ai物聯網工業

by Mariya Yao

姚iya(Mariya Yao)

人工智能和物聯網將如何改變行業 (How Artificial Intelligence & the Internet of Things will transform industries)

微軟首席技術官凱文·斯科特(Kevin Scott)訪談 (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.

作為“ AI促進增長”高管教育系列的一部分，我們采訪了成功應用AI來發展企業的全球領先公司的高層管理人員。今天，我們與Microsoft首席技術官Kevin Scott坐下來。

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.

作為微軟的首席技術官，凱文(Kevin)推動著該技術巨頭的AI戰略和服務。在本次采訪中，他重點介紹了AI和IoT的交叉點，并揭示了企業如何成功地利用這兩種新興技術的組合來推動真正的業務價值。

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.

他還分享了他對AI和自動化已被破壞的成熟行業的訪問中的見解，以及有關經理如何為未來的最佳人才準備提供了重要經驗。

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。歡迎來到我們的“人工智能促進增長”高管培訓系列，我們在此采訪成功將人工智能應用于企業問題的高層領導和公司。今天，我很高興能與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?

凱文(Kevin)，幾個星期前，我們在午餐時間進行了討論，您提到了AI和IoT結合對企業工作流程的巨大影響。您能否給我們的聽眾一種感覺，讓您在這個空間中看到最大的機會和最大的投資回報？

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.

凱文·斯科特(Kevin Scott)：一直向前回顧趨勢，這是自90年代初以來計算領域最激動人心的時刻之一。同時發生的一些事情以一種非常有趣的方式組合在一起。

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.

這些趨勢之一是物聯網本身正在爆炸。來自各種不同來源的研究不同。 Gartner對物聯網設備的研究表明，到2020年，我們可能將使用200億臺設備。

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.

這些是連接到Internet的計算設備，并且作為參考，我們現在可能擁有110億或120億個IoT設備。大約有十億臺PC和兩十億個智能手機，因此IoT領域比當今出現的最大計算平臺大一個數量級。

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

對于我們所有人來說，這本身就是一件令人興奮的事情，也是一個非常有趣的機會。

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.

當您將其與硅在加速中變得越來越強大的事實結合起來時，并且您正在考慮進行AI模型訓練和AI推理所需的硅類型，那么特定類型的計算能力可能會以以過去五年的價格表現而言，是10倍。我們看到，在未來八年左右的時間里，趨勢線可能還會繼續出現五個數量級。

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.

這對高端計算有著明顯的影響，在未來的幾年中，您將在云中擁有大量的額外容量，例如構建更復雜的模型。這也意味著，在此物聯網環境中，人工智能的力量正在進入云邊緣的消費者價格點設備。

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.

以此為前提，并且您認為這些IoT設備越來越配備傳感器，您確實擁有我們認為將成為一種新的計算范例的東西。

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.

我們之所以稱其為“智能邊緣”，是因為這不僅是因為計算正在變得無處不在并正在融入您的環境，您要進入的任何房間都可能擁有數十個這樣的設備，每個設備都能感知其環境內部發生的事情并對其做出明智的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.

在我們思考如何構建和管理這些系統的方式上，確實需要進行大量更改。

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，當這些邊緣設備上出現更多的AI應用程序時，將會吸引消費者。鑒于物聯網設備和AI的激增，以前可能無法實現哪些應用程序，但企業現在應該考慮什么。

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：您已經在智能揚聲器中看到了這些事情的早期階段，但是我認為這實際上只是冰山一角。

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

如果我們在未來幾年內做好工作，您將開始看到越來越多的應用程序。

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.

這些有趣的商店之一就是這些智能商店，它們是零售商店，在商店中，他們使用物聯網設備和攝像頭，貨架傳感器以及計算機視覺模型中的一堆AI來識別您進入商店時的身份，并且看看您將哪些物品放入購物車并從商店中取出，甚至不必在哪里結帳。

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.

這些商店越來越多地涌現為概念證明。并非一定有這種浪潮席卷零售業并重新定義一切。我們所有人都應該將其視為您可以用這項新技術完成的各種事情的靈感。

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.

舉個例子，我妻子今年年初做了手術。在海灣地區我們一家醫院的手術室恢復區里，我經常閑逛，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.

當您從手術中恢復時，發生的事情之一是醫生希望您進行一定程度的活動，但他們希望確保您不要過度活動。您剛做完手術可能會受傷。

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.

現在，他們監視您的活動的方式是有護士。在這家特殊的醫院中，整個病房有四個班次，而且這些護士無法密切關注每一個康復中的病人。

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，如果您使用照相機和計算機視覺模型來查看這些物聯網設備，那么對于我們來說，在這個新世界中編寫軟件將非常容易，該軟件可以識別我的妻子何時在公共區域四處走動，并且可以將其添加到她的記錄中活動。

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.

如果她的活動水平低于該水平，則可以在其工作站或移動設備上提醒護士，并說：“患者Scott今天還沒有達到規定的活動水平”。或者，如果他們過度活躍，它可以發送緊急警報以立即找到該患者并將他們送回房間。

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.

我認為這種軟件現在可以支持數十萬種方案。目前，該技術存在一些包裝問題。我們需要做更多的工作，以使更多的人更容易使用它，但是我應該說，問題或挑戰的一部分正在使人們想像在這個新世界中將會發生什么。

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，因為當您采用IoT并將其與AI結合使用時，您正在談論將兩個巨大的趨勢，兩個高度技術性和非常難于理解的技術結合在一起，因此在一個平臺上實現它肯定會遇到很多挑戰。企業規模。

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?

根據您的經驗，高管在實施此類AI + IoT應用程序時可以做哪些事情，以更好地準備和增加成功的機會？

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：我認為您可以做的最大的事情就是利用云中正在出現的一些通用基礎架構。

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，我們是在談論物聯網，而我提到的第一件事是云，但是擁有云是物聯網上發生的一切的協調背板。

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.

確保您的數據在云中，并已進入一個良好的狀態，以使自己對數據治理感到滿意，了解自己擁有和不擁有的數據片段，將真正有助于告知類型您將能夠構建的人工智能。

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，讓您的組織考慮當前可用的所有AI工具。其中的某些工具仍然非常出色，但某些工具卻變得異常簡單。

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.

像計算機視覺一樣，這里作為Microsoft的CTO有點自私自利。例如，您可以使用我們的Azure認知服務 API來進行計算機視覺。

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.

我們已經為您訓練了許多用于計算機視覺的基準模型，但是您可以根據自己的定制數據來找到我們，并且可以將數據添加到我們的模型中，并從其他模型中獲得定制的模型。為此，您可以執行一些操作，例如識別員工，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，則能夠識別您的庫存以及在制造過程中使用的零件……讓自己意識到這些功能是什么，我認為現在這真的很重要。

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?

另一件事是考慮您的安全策略是什么。這真的很重要。在連接設備的爆炸式增長中，我們所有人都必須重新考慮的一件非常有趣的事情是，它所帶來的安全挑戰要有趣得多，甚至比企業擁有的智能手機筆記本電腦BYOD問題集還重要。您是否允許某人攜帶智能物聯網設備并將其添加到您的公司無線網絡中？

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

一些公司已經在考慮這些智能揚聲器的功能。我已經與公司網絡上沒有Amazon Echoes或智能智能揚聲器的人們聊天。這可能是一種下意識的React，使您脫離了有趣的未來可能性

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.

我的觀點：隨著AI嵌入到萬物之中，人們自然會感到恐懼，尤其是媒體的加劇，認為AI與IoT的結合會破壞員工隊伍并使人們失業。

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?

我知道您已經花了很多時間對此進行思考，并且您認為完全不必如此。您可以在這個特定主題上分享更多的想法和故事嗎？

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：我們作為一個社會，我們作為一個技術產業，必須選擇走的路。技術行業正在構建這些工具和功能，行業，政府和社會的其他成員正在決定如何進行部署。

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

關于我的工作，有趣且超級有趣的事情之一是，我看到了相當廣泛的AI開發領域。

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，在過去的一年中，我在技術上看到的兩個最令人鼓舞的東西是精準醫學和精準農業的發展。以精密農業為例，我們正在進入一個智能化的時代，就像到處都有這些具有AI功能的設備(包括能夠將它們安裝在無人機中)一樣，它使您可以收集有關農業運營的更多有趣數據。

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.

幾年前，而且這可能仍然是最新技術，如果您想為農作物建立水文模型，例如了解田間干濕點的位置，并嘗試優化您的耕作方式正在輸送水以確保您浪費的水盡可能少，并且[確保]您獲得了[莊稼]所需的確切水量，因此您必須經歷這種難以置信的昂貴而乏味的工作練習在機械灌溉系統中的整個位置和流量計上放置一堆水傳感器。

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

您必須進行相當大規模的農業經營，這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.

現在，您可以購買一千美元的無人駕駛飛機，相當于運行計算機視覺模型的Raspberry Pi ，就像在田野上飛行一樣，他們可以為該領域建立相當準確的水文模型。然后，您可以[使用該數據]優化灌溉。它實際上是在超便宜的商品硬件上運行的免費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的流動，技術在這里創造了豐富的資源。它不是將權力集中在少數人的手中，而是使無數人無法獲得的東西增加了數個數量級。我看到這種趨勢在研發，農業等領域都正在發生，并且這些創新將在未來五到十年內流向經濟。

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.

醫學上也發生了同樣的事情，您將來自智能手表或健身手環的越來越多的人體數據結合在一起，然后將這些數據與當代的AI(例如深度神經網絡)結合起來能夠做到的事真是令人難以置信，例如，在預測有癥狀的患者之前，幾乎可以免費治療嚴重的健康狀況，而固定基本健康狀況要比患病后相對容易。

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?

這些事情可能會以這種積極的方式改變世界，而我們所獲得的世界將在很大程度上取決于我們是否在考慮人工智能。與集中控制權的縮小技術相比，這是一種創造豐富能力的授權技術嗎？

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

我是一個巨大的支持者，并對前者的潛力非常樂觀。

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：毫無疑問，人工智能對一個社會具有巨大的潛力，特別是在您提到的領域，即精準農業和精準醫學。

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?”

我想深入探討人們有時會拋棄的論點：他們會說：“好吧，既然您擁有一架使用AI進行這些水文模型的無人機，那將要手工建立這些模型的人會怎么做？？”

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.

對于其中一些工作被自動化的人意味著什么？您確實在工業應用中看到了這一點，人們的工作實際上已經自動化了。

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

您對此有何想法，以及您對這些不同行業在自動化實際發生情況上的分析有何經驗？

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：這里正在發生破壞，但是我真正看到的是，如果您是華盛頓州東部的一個本地有機農戶，并且我們有合作伙伴正在[適合]這個確切的概況上，之前沒有人建立水文模型。進行小型手術的人們無法使用該技術。

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.

您可以將其帶到發展中國家，在這里，我們確實看到了一些巨大的影響正在發生。絕對不是在印度農村的小農場上建立水文模型或建立能夠準確預測人們何時應該種莊稼的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.

以前沒有人在做這項工作，應用該技術所獲得的就是生產力更高，產品質量更高，對環境的有害副作用更少的產品。

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.

但是您是對的，在某些地方存在工作中斷的情況。我已經進行了很長時間，我建立的第一個機器學習系統大約是15年前，我想我們會看到的是，這些機器學習系統具有巨大的潛力，可以為人們創造機會做更高價值的工作。

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.

不是您要永久替換工作。通常，該機器可以使世界上最繁瑣的事情實現自動化，而您可以解放某人做的事情具有更高的價值。

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.

我再舉一個最后的例子：當我還是一名年輕工程師時，我的第一份工作就是為一家電子合同制造商工作。

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.

這家公司在弗吉尼亞州林奇堡的員工不足20人。您有極少數的人試圖使該業務正常運轉，因此您有一些人將對電路板進行質量檢查，他們將進行組裝，他們將進行組裝后測試。它們是在許多不同事物之間進行上下文切換。

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.

我曾想過要使用計算機視覺在過去的業務中進行質量檢查。這樣做會有所幫助，例如采用稱為紅外回流焊的過程，您可以將照相機完全放在此回流焊機的兩端。 [它將]在進入電路板之前先對其進行觀察，然后當電路板從機器中出來時，[AI]將基本上取代人類將要進行的外觀檢查。

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，它不會消除任何人的工作。這是一件很乏味的事情，它使(工人)從他們寧愿做的其他更高價值中分心，??并為公司創造了更多價值。

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.

人工智能的問題-我認為這是人們的誤解-不會出現這種類似于上帝的，類似于人類的事物，并取代了對人類和經濟的需求。這是一件可以進來的工作，使許多工作變得乏味……我的意思是成千上萬的[任務消失了]。 [AI應用程序有一個超長的尾巴，人們將以與數十年前PC無所不在的情況下構建數十萬個應用程序的方式相同的方式構建。

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

他們建造的東西，就像整個行業都從建筑物中創造出來的一樣，將創造大量的工作機會。他們正在自動化的事物正在減輕人們進行大量繁瑣工作的負擔，從而使他們能夠找到人類獨特的高價值事物來定位

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：非常感謝你的例子。凱文(Kevin)，我喜歡您喜歡用AI和物聯網解決的乏味事情的個人故事，因此，我非常感謝您的積極態度。當我們考慮可以為自己，為我們的公司以及為我們的社會構建的AI應用程序時，我們絕對需要更多。

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

凱文(Kevin)非常感謝您參與AI for Growth高管培訓系列。非常感謝您的評論。

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好吧，不要在這里停下來！加入TOPBOTS社區，我們將確保您獲得有關應用人工智能，機器學習和自動化的最佳內容。

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

最初于2018年6月25日發布在www.topbots.com上。