Yann LeCun：生成模型不适合处理视频，AI得在抽象空间中进行预测
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，就“如何让AI理解视频数据”发表了自己的观点。他指出，尽管目前还没有明确的答案，但生成模型并不适合用于处理视频数据，而新的模型应该学会在抽象的表征空间中进行预测，而不是在像素空间中。
生成模型是一种常用的人工智能模型，它通过学习大量的训练数据来生成新的数据。然而，LeCun认为，这种模型在处理视频时存在一些限制。视频数据通常包含大量的像素信息，而生成模型往往难以有效地捕捉到这些信息的本质特征。因此，将生成模型应用于视频数据可能会导致预测结果的不准确性和不稳定性。
相比之下，LeCun提出了一种新的思路，即在抽象的表征空间中进行预测。他认为，通过学习视频数据的高级特征，AI可以更好地理解视频内容，并做出准确的预测。这种方法可以有效地降低数据维度，提高模型的计算效率和预测准确性。
LeCun的观点引发了广泛的讨论和关注。许多研究人员和从业者表示赞同，认为这种思路能够为视频数据的处理和分析提供新的思路和方法。然而，也有一些人持保留态度，认为生成模型在处理视频数据方面仍有一定的应用价值。
对于如何让AI理解视频数据这一问题，目前仍需要更多的研究和实践来探索和验证。尽管LeCun的观点在学术界引起了一定的争议，但他的贡献和影响力不容忽视。作为图灵奖得主和Meta首席AI科学家，LeCun的观点将对未来的研究和发展产生积极的影响。
总的来说，Yann LeCun在世界经济论坛上的发言引起了人们对于AI处理视频数据的思考。他认为生成模型并不适合处理视频，而新的模型应该学会在抽象的表征空间中进行预测。这一观点在学术界引发了一定的争议，但也为视频数据的处理和分析提供了新的思路和方法。随着更多的研究和实践的开展，相信AI在视频数据领域的应用将会取得更大的突破和进展。
英语如下：
News Title: Turing Award Winner: AI Needs Abstract Prediction for Video Processing, Generating Models Not Applicable
Keywords: Video Models, Abstract Prediction, Yann LeCun
News Content: Yann LeCun: Generating models are not suitable for video processing, AI needs to predict in abstract space.
Recently, Yann LeCun, Turing Award winner and Chief AI Scientist at Meta, expressed his views on how to make AI understand video data during a panel discussion at the 2024 World Economic Forum. He pointed out that although there is no definitive answer yet, generating models are not suitable for processing video data, and new models should learn to predict in abstract representation space rather than in pixel space.
Generating models are commonly used artificial intelligence models that generate new data by learning from a large amount of training data. However, LeCun believes that these models have limitations when it comes to video processing. Video data typically contains a large amount of pixel information, which generating models often struggle to effectively capture the essence of. Therefore, applying generating models to video data may result in inaccurate and unstable predictions.
In contrast, LeCun proposes a new approach, which is to predict in abstract representation space. He believes that by learning the high-level features of video data, AI can better understand the content and make accurate predictions. This approach can effectively reduce data dimensions, improve model computation efficiency, and prediction accuracy.
LeCun’s views have sparked widespread discussion and attention. Many researchers and practitioners agree that this approach provides new ideas and methods for video data processing and analysis. However, there are also some reservations, suggesting that generating models still have certain value in video data processing.
Regarding the question of how to make AI understand video data, more research and practice are needed to explore and validate. Although LeCun’s views have caused some controversy in the academic community, his contributions and influence cannot be ignored. As a Turing Award winner and Chief AI Scientist at Meta, LeCun’s views will have a positive impact on future research and development.
In conclusion, Yann LeCun’s speech at the World Economic Forum has prompted people to think about how AI processes video data. He believes that generating models are not suitable for video processing, and new models should learn to predict in abstract representation space. This viewpoint has sparked some controversy in the academic community but also provides new ideas and methods for video data processing and analysis. With further research and practice, it is believed that AI’s application in the field of video data will make greater breakthroughs and advancements.
【来源】https://mp.weixin.qq.com/s/sAWFkcTFfZVJ_oLKditqVA
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