Lex Fridman Podcast - #109 - Brian Kernighan: UNIX, C, AWK, AMPL, and Go Programming

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.

Now what?

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.

Yeah, yeah.

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.

Linux, yeah.

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

and efficient.


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.

That’s true.

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.

Oh, absolutely.


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

like that.

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?

Oh, grep.


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

of machines.

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

in Windows.

It’s changed my life.

OK, what’s your perfect programming setup?

What computer, what operating system, what keyboard,

what editor?

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.

What’s ED?

That’s the original Unix editor.

Oh, wow.

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

of lines.

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.

Yeah, right.

Real paper.

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.

Without reprinting.

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’s cool.

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.

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.

Very strange.

That’s right.

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.

And Fortran.

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 the JavaScript.

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.

Dennis Ritchie.

Yeah, right.

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,

neatly formatted.

And especially if you follow that example,

there is something magical of doing something

that feels useful.

Yeah, right.

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.

So what do you think about another language of JavaScript?

That’s this…

Well, let me just sort of comment on what I said.

When I was brought up, sort of JavaScript was seen as

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,

JavaScript was deemed to be fairly irregular

and an ugly language, and certainly in the academy,

if you said you were working on JavaScript,

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 I’ve played with JavaScript translators

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,

which JavaScript, and actually most languages,

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,

you mentioned Python and JavaScript,

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,

absolutely nothing.

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

for JavaScript.

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

using JavaScript, essentially.

Probably not.

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

programming languages?

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?

Well, arguably.

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.

Yeah, exactly.

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.

Oh, absolutely.

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.

And we.

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.

Oh, interesting.

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.

What’s that?

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.

Yeah, right.

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.

That’s right.

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

from memory?

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?

Yeah, absolutely.

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.

Good fun.

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