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@JeffDean: Google Translate is turning 20! 🎉. There are 20 fun facts and tips in the thread below. Translate is one of my favorite Google products bec...

@JeffDean 3 信息等级 3 1 噪音/剔除;2 较弱;3 普通事实;4 重要行业动态;5 极重大事件。该分数是信息显著性,不是投资建议。 发布:2026-04-28T20:16 抓取:2026-05-05 04:05
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摘要

谷歌翻译成立20周年,Google AI负责人Jeff Dean回顾其发展历程:2006年部署基于5-gram语言模型的首个系统,实现质量飞跃;2016年转向深度神经网络,并依赖序列到序列模型和TPU定制芯片,将推理性能提升30-80倍,降低了延迟,使服务可行。

客观事实
  • 谷歌翻译2006年部署基于5-gram语言模型的系统,实现质量飞跃
  • 2016年转向深度神经网络,采用序列到序列模型
  • TPU将神经网络推理性能提升30-80倍,延迟降低15-30倍
Google Translate TPU Google

原文

Google Translate is turning 20! 🎉. There are 20 fun facts and tips in the thread below.

Translate is one of my favorite Google products because it brings us all closer together!

I've been involved with a couple of things over the years. The first was our deployment of the initial system in 2006, which provided a huge leap forward in quality because it used a much larger 5-gram language model trained on trillions of words of text (indeed, probably the first trillion token language model training in the world: paper has some nice heads showing scaling-law-like quality improvement from scaling to more data/compute).

See "Large Language Models in Machine Translation", Thorsten Brants, Ashok C. Popat, Peng Xu, Franz J. Och and Jeffrey Dean, https://t.co/QnK7lllpoj

The second major collaboration was in 2016 when we moved Translate over from a statistical machine translation approach to using deep neural networks.  This approach relied on two key innovations.  The first was Google's work on Sequence-to-Sequence models (https://t.co/W9c0a0PXoV).  The second was our development of TPUs, custom cups that improved the performance of inference for deep neural networks by 30-80X over existing CPUs and GPUs of the day (and reduced latency by 15-30X).  This made launching compute-intensive language model services like Translate feasible for hundreds of millions of users. See "In-Datacenter Performance Analysis of a Tensor Processing Unit",  Norman P. Jouppi et al.  https://t.co/qpJl7FM6EO

GNMT paper:
"Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation",  Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean, https://t.co/YasV0MEpxM

Most recently, we have advanced Translate further using Gemini models.

Each of these advances relied on research that have major quality leaps over the existing status quo translation approaches, bringing better quality and connectedness to all of our Translate users! 🎉

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