Alan Kay Interview for nerdear.la 2020

living organisms can survive in their own waste products

foreign

hello everybody uh

thank you for inviting me to talk

i thought

my bio

is very very brief

pretty much everything i did uh [Music]

came from my research community

and

nobody owes more to it than i do

i'm not going to talk much about the research community

xerox park and

arpa but occasionally i'll refer

to it and

the

talk today

really is in three short

parts because i would like to get

to the question and answer session

i think that would be the most interesting for

for all of us i'm going to try and

get through the talk uh quickly

enough to leave time for questions and answers

and uh

maybe some of the things i say will help

with the questions and answers so

three parts are

about pollution

the kinds of poor thinking that humans do that

gets us in trouble especially

software and other parts

of the world how to find better

than the ones most of us

pick and then we'll get into the question and

answer session

so the first uh

idea about pollution here is this very old slide

that goes all the way back to the 1950s

of a cartoon character named pogo

looking at all the trash and

he says we've met the enemy and he is us

and that's even more true today

because the amount of trash

we have is

much much larger in fact

we can see that

just one of the trash cycles

in the middle of the pacific is almost the size

of the united states

and this is

just the surface that we can see

so they estimate that perhaps 85 percent of

it is below the surface

and whenever i see pictures of this it kind of makes

me think about software

it's just a really

immense amount of it

bigger than most countries

and one of the reasons we have it it doesn't

seem like you have to do any real thinking

to use

trash make trash throw it away

put it somewhere else and

it just gathers up and gathers

and of course computing is that way

most of the software that's made in the world

is not done thinking about the future

in fact most of the costs of doing software

are because of what wasn't

done in the present to

make the 85 percent of the costs

of software in the future much much lower

instead everything is done right now in the present

it's done too quickly

and we wind up with this immense amount of

trash and of course the software doesn't go

in the middle of the pacific ocean

so it's perhaps it's a little bit

more like a slum that's

the size of the united states

perhaps we're living inside

of this

software trash

or slum that's still running

most of it hasn't been turned off

and there are many

go with this i'm just

going to pick one for this

talk so one of

the historical reasons for how we get

into these kinds of troubles is that in

our genes like many

animals many mammals

especially primates our

natural instincts are to tinker with things poke

see what happens if i do this what happens if

i do that and not worry about the consequences

it was hard to get hurt 200

000 years ago and

so trying this and that

's natural it's not something we want to get

rid of if you don't know what else to do it

is kind of fun to tinker

but take something like clay which

resembles a computer in that

you can make the clay

go anywhere you want you can push it around you can tinker

it forever but it's

really hard to tinker it into something great

you just don't get

by it tinkering

into something that's wonderful

like this statue of

uh voltaire

what you get is kind of a mess

to get voltaire you have to

learn a lot of things you have to think about a

develop skills

what you get when you tinker is

omething like this child making a little cup

and when you see something like

this with software

you can complain and say hey

that's not good enough and

these days what people say is well this is

a start when i complained about the world

wide web and especially the web browser

more than 25 years ago i said well

is just a start this is a start we'll we'll make it better

in fact they didn't

many of the things that were wrong then especially

with interaction and

end user creation of things

are as bad today or worse

so what you get when you start off badly

with a tinkering frame of mind is

something that looks like it was tinkered

into existence which is great when a child does it

but you really don't want adult professionals

to be putting a zillion of these out

into the world so if we come back

to tinkering historically we should ask well what came

after tinkering and the answer is

engineering which is

making things using principles

so by trying things out you find ways that

work you remember those ways that work

them up they may even be literally

a cookbook or they're like a cookbook

and then you have a set of things that

are likely to get you faster

where you're trying to go and to get you something that's

better now you still have to make the thing

so making is part

of both tinkering and engineering

but basically in

any real field

that involves making things we want to be able to move from tinkering

engineering so that the goal is engineering

and of course even bad engineering has

principles there are bad ways to cook

things using principles so we also have to ask

are the principles good enough

and after engineering

thousands of years after engineering

the kind of mathematics that we recognize today

was invented about 2500

years ago and it also involves

principles but the principles are about reasoning

and representation

and about 2 000 years later

what we call science today

was invented

one of the most important inventions of all time

because its principles

are about how we negotiate with

the bad stuff inside our heads our brains

ask the deal using

the kinds of belief structures and

memories and everything else we have in here with the phenomena

that we experience in the world

and so it winds up being a negotiation

and it is a deep skill

it's not something built into the human race

it was invented very recently and it's

one of the most powerful things that we've invented

it's also not understood very well

and people who are good these days

work

in a sweet spot

of all four of these things

and if you think of yourself as an engineer

these days you still know mathematics

you still know science you still know

tinkering you think about yourself as a scientist

you still know mathematics you still know engineering

so where you are on this scheme

depends partly on your personality

but basically the people who are really

good use all four of these things

and try to choose which ones are the most

reasonable at time to time so

get into this sweet spot is part of the

idea of being a modern

practitioner in a

stem field now

if we look at computing and ask where is it

it doesn't look so good it's

mostly tinkering still today a little bit of engineering

tiny about a bit of math and almost

no science and

most of the engineering is in the hardware

why is that well it's

get away with murder

with hardware software

people

can make things that are quite dangerous

but in fact because they can be debugged and fixed and patched

and so forth they get away with

much more than any piece of hardware

can so much of the engineering and computing is

in hardware and the vast

majority of software people

think it can be done without learning about engineering without

learning about mathematics and without learning

science and that is

kind of where this mess comes

from now when i started

off programming i started

off like most everybody else

which is not knowing anything

uh important about it i happen to start

a long time ago around 1961

in the air force

but i think my situation back then

was similar to what it is today and that somebody else was

choosing for example

what computers i worked on

they were choosing what operating system in

the case of this machine it didn't have an operating system

which in some ways made life simpler

they chose the programming language

for this machine it was assembly code

development system well it didn't have one

the design or lack of design was represented

in flowcharts back then

done by other people

the legacy system were huge

punch card systems

and the job for us

coders coders were

people who turned flowcharts into machine code

so we were essentially compilers of the higher

level language of flowcharting

and nobody cared

my opinions and i also didn't know

much so that was a situation of course

after after this i went on to

do slightly higher level programming on

supercomputers still mostly in machine code

still with

other people choosing the machines and the choices and so

forth so

now it had happened that i had gone to an engineering

high school and

i had gotten

a undergrad an undergraduate degree

mathematics and another one in molecular

biology so i knew

something about engineering i knew something about mathematics

i knew something about science but it

just wasn't in the computing part of things

which i used as a job to put

hrough college so i was a journeyman

programmer then in 1966

i accidentally wound up in grad school

my theory was spend a year

learning something about computing and avoid

getting a real job and avoid going to grad school

in math or biology and by

accident i round wound up in this

advanced research projects agency

research community that by 1966

had already invented quite a few things they didn't

invented the first parallel computers

real-time computers with displays

and pointing devices they'd invented

the first interactive graphics system

of a modern kind time sharing

uh artificial intelligence

there's already a first little personal computer

the tablet the mouse

hypertext and

they were starting to talk about uh making a

packet switching network which was called

the arpanet they hadn't done it yet

so that was the world that i found myself in

and it was full of people who were

completely unlike the people that i'd worked

with in the air force the people in the arpa community

they all had extensive

of them uh phd's

in engineering of some kind

like electrical engineering or mathematics

or science

and what they were trying to do

take what they knew about these developed fields

these difficult fields that have been developed

over many decades and hundreds of years

and to see what they could use

from this experience in this new

world of computing and

so pretty much everything i had learned

as a journeyman programmer was worthless

the way i went about doing it was worthless

so i wound up having to learn

again but this time

from this point of view of engineering math

and science that this community had

community not only made

its own software made its own programming languages

but it also made its own hardware whenever it needed it

so it's basically full spectrum

computing and in order to get

any kind of degree there you had to learn how to do this full

spectrum computing of hardware and software

and there are some

really famous people who will

be known to you today perhaps the patron saint

of programming don knuth

was there and he his

degree was in mathematics

and turned himself

into what i think we'd all think of

as a great programmer i thought he

was is still hanging

in there doing well and

one of the things that he tried to get people to understand

is this saying premature optimization

is the root of all evil

and it this was a

hard thing for him to say back then

because you could hardly get any computer back

in the 60s to do anything

without optimizing they were just really

slow like they were a million times slower

than your cell phone and

more than a million times smaller

but don pointed out that

most parts of most programs

doesn't matter how fast they run what you

really want to do is get the program right

identify the places that run too slowly and then find

a way of optimizing those

without disturbing the fact that you got the

running and

there are many other sages back then here's

bob barton um my

world's greatest uh computer designer

designer of the furrows b5000

he was a mathematician also and

the things he like to

drum into our heads is that good ideas don't

often scale and of course there

are many other principles from back then

i could make a a

three-day course out of just the principles

that these people used in the early

60s but for this talk i thought i'd just

stick with don's idea that

premature optimization is the root of all evil

and ask well what is it that we should be

thinking of then because we still want to

do something and if we can't optimize

what is it that we should do

well one thing we can do is try designing

that's a new thought to a lot of computer

people they just jump in and start

writing code and think about well we'll design

it later but it doesn't work so well on the other hand

you have to write something because it's

really hard to design a whole system before

trying things out it's too complicated

but you start designing

if we are not going to optimize prematurely

somehow have to separate meanings from optimizations

and this is something we can bring up in the question

and answer thing of what are the different ways of doing

this what does it mean

to separate a meaning from the optimization

for now let's just think of it as a meaning is the

write no matter how slow

that can be debugged

and

allows you to know that you

this meaning captured so like if it's

if your idea in one part of

the thing is you have the idea of sorting

then your little module layer

would be the simplest way to do a sort

it could take forever to sort but it guarantees to be

you something that

you can use for testing

things and also you can use for testing out the optimizations

and once you have the system running no matter how slowly

you can then carefully add in the optimizations

and you can add them in in a way that they

do not pollute the meanings in fact you

can use the meanings to test the optimizations

and by testing

the the optimizations you

want to constantly be able to turn the optimizations off

see whether this is that part of the system

still runs or not turn the optimizations on

see if you get the same results

you can have the meanings and the optimizations running

at the same time until you trust the optimizations

and so forth so there's a whole scheme of things you can do

here and in fact

these kind of things are done in regular engineering

disciplines what i call real engineering

so the designing and separating

from optimizations is called computer-aided design

in regular engineering

these lead to simulation

so you want to simulate

before you commit to doing

the much larger work of packaging

so you have to have a way of simulating what does that mean well we

can talk about that when you

add the optimizations in you better add them in through

the cad tool and then

let the simulator worry about how you

test the optimizations against the meanings

well you have real-time

requirements and your program

might be running too slowly so you just find

a super computer this is what the rpa community

did maybe these days

as you're purchasing super computer time online

from amazon or google

any organization that is targeting

things like this should be paying more

upfront in order to make their actual

program development time uh faster

with

and finally when you get everything going what

real engineering does is it starts thinking about fat

and fab sometimes

requires some additions into

the cat for instance when you're

making a bridge or a

table where you have two beams

connecting each other you might have to put a gusset

in there to help the connection

itself might not be strong enough

so the little things that you have to add in there but you want them to be

minor okay so

that is a typical thing that we find

over and over again in real engineering electrical

cad it's the way everything is done today

nothing electrical

especially no computer chip

is done without completely simulating every aspect

of it before it ever even goes to

masks and silicon

every form of mechanical cad

mechanical engineering

this and the

simulations are particularly critical

because in a jet

are many parts internally

in a jet engine that are actually

their melting point is

temperatures that are in parts of the jet engine

it's really interesting how you make jet engines

to allow these things that would melt

not to melt uh

biocad so here's something that's come along

after computing and

yep the biologists knew enough

to for bioengineering to make

real cad systems with real simulations to allow everything

to be done notice that

all of these things are done using computer

techniques computer displays computer power

to do all of this

so the other engineering fields are using computers to really

help them develop

nanocad

but when you look at

in if you go into most companies and look

to see how people are developing software

you don't find anything like this

what the

what is it whatever

it is it doesn't look like engineering and if you look closely

it's really a

set of facilities for kind of tinkering around

kind of organizing thing but it's mainly aimed at fab

it's much less

aimed at design

almost never almost never do you find

sim as a key component

it's mainly aimed as though

yeah we can do everything right

we're smart enough to be able to write the end

code and have all of the stuff and intertwine

the optimizations and so forth

so this is kind of a yikes

now

what if you don't have the cad

tools most people don't for software

partly because they don't exist but even

the great development systems many people don't develop

using them well what if you don't have those

and what if you don't have a super computer

and the lesson from the past

is you should still do these things

every single one of them you just

o put the optimizations

in more carefully and a little bit

earlier but you still must do the meanings

first so this is a discipline

fact the difference between

gineering and math

and science is the three math

engineering and science they're disciplines

disciplined when you're actually using them

so why don't most

computer people do this well let

me there are lots of reasons but

i'll just pick one is part of it is because our brain

really doesn't want to do all this work

that's why it's a discipline so

we're actually poor thinkers

and we have about 200

known ways that we think poorly

so i'm just going to pick one

way one little part of this

very complicated set

of bad machinery we have between our ears

and i'm going to use

as an analogy

our thoughts or our context as

like a flat surface

it's colored pink here

and our thoughts

will can move around

the surface like an ant crawling on a

table we can pick different directions

if we find an obstacle

we can figure it out how to go around it

so notice everything that we're doing

here is like what we call thinking

but every single thing is

pink and we don't know it's pink because we've never seen

anything but pink so this is a context

that we think of as reality rather

than something that we believe in or something that we've only

been brought up in

okay so

the other thing is uh

when we're seeking goals we tend to cling

on to our goals very very strongly

so if our goal is b

and we will often pick b

in there

so let's try and get to it

we'll start working on it but

in fact the problem space might not actually

flat it just seems flat to the ant

so we start moving towards b and

all of a sudden it starts getting really difficult and then we slide

into a ditch

why are we having so much trouble but we keep on fighting

towards b partly because

in school we've been told

we must solve this problem and

problems are usually given to us by other people

why do we cling so

strongly to these things well

let's go to africa here for a second and

the way

he's going to catch this baboon is he digs a hole in this

termite hill there's the young baboon there

watching it you

put some seeds

nice tasty seeds in the hole here

then he just backs off and

wondering what what's going on here

and that's curious remember baboons are primates

like we are

now looking around well the guy's over there

maybe i should take a look

now when he gets close

he can start smelling the seeds

reach us

in and he's got the seeds

but now we want to let go of the seeds

you can let go

anytime but he won't let go

so the hunter just comes over to him and

ties him up pulls

him out and he's got the baboon

so let me just tell you i won't tell

what what happens after you can find this

uh movie on youtube

and

the baboon does not get hurt

he hunter had a really interesting reason for wanting

to capture the baboon and you'll find that out

but for here uh what we need to understand

is the seeds

this

cognitive uh glitch

which we have baboons have

and many other animals have

it's called loss aversion once we have

something we don't want

to let it go it becomes much more valuable

more valuable to

this baboon than his life because the

hunter might have wanted to kill him and eat his brains

ame thing

with us that goal

becomes more valuable to us

sometimes in our life

and often uh

prevents us from actually solving

problems uh that need to be solved

well if you knew more like

actually a an explorer you know

well when things get tough maybe

we should re just explore around

maybe we don't have to go up over this

hill and then down the valley

and back up in the hill and stuff maybe there's a way

around and this seems like well

we're going we have to go away from the goal to do this

yeah you have to go away from the goal

going to take you a longer distance to get to the goal

but in fact it might take you less time

and less effort to get to the goal

in fact by looking off to the side boy there might

even be a super highway i've colored it blue because that's

my color for science science

is a super highway again you get go off out

of your way get onto this thing you can go three times as fast

and you get to the goal faster

but if you start using science

you might

decide to invent a plane just

fly over the darn thing

but once you start doing science

you've got this even bigger idea is wow there might

be a non-pink world

there might be a whole blue world up there and

only in this blue world is there a goal

c that's the one i really wanted

i didn't even know it

and by the way if you think

about this in terms of your education if you went

to a a really good university

then you probably

went in there with some goals like b a

really good university will change you so much

that it will show you things like see

and your life will never be the same again

so this idea of getting trapped

in a context

and the idea that there are other contexts and

it takes some real effort to get out of

seems real to these other contexts

that might be more powerful this

is the message from this section of the

talk

so how can we get

in the frame of mood to try and find

these bigger goals

these bigger visions

and again this is a huge area

the things uh i was

interested in still am

is the idea of personal computing

back in the 60s we used to say to people well person

is the first word in personal computing so you

better not try and do personal computing if you don't understand

uh persons and of course

people today do not understand

uh people at all and so the

most of the apps that are done are

have terrible user interfaces and

are actually not very good for people

so this is a first lesson but in

fact this is a little bit of a red

because a person all by themselves

is being punished

solitary confinement banishment

we actually exist in a

society of people and

so when we talk about personal computing we have

to talk about humans in a culture

we have to understand what humans

are

that we share with other humans the thing that makes us

human and that will give us great insights

into ourselves because it's

hard to look at ourselves when we're in the pink plane

and just see anything but pink

and in a society

well we've got duties like voting

we have duties to

the next generation whether or not we have children

every

culture has its own world view we need to understand

those and get more powerful ones

every culture

has some form of schooling we need to pay

attention to that there's this idea of richness

richness is

outside the purely

pragmatic we have large

respond to art and love

and friendship and

many other things that are not strictly

pragmatic we need to

care for those and then

we have the thing that most people are worried about way

too much which is livelihood how can i get

a job many people go into programming

because they think they can make money at

it and this is a very bad reason for going into programming

you really shouldn't if you're not going to go into

programming to try and improve the world you

should try and look elsewhere

and these things

here these seven

things are part

of especially in our day and age

are part of a much larger context of what

you might call grand deadly

important issues

i just have 12 here and you can think of more

these are the things that we

worry about also

and these things are global now

there's a large enough population

of the cultures

have enough power to affect the entire world

and are thus affecting all of these things for everyone

pandemic now

that is now worldwide and it gives

people perhaps a bit of a sense although

i think people still are not taking it seriously

and they certainly are not taking the climate seriously

enough because

this world we live on is dying and we

so a child born in 2020

is going to be 80

at the end of the century and you wonder what what is

going to be left for this child

but if you're taking the point of view

that i'm advocating here the other way to look

it is wow 80 years

things could be much better for everybody in the world

80 years from now if we just took all of

this seriously and learned how to think better

and the context is even bigger than the world

it's only been about

100 years or so since we realized that the universe

is vastly larger than our own galaxy

and it's only been a few hundred years

since we realized we lived in a galaxy

we are now aware of most

uh people on the planet in the form

thousands of different cultures all with different beliefs

now all in communication

through our technological structure this is a self-portrait

of the internet but it stands

for all of the technology that we've made

over the last few hundred years

and we ourselves

are a complex system

and our brains are

even more complex systems

so one of the contexts here is this idea

that what we need to be aware of is

ystems that we live in and the systems that we

and these systems are all interconnected

and intertwined so

most of them are invisible

so the four ideas here

first science

is not just about finding out about how

atoms work or how photons

work or how stars work

science the larger

idea of science is the set of heuristics

with to get around with

brains this is the way to

this is why everybody should learn science

systems

is one of the strongest ways we have

to think about complexity and almost

nobody learns systems it's not taught in classrooms

at least in the united states and

over here in the in the uk

and most computer people that i've talked to

outside of special

community most computer

people know very little about systems they think

programming is creating an algorithm

rather than designing

a system and being part of a system so this is

a very weak view then two ideas from einstein

we cannot

solve our problems with the same levels of thinking that we use to

create them this is true of the small in computing

the way people have been going about computing

are just going to make things worse the way we've gone

about health the way we've gone about climate

it's all going to make things worse if we

persist and einstein has this nice

definition of insanity which is

over and over and expecting

that's what we're doing and again you can

if you think small in computing that's what we're doing

computing is hardly different today

except in scale than

it was uh 60 years ago

it's really a shame

the scaling is just making things worse

because almost anything that was done 60 years ago

couldn't handle

scale very well we had to

invent entirely new methods to do things

just even like the ethernet or the internet

to make personal computing and the user interfaces

scale across billions of people

so scaling is another issue that we could be

talking about

so we can wind up here

talking

about the world's greatest hockey player

his name is wayne gretzky

a thousand more goals than anybody

history and he was just a little

guy wasn't big he wasn't tough

took a lot of shots on goals his his

his percentage of

getting goals for shots wasn't very good and some people

complained and he said well you miss 100

of the shots you don't take so

his idea here is you you have to show up and

shoot on goal you can't

worry you have to do all your planning ahead of time you

whether any particular shot is going to go in you have to keep

shooting and

he said well a good hockey player goes

to where the puck is a great one goes to where the

going to be and he didn't mean tracking

he meant getting to a place on

the ice where some teammate

could pass him the puck

where he would have a clean clear shot on the goal

so he would could understand

the patterns of uh all the players

on the ice he could see well if i get over there

somebody can get me a thing and then that will give me a shot

on goal there so this is a completely

different way because most sports especially

sports like like hockey or football

are basically tactical whereas

gretzky was the greatest

player in history because he was strategic

and

he furnishes a good analogy for

an example that i'll end with here from

so the first

idea is uh

you have to

have ways of coming up with ideas

and as i mentioned in the beginning my way was

by being embedded in this incredible community

that was full of rich visions and

interests in helping people so i didn't invent

the idea of personal computing but this

is what i worked on for my thesis

in 1968 and while i was working on it

i

ran into seymour papert who had

been he was a mathematician

like me and he had realized some really important

things about computers

and how children could

learn mathematics by taking advantage of what

could actually do and when i met

him in 1968 what

i saw was something that i understood

and understood beforehand except i didn't

understand it i was in the pink plane

and pappert was in the blue plane

he had seen something that was right there in front of

all of our eyes that combined

what he knew about children and what

he knew about computers and what he knew about mathematics

that provided a

glimpse into a complete revelation

about how

uh children's science and math education

changed and that just

completely blew my mind and so on the

plane flight back to utah i had a blue thought

not a pink thought the

oh this makes

computing really important makes it

more like reading and writing and if it's like reading and writing

children have to do it and pappert is showing us how

and that means children have to have their own

computer and it better be really portable because you don't

confine them inside they need to be able to use it outside

and arpa was working on uh

besides the arpanet working on a wireless version

so it would be connected wireless it would have a flat screen

display and so this cartoon

is what i drew on the plane back to utah

and i started

thinking about it and

community the thought process

went kind of like this well

you have this idea seems good

might be bad but one of the things you should

look around and see if there's are any exponentials that are going

to help in the future it's not possible now

so you can take the idea out 30 years

like to 1998 or 2000

and ask what does this

idea look like 30 some odd years from now

and the answer is oh yeah absolutely going to happen

moore's law will guarantee that we'll be able to

do this but we don't know how people will use it we

don't know what the user interface would be like so then the

critical part is bringing it back uh 10

to 15 years

bring it back to about 10 10 to 15

out in this case it was like 1985

1986 because

when you can get something within

10 to 15 years you can bridge the computing

gap by just paying money

you can build the

function of a computer 10 to 15 years

from now you can build it now

it's going to cost 10 20 times as much

it's going to be 10 or 20 times too large

but you can build it and you can build a bunch

of them so that's what we did we built

a little supercomputer for everybody at park in fact we

built about 2 000 of them and this

allowed us to have

a window to invent the software not

just the the operating

interface but a whole bunch

of the software that would be usable in 10

years or so from then

and this computer is

fast as what you could get from

time sharing interactive time sharing and allowed

us to do two things

there's a whole bunch of stuff that we didn't have to optimize

we could just program the meanings

of user interface ideas hundreds of them

and do a dozen experiments a day

is what we did to invent the

part gui that everybody uses today

the other thing we could do is by optimizing

uh

in the both of these in the way that i mentioned

uh that allowed us to do the

applications of the future uh

in 1973 1974.

this is microsoft word

as it existed in 1974 at xerox park

than 10 years before it appeared

uh commercially but it was quite

possible to do because this machine was actually more powerful

than a mac or an ibm pc

of 1984 85 or so

right so

the simplest way to think about making progress

here is always find ways of computing

in the future and even

if you don't have a super computer there are ways you

in the future because you can make a future

architecture just using

software alone that is going to get you much further along

okay

made it to the question and answer and with that

i'll turn it back to my friends

for the next phase of this

talk thank you very

much

is

in

can you both hear me okay yes

yes good so again

being with us alan and

so as i was saying i am not worthy to take this conversation

so i will just let you talk with

with hernan okay i'm not

worthy either but anyway i'll do it

no no

no thank you thank you very much alan it's it was

amazing the talk i i always enjoyed your talks

uh it's a new way your worth

ways of thinking and new ideas and

and that's great not too many people do that so that's

amazing and i like to start talking about science

because that's something that i know you like and

you talk about it also and uh you know you

always encourage scientific thinking in

in people and i remember uh an example

that you gave in the squeakers dvd like 20 years ago

to quincy jones where you

you showed you know you told us that we science

broaden our vision and in that

way we can you know see beforehand

going to happen a long time from

from now like for example aids and so on

um but in your

that we don't understand science very well

my question is why is that why it's so difficult for

understand science and to think scientifically and

how can we change that well i think

if we look historically

and if we think about science as

what we think of as modern science

we it started really uh

400 or maybe 450

years ago now the greeks had

scientists but they didn't have

science because science is not just

individual scientists but it's also a community

that helps debug fondly held

notions that

people as human beings have

right so zionists are humans as well and

so um they like their own theories

and

one of the one of the hallmarks of the existence

of science is debugging

of ideas and even

ideas that seem to be backed up by experimental

evidence the idea is it's not just

about what one person

thinks is going on or tries to demonstrate

is going on if you go back historically

we can trace back humanity at least several

hundred thousand years we know that

the uh the female line

of humanity goes back 200 000

years and so anybody

the question well how how could it possibly

have taken us 200 000 years to

invent science what was so unobvious

about it and part of the answer is

uh our social

uh cultures

relied on

storytelling so regular

language convolved

with storytelling and using stories as a way

of remembering things and explaining things

and a story is like math

it can be consistent

but it doesn't have to have anything at all to do with the real world

and the other part

of it is if you look at optical illusions

the thing that anybody

who experiences an optical illusion has to

realize is oh i'm not seeing what's out

i

just think i'm seeing what's out there but actually what i'm seeing is

something manifested inside my own head

and that's why i'm seeing some sort of parallel

you know the simplest one is just measuring

holding up your thumbs yeah and i can see

in the in the video that the closer

one is about half the size

but in fact what i'm experiencing is the closer one

is about 80 percent of the size

because our brain knows they're the same size

and so i'm seeing something about a third of

a second late that

is a manifestation of a combination of my

beliefs and understanding with some input

from the outside world putting together into

a kind of a story which is presented

back to me as reality and of course

the though right now we have a perfect

example for the world to see in the

american president that we have who basically

like all humans but

in a very public way is projecting his

beliefs out onto the world

and taking that as his reality

and that's basically what humanity is all about

you see it in computing all the time

also if you just look at the comment section in

any slash dot or

this stuff you see people constantly

projecting their own beliefs

and desires as reality

so it

also happens that this is the 400th anniversary

of one of the major

starting places for science

so in 1620

a guy in england here by the name of francis

bacon wrote a book

uh called the new organization of knowledge

novum organum scientia

science actually meant

knowledge and

the gathering of knowledge it doesn't didn't mean back then what

we mean it today and in this book

he points out that humans are terrible thinkers

in a many many ways

he picks four his four favorite ones

are were terrible because of our genetics

in other words our brains weren't made to think they were

made to do something else

our cultures

weren't made to think they were made to survive

our languages were made for

stories not for representing

ideas very accurately or being able to use

to help think and then our academics

are teaching

uh facilities uh

will often teach ideas that have long

been debunked

we are doomed basically

yeah close close to it so

so what uh what bacon called for is what

he said was we need a new science

and what he said is what what science is what

this new science is a new way to get knowledge

is basically to come up with all the heuristics

and methods we can come up with

to get around with what's wrong with our brains

so this is the big idea about science

and it's not taught in any school in the

united states that i know of no we are not even

close to that i mean yeah big idea about science

is not about uh

no life on mars and

certainly not what you find in a science museum the science

museums in the us and the uk have

hardly any science in them at all they're full of technology

which isn't the same thing at all

but bacon's idea was much larger

so just to make everybody feel better or maybe worse

the

this 400th anniversary of some of the most

important ideas of the last 400

years has not been mentioned once

in any of the british papers

so the the people in the uk are as innocent

of scientific knowledge and history

everybody else in the world and they have no idea

that science actually for real got started

what happened to us in our field

in this software development with you know the the

people that created the development is has

even a bigger problem which is

because uh

when you start off with software

you're starting off with something that probably isn't

going to kill anybody so it's not like

building a big bridge or building an airplane

or something and so you don't really

have to know very much about engineering

write a program you certainly don't have

anything about science to write a program

and you don't have to really know anything about math to write a program

yeah so

uh because simple programs will still do

something it's a little bit

more like this game that was around for a while called

guitar hero yeah where you

could pretend to be a guitar player

and things with things would happen and

a lot of the attraction of programming uh

when the 80s started up and the attraction of

the 8-bit micro computer was

just to touch it

just to feel part of this thing that was happening

the problem is none of the content happened and this is very

common in pop cultures

so pop culture will develop its own music

which is usually much much simpler than

uh develop music it will develop

its own notions of knowledge

and science and you can see it uh

[Music] all over the world

through uh social media

right so this is a universal

publishing system as

viewed by uh

by the majority of people who

haven't had the good fortune to undergo

education in basically the 20th or the

21st century so we have this enormous

culture that

is not very far

really from the middle ages

and we can see that by looking at

most of the world and most of the world's leaders

have reacted to uh the covid

think of it yeah that's right please because

anybody who's actually understood

an eighth grade biology course

these days those

should know exactly what's happened there's nothing that tricky about

it real question is

how infectious is it really but

as far as what a contagious

deadly disease without a cure can

do it should be something that every adult

on the planet

at least in the first and second worlds

should be able to respond to well

now new zealand did a great job yes yeah

they had the right kind of leader the leader was

able to get business people and the politicians

both parties together and get everybody to agree

to this how she did that i'm not

sure but he did

do it and it results in there so if

you prorate new zealand's 25 deaths

with a population of 4 million

you can see that almost every other country

in the world has been needlessly killing off

tens of thousands to hundreds of thousands

of people uh unnecessarily

just because hardly anybody is educated

enough in something that you learn in seventh

or eighth grade biology so because business

life basically

well i don't think they think of it that way you don't

no they just don't see it i

think if you held a gun to the head of a businessman

that said would

you rather live or would you rather

uh stay in business i think they

choose life most of the time most

of the time yeah most of the time there's a famous

there was a famous joke which i

is told better in the u.s okay

which i won't won't tell here but it's quite funny about

that so no i think the big problem

the

lack of imagination

so if you think about what that our brain imagine readily

well we can readily imagine gods

we've never seen them but these are things

that are part of our subconscious

and so and we have experienced them in

dreams so we can imagine things like that

more readily than we can anything that

that is related to science or to think scientifically

yeah if it's if it's really small if it's really

fast if it's really new

and this is where bacon comes in because what

is okay or or

sentence here so we can go okay

next question is yeah yeah so

you know the term artificial intelligence is

used widely and very loosely

these days

but if you think about it what is it what is artificial

intelligence well it's artifice

it means making something you have

so

somehow you're trying to make some sort of a

process that is going

to act intelligent and

impressed with any such process

unless it's more intelligent than a human

and so if we look for something on

the planet that is an artificial

process that's more intelligent than any human

there's only one and that's

science itself

science is a better scientist than any scientist

and science

itself is that process

that makes a group of people much more intelligent

than human genetics human culture

human language and human academics and

so uh

so this should be a simple idea

but partly be

because of the way education goes

uh science has been

relegated to just another belief system

yeah and it is a belief

system but it's a belief system that's backed

up with a lot more than most of the other belief systems

and that can reflect on itself and it

can change based on most mistakes that it makes

and those things that's basically what other belief

systems don't do like religions not really

i think argentina is a catholic

country and one of

the most famous philosophers in history was saint thomas

aquinas and reflecting on

christianity from the standpoint of

greek philosophy was exactly what he did do

so you don't want to you don't want to use that

i think the thing that's

interesting about science is

the particular methods it uses to

try and deal with the noisiness of our human brains it's

basically an error correct detecting and correcting

system if you learn how to do it

and it's basically a skill so

it's not something you know and some people

it's like in music some people have a little more talent than others

but in develop music talent

won't do it you say you have to

practice and develop skills and

the same thing is true for thinking and thinking

is uh in the large

is primarily the province of what's what science

is about so in computing

uh you can hardly see this recently

recently meaning the last 30 or 40

years so the in

the 60s and 70s i started in

like 1961 or 62

the people who are doing what

call computer science today

and what we call software engineering today

started off as real scientists

and started off uh

as real engineers yeah we weren't having undergraduate

degrees that's right there was no

no programming career or

computer science career that's right and so the people who went

into it were interested in it

and just like i did it from another perspective

and that allows them to to see

we as developers don't see

it was a special thing because don't forget there

still ibm yeah back there

and there was i i learned to program in the military

in the air force and the programming there

was just as uninspired

as it is today yeah

what was special uh was that the the

research community that i just luckily

stumbled into in the mid 60s

had started thinking about

what the computer actually meant

and for them it was the next 500 year invention

after the printing press

and many of the things that were going to be important

were how it could go qualitatively beyond

the civilization building inventions

of the past several thousand years like writing

mathematics science

press and so forth and that

o this idea and ibm

did not have this idea the air force did not have

this idea but these people did

and because they were in a good place and because the cold

war was going on there was extra money in

the department of defense uh

one of the people who had this idea got funded

in a big way and he spread

the result is most of the technologies

that we use today which are not invented by ibm

not invented in the air force so

a fortunate thing the processes

that led to those inventions

hardly exist today yeah

see those kind of research

and in current industry at all yeah

the opposite is all the opposite just short term

i want to do this very fast and

yeah the talks i've given

about this in the past the line i put

up on the screen is the goodness of the results

correlates most strongly with the goodness

of the funders so

the rarest thing is a good funder because

if you look at the bell curve at the top of the bell

curve in every generation you're going to get uh

you know super clever people

to draw and so the thing

different than was not us

we just happened to be the lucky people

there the difference was in in the funding

and the funders found us

and uh encouraged us to follow our instincts

and so we've got what we've got that's what we don't

have today yeah that's right so

let me go back just one more question about

science and then we can go to to software and and that

stuff so let's let me let me play a

devil's advocate here okay i

just for a little bit i'm not sure if i can do it right but

anyway uh you know science is great you

it as the most one of the most important achievement of humankind

and uh but science

is what brought us here i mean not science

we humans using science as a

tool is what brought us to the situation where we

waste everywhere with the climate

you know the burning woods and

those kind of things because science

in science in in some way allow us to

think better how to build stuff

but in the other way

not ready to use that powerful

tool so

scientific in thinking can help with

e consequences of things but the actual historical

fact is that

uh business

technology and the industrial revolution

led us to the problem we are what

science did was to make engineering

immense

and the industrial revolution that was starting

yeah was to

add on to this immensely powerful ways

it transformed engineering from something

that was essentially cookbooking

making cookbooks of things that worked

doing things by uh by principles

being able to actually derive uh

physical uh results

and estimations and new

kinds of materials and so forth so science

is a culprit in the sense that it opened

up the possibilities

and the power ratio

or what people can do

but basically when i look at

history what i see is

today what i see

is people uh

just trying to get ahead

in various ways and for instance in

america i don't think most heads of businesses

are really all that aware that they're part of a

country particularly

the multinational ones and certainly

uh if we go back 200

000 years ago we have to look

at something that we can find in all mammals

that

that we have also which is trade-offs

between two powerful forces

that are very different one is cooperation and

one is competition

the

more powerful one unfortunately is competition

in the end

it takes a very very special person

not to try and save themselves

even though we know cooperation

better than competition it is and not

only that uh you

know the reason uh there are social species

of which we are one of them is because cooperation

uh even if you're in a wolf pack

allows the pack to

and you're in

a baboon troop it allows the baboon so

uh cooperation

evolved as

uh all the other traits evolved

underneath that though

is competition and so

you know sociologically

people feel deprived if they are put into

solitary confinement or banished

but as soon as they get back into society

they start competing

and it radiates

out from them to their

you know there's a saying in some of the arab

countries that uh me against my

i against our family

our family against our neighbors

o these

uh tribal uh identity

things radiate outwards

and there are many science fiction stories written about

how to unite the human race will just uh

attack the world with aliens

and all of a sudden then the humanity can see itself

as a bigger tribe that's right and

get together and fight the aliens because yeah

so this this is the this is uh thinking of

completely immature beings who have

no real sense of of what's going on

the big deal in business

is at least in america

is most of the ceos i

know are not

really aware that the reason

they're making money at all is because

they exist within a cooperative structure

that was set up 250

years ago the wealth

that they are tapping into is there

because of the cooperative structure and then they're competing

underneath it so again just just to finish

this off to go back 200 000 years ago

you ask well

if we go back before there's hardly any

culture there's probably never

a time you know because uh primates have cultures

oh so before that even before

go back okay when we have

almost no culture

there's almost no language yeah

but we ask well what uh how did things

uh managed to survive back

then and the answer is genetically we

have drives to reproduce

that are very strong yeah we have

uh drive

to find food and water

so hunting and gathering

is predates any kind

of cooperation if you're

by yourself you're still going to be looking for food and water

yeah and if you think about hunting gathering

it doesn't scale well yeah

right hunting and gathering one of the scalings

of it is to steal things

one of the scalings of it is to

uh strip a

cooperative area dry yeah

so there are many many variations

of this and so one of the

complaints that you could have about school

is that schools do not

teach at least in the united states again

schools do not teach the

children anything important about their own species

these are kind of taboo subjects

what are human beings actually

and we just we aren't very

pretty as a species when you start looking at it from that standpoint

but the good part

of looking at it that way is we get to see

how a

civilization can be

kinder can be more cooperative can

be smarter all of these things

and that is something that uh you need to learn early

because certainly mo i would say most american adults have

idea about this at all from their standpoint

his battle for survival

yeah competition is is being taught in

high school and schools on sports

and all those things instead of instead of cooperation yeah

yeah and rhetoric and governments is we're

not competitive enough what

how could what does that mean on a finite planet

hat's crazy

i don't mean as a metaphor i mean this it is literally

insane yeah well it's what is happening to

yeah and of course the you know scientists

were aware of the climate problems

uh starting in the early 60s

yeah yeah they knew

about it and and the people the business people

tried to not to the idea to spread

out yeah yeah it's not if the business

people really understood it they

uh could actually see

one of the one of the biggest problems with lack of history

is besides

trying to avoid blunders that

done early

we reinvent the flat tire all the time

as you said reinvent the flat tire all the time

but the other thing is we miss opportunities

so for example whenever we've had

a big calamity

in the last 100 or more years

usually

things have not been prepared well enough for it

these glamorous are usually wars not always

but for example the result

of the wars

the aftermath of the wars is

usually prosperity

and the reason is is that it's in war when

there's the extra investing kind of investment

that conservative people don't like to

make or anything

yeah when you're really

in trouble you might give a smart

person you don't understand some money anyway

just right and so the kind

of funding research funding that's done during a war

is unfortunately almost the only

kind of research funding that is uh

that has been done it's when people this is why the

institute of health in the united states

gets more than three times the funding than all of the

national science foundation national

science foundation funds all the other sciences

but the national institute of health why because

people are afraid of dying so they're much

politically they they can

imagine their own death they're afraid of it and there

is a goal that unites them to do that funding

yeah in that case the real

problem with all of the the problems in

recreating arpa and xerox park

uh in recent times

have been that the the mostly billionaires that

have tried it uh they

want to uh direct the research

yeah and i've told any number of them

i said well you shouldn't be doing

this because if you look at why arpa and xerox

park succeeded

the people who directed the research were the people

who were going to do the research the people who chose the problems

were these uh top

researchers that's right and with all due respect

sir you've spent the last 20 years

becoming a billionaire

researcher and so the chances that you can

pick a good research problem are essentially zero

you just want to be a wannabe

you want to be part of this and you're

not satisfied with your billion you want to do this too

but you're incompetent to do it so in

government it's slightly different uh

where the people who are responsible

think they need to be in control because you

know they are responsible yeah that's right but the

made arpa and park different was that the people

who are responsible knew they couldn't be in control

yeah yeah big lighter and bob taylor they

allow you to do whatever you want

for trying to get a culture

going trying to find talent

responsible so taylor

never suggested a single research

project uh at park and

lick lighter stayed with his vision

and people asked well how are you going to do the vision

know but i'm going to fund people

know yeah and they'll take

30 or 40 this is you know

this is more like playing baseball yeah it's hard

to get and it is interesting because bob

taylor was a psychologist wasn't he

and lee glider that's right so they they

knew how people how to get people together

stuff i think there is some of that

both of them happen to be experimental psychologists

okay we're not clinical

yeah yeah and they're

different personalities lick was

just a very nice guy

and okay you're special though he was

one of the inventors of cognitive psychology

so he's special but his personality was

also special taylor

was a uh

much more aggressive

okay he adored

lick glider okay and

uh when he when he

was part of the arpa thing he put a lot of effort

exactly why what

lick lighter did was working so well

and when he became head of head of the

computing research at park he put those principles

he was not acting by instinct

he knew how to do it you were basically yeah

he said uh this is what lick lighter

did by instinct

uh we can we can do this by method

we'll just do do

it this way and park

was a concentration of both

talent and method yeah and it was

amazing wasn't it well

i mean everything that you've done there

yeah well i think the most amazing thing

was uh the bulk of the the work

that's known today was done by you know

25 or 30 people yeah yeah everyone

every part of it and so the concentration

of of abilities there was

large and the concentration of method

the taylor's application

of what he thought was a good way

to do this thing was more powerful than the

than the more ad hoc arpa

management way that he'd learn from

so yeah so

so i always i when i look to

when people ask me questions i said well you know

don't look at us look at look

at the you know the four funders at arpa were

lick lighter ivan sutherland

larry roberts and then taylor

came and did park and so if

you if you want to thank somebody thank them

and you know

uh when we've gotten metals in

the past uh i mean my

line is that well um you know metal

40 years after the fact is okay but

the if you if you want a real

reward think of the reward for being

this work yeah back

then to actually do that's the big deal

and then as much of a reward is the

fact that the funders were giving us gold medals

and a lot more gold than was in the

gold medals they were giving it us

before the fact knowing that most of it was going to turn

into lead so

when you get funding like that and you get to do the

work uh you don't need anything uh

that's it that's it yeah

place in the you know the right environment

you create it was great and we

knew it was great back then uh

and uh our

appreciation for it went up by about a factor of thousands

after it ended

yeah yeah yeah right because i was in

it i had been out

of the regular culture

because i was in it in grad school and then uh for 10

years at xerox park and so i've

been completely isolated from the outside computing

culture for 15 or 16

years and i was quite shocked

when you get out of there yeah

about everything i

can imagine yeah yeah i can imagine so let's

go to that subject now to software

if you don't mind because i know i know

that you can talk for an hour by yourself

i i need to do something you know

you should interrupt me uh by the way

much okay

okay yeah but i'm not sure when to interact

when to know but anyway so let's

let's talk about software and basically software design

uh you know for a long time software design has been

thought as drawing uh

on paper you know and to the design

was to draw basically what you

thought the organization of the system should be

while you were running the system in your head

so you were imagining the system and then

making some draws and you know that was designed

time that was the classic idea as you mentioned

flowcharts in the air force uh you

know that was maybe thought as design but

uh you know we know that that doesn't work

at least what you know the history

tell us that it doesn't work so what

is for you software design because you talk about it in your talk

you talk about cad and those kind of things so what

activities should i

should be done in software design

i think the first thing is

uh i'm sorry i'm sorry to interrupt

you to finish the question because you also

talk about meaning creation of meaning

and how do you relate that to design i think

that the key point is there so yeah

i think the first thing the simple

thing is that

you know the thing i had to get

over in a hurry when i went from

being a journeyman programmer

in the early 60s to accidentally

research community

was that the

the average program

in the early 60s was relatively

easy to do

the computers were small and

so the main problem in programming back

then was more than anything else

get completely

mired in optimization

because you had to optimize you didn't have enough memory you didn't

enough uh cycles

uh so that the tendency

was to convol as people do today

yeah yeah we're still doing that having

that old school in in our heads yeah

so the uh however arpa

was uh

basically the people who

were our mentors first

generation people who were funded by lick lighter

we're basically all systems people

they have many of them

sage air defense

system which was

24 installations of

two computers the size of a

softball a football field

wow there was one floor

of a four-story building the basement of the building

was the power supply for

these computers so we could think of two amazing

and then the third

floor was operations and the top floor

had 150 graphics terminals

that were run by these enormous

vacuum tube computers and

the government built 24 of these block

houses wow all connected

into the radar systems of the of the country

and so that's what these

people were doing in the 50s and

so the monumental scale

of

people who came out of world war ii

and i should mention here for

people who are interested in history if you want to understand

where the mental framework

came from for the

this uh research work in the 60s and 70s it

came out of the mostly out of the radar work

which was jointly between

uk and the uk yeah i'm

primarily at mit but started

off in in britain and there's a great story there's some great

books read about it and

a talk that you had with a

on youtube that where you talk

about that i've got a couple of talks about that i've written

some papers yeah about

it also another monumental

effort was the manhattan

project

so most people aren't who don't the manhattan project they spent

you think about what the us was doing in

world war ii the manhattan project itself

cost more than one percent

of all of the defense funding

war ii so

it involved about 800

000 people it involved

making new cities yeah

because they went wherever they could find cooling water and they

built new cities they brought in school teachers they bought

in doctors they built entire cities they built

plants of acres and acres they didn't know

the best way to refine

get refined uh material

for the for the bombs there are four

known ways and general grove said well let's let's

just go all out on all four of them

and he was the he was the head of this project

and so his history is worthwhile

reading to read about uh

you know something at scale that scale

yeah yeah and the same thing is and i've used

this many times in talks is my background partly

was in uh molecular

biology and you have my favorite book up there

yeah yeah behind you yeah

that's my favorite edition of the book the third is oh

cool the red one the right one not

one that's i think that was the sweet spot

and uh they haven't read it yet

little bit big one of the best

reads ever yeah it's about a thousand pages long

one week one week of reading

you're in uh

so if you come out of that background computers are

tiny tiny tiny little things

and systems

are even the

largest human-built systems are small compared

to what biology pulls

off and of course a lot of the stuff in biology

uh can't be applied to computing

just because of the nature

of the way the materials themselves work and

what things are like at sub microscopic

scales but a lot of it does

and another book that was very influential

was uh christopher alexander's

book which is his phd

thesis called notes on a synthesis of form

yes yeah he repudiated this book

because he got hippified uh

after he went to berkeley went into a different

subsequently wasn't that bad

but this phd thesis book was

really interesting and his

his main example

done after this fascinating

discourse of how you think about complexity

and design and the conflicts

and finding them and

modulizing things and it's just great it's

great today it's just a great book i have it here

somewhere yeah but the end

the the end example of it is

taking the uh

new uh a new

uh village in india from

scratch so it's going to have a

population of a few thousand people and it has

you know a thousand or two constraints

of every different

kind in the thing and how are you going to design this

and this is great because this is not a typical

if you're looking for computing stuff

uh people are start taught programming

examples that are nothing like

or should be nothing like anything they're going to do

for real yeah yeah yeah that's right

much better off dealing with us

a live running system

like say small talk yeah or even

javascript although the it's so ugly in there

but it's live so

system that you can look at

you're much better off learning how to program in

the context of the system because

then you start having to sift into

your mind yeah it's

because you have to to realize

that if you change something you can break it while running

so it you know you can do you can learn a

little bit of driving a car driving around

a field or a parking lot yeah with no cars

on it but really driving is learning how to drive on a

street where you have stop signs and you have that's right

there's all these things you have to worry about and it's very confusing

and the process of learning how to drive

is learning how to create things

in your mind for dealing with all

of the heuristics that have to be done in real time but

so design is again one of these thinking skills

and part of it part

skill is how to hold

in your mind simultaneously

uh things

that conflict or seem to

rather than trying to resolve them too early

and i forget whether i did it in this talk or not but

about you know our ideas made

or like categories where they can't interpenetrate

or

they

hello

i think i lost i think

we lost yeah oh we lost you

the last yeah 30 seconds chris

from my point yeah so the question is are ideas made out

of matter yeah

so one idea can't is basically

antagonistic to another or rd is made out of radiation

so you can shine lots lots of colored

lights at a wall and they superpose

so you can see all of them there they don't interfere

with each other you see interesting combinations

and that's one of the states you have to get into

when you design or when you think about anything

when you're dealing with complexity

the last thing you should try to do is solve a problem

the number one thing you have to do when you're dealing

with complexity is to try and find out what's going on

yeah try to find a way of looking at what's going

yeah the problem the problem finding instead of

the problem solving grinding is and that was the big

one of the big things i got in arpa yeah

they realized the problem finding is that and so

when you get to software

and think about what's the software possible the

pro software process is

trying to deal with

e kinds of things that alexander was dealing with

and the first thing you have to have is some

understanding of what the meaning is

before you start trying to to optimize so

alex so if you look at alexander the i should

have brought brought the book

i have it in my room i don't have it here but yeah

i have it here

oh my

god

this is london so i don't have my real

library here but

i have about a thousand

whoops i have about a thousand books here

just just a few and i

need some yeah so this book

yeah you can't

uh praise it too highly

eah

i read it it's really interesting it opens your

mind yeah so here's his final

design of the village

uh but of the most interesting

things is how careful he is

yeah so appendix

one is the worked example where he

looks at hundreds of constraints

and tries to put them down very carefully

if you look at the requirements and of course

the

real question is are you actually saying anything when

requirement down in a lot in a natural language

so

the belief we had when we started thinking

about this stuff and you bet again

the arpa community

invented computer-aided design

and general motors

was doing also doing a project but

i think was most strongly invented

in sketchpad by ivan sutherland

and then especially by his brother bert

and others at lincoln lab and

mit had a huge numerically

tool project

that was also about computer-aided design so

the idea is you and a lot of the computer graphics that was

then was to try and see whether you could define

shapes using

graphics that a program

could look at the shape and see what a

five-axis milling machine

would have to do to make that shape out of a

piece of metal and so there's a lot of

that and of course the

shape that you want like when you make a flange

yes uh

that flange might not be strong enough

in its yeah

in its simple flangeness

yeah you might have to put a fillet

you know which is extra metal yeah to make it stronger

on the bend part so you can think of that

as something that would be revealed

in simulation so

sketchpad when you designed something in sketchpad

you didn't just you weren't just doing a drawing

you would get something that because you had all the

constraints and the simulation to see what yeah

so and and going back sorry to interrupt

you because we're running out of time and i have a bunch of

questions and i want i want to go to one at least one

questions from the people i don't want to be the only one asking

but um you know you mentioned small talk and

you know that amazing uh system

dan ingalls and adele goldberg

other people and you know working is

with small talk as you said it's like working in a living

system where you are changing it while the

system runs but also one of the main

features of small talk is the immediate feedback and

you you know brett victor he he gave a great talk about

immediate feedback and how important that is to design

stuff to work with staff so how do you see

immediate feedback in in software development how important

you think i think you know

put into my talk

session i just did another talk

okay okay no but you didn't talk about immediate feedback

in your talk yeah but i might have thrown in

uh four stages

yes yes uh the tinkering

engineering followers that's right and i was going

you forgot art why you didn't

put art in there also oh it all

is oh okay okay because what art

art the the greek

word uh art comes from the

latin ours and

the the greek root which goes back even earlier

is techni okay so technology

literally means anything that

humans make anything

okay so what what

happened is in the 19th century uh

painting and sculpture took the term

art oh it used to be called the fine

arts okay okay let's see i

see yeah that's like uh it's like

uh a.i used to mean something different than stuff today

that's right yeah yeah object oriented

used to mean something new today

so people people do this but in fact

in many of the presentations i do i show

these four guys sitting

the larger context around them

is art because it's everything everything

that's that's why artificial

means something special

yeah doesn't mean

something bad perfectly means

something that's made

okay is

intelligence that is made that is made yeah

so yeah so

uh so if you look at those four things

uh and the point i make is

in modern times you want to be

in the sweet spot at the center

ight yeah that combines diagrams

yeah so you want to choose when am i going to tinker

scientist tinker

engineers tinker mathematicians tinker so you have to

tinker that's right yeah you have to be you

have to make things principled it's not

just an engineering but uh like i have also

have a degree in pure math so if you're

going to make a mathematical proof it's at

least as engineered as a bridges

it has to have that integrity

connectiveness in order to be

considered a good proof yeah so

yeah and then science has this extra

important way of being a

between what's inside of our

heads and the kinds of phenomena that we

deal with out there so it's it's a meta

thing that's much more complex than the rest of these

ideas and much more powerful

so so if you try and apply that

map software into it it's usually

way off uh yeah yeah

bit and wearing

uh hardly any math no real

science so if you try

and map that into some modern system

that you might try to make

one of the number one things i think anybody would do

and certainly we did

what we could do back in the 70s

was to say yeah we have to do cad and sin

you have to be able to do computer-aided design

which means we have to be able to continue it's

graphics that we have to continually develop

the thing we have

in mind so we can at least fasten on the meaning

of it so the compute

the cad part is meaning it's semantics

and the

simulation part is because

beyond a few sentences

you shouldn't trust 20

or 30 or 100 requirements yeah

that's right without debugging that's right

that's that's something we do actually in software development

with testing you know there is a

test-driven development i don't know if you heard about it uh

yeah sure but that the problem with that

is that you know

if it were good let's let's

you're doing it well yeah and

in theory you should be able to run

uh you should be able to on a supercomputer you should be able

bring uh the thing to life right if

is covering everything uh the truth

at's right some parts of

it yes so the biggest problem with

want to have tests but the problem

is it loses the larger integrity

of what we need in designing a system

that's right yeah so i think

it's it's not that you don't want to have tests but i think

trying to start with tests is

and again

sometimes i throw in a lump of clay yeah

yeah you did can you i did okay so

it's pretty hard to debug a lump of clay into

a really nice piece of sculpture

yeah yeah and but you got to test

everything continuously right yeah you

have to have a vision of what it is that

you do yes but starting starting with the test

it's like thinking about the meanings first instead

of thinking about the no no it's it's thinking about

criteria the question is is

you know the way to think about the meanings

is to think about the simplest thing that is

like the thing that you want

that gets you no so starting off uh

uh asking

about bathroom facilities

is not going to lead you to a village design

no of course so what you need to think about

is what is the simplest thing that's like a village okay

okay let's see that gives you the system

okay that gives you many of the main things that the system

has to have and then that's not going to be

uh nearly

detailed enough or scaled

enough but it has its it's

a vision of the whole yeah

i think we don't have too much time

i'm sorry to interrupt you again i think we need like six hours

three ten days to talk about everything

uys are organizing you you

know too much alan you know too much it's

difficult it's difficult too

but let me at least ask you one question

from the audience um we have one from maximo

prieto he he admires you because

and he his question

is what can we as simple programmers

not the owners of the business do to

provoke a change in the way we are forced to program

today i think we deserve to program in a better

way yeah so well i think that's the big problem when i

was a professional programmer

you know it was somebody else's machine somebody

else's software somebody else's

problem so i was

you know at the bottom of this

of this machinery on on the other hand

what we did have

lot of this was in machine code

but the nice thing is uh the

machine code systems back then

many of them had really good macro systems

and so anybody

who wanted to survive back then and particularly

with regard to this question

the first thing we would do

a bunch of us who are involved in these projects

is to spend a fair amount of our time

each week often each day

working with each other on macros

that would give us higher level

blocks so basically programming language

dsl basically making

because otherwise you're at

the you're in the wrong place

for uh changing your mind so

about one of the ways of thinking about this thing is

uh and certainly was the way

people approached it and the arpa community really approached

it was let's

admit that human beings are terrible thinkers

that means we are terrible thinkers

what can we do about that well it means that

most of our ideas are going to be mediocre down to bad

let's just put that right

hat's right let's

as a you know that's the yeah let's

face it we still want to make progress so what

can we do well what we need to do is to be able to

fix things so we can change our

mind and what are those things we can do well

starting to happen back when i was a

uh when i was a programmer like one

of the machines i programmed on back then did

not have any index registers

really basic yeah

but you don't need an index register you

can go in and modify addresses that's right

what index registers are is late

binding something

yeah give you both safety and

a way of changing your mind more easily

every time you use a an indirect pointer

you are allowing

allowing

the possibility of sticking something in between

that's right to change that that the

other way would be static but in that way it's dynamic

it later on yeah idea

is virtually nothing that's important

should you know the actual address of

and one other thing and i've

this you know long ago a big

although it wasn't nearly as big as

it as it should have been became a really big

revelation later on

but the the file system

uh on the borough's beach

uh rose 220

in the air force so there were no operating systems

then and they wanted to exchange

tapes with files on them

and the way they hit on doing this

and i don't know who did it but

the idea was that the and these

piles were long because i won't go into the

weirdness of the tape drives back then but

so the front part of this was

bunch of a vec you know

an array yeah of pointers

further on down in the file

into the second part of the file which is a bunch of borrows

220 programs

machine codes yeah those are machine code programs

yeah and then the last part which could be

the size of the rest of the tape were all

the data records and the way

you way you uh read a tape is the tape

always got read into the same place in memory

and the only thing that was standardized was

what the index positions in this

first array actually meant

like so it was basically a

class with messages pointing to

methods basically that yeah yeah and i

real you know and it was great

we we called that data driven programming back then

yeah okay because what what

want to do is write your own code

to understand what you needed to do

was just understand you know if you want to find record

150 and of course there are different

sizes but it didn't matter because the code

there knew what size these guys were it knew

whether where the data is right

so nothing nothing was fixed but it was

dynamically decided by the

code yeah i got there in 1961 and

that was that was already in place

yeah wow yeah so that

was how you did things on the on the

borough's 220 for air

training command records

and but you know if you look at the principle

behind it yes it's like the principle of oop

that's right basically your abstracting and sketchpad

which was done about the same time had

the same idea sketchpad object

was

mostly a bunch of indirect points of the

thing called display and sketchpad

was just a pointer at a

specific position in this data

record for the thing and you just

jumped in direct through it and it would take you to the procedure

that was most well suited for displaying

that particular kind of thing there so

even though in the sketchpad those things

were more like prototypes than that classes is that

i don't know if you look if you read the thesis

yeah everybody should because no he

had uh even something like

inheritance oh really well

no no it was

sketch paper they were not prototypes like

that you cloned because i thought that they were like

cloned to have another one

not like no no okay okay

okay so but anyway once

you know i saw a sketch pad

a few years later in 66 in my first

in grad school along with simula and

it was having seen

this this idea like five or six times

a row because the borough's b5000

had a variant of it also and

in some ways even more sophisticated

and i was just dumb you know it just took me

four five six times to see the thing then finally

error is holy this is really an enormous

good idea it is actually

cosmically important so

let's make it scale now that's what you

did basically yeah so once

course once i saw it was

out to

yes

you know and again again it's

it's pragmatics that hurts

the idea like it's the simplest idea

that there is that if you have a computer

you can do any computation

you can represent any data structure

yeah so it should be a complete

instant

deduction that oh if i want to make

a computing system i should just make virtual computers

because that will allow me

to define anything else and i have

a universal way of doing it and people still don't use

what they call oop today to do that yeah

they just can't see it

here's something about a computer even today when they're

a lot smaller that with

where the pragmatic reality of the

computer you know contrasted

virtually or what it is semantically

yeah yeah so what's once you can

move from pregnant so one of the ways of looking

at is one of the biggest problems in programming in every

era and i don't even know what

decade this is for me i've encountered them

but it's going to be 60 60 years pretty soon

six years next year

six decades wow but in every year

it's the same thing with the same problem

get caught up with the pragmatics

yeah with the immediate with the immediate

instead of the yeah and the number one

optimization and pragmatics

hat on

and we can all do it once you put that

on it is almost impossible to design after

that's right completely incompatible

idea it's very difficult

to change it it's very difficult to say

to understand that you did something wrong it's very difficult to

see that you see that you did something wrong

so yeah so for me uh

you know there are a lot of different ways of using a dynamic system

and it depends on the personality for me

i like to do

when i was i

like to do this idea what's the smallest

village and i

just do that from scratch okay even

in small talk in small talk i wouldn't go in and define a lot

classes or any of that stuff i'd use the

the thing and called workspace which

had grass space yeah in it

and i would use a rebel with

i say like a rebel but with asteroids

because you know in a rebel you're like working

in one line but in a workspace you have the whole thing yeah yeah

you can have many many things in there and

it has its own uh address space

so what i would do is just do

a complete system the smallest thing

that's a village and everything i mean i would do the

loop i do the user interface i do every little thing the

could do trying to get

into my head what the

uh primary focus of the design

because the important thing about what

what oop is at least the oops stuff that we did

is that it's a module

system for us encapsulation

percent yeah yeah

yeah it's not there uh you know

occasionally you might have to fall back and define something like a data

structure but and when you

do that you're actually recording danger

basically it's a module system and the problem with

system is it doesn't tell you what modules

hould come up with right the module

system just gives you a way of protecting one set of things from

another

the whole front part of it is how to think

in terms of modules and what

to do in his his thesis program which

he wrote in fortran was a thing that attempted

to find you the

best set of modules out of a complex

set of constraints still a good idea yeah yeah

but basically and to me that's getting

the meaning of the thing down so to

me meetings dominate and then you have

this interesting thing in

most programming languages today

is there's nothing that because of this

fact that you can use in directions

that you can have a set of meanings

and they might include some tests

but if you think of the uh

yeah suppose it's just something like sorting yeah

and so

the you know the the meaning

for sorting is that the

output is a permutation of the input yeah

that obeys some some relationship

and prolog will even uh

sort for you just on that basis

does it really does it the hard way but

it it gives you the and of course

permutation is not easy thing to describe

it's a trickier kind of

thing but uh but there are 50

known ways to sort oh sort yeah

and they all have different uh

pragmatic ranges

and they have different conditions and so you can imagine

if you're going to do a sorting thing you have something that's

going to protect the meaning

so you can grind let it grind if you want

yeah yeah or it could be a really simple sorting

program that you're sure really does sort

yeah maybe or maybe we combined with tests

because both of these things but you keep that over on

the side and then on the on the right hand side of the page

you write down your 50 sorting routines

and you head them with the conditions

under

which they should be invoked so

they look this is just what we did when we were writing macros

force that's right basically

inputs oh this is an enormous

array so uh

i better not do a bubble sort here

or one of the side conditions

here is uh

this uh this

system has to be uh

easily updatable

so you might want to use a b tree

right you know you don't want to use a hard

array you might be better off going to a b tree

which gives you incremental updating

on the thing and still gives you pretty fast sorting

yeah yeah of course small talk has any number of

these yes different uh kinds of things

and then your module

is which is called sort

is the thing that

done the cad sim part

fab part

is all the all these

routines and the rules should be you should be able

to turn off any and all of the optimizations in any part

of the system and just have the system slow down

that's right but still working if and

that isn't true then you've done

a bad design period

that's simple yeah you're just playing at being an

engineer yeah in computing or you're just

flying at being a programmer even

yeah but going to to the last question

so what what would you tell to a programmer

today to you know

are working what can we

do you know a tiny people can we

do something or not i mean

because as you've mentioned many times complexity

currently in in our you know development software right now

is so complex uh you know working

doing web application is so much

difficult today that doing applications like

20 years ago yeah but so okay it's in

some cases you're just not

going to beat the system yeah

that's that's some cases just that because

the the what's imposed

on you is just too much however

like suppose you are doing web stuff yeah

uh so the

the first thing to notice is that

underneath javascript

is a dynamic

language subset of javascript

can be used as a real

target from uh

other language development yeah yeah

because uh you know there's a

good garbage collector yeah

yeah and quite a bit of it

lacks a couple of reflection

but i i advise people

in javascript to do a thing that

ago which was to

make a preprocessor for javascript

yeah there are

many there are many things like that today yeah it just parses

so you feed javascript to it

and it writes

things in a way that uh what you do

is reflective because it adds

that stuff in there that's right okay okay

so the basic idea is that for almost any

programming language uh

the chances that it's going to really fit what you need is

low and the whole point we do

should be open and with all the source

code available like small talk that helps

that helps a lot yeah i'm sorry sorry sorry

to interrupt you yeah but in

but you know small talk i don't advocate small talk today

because it was the world's greatest thing

really in the 70s but

uh you know what's

mall talk today is how well it compares

with more modern things

but that's because the more modern things are not very good

small talk is not

what i would use if i was going to do

a major project today i would

make i would do what we did you know the other thing about

that community back then is uh it didn't bother

us to do major tools yeah that's right sometimes

our part of our skill set so yes something that

today we're still difficult it's like

we build tools for all the other that's right

for other people but not for us uh

it's that but it's also just people not learning their

field yeah most people

are are happy to get paid for doing x

yeah and uh that's not very professional

so if you're professional you learn

field and our our field

is primarily

there because software

was invented so we didn't

have to put up with a fixed set of

facilities from hardware yeah let's think

about what that means it means that if you have software

you should never have to put up a fixed

set of anything well if you're

just treating uh

you're treating really badly design software

most of the time as though it's some sort of machine

that you can't do anything with

so this indirection principle yeah

so i i suggest just being subversive

you basically

you start working on

a much better way

of doing little parts of things

and at some point you can

uh see if you can attract more

people to doing this you gotta

you know you you do it over lunch with

beer you gradually

but but before you do that

it helps you know the first ones you do

you're not going to want to use as tools

yeah but you have to get started

and if you are going to try and

get around the problem that we have today

then you're going to have to be a lot more skilled

than most programmers are

yeah so i

like i said i was uh

kind of a standard program because i was used

programming to work my way through college so i didn't think of it

as a central thing at all

i was i had a got a math degree in a biology

and i programmed uh

to pay my tuition yeah but when i when

i got into grad school i was in this research

community that was really serious

about big things

and so but they also gave

the grad students a lot of leeway

so i had the time

and the freedom and the

resources there to just

learn a whole bunch of things that if i'd

been more of a professional earlier on i would

idn't know how to do a language i didn't know how to do an operating

system i didn't know how to do a

system even okay doing

programs and uh you know and

but a couple of years later

uh i was more

skilled on this and i understood

process of

because that graduate school

arpa

did not require

people to do individual

theses although usually

ou did but usually

they allow you they gave you the freedom to

do whatever you thought it was it was in a

big context and so the uh

you could work on big problems and

you know write up the first two

a big problem if that was good

they'd give you a phd if you worked on smaller problems

they wanted you to finish something in two years

but they didn't care what

they wanted was two years of world-class

work to get to give out a phd and

you didn't have to write little papers

with your professor's name on them or any of that stuff

okay they didn't want you to write papers

well completely different to what

oday what they want you to do is to write a thesis

right that's the whole reason

grad school it's not to write papers that's right

they want you to do

uh world-class stuff for two

row and write it up and out you go

so i was in grad school for two and a half

years and uh

and i wasn't i was not

nearly the fastest fastest one out

during my year uh in that community

was john warnock who

is famous for doing adobe oh

okay but john was a uh

was a staff program he had a math

degree had a master's in math and he

working had a wife and a kid and he was working as a

staff programmer for the university

and one of the kids

on the grads uh grad school

students went to him and asked them a question and

john saw a big answer to it that happened

to be the first really good way to do continuous

tone 3d graphics

he was the inventor of at that time

of that and so his

he was a grad student less than six months

and his thesis

was actually 16 pages of text

and nine pages of pictures

bingo out he went

and falcon is a multi-millionaire because of adobe

that

uh so that was a

good way of doing things back then because it it

emphasized not what you're doing

school school was basically

something that was a support system for

doing the kinds of things you were going to do after school

that was the way they thought of it and um

so the so the main thing about it is

uh there was a lot of scrambling but there was

not a lot of competition yeah that's right because

these are grants for were for entire

departments and so the professors weren't competing

the professors didn't have to worry about tenure

graduate students weren't competing

was a completely different environment

today but i like i like what you said that uh

should be subversive to

that's what that's what software

is that's right that's right that's science

the big problem with it is bad software is subversive

in really terrible ways

like the like the pollution

that i did and yeah yeah so

if you think about it uh you know in medicine

you can you're allowed to put on a band-aid

without a doctor's uh

degree but not allowed

to do a hard operation without

being certified and nothing like that exists in software

and so forth yeah yeah yeah and and if when you get

out of of college in in medicine you have to

stay like four years in

stuff you have to

learn yeah yeah and so

uh so to go to a serious

subject it's if you know if you know about

the boeing 737 max

yeah yeah yeah so my one of

is a pilot and he used to to pilot

that one so yeah so there you have

an artificial intelligence that knows nothing

doing and

uh it was allowed to be made

right we know there are people on board

it doesn't know anything about flying

because if you're at an altitude of a couple hundred feet

you don't correct for a stall by diving

the plane at the ground that's right every pilot

so so uh

boeing allowed people to

make a technological device

that uh should be

called in artificial intelligence

because it does things that humans do

and uh but it has no

certification the people would

have no certification and so

none of none of that is being

seriously in medicine the number

the first thing in the hippocratic oath

for doctors says above

all do no harm that's right that's right yeah

ave that those kind of things no such thing

no no and the equivalent in engineering

is the bridge must not fall

the plane must not crash

and engineering is now starting to violate that

yeah they let that plane

there's no way you should ever let a plane

right and so

what's happening is this pop culture

amateur and

i should i i like the term amateur because

lover okay

but basically people who are not skilled enough

for the responsibilities they have

are and may not

realize it may not have the faintest

idea they may think that's right safety bug one

bubble sort program they know how to program

and they're being hired right

and writing gazillions

of terrible code much of which is going

to be is almost impossible

to deal with you know so i i say

well this is like a big fire

so in a big fire you have to decide what parts you're

going to just learn let burn out

and other parts you have to isolate by driving

okay through it and so forth and

think about what software is it's exactly the same

idea right you have to

uh the

much of the stuff look

just to retreat back to something that should never have

happen is the way the web and the web

was done i was i was going to ask

question because there is one number one

the number one telling thing to me is

virtually no student i've met in

undergraduate or graduate school at ucla

can tell me what's wrong with the web

in the web browser

that means uh the education

is completely failed yeah because

they only teach you what we use right now and

know what used to be or the principles or the problems

full circle to where we started which is

people are projecting their beliefs on the world

like one of the most pernicious beliefs in computing

is uh simple

darwinism yeah we must

have the best stuff in the

and i used to give lots of talks until i gave up

showing stuff from the past that's infinitely better than the

stuff today no because they think darwinism

just fits

yeah that's right if you have a stupid environment

you're going to wind up with with a

stupid result of evolution and that's what

we've got remember the

really important thing is that

people were probably as clever from

the iq standpoint 100 000 years ago

as they are today yeah right

but uh the

so uh ignorance resembles

you can be clever as hell but if you don't know anything

you might not be clever enough to

around that and liam my grandfather

my grandfather used to say that it is worse an ignorant

person that a dumb person because

is ignorant doesn't know it

and that makes more more harm than

somebody that yeah yeah if you have a smart

hey know it and they don't yeah so

and that's basically i think computing attracts

that kind of personality there

used to be and also it attracts a personality

that tends not to like humans very much

they're more common

well i think it's because uh

you know many people in computing uh

have asperger's i have a bit myself

and it's comforting to deal with machinery

yeah yeah because you don't have to negotiate

with it yeah that's right that's right and

it doesn't have all of these you don't have to be polite

yeah yeah uh you don't have to try and

figure out what the the other person

is thinking about you don't have to do all of these things yeah

yeah and so the thing that saved me

was doing theater whereas

theater is the perfect thing for

a person who is uncomfortable around

people because you have

to because it's theater is basically the

an anthropological way of getting at the anthropology

of humanity

interesting yeah because

understand why theater can work

if you think about what it is and think about

why people can wind up crying

in a completely artificial situation yeah

okay and how to arrange things

happens you understand a lot

if you're still uncomfortable with them

but you understand more about what's going on

and you also have the understanding

needed to design a decent user interface

if you don't have that

uh the user interfaces you'll make are hopeless

yeah and you'll retreat into

world of simple machinery

simple yeah

so it's very possible to for people to

create these bubbles that they

live in that really have nothing to do certainly

if you look at uh the software i've looked

at it's too much software to make a blanket statement

world now but

the software that i've looked at in

mainly in very large companies and in

the government uh indicates

that hardly anybody

who is good at design was ever in the

process because the

objective is to get things done quickly

on time and in budget not

worry about the future and that's where we get all the trash

in the pacific ocean from that's right it's all about

getting the bottle of water to you quickly

cheaply and who cares where the bottle

afterwards that's right that's right that's basically

why i use that analogy to start off

my talk that it's it works very well

uh unfortunately software is much more invisible

than the trash in the pacific ocean

because you can't take pictures

of

you know gazillions of terabytes

of crap

you can get an idea by looking at

a company that should be doing a lot better

uh like google

and ask the question

how is it possible that when you retrieve something

oogle that it's not showing

summaries of those web

just picking random

stuff out of the web pages but it has to index the

web pages to be able to do the retrieval at all

so why aren't they indexing the retr uh the web

pages are about

and putting that in there is meta information

why after all of these year now of course i've asked

my friends at google many times

you guys are doing all of this stuff you

got ai chips to do this and

but you can't do the most elementary thing

in your primary product

to make it any better than it was uh 25

years ago right so

what's the

problem

can you hear me

hello

hello can't hear you

see i was complaining about

the web and it took

revenge

hello hernan

yes can you hear me now yes

yes no i don't know what happened something was wrong

with my well i was i was complaining about the web

and it took revenge yeah

so what i i was asking you what did i say

when you told them that when you asked them that well

you know if i i haven't tried it on

recently but i used to every couple of months i'd send

norvig an email he's a good guy

charge of all this stuff and well

he went to the basically the

the one of the ways of

looking at it and you could ask them why

the things that really did happen

and i think it has to do with

how school once you start valuing

a's in school

pro process

that makes the problems progressively easier

right because people complain

if they don't get an a if they're valuable that's right

the only goal for them is that yeah

then you have to go to easier and easier problems rather than

uh and so i think what

happened is certainly with uh

ai which is actually a hard problem

what they did was to substitute uh

various forms of machine learning

and uh you know perceptron

type stuff which is which

you know one part of our

brain for a fair amount of our brain does a lot

of that but so do so do pigeons

yeah they decided to uh

and the nice for them the nice thing is

cohonen years ago showed that

certain forms of matrix algebra

were isomorphic to simple perceptrons

and all of a sudden that meant so

that wasn't always known okay

was a guy tubo

i don't know yeah well he was the guy who did that

and uh it put a mathematical

basis on it

and it suddenly allowed academics to write

papers with math in them

and since they were supposed to write papers

uh all of a sudden

academic ai shifted

abruptly yeah from just

just machine learning and giving up what

is now called general ai yeah yeah

this is like i have to call object-oriented

programming dynamic object going to program

now

because the term i

away from me and the same thing

as the term that mccarthy made up

which was you know in which machine learning

was a tiny tiny part that's right that's right

take it away from him and

change and change it change the meaning of the world yeah

and so uh a friend of a friend of mine

actually wrote a paper somewhere about this

thing and he called it colonization

when something is

successful everybody wants in on it

and so the they get it by colonizing

the term

in american

uh k-12

uh mathematics yeah

uh they tried to reform it

yeah they got so much pushback that

solved the problem by renaming arithmetic mathematics

that was the solution mathematics in school

have

to realize this is the world that we live in the pop culture that

in yeah that's right it's all about labels

it's about designer jeans

buying a t-shirt with a label

got one right on on you right now there you go

that gives

you the illusion that you understand what i mean

yeah of course gives

other people the illusion

thank you thank you for you know make

me feel like that think

about it do you know what i really meant well i

think so but i'm not sure you know

i'm on email so

i i i know the history i i

'm not sure if i understand exactly

what you meant i've gotten very few emails

about this because people love these

slogans yeah this is why people love

organized religion because

the slogan sounds good yeah

yeah but you have to realize the slogan is this the

can tell that's right that's right

have the context so it's the meaning

yeah and

so well

no the meaning can be what they want it to be

oh yeah that's that's better yeah that's the way there is

a university building in padelbourne germany

yeah that has your phrase in there yeah

building and when i went they asked me to come

celebrate it and i asked the audience whether they

knew what i meant

so i showed them some of the variations

okay okay and it's like like point of

view is worth 80 iq points which is another favorite

one it's on t-shirts and yeah but

i i didn't say whether the sign was plus or minus

good point

and having a weak point

view uh makes you

appear less smart than you actually are

and the same thing with the best way to predict the future is to

invent it doesn't say anything about uh

whether it's a good future

see people inventing the future take a look at washington

and wall street

yeah

so yeah this is what i'm saying these

slogans i started making up see i think in terms

tell by the fact that i talk

and talk and the reason is that i'm basically

a book guy i don't think

in terms of sentences and i don't think in terms of slogans

but i started making up these slogans when

at xerox when when

i realized the executives couldn't deal with threesome with this

slogan and what they needed was

something that sounded good to them

a good way to

make them think what you wanted it's like

when i give a talk uh

the talk is just a commercial

for doing a lot of work

and most people uh try to use the talk as

ource of knowledge and it can't be

look at the world that we live in this world was not made

oral discourse this world was made by

thousands and thousands of these things

yeah these books that's right not bad from what

we're doing right now so if if this

session you know had

help us to understand

a little bit more of what is going on and i think the number

one thing to take from any talk of mine is wow whatever

thinking might not be right that's right

yeah

that's right you have to doubt about

o get you know

so i think make

the mistake of thinking that i know the answers yeah

don't but i am a professional thinker

so what i do know how to do is to

my own thinking yeah so most people

don't do that alan i you know

we have to uh you know the time

is up uh i

you know i would love to be like talking

for more like i don't know 20 hours 40

hours all the time you could give us but uh

this time we have to to end and

i think the last thing the last thing that you said is

how we can you know wrap up uh

thing um i really appreciate

your talk and it was amazing for me a pleasure

to be able to talk to you personally i i wanted

to go to london this year as i told you in the email

to the conference of the history programming languages sadly

it wasn't done because of

the covet but anyway um i think everybody

enjoyed your talk and everything

us today and you told us today

and i hope to see you someday and

for everybody to keep enjoying

what you think london is a great place to live

oh well i haven't i've been out

of my flat here since the last week

february whoa

because yeah yeah

they have one they have one of the worst death rates

uh in the world here in the uk so

and i'm a former biologist so

better be careful we want you to leave more

i am my wife and i

are casual we're really here okay

okay alan thank you very much i i

don't know if you want to finish saying something a small

than enough okay

it's been it's been a tremendous pleasure thank

you thank you very much bye bye

okay thank you alan

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