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As Boeing China’s regional director of airspace and airport programs, Helen Lee is helping the aerospace giant work toward improving airport and airspace operational efficiency and enhancing flight safety for its aviation customers. In this episode of the Me, Myself, and AI podcast, Helen discusses ongoing research that involves using AI to analyze the wake turbulence of aircraft with computer vision systems, using speech recognition to analyze interactions between pilots and air controllers to minimize the potential for human error, and using image recognition to scan planes for needed repairs. Helen also talks about the challenges of implementing such technology across a complex industry in which there’s no tolerance for error and systems must be impenetrable to hackers.
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Transcript
Sam Ransbotham: Many tasks could benefit from advanced technologies, but how can organizations use emerging technologies in high-stakes situations? Find out how the aerospace industry is using AI in today’s episode.
Helen Lee: I’m Helen Lee from Boeing, and you’re listening to Me, Myself, and AI.
Sam Ransbotham: Welcome to Me, Myself, and AI, a podcast on artificial intelligence in business. Each episode, we introduce you to someone innovating with AI. I’m Sam Ransbotham, professor of analytics at Boston College. I’m also the AI and business strategy guest editor at MIT Sloan Management Review.
Shervin Khodabandeh: And I’m Shervin Khodabandeh, senior partner with BCG, and I colead BCG’s AI practice in North America. Together, MIT SMR and BCG have been researching and publishing on AI for six years, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities and really transform the way organizations operate.
Sam Ransbotham: Shervin and I are excited to be talking today with Helen Lee, regional director of air traffic management and airport programs in China for The Boeing Company. Helen, thanks for taking the time to talk with us. Welcome.
Helen Lee: Thank you for having me.
Sam Ransbotham: Let’s get started. Helen, can you tell us about your current role at Boeing?
Helen Lee: I’m currently working at Boeing China in the Beijing office. My main responsibility is to oversee Boeing’s airport and air traffic management [ATM] programs in China. What we try to do here is to improve airport and airspace operational efficiency and at the same time enhance flight safety.
I’ve been doing consulting for airports and ATM for 10 years, and the interesting part is every project is very different; I’m able to work with different groups of people. Air traffic management is not the core business for Boeing, but what we really want is to help our customers. And because airlines are our customers, we want them to operate more efficiently and also enhance their flight safety.
Other than the design and manufacture of aircraft, we also provide a lot of services to our airline customers. It’s kind of like the entire ecosystem that Boeing and actually other partners are working on together. For example, we provide flight planning to airline customers, and that will help them to better plan their flights and optimize their flight paths, and that also will reduce their fuel consumption and emission of carbon dioxide.
Shervin Khodabandeh: Air traffic management has always been quite fascinating to me because I always think, there’s tens of thousands of aircraft at any given time in the air, and you have different systems trying to do air traffic management or air traffic control for that aircraft. It feels like it’s very data centric. It’s also somewhat, maybe, chaotic, where unpredictable things can happen. And then you probably have — I don’t know, you tell us — some interaction effects between different ATMs, or air traffic management systems, but can you just educate us and our audience a bit on what it is and how it works and how sophisticated and complicated it is?
Helen Lee: So you probably all know that a flight usually takes six phases: testing on the ground and either testing in or testing out; and then takeoff from the runway; and then climbing; and then en route; and then start descent; and landing. So there are six phases of it. And in each phase, there are air traffic controllers controlling the aircraft. General aviation in the U.S. is very different. [A plane] could fly in controlled airspace, but for most of our commercial flights, you need to go through the six phases.
First, you have a controller at the ground, and you probably see the airport’s high tower — that’s the ATC tower. They have the ground controller that controls the ground movement, and then there’s the tower controller, who controls the takeoff and landing. And once the aircraft climbs to a certain altitude, you will be handed over to an approach controller, or departure controller; you can say that. So that controller will handle the aircraft to about 30 to 60 nautical miles from the airport, and then you are passed to the en route controller. And then the en route controller usually has to go through several centers, and in each center, there could be many sectors, and each sector will usually be managed by one controller. And so they hand off one by one, all the way through to the landing site. So that’s how that works.
But I would have to say, AI is still not widely used in ATM systems. There are many, many challenges; we can talk about that later. They’re still using voice communication mostly. And so that’s the part that hasn’t been replaced by this data-link communication yet. But we’re moving in that direction.
One application that’s widely being used is computer vision, like image recognition. And so, for example, what we are doing right now is, in one of the studies, we’re [using] that to recognize wake turbulence — the wake vortex right after a landing aircraft, or maybe for an aircraft in approach. And so we are using a lidar machine to observe that wake, and then we use the AI algorithm to help us to capture those. And so we can train the machine to recognize the location and the strength of that wake vortex. That would be something we might be applying in the future to shorten the wake turbulence separation.
Another one is, as I mentioned earlier, about speech recognition. That is something that we are doing a lot of research on — and not just Boeing, but other parts of the industry — to speech-recognize the cockpit conversation with the controller. Because some of the instructions from the controller are kind of the same from one aircraft to another aircraft, that part may be able to be digitized and just use a data form and have a display in the cockpit instead of having the controller repeat it all the time.
And … if an aircraft has to be rerouted to another path, that would be another thing that can be digitized using speech recognition. The benefit of it is, if it’s all digitized, it’s all coming from the controller, and then we don’t need the pilot to punch in the paths, the waypoints, into the system and they can be uploaded directly to the flight management system. So they will avoid some of the errors made by the pilots.
Shervin Khodabandeh: So we’re hearing there are a lot of potential applications but not yet widespread use. What are some of the reasons for that? You said voice recognition, for example, right?
Helen Lee: Yes.
Shervin Khodabandeh: Like my … Siri doesn’t have any problems recognizing my voice, although I have heard, on YouTube, some of the conversations between air traffic control and pilots, and it’s as strange to me as deciphering my doctor’s prescription. But tell us more: Why is that hard?
Helen Lee: One is the reliability. You know, how reliable [are they] when they recognize the voice? Because it cannot make any mistakes. In the air traffic management world, there’s zero tolerance for mistakes. So it’s not like your Siri, where if you make a mistake, that’s fine; you just say it again. But not here; you cannot do it. And so that’s one thing.
And the other one that I was saying, probably with data link communication, is the security — whether we have a very secure environment that nobody could hack into or things like that. That’s why so far, we haven’t seen any application being certified yet.
Shervin Khodabandeh: If I’m understanding you correctly, one of the biggest hurdles is the need for absolute precision, zero error tolerance, and just how much is at stake that it’s —
Helen Lee: Correct.
Shervin Khodabandeh: — maybe unlike many other things.
Sam Ransbotham: Yes, Shervin, I think a lot that we talk about is corporate application, where people make a recommendation or a loan approval. It’s not real time; it’s not critical in the moment, whereas this is a very different scenario. But not all the scenarios are very different. Like, for example, the wake turbulence is something that doesn’t have to be real time. That could be an after-the-fact analysis; runway configuration could be after the fact.
Shervin Khodabandeh: And it’s not just real time, either. It’s like, if you think about algorithmic trading that’s going on, it’s near real time, or the credit card authorization is real time. I think it’s real time, but also, how much is at stake — like, [what is] the cost of being wrong? Like with image recognition, for example, or video, like in medical applications, you still have a doctor, and if there’s a mistake, God forbid, it’s one life; it’s not hundreds of lives. Are we getting it, that it’s really the gravity of the situation? Is that what prevents these things from being widely adopted in the flight life cycle?
Helen Lee: Actually, in the last couple decades, the industry has been really preparing for this technology to be applied in this industry, so a lot of work has been done to [support] the automation of the system. So that’s one part. Now we know most of the aircraft, especially new aircraft … the advantage to using this in China is, they have almost all new aircraft in China, because most of their aircraft is less than 10 years old. So that means they’re all equipped with the latest technology on board. And so that’s one thing. And also, their control surfaces — they use the most advanced technology as well. I would say the basics for AI to be applied is to have some of the automation system be there.
One thing that we are also doing … you know, aircraft can do [autopilot]. Before, many times we used the procedure of … landing and departing from an airport. But now, we are really promoting performance-based navigation. And since most aircraft already have that navigation system equipped, we are able to give an aircraft a more precise route for it to climb out or descend to an airport. And that means it’ll be much easier later on if we try to manage those aircraft.
So that’s one thing: Kind of create a base so we can build upon it and use more advanced technology in this system. And there are many new studies coming out, and there are road maps and plans for using AI in air traffic management. So I would say in the next decade, we’ll probably see a lot more things come up using AI.
I would have to say another challenge we have in using AI is, usually the AI, if you apply it to an air traffic management system, we might rely on a knowledge-based expert system. So it is very hard to build a good expert system; especially in different environments, their expert system may be completely different because their operation is different. They may have a [different] terrain, they may have different runway configurations and all that. So that’s another part: You cannot just build one expert system to use everywhere.
Sam Ransbotham: So there’s a lot of preparation work, and what struck me as particularly interesting about what you are saying is how coordinated that needs to be with lots of different people, lots of different organizations, different airlines. This isn’t just a thing that one organization can put in place and dictate to their people that they use. It’s something that has to coordinate across lots of different organizations. With equipment like airplanes, you can’t just [say], “Oh, well, let’s just all get new airplanes next week so they have the new technology.” So it’s complicated.
Helen Lee: You’re very right about that. And that’s why everybody’s working on what we call SWIM, that’s “systemwide information management.” So that means we are able to share information between different players; that could be the airline, could be pilots in the cockpit, could be air traffic controllers. And then we will have the meteorology data, and everything will come together. So everything will be shared within the system. Different players will be able to see the information and data they need to better operate their own system.
Sam Ransbotham: You pointed to a lot of forward-looking aspects of artificial intelligence. Is there something you’re using right now? Is there something that Boeing is doing right now that maybe we don’t know about or that is behind the scenes that’s hard for people to see? What kinds of artificial intelligence are currently in use right now?
Helen Lee: One thing that’s coming very close to application is using image recognition. I listened to one of your peer’s [episodes] — [Gina Chung] from DHL. She mentioned a similar technology. So, for example, when an aircraft is coming in, we can use a robot camera to take pictures of the aircraft, to take pictures of the fuselage, to see if there’s any damage and then use AI to recognize whether that’s an important thing that we need to take care of — whether it needs to go into the hangar to be fixed, you know, things like that.
Sam Ransbotham: That seems like a great application.
Helen Lee: Yes, but before, you know, you have to have a human being walk around the aircraft to identify all those [things] and then make decisions.
Sam Ransbotham: I think what’s difficult about some of those things is what you don’t notice is when they don’t happen. Let’s say you do a great job of inspecting the plane beforehand and finding a problem and preventing it, or recognizing a part needs service before people are actually on the plane. These are not things that people notice. You only notice when it doesn’t work.
Helen Lee: Correct!
Sam Ransbotham: It’s the classic engineering problem.
Helen Lee: You’re absolutely right. We are in the process of collecting data because we need a lot of data to train the machine. And the most important part is to collect all those data. And nowadays, with the new aircraft, like the 787, there’s a lot of data we can collect. It’s not like the older 747 that was built decades ago. But the new aircraft that we make today, we are able to have a lot of data, and then those data will help us to analyze the health of the aircraft.
Sam Ransbotham: That seems great. So, Helen, we have a new segment, and we ask our guests a series of rapid-fire questions. So just answer the first response that comes to your mind. You don’t have to think about it too much. Just [go with] your first reaction. So, what’s been your proudest moment of using artificial intelligence?
Helen Lee: It’s hard, because I don’t use that every day. [Laughs.]
Shervin Khodabandeh: I think that was the response. [Laughs.]
Sam Ransbotham: Exactly. That may be your answer.
Shervin Khodabandeh: No response is the response.
Helen Lee: Yeah.
Sam Ransbotham: OK. Well, what worries you about artificial intelligence?
Helen Lee: The challenge we were talking about. You know, how safe it can be if it really is applied to the air traffic control environment.
Sam Ransbotham: What’s your favorite activity that involves no technology?
Helen Lee: Oh, that’s something I couldn’t do now: I used to do kayaking when I was living in Atlanta.
Sam Ransbotham: Oh, OK. Well, I’m actually from Atlanta, so yeah.
Helen Lee: Oh!
Sam Ransbotham: We probably kayaked the same waters then. I see a picture of you snowboarding on your background there.
Helen Lee: Yes, that’s in Beijing. I used to do snowboarding when I was living in the D.C. area.
Sam Ransbotham: What was your first career that you wanted when you were a child?
Helen Lee: I would have to say mechanical engineer, because that was what my mom had been doing, because she designed household electrical appliances. I used to watch her draw those engineering drawings when I was little, and I thought, “Oh, that’s amazing.” And then you can see the product, and that’s fun. So that’s why my major in college was mechanical engineering, before I changed to aerospace engineering.
Sam Ransbotham: Both Shervin and I are chemical engineers, and so I have to bring that up every [episode]. And so we believe chemical engineering is better than all the other engineering.
Helen Lee: [Laughs.] Well, yeah, in some of the universities, aerospace engineering is part of the mechanical engineering department.
Sam Ransbotham: What’s your greatest wish for AI in the future?
Helen Lee: I wish all the aircraft would be able to be controlled by AI, and also that the air traffic control would be conducted by AI so it’s always just machine-to-machine talk, and so there would be fewer errors, less chance for mistakes, and [greater efficiency].
Sam Ransbotham: I think we all want those things. Helen, it was great meeting you and talking with you. I think one thing that impressed me about this is what a complex environment you’re in, coordinating lots of different organizations with equipment that’s really out of your control. And it’s a very difficult situation compared to a lot of the people who we talk to. Thank you for taking the time to talk with us. We really enjoyed it. Thanks.
Shervin Khodabandeh: Thank you very much. It’s been really enlightening.
Helen Lee: Thank you for having me.
Sam Ransbotham: Next time, Shervin and I talk with Sowmya Gottipati, vice president of global supply chain technologies at the Estée Lauder Company. We hope you can join us.
Allison Ryder: Thanks for listening to Me, Myself, and AI. We believe, like you, that the conversation about AI implementation doesn’t start and stop with this podcast. That’s why we’ve created a group on LinkedIn specifically for leaders like you. It’s called AI for Leaders, and if you join us, you can chat with show creators and hosts, ask your own questions, share your insights, and gain access to valuable resources about AI implementation from MIT SMR and BCG. You can access it by visiting mitsmr.com/AIforLeaders. We’ll put that link in the show notes, and we hope to see you there.
