Well, good morning.
My name is Steve Ward, and I have the intimidating honor
of chairing a panel of distinguished Turing Award
winners.
As you know, the Turing Award is the most prestigious award
available in computer science.
And our panelists include Professor Andrew Yao,
who has been on many faculties, including MIT's, and is
currently from Tsinghua in Beijing.
MIT's Ron Rivest, and MIT's Barbara Liskov,
and Butler Lampson, who hails from both MIT and Microsoft,
and Professor Fernando Corbató of MIT.
I thought we would get started by asking each panelist in turn
to say a few retrospective words about the work that earned them
the Turing Award, perhaps shedding
a little light on their motivation
and what their retrospective view is
of the impact of their work.
And I thought we would do this in kind of chronological order.
So I'll start with Corby, who was cited for pioneering work,
organizing the concepts and leading
the development of the general-purpose, large-scale,
time-sharing, and resource-sharing computer
systems CTSS and Multics.
Corby?
Thank you.
Well, I'm reminded that--
first of all-- that it was 50 years ago this spring that MIT
had its 100th anniversary.
And right here, in Kresge, there was a series of symposia.
One of which was chaired by Martin Greenberger of the Sloan
School, who had a symposium called
Management and the Computers of the Future.
That was published eventually in a book by MIT Press,
and it got relabeled Computers and the World of the Future.
It's still in print, actually, thanks to demand printing,
and for $40 you can get it.
Most notably, in that particular session on computing,
there was a lecture by John McCarthy,
who subsequently went to Stanford
and got a Turing Award also.
But his seminal work began here at MIT.
But he, in particular, in his talk in that symposium,
advocated time-sharing and made a good case for it,
and the notion of a computer utility.
Well, the other thing is that computing 50 years ago was only
a little over 10 years old.
10 years old, modern computing that is, programming.
And the thing that was staggering at the time
was how frozen the attitude of the industrialists were,
and the computing manufacturers.
They really didn't understand why
they needed to change anything, and time-sharing
was against the grain.
So CTSS was started out--
it was almost kind of a hack, it was going to be a demo.
It gradually, as it succeeded, grew and grew
and was quite successful.
One of the other things I would notice
is that time-sharing was the phrase,
but the real goal was man-machine interaction.
Or as Licklider said, man-machine symbiosis.
There were two key people in that evolution,
one was Licklider, who was an inspiring advocate,
and Bob Fano, my friend, who was also very wise
and had the leadership to start Project Mac, from which CSAIL
evolved directly.
Fano gave a presentation in anticipation
of this celebration which is on the web, I believe.
And then finally, a last remark would
be that CTSS and Multics, which succeeded it,
were precursors to the contemporary systems
and networks.
And all of the interfaces and ideas that have shown up now
have set the stage for personal computing
and the Altos, which Butler I'm sure will talk about.
And the PC world that we know today got
started in time-sharing, as people
began to learn to interact in a real time basis.
It also led to things like Unix, and the C programming language,
and ultimately today is being rediscovered
in cloud computing.
Well thank you Corby.
And if Corby was the consummate system builder of the 1960s,
I think Butler Lampson certainly deserves
that honor for the '70s.
He was cited in 1992, when he earned his Turing Award,
for contributions to the development of distributed
personal computing environments and the technology
for their implementation, workstations, networks,
operating systems, programming systems, displays, security,
and document publishing.
It's hard to know what got left off of that list.
Butler?
I can tell you exactly what got left off.
Spreadsheets and the web got left off.
When Xerox started the computing part of the Xerox Palo Alto
Research Center in 1969, their short-term motivation
was that they had just made a foolish mistake
and bought a computer company.
Their longer term motivation was that they
were able to see that the light lens copying
business, over which they had a monopoly at the time,
was going to be very nice for a decade or two,
but it was not going to last forever.
And the senior management of Xerox
thought that they ought to prepare for the future,
so they started this lab and they
told us to invent the office of the future.
Of course, they had no idea what that meant, and neither did we.
But it turned out to be a very fruitful instruction
to give us, because based on that,
we were able to develop pretty much all of the things
that you're now accustomed to in the world
of personal computing.
Networking, and local systems, and drawing programs,
and document editing and the first internet
was actually built at Xerox PARC.
For some mysterious reason, Xerox
decided that we shouldn't publish anything about it,
so the people who did the work never got much credit.
But they did go to the seminar at Stanford
where Vint Cerf was developing the internet protocols
and they weren't allowed to talk about the internal network,
but they were allowed to ask questions.
And they asked a lot of questions.
And finally Vint looked at one of them and he said,
you've done this before, haven't you?
So we did all that stuff.
We didn't do spreadsheets, that would have been technically
possible on the equipment that we had
but we had no personal use for spreadsheets.
Since, although their use has expanded a lot more recently,
originally they were meant for accountants,
and we didn't do accounting.
And we didn't get along very well with the people
at PARC who did do accounting.
So that's why we didn't do spreadsheets.
And the reason we didn't do the web
was that we didn't have a big enough sandbox to play in.
My theory about the reason that the web came along when it did
is that that was the first time the internet was big enough
to make it worthwhile for people to actually post things on it.
Anyway, we did invent all this stuff,
and it's pretty much the direct predecessors of all the stuff
that people use today.
Even the first quote laptop unquote
was built at PARC in the late 70s.
OK, thank you.
Andy, you were awarded the Turing Award in the year 2000
in recognition of fundamental contributions
to the theory of computation, including
the complexity-based theory of pseudorandom number
generation, cryptography, and communication complexity.
Can you give us your retrospective view
of that work, and its impact and your motivation?
I think my Turing Award was mainly due to the proposal
of two concepts around 1980.
These concepts would lead to the creation of two subfields
in theoretical computer science, which over the years
have shown a lot of depth and applications.
And the first of these is communication complexity,
which captures the amount of communications needed when
several computers want to perform a joint computation
based on their private inputs.
And the second concept that I proposed
was about the perfect pseudorandom number generator.
Pseudorandom number generation had been a very important
subject in simulations and in other fields,
and the standard statistical theory about it
is that whether a pseudorandom number generator is good or not
depends on the application.
And typically you have some sort of
mathematical, statistical test to see whether it's good
or not.
Now with the advent of cryptography,
we have to be more stringent, and we
have to think about is there a way so that we can define
a good definition of pseudorandom number generator
so that it can satisfy the very rigorous requirements.
And so I proposed a definition, which I think
is pretty outrageous.
I was asking the question, is it possible to have a pseudorandom
number generator that can pass all the statistical tests
that people can think of?
And it actually turned out to be the case.
And with the help of computational complexity,
and myself and many of my colleagues
together, we have created to make it work.
And now in retrospect, I think it probably should not
have been a surprise that concepts like this
would lead to ramifications.
The reason is that around 1980, there
were two seminal intellectual trends that started
to happen in computer science.
One is the change from single machine to networked setting,
I think as pioneered by people like Butler and others.
So the cost consideration would no longer
be just the computation time for one single machine,
but actually you have to take the communication
cost into account.
And that the second seminal intellectual trend
that was started at time, is that at that time
the proposal of the public-key cryptography
led farsighted people to think about in the future,
maybe we will have a big network and we
can do electronic commerce, and cryptography will
be very useful and important.
And now when you think about cryptography,
the traditional theory was the Shannon Theory of Cryptography.
But Shannon's theory was very rigorous.
However, it's perhaps too rigorous.
If you insist on that security then you practically cannot do
any electronic commerce.
And so I think at that time, near the end of the 1970s
and the beginning of the 1980s, many of us
were struggling to try to break away from the Shannon cage.
And instead, we would embrace the computational complexity
to become our savior, so that we can construct frameworks
so that cryptography will be rigorous
and will be meaningful.
And so I think that given those two trends,
I think that it should have come as no surprise
that those of us who were lucky enough in those early days, who
started to think about such issues,
would come up with concepts that would become very important.
And perhaps I should explain why I'm here.
I think all my distinguished friends here
are MIT faculty members.
I think that firstly, I was a faculty at MIT
during 1975 to 1976.
And secondly, I think my work has
been influenced by a lot of the MIT people who are here today.
I think, for example, in the communication complexity
that Harold Abelson and the pseudorandom number generation
by Ron Rivest, Shafi Goldwasser, and Sylvia McCauley.
And I think there's an interesting story
that I think--
Ron if you'll forgive me-- that I will just repeat it.
I think Ron always joked with me that I missed
the boat because I left MIT in 1976,
otherwise I would perhaps be able to get a Turing
Award together with them.
But fortunately, I went to Stanford,
and then later on had the opportunity
to mix with the Berkeley crowd.
And so I guess I got into cryptography on the West Coast,
and I guess everything is probably preordained after all.
Thank you.
Thank you, Andy.
Ron, you were awarded the Turing Award in 2002
for ingenious contribution for making public-key cryptography
useful in practice.
Your retrospective view?
Yes, sure.
So Andy talked about some of the themes
that were around in those days.
So our work was inspired by the Diffie-Hellman paper talking
about public-key cryptography, and it asked the question
whether public-key cryptography was possible.
And with Len Edelman and Adi Shamir,
who are also members of--
not CSAIL, but LCS at the time-- although members
of the math department, and that shows
the strength of the inter-departmental character
of the enterprise of computer science.
We came up with a proposal known as the RSA scheme.
Now I guess if you'd stayed around,
it might have been RSAY or--
Or YRSA.
YRSA or whatever, yeah.
So, anyway, we came up with a proposal,
which has so far stood the test of time,
based on the difficulty of factoring the product of two
large prime numbers and doing some modular exponentiation
for encryption and for decryption.
We still don't know, in the end, whether it's secure or not.
We have many open problems in the field of computer science
and theory, and showing that a problem is hard
remains one of the most challenging problems we have.
And we don't know whether specific problems
like factoring are hard, or particular cryptosystems
are hard.
But cryptography has thrived, and one
of the impacts that our work had, I think,
is to really help establish the character of the questions
that people ask.
And the kind of questions you can ask and answer
is can you design cryptosystems so
that the difficulty of breaking that cryptosystem
is closely related or identical to some hard problem
that you believe to be hard.
You make explicit assumptions that factoring is hard,
or discrete logarithm is hard, or doing discrete log
and elliptic curves is hard, or something like that.
And the field has really blossomed
in a very nice way, in spite of our inability
to show that these particular problems are hard, because we
can make very crisp assumptions saying if you assume
that these problems are hard, then
you can achieve these cryptographic objectives.
And so the field has really blossomed in a wonderful way
and achieved a lot of paradoxical things.
I think the public-key work itself demonstrates
the paradoxical character that cryptography can have,
that you can ask something which seems like you shouldn't be
able to do it, and yet you can at the
end with the clever use of the tools at hand.
And that's a theme that appears over and over again,
is that by making the right assumptions, by being
careful about your reductions and asking
what appear to be paradoxical questions,
paradoxical set of requirements, you can actually
achieve some of these things.
I'll stop there.
OK.
Thank you, Ron.
And Barbara, your 2008 Turing Award
cited contributions to practical and theoretical foundations
for programming language and system design, especially
related to data abstraction, fault tolerance,
and distributed computing.
OK.
Well, Corby started by mentioning John McCarthy,
so I wanted to say that John McCarthy was my thesis
advisor at Stanford.
And I was working in AI at the time,
but as soon as I got my PhD I switched into computer systems.
I went to work at the Miter Corporation,
and while I was at Miter I started
to work on programming methodology,
because there was a huge concern at the time.
And of course, there's still a concern about what they then
called the software crisis.
By which they meant we had no idea
how to build software that actually
did what it was supposed to do.
And I found this problem very intriguing,
and so I was thinking about it.
And I moved to MIT in 1972, and this
was the problem that was consuming me,
what would be the right way to organize software
so that we could actually control the complexity
and reason about the resulting programs.
And I had in mind a particular way
of organizing programs, which I referred to
as the multi-operation module.
So the idea was that I had a big black box,
and inside there was complicated data structures
and very complex code, but it was all hidden inside the box.
And the box provided an interface
that consisted of a number of operations, and outside the box
you could call these operations but that was the only way you
could interact with the box.
And this seemed to me like a really good way
to organize programs.
And there were other people that had similar ideas,
like Dave Parnas at the same time,
but I felt that it was very hard to communicate this idea
to normal programmers.
And so, in the fall of 1972 shortly after I got to MIT,
I had this wonderful aha moment.
And very few people are lucky enough
to have a moment like this.
And what happened to me was I all of a sudden
saw that I could relate this idea
of a very abstract, multi-operation module
to data types.
And I felt that making that connection
was going to be really beneficial, because programmers
understood data types.
They programmed with data types all the time.
And the connection was that the module was a data object,
the operations were the operations of the object
that you use to manipulate it, and people already
use things like arrays and they understood
that you could add something to array,
remove something from the array, and they also
understood that they thought about these things
abstractly and did not have to actually understand how things
were implemented underneath.
And so I had this idea of abstract data types.
And then, of course, you know you're lucky enough
to have an idea like that, but of course it
doesn't stop there.
I spent several years trying to figure out
what this idea really meant, how did you define it formally,
what was an abstract data type conceptually,
how did you specify them, how did you reason
about their correctness, how did you design programs
using the idea, and so on.
Around about 1980 I switched into distributed systems,
so that was the other part of the award.
And so that was just an example of what
I was doing in my career, I was opportunistically
switching from one field to another
as I saw really important problems.
And when working in distributed systems, where I work today,
I got very interested in replication and so forth.
So I've had a lovely career, and I was lucky enough
to have some good ideas.
And that's where I ended up.
Well, thanks Barbara.
I have here a handful of questions
that have been submitted by attendees,
and I'm unlikely to be able to get to them all,
but let me start with several ones
that I think should be easy that deal
with the institution of the Turing Award itself.
The first one is, in 25 words or less,
how did the Turing Award change your life?
Would anybody like to respond to that?
OK.
Well if you decline responding to that,
let me get to one that's even shorter and considerably more
blunt.
Are Turing Awards really a good idea?
I'm guessing this was submitted by somebody who
doesn't have a Turing Award.
If I had to guess, I would be able to guess a one-bit answer
that each of the awardees would give, but.
Is this meeting a good idea?
Is anything a good idea?
I think they're very closely related questions.
Perhaps, but I make the assumption that none of you
did the seminal work that you did specifically
in order to win a Turing Award.
OK.
So if it didn't have that effect on your behavior,
did it in fact have any effect on your behavior?
As a social institution, is it a positive thing?
Anybody want to comment on that?
Well, it's not for individuals.
You don't do research because you're
thinking about the Turing Award, you
do research because you find things
that you are fascinated with that are interesting,
useful to work on.
It's probably good for the field,
because it gives us a history, it recognizes contributions,
it gives people a way of thinking
about the history of the field.
Of course, it's not perfect.
Because the people who get the award,
they have to be nominated, the nomination
has to be phrased properly, the committee has to be receptive,
and so clearly there are going to be people who didn't
get the award who deserved it.
And there might be people that got the award who
didn't deserve it, who can say.
But I think if you think about it, sort of on average,
it's a useful thing for the field.
Is it fair to have the model that, like tenure, people who
have it think it's a good idea?
Well, let me move on to something
that's closely related.
To also answer that, if I may, I think I do have an opinion.
I think the Turing Award is great,
and it adds some value to the field,
but I think more important to the field
are awards going to younger people before their promotion
and tenure and so on, too.
I think that's a critical thing, to recognize accomplishment
early in people's career, and the Turing Award
tends not to do that.
OK.
A related question is, can you think
of work that has already been done
that deserves a Turing Award and hasn't yet received one?
For example, would you like to make any predictions
as to the 2012 Turing Award winner?
You, after all, are pundits, and you
need to exercise your crystal ball a little bit.
I'll step into that one.
I'm not sure I know exactly who to credit,
but I think there probably is not
a person in this room that doesn't
recognize the profundity of the contribution of Google.
It's become a verb.
People have thrown away their encyclopedias,
and it's a wonderful contribution to our well-being
and so forth.
It had a predecessor, which isn't well known, AltaVista.
Butler maybe can talk about that.
Google has succeeded for reasons I don't fully
understand myself, but it's miraculously fast.
It's fast because they have a lot of money.
And they're clever, too, but it takes a lot of money.
And the reason they have a lot of money
is that they figured out what very few people did, which
is that you could make a lot of money
out of targeted advertising.
Well aren't there similar institutions?
I'm thinking of things like Wikipedia,
that doesn't have a lot of money, that's
also had a significant impact.
Yes, I would put that in the same ballpark.
So money isn't essential.
OK.
Whether money is essential depends
on what it is you're trying to accomplish.
Perhaps.
For going to the moon, you need a lot of money.
To index the internet, you need a lot of money.
OK.
Let me move on to another question, which
may be viewed as superficial or profound
depending on your view, but it should
be easy to answer it actually.
I'll read it in its entirety.
It's addressed to each of the panelists.
And the question is this, do you have a Facebook account--
I haven't finished-- a Twitter account, and why not?
So.
So Barbara, fess up.
Do you have a Facebook account?
Absolutely not.
A Twitter account?
Neither Facebook nor Twitter.
Do you have an email account?
Of course I have an email account.
I'm not particularly interested in that kind of exposure.
I'm not really interested in following people or having
them follow me, but I'm a person of my generation.
And if I were 40 years younger I would probably
have a completely different answer, because I
do think it's generational.
We grew up in a certain way, we're
used to a certain way of living, and you sort of
settle into a rut.
Yeah.
I do think, though, that there's something quite dangerous going
on in the internet.
And Facebook is a good example of that, because of the way
people expose themselves, not understanding how
they might regret that later.
And I'm keenly aware of the dangers of the internet.
And thank God for Ron, and all the crypto,
so I can at least do online banking,
but I'm very careful even with email
and what I will send because--
and you think politicians would learn this after a while.
Because everything you do like this is exposable.
And I just don't care to be exposed.
So actually, I do have a Facebook account.
I do have a Twitter account.
I make sure to log in to Facebook at least once
a quarter, Twitter at least once a decade.
OK, so my follow on question for you, Ron,
is what are you going to do about the 600 friend requests
that you get as a result of that announcement?
No promises.
OK.
It's sometimes argued that all branches of computer science
have enjoyed success because of the remarkable exponential
growth, the Moore's law growth, for over five decades.
And so an interesting question is,
would your work be as celebrated as it is now?
Would you even have done it if it
hadn't been for this kind of Moore's law
growth, this exponential growth, and the impact of computers
themselves?
And there is kind of a rising tide lifts all boats argument,
that maybe good research has turned
into great research because of the economic impact of all
of this growth.
Certainly.
It's certainly true for most.
It's certainly true for?
For most practical systems work.
We couldn't have done what we did it
at PARC five years earlier.
Just with the hardware, would have been impossible to build.
Were you excited about building that hardware in anticipation
that someday it would become cheap enough?
Absolutely.
That people could put it in their pockets
and have it at home?
Absolutely.
I wrote a guest editorial on this subject in 1972,
it was crystal clear that that would happen.
But I think it's fair to say that was only--
I suppose from the point of view of the welfare of the Xerox
corporation, that was quite essential.
We would probably have tried to do the work anyway,
even if we couldn't have foreseen that future.
But we certainly did foresee it.
Yeah.
I think in backing up what Butler
said, during the '70s and '80s, everybody working
in any central area of computer science
was keenly aware of the fact that computers
were getting faster and faster.
And so you could envision doing things today
that weren't going to be practical today,
but you were laying the framework for something
that would be practical a little bit down the line.
I think even for theorists, I think
that it's very important to do theory problems that
are related to technology.
Somehow the advances of technology
bring out a lot of really interesting problems,
so that I don't know whether it's a coincidence
or there is a correlation that somehow things
that have very heavy technological content
also have very rich theoretical content.
And I think that I was pretty lucky in getting
into this field and I have been quite interested
in combinatorial problems like [INAUDIBLE] problem.
And all those problems were very fascinating to me
at the time when I entered computer science.
And I couldn't really decide whether to work
in combinatorial problems or doing something else,
like computer science.
And at some point, it suddenly hit me
that there were so many interesting mathematical
problems in the world that how do you decide
which problems to work on?
And computer science, being a very rapidly advancing field,
kind of breaks the symmetry.
Somehow it singles out problems that
is different from the typical mathematical problems,
and I thought that was a good choice.
And I still don't understand whether there's a correlation,
but somehow it seems so.
Well, to put it more indelicately,
was there a bit of technological opportunism
in your career choice, then?
Not really.
I think that actually I first got
into the theoretical computer science from Duncan notebooks.
And so I think that when I first read the manuscript
and did the exercises and open problems,
I didn't really think of the opportunism in technology.
But somehow, later on, when I thought
about deciding whether to pursue computer science or not,
then I realized that beside of an interest
in problems, computer science or so is a new discipline.
I think that at that time, I understood
computer science was very, very new compared with mathematics.
But now, after 40 years when I think back,
I realized that how close to the beginning
I was entering into that field.
Because when I entered the field there
were already many big names and people, but I think that--
I think that it is very important
to get into an area early, so I think that perhaps all of us
have that advantage here.
I think the evolution of the technologies
was kind of foreseen by most people in a misty way,
without recognizing everything.
Clearly, some of the many computer vendors
didn't see the impact of the processor on a chip very well
and are out of business.
But from the earliest days of time-sharing,
we had already discovered social interaction
in the form of messages to each other,
which later evolved into email.
Initially, they were just messages within the system.
And today, the social interaction
extends to Facebook and now even Twitter.
Yes, I do have a Facebook account, but I'm like Ron,
I don't log in very well.
I was coaxed into it by my grandchildren.
But, I too share with Barbara the hesitation
to expose more than I have today on my website, which
is a brief bio, which is comparable to what
you can find just by any normal Google search.
So I think some of those reservations
are because maybe we've been around long enough to recognize
some of the disasters that occur when people make mistakes.
But I think most people have that aha moment when they send
an email, they regret it, and they
realize they can't get it back and there's
nothing they can do.
And that was a revelation to everybody
when they first discovered.
Well maybe the next Turing Award winner
should be the person who invents the unsend key.
Steve, I wanted to respond to your question, too, if I could.
Because theory is a bit different, I think.
And the ability to do theory untethered
to the technology of the day is a characteristic of theory,
sort of by definition.
And we see that today with quantum computing, right.
It's not clear at all whether we can build quantum computers.
There may be possible, but there's
a wonderful theory involving.
Scott Aaronson and other people are working on this.
And it's an example of theory evolving, independent
of the technological underpinnings.
And I think had, in response to your question,
had the computer technology frozen in the '60s or the '70s,
we would have seen much of the same theory evolve too.
Maybe there wouldn't have been as much funding, or something,
I don't know.
But it would have been the kinds of questions asked
and the kinds of evolutions might
have been very much the same.
Recall that NP completeness came about that time too.
Of course NP completeness was sort of
on the pessimistic side of theory,
what you can't do maybe, so you don't
need the hardware to do that.
But even public-key was ahead of its time,
in the sense that it really didn't
have a utility until the web was invented till Tim.
But if the market hadn't developed
for public-key cryptography, you think
you still would have done it?
You wouldn't just be an obscure number theorist instead
of a Turing award winner?
There was a conference series started in the early '80s,
but the market for cryptography didn't really
happen until the '90s.
So it's a whole decade of vigorous theoretical
exploration before the market happened there.
So theoretical work and cryptography is part of that.
There's been a big market for cryptography
for hundreds of years.
Well big in the sense of the military market
has always been there, of course.
OK.
Before leaving the Moore's law thing,
can we look at potentially the dark side of it?
Is it possible that the Moore's law growth of technology
has made too much function available too fast,
that we haven't actually had time to develop
mature engineering disciplines?
There's a lot of complaints about the quality of software
these days, for example.
It's the law of nature that there's always
going to be a lot of complaints about the quality of software.
Why is that?
Because we always raise our aspirations
to the point where we can't quite do
it or we can just barely do it.
Yeah.
So that's just-- words fail me.
But I actually think that the much more serious implications
are the sociological implications of computing.
I mean, we heard yesterday in the talks
about the financial markets about the danger of completely
computer-controlled trading.
And I think if you look across our technology,
you see this in many sectors.
That we have developed the technology--
I mean, I don't think the problem of the software
is the big problem, I think it's the control of technology.
But of course, this is not limited to our technology.
Yeah it's not as bad as nuclear fission.
Just look at it that way when you start wringing your hands.
But nevertheless, it's a serious problem.
And it takes time for a society to catch up
with the innovations.
It's not as bad as the spinning jenny, either.
OK, let's exercise your crystal ball a little bit more.
Put yourself in the position of a new PhD student
just starting your career.
What problem would you choose to work on?
What do you think are the important problems that
are facing computer science now, and the ones where
there are still dramatic progress to be made?
I'll give the same answer I gave last time a similar question
was asked on a panel.
P equals NP.
[LAUGHTER]
Well I would venture--
Not always silence is ever met.
For a short time.
Would you actually, as a PhD supervisor with a new PhD
student, would you advise him to tackle
as a thesis topic settling the P equals NP question?
No, that would be unwise.
Think that would be bad career advice.
It's not quite what was asked.
Well I would advise a really brilliant PhD student
to work on the factoring of large integers
by classical means.
Which may irk Ron a bit, but I think
that that's the right problem.
It's the question we want to know the answer to.
Yeah.
Yeah, so.
Yeah.
I think machine learning is a really promising area.
It's vibrant right now, a lot happening.
And I think new students with a lot
of mathematical or other skills could make progress there,
and I think that's an area which is going to be recommended.
Well Ed Lazowska already gave my spiel on this subject
yesterday.
I think it's very clear that although sort
of classical simulation and communication
applications of computing will continue
to be interesting and important, the really hot area is going
to be what I like to call embodiment
and what the NSF calls by the bureaucratic name
cyber-physical systems.
Which to me means computer systems
that have non-trivial interactions
with the physical world.
This is something that's just getting started,
and that's going to be even more interesting
and important than the areas that have already been mined.
So I thought Ed's talk yesterday was great,
and you gave me hope about systems again.
I think that the characterization of systems
going up and down the trough of despair
and so forth, I think that's really right on.
And it's very hard to answer the question for any of us
I think, of what we would do today,
because we're not young anymore.
And when you're young you see the world a little differently
than when you're not so young.
My thought about today is that I still
think there are very interesting issues and systems,
but I also think that it's not really embodiment so more,
as much as the combination of computer science and something
else.
Like, I do think computational biology is really interesting,
and so if I were starting off today that's the kind of thing
I might do.
I think all of us benefited from the fact
that we happened to see this new field
and we were lucky enough to get in at the beginning of it.
And I think that's a way that you have a really interesting
career, because there are just so many open problems that you
can work on that people have not solved before.
But it's very hard for us today to see what goes--
Well, isn't the flip side of that argument,
to put it very cynically, that you guys have taken
all the low hanging fruit?
You've built these great systems in the '60s and '70s
and all the new PhDs can do is kind of fine tune
the fundamentals that you've laid down?
Absolutely not.
Somehow they find interesting things.
That's right.
OK.
So another question from an attendee, directed to all.
And it reads, isn't it time for parallel programming techniques
that mortals can understand?
Yeah.
It is, indeed.
Yes it is.
That's a really important question,
and it's not clear to me that the answer is yes.
That in the end we will succeed.
And if we can't succeed, then that's
going to limit the applicability of
the huge multi-core computers.
And that's not necessarily bad, it just
means that the technology will go in one way or another.
God did not say that computers had to have thousands of cores.
But I think as a technical challenge,
it's a huge interesting problem right now.
Is it a programming language problem, do you think?
I think so, yeah.
It's a programming language problem
and it's a methodology problem, even more.
Because programming languages follow methodology,
so it's a methodology problem.
If we can figure out how to organize those programs,
that's the first thing we have to do.
Then we can invent programming languages
that support the methodology.
But so far we don't have too much
of a clue about what to do with this.
This one striking counter-example
to that, which is MapReduce.
Yes, but I don't think MapReduce goes all that far
in solving the general class of problems.
It goes nowhere in solving the general class of problems,
that's certainly true.
But there is a large class of problems where we actually
do have a reasonable answer.
Well should the question be actually limited
to parallel programming?
I mean, what about just everyday programming?
One could ask the skeptical question
that despite all that you've taught us about programming
languages, Barbara, JavaScript has emerged
as the most popular programming language in the world
and that's hardly an exemplar of your lessons.
Can that be interpreted as evidence
that programming languages don't really matter that much?
The correct way to look at JavaScript
is it's a machine language.
Well it's a machine language that a lot of lines of code
are written in every year.
Only for relatively small programs.
People want to write--
It's changing.
People want to write big programs, I don't think so.
People want to write big programs,
they write them in something else
and they compile them into JavaScript
so that the browser will run them.
Yeah.
I mean, it's a sad statement about our field
that a lot of big programs start off with small programs.
And when you're building a small program,
you don't have to pay much attention to methodology
because you can just do it through brute force.
Willpower.
Brute force.
It's a form of willpower, right.
And then, so this is why there's so much
stuff written in languages that are not really all that great.
Because you start off this way, you
don't have to think about it hard.
Then when you get to the big problems, you're in trouble.
But it's, I don't know how to solve this.
Yeah but we're OK, for the reason that I said.
JavaScript is a machine language,
you can write your program in any language you want.
OK.
I have a question addressed to Butler, Barbara, and Corby.
The question is this.
As computation becomes increasingly distributed,
should we rethink the role of the operating system?
So there's a very healthy operating system community that
thinks about this all the time.
My basic belief is that distributed computing
is probably the source of inspiration
for parallel computing.
Distribute computing actually is not in bad shape,
and it is the way that Google does all its work today.
It is the way that in fact, companies
that have lots and lots of processors,
those are parallel machines, in a sense.
It's just that they use a particular memory
model and a particular way of thinking about how computation
is organized.
They run MapReduce.
Some of them run MapReduce, some of them don't.
Most of them do, actually.
If you get down to the systems level,
they're not running MapReduce, they
are implementing MapReduce.
That is undeniably true.
They're running some boring variant of Unix
down at the system level.
Yeah, it's not clear to me that the operating systems
are different.
That the problems that people look at in the operating system
may change a little bit, the way you
do load balancing, your notions of virtualization,
they may evolve a bit.
But it's not clear that they're very
different than what they were.
Not much has changed in operating systems
since the mid '70s.
In my opinion it's not for commercial reasons,
it's because we haven't had any great new ideas.
Aren't there people who argue that the web is now
our operating system?
I have no idea what that means.
Yes, certainly, there are people who argue that,
but I have no idea what it means.
OK.
Here's a question, perhaps for our theorists.
Has there been any progress made in the question of P equals
NP over the last 20 years?
And do people do people still work on this problem?
I think that there are people who still work on this problem,
as evidenced by the claim of solving
P equal to NP or the other way every few years.
I think that the most recent one appeared with a very high
publicity about a year ago.
So I think there are people who still work on it,
and I think the last one got some--
I think that most of the claims, people just brushed off.
But somehow the last one got a little bit more esteem.
My feeling is that there has not been any real progress of P
equal to NP.
I think that if you want to win a Turing Award
by working on P equal to NP--
Butler, sorry-- I think that it probably is not
a good idea for a PhD student.
I don't deny that.
We have made some progress, but of a negative sort.
I mean, we do know why certain classes of arguments
don't work.
Some of the arguments relativize,
or there's other issues with them,
so we're getting more frustrated.
We're understanding the narrow path
we have to follow to come up with a proof, maybe.
OK.
Let me ask you to turn up the gain on your crystal
ball a lot.
There will presumably be a celebration like this in 2061
of MIT'S 200th anniversary.
Will it be a Turing session?
Will there be Turing Awards?
Will it be computer science, will computer science
have just assimilated into other disciplines like math
and physics?
Any ideas?
Well I could speak up and say I don't expect to be there.
Well we will certainly reserve a place for you, nonetheless.
Thank you.
Well, I have a feeling that at MIT 200
there will still be Turing Awards.
And actually, at that time, I think
contrary to what you think, I think
that computer science has absorbed physics and biology
and everything else.
That's a great answer.
OK.
The person moderating the panel, Steve, will be a robot.
It won't be a person.
OK.
Well the panelists be robots as well?
We don't know.
Intriguing possibility.
OK.
At the ACM's 50th anniversary celebration, which was in 1997,
my then boss Nathan Myhrvold made a prediction
about what would happen.
What would be going on 50 years in the future?
They asked a lot of people to do this.
I would never have dared to make a prediction about what
computing is going to be like in 50 years,
but Nathan is fearless.
He predicted uploading.
So uploading means you get to upload your consciousness
into a computer.
Why would you want to do that?
Well, you can be immortal.
Great.
And it seems to me, if you're a materialist,
you can't really rule this out.
OK.
Well.
On that profound thought, I'd like
to close this session by resurrecting an old tradition
that Mike Dertouzos started many decades ago.
He hosted the distinguished lecturer series
of LCS, which is now called the Dertouzos lectures, and made
a practice of asking each of his distinguished lecturers
to summarize their life experience into a one sentence
lesson.
So I'm going to put each of you on the spot
and ask you for your sort of one sentence words of wisdom.
And of course, you're all Turing Award panelists,
so you have the strong advantage that you
can say something that's merely inscrutable
and the audience will interpret it as profound.
So Corby, can I ask you to start?
I'll give it a stab.
I think if there's any single thing that--
a piece of advice one could give,
it is to follow your enthusiasm, whatever
that may turn out to be.
And in particular, take advantage
of the ambience of a place like MIT,
where one has to recognize in order to enjoy your life
you have to consider yourself a lifelong student,
and you really should take a drink from the fire hose.
Thank you, Corby.
Butler?
I think Corby said it very well, you
should do something that you really like doing.
And if you like doing lots of things,
I suggest that you consider either biology, or chemistry,
or computing as the hot fields for the next two
or three decades, at least.
Good.
Barbara?
So I think you should be opportunistic.
I think opportunism is a good thing.
And what it means is you look around
and you see what's moving and what's open
and then you should focus on what are the important problems
and avoid doing incremental work.
And if it requires changing your field,
then you shouldn't be afraid to do that.
Ron?
I guess it's very much in the same sort of spirit.
The thing I would advise students
would be to ask questions based on the technology,
the hot technology of the day, but to ask them in such a way
and try to derive answers that will
be interesting a couple of decades from now.
Good.
Andy?
My advice would be, for the students,
would be to always try to do something
that is just a little bit beyond your ability.
Great.
Well, I'd like to thank our distinguished panelists.
We have 4 and 1/2 minutes.
What would you like to talk more about?
This is the first time I've ever seen one of these things cut
off before its deadline.
I was going by my watch.
Oh, so there's--
Other questions?
Shall we wing it and take a question from the audience?
Certainly.
Sure, why not?
Sure.
Yeah.
It's pretty scary.
A real-time question.
Yeah.
On the P equals NP problem, what about proving
that neither P equals NP nor P not equals NP is provable?
I would say it's possible.
But you may not be able to know whether that's true or not.
Do you think you can entertain another question?
Sure.
Stand close to the mic.
I just-- sorry-- I just wanted to make sure
that I was allowed to.
You were talking about the future,
I was just wondering what you might think.
One of you said machine learning was
a very hot topic in the future, and I might have been busy
and haven't heard the Turing IBM Watson computer.
And of course, at the feet of humanity as a search engine
because it can do more than what Google can do.
It can take care of human language, idiosyncrasies
and puns, and many other things.
So I think that with very little changes
it could be a stronger search engine
than Google and content search.
Also that, it only runs at 80 petaFLOPS,
which is a rather slow speed, because that speed has
been around for a year or two, maybe a year or so.
Even Anton chips with DE Shaw, even those specialized,
will probably be running faster floating point operations
than that.
Even though application is not a natural language.
The other thing I want to ask you about it, if you want
to comment on it, besides the Watson Computer's machine
learning success and search engine success,
is what do you think of Google and YouTube broadcast
as network like MTV?
The public doesn't like newspapers,
the public likes to generate their own news
because they think the newspapers and broadcast
news are a little bit corrupt, have their own agenda.
So if YouTube and broadcast and watch their own news,
it would be kind of like MTV for the young.
Because YouTube used to be the young, but now
it's replaced by Facebook.
So if it weren't cable TV, it could be the MTV [? change ?]
for Facebook, which is really YouTube with all the pictures.
Is that a question?
There's a lot there in that question.
I think my piece of advice would be to focus.
Focus.
[LAUGHTER]
Focus and get things narrowed down.
Well there is the whole YouTube kind
of revolution, where the citizenship, the browsers,
and so on are generating neural content.
We retract your invitation.
Sorry, but we are in fact now, officially out of time.
So, I thank the panelists.