Deep learning works, and we will solve the remaining problems.

深度学习有效，我们将解决剩余的问题。

Jean Liu: With deep learning, everything.

还有深度学习和其他的技术。

Number one is you know the deep learning technology.

第一种就是深度学习技术。

In three words: deep learning worked.

深度学习奏效了。

They are able to learn from experience - something called deep learning.

他们能够从经验中学习——这就是所谓的深度学习。

The craft of communicating with these deep learning models has been dubbed " prompt engineering" .

与这些深度学习模型进行交流的技术被称为“即时工程”。

And this is all based on our machine learning and deep learning algorithms.

这一切都基于我们的机器学习和深度学习算法。

It noted China was especially strong in the fast-growing area of " deep learning" .

报告指出，中国在快速发展的" 深度学习" 领域尤为突出。

Complex, nuanced sentences like this one are now understandable with deep learning algorithms.

像这样一个细小复杂的语句对深度学习来说是相对易于理解的。

So imagine if we can help enhance their efficiency using these deep learning approaches?

所以想象一下，是否我们能够用深度学习的方法 帮助他们提高效率？

That's what deep learning algorithms do as they go through all the training data.

这就是深度学习算法在处理所有训练数据时所做的事。

During his career, the 75-year-old Hinton has pioneered work on deep learning and neural networks.

在他的职业生涯中，75岁的辛顿在深度学习和神经网络方面开创了先河。

So, remember: repetition, focus and deep learning are the memory muscles we need.

所以，记住：重复、专注和深度学习是我们需要的肌肉记忆。

That software was a piece of artificial intelligence called a deep evolutionary reinforcement learning algorithm, or derl.

该软件是一款人工智能软件，叫做深度进化强化学习算法，简称derl。

" Data is profoundly dumb about causality, " claims Pearl. Hill puts it more bluntly: " Deep learning is not that deep."

“数据对因果关系是极其愚蠢的，”Pearl声称。Hill则说得更直接：“深度学习没那么深奥。”

The hub is home to various AI applications such as security and protection, voice recognition and deep learning.

这里是许多人工智能应用的孕育基地，比如安保领域、声音识别领域以及深度学习领域等。

Each of these sentences was generated by a deep learning algorithm to describe each of those pictures.

每一句话都是深度学习算法对图片进行的描述。

In 15 words: deep learning worked, got predictably better with scale, and we dedicated increasing resources to it.

深度学习奏效了，随着规模的扩大，它变得可预测地更好，我们投入了越来越多的资源。

Whereas a computer can eventually figure out a method that works — that's what deep learning does.

而计算机最终可以找到一种有效的方法——这就是深度学习的作用。

Still, harnessing these " deep learning" neural networks into a structure that has any predictive power requires some elegant algorithm-building.

不过，要将这些“深度学习”神经网络转化为具有预测能力的结构，需要一些简炼的算法构建。