5th HLF – Interviews with mathematics and computer science laureates: John E. Hopcroft
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Internet forumComputerMathematicsTuring testMusical ensembleComputer animationJSONXMLUML
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Turing testStudent's t-testNumerical analysisNeuroinformatikMathematicsElectric generatorProcess (computing)Green's functionVideo gameAreaField (computer science)InformationGoodness of fitFaculty (division)Task (computing)Bit rateService (economics)Cycle (graph theory)Event horizonStructural loadPlanningSystem callPhysicalism40 (number)Functional (mathematics)Computer scienceUniverse (mathematics)Device driverElectronic data interchangeMultiplication signSingle-precision floating-point formatDegree (graph theory)MereologyFamilyComputer scientistOffice suiteOperator (mathematics)Online helpSurface of revolutionRoboticsPhase transitionLevel (video gaming)MassMeeting/Interview
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Turing testForm (programming)MathematicsComputerInternet forumField (computer science)Different (Kate Ryan album)Functional (mathematics)Musical ensembleEvent horizonPredictabilitySurface of revolutionGroup actionPattern recognitionMedical imagingSoftwareStudent's t-testBitShape (magazine)Speech synthesisCategory of beingMoment (mathematics)AerodynamicsMathematicsDimensional analysisAreaSpacetimeMeeting/Interview
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Internet forumComputer animationJSON
Transcript: English(auto-generated)
00:22
Which HLF is this for you? How many have you been to? I think this is my fifth. I've been to all of them. Really? Yes. Have you noticed any changes over the years? No, they're actually pretty much the same. Now, with the mathematics, that's not surprising because mathematics doesn't change quickly.
00:42
But computer science is changing so quickly that it is a little surprising. But I guess AI is a lot bigger now. But we don't hear it in the Laureates talks. But over lunch, when we're talking to students,
01:02
there's a lot more students interested in AI than a few years ago. And that's interesting. And as I've talked to Laureates throughout the day, of course, we're talking about mentoring. And it sounds like you're getting a lot from the students. You're finding out sort of what's current. Well, students seem to understand where the world is going
01:22
much faster than faculty do. And I noticed this at Cornell that students are sort of voting with their feet. The number of majors in computer science has increased tremendously. And our enrollment in courses has gone up
01:40
because students in other majors realize they better take some computer science. But faculty haven't shifted. The university didn't shift until just recently. Now they're told us we can have three new faculty lines per year for the next few years. But they should have probably done it five, six years ago.
02:03
So how has that affected? Now, are you still actively engaged in Cornell? Yeah. I teach full time. So I have my TA give a review session for one class this week and give an exam the other class. That's how I was able to get away for this event.
02:22
Well, it's apparently important to you. Tell me a little bit of what you get from coming to HLF. Oh, there are lots of things in different ways. I enjoy teaching and enjoy helping students, mentoring students. And I think I can help a lot of them by giving them what I've learned during my lifetime.
02:43
Just telling them that they're going to do fundamental research. It's got to be research that they're excited about, not research that their advisor tells them to work on. And just simple things like that. But also, I'm doing other things and their advantages.
03:03
I work in China. And I chair two faculty recruiting committees in China. And so there are a number of Asian students here who may be looking for faculty positions when they get their PhD. And so I can spend time recruiting. And there's lots of different factors.
03:23
Now, you have an interesting, well, perhaps unique perspective of mentoring young researchers in both China and the United States. Are there any notable differences that are worth mentioning? Oh, yeah. China has one fifth of the world's talent. Talent is uniformly distributed around the world,
03:42
but opportunity is not. And the university educational system in China is really not working. And they've only had a stable government for the past 30 years. And in the last 20 years, they've been enormously expanding university education.
04:02
There were six million students in universities 20 years ago. Today, there are 20 million. And they've been basically building the equivalent of 50 universities a year the size of Cornell. And you can see that there is a question with quality.
04:20
They increased the faculty from 300,000 to a million. And you have to ask the question, where did they find 700,000 faculty? But now, they've satisfied the quality. Excuse me, the quantity. And now, they will focus on quality. And they're putting real effort into bringing it up to world standards.
04:43
Now, since this is the fifth Heidelberg Laureate Forum, let's look ahead five years in education both in China and the United States. Where do you hope it will be? Well, I hope that education will improve. Well, in China, it clearly will. And one of the things, it's the graduates from the elite programs in China
05:04
which are driving U.S. PhD programs. If you look at the number of Asian PhD students, it's a significant fraction of the total. And the one thing, though, that I really hope is that the two nations will become very close friends.
05:25
The United States has a very close relationship with Europe. And we should develop that kind of relationship with China. I mean, China probably is going to become the world's power simply because of the size of its economy.
05:41
And nations are becoming so interdependent that I hope people will realize that we're one world rather than a bunch of countries competing with one another. And this Heidelberg event, one of the things it does is it brings in students from all over the world.
06:01
If you look at the distribution of where these students came from, it's quite impressive. I mean, don't just look at the institution they're at at the moment. That's not so impressive. But if you look where they did their undergraduate work, there are Africa, South America, Russia, China, everywhere.
06:21
And these students meet one another and talk to one another. And while it's a small fraction of the world's population, at least these people will understand other cultures and so on. Now, the last person I had in here, Dr. Sudhan, he's an active professor and he mentors a lot.
06:42
And he was talking about how a Ph.D. student he might see for five years, whereas here you only get one week. So what do you think is the thing that you can convey or that you think people pick up within one week? I think the only thing that they can pick up is something like what I said before,
07:01
is that they should do what they enjoy. That if you're working for your advisor, it's work. But if you're exploring what you're really curious about, you're much more likely to do something fundamental. And just you can convey some things like this.
07:22
And you can convey things that there's a real pleasure in helping other people. I mean, one of the rewards of being a faculty member is occasionally I'll be in my office and someone will wander in who's about 50, 55 years old and he'll say, you don't remember me, I bet,
07:40
but I took the following course from you 30 years ago and it had a profound effect on my life. And the reward that you get from that is fantastic. And if you can convey to young researchers the reward structure of teaching and helping other students,
08:00
not to just focus on your research, that's important, but helping other students is where the real reward structure comes from. So let's look back to when you were 22, 27. Let's just look at that age period since that's similar to what people here are, maybe a little bit older.
08:22
What was your process? Who are your mentors? What was life like for you during that time? Well, one of the things is I did not have a strategic plan. I was not very sophisticated, to be really honest with you. I grew up in Seattle and I went to Seattle University,
08:41
which is a small Jesuit college there, and I was going to stay in Seattle. I was going to go to the University of Washington to get my PhD. And I decided to go over and talk to a faculty member there just to get acquainted. And he didn't, for some reason, he was down on Seattle U and he said, we cannot admit you to the University of Washington
09:00
because you're going to an unaccredited engineering college. I realized that I would probably be admitted anyway, but I went back and talked to my department chair. And he said, why are you applying to the University of Washington? Why don't you go to Stanford? And I would never have thought of going to Stanford, but I thought, okay, I'll apply.
09:21
And Stanford was happy to accept me, so I went to Stanford. But it was not part of a strategic plan. It was purely accidental. And when I got my degree at Stanford, I was going to come back and teach at Seattle University. But one day I was walking past my advisor's office, and he said, come in.
09:41
And he was talking on the telephone to Ed McCluskey at Princeton. And Ed had called him to find out if there's anybody he could recruit to Princeton. And I was given the telephone, talked to Ed McCluskey, and Ed invited me back for an interview. And I would never have thought of applying or going back.
10:03
So anyway, I thought I'd go back and see what it would be like to be at an Ivy League university. And when they made me an offer, I thought, well, you know, I'll spend three years there. But it was just accidental things, nothing that I planned or things that made it work.
10:21
And it was an electrical engineering department that I was hired in. My degrees were in electrical engineering. And at that time there were no computer science departments and no computer science books. But the chair of the department knew that computing was going to be important. And so he said, I want you to develop a computer science course.
10:41
What year would this have been? This was 64. And I had to ask, what's in a computer science course? And he gave me four research papers and said, if you cover the material in here, it'll probably be good. But what I hadn't realized is his having me develop that course made me one of the world's first computer scientists.
11:03
And there's an advantage. If I had been in high-energy physics, I would still be waiting today for the senior faculty ahead of me to retire. But because there were no senior faculty ahead of me, I got a number of opportunities at an early age that you would never normally get.
11:20
One of them I got a call from our president and said he wanted to appoint me to the National Science Board that oversees science funding. This is when I was just in my 40s. And this would never have happened if I had been in physics. And so I spent six years on the National Science Board. But these were all things that happened purely by accident.
11:44
So the students today seem to be much more sophisticated. And they have strategic plans. And I'm kind of curious whether their sophistication is going to help them more than just random events. Well, there are two things that stand out to me from that story.
12:03
One is that you were engaged enough in your department to be walking by your advisor, and your advisor knew you well enough and liked you well enough to call you into the phone. And the other was that you were in a greenfield area. So I guess that leads to two things. First, are there greenfield areas for students today?
12:24
Yeah. So when I tell students this story about my things, they point this out. They say, well, you just happened to be lucky that you graduated in 64. But what I tell them is that the world is undergoing a fundamental change now.
12:41
And computer science in their career is going to be fundamentally different than computer science in my career. And if they position themselves for the future, rather than do what some senior faculty member suggests they do, which is work on problems that were important during his career, they will have the same opportunity.
13:04
I mean, AI, there's an information revolution taking place. And it's going to be as profound as the industrial revolution or the agricultural revolution. And if they position themselves for the future,
13:21
they will have tremendous opportunities. So how do you think they can do that? And again, we're looking at people like, let's say, a 25-year-old researcher here planning the next five years. What advice would you give them? Well, the first thing is I think they have to develop their professional reputation, because if they want to do other things, that reputation is going to be important.
13:45
See, one of the changes in the world, I listen to a lot of people who are conservative, and they tell me things which I actually believe, but they don't tell me the whole story. For example, they say they were very poor and they worked hard and they made themselves successful.
14:01
You mean politically conservative or personally? Politically and personally. And they say because someone works hard and so forth, they should be rewarded for that. And I actually believe in that. But what they haven't said is that the playing field is not level for everybody. And I think the next generation should really start to be concerned
14:24
with leveling that playing field. If you're born in a middle-class family, in a stable family with relatively well to do, you have tremendous opportunities that someone, let's say, who is born in an inner city to a single mom who's on drugs.
14:42
And the question is, how do we level that playing field? And one of the things we've done legally in the United States, we've done away with racism. But we haven't done away with what I'll call economic racism. And until we break that cycle, we're going to have a problem in our country.
15:03
And there's just a whole host of these things that this next generation is going to have to solve. But they have to first build their international research reputation. And it may be more than five years, it may be 20 years. And your goals in life change.
15:21
Back in 64 when I started, I wanted to build my research reputation. And that's what I focused on. But today, there's nothing I'm going to do which is going to change my research reputation. But my goal now is I'd like to do something to make the world better for a significant number of people. And that's one of the reasons I'm working in China.
15:43
There's a once-in-a-lifetime opportunity there. If I can help improve university education, it will make lives better for a million students. And so your goals change in your career. And I think to let people understand that,
16:04
that what really their goals are now may not be their goals 20 or 30 years from now. And that does seem to be an opportunity for mentorship because knowing the different phases of a career, I suppose. Well, also telling them their world is going to be fundamentally different
16:20
than the world I lived in. And in the past, if you go back a generation before me, changes in the world were slow enough that you could pass information from one generation to the next. But I think these young researchers, things are going to be so fast, the world is going to change during their generation.
16:43
And it may be that with automation of intellectual tasks, we may reduce the percentage of the population that's needed to produce all the goods and services we need. I'm sorry, automation of intellectual tasks. What do you mean by an intellectual task?
17:00
Well, driving a car. We used to think that was something that required a human. Car manufacturers and other companies believe they're going to mass produce self-driving vehicles at four years from now. And that's going to change the number of jobs.
17:21
In the US, there are 3.5 million truck drivers. Those jobs are going to go the same way elevator operator jobs went. And there's another 5.5 million that load the trucks. That's going to certainly be automated. And if you just look through jobs, it may be that people are not going to work in the next generation.
17:44
Working for a company is a relatively recent phenomenon. 150 years ago, there weren't big companies that you went to work for. And it may only be 150 years that this existed, that in the future, things will be produced automatically
18:02
by robots and so forth, and the cost of goods are essentially going to be free, many of them, that we're going to have to figure out how we're going to engage humans in meaningful activities. I mean, right now, I think of my job almost as part of me.
18:20
But if I didn't have a job, what would I be? And so they're going to live in a fundamentally different world and how they're going to make sure that that world functions correctly is important, at least getting them aware that changes are coming at a very rapid rate.
18:43
Well, let's talk specifically about your field a little bit. What changes do you hope to see? Well, right now, so deep learning is the area that I'm working in at the moment. And right now, researchers in many different fields, like image recognition, speech recognition, finance,
19:01
and so forth, are applying deep learning technology very successfully, just incredibly successfully. But nobody seems to understand why it works. And so what I see as one of the major advances is figuring out why deep learning is so successful.
19:20
And this is true in most engineering areas. I mean, we learned how to make airplanes fly before we really understood aerodynamics. And then afterwards, we developed the science to make them fly better. Same with internal combustion. We made engines work, and then we figured out really how the science of combustion
19:42
made them much better, less polluting and so forth. And so I think one of the major advances will be to really understand deep learning and make it work even better, even though I've got to admit it's very successful. Now, five years from now, where do you think? I know the predictions are always dangerous,
20:01
especially in artificial intelligence and deep learning. Well, I should tell you, in deep learning, it's really pattern recognition in high-dimensional space. And one thing, if you were training a deep learning network to recognize a bicycle, it would be by the shape of the bicycle, not by its function.
20:21
And if a human being looks at a bicycle, they extract the function. Oh, that's something that I can use to get from here to there. And I think the next revolution in deep learning and AI will be extracting the function and other properties other than just the shape or the image.
20:43
Anything else you'd like to say either about the HLF or anything? Well, I think the HLF is very helpful for these students that come here. In fact, now that it's been here five years, I would like us to go back and look and see
21:01
how that first-year group, what they're doing, because many of them will just now be getting their PhDs, I think. It may still be just a little earlier. But it would be interesting to talk to some of them and see what impact this event had on their lives. Well, thank you. That's a good ending.