Yann LeCun recently expressed his insights on the development direction of AI at the World Economic Forum. He emphasized the limitations of current generative models in video processing and pointed out that future AI needs to make predictions in abstract space rather than pixel space. This has triggered in-depth thinking on the architecture and development direction of AI models, and also indicates that AI research will face new challenges and opportunities. The article focuses on the difficult problems encountered in video processing and the new methods and technologies required to solve these problems.
Yann LeCun, Turing Award winner and Meta's chief AI scientist, pointed out at the World Economic Forum that generative models are not suitable for processing videos, and AI needs to make predictions in an abstract space. As text data on the Internet dries up, AI researchers are turning their attention to videos and realizing that understanding causal relationships is crucial to future AI systems. Therefore, new models should learn to predict in abstract representation space rather than in pixel space. The difficulty in video processing lies in the complexity of pixel space, so new architectures are needed to process video inputs and make predictions in abstract representation spaces. In order to solve the difficult problems in video processing, new scientific methods and technologies need to be created to enable AI systems to utilize information like humans.LeCun's point of view points out the path for future research in the field of AI, posing new challenges in terms of data scarcity and understanding of causality, and also indicates that AI technology will develop in a more intelligent and understanding direction. In the future, breaking through the limitations of pixel space and making predictions in abstract space will become a key breakthrough point in AI research.