What I Worked On

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| February 2021Before college the two main things I worked on, outside of school,
were writing and programming. I didn't write essays. I wrote what
beginning writers were supposed to write then, and probably still
are: short stories. My stories were awful. They had hardly any plot,
just characters with strong feelings, which I imagined made them
deep.The first programs I tried writing were on the IBM 1401 that our
school district used for what was then called "data processing."
This was in 9th grade, so I was 13 or 14. The school district's
1401 happened to be in the basement of our junior high school, and
my friend Rich Draves and I got permission to use it. It was like
a mini Bond villain's lair down there, with all these alien-looking
machines � CPU, disk drives, printer, card reader � sitting up
on a raised floor under bright fluorescent lights.The language we used was an early version of Fortran. You had to
type programs on punch cards, then stack them in the card reader
and press a button to load the program into memory and run it. The
result would ordinarily be to print something on the spectacularly
loud printer.I was puzzled by the 1401. I couldn't figure out what to do with
it. And in retrospect there's not much I could have done with it.
The only form of input to programs was data stored on punched cards,
and I didn't have any data stored on punched cards. The only other
option was to do things that didn't rely on any input, like calculate
approximations of pi, but I didn't know enough math to do anything
interesting of that type. So I'm not surprised I can't remember any
programs I wrote, because they can't have done much. My clearest
memory is of the moment I learned it was possible for programs not
to terminate, when one of mine didn't. On a machine without
time-sharing, this was a social as well as a technical error, as
the data center manager's expression made clear.With microcomputers, everything changed. Now you could have a
computer sitting right in front of you, on a desk, that could respond
to your keystrokes as it was running instead of just churning through
a stack of punch cards and then stopping.
[1]The first of my friends to get a microcomputer built it himself.
It was sold as a kit by Heathkit. I remember vividly how impressed
and envious I felt watching him sitting in front of it, typing
programs right into the computer.Computers were expensive in those days and it took me years of
nagging before I convinced my father to buy one, a TRS-80, in about
1980. The gold standard then was the Apple II, but a TRS-80 was
good enough. This was when I really started programming. I wrote
simple games, a program to predict how high my model rockets would
fly, and a word processor that my father used to write at least one
book. There was only room in memory for about 2 pages of text, so
he'd write 2 pages at a time and then print them out, but it was a
lot better than a typewriter.Though I liked programming, I didn't plan to study it in college.
In college I was going to study philosophy, which sounded much more
powerful. It seemed, to my naive high school self, to be the study
of the ultimate truths, compared to which the things studied in
other fields would be mere domain knowledge. What I discovered when
I got to college was that the other fields took up so much of the
space of ideas that there wasn't much left for these supposed
ultimate truths. All that seemed left for philosophy were edge cases
that people in other fields felt could safely be ignored.I couldn't have put this into words when I was 18. All I knew at
the time was that I kept taking philosophy courses and they kept
being boring. So I decided to switch to AI.AI was in the air in the mid 1980s, but there were two things
especially that made me want to work on it: a novel by Heinlein
called The Moon is a Harsh Mistress, which featured an intelligent
computer called Mike, and a PBS documentary that showed Terry
Winograd using SHRDLU. I haven't tried rereading The Moon is a Harsh
Mistress, so I don't know how well it has aged, but when I read it
I was drawn entirely into its world. It seemed only a matter of
time before we'd have Mike, and when I saw Winograd using SHRDLU,
it seemed like that time would be a few years at most. All you had
to do was teach SHRDLU more words.There weren't any classes in AI at Cornell then, not even graduate
classes, so I started trying to teach myself. Which meant learning
Lisp, since in those days Lisp was regarded as the language of AI.
The commonly used programming languages then were pretty primitive,
and programmers' ideas correspondingly so. The default language at
Cornell was a Pascal-like language called PL/I, and the situation
was similar elsewhere. Learning Lisp expanded my concept of a program
so fast that it was years before I started to have a sense of where
the new limits were. This was more like it; this was what I had
expected college to do. It wasn't happening in a class, like it was
supposed to, but that was ok. For the next couple years I was on a
roll. I knew what I was going to do.For my undergraduate thesis, I reverse-engineered SHRDLU. My God
did I love working on that program. It was a pleasing bit of code,
but what made it even more exciting was my belief � hard to imagine
now, but not unique in 1985 � that it was already climbing the
lower slopes of intelligence.I had gotten into a program at Cornell that didn't make you choose
a major. You could take whatever classes you liked, and choose
whatever you liked to put on your degree. I of course chose "Artificial
Intelligence." When I got the actual physical diploma, I was dismayed
to find that the quotes had been included, which made them read as
scare-quotes. At the time this bothered me, but now it seems amusingly
accurate, for reasons I was about to discover.I applied to 3 grad schools: MIT and Yale, which were renowned for
AI at the time, and Harvard, which I'd visited because Rich Draves
went there, and was also home to Bill Woods, who'd invented the
type of parser I used in my SHRDLU clone. Only Harvard accepted me,
so that was where I went.I don't remember the moment it happened, or if there even was a
specific moment, but during the first year of grad school I realized
that AI, as practiced at the time, was a hoax. By which I mean the
sort of AI in which a program that's told "the dog is sitting on
the chair" translates this into some formal representation and adds
it to the list of things it knows.What these programs really showed was that there's a subset of
natural language that's a formal language. But a very proper subset.
It was clear that there was an unbridgeable gap between what they
could do and actually understanding natural language. It was not,
in fact, simply a matter of teaching SHRDLU more words. That whole
way of doing AI, with explicit data structures representing concepts,
was not going to work. Its brokenness did, as so often happens,
generate a lot of opportunities to write papers about various
band-aids that could be applied to it, but it was never going to
get us Mike.So I looked around to see what I could salvage from the wreckage
of my plans, and there was Lisp. I knew from experience that Lisp
was interesting for its own sake and not just for its association
with AI, even though that was the main reason people cared about
it at the time. So I decided to focus on Lisp. In fact, I decided
to write a book about Lisp hacking. It's scary to think how little
I knew about Lisp hacking when I started writing that book. But
there's nothing like writing a book about something to help you
learn it. The book, On Lisp, wasn't published till 1993, but I wrote
much of it in grad school.Computer Science is an uneasy alliance between two halves, theory
and systems. The theory people prove things, and the systems people
build things. I wanted to build things. I had plenty of respect for

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