Marcus Hutter, a leader in the field of general artificial intelligence, and Xuan Xiaohua, founder of computing technology of Huayuan, in an interview, discussed in depth the development trends and potential impacts of large language models. They pointed out that the large language model is expected to undertake 50% of human work tasks in the future, and this prediction has triggered widespread thinking in the industry about future work scenarios. However, they also emphasized that the realization of this goal faces many challenges, including cost control, improvement of reasoning capabilities, and the accuracy of data labeling.
When discussing the cost control of large models, Hutter and Xuan Xiaohua pointed out that although large language models have made significant technological progress, their high training and operational costs are still the main factors that restrict their widespread use. To reduce costs, they recommend sustainable development by optimizing algorithms, increasing hardware efficiency, and exploring more economical training methods. In addition, they also mentioned that improving reasoning capabilities is the key to ensuring that the big model runs efficiently in practical applications.
The accuracy of data annotation is also a challenge that cannot be ignored in the development of large models. Hutter and Xuan Xiaohua emphasized that high-quality data annotation is the basis of model training, but in actual operations, the accuracy and consistency of data annotation are often difficult to ensure. To solve this problem, they suggest a combination of automated annotation tools and manual audits to improve the quality and efficiency of data annotation.
In terms of the development trends of open source and closed source models, Hutter and Xuan Xiaohua believe that open source models and closed source models have their own advantages. The open source model is transparent and scalable, which can attract more developers and researchers to participate and promote the rapid iteration of technology; while the closed source model focuses more on commercialization and intellectual property protection, which can bring greater competitive advantages to enterprises. They predict that in the future, open source and closed source models will develop in parallel in different fields, each playing their unique roles.
In addition, Hutter and Xuan Xiaohua also emphasized the application potential of large models in vertical fields. They believe that the application of big models in specific fields such as healthcare, finance, and education will bring about revolutionary changes. By custom-developed to the needs of specific industries, large models can provide more accurate and efficient solutions, thereby promoting digital transformation in these industries.
Overall, Hutter and Xuan Xiaohua's discussions provide profound insights into the development of the big model. Despite many challenges, the future application prospects of large language models are still broad. With the continuous advancement of technology and the gradual reduction of costs, big models are expected to realize their huge potential in more fields and have far-reaching impacts on human society.