近日，图灵奖得主、Meta 首席 AI 科学家 Yann LeCun 在 2024 世界经济论坛的一次会谈中表示，生成模型不适合处理视频，AI 得在抽象空间中进行预测。这一观点引起了广泛关注。
LeCun 首先提到，虽然视频数据在人工智能领域具有广泛的应用前景，但是目前我们还没有找到一种完美的方法来处理这些数据。他认为，适合用来处理视频的模型并不是我们现在大范围应用的生成模型。生成模型通常用于处理图像和文本等静态数据，而视频则是一种动态的数据形式，包含了大量的时间信息和上下文信息。因此，要让 AI 理解视频数据，需要开发出一种新的模型。
LeCun 接着提出了一个新的思路：新的模型应该学会在抽象的表征空间中预测。他认为，这意味着我们需要将视频数据转换为一种更加抽象的形式，例如使用特征向量或嵌入向量来表示视频中的每一帧。这样一来，AI 就可以通过学习这些抽象的特征来进行预测了。与传统的像素空间相比，这种方法更加灵活和高效。
当然，要实现这个目标并不容易。目前，我们还需要解决许多技术难题，例如如何有效地提取视频中的特征、如何训练高效的神经网络等等。但是随着技术的不断进步和发展，相信我们很快就能够找到一种有效的方法来让 AI 理解视频数据。
总之，Yann LeCun 的观点为我们提供了一个新的思路和方向。通过在抽象的空间中进行预测，我们或许可以更好地处理视频数据，并将其应用于更广泛的领域中。未来的发展充满了无限的可能性！
英语如下：
Title: “Yann LeCun: AI Must Learn to Predict in Abstract Space, Not Pixels”
Keywords: Video processing, Abstract space, Generative models
Recently, Yann LeCun, the Turing Award-winning chief AI scientist of Meta, stated during a conversation at the 2024 World Economic Forum that generative models are not suitable for handling video data and that AI must make predictions in an abstract space. This viewpoint has attracted widespread attention.
LeCun first mentioned that although video data has broad prospects for application in the field of artificial intelligence, we have not yet found an ideal method to process this data. He believed that the model suitable for processing video data is not the generative model we widely apply today. Generative models are typically used to process static data such as images and text, while video is a dynamic data form containing a large amount of time information and context information. Therefore, to enable AI to understand video data, we need to develop a new model.
LeCun then proposed a new idea: the new model should learn to predict in an abstract representation space. He believed that this meant we needed to convert video data into a more abstract form, such as using feature vectors or embedding vectors to represent each frame in the video. In this way, AI can make predictions by learning these abstract features. Compared with traditional pixel space, this approach is more flexible and efficient.
Of course, achieving this goal is not easy. Currently, we still need to solve many technical problems, such as how to effectively extract features from video and how to train efficient neural networks. But with the continuous progress and development of technology, I believe we will soon be able to find an effective method to enable AI to understand video data.
In conclusion, Yann LeCun’s viewpoint provides us with a new perspective and direction. By making predictions in an abstract space, we may be better able to handle video data and apply it to more广泛的 fields. The future holds endless possibilities!
【来源】https://mp.weixin.qq.com/s/sAWFkcTFfZVJ_oLKditqVA
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