【新闻】 图灵奖得主 Yann LeCun 在世界经济论坛上表示，当前广泛应用的生成模型并不适合处理视频数据，而应该寻找一种新的方法，在抽象的表征空间中进行预测。
Yann LeCun 表示，尽管如何让 AI 理解视频数据的问题还没有明确的答案，但他认为现有的生成模型在处理视频时存在一些限制。他指出，这些模型往往过于关注像素级别的细节，而忽视了视频中的更高层次的信息和模式。
因此，LeCun 认为，未来的 AI 模型应该学习在抽象的表征空间中进行预测，以便更好地理解和解释视频数据。这种方法可以帮助 AI 更准确地理解视频中的动作、事件和情感等内容，从而在诸如视频分析、自动驾驶等领域发挥更大的作用。
此外，LeCun 还强调了人工智能的发展需要更多的跨学科合作。他认为，计算机科学、心理学、哲学等多个领域的知识和技术都可以对 AI 的发展产生重要影响。只有通过多方面的努力，才能推动人工智能技术的进步，并将其应用于更多的领域。
英语如下：
News Title: ” Turing Award Winner Reveals: Future AI May Say Goodbye to Pixel Space, Move Towards Abstract Understanding of Videos”
Keywords: AI understanding videos, generative models, abstract space
News Content:
【News】Yann LeCun, a Turing Award winner, stated at the World Economic Forum that the widely used generative models are not suitable for handling video data and we should seek new ways to make predictions in an abstract representation space.
LeCun pointed out that although there is no clear answer to how AI can understand video data, existing generative models have some limitations when dealing with videos. These models often focus too much on pixel-level details and ignore higher-level information and patterns in videos.
Therefore, LeCun believes that future AI models should learn to make predictions in an abstract representation space to better understand and interpret video data. This method can help AI more accurately understand video content such as actions, events, and emotions, thereby playing a greater role in fields such as video analysis and autonomous driving.
In addition, LeCun also emphasized the importance of interdisciplinary collaboration in the development of artificial intelligence. He believes that knowledge and technologies from multiple fields such as computer science, psychology, and philosophy can all have important impacts on the development of AI. Only through joint efforts from various aspects can we promote the progress of artificial intelligence technology and apply it to more fields.
【来源】https://mp.weixin.qq.com/s/sAWFkcTFfZVJ_oLKditqVA
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