ai物联网工业

by Mariya Yao

姚iya(Mariya Yao)

人工智能和物联网将如何改变行业 (How Artificial Intelligence & the Internet of Things will transform industries)

微软首席技术官凯文·斯科特(Kevin Scott)访谈 (An interview with Microsoft CTO Kevin Scott)

As part of our AI For Growth executive education series, we interview top executives at leading global companies who have successfully applied AI to grow their enterprises. Today, we sit down with Kevin Scott, Chief Technology Officer at Microsoft.

作为“ AI促进增长”高管教育系列的一部分 ，我们采访了成功应用AI来发展企业的全球领先公司的高层管理人员。 今天，我们与Microsoft首席技术官Kevin Scott坐下来。

As the CTO of Microsoft, Kevin drives the technology giant’s AI strategy and services. In this interview, he focuses on the intersection of AI and IoT and reveals how enterprises have successfully leveraged the combination of these two emerging technologies to drive real business value.

作为微软的首席技术官，凯文(Kevin)推动着该技术巨头的AI战略和服务。 在本次采访中，他重点介绍了AI和IoT的交叉点，并揭示了企业如何成功地利用这两种新兴技术的组合来推动真正的业务价值。

He also shares insights from his visits to industries ripe for disruption by AI and automation, and key learnings for how managers can best prepare their workforces for the future.

他还分享了他对AI和自动化已被破坏的成熟行业的访问中的见解，以及有关经理如何为未来的最佳人才准备提供了重要经验。

Mariya Yao: Hi everyone, this is Mariya with TOPBOTS. Welcome to our AI for Growth executive education series, where we interview the top leaders and companies that are successfully applying AI to enterprise problems. Today, I’m very excited to be joined by Kevin Scott, who is the CTO of Microsoft.

Mariya Yao：大家好，我是TOPBOTS的Mariya。 欢迎来到我们的“人工智能促进增长”高管培训系列，我们在此采访成功将人工智能应用于企业问题的高层领导和公司。 今天，我很高兴能与Microsoft的CTO Kevin Scott一起加入。

Kevin, a couple weeks ago we were having a discussion over lunch, and you mentioned the extraordinary impact of the combination of AI and IoT that’s transforming enterprise workflows. Can you give our audience a sense of where you’re seeing the most opportunity and the most ROI in this space?

凯文(Kevin)，几个星期前，我们在午餐时间进行了讨论，您提到了AI和IoT结合对企业工作流程的巨大影响。 您能否给我们的听众一种感觉，让您在这个空间中看到最大的机会和最大的投资回报？

Kevin Scott: Just stepping all the way back and looking at the trends, [this is] one of the more exciting times in computing since the early 90s. There are a few things that are happening at once that are combining in a very interesting way.

凯文·斯科特(Kevin Scott)：一直向前回顾趋势，这是自90年代初以来计算领域最激动人心的时刻之一。 同时发生的一些事情以一种非常有趣的方式组合在一起。

One of those trends is that IoT itself is exploding. There are different studies from a variety of different sources. The Gartner study on IoT devices indicates that we’re probably going to go to somewhere north of 20 billion devices by 2020.

这些趋势之一是物联网本身正在爆炸。 来自各种不同来源的研究不同。 Gartner对物联网设备的研究表明，到2020年，我们可能将使用200亿台设备。

These are computing devices connected to the Internet, and [as] a frame of reference, we’re probably at 11 or 12 billion IoT devices right now. There are about a billion PCs and two and a half billion smartphones, so the IoT sector is an order of magnitude larger than the largest computing platform that has emerged today.

这些是连接到Internet的计算设备，并且作为参考，我们现在可能拥有110亿或120亿个IoT设备。 大约有十亿台PC和两十亿个智能手机，因此IoT领域比当今出现的最大计算平台大一个数量级。

That is in and of itself an incredibly exciting thing and a really interesting opportunity for all of us.

对于我们所有人来说，这本身就是一件令人兴奋的事情，也是一个非常有趣的机会。

When you combine that with the fact that silicon is becoming much more powerful at an accelerating clip [and] you are thinking about the types of silicon required for doing AI model training and AI inference, that particular type of computing power is growing by maybe a factor of 10X in terms of price performance over the past five years. We see that trend line continuing for probably another five orders of magnitude emerging over the next eight years or so.

当您将其与硅在加速中变得越来越强大的事实结合起来时，并且您正在考虑进行AI模型训练和AI推理所需的硅类型，那么特定类型的计算能力可能会以以过去五年的价格表现而言，是10倍。 我们看到，在未来八年左右的时间里，趋势线可能还会继续出现五个数量级。

That has obvious implications for the high-end of computing, where in the cloud you’re gonna have huge amount of additional capacity over the coming years, like build more sophisticated models. It also means that the power of AI is coming to consumer price point devices on the edge of the cloud in this IoT environment.

这对高端计算有着明显的影响，在未来的几年中，您将在云中拥有大量的额外容量，例如构建更复杂的模型。 这也意味着，在此物联网环境中，人工智能的力量正在进入云边缘的消费者价格点设备。

Take that, and you take the fact that these IoT devices are increasingly sensor-equipped, you really do have what we think is gonna be a new computing paradigm.

以此为前提，并且您认为这些IoT设备越来越配备传感器，您确实拥有我们认为将成为一种新的计算范例的东西。

We’re calling it the “Intelligent Edge”, because it’s not just about the fact that computing is becoming ubiquitous and merging into your environment where any room that you’re gonna step into is potentially gonna have tens of these devices, each capable of sensing what’s going on inside of its environments and reacting intelligently to it.

我们之所以称其为“智能边缘”，是因为这不仅是因为计算正在变得无处不在并正在融入您的环境，您要进入的任何房间都可能拥有数十个这样的设备，每个设备都能感知其环境内部发生的事情并对其做出明智的React。

It really is gonna require a bunch of change in the way that we’re thinking about how we build and manage these systems.

在我们思考如何构建和管理这些系统的方式上，确实需要进行大量更改。

MY: What are some examples? You mentioned so many more AI applications are going to come to consumers when it comes on these edge devices. What are some applications that maybe weren’t possible before, but that businesses should now be thinking about, given the proliferation of IoT devices and of AI.

我：有哪些例子？ 您提到，当这些边缘设备上出现更多的AI应用程序时，将会吸引消费者。 鉴于物联网设备和AI的激增，以前可能无法实现哪些应用程序，但企业现在应该考虑什么。

KS: You’re already seeing the early stages of these things in the intelligent speakers that are coming out, but I think that’s really just sort of the tip of the iceberg.

KS：您已经在智能扬声器中看到了这些事情的早期阶段，但是我认为这实际上只是冰山一角。

If we do our job right over the coming years, you’re gonna start to see more and more applications.

如果我们在未来几年内做好工作，您将开始看到越来越多的应用程序。

One of the interesting ones that has been written about are these smart stores that are retail outlets where they’re using IoT devices and cameras, shelf sensors and a bunch of AI in computer vision models to identify you as you come into the store and just look at which items you are putting in your shopping cart and taking out of the store, where you don’t even have to check out.

这些有趣的商店之一就是这些智能商店，它们是零售商店，在商店中，他们使用物联网设备和摄像头，货架传感器以及计算机视觉模型中的一堆AI来识别您进入商店时的身份，并且看看您将哪些物品放入购物车并从商店中取出，甚至不必在哪里结帐。

There are more and more of these stores popping up as proofs-of-concept. It’s not that [there] necessarily is going to be this wave that sweeps through retail and redefines everything. We should all look at that as an inspiration for the sorts of things that you could do with this new technology.

这些商店越来越多地涌现为概念证明。 并非一定有这种浪潮席卷零售业并重新定义一切。 我们所有人都应该将其视为您可以用这项新技术完成的各种事情的灵感。

Just by way of an example, my wife had surgery early this year. I was getting to hang out a lot in the surgical unit recovery area at one of our hospitals here in the Bay Area, and I was noticing all of the processes and workflows.

举个例子，我妻子今年年初做了手术。 在海湾地区我们一家医院的手术室恢复区里，我经常闲逛，我注意到了所有流程和工作流程。

One of the things that that happens when you’re in recovery from surgery is the doctors want you to get some level of activity, but they want to make sure that you’re not overly active. You might injure yourself after the surgery you’ve just had.

当您从手术中恢复时，发生的事情之一是医生希望您进行一定程度的活动，但他们希望确保您不要过度活动。 您刚做完手术可能会受伤。

Right now, the way that they monitor your activity is they have nurses. In this particular hospital, there were four on shifts for this entire ward, and there’s no way that these nurses can keep a close eye on every one of the patients that were in recovery.

现在，他们监视您的活动的方式是有护士。 在这家特殊的医院中，整个病房有四个班次，而且这些护士无法密切关注每一个康复中的病人。

But if you look at these IoT devices with cameras and computer vision models, it should be very easy for us to write software in this new world that would identify when my wife is in the common area walking around, and they can add to her tally of activity.

但是，如果您使用照相机和计算机视觉模型来查看这些物联网设备，那么对于我们来说，在这个新世界中编写软件将非常容易，该软件可以识别我的妻子何时在公共区域四处走动，并且可以将其添加到她的记录中活动。

If she’s below her activity level, you can alert the nurses at their workstation or on their mobile device and say, “Patient Scott isn’t getting up to their daily level of prescribed activity today”. Or if they’re overactive, it can send an urgent alert to go find this patient right now and get them back to their room.

如果她的活动水平低于该水平，则可以在其工作站或移动设备上提醒护士，并说：“患者Scott今天还没有达到规定的活动水平”。 或者，如果他们过度活跃，它可以发送紧急警报以立即找到该患者并将他们送回房间。

I think there are going to be hundreds of thousands of scenarios that this flavor of software can power right now. Right now, we’ve got some packaging issues with the technology. We need to do some more work to make it more accessible to more folks, but part of the problem or challenge, I should say, is getting people to imagine what’s going to be possible in this new world.

我认为这种软件现在可以支持数十万种方案。 目前，该技术存在一些包装问题。 我们需要做更多的工作，以使更多的人更容易使用它，但是我应该说，问题或挑战的一部分正在使人们想像在这个新世界中将会发生什么。

MY: Right, because when you take IoT and put it with AI, you’re talking about bringing two huge trends, two highly technical and very difficult to understand technologies together, so there’s definitely going to be a lot of challenges implementing that on an enterprise scale.

我：是的，因为当您采用IoT并将其与AI结合使用时，您正在谈论将两个巨大的趋势，两个高度技术性和非常难于理解的技术结合在一起，因此在一个平台上实现它肯定会遇到很多挑战。企业规模。

In your experience, what are some of the things that executives can do to better prepare and increase their chances of success when implementing these kinds of AI + IoT applications?

根据您的经验，高管在实施此类AI + IoT应用程序时可以做哪些事情，以更好地准备和增加成功的机会？

KS: I think the biggest thing that you can do is availing yourself of some of the common infrastructure that’s emerging right now in the cloud.

KS：我认为您可以做的最大的事情就是利用云中正在出现的一些通用基础架构。

Basically, we’re talking about IoT and the first thing that I mentioned is the cloud, but having the cloud is the sort of coordination backplane for everything that’s happening on IoT.

基本上，我们是在谈论物联网，而我提到的第一件事是云，但是拥有云是物联网上发生的一切的协调背板。

Making sure that your data is in the cloud, that you’ve gotten yourself into a good state where you’re comfortable with your data governance, you understand what pieces of data you do and don’t have, will really help inform the types of AI that you’re gonna be able to build.

确保您的数据在云中，并已进入一个良好的状态，以使自己对数据治理感到满意，了解自己拥有和不拥有的数据片段，将真正有助于告知类型您将能够构建的人工智能。

Then, getting your organization thinking about all of the AI tools that are available right now. Some of these things are still incredibly elite, [but] some of the tools though are getting to be incredibly easy.

然后，让您的组织考虑当前可用的所有AI工具。 其中的某些工具仍然非常出色，但某些工具却变得异常简单。

Like the computer vision things — it’s being a little self-serving here as CTO of Microsoft — you can use our Azure cognitive services APIs to do computer vision stuff, for instance.

像计算机视觉一样，这里作为Microsoft的CTO有点自私自利。例如，您可以使用我们的Azure认知服务 API来进行计算机视觉。

We have trained a bunch of baseline models for computer vision for you, but you can come to us with your bespoke data of things that are unique to you, and you can add your data to our models and get a customized model out of the other end that lets you do things like identify the faces of your employees, friends, and so on.

我们已经为您训练了许多用于计算机视觉的基准模型，但是您可以根据自己的定制数据来找到我们，并且可以将数据添加到我们的模型中，并从其他模型中获得定制的模型。为此，您可以执行一些操作，例如识别员工，朋友的面Kong等。

Or if you are in manufacturing, for instance, being able to identify your inventory and your parts that you are using in your manufacturing processes… Making yourself aware of what these capabilities are, I think it’s a really important thing right now.

或者，例如，如果您正在制造中，则能够识别您的库存以及在制造过程中使用的零件……让自己意识到这些功能是什么，我认为现在这真的很重要。

The other thing is thinking through what your security policies are. It is really important. One of the really interesting things again that we all will have to think through with this explosion of connected devices is that it’s gonna present a security challenge that is far more interesting, even than the smartphone laptop BYOD sets of issues that enterprises have. Do you allow someone to take a smart IoT device and add it to your corporate wireless network?

另一件事是考虑您的安全策略是什么。 这真的很重要。 在连接设备的爆炸式增长中，我们所有人都必须重新考虑的一件非常有趣的事情是，它所带来的安全挑战要有趣得多，甚至比企业拥有的智能手机笔记本电脑BYOD问题集还重要。 您是否允许某人携带智能物联网设备并将其添加到您的公司无线网络中？

Some companies are already thinking through this with these smart speakers. I’ve chatted with folks who have no Amazon Echoes or intelligent smart speakers on their corporate networks. That may be a knee-jerk reaction that cuts you off from interesting future possibilities

一些公司已经在考虑这些智能扬声器的功能。 我已经与公司网络上没有Amazon Echoes或智能智能扬声器的人们聊天。 这可能是一种下意识的React，使您脱离了有趣的未来可能性

MY: As AI becomes embedded in everything, there is a natural fear, especially exacerbated by the media, [that] the combination of AI with IoT is going to disrupt workforces and put people out of jobs.

我的观点：随着AI嵌入到万物之中，人们自然会感到恐惧，尤其是媒体的加剧，认为AI与IoT的结合会破坏员工队伍并使人们失业。

I know that you’ve spent a lot of time thinking about this, and you believe that that does not have to be the case at all. Can you share more of your thoughts and stories on this particular topic?

我知道您已经花了很多时间对此进行思考，并且您认为完全不必如此。 您可以在这个特定主题上分享更多的想法和故事吗？

KS: We as a society and we as a technology industry get to choose the path that we walk down. The technology industry is building these tools and capabilities, and the rest of the industry, government, and society are deciding how to get deployed.

KS：我们作为一个社会，我们作为一个技术产业，必须选择走的路。 技术行业正在构建这些工具和功能，行业，政府和社会的其他成员正在决定如何进行部署。

One of the interesting and super fun things about my job is [that] I get to see a fairly broad spectrum of AI development.

关于我的工作，有趣且超级有趣的事情之一是，我看到了相当广泛的AI开发领域。

For instance, two of the most inspiring things that I’ve seen technologically over the past year are the developments in precision medicine and precision agriculture. Precision agriculture, for instance, we are entering an era where this intelligent edge, like having these AI-capable devices everywhere including [and] being able to mount them in drones, is allowing you to gather more interesting data about agricultural operations.

例如，在过去的一年中，我在技术上看到的两个最令人鼓舞的东西是精准医学和精准农业的发展。 以精密农业为例，我们正在进入一个智能化的时代，就像到处都有这些具有AI功能的设备(包括能够将它们安装在无人机中)一样，它使您可以收集有关农业运营的更多有趣数据。

A few years ago — and this is probably still state of the art — if you want to build a hydrology model for your crops, [such as] to understand where the wet and dry spots are in a field, to try to optimize how you’re delivering water to make sure that you’re wasting as little water as possible, and [making sure] you are getting the exact amount of water that [your crops] need, you’d have to go through this incredibly expensive and tedious exercise of putting a bunch of water sensors all over the place and flow meters inside of your mechanical irrigation systems.

几年前，而且这可能仍然是最新技术，如果您想为农作物建立水文模型，例如了解田间干湿点的位置，并尝试优化您的耕作方式正在输送水以确保您浪费的水尽可能少，并且[确保]您获得了[庄稼]所需的确切水量，因此您必须经历这种难以置信的昂贵而乏味的工作练习在机械灌溉系统中的整个位置和流量计上放置一堆水传感器。

You’d have to have fairly large-scale agricultural operations to do this, and it was an elite thing.

您必须进行相当大规模的农业经营，这是一件很了不起的事情。

Now, you can take a thousand-dollar drone that’s got the equivalent of a Raspberry Pi running a computer vision model, like flying over a field, and they can build a fairly high accuracy hydrology model for that field. You can then optimize your irrigation [with that data]. It’s virtually free AI running on super cheap commodity hardware.

现在，您可以购买一千美元的无人驾驶飞机，相当于运行计算机视觉模型的Raspberry Pi ，就像在田野上飞行一样，他们可以为该领域建立相当准确的水文模型。 然后，您可以[使用该数据]优化灌溉。 它实际上是在超便宜的商品硬件上运行的免费AI。

That is a flow of AI, where the technology is creating abundance. It’s not concentrating power into the hands of the few, it is making things that were inaccessible to tons and tons of people to orders of magnitude more people. I see that trend happening across the board in R&D, in agriculture, and these innovations will be flowing out into the economy over the next five to ten years.

那就是AI的流动，技术在这里创造了丰富的资源。 它不是将权力集中在少数人的手中，而是使无数人无法获得的东西增加了数个数量级。 我看到这种趋势在研发，农业等领域都正在发生，并且这些创新将在未来五到十年内流向经济。

The same thing is happening with medicine, where you take this combination of increasingly ubiquitous data about the human body that’s coming from smartwatches or fitness bands, then coupling this data with contemporary AI, like deep neural networks, and the things that you’re going to be able to do are really incredible, like predicting serious health conditions for virtually free before a patient is symptomatic when it’s relatively easier to fix the underlying health condition than it is after the patient is sick.

医学上也发生了同样的事情，您将来自智能手表或健身手环的越来越多的人体数据结合在一起，然后将这些数据与当代的AI(例如深度神经网络)结合起来能够做到的事真是令人难以置信，例如，在预测有症状的患者之前，几乎可以免费治疗严重的健康状况，而固定基本健康状况要比患病后相对容易。

These things can potentially transform the world in this positive way, and what world we get is going to depend largely on whether we’re thinking about AI. Is this an empowering technology that creates abundance versus this narrowing technology that concentrates control?

这些事情可能会以这种积极的方式改变世界，而我们所获得的世界将在很大程度上取决于我们是否在考虑人工智能。 与集中控制权的缩小技术相比，这是一种创造丰富能力的授权技术吗？

I’m a huge proponent and hugely optimistic about the potential of the former.

我是一个巨大的支持者，并对前者的潜力非常乐观。

MY: There’s no doubt that AI has so much benevolent potential for a society, especially in the areas that you mentioned, precision agriculture and precision medicine.

我：毫无疑问，人工智能对一个社会具有巨大的潜力，特别是在您提到的领域，即精准农业和精准医学。

I want to dig into this argument that people will sometimes throw out: they’ll say, “Okay, now that you have a drone with AI doing these hydrology models, what happens to the guy whose job it was to build these models by hand?”

我想深入探讨人们有时会抛弃的论点：他们会说：“好吧，既然您拥有一架使用AI进行这些水文模型的无人机，那将要手工建立这些模型的人会怎么做？ ？”

What does it mean to some of these people whose jobs are being automated? You are really seeing this in industrial applications, where people’s jobs have literally been automated.

对于其中一些工作被自动化的人意味着什么？ 您确实在工业应用中看到了这一点，人们的工作实际上已经自动化了。

What is your thought on that, and what has been your experience analyzing these different industries on what’s really happening with automation?

您对此有何想法，以及您对这些不同行业在自动化实际发生情况上的分析有何经验？

KS: There is disruption happening, but what I’m really seeing with these things is, if you’re a small local organic farmer in eastern Washington State [and] we have partners we’re collaborating on [that fits] this exact profile, there was no guy building a hydrology model before. This technology wasn’t accessible to folks who were running a small operation.

KS：这里正在发生破坏，但是我真正看到的是，如果您是华盛顿州东部的一个本地有机农户，并且我们有合作伙伴正在[适合]这个确切的概况上，之前没有人建立水文模型。 进行小型手术的人们无法使用该技术。

You take that to the developing world, where we’re really seeing some huge impacts happening right now. This definitely wasn’t a guy on the small farm in rural India building hydrology models or building AI that’s accurately predicting when folks should be planting crops.

您可以将其带到发展中国家，在这里，我们确实看到了一些巨大的影响正在发生。 绝对不是在印度农村的小农场上建立水文模型或建立能够准确预测人们何时应该种庄稼的AI的人。

There was no one doing the work before, and what you get when you apply the technology is just more productivity and better quality products with less detrimental side effects like to the environment.

以前没有人在做这项工作，应用该技术所获得的就是生产力更高，产品质量更高，对环境的有害副作用更少的产品。

But you are right, there are places where there’s job disruption. I’ve been doing this for a really long time, the first machine learning system I built was about 15 years ago, and the thing that I think we will see is that these machine learning systems have this huge potential to create the opportunity for people to do higher value work.

但是您是对的，在某些地方存在工作中断的情况。 我已经进行了很长时间，我建立的第一个机器学习系统大约是15年前，我想我们会看到的是，这些机器学习系统具有巨大的潜力，可以为人们创造机会做更高价值的工作。

It’s not that you’re permanently displacing jobs. Usually the machine automates the most tedious things in the world, and the thing that you can free someone up to do is much higher value.

不是您要永久替换工作。 通常，该机器可以使世界上最繁琐的事情实现自动化，而您可以解放某人做的事情具有更高的价值。

I’ll give you one final example: when I was a young engineer, one of my first jobs was working for an electronics contract manufacturer.

我再举一个最后的例子：当我还是一名年轻工程师时，我的第一份工作就是为一家电子合同制造商工作。

This is a company that was less than 20 people in Lynchburg, Virginia. You had a very small number of people trying to make this business work, so you had people who would do QA on circuit boards, they would do assembly, they would do post assembly testing. They were context switching across a bunch of different things.

这家公司在弗吉尼亚州林奇堡的员工不足20人。 您有极少数的人试图使该业务正常运转，因此您有一些人将对电路板进行质量检查，他们将进行组装，他们将进行组装后测试。 它们是在许多不同事物之间进行上下文切换。

I’ve imagined using computer vision for doing QA in my old business. It would have helped out with things, like with this process called infrared reflow soldering, you could totally put a camera on either end of this reflow solder machine. [It would] look at a circuit board before it goes in, and when the circuit board comes out of the machine, [AI would] basically replace the visual inspection that a human being would be doing.

我曾想过要使用计算机视觉在过去的业务中进行质量检查。 这样做会有所帮助，例如采用称为红外回流焊的过程，您可以将照相机完全放在此回流焊机的两端。 [它将]在进入电路板之前先对其进行观察，然后当电路板从机器中出来时，[AI]将基本上取代人类将要进行的外观检查。

In the context of [my] old company, it wouldn’t have eliminated anyone’s job. It would be the super tedious thing that was distracting [the workers] from something else higher value that they would prefer to be doing and generated more value for the company.

在[我的]老公司的背景下，它不会消除任何人的工作。 这是一件很乏味的事情，它使(工人)从他们宁愿做的其他更高价值中分心，​​并为公司创造了更多价值。

The thing with AI — this is the misconception I think people have — is [that] it is not going to be this sort of god-like, human-resembling thing that comes in and replaces that the need for human beings and the economy. It’s a thing that can come in and make tedious work across a bunch… I mean hundreds of thousands [of tasks disappear]. [There’s a] super long tail of AI applications that people are gonna build the same way that people built hundreds of thousands of applications when PCs became ubiquitous a few decades ago.

人工智能的问题-我认为这是人们的误解-不会出现这种类似于上帝的，类似于人类的事物，并取代了对人类和经济的需求。 这是一件可以进来的工作，使许多工作变得乏味……我的意思是成千上万的[任务消失了]。 [AI应用程序有一个超长的尾巴，人们将以与数十年前PC无所不在的情况下构建数十万个应用程序的方式相同的方式构建。

The things that they build, like there’s going to be this whole industry that gets created out of the building, it’s going to make a gazillion jobs. The things that they’re automating is going alleviate people from doing a whole bunch of tedious work, so that they can find the higher value things that human beings are uniquely situated to doing

他们建造的东西，就像整个行业都从建筑物中创造出来的一样，将创造大量的工作机会。 他们正在自动化的事物正在减轻人们进行大量繁琐工作的负担，从而使他们能够找到人类独特的高价值事物来定位

MY: Thank you so much for that example. Kevin, I love the personal story of this tedious thing that you would love to fix with AI and IoT, so I really appreciate the positive attitude. We definitely need more of that when we are thinking about AI apps that we can build for ourselves, for our companies, and for our society.

我：非常感谢你的例子。 凯文(Kevin)，我喜欢您喜欢用AI和物联网解决的乏味事情的个人故事，因此，我非常感谢您的积极态度。 当我们考虑可以为自己，为我们的公司以及为我们的社会构建的AI应用程序时，我们绝对需要更多。

Thank you so much, Kevin, for being on the AI for Growth executive education series. Really appreciated your commentary.

凯文(Kevin)非常感谢您参与AI for Growth高管培训系列。 非常感谢您的评论。

喜欢你刚刚读的书？ (Love What You Just Read?)

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好吧，不要在这里停下来！ 加入TOPBOTS社区，我们将确保您获得有关应用人工智能，机器学习和自动化的最佳内容。

Originally published at www.topbots.com on June 25, 2018.

最初于2018年6月25日发布在www.topbots.com上。

翻译自: https://www.freecodecamp.org/news/how-artificial-intelligence-the-internet-of-things-will-transform-industries-f3b3fd161c01/

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