The following is a conversation with Brian Kernighan,
a professor of computer science at Princeton University.
He was a key figure in the computer science community
in the early Unix days, alongside Unix creators,
Ken Thompson and Dennis Ritchie.
He coauthored the C programming language with Dennis Ritchie,
the creator of C, and has written a lot of books
on programming, computers, and life,
including The Practice of Programming,
the Go programming language, and his latest,
Unix, A History and a Memoir.
He cocreated AUK, the text processing language
used by Linux folks like myself.
He co designed Ample, an algebraic modeling language
that I personally love and have used a lot in my life
for large scale optimization.
I think I can keep going for a long time
with his creations and accomplishments,
which is funny because given all that,
he’s one of the most humble and kind people
I’ve spoken to on this podcast.
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And now, here’s my conversation with Brian Kernighan.
Unix started being developed 50 years ago.
It’d be more than 50 years ago.
Can you tell the story like you describe in your new book
of how Unix was created?
Ha, if I can remember that far back,
it was some while ago.
So I think the gist of it is that at Bell Labs,
in 1969, there were a group of people
who had just finished working on the Multics project,
which was itself a follow on to CTSS.
So we can go back sort of an infinite regress in time,
but the CTSS was a very, very, very nice time sharing system.
It was very nice to use.
I actually used it that summer I spent in Cambridge in 1966.
What was the hardware there?
So what’s the operating system, what’s the hardware there?
What’s the CTSS look like?
So CTSS looked like kind of like
a standard time sharing system.
Certainly at the time, it was the only time sharing.
Let’s go back to the basics.
What’s a time sharing system?
Okay, in the beginning was the word
and the word was the system.
And then there was time sharing systems.
Yeah, if we go back into, let’s call it the 1950s
and early 1960s, most computing was done on very big
computers, physically big, although not terribly powerful
by today’s standards, that were maintained
in very large rooms and you use things like punch cards
to write your programs on and talk to them.
So you would take a deck of cards,
write your program on it, send it over a counter,
hand it to an operator and some while later
back would come something that said,
oh, you made a mistake and then you’d recycle.
And so it was very, very slow.
So the idea of time sharing was that you take
basically that same computer, but connect to it
with something that looked like an electric typewriter.
They could be a long distance away, it could be close,
but fundamentally what the operating system did
was to give each person who was connected to it
and wanting to do something a small slice of time
to do a particular job.
So I might be editing a file, so I would be typing
and every time I hit a keystroke,
the operating system would wake up and said,
oh, he typed character, let me remember that.
Then it’d go back to doing something else.
So it’d be going around and around a group of people
who were trying to get something done, giving each
a small slice of time and giving them each the illusion
that they pretty much had the whole machine to themselves
and hence time sharing, that is sharing the computing time
resource of the computer among a number of people
who were doing it.
Without the individual people being aware
that there’s others in a sense, the illusion,
the feelings that the machine is your own.
Pretty much that was the idea.
Yes, if it were well done and if it were fast enough
and other people weren’t doing too much,
you did have the illusion that you had the whole machine
to yourself and it was very much better
than the punch card model.
And so CTSS, the compatible time sharing system
was I think arguably the first of these.
It was done I guess technically in 64 or something like that.
It ran on an IBM 7094, slightly modified
to have twice as much memory as the norm.
It had two banks of 32K words instead of one.
32K words, yeah.
Each word was 36 bits, so call it
about 150 kilobytes times two.
So by today’s standards, that’s down in the noise.
But at the time, that was a lot of memory
and memory was expensive.
So CTSS was just a wonderful environment to work on.
It was done by the people at MIT,
led by Fernando Corbato, Corby who died just earlier
this year, and a bunch of other folks.
So I spent the summer of 66 working on that,
had a great time, met a lot of really nice people
and indirectly knew of people at Bell Labs
who were also working on a follow on to CTSS
that was called Multics.
So Multics was meant to be the system
that would do everything that CTSS did
but do it better for a larger population.
All the usual stuff.
Now the actual time sharing, the scheduling,
what’s the algorithm that performs the scheduling?
What’s that look like?
How much magic is there?
What are the metrics?
How does it all work in the beginning?
So the answer is I don’t have a clue.
I think the basic idea was nothing more
than who all wants to get something done.
Suppose that things are very quiet
in the middle of the night,
then I get all the time that I want.
Suppose that you and I are contending at high noon
for something like that,
then probably the simplest algorithm is a round robin one
that gives you a bit of time, gives me a bit of time.
And then we could adapt to that.
Like what are you trying to do?
Are you text editing or are you compiling or something?
And then we might adjust the scheduler
according to things like that.
So okay, so Multics was trying to just do some of the,
clean it up a little bit.
Well, it was meant to be much more than that.
So Multics was the multiplexed information
and computing service and it was meant to be
a very large thing that would provide computing utility.
Something that where you could actually think of it
as just a plug in the wall service.
Sort of like cloud computing today.
Same idea, but 50 odd years earlier.
And so what Multics offered
was a richer operating system environment,
a piece of hardware that was better designed
for doing the kind of sharing of resources.
And presumably lots of other things.
Do you think people at that time had the dream
of what cloud computing is starting to become now,
which is computing is everywhere.
That you can just plug in almost,
and you never know how the magic works.
You just kind of plug in, add your little computation
that you need to perform and it does it.
Was that the dream?
I don’t know where that was the dream.
I wasn’t part of it at that point.
I remember I was an intern for summer.
But my sense is given that it was over 50 years ago,
yeah, they had that idea that it was an information utility.
That it was something where if you had a computing task to do,
you could just go and do it.
Now I’m betting that they didn’t have the same view
of computing for the masses, let’s call it.
The idea that your grandmother would be shopping on Amazon.
I don’t think that was part of it.
But if your grandmother were a programmer,
it might be very easy for her to go and use
this kind of utility.
What was your dream of computers at that time?
What did you see as the future of computers?
Because you have predicted what computers are today.
Oh, short answer, absolutely not.
I have no clue.
I’m not sure I had a dream.
It was a dream job in the sense that I really enjoyed
what I was doing.
I was surrounded by really, really nice people.
Cambridge is a very fine city to live in in the summer,
less so in the winter when it snows.
But in the summer, it was a delightful time.
And so I really enjoyed all of that stuff.
And I learned things.
And I think the good fortune of being there for summer
led me then to get a summer job at Bell Labs
the following summer.
And that was quite useful for the future.
So Bell Labs is this magical, legendary place.
So first of all, where is Bell Labs?
And can you start talking about that journey
towards Unix at Bell Labs?
Yeah, so Bell Labs is physically scattered around,
at the time, scattered around New Jersey.
The primary location is in a town called Murray Hill,
or a location called Murray Hill is actually
across the boundary between two small towns in New Jersey
called New Providence and Berkeley Heights.
Think of it as about 15, 20 miles straight west
of New York City, and therefore about an hour north
of here in Princeton.
And at that time, it had, make up a number,
three or 4,000 people there, many of whom had PhDs
and mostly doing physical sciences,
chemistry, physics, materials kinds of things,
but very strong math and rapidly growing interest
in computing as people realized you could do things
with computers that you might not have been able
to do before.
You could replace labs with computers
that had worked on models of what was going on.
So that was the essence of Bell Labs.
And again, I wasn’t a permanent employee there.
That was another internship.
I got lucky in internships.
I mean, if you could just linger on it a little bit,
what was the, what was in the air there?
Because some of the, the number of Nobel Prizes,
the number of Turing Awards and just legendary
computer scientists that come from their inventions,
including developments, including Unix,
it’s just, it’s unbelievable.
So was there something special about that place?
Oh, I think there was very definitely something special.
I mentioned the number of people,
it’s a very large number of people, very highly skilled
and working in an environment
where there was always something interesting to work on
because the goal of Bell Labs,
which was a small part of AT&T,
which provided basically the country’s phone service.
The goal of AT&T was to provide service for everybody.
And the goal of Bell Labs was to try and make that service
keep getting better, so improving service.
And that meant doing research on a lot of different things,
physical devices, like the transistor
or fiber optical cables or microwave systems,
all of these things the labs worked on.
And it was kind of just the beginning of real boom times
in computing as well.
Because when I was there, I went there first in 66.
So computing was at that point fairly young.
And so people were discovering
that you could do lots of things with computers.
So how was Unix born?
So Multics, in spite of having an enormous number
of really good ideas and lots of good people working on it,
fundamentally didn’t live up, at least in the short run,
and I think ultimately really ever,
to its goal of being this information utility.
It was too expensive and certainly what was promised
was delivered much too late.
And so in roughly the beginning of 1969,
Bell Labs pulled out of the project.
The project at that point had included MIT, Bell Labs,
and General Electric, General Electric made computers.
So General Electric was the hardware operation.
So Bell Labs, realizing this wasn’t going anywhere
on a timescale they cared about, pulled out of the project.
And this left several people with an acquired taste
for really, really nice computing environments,
but no computing environment.
And so they started thinking about what could you do
if you were going to design a new operating system
that would provide the same kind of comfortable computing
as CTSS had, but also the facilities of something
like Multics sort of brought forward.
And so they did a lot of paper design stuff.
And at the same time, Ken Thompson found
what is characterized as a little used PDP 7,
where he started to do experiments with file systems,
just how do you store information on a computer
in a efficient way, and then this famous story
that his wife went away to California for three weeks,
taking their one year old son, and three weeks,
and he sat down and wrote an operating system,
which ultimately became Unix.
So software productivity was good in those days.
So PDP, what’s a PDP 7?
So it’s a piece of hardware.
Yeah, it’s a piece of hardware.
It was one of early machines made
by Digital Equipment Corporation, DEC,
and it was a mini computer, so called.
It had, I would have to look up the numbers exactly,
but it had a very small amount of memory,
maybe 16K, 16 bit words, or something like that,
relatively slow, probably not super expensive.
Maybe, again, making this up, I’d have to look it up,
$100,000 or something like that.
Which is not super expensive in those days, right?
It was expensive.
It was enough that you and I probably
wouldn’t be able to buy one,
but a modest group of people could get together.
But in any case, it came out, if I recall, in 1964.
So by 1969, it was getting a little obsolete,
and that’s why it was little used.
If you can sort of comment,
what do you think it’s like
to write an operating system like that?
So that process that Ken went through in three weeks,
because you were, I mean, you’re a part of that process.
You contributed a lot to Unix’s early development.
So what do you think it takes to do that first step,
that first kind of, from design to reality on the PDP?
Well, let me correct one thing.
I had nothing to do with it.
So I did not write it.
I have never written operating system code.
And so I don’t know.
Now an operating system is simply code.
And this first one wasn’t very big,
but it’s something that lets you run processes,
lets you execute some kind of code that has been written.
It lets you store information for periods of time
so that it doesn’t go away when you turn the power off
or reboot or something like that.
And there’s kind of a core set of tools
that are technically not part of an operating system,
but you probably need them.
In this case, Ken wrote an assembler
for the PDP 7 that worked.
He needed a text editor
so that he could actually create text.
He had the file system stuff that he had been working on,
and then the rest of it was just a way
to load things, executable code from the file system
into the memory, give it control,
and then recover control when it was finished
or in some other way quit.
What was the code written in,
primarily the programming language?
Was it in assembly?
Yeah, PDP 7 assembler that Ken created.
These things were assembly language
until probably the, call it 1973 or 74, something like that.
Forgive me if it’s a dumb question,
but it feels like a daunting task
to write any kind of complex system in assembly.
It feels like impossible to do any kind
of what we think of as software engineering with assembly,
because to work on a big picture sort of.
I think it’s hard.
It’s been a long time since I wrote assembly language.
It is absolutely true that in assembly language,
if you make a mistake, nobody tells you.
There are no training wheels whatsoever.
And so stuff doesn’t work.
There’s no debuggers.
Well, there could be debuggers,
but that’s the same problem, right?
How do you actually get something
that will help you debug it?
So part of it is an ability to see the big picture.
Now these systems were not big in the sense
that today’s pictures are.
So the big picture was in some sense more manageable.
I mean, then realistically,
there’s an enormous variation
in the capabilities of programmers.
And Ken Thompson, who did that first one,
is kind of the singularity, in my experience, of programmers.
With no disrespect to you or even to me,
he’s in several leagues removed.
I know there’s levels.
It’s a fascinating thing that there are unique stars
in particular in the programming space
and at a particular time.
You know, the time matters too,
the timing of when that person comes along.
And a wife does have to leave.
There’s this weird timing that happens
and then all of a sudden something beautiful is created.
I mean, how does it make you feel
that there’s a system that was created in three weeks
or maybe you can even say on a whim,
but not really, but of course, quickly,
that is now, you could think of most of the computers
in the world run on a Unix like system?
How do you interpret, like,
if you kind of zoom from the alien perspective,
if you were just observing Earth,
and all of a sudden these computers took over the world
and they started from this little initial seed of Unix,
how does that make you feel?
It’s quite surprising.
And you asked earlier about prediction.
The answer is no.
There’s no way you could predict that kind of evolution.
And I don’t know whether it was inevitable
or just a whole sequence of blind luck.
I suspect more of the latter.
And so I look at it and think, gee, that’s kind of neat.
I think the real question is what does Ken think about that?
Because he’s the guy arguably from whom it really came.
You know, tremendous contributions from Dennis Ritchie
and then others around in that Bell Labs environment.
But, you know, if you had to pick a single person,
that would be Ken.
So you’ve written a new book,
Unix, a history and a memoir.
Are there some memorable human stories,
funny or profound from that time
that just kind of stand out?
Oh, there’s a lot of them in his book.
Oh, there’s a lot of them in a sense.
And again, it’s a question of can you resurrect them
in real time?
His memory fails.
But I think part of it was that Bell Labs at the time
was a very special kind of place to work
because there were a lot of interesting people
and the environment was very, very open and free.
It was a very cooperative environment,
very friendly environment.
And so if you had an interesting problem,
you go and talk to somebody
and they might help you with the solution.
And it was a kind of a fun environment too,
in which people did strange things
and often tweaking the bureaucracy in one way or another.
So rebellious in certain kinds of ways.
In some ways, yeah, absolutely.
I think most people didn’t take too kindly
to the bureaucracy and I’m sure the bureaucracy
put up with an enormous amount
that they didn’t really want to.
So maybe to linger on it a little bit,
do you have a sense of what the philosophy
that characterizes Unix is, the design?
Not just the initial, but just carry through the years,
just being there, being around it.
What’s the fundamental philosophy behind the system?
I think one aspect of fundamental philosophy
was to provide an environment that made it easy to write
or easier, productive to write programs.
So it was meant as a programmer environment.
It wasn’t meant specifically as something
to do some other kind of job.
For example, it was used extensively for word processing,
but it wasn’t designed as a word processing system.
It was used extensively for lab control,
but it wasn’t designed for that.
It was used extensively as a front end
for big other systems, big dumb systems,
but it wasn’t designed for that.
It was meant to be an environment
where it was really easy to write programs.
So the programmers could be highly productive.
And part of that was to be a community.
And there’s some observation from Dennis Ritchie,
I think at the end of the book,
that says that from his standpoint,
the real goal was to create a community
where people could work as programmers on a system.
And I think in that sense, certainly for many, many years,
it succeeded quite well at that.
And part of that is the technical aspects
of because it made it really easy to write programs,
people did write interesting programs.
Those programs tended to be used by other programmers.
And so it was kind of a virtuous circle
of more and more stuff coming up
that was really good for programmers.
And you were part of that community of programmers.
So what was it like writing programs in that early Unix?
It was a blast.
It really was.
You know, I like to program.
I’m not a terribly good programmer,
but it was a lot of fun to write code.
And in the early days, there was an enormous amount
of what you would today, I suppose,
called low hanging fruit.
People hadn’t done things before.
And this was this new environment
and the whole combination of nice tools
and very responsive system and tremendous colleagues
made it possible to write code.
You could have an idea in the morning.
You could do an experiment with it.
You could have something limping along that night
or the next day and people would react to it.
And they would say, oh, that’s wonderful,
but you’re really screwed up here.
And the feedback loop was then very, very short and tight.
And so a lot of things got developed fairly quickly
that in many cases still exist today.
And I think that was part of what made it fun
because programming itself is fun.
It’s puzzle solving in a variety of ways,
but I think it’s even more fun when you do something
that somebody else then uses.
Even if they whine about it not working,
the fact that they used it is part of the reward mechanism.
And what was the method of interaction,
the communication, that feedback loop?
I mean, this is before the internet.
Certainly before the internet.
It was mostly physical right there.
Somebody would come into your office and say something.
So these places are all close by,
like offices are nearby, so really lively interaction.
Bell Labs was fundamentally one giant building
and most of the people were involved in this unique stuff.
We’re in two or three quarters and there was a room.
Oh, how big was it?
Probably call it 50 feet by 50 feet.
Make up a number of that which had some access
to computers there as well as in offices
and people hung out there and it had a coffee machine.
And so it was mostly very physical.
We did use email, of course.
But it was fundamentally, for a long time,
all on one machine.
So there was no need for internet.
It’s fascinating to think about what computing
would be today without Bell Labs.
It seems so many, the people being in the vicinity
of each other, sort of getting that quick feedback,
working together, so many brilliant people.
I don’t know where else that could have existed
in the world given how that came together.
Yeah, how does that make you feel
that little element of history?
Well, I think that’s very nice,
but in a sense it’s survivor bias
and if it hadn’t happened at Bell Labs,
there were other places that were doing
really interesting work as well.
Xerox PARC is perhaps the most obvious one.
Xerox PARC contributed an enormous amount
of good material and many of the things
we take for granted today in the same way
came from Xerox PARC experience.
I don’t think they capitalized in the long run as much.
Their parent company was perhaps not as lucky
in capitalizing on this, who knows?
But that’s certainly another place
where there was a tremendous amount of influence.
There were a lot of good university activities.
MIT was obviously no slouch in this kind of thing
and others as well.
So Unix turned out to be open source
because of the various ways that AT&T operated
and sort of it had to, the focus was on telephones.
I think that’s a mischaracterization in a sense.
It absolutely was not open source.
It was very definitely proprietary, licensed,
but it was licensed freely to universities
in source code form for many years.
And because of that, generations of university students
and their faculty people grew up knowing about Unix
and there was enough expertise in the community
that it then became possible for people
to kind of go off in their own direction
and build something that looked Unix like.
The Berkeley version of Unix started with that licensed code
and gradually picked up enough of its own code contributions,
notably from people like Bill Joy,
that eventually it was able to become completely free
of any AT&T code.
Now, there was an enormous amount of legal jockeying
around this in the late, early to late 80s, early 90s,
something like that.
And then, I guess the open source movement
might’ve started when Richard Stallman started
to think about this in the late 80s.
And by 1991, when Torvalds decided he was going
to do a Unix like operating system,
there was enough expertise in the community
that first he had a target, he could see what to do
because the kind of the Unix system call interface
and the tools and so on were there.
And so he was able to build an operating system
that at this point, when you say Unix,
in many cases, what you’re really thinking is Linux.
But it’s funny that from my distant perception,
I felt that Unix was open source
without actually knowing it.
But what you’re really saying, it was just freely licensed.
It was freely licensed.
So it felt open source in a sense
because universities are not trying to make money,
so it felt open source in a sense
that you can get access if you wanted.
Right, and a very, very, very large number of universities
had the license and they were able to talk
to all the other universities who had the license.
And so technically not open,
technically belonging to AT&T, pragmatically pretty open.
And so there’s a ripple effect
that all the faculty and the students then all grew up
and then they went throughout the world
and permeated in that kind of way.
So what kind of features do you think make
for a good operating system?
If you take the lessons of Unix,
you said make it easy for programmers.
That seems to be an important one.
But also Unix turned out to be exceptionally robust
So is that an accident when you focus on the programmer
or is that a natural outcome?
I think part of the reason for efficiency
was that it began on extremely modest hardware,
very, very, very tiny.
And so you couldn’t get carried away.
You couldn’t do a lot of complicated things
because you just didn’t have the resources,
either processor speed or memory.
And so that enforced a certain minimality of mechanisms
and maybe a search for generalizations
so that you would find one mechanism
that served for a lot of different things
rather than having lots of different special cases.
I think the file system in Unix is a good example
of that file system interface in its fundamental form
is extremely straightforward.
And that means that you can write code
very, very effectively for the file system.
And then one of those ideas, one of those generalizations
is that gee, that file system interface works
for all kinds of other things as well.
And so in particular, the idea of reading
and writing to devices is the same as reading
and writing to a disc that has a file system.
And then that gets carried further in other parts
of the world.
Processes become, in effect, files in a file system.
And the Plan 9 operating system, which came along,
I guess, in the late 80s or something like that,
took a lot of those ideas from the original Unix
and tried to push the generalization even further
so that in Plan 9, a lot of different resources
are file systems.
They all share that interface.
So that would be one example where finding the right model
of how to do something means that an awful lot of things
become simpler, and it means, therefore,
that more people can do useful, interesting things
with them without having to think as hard about it.
So you said you’re not a very good programmer.
You’re the most modest human being, okay,
but you’ll continue saying that.
I understand how this works.
But you do radiate a sort of love for programming.
So let me ask, do you think programming
is more an art or a science?
Is it creativity or kind of rigor?
I think it’s some of each.
It’s some combination.
Some of the art is figuring out what it is
that you really want to do.
What should that program be?
What would make a good program?
And that’s some understanding of what the task is,
what the people who might use this program want.
And I think that’s art in many respects.
The science part is trying to figure out how to do it well.
And some of that is real computer sciencey stuff,
like what algorithm should we use at some point?
Mostly in the sense of being careful to use algorithms
that will actually work properly, scale properly,
avoiding quadratic algorithms
when a linear algorithm should be the right thing,
that kind of more formal view of it.
Same thing for data structures.
But also it’s, I think, an engineering field as well.
And engineering is not quite the same as science
because engineering, you’re working with constraints.
You have to figure out not only what
is a good algorithm for this kind of thing,
but what’s the most appropriate algorithm given
the amount of time we have to compute,
the amount of time we have to program,
what’s likely to happen in the future with maintenance,
who’s going to pick this up in the future, all
of those kind of things that if you’re an engineer,
you get to worry about.
Whereas if you think of yourself as a scientist,
well, you can maybe push them over the horizon in a way.
And if you’re an artist, what’s that?
So just on your own personal level,
what’s your process like of writing a program?
Say, a small and large sort of tinkering with stuff.
Do you just start coding right away
and just kind of evolve iteratively with a loose notion?
Or do you plan on a sheet of paper first
and then kind of design in what they teach you
in the kind of software engineering courses
in undergrad or something like that?
What’s your process like?
It’s certainly much more the informal incremental.
First, I don’t write big programs at this point.
It’s been a long time since I wrote a program that
was more than I call it a few hundred or more lines,
something like that.
Many of the programs I write are experiments
for either something I’m curious about
or often for something that I want to talk about in a class.
So those necessarily tend to be relatively small.
A lot of the kind of code I write these days
tends to be for sort of exploratory data analysis
where I’ve got some collection of data
and I want to try and figure out what on earth is going on in it.
And for that, those programs tend to be very small.
Sometimes you’re not even programming.
You’re just using existing tools like counting things.
Or sometimes you’re writing OX scripts
because two or three lines will tell you
something about a piece of data.
And then when it gets bigger, well, then I
will probably write something in Python
because that scales better up to call it a few hundred lines
or something like that.
And it’s been a long time since I wrote programs
that were much more than that.
Speaking of data exploration and OX, first, what is OX?
So OX is a scripting language that
was done by myself, Al Aho, and Peter Weinberger.
We did that originally in the late 70s.
It was a language that was meant to make it really easy
to do quick and dirty tasks like counting things
or selecting interesting information from basically
all text files, rearranging it in some way or summarizing it.
It runs a command on each line of a file.
I mean, it’s still exceptionally widely used today.
It’s so simple and elegant, sort of the way to explore data.
Turns out you can just write a script that
does something seemingly trivial in a single line,
and giving you that slice of the data
somehow reveals something fundamental about the data.
And that seems to work still.
Yeah, it’s very good for that kind of thing.
That’s sort of what it was meant for.
I think what we didn’t appreciate
was that the model was actually quite good for a lot of data
processing kinds of tasks and that it’s
kept going as long as it has because at this point,
it’s over 40 years old, and it’s still, I think, a useful tool.
And well, this is paternal interest, I guess.
But I think in terms of programming languages,
you get the most bang for the buck by learning AUC.
And it doesn’t scale the big programs,
but it does pretty darn well on these little things
where you just want to see all the somethings in something.
So yeah, I probably write more AUC than anything else
at this point.
So what kind of stuff do you love about AUC?
Is there, if you can comment on sort of things
that give you joy when you can, in a simple program,
reveal something about the data.
Is there something that stands out from particular features?
I think it’s mostly the selection of default behaviors.
You sort of hinted at it a moment ago.
What AUC does is to read through a set of files,
and then within each file, it writes
through each of the lines.
And then on each of the lines, it has a set of patterns
that it looks for.
That’s your AUC program.
And if one of the patterns matches,
there is a corresponding action that you might perform.
And so it’s kind of a quadruply nested loop or something
And that’s all completely automatic.
You don’t have to say anything about it.
You just write the pattern and the action,
and then run the data by it.
And so that paradigm for programming
is a very natural and effective one.
And I think we captured that reasonably well in AUC.
And it does other things for free as well.
It splits the data into fields so that on each line,
there is fields separated by white space or something.
And so it does that for free.
You don’t have to say anything about it.
And it collects information as it goes along,
like what line are we on?
How many fields are there on this line?
So lots of things that just make it so that a program which
in another language, let’s say Python,
would be five, 10, 20 lines in AUC is one or two lines.
And so because it’s one or two lines,
you can do it on the shell.
You don’t have to open up another whole thing.
You can just do it right there in the interaction
with the operatives directly.
Is there other shell commands that you love over the years
like you really enjoy using?
Grep’s the only one.
Yeah, grep does everything.
So grep is a simpler version of AUC, I would say?
In some sense, yeah, right.
What is grep?
So grep basically searches the input
for particular patterns, regular expressions,
technically, of a certain class.
And it has that same paradigm that AUC does.
It’s a pattern action thing.
It reads through all the files and then
all the lines in each file.
But it has a single pattern, which
is the regular expression you’re looking for,
and a single action printed if it matches.
So in that sense, it’s a much simpler version.
And you could write grep in AUC as a one liner.
And I use grep probably more than anything else
at this point just because it’s so convenient and natural.
Why do you think it’s such a powerful tool, grep and AUC?
Why do you think operating systems like Windows,
for example, don’t have it?
You can, of course, I use, which is amazing now,
there’s Windows for Linux.
So which you could basically use all the fun stuff
like AUC and grep inside of Windows.
But Windows naturally, as part of the graphical interface,
the simplicity of grep, searching
through a bunch of files and just popping up naturally.
Why do you think that’s unique to the Linux environment?
I don’t know.
It’s not strictly unique, but it’s certainly focused there.
And I think some of it’s the weight of history
that Windows came from MS DOS.
MS DOS was a pretty pathetic operating system,
although common on an unboundedly large number
But somewhere in roughly the 90s,
Windows became a graphical system.
And I think Microsoft spent a lot of their energy
on making that graphical interface what it is.
And that’s a different model of computing.
It’s a model of computing where you point and click
and sort of experiment with menus.
It’s a model of computing works rather well for people
who are not programmers and just want to get something done,
whereas teaching something like the command line
to nonprogrammers turns out to sometimes be
an uphill struggle.
And so I think Microsoft probably
was right in what they did.
Now you mentioned Whistle or whatever
it’s called, the Winix, Linux.
I wonder what it’s pronounced.
WSL is what I’ve never actually pronounced.
Whistle, I like it.
I have no idea.
But there have been things like that for longest.
Cygwin, for example, which is a wonderful collection of take
all your favorite tools from Unix and Linux
and just make them work perfectly on Windows.
And so that’s something that’s been going on
for at least 20 years, if not longer.
And I use that on my one remaining Windows machine
routinely because if you’re doing something that
is batch computing, suitable for command line,
that’s the right way to do it.
Because the Windows equivalents are, if nothing else,
not familiar to me.
But I would definitely recommend to people
if they don’t use Cygwin to try Whistle.
I’ve been so excited that I could write scripts quickly
It’s changed my life.
OK, what’s your perfect programming setup?
What computer, what operating system, what keyboard,
Yeah, perfect is too strong a word.
It’s way too strong a word.
What I use by default, I have, at this point,
a 13 inch MacBook Air, which I use
because it’s kind of a reasonable balance
of the various things I need.
I can carry it around.
It’s got enough computing, horsepower, screen’s
big enough, keyboard’s OK.
And so I basically do most of my computing on that.
I have a big iMac in my office that I use from time to time
as well, especially when I need a big screen,
but otherwise, it tends not to be used that much.
I use mostly SAM, which is an editor that Rob Pike wrote
long ago at Bell Labs.
Sorry to interrupt.
Does that precede VI?
Does that precede iMac?
It post dates both VI and iMacs.
It is derived from Rob’s experience with ED and VI.
That’s the original Unix editor.
Dated probably before you were born.
So actually, what’s the history of editors?
Can you briefly, because it’s such a fact.
I use Emacs, I’m sorry to say.
Sorry to come out with that.
But what’s the kind of interplay there?
So in ancient times, call it the first time sharing systems,
going back to what we were talking about.
There was an editor on CTSS that I don’t even
remember what it was called.
It might have been edit, where you could type text, program
text, and it would do something, or document text.
You could save the text.
And save it.
You could edit it.
The usual thing that you would get in an editor.
And Ken Thompson wrote an editor called QED, which
was very, very powerful.
But these were all totally A, command based.
They were not mouse or cursor based,
because it was before mice and even before cursors,
because they were running on terminals that printed on paper.
No CRT type displays, let alone LEDs.
And so then when Unix came along, Ken took QED
and stripped it way, way, way, way down.
And that became an editor that he called ED.
And it was very simple.
But it was a line oriented editor.
And so you could load a file.
And then you could talk about the lines one
through the last line.
And you could print ranges of lines.
You could add text.
You could delete text.
You could change text.
Or you could do a substitute command
that would change things within a line or within groups
So you can work on parts of a file, essentially.
You can work on any part of it, the whole thing or whatever.
But it was entirely command line based.
And it was entirely on paper.
And that meant that you changed it.
And so if you changed a line, you
had to print that line using up another line of paper
to see what the change caused.
So when CRT displays came along, then you
could start to use cursor control.
And you could sort of move where you were on the screen.
Without reprinting every time.
And there were a number of editors there.
The one that I was most familiar with and still use
is VI, which was done by Bill Choi.
And so that dates from probably the late 70s, as I guess.
And it took full advantage of the cursor controls.
I suspect that Emacs was roughly at the same time.
But I don’t know.
I’ve never internalized Emacs.
So at this point, I stopped using ED, although I still can.
I use VI sometimes, and I use SAM when I can.
And SAM is available on most systems?
It is available.
You have to download it yourself from, typically,
the Plan 9 operating system distribution.
It’s been maintained by people there.
And so I’ll get home tonight.
I’ll try it.
It sounds fascinating.
Although my love is with Lisp and Emacs,
I’ve went into that hippie world of.
I think it’s a lot of things.
Religion, where you’re brought up with.
Yeah, that’s true.
Most of the actual programming I do is C, C++, and Python.
But my weird sort of, yeah, my religious upbringing is in Lisp.
So can you take on the impossible task
and give a brief history of programming languages
from your perspective?
So I guess you could say programming languages started
probably in, what, the late 40s or something like that.
People used to program computers by basically putting
in zeros and ones.
Using something like switches on a console.
And then, or maybe holes in paper tapes.
Something like that.
So extremely tedious, awful, whatever.
And so I think the first programming languages
were relatively crude assembly languages,
where people would basically write
a program that would convert mnemonics like add ADD
into whatever the bit pattern was
that corresponded to an ADD instruction.
And they would do the clerical work of figuring out
where things were.
So you could put a name on a location in a program,
and the assembler would figure out
where that corresponded to when the thing was all put together
and dropped into memory.
And early on, and this would be the late 40s and very early
50s, there were assemblers written for the various machines
that people used.
You may have seen in the paper just a couple of days ago,
Tony Berker died.
He did this thing in Manchester called AutoCode, a language
which I knew only by name.
But it sounds like it was a flavor of assembly language,
sort of a little higher in some ways.
And it replaced a language that Alan Turing wrote,
which you put in zeros and ones.
But you put it in backwards order,
because that was a hardware word.
Yeah, yeah, that’s right.
So assembly languages, let’s call that the early 1950s.
And so every different flavor of computer
has its own assembly language.
So the EDSAC had its, and the Manchester had its,
and the IBM whatever, 790 or 704, or whatever had its,
and so on.
So everybody had their own assembly language.
And assembly languages have a few commands, additions,
subtraction, then branching of some kind,
if then type of situation.
Right, they have exactly, in their simplest form at least,
one instruction per, or one assembly language instruction
per instruction in the machine’s repertoire.
And so you have to know the machine intimately
to be able to write programs in it.
And if you write an assembly language program
for one kind of machine, and then you say,
gee, it’s nice, I’d like a different machine, start over.
OK, so very bad.
And so what happened in the late 50s
was people realized you could play this game again,
and you could move up a level in writing or creating languages
that were closer to the way that real people might think
about how to write code.
And there were, I guess, arguably three or four
at that time period.
There was FORTRAN, which came from IBM,
which was formula translation, meant
to make it easy to do scientific and engineering
I didn’t know that, formula translation, that’s wow.
That’s what I stood for.
There was COBOL, which is the Common Business Oriented
Language that Grace Hopper and others worked on,
which was aimed at business kinds of tasks.
There was ALGOL, which was mostly
meant to describe algorithmic computations.
I guess you could argue BASIC was in there somewhere.
I think it’s just a little later.
And so all of those moved the level up,
and so they were closer to what you and I might think of
as we were trying to write a program.
And they were focused on different domains, FORTRAN
for formula translation, engineering computations,
let’s say COBOL for business, that kind of thing.
And still used today, at least FORTRAN probably.
Oh, yeah, COBOL, too.
But the deal was that once you moved up that level,
then you, let’s call it FORTRAN, you
had a language that was not tied to a particular kind
of hardware, because a different compiler would compile
for a different kind of hardware.
And that meant two things.
It meant you only had to write the program once, which
is very important.
And it meant that you could, in fact,
if you were a random engineer, physicist, whatever,
you could write that program yourself.
You didn’t have to hire a programmer to do it for you.
It might not be as good as you’d get with a real programmer,
but it was pretty good.
And so it democratized and made much more broadly available
the ability to write code.
So it puts the power of programming
into the hands of people like you.
Yeah, anybody who is willing to invest some time in learning
a programming language and is not then tied
to a particular kind of computer.
And then in the 70s, you get system programming languages,
of which C is the survivor.
And what does system programming language mean?
Programs that, programming languages
that would take on the kinds of things
that were necessary to write so called system programs.
Things like text editors, or assemblers, or compilers,
or operating systems themselves.
Those kinds of things.
They have to be feature rich.
They have to be able to do a lot of stuff.
A lot of memory management, access processes,
and all that kind of stuff.
It’s a different flavor of what they’re doing.
They’re much more in touch with the actual machine,
but in a positive way.
That is, you can talk about memory in a more controlled
You can talk about the different data types
that the machine supports, and more ways
to structure and organize data.
And so the system programming languages,
there was a lot of effort in that in the,
call it the late 60s, early 70s.
C is, I think, the only real survivor of that.
And then what happens after that?
You get things like object oriented programming languages.
Because as you write programs in a language like C,
at some point scale gets to you.
And it’s too hard to keep track of the pieces.
And there’s no guardrails, or training wheels,
or something like that to prevent you
from doing bad things.
So C++ comes out of that tradition.
And then it took off from there.
I mean, there’s also a parallel, slightly parallel track
with a little bit of functional stuff with Lisp and so on.
But I guess from that point, it’s
just an explosion of languages.
There’s the Java story.
There’s all the stuff that the cool kids these days
are doing with Rust and all that.
So what’s to you?
You wrote a book, C Programming Language.
And C is probably one of the most important languages
in the history of programming languages,
if you kind of look at impact.
What do you think is the most elegant or powerful part of C?
Why did it survive?
Why did it have such a long lasting impact?
I think it found a sweet spot of expressiveness,
so that you could rewrite things in a pretty natural way,
and efficiency, which was particularly important when
computers were not nearly as powerful as they are today.
You’ve got to put yourself back 50 years,
almost, in terms of what computers could do.
And that’s roughly four or five generations,
decades of Moore’s law, right?
So expressiveness and efficiency and, I don’t know,
perhaps the environment that it came with as well,
which was Unix.
So it meant if you wrote a program,
it could be used on all those computers that ran Unix.
And that was all of those computers,
because they were all written in C.
And that was Unix, the operating system itself,
was portable, as were all the tools.
So it all worked together, again,
in one of these things where things
fit on each other in a positive cycle.
What did it take to write sort of a definitive book,
probably definitive book on all of program,
like it’s more definitive to a particular language
than any other book on any other language,
and did two really powerful things,
which is popularized the language,
at least from my perspective, maybe you can correct me.
And second is created a standard of how, you know,
how this language is supposed to be used and applied.
So what did it take?
Did you have those kinds of ambitions in mind
when working on that?
Is this some kind of joke?
No, of course not.
So it’s an accident of timing, skill, and just luck?
A lot of it is, clearly.
Timing was good.
Now, Dennis and I wrote the book in 1977.
And at that point, Unix was starting to spread.
I don’t know how many there were,
but it would be dozens to hundreds of Unix systems.
And C was also available on other kinds of computers
that had nothing to do with Unix.
And so the language had some potential.
And there were no other books on C,
and Bell Labs was really the only source for it.
And Dennis, of course, was authoritative
because it was his language.
And he had written the reference manual,
which is a marvelous example
of how to write a reference manual.
Really, really very, very well done.
So I twisted his arm until he agreed to write a book,
and then we wrote a book.
And the virtue or advantage, at least,
I guess, of going first is that then other people
have to follow you if they’re gonna do anything.
And I think it worked well because Dennis
was a superb writer.
I mean, he really, really did.
And the reference manual in that book is his, period.
I had nothing to do with that at all.
So just crystal clear prose and very, very well expressed.
And then he and I, I wrote most of the expository material.
And then he and I sort of did the usual ping ponging
back and forth, refining it.
But I spent a lot of time trying to find examples
that would sort of hang together
and that would tell people what they might need
to know at about the right time
that they should be thinking about needing it.
And I’m not sure it completely succeeded,
but it mostly worked out fairly well.
What do you think is the power of example?
I mean, you’re the creator, at least one of the first people
to do the Hello World program, which is like the example.
If aliens discover our civilization hundreds of years
from now, it’ll probably be Hello World programs,
just like a half broken robot communicating with them
with the Hello World.
So what, and that’s a representative example.
So what do you find powerful about examples?
I think a good example will tell you how to do something
and it will be representative of,
you might not want to do exactly that,
but you will want to do something that’s at least
in that same general vein.
And so a lot of the examples in the C book were picked
for these very, very simple, straightforward
text processing problems that were typical of Unix.
I want to read input and write it out again.
There’s a copy command.
I want to read input and do something to it
and write it out again.
There’s a grab.
And so that kind of find things that are representative
of what people want to do and spell those out
so that they can then take those and see the core parts
and modify them to their taste.
And I think that a lot of programming books that,
I don’t look at programming books
a tremendous amount these days, but when I do,
a lot of them don’t do that.
They don’t give you examples that are both realistic
and something you might want to do.
Some of them are pure syntax.
Here’s how you add three numbers.
Well, come on, I could figure that out.
Tell me how I would get those three numbers
into the computer and how we would do something useful
with them and then how I put them back out again,
And especially if you follow that example,
there is something magical of doing something
that feels useful.
And I think it’s the attempt,
and it’s absolutely not perfect,
but the attempt in all cases was to get something
that was going to be either directly useful
or would be very representative of useful things
that a programmer might want to do.
But within that vein of fundamentally text processing,
reading text, doing something, writing text.
So you’ve also written a book on Go language.
I have to admit, so I worked at Google for a while
and I’ve never used Go.
Well, you missed something.
Well, I know I missed something for sure.
I mean, so Go and Rust are two languages
that I hear very, spoken very highly of
and I wish I would like to, well, there’s a lot of them.
There’s Julia, there’s all these incredible modern languages.
But if you can comment before,
or maybe comment on what do you find,
where does Go sit in this broad spectrum of languages?
And also, how do you yourself feel
about this wide range of powerful, interesting languages
that you may never even get to try to explore
because of time?
So I think, so Go first comes from that same
Bell Labs tradition in part, not exclusively,
but two of the three creators, Ken Thompson and Rob Pike.
So literally, the people.
Yeah, the people.
And then with this very, very useful influence
from the European school in particular,
the Claude Speer influence through Robert Griesemer,
who was, I guess, a second generation down student at ETH.
And so that’s an interesting combination of things.
And so some ways, Go captures the good parts of C,
it looks sort of like C, it’s sometimes characterized as C
for the 21st century.
On the surface, it looks very, very much like C.
But at the same time, it has some interesting
data structuring capabilities.
And then I think the part that I would say
is particularly useful, and again, I’m not a Go expert.
In spite of coauthoring the book,
about 90% of the work was done by Alan Donovan,
my coauthor, who is a Go expert.
But Go provides a very nice model of concurrency.
It’s basically the cooperating,
communicating sequential processes that Tony Hoare
set forth, jeez, I don’t know, 40 plus years ago.
And Go routines are, to my mind, a very natural way
to talk about parallel computation.
And in the few experiments I’ve done with them,
they’re easy to write, and typically it’s gonna work,
and very efficient as well.
So I think that’s one place where Go stands out,
that that model of parallel computation
is very, very easy and nice to work with.
Just to comment on that, do you think C foresaw,
or the early Unix days foresaw threads
and massively parallel computation?
I would guess not really.
I mean, maybe it was seen, but not at the level
where it was something you had to do anything about.
For a long time, processors got faster,
and then processors stopped getting faster
because of things like power consumption
and heat generation.
And so what happened instead was that instead
of processors getting faster,
there started to be more of them.
And that’s where that parallel thread stuff comes in.
So if you can comment on all the other languages,
is it break your heart that you’ll never get to explore them?
How do you feel about the full variety?
It’s not break my heart,
but I would love to be able to try more of these languages.
The closest I’ve come is in a class
that I often teach in the spring here.
It’s a programming class, and I often give,
I have one sort of small example that I will write
in as many languages as I possibly can.
I’ve got it in 20 languages.
At this point, and that’s so I do a minimal experiment
with a language just to say, okay,
I have this trivial task, which I understand the task,
and it takes 15 lines in awk,
and not much more in a variety of other languages.
So how big is it?
How fast does it run?
And what pain did I go through to learn how to do it?
And that’s like anecdotal, right?
It’s very, very, very, very, very, very, very,
very, very narrowly focused.
I think data, I like that term.
So yeah, but still, it’s a little sample,
because you get to, I think the hardest step
of the programming language is probably the first step,
right, so there you’re taking the first step.
Yeah, and so my experience with some languages
is very positive, like Lua,
a scripting language I had never used,
and I took my little program.
The program is a trivial formatter.
It just takes in lines of text of varying lengths,
and it puts them out in lines
that have no more than 60 characters on each line.
So think of it as just kind of the flow of process
in a browser or something.
So it’s a very short program.
And in Lua, I downloaded Lua,
and in an hour, I had it working,
never having written Lua in my life,
just going with online documentation.
I did the same thing in Scala,
which you can think of as a flavor of Java, equally trivial.
I did it in Haskell.
It took me several weeks.
But it did run like a turtle.
And I did it in Fortran 90, and it was painful,
but it worked, and I tried it in Rust,
and it took me several days to get it working
because the model of memory management
was just a little unfamiliar to me.
And the problem I had with Rust,
and it’s back to what we were just talking about,
I couldn’t find good, consistent documentation on Rust.
Now, this was several years ago,
and I’m sure things have stabilized,
but at the time, everything in the Rust world
seemed to be changing rapidly,
and so you would find what looked like a working example,
and it wouldn’t work with the version
of the language that I had.
So it took longer than it should have.
Rust is a language I would like to get back to,
but probably won’t.
I think one of the issues,
you have to have something you want to do.
If you don’t have something that is the right combination,
if I want to do it, and yet I have enough disposable time,
whatever, to make it worth learning a new language
at the same time, it’s never gonna happen.
Well, let me just sort of comment on what I said.
probably like the ugliest language possible,
and yet it’s quite arguably, quite possibly taking over,
not just the front end and the back end of the internet,
but possibly in the future taking over everything,
because they’ve now learned to make it very efficient.
And so what do you think about this?
Yeah, well, I think you’ve captured it in a lot of ways.
When it first came out,
and an ugly language, and certainly in the academy,
people would ridicule you.
It was just not fit for academics to work on.
I think a lot of that has evolved.
The language itself has evolved,
and certainly the technology of compiling it
is fantastically better than it was.
And so in that sense,
it’s absolutely a viable solution on back ends,
as well as the front ends.
Used well, I think it’s a pretty good language.
I’ve written a modest amount of it,
and things like that.
I’m not a real expert,
and it’s hard to keep up even there
with the new things that come along with it.
So I don’t know whether it will ever take over the world.
I think not, but it’s certainly an important language,
and worth knowing more about.
There’s, maybe to get your comment on something,
sort of Python, such a big part of the experience
of programming with those languages includes libraries,
sort of using, building on top of the code
that other people have built.
I think that’s probably different from the experience
that we just talked about from Unix and C days,
when you’re building stuff from scratch.
What do you think about this world
of essentially leveraging, building up libraries
on top of each other and leveraging them?
Yeah, no, that’s a very perceptive kind of question.
One of the reasons programming was fun in the old days
was that you were really building it all yourself.
The number of libraries you had to deal with
was quite small.
Maybe it was printf, or the standard library,
or something like that, and that is not the case today.
And if you want to do something in,
and those are the two fine examples,
you have to typically download a boatload of other stuff,
and you have no idea what you’re getting,
I’ve been doing some playing with machine learning
over the last couple of days,
and geez, something doesn’t work.
Well, you pip install this, okay,
and down comes another one,
okay, and down comes another gazillion megabytes of something
and you have no idea what it was.
And if you’re lucky, it works.
And if it doesn’t work, you have no recourse.
There’s absolutely no way you could figure out
which of these thousand different packages.
And I think it’s worse in the NPM environment
I think there’s less discipline, less control there.
And there’s aspects of not just not understanding
how it works, but there’s security issues,
there’s robustness issues,
so you don’t wanna run a nuclear power plant
So speaking to the variety of languages,
do you think that variety is good,
or do you hope, think that over time,
we should converge towards one, two, three
You mentioned to the Bell Lab days
when people could sort of, the community of it,
and the more languages you have,
the more you separate the communities.
There’s the Ruby community,
there’s the Python community,
there’s C++ community.
Do you hope that they’ll unite one day
to just one or two languages?
I certainly don’t hope it.
I’m not sure that that’s right,
because I honestly don’t think there is one language
that will suffice for all the programming needs of the world.
Are there too many at this point?
But I think if you look at the sort of the distribution
of how they are used,
there’s something called a dozen languages
that probably account for 95% of all programming
at this point, and that doesn’t seem unreasonable.
And then there’s another, well, 2,000 languages
that are still in use that nobody uses,
and, or at least don’t use in any quantity.
But I think new languages are a good idea in many respects,
because they’re often a chance to explore an idea
of how language might help.
I think that’s one of the positive things
about functional languages, for example.
They’re a particularly good place
where people have explored ideas
that at the time didn’t seem feasible,
but ultimately have wound up
as part of mainstream languages as well.
I mean, just go back as early as Recursion Lisp
and then follow forward functions as first class citizens
and pattern based languages,
and gee, I don’t know, closures,
and just on and on and on.
Lambda’s interesting ideas that showed up first
in, let’s call it broadly,
the functional programming community,
and then find their way into mainstream languages.
Yeah, it’s a playground for rebels.
Yeah, exactly, and so I think the languages
in the playground themselves are probably not going
to be the mainstream, at least for some while,
but the ideas that come from there are invaluable.
So let’s go to something that, when I found out recently,
so I’ve known that you’ve done a million things,
but one of the things I wasn’t aware of,
that you had a role in Ample,
and before you interrupt me by minimizing your role in it.
Ample is for minimizing functions.
Yeah, minimizing functions, right, exactly.
Can I just say that the elegance and abstraction power
of Ample is incredible,
when I first came to it about 10 years ago or so.
Can you describe what is the Ample language?
Sure, so Ample is a language for mathematical programming,
technical term, think of it as linear programming,
that is setting up systems of linear equations
that are of some sort of system of constraints,
so that you have a bunch of things
that have to be less than this, greater than that,
whatever, and you’re trying to find a set of values
for some decision variables that will maximize
or minimize some objective function,
so it’s a way of solving a particular kind
of optimization problem,
a very formal sort of optimization problem,
but one that’s exceptionally useful.
And it specifies, so there’s objective function constraints
and variables that become separate
from the data it operates on.
So that kind of separation allows you to,
put on different hats,
one put the hat of an optimization person
and then put another hat of a data person
and dance back and forth,
and also separate the actual solvers,
the optimization systems that do the solving.
Then you can have other people come to the table
and then build their solvers,
whether it’s linear or nonlinear,
convex, nonconvex, that kind of stuff.
So what is the,
to you as, maybe you can comment
how you got into that world
and what is the beautiful or interesting idea to you
from the world of optimization?
So I preface it by saying I’m absolutely not an expert
on this and most of the important work in AMPL
comes from my two partners in crime on that,
Bob Forer, who was a professor
in the Industrial Engineering
and Management Science Department at Northwestern,
and my colleague at Bell Labs, Dave Gay,
who was a numerical analyst and optimization person.
So the deal is linear programming.
Preface this by saying I don’t.
Let’s stay with linear programming.
Yeah, linear programming is the simplest example of this.
So linear programming, as it’s taught in school,
is that you have a big matrix,
which is always called A,
and you say AX is less than or equal to B.
So B is a set of constraints,
X is the decision variables,
and A is how the decision variables are combined
to set up the various constraints.
So A is a matrix and X and B are vectors.
And then there’s an objective function,
which is just a sum of a bunch of Xs
and some coefficients on them,
and that’s the thing you want to optimize.
The problem is that in the real world,
that matrix A is a very, very, very intricate,
very large and very sparse matrix
where the various components of the model
are distributed among the coefficients
in a way that is totally unobvious to anybody.
And so what you need is some way
to express the original model,
which you and I would write,
you know, we’d write mathematics on the board,
and the sum of this is greater
than the sum of that kind of thing.
So you need a language to write those kinds of constraints.
And Bob Forer, for a long time,
had been interested in modeling languages,
languages that made it possible to do this.
There was a modeling language around called GAMS,
the General Algebraic Modeling System,
but it looked very much like Fortran.
It was kind of clunky.
And so Bob spent a sabbatical year at Bell Labs in 1984,
and he and, there’s only the office across from me,
and it’s always geography,
and he and Dave Gay and I started talking
about this kind of thing,
and he wanted to design a language that would make it
so that you could take these algebraic specifications,
you know, summation signs over sets,
and that you would write on the board
and convert them into basically this A matrix,
and then pass that off to a solver,
which is an entirely separate thing.
And so we talked about the design of the language.
I don’t remember any of the details of this now,
but it’s kind of an obvious thing.
You’re just writing out mathematical expressions
in a Fortran like, sorry,
an algebraic but textual like language.
And I wrote the first version of this Ample program,
my first C++ program, and.
It’s written in C++?
And so I did that fairly quickly.
We wrote, it was, you know, 3,000 lines or something,
so it wasn’t very big,
but it sort of showed the feasibility of it
that you could actually do something that was easy
for people to specify models
and convert it into something that a solver could work with.
At the same time, as you say,
the model and the data are separate things.
So one model would then work with all kinds
of different data in the same way
that lots of programs do the same thing,
but with different data.
So one of the really nice things
is the specification of the models,
human, just kind of like, as you say, is human readable.
Like I literally, I remember on stuff I worked,
I would send it to colleagues
that I’m pretty sure never programmed in their life,
just to understand what the optimization problem is.
I think, how hard is it to convert that?
You said there’s a first prototype in C++
to convert that into something
that could actually be used by the solver.
It’s not too bad,
because most of the solvers have some mechanism
that lets them import a model in a form.
It might be as simple as the matrix itself
in just some representation,
or if you’re doing things that are not linear programming,
then there may be some mechanism
that lets you provide things like functions to be called,
or other constraints on the model.
So all AMPL does is to generate that kind of thing,
and then solver deals with all the hard work,
and then when the solver comes back with numbers,
AMPL converts those back into your original form,
so you know how much of each thing you should be buying,
or making, or shipping, or whatever.
So we did that in 84, and I haven’t had a lot to do
with it since, except that we wrote a couple of versions
of a book on it.
Which is one of the greatest books ever written.
I love that book.
I don’t know why.
It’s an excellent book.
Bob Farrer wrote most of it,
and so it’s really, really well done.
He must have been a dynamite teacher.
And typeset in LaTeX.
No, no, no, are you kidding?
I remember liking the typography, so I don’t know.
We did it with DROF.
I don’t even know what that is.
You’re too young.
Uh oh, oh boy.
I think of DROF as a predecessor
to the tech family of things.
It’s a formatter that was done at Bell Labs
in this same period of the very early 70s
that predates tech and things like that
by five to 10 years.
But it was nevertheless, I’m going by memories.
I remember it being beautiful.
Yeah, it was nicely done.
Outside of Unix, C, A, Golang,
all the things we talked about.
All the amazing work you’ve done.
You’ve also done work in graph theory.
Let me ask this crazy out there question.
If you had to make a bet,
and I had to force you to make a bet,
do you think P equals NP?
The answer is no,
although I’m told that somebody asked Jeff Dean
if that was, under what conditions P would equal NP,
and he said either P is zero or N is one.
Or vice versa, I’ve forgotten.
This is why Jeff Dean is a lot smarter than I am.
So, but your intuition is, uh.
I have no, I have no intuition,
but I’ve got a lot of colleagues who’ve got intuition
and their betting is no.
That’s the popular, that’s the popular bet.
Okay, so what is computational complexity theory?
And do you think these kinds of complexity classes,
especially as you’ve taught in this modern world,
are still a useful way to understand
the hardness of problems?
I don’t do that stuff.
The last time I touched anything to do with that
was before. Many, many years ago.
Was before it was invented.
Because I, it’s literally true.
I did my PhD thesis on graph.
Before Big O notation.
Before, I did this in 1968,
and I worked on graph partitioning,
which is this question.
You’ve got a graph that is a nodes and edges kind of graph,
and the edges have weights,
and you just want to divide the nodes into two piles
of equal size so that the number of edges
that goes from one side to the other
is as small as possible.
You developed, so that problem is hard.
Well, as it turns out,
I worked with Shen Lin at Bell Labs on this,
and we were never able to come up with anything
that was guaranteed to give the right answer.
We came up with heuristics that worked pretty darn well,
and I peeled off some special cases for my thesis,
but it was just hard.
And that was just about the time that Steve Cook
was showing that there were classes of problems
that appeared to be really hard,
of which graph partitioning was one.
But this, my expertise, such as it was,
totally predates that development.
So the heuristic, which now,
carries the two of yours names
for the traveling salesman problem,
and then for the graph partitioning.
That was, like, how did you,
you weren’t even thinking in terms of classes.
You were just trying to find.
There was no such idea.
A heuristic that kinda does the job pretty well.
You were trying to find something that did the job,
and there was nothing that you would call,
let’s say, a closed form or algorithmic thing
that would give you a guaranteed right answer.
I mean, compare graph partitioning to max flow min cut,
or something like that.
That’s the same problem,
except there’s no constraint on the number of nodes
on one side or the other of the cut.
And that means it’s an easy problem,
at least as I understand it.
Whereas the constraint that says
the two have to be constrained in size
makes it a hard problem.
Yeah, so Robert Frost says that poem
where you had to choose two paths.
So why did you,
is there another alternate universe
in which you pursued the Don Knuth path
of algorithm design, sort of?
Not smart enough.
You’re infinitely modest,
but so you pursued your kind of love of programming.
I mean, when you look back to those,
I mean, just looking into that world,
does that just seem like a distant world
of theoretical computer science?
Then is it fundamentally different
from the world of programming?
I don’t know.
I mean, certainly, in all seriousness,
I just didn’t have the talent for it.
When I got here as a grad student to Princeton
and I started to think about research
at the end of my, I don’t know,
first year or something like that,
I worked briefly with John Hopcroft,
who is absolutely, you know,
you mentioned during award winner, et cetera,
a great guy, and it became crystal clear
I was not cut out for this stuff, period, okay.
And so I moved into things
where I was more cut out for it,
and that tended to be things like writing programs
and then ultimately writing books.
You said that in Toronto as an undergrad,
you did a senior thesis or a literature survey
on artificial intelligence.
This was 1964.
What was the AI landscape, ideas, dreams at that time?
I think that was one of the,
well, you’ve heard of AI winners.
This is whatever the opposite was,
AI summer or something.
It was one of these things where people thought
that, boy, we could do anything with computers,
that all these hard problems, we could,
computers will solve them.
They will do machine translation.
They will play games like chess.
They will do, you know, prove theorems in geometry.
There are all kinds of examples like that
where people thought, boy,
we could really do those sorts of things.
And, you know, I read The Kool Aid in some sense.
There’s a wonderful collection of papers
called Computers and Thought that was published
in about that era and people were very optimistic.
And then of course it turned out that
what people thought was just a few years down the pike
was more than a few years down the pike.
And some parts of that are more or less now
sort of under control.
We finally do play games like Go and chess
and so on better than people do,
but there are others and machine translation
is a lot better than it used to be,
but that’s, you know, 50, close to 60 years of progress
and a lot of evolution in hardware
and a tremendous amount more data up on which
you can build systems that actually can learn
from some of that data.
And the infrastructure to support developers
working together, like an open source movement,
the internet, period, is also empowering.
But what lessons do you draw from that,
the opposite of winter, that optimism?
Well, I guess the lesson is that in the short run
it’s pretty easy to be too pessimistic
or maybe too optimistic and in the long run
you probably shouldn’t be too pessimistic.
I’m not saying that very well.
It reminds me of this remark from Arthur Clarke,
a science fiction author, who says, you know,
when some distinguished but elderly person
says that something is possible, he’s probably right.
And if he says it’s impossible, he’s almost surely wrong.
But you don’t know what the time scale is.
The time scale is critical, right.
So what are your thoughts on this new summer of AI
now in the work with machine learning and neural networks?
You’ve kind of mentioned that you started to try to explore
and look into this world that seems fundamentally different
from the world of heuristics and algorithms like search,
that it’s now purely sort of trying to take
huge amounts of data and learn from that data, right,
programs from the data.
Yeah, look, I think it’s very interesting.
I am incredibly far from an expert.
Most of what I know I’ve learned from my students
and they’re probably disappointed
in how little I’ve learned from them.
But I think it has tremendous potential
for certain kinds of things.
I mean, games is one where it obviously has had an effect
on some of the others as well.
I think there’s, and this is speaking from
definitely not expertise,
I think there are serious problems
in certain kinds of machine learning at least
because what they’re learning from
is the data that we give them.
And if the data we give them has something wrong with it,
then what they learn from it is probably wrong too.
And the obvious thing is some kind of bias in the data.
That the data has stuff in it like, I don’t know,
women aren’t as good as men at something, okay.
That’s just flat wrong.
But if it’s in the data because of historical treatment,
then that machine learning stuff will propagate that.
And that is a serious worry.
The positive part of that is what machine learning does
is reveal the bias in the data
and puts a mirror to our own society.
And in so doing helps us remove the bias,
you know, helps us work on ourselves.
Puts a mirror to ourselves.
Yeah, that’s an optimistic point of view.
And if it works that way, that would be absolutely great.
And what I don’t know is whether it does work that way
or whether the AI mechanisms
or machine learning mechanisms reinforce
and amplify things that have been wrong in the past.
And I don’t know, but I think that’s a serious thing
that we have to be concerned about.
Let me ask you an out there question, okay.
I know nobody knows, but what do you think it takes
to build a system of human level intelligence?
That’s been the dream from the 60s.
We talk about games, about language,
about image recognition, but really the dream
is to create human level or superhuman level intelligence.
What do you think it takes to do that?
And are we close?
I haven’t a clue and I don’t know, roughly speaking.
I mean, this was Turing.
I was trying to trick you into a hypothesis.
Yeah, I mean, Turing talked about this
in his paper on machine intelligence back in, geez,
I don’t know, early 50s or something like that.
And he had the idea of the Turing test.
And I don’t know what the Turing test is.
It’s a good test of intelligence.
I don’t know.
It’s an interesting test.
At least it’s in some vague sense objective,
whether you can read anything into the conclusions
is a different story.
Do you have worries, concerns, excitement
about the future of artificial intelligence?
So there’s a lot of people who are worried
and you can speak broadly
than just artificial intelligence.
It’s basically computing taking over the world
in various forms.
Are you excited by this future,
this possibility of computing being everywhere
or are you worried?
It’s some combination of those.
I think almost all technologies over the long run
are for good, but there’s plenty of examples
where they haven’t been good either over a long run
for some people or over a short run.
And computing is one of those.
And AI within it is gonna be one of those as well,
but computing broadly.
I mean, for just a today example is privacy,
that the use of things like social media and so on
means that, and the commercial surveillance
means that there’s an enormous amount more known about us
by people, other businesses, government, whatever,
than perhaps one ought to feel comfortable with.
So that’s an example.
So that’s an example of a possible negative effect
of computing being everywhere.
It’s an interesting one
because it could also be a positive, if leveraged correctly.
There’s a big if there.
So I have a deep interest in human psychology
and humans seem to be very paranoid about this data thing
that varies depending on age group.
It seems like the younger folks.
So it’s exciting to me to see what society looks like
50 years from now, that the concerns about privacy
might be flipped on their head
based purely on human psychology
versus actual concerns or not.
What do you think about Moore’s Law?
Well, you said a lot of stuff we’ve talked,
you talked about programming languages in their design,
in their ideas that come from the constraints
in the systems they operate in.
Do you think Moore’s Law,
the exponential improvement of systems
will continue indefinitely?
There’s a mix of opinions on that currently,
or do you think there’ll be a plateau?
Well, the frivolous answer is no exponential
it can go on forever.
You run out of something.
Just as we said, timescale matters.
So if it goes on long enough, that might be all we need.
Yeah, right, won’t matter to us.
So I don’t know, we’ve seen places
where Moore’s Law has changed.
For example, mentioned earlier,
processors don’t get faster anymore,
but you use that same growth of the ability
to put more things in a given area
to grow them horizontally instead of vertically as it were
so you can get more and more processors
or memory or whatever on the same chip.
Is that gonna run into a limitation?
Presumably, because at some point
you get down to the individual atoms.
And so you gotta find some way around that.
Will we find some way around that?
I don’t know, I just said that if I say it won’t,
I’ll be wrong, so perhaps we will.
So I just talked to Jim Keller and he says,
so he actually describes, he argues
that the Moore’s Law will continue for a long, long time
because you mentioned the atom.
We actually have, I think, a thousand fold increase,
still decreased in size, still possible
before we get to the quantum level.
So there’s still a lot of possibilities.
He thinks he’ll continue indefinitely,
which is an interesting optimistic viewpoint.
But how do you think the programming languages
will change with this increase?
Whether we hit a wall or not,
what do you think, do you think there’ll be
a fundamental change in the way
programming languages are designed?
I don’t know about that.
I think what will happen is continuation
of what we see in some areas, at least,
which is that more programming will be done
by programs than by people, and that more will be done
by sort of declarative rather than procedural mechanisms
where I’ll say, I want this to happen.
You figure out how.
And that is, in many cases, at this point,
domain of specialized languages for narrow domains,
but you can imagine that broadening out.
And so I don’t have to say so much, in so much detail,
some collection of software, let’s call it languages
or programs or something, will figure out
how to do what I want to do.
Interesting, so increased levels of abstraction.
And one day getting to the human level,
where we can just use natural language.
Could be possible.
So you taught, so teach a course,
Computers in Our World, here at Princeton,
that introduces computing and programming to nonmajors.
What, just from that experience,
what advice do you have for people
who don’t know anything about programming
but are kind of curious about this world,
or programming seems to become more and more
of a fundamental skill that people need to be
at least aware of?
Yeah, well, I couldn’t recommend a good book.
The book I wrote for the course.
I think this is one of these questions of,
should everybody know how to program?
And I think the answer is probably not,
but I think everybody should at least understand
sort of what it is, so that if you say to somebody,
I’m a programmer, they have a notion of what that might be,
or if you say this is a program,
or this was decided by a computer running a program,
that they have some vague intuitive understanding
and accurate understanding of what that might imply.
So part of what I’m doing in this course,
which is very definitely for nontechnical people,
and a typical person in it is a history or English major,
try and explain how computers work,
how they do their thing, what programming is,
how you write a program,
and how computers talk to each other,
and what do they do when they’re talking to each other.
And then I would say nobody, very rarely,
and does anybody in that course go on
to become a real serious programmer,
but at least they’ve got a somewhat better idea
of what all this stuff is about, not just the programming,
but the technology behind computers and communications.
Do they try and write a program themselves?
Oh yeah, yeah, a very small amount.
I introduced them to how machines work at a level below,
high level languages, so we have kind of a toy machine
that has a very small repertoire, a dozen instructions,
and they write trivial assembly language programs for that.
Wow, that’s interesting.
So can you just, if you were to give a flavor
to people of the programming world,
of the competing world,
what are the examples they should go with?
So a little bit of assembly to get a sense
at the lowest level of what the program is really doing.
Yeah, I mean, in some sense,
there’s no such thing as the lowest level
because you can keep going down,
but that’s the place where I drew the line.
So the idea that computers have a fairly small repertoire
of very simple instructions that they can do,
like add and subtract and branch and so on,
as you mentioned earlier,
and that you can write code at that level
and it will get things done,
and then you have the levels of abstraction
that we get with higher level languages,
like Fortran or C or whatever,
and that makes it easier to write the code
and less dependent on particular architectures.
And then we talk about a lot of the different kinds
of programs that they use all the time
that they don’t probably realize are programs,
like they’re running Mac OS on their computers
or maybe Windows, and they’re downloading apps
on their phones, and all of those things are programs
that are just what we just talked about,
except at a grand scale.
And it’s easy to forget that they’re actual programs
that people program.
There’s engineers that wrote those things.
And so in a way, I’m expecting them
to make an enormous conceptual leap
from their five or 10 line toy assembly language thing
that adds two or three numbers to something
that is a browser on their phone or whatever,
but it’s really the same thing.
So if you look in broad strokes at history,
what do you think the world,
how do you think the world changed because of computers?
It’s hard to sometimes see the big picture
when you’re in it, but I guess I’m asking
if there’s something you’ve noticed over the years
that, like you were mentioning,
the students are more distracted looking at their,
now there’s a device to look at.
I think computing has changed a tremendous amount,
obviously, but I think one aspect of that
is the way that people interact with each other,
both locally and far away.
And when I was the age of those kids,
making a phone call to somewhere was a big deal
because it costs serious money.
And this was in the 60s, right?
And today people don’t make phone calls,
they send texts or something like that.
So there’s an up and down in what people do.
People think nothing of having correspondence,
regular meetings, video, whatever,
with friends or family or whatever
in any other part of the world,
and they don’t think about that at all.
And so that’s just the communication aspect of it.
Do you think that brings us closer together
or does it make us,
does it take us away from the closeness
of human to human contact?
I think it depends a lot on all kinds of things.
So I trade mail with my brother and sister in Canada
much more often than I used to talk to them on the phone.
So probably every two or three days,
I get something or send something to them.
Whereas 20 years ago,
I probably wouldn’t have talked to them
on the phone nearly as much.
So in that sense, that’s brought my brother and sister
and I closer together.
That’s a good thing.
I watch the kids on campus
and they’re mostly walking around with their heads down,
fooling with their phones
to the point where I have to duck them.
I don’t know that that has brought them closer together
in some ways.
There’s sociological research that says people are,
in fact, not as close together as they used to be.
I don’t know where that’s really true,
but I can see potential downsides
and kids where you think,
come on, wake up and smell the coffee or whatever.
But if you look at, again, nobody can predict the future,
but are you excited?
Kind of touched this a little bit with AI,
but are you excited by the future in the next 10, 20 years
that computing will bring?
You were there when there was no computers really.
And now computers are everywhere all over the world
and Africa and Asia and just every person,
almost every person in the world has a device.
So are you hopeful, optimistic about that future?
It’s mixed, if the truth be told.
I mean, I think there are some things about that
that are good.
I think there’s the potential for people
to improve their lives all over the place
and that’s obviously good.
And at the same time, at least in the short run,
you can see lots and lots of bad
as people become more tribalistic or parochial
in their interests and it’s an enormous amount
more us than them and people are using computers
in all kinds of ways to mislead or misrepresent
or flat out lie about what’s going on
and that is affecting politics locally
and I think everywhere in the world.
Yeah, the long term effect on political systems
and so on is who knows.
Who knows indeed.
The people now have a voice which is a powerful thing.
People who are oppressed have a voice
but also everybody has a voice
and the chaos that emerges from that
is fascinating to watch.
Yeah, yeah, it’s kind of scary.
If you can go back and relive a moment in your life,
one that made you truly happy outside of family
or was profoundly transformative,
is there a moment or moments that jump out at you
I don’t think specific moments.
I think there were lots and lots and lots of good times
at Bell Labs where you would build something
and it worked.
Did you say it worked?
So the moment it worked.
Yeah, and somebody used it and they said,
gee, that’s neat.
Those kinds of things happened quite often
in that sort of golden era in the 70s when Unix was young
and there was all this low hanging fruit
and interesting things to work on
and a group of people who kind of,
we were all together in this and if you did something,
they would try it out for you.
And I think that was in some sense,
a really, really good time.
And AUK was, was AUK an example of that?
That when you built it and people used it?
And now millions of people use it.
And all your stupid mistakes are right there
for them to look at, right?
So it’s mixed.
Yeah, it’s terrifying, vulnerable
but it’s beautiful because it does have a positive impact
on so, so many people.
So I think there’s no better way to end it.
Brian, thank you so much for talking to us, it was an honor.
Okay, my pleasure.
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