Geoffrey Everest Hinton CC FRS FRSC是一位英裔加拿大认知心理学家和计算机科学家，最著名的工作是人工神经网络。自2013年以来，他一直在谷歌和多伦多大学工作。2017年，他共同创立并成为多伦多病媒研究所的首席科学顾问。
Humans are still much better than computers at recognizing speech.
人类在识别语音方面仍然比计算机强得多。
The pooling operation used in convolutional neural networks is a big mistake, and the fact that it works so well is a disaster.
卷积神经网络中使用的池运算是一个很大的错误，而它如此有效的事实是一场灾难。
Making everything more efficient should make everybody happier.
让每件事更有效率应该会让每个人都更快乐。
Most people at CMU thought it was perfectly reasonable for the U.S. to invade Nicaragua. They somehow thought they owned it.
CMU的大多数人认为美国入侵尼加拉瓜是完全合理的。他们不知怎的以为是自己的。
In a sensibly organised society, if you improve productivity, there is room for everybody to benefit.
在一个组织合理的社会里，如果你提高生产力，每个人都有受益的空间。
Most people in AI, particularly the younger ones, now believe that if you want a system that has a lot of knowledge in, like an amount of knowledge that would take millions of bits to quantify, the only way to get a good system with all that knowledge in it is to make it learn it. You are not going to be able to put it in by hand.
大多数人工智能领域的人，尤其是年轻人，现在都相信，如果你想要一个拥有大量知识的系统，比如一个需要数百万比特才能量化的知识量，那么获得一个拥有所有这些知识的好系统的唯一方法就是让它学会。你不能用手把它放进去。
We now think of internal representation as great big vectors, and we do not think of logic as the paradigm for how to get things to work. We just think you can have these great big neural nets that learn, and so, instead of programming, you are just going to get them to learn everything.
我们现在认为内部表征是一个巨大的向量，而我们不认为逻辑是如何使事物运转的范例。我们只是认为你可以用这些伟大的神经网络来学习，所以，不用编程，你只需要让他们学习一切。
In A.I., the holy grail was how do you generate internal representations.
在人工智能中，圣杯就是如何生成内部表示。