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。
My father was an entomologist who believed in continental drift. In the early '50s, that was regarded as nonsense. It was in the mid-'50s that it came back. Someone had thought of it 30 or 40 years earlier named Alfred Wegener, and he never got to see it come back.
我父亲是昆虫学家，他相信大陆漂移。在50年代早期，这被认为是无稽之谈。它是在50年代中期回来的。早在三四十年前，就有人想到过它，名叫阿尔弗雷德·韦格纳，但他再也没有看到它回来。
The brain has about ten thousand parameters for every second of experience. We do not really have much experience about how systems like that work or how to make them be so good at finding structure in data.
大脑每一秒的经验都有大约一万个参数。对于这样的系统是如何工作的，或者如何使它们能够很好地发现数据中的结构，我们并没有太多的经验。
I got fed up with academia and decided I would rather be a carpenter.
我受够了学术界，决定宁愿当木匠。
All you need is lots and lots of data and lots of information about what the right answer is, and you'll be able to train a big neural net to do what you want.
你所需要的只是关于正确答案的大量数据和信息，你就能训练出一个大的神经网络来做你想做的事情。
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。