作为微软的首席技术官，凯文·斯科特面临的挑战在于：如何让微软在各种科技趋势中立于不败之地。

斯科特于两年前成为了微软的首席技术官，在此之前曾执掌领英的软件工程业务，长达六年。2016年，微软以260亿美元收购了领英。他在微软的首项任务便是弄清楚公司纷繁复杂的业务部门所使用的各种科技，并且衡量其实用性，然后确保每个部门都能用上最受欢迎的技术。

斯科特说，这一举措反映了微软首席执行官萨蒂亚·纳德拉所奉行的理念。纳德拉花费了大量的时间来思考“该做什么”，以及“我们会因不做什么而感到后悔。”

微软错过了几场技术革命，尤其是智能手机的崛起，而其对手苹果和谷歌则把握住了这一机会。纳德拉希望斯科特能够确保类似事情不会再次上演。

在以下经过编辑的《财富》采访中，斯科特讨论了人工智能、微软继续开发混合现实技术[微软对虚拟和增强现实技术的统称]的举措，以及深度学习所带来的挑战。

《财富》：微软的人工智能与其他公司有何不同？

凯文·斯科特：从骨子里来讲，微软是一家平台公司。听过比尔·盖茨长期以来对平台公司定义的人可能会知道，平台公司致力于打造能够创造各种机遇的技术，而利用这些机遇的公司有着各自不同的经济价值观。我们正在做大整个市场的规模。例如，个人电脑带来了一个巨大的经济机遇。在我们看来，人工智能基本上也是如此。

当我想到平台时，我会想到Windows，其他公司可以利用这一系统打造各种应用。你觉得人工智能也是如此吗？

如今我们所力推的事情是：众多人工智能技术如今依然十分复杂，很多公司难以跟上其步伐。如今，数以万计的开发人员可能都是机器学习/数据科学的坚实拥趸。几乎我们所接触过的每一位客户都在思考如何使用人工智能来帮助其更好地经营业务。我们不能指望所有公司都能请得起那些专业领域的博士和机器学习工程师。按照当前人工智能行业的发展速度，现有的人才数量难以满足市场需求。

我们所面临的一个挑战是打造能够降低准入门槛的科技，这样，更多的开发人员便可以在其产品和服务中使用机器学习。在这一领域，微软自身可谓是自成一派，因为我们有约5.5万名开发人员，但并非所有人都是机器学习/数据科学专家。

我觉得，学会接受“很多实验都将失败”这个事实是探索深度学习人工智能技术的公司所面临的一个挑战。

作为平台提供商，微软有责任为人们提供更好的工具，引导人们走上能够通往成功的康庄大道。

我觉得，人们确实得有心理准备，因为会有失败的情形。人们必须抱着试验的心态来从事这件事情。它不是定理证明，并非只是按照固定的套路就能搞定，而且不会出什么意外。它更像是实验室的工作。

那些深谙技术的公司也都已经适应了这种“实验然后失败”的流程。我们只是通过自己的努力了解到，第一个实验没有获得成功，接下来还得再接再厉。当你获得成功的时候，在实验中付出的所有成本都是值得的。

你在人工智能方面都有哪些经历？

我正在写一本有关人工智能的书，内容涉及人们为什么应该乐观看待存在人工智能的未来。而且反过来讲，我认为人工智能甚至对美国农村地区的人也是有百利而无一害。

我来自弗吉尼亚中部坎贝尔郡农村的一个贫穷家庭，我所在的这个小镇名为格拉迪斯。为了写书，一年前我回访了小镇。镇上所有的工业早在数年前就已经消失。烟草、纺织品、家具制造业都不见了。但当地出现了一些有意思的行业，其中一些便是基于人工智能和高级自动化。

格拉迪斯发生了什么变化？

在我的同学中，有的家里连续5代人都是烟草种植者。在烟草市场没落后，他们的生意便一落千丈，不得不另谋出路。这些人有着不俗的创业头脑，而且认为科技将在其业务中发挥重要的作用。

他们以前用于种植烟草的地块如今已经种上了草皮，其单位经济效益并不比烟草差。部分原因在于他们使用了多种高级自动化拖拉机以及较为复杂的科技，并运用它们在异常辽阔的土地上种植草皮。这个工作的劳动力需求比种植烟草更大，但借助这一技术，他们雇用的人数没有发生变化。因此科技并没有削减工作岗位。

在地平线上，你可以看见无人机从作物上飞过，从空中进行检查。这并不是说不需要人工，而是可以让无人机更加频繁地飞越地块，并获得更多有关地块情况的数据，以便让人们更好地调整肥料和水。

得益于这一技术，公司无需为了获得最佳单位经济效益而建造一个大型工厂，并雇员数千名员工。人们可以在当地设立一家企业，然后雇用30名员工，然后将这个只有30人的弗吉尼亚坎贝尔郡企业发展成为一家国际性公司。有人认为，如果存在100家企业，每个企业有1万名员工，工作机会并不会回到城镇，然而，如果当地拥有10万家企业，每家公司有100个高技能岗位，情况就会不一样。

这类工作的薪资更高吗？

是的，确实如此。

有人担心，尽管自动化会让公司更加高效，但受益的是管理层，不是雇员。

我觉得这两种事情都可能发生，因此我们应该谨慎对待。在写这本书时，包括与沃尔玛这种规模的客户以及中小企业的客户进行交谈时，我发现有很多事情都值得我们期待。

虚拟现实和增强现实似乎在三年前异常火爆，但由于回报速度较慢，如今很多风投资本家已不再像以前那样关注这一行业。如果某项技术并没有出现预期的热度，你会采取什么样的措施，并如何调整？

我的工作职责之一就是确保公司能够长期维持对某些投资的专注和重视。可以确定的是，微软并未减少在混合现实方面的投资[微软制作了HoloLens增强现实头戴设备]。不但没有减少，反而还有所增加，虽然不是大幅增长，但确实在增长。

如果你自认为是一家平台公司，你就必须对平台的未来进行构思。我们认为有三种技术将发展为重要的平台，只是它们目前正处于不同的开发阶段。

一个是量子计算，它在今后将变得非常重要；再就是混合现实，我们认为它将先于量子计算，成为一个异常重要的平台；然而在这之前，“智能边缘”这一理念将大行其道，人们可以将其看作是物联网[与互联网相连的设备]、传感器和人工智能的结合体。

我们认为，上述三项事物将成为未来极为重要的平台。同时，如果要让某个全球性的平台得以运转，人们必须进行投资，并相信它是可行的，而且它的实现只是时间上的问题，而不是能否的问题。（财富中文网）

译者：冯丰

审校：夏林

As Microsoft’s chief technology officer, Kevin Scott has the challenging job of keeping his company atop all of the tech trends.

Scott became Microsoft’s CTO two years ago after six years directing software engineering at LinkedIn, which Microsoft bought in 2016 for $26 billion. One of his first jobs at Microsoft was to identify all the technologies used by the company’s sprawling business units—to gauge their usefulness—and then make sure that the popular ones were available to every division.

The exercise reflects the philosophy of Microsoft CEO Satya Nadella, Scott says. Nadella not only spends a lot of time thinking about what to do, but also “what are we not doing that we’re going to regret.”

Microsoft missed out on a few tech revolutions, in particular the rise of smartphones, which rivals Apple and Google ended up capitalizing on. Nadella wants Scott to make sure that nothing similar happens again.

In this edited interview with Fortune, Scott talks about artificial intelligence, Microsoft’s continued push into mixed reality [Microsoft lingo for both virtual and augmented reality tech], and the challenges of deep learning.

Fortune: How do you distinguish Microsoft’s AI from other companies?

Kevin Scott: We’re a platform company by DNA. If you listen to how Bill Gates has always defined what a platform company is, it’s one that builds technology that creates all of this opportunity in which you don’t have all of the economic value concentrated in one company. We’re increasing the overall size of the pie. The PC, for instance, created an enormous economic opportunity. We see AI as essentially the same thing.

When I think of platforms, I think of things like Windows, which other companies can build apps on top of. Is this how you see AI?

The thing that we’re pushing hard on is that a lot of AI right now is still unnecessarily difficult for many people to get up to speed on. There are maybe in the high tens of thousands of developers out there who are hardcore machine learning/data science folks. Almost every customer we interact with is thinking about using AI to help its business run better. And you can’t expect each and every one of them to hire a bunch of Ph.Ds and machine learning engineers. At the rate AI is unfolding, not enough of those people exist.

One of our challenges is to build technologies that lower the barriers to entry so a much larger pool of developers can use machine learning in their products and services. Microsoft itself is a microcosm for this because we have about 55,000 developers in the company and not all of them are machine learning/data science experts.

I imagine it’s a challenge for companies exploring the AI technique of deep learning to get used to the idea that a lot of their experiments will fail.

It’s incumbent upon us as platform providers to give people better tools—to guide you in better ways toward paths that get you to success.

I think you do have to expect some of this stuff not to work. You have to get into it with this experimental mindset. It’s not like you’re proving a theorem and you walk through the steps and it’s done and predictable. It’s more like lab science.

The most technologically-savvy companies are used to this trial-and-error process. We just know through our own efforts that the first thing is not going to work, and you have to push and push. When you get the win, it totally covers all of the costs of the experimentation.

What’s your background in AI?

I’m writing a book on AI right now. It’s about why we should be optimistic about a future that includes AI. The contrarian thing is that I think it’s net beneficial even to people in rural parts of the country.

I was a poor kid from rural central Virginia—Campbell County, a little town called Gladys. I went back there a year ago for the book. All the industry there evaporated years ago. Tobacco, textiles, furniture manufacturing all went poof. But some interesting things are emerging there now, some of which is powered by AI and advanced automation.

What’s going on in Gladys?

I went to school with people whose families’ have been tobacco farmers for five generations. Their business basically went sideways when the tobacco markets collapsed, and they had to figure out what to do. They were fairly entrepreneurial and they knew technology would play a role in what they were doing.

All the land that they used to plant tobacco on is sod now, and the unit economics is about as good as tobacco. Part of the reason is that they use a bunch of advanced automation—tractors, and fairly sophisticated technology to let them grow sod on these very large tracks of land. It’s more labor intensive than tobacco was, but with the technology they have about the same number of employees. So technology hasn’t reduced jobs.

On the horizon are things like drones that can fly over crops to do aerial inspections. It’s not that you don’t need a human being, but you can fly over it more frequently and get more data about what’s going on in your field so you can better adjust fertilizers and water.

Because of the technology, you don’t need a giant factory with thousands of people in order to just get your unit economics right. You can start a business and have 30 people working in this place and have that 30-person business in Campbell County, Va. be a global business. Some people believe you won’t have jobs coming back where there are 100 companies with 10,000 jobs a piece, but you’ll have 100,000 companies with 100 higher-skilled jobs each.

Will those jobs pay more?

Yeah. I know for sure.

Some people are concerned that while automation will make companies more efficient, only management will benefit and not the workers.

I think both can happen and I think we should be cautious. What I’ve seen working on this book and talking with customers the size of Walmart all the way down to small and medium sized businesses is that there’s lots of things to be hopeful about.

Virtual reality and augmented reality seemed really big three years ago, and now many venture capital investors aren’t as focused on it because they couldn’t get returns fast enough. How do you plan for and adjust when a technology hasn’t caught on as fast as hoped?

Part of my job is making sure that we maintain our focus and our commitment to some of these investments over long periods. The thing I can say is we have not reduced our investments in mixed reality [Microsoft makes the HoloLens augmented reality headset]. If anything, we increased things—not dramatically up, but it’s growing.

If you’re thinking of yourself as a platform company, you have to be thinking about what the future platforms are going to be. We have three things that we believe are going to be important platforms that are in different stages of development.

One is quantum computing, which at some point is going to be very important. There’s mixed reality, which we think is probably in a shorter time horizon is going to be a very important platform. And on a shorter time horizon than that, this notion of an intelligent edge, which you can think of as a mashup of IOT [Internet-connected devices], sensors, and AI.

We believe all three of those will be extremely important platforms in the future. And to make a global scale platform work, you have to invest and believe it’s real. It’s a question of when and not if.