在2024世界经济论坛的一次会谈中，图灵奖得主、Meta首席AI科学家Yann LeCun针对“如何让AI理解视频数据”的问题发表了自己的见解。LeCun指出，尽管目前还没有明确的答案，但目前广泛应用的生成模型并不适合处理视频数据。他认为，新的模型应当学会在抽象的表征空间中进行预测，而不是在像素空间中。
LeCun的这一观点对于当前AI视频处理领域的研究具有重要的指导意义。目前，AI在视频处理方面的应用已经取得了显著的成果，例如视频识别、视频生成等。然而，由于视频数据的高维度和复杂性，如何让AI更好地理解视频数据仍然是一个挑战。
据机器之心报道，LeCun的建议可能意味着，研究人员需要开发新的模型架构和训练方法，以使AI能够在更高的抽象层次上处理视频数据。这一突破可能会带来AI视频处理技术的下一次革命，从而使AI在抽象概念、复杂场景的理解和生成方面取得更大的进步。
英文标题：Meta’s Chief AI Scientist Says Generative Models Are Inadequate for Video Processing
Keywords: AI, Video Processing, Abstract Representation
News content:
At the 2024 World Economic Forum, Yann LeCun, a Turing Award winner and the Chief AI Scientist at Meta, shared his insights on “How to Make AI Understand Video Data.” LeCun stated that although there is no clear answer yet, the generative models currently widely used are not suitable for processing video data. He believes that new models should learn to predict in the abstract representation space rather than in the pixel space.
LeCun’s viewpoint has significant guidance for the research in the field of AI video processing. Currently, AI has achieved remarkable results in video processing applications, such as video recognition and generation. However, the high-dimensional and complex nature of video data makes it a challenge for AI to understand it better.
According to a report by机器之心, LeCun’s suggestion may imply that researchers need to develop new model architectures and training methods to enable AI to process video data at a higher level of abstraction. This breakthrough could lead to the next revolution in AI video processing technology, enabling AI to make greater progress in understanding and generating abstract concepts and complex scenes.
【来源】https://mp.weixin.qq.com/s/sAWFkcTFfZVJ_oLKditqVA
Views: 1