Alan Kay: Programming and Scaling

so

of course we never want to spend too  much time looking

back especially when  talking to students

because the world is  in front of you

but one

point I wanted  to make and this has always been

true  and has never reflected adequately

in  any rewards that a person might receive

  and that is that when

good work gets  done it almost always gets done

first in  a powerful research

community of

very  friendly and very critical

colleagues  who know how to argue

the right way so  as you're looking

ahead for yourself  finding

these people in general is

  critical and finding them in particular

is critical so these are just some of  the people that

have

associated with some of the work we've  been doing it

viewpoints Research  Institute

and

very few awards actually

  are given to groups

that this is a real  mistake in our field

because I believe  in our field that you not only have

ideas you have to get them  implemented and

it is often the case  that people

with ideas are not so good  at implementing and so

you need to have  a kind of a team of

different kinds of  talents who are able to

work together

one little look-back though

it could be  so I as we as the

term is meant today I  made up the term object-oriented

  programming but I made it up for  something

different than the term stands  for today

and what the term stands for

  today this could be the 50th anniversary

  of it so that's quite surprising to  think it

nineteen  61

and it goes back to a very surprising

  form which is

actually a machine so

there's no object-oriented programming  language in 1961

that I'm aware of  I actually started

in computing myself  in 1961

so there was nothing like an  object-oriented programming

back then but there is a piece of  hardware that

can most easily be

  explained to you is that this was a

  computer hardware that

executed a

set of  instructions that look like the byte

  coded VMs that you're familiar with from  Java

  in fact those byte coded VMs that Java

  have came originally because they were  the

basic instruction set on this  machine

so this is one of the first  attempts

at a building a higher-level  machine

to reflect the idea in the early  60s that

we were going to program in  levels of language

higher than C and

Bob  Barton here invented

this machine and  sadly the

hardware of the day is not as  advanced as this machine

was so for  those of you

who think we've been  engaged in steady progress

in some form

  of Darwinian processes that are making

things better for us it's actually quite  the opposite quite

the opposite in  history

you can find really incredible

  geniuses like this man who anticipated

  so strongly that we

can only emulate his  ideas and software

today intel hasn't  gotten around to understanding

them yet  so this I find

really quite sad on the  other hand it

gives you a sense of this  person

and if you want to read an  interesting paper probably

the shortest  greatest paper ever written

in computer  science you can read this one

written in  1961 by Bob

Barton  new approach to the functional design

of  a digital computer it's only six pages  long

and it lays out what

a felicitous

  execution environment for

language would be like and how it would  be realized in

a computer this computer  was built by

the way it lived on longest  in Germany the

German banking system  loved it

because it was uncrashable why  was it uncrashable

because it was the  first hardware or software system

to  have what we call capabilities today

an  application programmer simply could not

  write a program that could hurt any

  other program on the computer that was

built directly into the into the  hardware of this

machine and the reason

  I'm showing you this just because

part  of it is everybody should understand the  history

of our field if we were  physicists and we failed

to understand  what Newton did we should be shot and

  but in fact in computing we

are very  happy not to understand what most

of the  founders of our field actually did so

I  always like to pick one of them in

the  generation before me and

explain what he  did but reason I brought it up

here is  that I had a course

from this guy this  guy was took

a leave of absence from  borrows to come teach

at the graduate  school I was at and in

this course which  was

about advanced systems design  instead of teaching

this course he  handed out a list

of all the papers we  should read he

said there are a few  things known about advanced

systems  design in 1966

they're written down in  these papers and I expect you to read

every single one of these papers and  understand them and

then he said but it  is my job

to firmly disabuse

you of any  fondly held ideas

you might have brought  into this classroom and translated

into  less flowery English what he proposed

to  do is to take all of our beliefs

  about computing and destroy them

and  that is what this class was

the best  class I've ever had in a

university or  graduate school and

why was this a good  idea well

we'll see in a second that the  biggest problem

we have as human beings

  is that we confuse our beliefs with

  reality so we seem

to live in a reality  here but actually it's a construction

  that we look at one way and

another culture might look at it another  way and my

little dog Watson might look  at this

whole thing from a completely  different point of view and when

we  treat a construction as

reality you are  actually binding the kinds of thoughts

that we can actually have and worse than  that we're picking a

tiny part of the  past that led to this

particular  construction as the only past

we ever  drawn so

what this guy did is just  demolished everything that

we thought  including things that he actually  believed

in so he demolished everything  no no

proposition was safe

and at the  end of it we

actually were free so we  could

then learn computing from the  point

of view of it being simply  opinions

and a little bit

hat have been done so far up that way  but might

not have anything particular  to do with

a desirable future so here's  a paradigm

that existed back then and

is  pretty much what we do today and I call  it the make

and fix paradigm and it

started off with data structures and  procedures they

were like tinker toys  maybe a little bit like clocks

we fit we

  make gears we fit them together called

  api's they have to fit pretty closely

  for the thing to work and

that's pretty  much the only architecture we have and

of course you can make a tic-tac-toe  computer

out of tinker toy this is Danny  Hillis has a few

it  but like

such things was once dismantled  to

be shipped to a museum and when they  put it back together

and never worked  again and the reason

was it was just  luck that all

of these Tinkertoy  actually work because there was play  there

there strings inside the

strings weren't the same length  anymore and

that reminds you of any  programs and

this is this murky thing is  actually Mexico

City has anybody ever  been to Mexico City

so it's 20 million  people

couldn't be more smoggy

and it's  kind of a mess

and it's just the kind of  it's just a slightly

more extreme  version of a lot of American cities

  where individuals

have carpenters have

decided to do a house here or a house  there and

the problem is is that each

  individual little ant doing something

  has led to a

totality that is almost  unbearable

and yet 20 million people are  forced to live in it and to

me this is  what software today is kind of

like it's

  basically using almost the exact same  architectures

as we're known 50

years  ago because even

the so called  object-oriented languages today have  lots

of setters in the objects and when  you have a setter in an object

urned an object back into a data  structure and

so you get this huge

  amount of scaling of

complications but  you don't get a lot

of bang for the buck

  it's not clear what to do with it but

  what

yeah

so if

we look at software in terms  of lines

of code for instance

a one book

of software is about 20,000 lines of  code and a

foot of books is about I have

  it here a meter is about a million lines

  of code so if you stack up a meters  height

high a book it's about only a  million lines of code

okay so

if we look  at some software systems

for instance  Vista has

120 million lines of code in  it so

that's a hundred and twenty meters  of

books to read and

six thousand books  and another

140 million for the apps  another seven thousand

books so we're  talking thirteen thousand books

for the  simplest part of

personal computing that  we're all used to anybody

here read  thirteen thousand books

anybody here  read one book

okay just checking

so

there's a lot and I know a

big  software company whose

name you might  have heard that has

to deal with a live

  base of about 350 million lines of code  and

as they say about 145,000

screens so  screens

is a term that was used a long  time ago for 3270

terminals and so if  they call

their user interface today  made out of screens it's probably that  they

just adapted those old 3270

screens  so this is about seventeen

thousand five  hundred books worth of software

nobody  in that company knows what it is

but  they have thousands and thousands of  people

maintaining it so the question we  should be asking as

computer scientists  first let alone engineers

there's  computer scientists we should be asking

  is that how

rite  to get that much

functionality or is  something

wrong

so one way of looking at  it is if your

are two curves so

the  complication curve

here I just put up

you know about a a factor

of a hundred  this

and so

we could see that while we  like the complication curve to

run at  the same rate as the complexity curve it  actually

is way ahead of the game so for

  a certain level of complexity like say  in personal

computing the actual  complication

might be a factor of a  hundred or a factor of a thousand

like  all

but one tenth of one percent in the  Microsoft

software suite might be  useless

code code that

is there perhaps  for compatibility reasons code

that they  don't dare take out

in fact I know that  for

example if everybody here is  probably used Microsoft

Word and it's  quite surprising that

Microsoft Word  when you have paragraph justification

on  actually has selection

errors to this  day if you ever had

that happen got  justification on you click in

there and  you start typing and the type

character  goes somewhere else that bug

there for more than twenty five years  it's been reported

literally thirty or  forty thousand times and

they cannot  find the bug

this is the most elementary

thing that you ever do in a word  processor is to

be able to select where  you want the character go

they can't  find it and fact

I've heard that part of  Microsoft words listing has

been lost they've had to recreate it  from

from assembly code

so that's kind  of a state of affairs that

we probably  should be thinking about

because when you start getting into  factors

of a hundred and factors of a  thousand and factors of 10,000

actually talking about the difference  between somebody

being able to  comprehend something and maybe

not be  able to comprehend it at all

so

if we go  back to our diagram here we

can pick up  some issues that we might care

about now

say something about the communication  with aliens issue

a little bit later  what that is

and

if we look in this  morass

of stuff there's one interesting

  thing that isn't like this that

is huge  and yet it is tidy

its level of  complication

exactly balances its level

  of complexity and that's the Internet so

it's worthwhile thinking about that that  even in a poorly

done C program the  Internet is actually controlled

than twenty thousand lines of code  itself

and that twenty thousand lines of  code can be written in

about a thousand  lines of code so tcp/ip is

a kind of a  universal DNA

for an architecture of  billions

of nodes in a

dynamic system  that has never been stopped

has never  been stopped from when it first

got  turned on in September 1969

  so replaced all of its atoms at

least  once and has never been stopped

it has  been changed

it has been improved

but in  fact it's an eternal system

it is scaled  well and so forth and I'm

not talking  about the web of course web is

a mess  but the internet was done by

experts you  had a couple of those guys Vint Cerf and

  Bob Kahn here at different times these

  guys knew what they were doing and boy

  when you look at this I

think why it is  why

did the internet turn out so well  and why can't we

that way because you could imagine  taking the

inner  as a model for doing software modules  why

don't people do it

and it's the way  I look at

it is a little

jump of

qualitative kind happen here and that

jump is kind of a little jump from gear  type things to

biological type things

  and in fact those of us who had

been  biologists contribute some pilot

biological ideas to the design of the  Internet  because

of course the Internet is one of  the few human

artifacts that

we've made  that behaves like about like something

  that's alive in that

it repairs itself  while it is

still running it is able

to  expand and grow and so forth and

so

we  can think of the Internet is in

it was  made but

it has some of these biological  ideas in it

so some of the

same kinds of  ideas for scaling

were done in software  also around the

same time and some of  those were successful

at Xerox PARC but

  one of the problems

of these

some of  these semi new ideas here is when

they  got translated into sea based forms

in  order to be recognizable

to people the  things

that were like this old paradigm  were emphasized

here so for example you  got this

really terrible idea of having  a so-called object-oriented

system and  turning it back into data structures by  putting

setters in which allow the very  same disastrous

remote assignment

statements to to be done and turns the  system into

a much more fragile way of  thinking about things

but I think that

  what's actually going on here

is that  there's a new way of thinking

things that represents  a brighter future

for programming and if

  this is makin fix for

instance you can  fix a clock but you have to negotiate  with

the system so at some point the

systems are too complicated for a human  being to go into too many

feedback loops  too many self regulatory

systems and  stuff and you wind up at

best  negotiating with the system now the

  stuff we're doing in here so far is

in

  between we built

it by hand but we made  a system

as we've made up many other  systems

in software but we haven't made  the systems capable

enough to be able to  be negotiated

with so I just want to  have you think

of it in your head of  what it would be like if you're

doing a  software system in some new form

of  development

interactive development kit

  what would that software system actually  do

besides just doing what you asked it  to

do could it for

instance participate  in readapting

the software for future  needs about

80% of the cost of all

  software that is deployed in industry

  comes after the software

has been  successfully put out the first time

so  the life cycle changes

that are required  of the software and the integration and

reintegration  actually dominate

all the costs of  software but

because of the way  companies have

to report their earnings  and their expenses

companies cannot  charge

upfront for

all of the stuff that  you really need

to do to software to  allow it to be changed downstream so

what they tend to pay for is just enough  money to make the thing

run to its  original specs and hardly any money to

  be able to fix it up later because they  just amortize

those costs of the future  and the costs are enormous

o

if we take the

internet as one area  and then a slightly

more advanced way of  thinking about things is that

once we  start thinking in biological terms

we  also are thinking about Ecology's

and I  hope many

of you have thought enough  about the Internet to realize that there

  are levels of communication protocols

  that could be put on top of

tcp/ip

for  example interoperation protocols

that  could eliminate the

dependence on  specific operating systems

and I hope to  get to that in

a few minutes  okay

way down the road

what

is actually  being put together is something

that is  going to have to be discussed in  psychological

or mental terms because

  the if this

is manipulation

up there  just wanted to make a little comment  about

biology because

sometimes

if we  study computers we don't learn

other  things

so we can I'm going to read this  diagram both

way so at the

inch scale

now there's

what twenty five

thousand  Mike

twenty five thousand four hundred

  microns per inch and a ecoli

is about  two microns long so it's

about 13,000  microns

per inch a little over ten  thousand per

per centimeter

and so if we  blow it up by

eighty thousand we get to  see this and

of course it's very active  so we wouldn't

see this we would see a  blur and if

we take the next section up  there

we get something that looks like this  where we see a bunch of large molecules

  we take a section of that so

we're now a  magnification of about 10

million we see  a medium-sized molecule

has about five  thousand atoms in it and these are  actually

water atoms so if you like  books

like this it's called the  machinery light of life

by David  Goodsell this is a molecular

biologist  who likes to paint and he

realized that  doing watercolors of

what is actually  going on there could be more clear than

  computer graphics or anyway this is

marvelous book for getting an idea so  these are individual water molecules  these

are calcium ions 5,000

atoms or so

  and if

you see the size of this thing  compared to it in there

and see it in  the size of the larger thing they're

  about 120 million molecules

that are not  water in

a single living thing

so one of  the things to think about in computer

  terms is the scale jump from the

lower level of architecture which is  polymers like

proteins chains of

atoms  to make

long molecules the scale jump to  what what

it needs to do to make a  living thing is

incredible compared to  the scale jump we do when we try

to make  objects and I think one of the

mistakes  we made years ago was to make objects  too

small and we missed

this idea that  you might get a lot more out of

making a  much more capable more universal

object  and try to build things in terms of that

  and just for comparison

there are just  slightly fewer tinker toys

in Danny  Hillis tinker toy

tic-tac-toe playing  computer as

there are in one of these  proteins so so

a protein you could  imagine assembling by hand but you could  not imagine assembling

a bacterium by  hand

  okay got aggro on

  okay

so here's a

piece of context that

I  always like to ask students about

so  here's an interesting thing

about all of  us is pretty

much from the time we were  born every

person in this room was  praised for being bright

right

  we just talked up a

storm and we were  interested in this and we were so

clever  and we were encouraged most

of the time  to be clever

it

seems like a good thing  and maybe it is because here

we are but  imagine

being born with twice the

  intellectual capacity of Leonardo

  smarter than any of us no

question twice  the

intellectual capacity of Leonardo  but in 10,000

BC how

far are you going  to get how

far did Leonardo get Leonardo  could not invent a single

engine for any  of his vehicles think about that

maybe  the smartest person of his time but he's  born in

the wrong time his IQ could not  transcend

so IQ is

really not worth that much  because

talking about vehicles here's

  Henry Ford nowhere

near Leonardo but he  happened to be born in the right century

  century in which people had already done

a lot of work in making mechanical  things and so

it was Duck Soup to make a  car that worked and sell

millions of  them for four hundred and seventy five

  dollars apiece and of

course we have  Daimler and Benz here

in Germany so  basically when you born at

the right  time a whole bunch of things are open to  you

  so knowledge in many

many cases just  Trump's IQ

I

and this is because

there are certain  special people who invent new

ways of  looking at things so

Ford was powerful  because Isaac

Newton changed the way new  with the way Europe thought

about things  so he changed

an entire way of things  and it's one

of the wonderful things  about the way knowledge works if you can  get a supreme

genius to invent calculus  those

of us with more normal IQs can  learn it

so we're not shut out from

what  the genius does we just can't

calculus by ourselves but once one of  these guys turns

things around the  knowledge

of the era changes completely  this

is why in the United States at  least computer science

is almost dead  because

of course I would get a lot of  argument about that because there are

  many people there but the

outlook there  has gotten pretty weak

and so the way I  think of it

is the

outlook at you have  on something is what is

going to give  you those extra IQ points but

have a really powerful outlook the  knowledge you'll gather

will be powerful  and then if you have some extra

brights  if you happen to be clever

when you have  those first couple of things then

you  can be very powerful

so I think of  knowledge

as silver and outlook as gold

  and IQ is kind of a lead weight

this is  killing

computer science because a lot  of people in computing don't

bother  learning anything because it's

not that  difficult to learn how to program badly

  right and you can be clever and

write a  program in JavaScript to do lots of  different

kinds of things the problem is  you there's no

place to go with it

so when I look at our field today

from  this 50-year perspective

well

looking  back I would

say the absolute number of  four

four and five Sigma IQs is

actually  has actually increased

so if we were to  take all

of the people in our field  vastly

more and look at the superbright  there's

just more than there were 40 50  years ago

but the relative number is  much

worse because the

total number is  increased tremendously and the

number of the superbright is not  increased commensurately

and so there's  an enormous amount of noise

from the  middle today in the field

an enormous  number of artifacts from them

similarly  there's

a lot more knowledge of computer  science today than there was

40 50 years  ago

but the general knowledge of

the practitioners has only increased a  little bit the

level the small size

of  this increase is almost staggering

given  what

there is to know that has been  written

down in various places and

then  the

number of outlooks that are powerful

  has gone way up

over those years and

but  the general outlook is hardly more  powerful

than it was I'm talking about  programming here this

is quite static so  something

something is going on that  reminds me a lot of pop music

right  there if

you sit down and learn music  there and

look back over the last six  centuries and

it's just some of the most  incredible

talking just about classical  music I'm talking

about jazz any kind of  develop music it  just

amazing the riches that are there

  yet pop music uses 1/100

of 1% of that  and

so I think what what's happened is  computing

has turned into a pop culture  and

the universities are not helping

in  general at least in the u.s.

so Cicero  anybody

know a good quote about Cicero  having to do with

present and past let's

  check out your classical education here

  so you know who Cicero was

he was one of  those old Roman guys

so Cicero once  wrote

he who knows only his own  generation remains

forever a child

and

basically what McLuhan

is saying  here is we tend to be trapped

in a  single conception of the present

makes it hard for us to see it in any  terms except

the terms that led up to  the present that we're in

and he said  and

that means it's hard to conceive of  a future that

is an incremental on the  present makes

sense right the president  is

overwhelming to us and there are all  these little things that we can do

a  little bit better on it

that gives us a  kind of a model that's like this

so

  here's some rain falling outside making

  a little gully by random

and it happens  that a gully by random

just happens to  be more efficient

at making the gully  bigger so

if you just let that go on for  a bit you

get these guys there's no

  reason for where these are just some

  random rain disturbing some things

there  but once it got set up it

got interested  in itself just

like our brains do so  this

is not a bad model of the way human  memory

works  we start learning something it was very  hard

for us to see what else is going on  and

so there's this famous picture of  Conrad

Lorentz who discovered that the  first thing

a duckling saw when it came  out of the egg it thought was his mother

  and so if Lawrence

arranged it so he was  the first thing they saw so

the little  ducks would follow him around and when  he

went out swimming they'd follow him  around and so

I think that's the way  people often approach well

your first  programming language right

so probably  the most disastrous thing

learn is your first programming language  even if it's a good programming

language  and the reason is is it's tends

to  become computing and

so it might be a  better idea to learn two or three

programming languages at the same time  even though that's a different

kind of  struggle but at least relativize

what  people think  computing

might be so we got this  present

it

comes out of one set of  things in the past that

we're daily  aware of it

gives rise to an incremental  future this

is a lot like academic paper  writing in the US

I don't know whether  you have to do it here but so

almost all  the papers written in in the US and  computing

academia are counted so the  University

of California counts all  papers written by

academics they don't  care whether the papers are good or bad  they

don't care whether a professor

has  a really great idea every five years  they

do care if the professor writes 10

  or 15 or 20 papers each year

and the  same with graduate

students because  graduate students are taught to follow  in

this mold and often the professor has  his name on that very same paper

the  graduate student in increases his count

  so there are a lot of factors here

but  the truth is that the

past is vast

enormous  there are billions

of people  contributing to the past and every

time  we think the present is real we

actually  cannot see the rest of the past

so we have to destroy the present

one of  the ways of doing it is to

try spend a  little time each day trying

to think of  what you take as reality as

being just a  result of

that rain falling on the  ground and

being intensified

get rid of  it

  once you get rid of it

it's a scary  situation

because you've said I'm not  going to have anything based

on the  course that's not possible you're

just  trying but sometimes you get

a little  feeling this

is not an idea it's just a  feeling

it's like

an odor of perfume

but  the fun thing is that little feeling can

  actually lead you to look in the past in  different

places then

you would normally  do and you can bring those up

to that  feeling and once

you do that that  feeling starts

expanding into a vision  and the

vision expands into an actual  idea

so this is one way of doing

it and  some of the most creative people I know  actually

operate this way this is where  these ideas come

from that are not just  incremental to the

present they come out  of

vague even muscular sensations

that  you have to go chasing to try

out what they are you try and get the  idea too early it

can only be in terms  of the present

what we do

  so everything that human humans

make is  technically technology

so I'm going to  and we

have a different meaning for  technology so I'm going to change the

  Greek root of technology is technique

  technique means whatever human make

  humans make don't let's call it the arts

and

in the arts we have tinkering I'm

  going to do these in historical order so

mammals are creatures who tinker  especially

primates especially us and

  engineering is a kind of principle

  tinkering so

it came before math and  science

what does it mean by principles

if you don't have math and science well  if you build a building

and it falls  down you just build it with twice the

cross-sections of all the beams you  don't need any math and

if it doesn't fall down you write it  down in a book and

you wind up with this  big cookbook that by

the way is the way  Babylonians did math the

Greeks hated  long books of things like that that

led  them to invent the company math we use  today

that came next

science as we know  it today came

about 400 years ago so

  anybody here know how

long our strain of  humans has been on the planet

how

long  have we been here this is it's kind

of  important isn't it for context

so you  know about so

it happens that we inherit  more from our mother

than our Father  because

mitochondria and its

DNA don't  fit into sperm but they do fit into OVA

  and so when

we got a fertilized egg we  cannot only get the union of

the two  nuclear DNA s

but we also get the female  mitochondrial

DNA and mitochondria are a

little bacteria that were captured a  long time ago

to help with energy  production in cells they were originally  separate

organisms so it turns out you  can actually

run computer simulations

on  the amount of change in the

female line  of humanity should

do this sometime  because it's kind of interesting and it

  turns out that if you run it back we  were all defendi

descended from one  female

now of course there are other females  around back then but

that female lived  192 thousand

years ago so our particular  strain

of humanity has been here but for  about 192 thousand

that  we only invented science

400 years ago

  that's how dumb we are think

about it  that's why IQ doesn't count

we never  were smart enough to invent science

we  blundered around

amusing ourselves with  stories for

hundreds of thousands of  years so

almost everybody in these  fields winds

up doing a combination of  them and you kind of want to be in the  sweet

spot you want to do a little  tinkering a little engineering

a little  math a little science and really

difference between the fields is more of  temperament than the kinds

of things  that you learn some people are idealist

  some are pragmatists

now if we look at  computing

it's mostly tinkering a

little bit of  engineering

maybe a little math hardly  any science

and when we say engineering

  we don't mean real engineering real

  engineering uni Empire State Building in  New

York was built in less than a year  by less than three thousand people

  including dem Demmel it demolishing

the  original site and occupancy

so it's  built at the rate of two stories a

day  the steel was still

110 degrees 40  degrees centigrade when

it came from the  factories in Pittsburgh

to make the  Empire State Building

  that's Engineering so whatever it is we  do is

like maybe what Egyptians did  making

pyramids piling things on top of  each other but

in computing we

could not  organize 3,000 people to do a major

  thing in less than a year right

have you  ever heard of it ever

No so

the word engineering seriously and I do  I

don't mean it as a metaphor I'm

saying  engineering let's not do away with

engineering has meant the last three or  four thousand years we

take that word  seriously we haven't got it yet in

  software we really

don't know how to do  it okay

so this is my favorite hairball

  it's actually from

a woolly mammoth so  that's a big

hairball I'm

gonna get rid  of it because but you

something complicated I'm going to  replace it with stars because

nicer to look at but if you think of  we're given

this phenomena that's the  basic

thing it could be a hairball it  could be they're

hairballs and what we want to do is make  sense of

it in science and we

have  language which is computer

scientists  you know can all be made out

of the  operation NAND or nor which

invented by a guy by the name of Charles  purse in

the 19th century  so

we can make all things having to do  with language out

of that one operation  and

what we

want to do is to come up  with something in language

that closely  mimics mimics the phenomena

that we're  interested in and forget in

fact because  of the limitations of our brain that's

about as far as we can go because the  brain contains

various kinds of  languages that does not contain stars  and

so we're interested in the  relationships

between those so the  reason

I'm explaining this is because  when I ask graduate

United States what is their meaning of  computer science

they always give me an  engineering definition which implies

that they have no idea what the word  science actually means this

is what it  means

but science

doesn't care whether  nature made it or whether

humans made it  so we can take a bridge do

exactly the  same thing

make a t-shirt explains

how  this bridge works and what's cool about  engineering

is once you make that  t-shirt you

can make another bridge get

hose engineers to make more interesting  bridge for a scientist

to look at and  you can take

this rather far those are

  my favorite bridge in Japan longest

  suspension bridge in the world and to  give

you an idea of how big it is

there's the Great Pyramid of Egypt and  the

Empire State Building next to it so  it its towers

are actually the size of  the

greatest building created in the  early 30s

this is some kind of bridge

makes

you feel good to be an engineer at  least it makes me

feel good because I  this is you know the

ability to really  make engineering and science

harmonize  with each other there's no greater

so computing we've

got computers we got  software here's

John McCarthy he

the abstraction of all that stuff on a  t-shirt that's

the Lisp interpreter in  itself one

of my favorite things for  more than 50 years

ago 1958

if you don't  have

a little tear in your eye when you  look at that you're missing something

  really really important about our field  this

is like one of the greatest things  that has ever done because

the same  thing with the bridge once somebody does  that

man you

can start inventing things  that are much more

you can invent  computer things that are like

like  living things and make

Internet's and so  forth so this is

really cool stuff and  you

can also look at personal computing  and ask

how many t-shirts we

Empire State Building worth of code from  Microsoft

but we should ask how many  t-shirts does it really take

and so one

of the things that we've done in our  approach is

finding that problem  difficult

it's really hard

thing through because geez  we

murse tin this stuff it looks large  it's

complicated so

what you can do is  make

a little Frankenstein's monster of

  a system it isn't

the one you actually  want exactly but it's

kind of cute and  it's kind of scary and

if it's also kind  of small then

you've actually made the  thing that will allow you to think

about  this other

guy

and Olay enable you to  make a baby step

towards capturing  millions

of lines of code in a few

tens  of thousands of lines of code

that's one of the things we've been  doing so

my website the website of

viewpoints  search Institute is Vpr

i.org  and

there you will read

more about this  project and I'll just show you

one  example here

from a bunch of principles

  one will be the math wins one

tiny Model  T

t-shirt programming and a little bit  of particles

and fields so this is one

  of our colleagues dan amma lang and

he  was charged with the

goal of doing all  of the computer graphics that you see

on  a personal computer and so

that's  several millions

of lines of code for  instance we took Cairo as an

Cairo is the rendering system and  composing

system that's used by Firefox  so

it does most of the graphical tricks  a few million lines of

code and turns  out dan has a mother

and his mother as

high school geometry teacher and she  asked him well what are you doing son

a  few years ago and he told her and

they  sat down didn't come back to work sat

down between Thanksgiving and Christmas  and worked out a new way

of thinking  about the involvement of arbitrary  polygons

with pixels in

math and it  looks like this

so this is the formula  they came

up with so with that this is  without any super

sampling or anything  else like that you just execute do what  that formula

says and it will give you  the exact shading for

perfect  anti-aliasing so

of course you can't run  that formula so

the next thing he did  was to make up a language

there was  rather like this mathematical

language  but which was executable so

you can  think of this as a domain-specific  language

although it's fairly general  and

it has math in it it's stream-based

  it's highly parallel

and so forth and  these the 40

lines of math over here  comes out to

be about 80 lines of  runnable code now you can run

it

and you  get

something like this  so the

rendering part will render high  quality

characters and simple shading  and

and so forth like that so so that's  80 lines

of code and of course

there's  more features and Dan also has

a brother  and this brother is very

good at math  also so he and Dan one summer made

up  the 26 standard

compositing rules that  are used in

Cairo and all of them  together was

about 90 lines of code all  right

because if you think about it if  you don't worry about optimization

or  anything else like that the mathematics

  here in the right kind of language

it  just falls out you're actually

down what a mathematician to write down  is the actual relationships

and you get  each one of these

things does a  different compositing there's the  rendering

stuff sampling for making  pictures

pen stroking so

the total today  is a little bit under 400 because

we  added a few more features to it but

  basically all of the graphics that were  used

to in 2d on a personal computer

can  be done in under 400 lines of code okay

  that is a big difference compared to  maybe

one or two millions of lines of  code in Cairo like you

can look at all  of it 400 lines of code is just a few

  pages well as a hitch

and I'm going to  end this talk with just

we get written the hitch of course is  you have to make this language run

right  so you don't want to spend three

year  you'll come up with the perfect language  and

then spend three years trying to get  it run so you have to have

another  language and

this is based out of a  paper

from 1964 about how

to make the  swirls

simplest compiler compiler done

  by a guy at UCLA Val sharee

  and we did a modern version

here's Alex Werth who did it  and so

the parser for making

the  graphics language is 130 lines of this  language

which is called Omata  and the

the optimizer

and the compiler  to a language like C is

another seven  hundred some-odd lines so that's what it  takes

to make the graphics language so  it's a little bit

larger but the nice  thing about this compiler

compiler  language is you use it for each of the  domain-specific

languages that you're  making things from and

of course you  have to make that language but

what's  nice about it it can make itself and it  can make itself

in only 76 lines of code  and it can optimize itself

in 30 lines  of code so you get the idea here all

right so the idea here is that each  problem space

has a particularly

  felicitous point of view

point of view  is worth a D IQ points if you find

point of view you're going to be able to  write down the

relationships more or  less directly

and that helps you think  about the problem you're working on and

then if it's easy to make that  domain-specific language

you just came  up with run you're

in business because  all of a sudden you've got a system

  that's actually it's actually working so

  I'm gonna so we're out of time

here and  I think I'll leave you

with that I was  gonna I think that's more than enough  information

but this is I think one

the points here that's interesting is  that this idea

is not only not new today  that's

why they're called dsls today  it's a

term we have today  turns out this idea was in full

flower  50 years ago nobody

knows about it today  because the terms change it

were called problem oriented languages  50 years ago POS

and for

a variety of  reasons this whole technique of

making  software systems very quickly and very

  in small form got dropped

when the  commercialization of personal computing

  happened in the early 80s note it's not

  taught as a as a major technique  and very

few kids in the United States  know that it's easy to make a

compiler  compiler language that is much more  general

and much easier to use than say  yak so

you learn yak and you're  convinced that it is

hard to make a  language but in fact yak is bad

the  actual techniques of making a

language  or actually run much easier to do so

let  me leave you with this idea

whatever it is that we think we know  today

we should shut it off periodically  and

see if we can find other things to  learn about thank