professor and vice dean of Tsinghua University School of Economics and Management.
Professor Li Jizhen first introduced the relevant background of the conference and delivered a speech on behalf of Tsinghua University School of Economics and Management, welcoming the guests and all the audience. Then, Professor Li Jizhen invited Du Xiaoman CTO Xu Dongliang and MIT Technology Review China Deputy Publisher Zhang Lan to give speeches on stage. After the speeches, the guests gave keynote speeches and shared their insights.
Photo: Sun Kewei, Technical Director of ICBC Technology
Sun Kewei, Technical Director of ICBC Technology: A new paradigm of human-machine collaboration
Sun Kewei, technical director of ICBC Technology, shared his views on the topic of "Application of Big Models in the Field of Financial Technology".
Sun Kewei pointed out that with the advent of big models, AI applications can be divided into three stages. The first is the initial investment and construction stage of AI, which is mainly carried out by purchasing. The second is the multi-dimensional trial layout stage, which adopts a combination of self-research and development and diversified attempts. The last is the deep and mature application stage, where large financial institutions are more inclined to self-research, supplemented by procurement.
In Sun Kewei's view, the construction of the artificial intelligence technology system in the financial field involves six aspects. The first is computing power, algorithms and modeling. Computing power corresponds to the technical platform, and algorithms correspond to the crystallization of the highest wisdom. Then there is capability building, which relies on data accumulation and consideration of the application framework. The capabilities, data and frameworks are all centered around the business.
From a business perspective, AI big models can be divided into four categories: basic big models, industry big models, enterprise big models, and task big models. Another change brought about by big models is the creation of a new paradigm of human-machine collaboration. Sun Kewei believes that after the introduction of big models, they can replace some human work, which brings about a challenge of role and paradigm change.
"(We) need to solve problems of technical stability and technical logic, comprehensively consider the technical advantages of large model technology in content generation, multimodality, small samples, and the uncontrollable risks it generates, and adhere to problem-oriented and demand-oriented approaches," he explained.
Amid heated discussions and endless exchanges, the 2024 Financial Industry Generative AI Application Report and Seminar was successfully concluded. It can be seen that despite the many difficulties and uncertainties in the implementation of generative AI, all participants are optimistic and expectant about its future. It is clear that the changes brought about by generative AI have quietly begun in both finance and other industries. Looking ahead to 2024 and beyond, it is increasingly important for the financial industry to actively embrace the transformative technology of generative AI and embark on a responsible AI governance strategy, which will enable society to fully utilize the transformative power of generative AI and better enhance human well-being.