Rethinking Design, Risk, and Software (2012)

Introduction by Joel Orr

⊕(time: 0:06)

Joel Orr was Chief Visionary (Emeritus) at Cyon Research.

I'm Joel Orr.

this title the topic central topic of  this symposium and I'm

also using the  words engineering and science and

I'm taking the

larger sense of those terms larger

sense of engineering and the larger sense

of what science is and I want to apply

it to our thoughts

and needs about software

viewpoints Research Institute is a 501c3 nonprofit public benefit organization so so we don't have a product, we're not trying to sell you anything.

Brad invited me here to provide some alternative points of view on how we might think about software, and how we might make software because the time is going to be dire

I put my email address up there. I should put it at the end but I'll put it here so that if people have a question that didn't get answered while I'm here. I welcome email I like email.

I love it better than almost any other form of communication because I can decide when not to use it.

You all know what I mean. Okay, let's tackle engineering first.

One way of thinking about engineering is that in the world we're presented with situations. Here's a really nice beautiful one but it has a couple of drawbacks from practical human need that gives rise to an idea.

We design something we make a thing that caters to that

And, if we don't extract principles from this thing, then we're not doing engineering yet we're just tinkering.

tinkering is kind of the thing that is built

in to many mammals if you have

a at imagine what a cat would be like

if it had hands it would be basically

a monkey it would you'd come home from

vacation in your house would be dismantled

so we want to extract principles we

want to push them back into the things

so we start doing principles building of things

those principles leak back into how we design things

and they even leak back into

how we have ideas and so

this has been going along on a lot longer than

mathematics and and science and

in the twentieth century one of the ideas that gave

rise to was a really really tall

building done very

quickly here's what it looked like when

they got done with it and I'm not going to spend a

lot of time with this but when

you talk about engineering this is one of the three

that comes up is the Empire State Building

so here's an article in one

the engineering magazines that says planning

control permit erection of 85 stories of steel

in six months and wasn't just 85 stories of steel

it was 85 stories of steel and and granite

and here's what it looked like here's the site

after they demolished the old Waldorf Astoria Hotel and

then Bing Bing Bing Bing

Bing and

so they clad the thing as they went up

and the entire

building of the Empire State

Building took less than 12 months and was done by about 3,000

people so we could not muster

3,000 people to do something major in less than

a year in computing so whatever we mean

when we say software engineering we don't mean real

engineering we mean something that we're aspiring

to and this is the way the

term software engineering was intended back

in the 60s at the Garmisch conference in 1968

where it was coined was an aspiration

today if you go to a university

or many companies and ask them what is software engineering that

hey'll say it's what we're doing that is

manifestly not the case

and there's some really interesting things

so anybody who aspires to being an engineer

of any kind should be intimately familiar with

how this was done

and what's wonderful

if there are many good books about

it including this facsimile of the Foreman's

notebook one of the foremen every

night typed on his pike a

typewriter and took a picture and pasted

it in there and this has come down to us but we don't

know who the foreman was and a few years

ago it was a facsimile into

a book that you can buy and are many interesting things

in there one of

the questions that was asked

the aspiring contractors was

what tools do you have for this job one of

them was Paul Starret who was one of two

Starret brothers they had built some very

large buildings in Manhattan previously

they were one of three big companies bidding for this job

in Paul Starr and they'd all said to

this question well we have all the tools we need in blah blah blah

blah what Starrett said is not a blankety-blank

thing not even a pick and shovel gentlemen

this building of yours is going to present unusual problems

ordinary building equipment won't be built

worth a damn on it will buy and make new stuff fitted

for the job that's what we do on every big job it

costs less than renting secondhand stuff and it's

more efficient okay now in software

a first order theory that's obtained

long as I've been in the field which is 50 years is

that you should never build your own

tools it's a black hole don't

build your own operating system don't build your own computer

for god sakes don't build your own programming

languages get all this stuff from the vendors and that

way you're actually going to and speed

up your ability to

produce the thing that you want to do it turns

out this isn't true or it's true

if you can't if you don't know how to build your own tools

you can certainly get into a black hole we all

know of them but in fact the second-order

theory is also true which is if you know how to build

your own tools then you better because

you can bypass the not just

workarounds that you have to do but you bypass

an entire set of perspectives that may have

nothing whatsoever to do with what you're trying to do

and they might even be antagonistic to it so

these guys did a lot of wonderful things like this is

a narrow gauge railway that was on every floor

Building they also built

twice as many elevators that are then are there

now just as temporary

elevators but going up 85

stories in order to move equipment

around and when they got the building up they

took down these elevators sounds

ridiculous right not really

now the other thing about engineering

real engineering that makes it

rather different still from what we attempt to do in software

is this problem this

is the Tacoma Narrows Bridge

we have much

of our infrastructure is crumbling here's one

that wasn't as funny as the two Acoma net Tacoma Narrows Bridge was

funny because nobody got hurt

it was predicted by a University

of Washington professor and the

reason they got good movies of it is he said when a wind comes

up that's over 55 miles an hour this bridge is

going down and so when that wind came up they went down

to a now-famous camera

store in Seattle famous for supplying the

movie cameras which they then took out to the bridge set them

up and watch the thing shake

itself apart this one though

in Minneapolis was not so

funny because many people got killed and

a whole school bus full of children

just missed getting demolished and

so there are many casualties on this

the trace back on this

one was first they thought the

rivets were substandard but they turned out

to be right and they finally found out that those gusset

plates those square things that the rivets are

joining those beams with

by a clerical error were

about 3/8 of an inch not

3 3/16 of an inch too

thin and 20 years

later that defect

in the structural engineering plus the clerical Arab

brought down the entire bridge

so the point here is that

one of the reasons you can fly in a jet plane and

not feel upset and if you

knew what was going on in the jet engine that's right outside there

and you should if your aspire

to be an engineer you will know that inside

that jet engine there are temperatures that

are higher than the melting point of any of the materials in

the jet engine it's

just one of those great things and

but jet engines run

for thousands and thousands and thousands of hours

they're one of the most

perfect engineering creations

especially given the the

difficulties that they labor under they

have to spin at 30,000 rpm they have

these temperature problems they have enormous shear

everything else going on they're just fan fantastic

so the the thing that has

allowed engineering to advance is that people

get really pissed off when

their friends and relatives wind

up dying on some engineering failure and

so the forces of

nature plus the social forces have

conspired to make engineers rather careful

and of course if you think about the parallel

situation inside of a computer the forces

and the masses are slight so

many software systems are starting a crash

that will take 10 years to happen

from this instant they are actually deployed they're

completely buggy but the gusset plate is

going to take a long time

to gradually come apart as

it gets more and more complicated and so

when you move from something where

doing something out in the world of forces

and masses

into a world where you don't have

these you have to have an artificial sense of discipline this

omething we are still learning how to do

now we don't know what happened

to the Roman engineers who failed

I

cement the world has ever known took a long time for us to find out just

really about 10-15 years ago to find

exactly what the secrets were of the Roman

cement and how about

this one same age again

if you walk right up to it looks holy smokes

this thing looks like it was built yesterday if

ever been to how many people have ever been to the Pantheon in Rome she's

been to Rome you've been to the Pantheon

first time I went I thought wow

you know looked like it was made out of granite and then I found

it was made out of reinforced concrete how many people Rios

people here might have found Atta do you realize how

many people realize it was made out of reinforced concrete

far to say that the big art forms of the 20th century were science

and engineering that's a lot to

live up to for something that wants to call itself

a science like computer science and wants to call itself

an engineering discipline like software engineering

some comparisons here I just note that a 400 page book has

20,000 lines of text in

it each one of those could be a line of

code so 20,000 line program

is 1 400 page book and foot

of books is about 300 thousand

lines million lines of code per meter there's

an easy one to remember and

the Empire State Building is about 441

million lines of code high as stacked

books is 20,000

5050 lines of code at a time stacked

up that

sounds like a lot but in fact a

companies are wrestling with about this much code

personal computing

which I had something to do with a long time ago Microsoft's

essays into it provide

operating systems over a hundred million lines of code and

application Suites of well

over a hundred million lines of code you

look at that you think good that are

we really getting that much bang for a

couple hundred billion lines of code I hope

you're saying no I hope you haven't

gotten complacent about this so personal

computing 250 say 200 million

lines of code but of course it's not really

that much the problem is that when you make a lot of code

there's a point where people their dependencies

are so intertwined the engineering has been done

so poorly that people start becoming

afraid to remove things so

they just let it stay there and they start layering over

it

big this big software

company has now gone over 400 million lines of

code I haven't changed the slide yeah I used to use this

analogy because I mean a pyramid is kind

of a garbage dump plastered over with limestone so it looks

nice just a big accretion but

when I look at this picture I think I can't use that metaphor

anymore because look at how modular these pyramids are

we would love to have that modularity in the

code that we write so I think the picture

that fits our code better is a slum

bulldozers out some stuff but just leaves the stuff lying

around never carts it off and and so forth

so the real question is not whether we

can improve on this but

what is the actual level

of improvement which is

tantamount to asking how

complex is the

actual problems were trying to solve compared

to the complexity we're creating by just bumbling around

so let's

ask a question about what science is now that

we've talked about engineering so real science

looks at hairballs this is my favorite hairball

it's about this big recovered from the stomach

of a woolly mammoth found in a glacier

but because hair balls are ugly to

look at but it got your attention especially

and I say how big it is

and it represents complications

but know the universe is good enough

the starry night it's pretty air but it's still

spent thousand of years misunderstanding

it in a bunch of ways and

we little creatures with our brains have languages

we can make all of our languages out of

the scheffers stroke here this is the NAND

operator and

because we have tiny little brains we

invented mathematics and we

up with something like Maxwell's equations or heavy sides

version of Maxwell's equation and put

a large part of the phenomena

that we're looking at out there on a t-shirt and

this represents complexity so

the complexity of the electromagnetic field

in large amounts

of it enough to invent a radio

and radar and etc etc

can be represented by a couple of these equations and

these equations should

be symmetrical

because the magnetic field and the electric field are

trade off against each other with the art so

if you worry about that and you happen to be

named Einstein you will come up with the special

theory of relativity which will symmetric

put symmetry into the whole thing and you get down to two

equations just like you should have one for the electric

field and one for the magnetic field now science is not

this so the problem is when most people take

science courses they're taught the t-shirt but

science is actually that

science is the the

realm of the relationship

between the phenomena that we can't get to and

similarly we can

do science with engineering

because it's an artifact that has phenomena so

we can actually look at that bridge that we

built there and scientists can look at what engineers

do and make a t-shirt of it so

we have t-shirts of what bridges are

their theories of bridges then the cool thing this

is why it's great to be alive today the

cool thing is you can take that t-shirt and

make one hell of a bridge

so engineering was around for a long time but it didn't hit

its stride until it had this beginning

the yang here event of making things and

looking at them the special way that science does

and how

big is this bridge well see those little boats down there those

are super tankers so

just for comparison there is our Empire State

Building so the pylons on this bridge are the height of the Empire

Building and there's the Great Pyramid of Egypt so

this is the asahi bridge in Japan

just can't beat it so

lovely and so this is

what we want to think about we want to think about real engineering

we want to think about real science computer

science is not real science ask anybody to give

will give you an engineering definition and part

of is because computer science persists in only building

doesn't spend a lot of time trying to understand them

so

you can look you know computers

we're built there you have their

complicated artifacts programming languages a complicated artifacts

you can do the same thing John McCarthy looked

at them and being a mathematician you

want a G shirt so this is the

programming language Lisp on the t-shirt and once

you look at that you realize whoops programming is not

as complicated as I thought all the semantics

I really care about are not in these in humongous

Fortran or Java compilers but they

actually very small and when I make them

small enough for my tiny little brain I can actually think about to manipulate

them whereas trying to go

over on the other side and mess around with

directly to make it better it just never happened so

John came up with a mathematical theory of computation

and those

of us who came in the next generation after McCarthy

got to do some really fun things with

this idea I should mention that what

I'm talking to you about actually happened

in the 60s and it

bore some real fruits but in fact it never made

it into the world of the 60s which was

dominated by IBM or the world of

the 80s and 90s which is dominated by Microsoft

didn't happen so most

people program in a way that is strikingly

similar to the way programming was done around 1965

so let's take a look at the idea

of tactics versus strategies the

simplest thing when we have materials is to think tactically

have a bunch of these

materials piles and stacks

simplest things we can get out of those hardly

any design effort at all as pyramids and walls and people

did this with bricks for thousands of

years before somebody had a strategic thought

which is hey

let's make something out of the bricks before we make the thing

let's make a new kind of building component

a different kind of structural integrity than

a pyramid does and all of a sudden we can kick ass

took thousands

of years to get beyond what

the brick forced into our minds by being in front of us

to this very odd thing that

requires more building materials to make than

when you're done this is one of the hard things about an arch it's

hard to imagine it because it doesn't work until

it works so people just ignored it

and the same thing happened in computing

where we build things out of Nan's

basically computing is all about comparing

things so you can take

materials like even two

something that first graders love is they can do any

fractional arithmetic problem

their ten-year-old brother can't do and

they can do it in two seconds with two rulers

all right because the ruler is an addition slide rule you

show this to a kid they'll love you for life

just compute the whole thing out ahead of time

and say here's

what it is and by the way you're teaching them vectors at the same time

and so it beats regular fractions in many many

ways and of course the Romans and the Greeks

Romans had a socially

accepted QWERTY form of numbers called Roman numerals

but they didn't use them most

people use this the wrong way the Romans and the Greeks had

abacuses to compute with this is

a Roman abacus and those stones are called calculi the

calculus came from this

notion of what they pull out of your teeth

also when they you go to the dentist to get the plaque

removed they call them calculi and

flip-flops didn't come

people wanted to do computers but because a couple

of Brits wanted to see if they could make a memory

that could do some of the things human memory could do

out of materials because

there's a vitalism was a big deal back then

and ENIAC

but the problem is matter is inconvenient for

this it's just

you know basically the scaling problems

overwhelm you very little return so

you need to go strategically and

of course this was done a long time before with

a jacquard loom von Neumann architecture x'

by anointment style programming languages and

even the programming languages of today like Java which

is also a von Neumann style language in

spite of a few trappings it has on it and

basically the programming that we're doing

today has

not stirred from this in 50 years

there have been about 3000 different programming languages

invented and some of them really useful but

style of programming that is used today is almost

entirely this style and it's

tyle where the programming language is rather similar

to the underlying storage mechanisms

and control structures of the machine there

are few convenience is there but not a lot and not

enough but of

course there are always weird people out on the fringes

who like Turing himself

who did things are completely

different for instance sketchpad which is having its 50th anniversary this

year computed by

you programmed it by putting in

constraints that sketchpad had to figure out

so instead of writing solutions

to problems you just gave sketchpad what the problem what

the nature of the problem was what the nature that would

characterize a solution and sketchpad had three problem solvers

and in 1962 it

would solve those problems for you by

the way he also invented computer graphics while

it and also this is the first object-oriented

system I know of so those three things were

done by Ivan Sutherland as a thesis project in one

year one person and I once they asked

Ivan how could you do these three things in

one year he says well I didn't know it was hard

Ivan as a genius but

he had also had the advantage of just

aiming for what we really needed he

worry about whether it was hard because nobody knew

what was easy and hard in 1962 he just went for

what we needed which is an interactive system that

you could show sort of what

you wanted then tell it what the criteria were to

the job up and it finished the job up great

too bad we don't have it today the

Internet another thing which

is almost completely ignored by computer people why

because it works too well most

computer people certainly most people in the world but most computer

people even don't even think of this as technology it's

completely different from the technology

they're used to the internet has never gone down

started running in 69 but

not continuously since then to replace all of its atoms and

it's bits without ever being shut down

people are shutting down their software systems

the time they shut down individual servers even though

a server is just a name you never have to do that

so what we've got is two cultures here we got

culture that was able to make something get scaled successfully

of magnitude we've got

another culture who that who can't even appreciate the

feat of engineering and science that

took to do so

the Internet is really the only extant true object-oriented

system in the world right now and then

there's of course AI which

people have lost interest in just as

it was starting to get really well work done

so we should

be spending more time thinking about about

this now one little

blast from the past 1973

we showed this machine and this the

system notice

overlapping windows there was a view

oriented object-oriented system

desktop publishing was

essentially the views

without the borders on them these

split up when they came out into the real world but it's Xerox

theory one thing there was no operating system

because you don't need one

okay and

so when Xerox asked us what we

were we're doing we gave them

our own version of Paul starts

speech not even a pick and shovel so so

part of the deal at Xerox PARC was we built every bit

of the hardware and software ourselves from scratch

we did not go out and buy

commercial computers we did

not go out and buy commercial software

the reason is this stuff was so different that

we would have spent all of our time running

into walls that were simply irrelevant

to what we were trying to do

then the cool thing was this was

a tiny machine 128 K Ram

and we used half of it for the display

so it had a display about 800

by 600 and

all the software from the end-user down to

the non-existent operating system down to the

metal of the machine was only about 10,000

of code it was done in a language we invented specifically

for the purpose so

one way of thinking about this is in the end no

matter what it is you think you're doing

math wins the

thing that is math the mathematize ation

of an idea dominates

all other things once you've done the

spadework around the edges at some point you have to

sit down and do something like mathematical thinking in

order to collapse all the

things that seem to be artificially dissimilar

into things that are similar this is called

making an algebra and this

was a technique that we use pretty generally at Parc and

so in a

few years we did this the

bitmap screen the GUI

WYSIWYG in desktop

publishing real objects laser

printers Ethernet pierre-pierre

and client-server and about half of the internet

now what was interesting about that perhaps is

it was only two dozen people

any company

here could have afforded to do this two

dozen people just ten million dollars a year in

today's dollars you could do it right now

you won't

do it though nobody is doing it you

won't do it in spite

fact that it returned 30 plus trillion bucks

returning about a trillion dollars a year still

so this is a huge win but

in fact it is against what

most people think is good practice I

don't mean in computing only

I mean in management okay

so we'll leave that because we don't want to talk about the past too much but

I just most people do not realize it was only two dozen

people that is only four to five years

to do all this stuff and doing it all from scratch

okay

now let's take a look at a

science project in the present same idea

here so all of these ideas I've been talking about here

we're going to apply them to this big thing

called personal computing and it's got all kinds of stuff

in it so

again we're interested in this area that

goes from the end-user

all the way down to the metal so there's a lot of

stuff we want to know

how many t-shirts does it take right

so this is a completely different thing

here we are not interested we're doing

science here now we're going to have to do a little bit

of engineering because the only way we can validate this science is by

making it run but this is

a science project we want those t-shirts

so let's take a look

so let's say half of

what Microsoft or Moore has in there is just

code they can't get rid of but still most

of these you know Linux and

OpenOffice and stuff

it's like a hundred billion lines of code so it's a lot

of a lot of stuff of different

kinds and

so

now here here we have the

we're kind of behind the eight-ball of

having a brief talk so this part

to do this part in a

reasonable way would take a couple of days so

I'm and I hate bullets so please do

not regard these as bullets just look they're ten blobs

out of twelve or thirteen blobs that

we used and what they

are are things a couple

of those we invented but most of them were things that have been around

for 50 years that didn't make it into

the particulars einer that

computing is see the difference here

is nature helps physicists be

honest because in the end you're

supposed to submit your theories back to nature but the problem is

we're a design field so our trade is bullshit

that's what design is and it's

good and there's bad and there's indifferent a lot

a lot of stuff in between and so

we can have a fad like

for instance semaphores semaphores

were known to be a bad idea when they were first invented by

Dykstra and whore but in fact

in that culture they took

on and for a variety of different reasons

being taught today and they're still being used today why

are they a bad idea anybody know

what's wrong with a semaphores

gets a

system lock up and there

is no way to do something

time that will tell you you won't get system melaka so

this is like the world's worst idea to

let the cpu control the time base

of your computations you're screwed

may take a while may

happen quickly but it's just basically

a bad idea and there was already a good idea also thought

up by John McCarthy in the early 60s

that actually works and works much

it's just not generally used and it won't be used for

a while because these things take decades

to get rid of things that are you

know de facto religions so I'm

just going to pick on a couple of these I already talked about math wins

and I want to talk about this

now in this area

up

around in here because we have to

one of the things we have to do in personal computing

today is make anti-alias two and a half d computer

graphics and we have to make it look really

good and if we didn't have it look really

good then we will be sloughing

off part of making something

that is like computer graphics that

we can recognize today so

of course whenever you have a hard problem you

get a graduate student to do it because

they are just so much smarter than we are

a mom of course we all do but his mom happens to be a high

school geometry teacher of that special kind that

actually understands math and so

they

do exist and so he went home Thanksgiving

three four years ago and his

well son what are you doing at viewpoints and he

said well I'm I need to reduce

the amount of code to do computer graphics on

a personal computer by a factor of a thousand and

she said oh that

sounds interesting what are you

going to do and in fact her expertise was in

projective geometry so Dan

did not come back from Thanksgiving he stayed

working with his mom he and his

mom worked together on this thing through Janney finally

showed up in January with this formula

and so this is one of these serendipitous

things that you can't completely

supply by method but because this stuff has been kicked

around by the best people in the world for

forty five years but in fact they found a new way of thinking

about the involvement of an arbitrary polygon

with a pixel that computes

exactly the right shade of the pixel

to give you perfect anti-aliasing and it's

only that big and the next thing Dan

did was to make a math

a language out

of the mathematics that looked like the mathematics but

could run on a computer and when you tell this

these 45 lines of code to do it

they can do things like this they can render anti-alias text

and anti-aliased

graphic objects so that was a great

start and this

language that he devised he decided to

make it a dataflow language with kind of

functional engines at the nodes and

because of that it is highly amenable to automatic

use of parallel resources

so here's

one of our benchmarks so this is 5000

anti-aliased

translucent characters

being re rendered every time

by this these 45

lines of code so there's no caching or anything

here and we can take a look at the CPU usage

and we can see that it's basically using so

this is a fork or Mac but

the the cores can handle two highly

interleaved threads at once so

seeing that one of those is being devoted to this task and

the fan has come on and

as I say go faster it says

okay give me another thread there it is

go faster and faster it starts interleaving them I can start zooming in

here so you can sort of see what's going on a little better you

got enough monkeys

yeah so

so we're actually quite surprised that

this is a lot of computing

but

we're getting a lot of a lot out of it so

I thought was wow we

should be able to make a whole system just out of this math

and so this is in fact

what I'm using so I'm not using PowerPoint here it's

probably obvious by now but here is this

system that

is this replacement

for personal computing and

now here's my next slide and

here's Dan's brother and here's all the compositing rules and

so for instance here's

this one is invert

here's another

combination rule here so

it's 95 lines to do all of the compositing rules that

you use so now that you've now

that you've seen and of course there

are those

45 lines there's gradients so

I should actually now this user interface

here is made out of the system also because

it's all one sort of one big desktop publishing document

and I'm going to the system is also live

so this has not been compiled away into

C code down there so I'm actually going

to pick an object here like this

this menu

here and I'm going to say

okay let's change the gradient fill on this

and

yeah something like that maybe or maybe we

don't want as hard an edge so we'll zoom

this out okay get the idea so the whole system

is live it's using its own stuff

here and

we keep on chugging along we

can see here's pen stroking

filtering and so all of

the graphics for personal computing in this little science exercise

came out to be four hundred and fifty seven lines of code

how do we know is 457 because you can count

457 things

it's hard to count 400 thousands or

440 million or so on so this

is about a thousand times smaller than the way it is

done say in Firefox right

so a thousand should get your attention right you

have to learn a new language on the other hand here

it is the whole thing is just a

few pages of code and it's live and you you

can run and use it okay

so now

of course where did this language

come from I have to build that and so

there's actually quite a distance between one of these compositing

rules a one-liner of code and getting

the result at the other end and so I might need

remove the hood and show you what's there and this looks a little more daunting

because I have to

translate this language into a standard

form I have to

translate this into another language that is sort of like see

I have to translate to

something that eventually will run on the 86 architecture

that's on this machine that looks

like a lot of work and of course I run the system

runs on multiple processors so I have to do that and

so one way of thinking about it is gee looks

like there's a lot of work in these square boxes here

and all so we need another special

language so we've just showed

you a domain-specific language

in the old days you are called problem

oriented languages I like old terminology because I'm old

and so we need another problem oriented

line language here whose problem domain

is transforming languages and of course there are tools

around for doing this but this is one of the

big stoppers for people doing this themselves is

making a language the way it is

a daunting procedures usually taught through

some set of tools like yak this is not the way

to do it but the poor student comes

out either not understanding it all or convinced that it's

too hard but in fact you

can make a much better language so here's

one we did called Omata and here's a simple

arithmetic expression and here's

the program and if I say do it it

transforms that expression into a tree

and if I come down here

and

pick this more

complicated expression up and paste it in

here and say do it I

get that and so that this

translation system isn't just for translating

things that have strings it translates any kind

of objects into any other kind of objects so you want to

do it in that generality because you're going to use this everywhere

and so

this graphical language winds up being 130

lines of code to make the trees and another 700

lines of code to make the

lower-level stuff that is actually run on the machine so

you could say a thousand lines of code 900 lines of

code to make that graphical language

but this

like a shaggy dog story right because well now I

have to make the language transforming language and but

of course it can make itself and

Alex Wirth did it particularly nicely so it can make

itself in 100 lines of code and

as you get closer and closer down

to the bottom of the system you start using these base

things over and over again and the

problem oriented language is stay on the top

okay but we have to do

the internet so tcp/ip

20,000 lines of

c-4 that so one book one of

those books this happened the C code and most

computers is a good nice job and

tcp/ip was invented by experts and

so we didn't expect to get a

factor of thousand on this in

pia Marta only got a little more

than a factor of a hundred so

tcp/ip is about 160 lines of code

using these techniques and if you're interested I can

exceed this is particularly elegant the way he did it I'll

explain it later for people who are interested so

this is represents taking

something it was done by very good people but in the wrong language

and done essentially the in a way that

generated unnecessary code the

big key here was not so much the language

as the method

the method came first and then we did the language

too to do it and so the whole thing collapses down

okay

let's take another one of these metaphors particles

and fields everybody understands this as a kind

of a metaphor for iron filings

in a magnetic field the iron filings seem

to know what to do they're feeling this field

and they're doing individual things that look like they're somewhat

coordinated and in fact there

are animals that do this also like ants

so here's a little ant simulation

this is their nest these guys are

their food and if we start this thing going

then we see that the

ants are finding the food going to

the nest and they're leaving this pheromone trail so this is using

about eleven thousand parallel

processes because each little cell is

being used to diffuse the perfume

the ants lay down you see right now all

of the ants have been captured by the field

they're not communicating directly with each other they're communicating

in directly with each other and we would call this

loose coupling well

if you have crazy brains you

could imagine that a paragraph of text

is one of those things

who's who said you could do the encyclopedia

over here thank you

simplest thing that you can get fifth graders to figure out is well when you have to do

something follow the guy who is in front of you and if there's nobody in front of you

go to the upper left-hand corner and if

you find yourself over the the right-hand margin tell

the guy in front of you and eventually somebody will know what

to do like go to the next line right so

let's just say go do this

so I just gave you like a three

line formatter

right so this is bath

I'm going to set it off again

and I'm going

to point out that I can edit this let's let it

so I can turn general here into

justify

and it's

just redoing this over and over again all

right so the editor is actually working

faster is to say hey don't follow the leader here because this is just for the crowd

when it's time to

wait until the guy in front of you gets to the right place

and then go right behind him so this

is called jump and here's what that looks like now

they're all standing still

okay

and so if I start jump here and I just say now do this in between frame times

bunk

okay so

all of the editing here is done like this and

in fact this is another

one of the documents in this Universal document system

remember we have to do Microsoft Office here but

why would anybody in the right mind give you

seven applications that all do almost the same thing

but not quite

well the answer is

simple because amazingly people will put

up with this silliness enough to buy it from them and thus

encourage them further

this is nonsense

really

what you want is a universal document in just different

ways really don't

a different system for the World Wide Web do

you know because it's just a different way of accessing

these multimedia Docs so

this is a this is actually how we write code so

this is an explanation to somebody

like you to

show in a

little essay how the how the code actually how

the code actually works so

we start off here and

thing we want to do is just get these guys

out there randomly

so these two lines of these two little boxes of

code do this and here's

one that just does this without the worrying

about the next line

and here

are some special cases

so

this is a reminiscent of the way Knuth sometimes

likes to program so

you actually learn the thing while you're while

you're looking at it you can do little experiments and

by the time you've done that bingo

you've got the thing

and so here the this

is a yet another problem oriented language but with

rules this time so rule base

so two seven rules for doing the layout

and

the whole editor

like microsoft word paragraphs

is 35 rules

okay so that's all you have

to do for that

's very useful to have is problem-solvers

but the problem with problem solvers and

these these problem oriented languages they're problem solvers they

aren't integrated so you

wind up with a hodgepodge of different things

serving different things and you want to be able to integrate them so

so here are we can have a simplex solver

differential equations propagation dynamic programming

relaxing the rules for

the paragraph these all have different problem domains

and what we want to do is to approach

strategically as I've been talking about here through

general relational languages that

talk to a kind of a little

operating system for solvers and often

with the help of an expert system that helps make

a strategy for using these things the

further stuff here is beyond the scope of this

short talk so I'll just move on here

okay

here's an important idea too

many ideas in this talk right but

here's something that happened in

the 60s except nobody noticed it except maybe

the ARPA researchers and the outgrowth

of that to Xerox PARC and that is that

the entire paradigm of computing because

of Moore's law was going to be able to shift

from a gear like way of doing things

basically tight coupling a

brittle code to something

more like ecology something more biological and

we use these ideas very strongly in

the invention of the internet and in the invention

object-oriented techniques that we used for software at

Xerox PARC and the deal here is

you can fix a clock but you can only make clocks of a

certain size maybe a thousand gears B for the

dis class this is what happens in today's programming

you have to negotiate with the system and

a lot of what growing the old world into this new world

is learning what it means to do this kind of negotiation as

you add more system elements so

the

is just a couple of minutes of gloss because

I'm getting close to the end here but I wanted

to say something about systems and

three of the ideas here are that

we want to control time I mentioned this a little

bit before we do not want to CPU to control time first we

control time and we'll do it by simulating

our own time we don't want to have

any preferred center so we want to have something like the internet

all the way down and we want loose coupling

now of course this has all been done before again

with networks and

here's here now we're looking at physical computers on the

internet or Ethernet and ideally

we'd like our computations

to be software versions of

these hardware networks why

because

in most cases we will need to do load

we'll be able to run all of these guys on a single

machine in which case we just have increased

integrity and ability to

design quickly but a lot of the time

especially now with mobile we want to have the same

computations able to drift around

the network to different kinds of devices

and in some cases not all of the computation

is going to want to be in the machine that is next

to the user interface so if you think of the user

interface here it's basically a set of views

of processes that are

giving it images to integrate

up on the surface so

here's a great thing if you're interested Dave reads

1978 PhD thesis at

MIT the design of

an operating system for the internet was

never never done but we validated it a

viewpoints so this

is what if you want to make a system that is the size of the internet

hat is a software system what is it that you have to

do to absolutely ensure that

your going to be that your you're going

to have what people like to call data integrity

always so that no matter where you

ask a question anywhere on the network about anything anywhere

on the network you will get the same answer for

the pseudo time referent

of that question and

a working version of a

migratory system was done by Jerry

Popek I put this up here because this

written in the 80s you can still get it from MIT press

the locusts distributed system architecture you'll

find the entire book interesting just a

little volume but the first two chapters are a classic

still of the issues you have to

think about and solve in order to do this but

again this has all been done this is almost 30 years

old now but nobody uses it

but if

you were to use it all applications are now just mashups

right we don't want applications as smoke stacks

because we want to integrate so we didn't have

at Xerox PARC and we did not have operating systems

and the current web

which is getting more and more complicated can immediately get

simple except for all the legacy stuff that

has been done so far so

here's something that viewpoints has

on its list but it's we

expect not to be able to solve this at least in

this path of doing things and it

may be beyond may require a much larger

effort than a small nonprofit

can do but if you think about analogies

to what's happening with the scaling that's

going on here it basically starts looking like

a biological ecology

and these have their own dynamics

and they need to be thought about in special ways

so the ability for us

to scale to what Moore's law

allowing right now is going to require us to start thinking more and

this and we did this to some extent when we did the internet

like I have a degree in molecular biology in my

misspent youth and so

a lot of the ways I think about this stuff is through

tissue biology and how

the hundred trillion cells in our body work

without having a dedicated Center and so forth

but in computing we have problems of

our our own that are special then the next step

beyond this the best book I've ever seen about it is

Minsky society of Mind book which is actually

about a model for human psychology but in

fact it is a very good model for

what the Internet is going

to turn into okay

so the punchline here we've got three main operating systems

I won't say which one is the lemon and

down

on the bottom here our little

Frankenstein monster that we made is that I shall

been showing you today is less than 20,000 lines of code so

it's worth pondering we

are not using any of the Macintosh software

in order to do this demo

and

here's just to kick off I

think I'll just show this slide and then quit

I organized the talk so I can

stop now at any place and I need to in about a minute

but I'll just leave this one thought

with you maybe we can have a few questions and then

maybe more will happen I think there's

a smaller group question session

coming up later but

let's think about this idea something appears and we've

got two things about it that are very different there's

news and new news is the stuff that

is incremental to the categories

that we already know almost every bit of news

is a specific parameter

into some category that we already understand

so it's this war that killing

this marriage

that hems is that

right and you can get a quite a bit

of this out in a few minutes

so there's almost no context to news that it's

not already inside of our own head

new on the other hand real new

not news

real new is invisible we don't

have a category McLuhan said until

I believe it I can't see it that

is the way it works in the human nervous system and

so news is something

that's been going on for the entire existence

of the human race about two hundred thousand years

it's basically campfires and this is what we're doing right now this

is a campfire I'm doing the best I can do

in an hour of telling stories in a campfire

but the problem is new

can take two to five years

to get the new categories

that you need in order to actually see it and one

of the unfortunate things that happens is that

new when you try to talk about it and people make

on it they usually transform it

back into news so for instance

we did this as a way of boosting

mankind it was

all about learning by doing but

in fact almost everybody in the world uses it only as a

consumable device for their

own convenience I would spend

thousand five hundred dollars which is the price of an average

American car for a laptop if

I could because I know what computers are good for

but in fact people only value them as as much as

they value their television sets and they use them roughly the way they

use their television sets so the big

problem whenever something new comes along like

personal computing and the internet is that

people when they see a

convenience to themselves they recast it back

the forms that they know about so for instance object-oriented

programming never made it outside of Xerox PARC

only the term did we got designer jeans

but designer jeans are just dungarees

with a fancy label on them thank