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This March, I spent two days at the Microsoft headquarters, just outside Seattle.
今年3月，我在微软总部度过了两天，就在西雅图郊外。
Microsoft is one of the tech incumbents that is most “all in” on AI.
微软是最“全力以赴”人工智能的科技巨头之一。
To prove just how much, they brought in journalists from around the world to participate in an “innovation campus tour”, which included a dizzying run of talks and demos, meals in the seemingly neverending supply of on-site restaurants, and a couple of nights in the kind of hotel writers usually cannot afford.
为了证明这一点，他们邀请了来自世界各地的记者参加“创新校园之旅”，其中包括一系列令人眼花缭乱的演讲和演示，在似乎永无止境的现场餐厅用餐，以及在作家通常负担不起的酒店里住上几晚。
We were walked through a research centre, wearing earplugs to block out the drone of a mini field of fans.
我们穿过一个研究中心，戴着耳塞以阻挡迷你风扇场的嗡嗡声。
We attended numerous panels: how teams were integrating AI, how those involved with “responsible AI” are tasked with reining in the technology before it goes awry.
我们参加了众多的小组讨论：团队如何整合人工智能，那些参与“负责任的人工智能”的人如何在技术出错之前控制它。
There was lots of chatter about how this work was the future of everything.
有很多关于这项工作如何成为一切的未来的讨论。
In one session, the affable Seth Juarez, principal programme manager of AI platforms, spoke of AI as being like moving from a shovel to a tractor: that it will, in his words, “level up humanity”.
在一次会议上，和蔼可亲的人工智能平台首席项目经理塞斯·华雷斯谈到人工智能就像是从铲子升级到拖拉机：用他的话来说，它将“提升人类的水平”。
Some of the things we saw were genuinely inspiring, such as the presentation by Saqib Shaikh, who is blind and has spent years working on SeeingAI.
我们看到的一些事情确实令人鼓舞，比如萨奇布·谢赫的演讲，他是盲人，多年来一直在研究SeeingAI。
It's an app that is getting better and better at labelling objects in a field of view in real time.
这是一款应用程序，它在实时标记视野中的对象方面变得越来越好。
Point it at a desk with a can and it will say, “A red soda can, on a green desk.”
把它指向一张放着一个罐子的桌子，它会说：“一个红色的汽水罐，在一张绿色的桌子上。”
There were other hopeful ideas for how AI could be used: to preserve dying languages, improve the accuracy of scans for tumours, or better predict where to deploy disaster response resources – usually by processing large amounts of data and then recognising and analysing patterns within it.
还有其他一些关于如何使用人工智能的有希望的想法：保护濒危语言、提高肿瘤扫描的准确性，或更好地预测在哪里部署救灾资源——通常是通过处理大量数据，然后识别和分析其中的模式。
At the end of a panel at Microsoft on AI research, we were each offered a copy of a book titled AI for Good, detailing more altruistic uses.
在微软关于人工智能研究的小组讨论结束时，我们每个人都得到了一本名为《人工智能为善》的书，详细介绍了更多的利他用途。
One of the projects mentioned in the book involved using machine learning to predict where best to put solar panels in India.
书中提到的一个项目涉及使用机器学习来预测在印度哪里放置太阳能电池板最好。
That's encouraging stuff, the sort of thing that lets one momentarily feel a hint of relief or hope that, maybe, some things are going to get better.
这是令人鼓舞的事情，这种事情会让人暂时感到一丝宽慰或希望，也许，有些事情会变得更好。
But many of the problems preventing, say, the deployment of solar power in India do not relate to a lack of knowledge.
但是，许多问题，比如说，阻碍印度部署太阳能的问题，与缺乏知识无关。
More often, the problems are the lack of resources, the absence of political will, the power of entrenched interests and, more plainly, money.
更多时候，问题在于资源匮乏、缺乏政治意愿、既得利益的权势，更直白地说，是资金问题。
