Alan Kay Speaks at BAAI Conference

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see here do you know

welcome to the BAA I a

conference and we

are going to have this you know almost an

hour you to talk about few things

you right away here is a

very enthusiastic about

particular you know you you have this particular

note of how the big hall

so I assume you

have a presentation first and now let's go the

presentation then we will come back with our dialogue

ok I'm gonna need to share my screen

sure here

networking yes perfect okay

okay so this

is just a few notes

to help lead

us into the discussion which is the main point of this

and these five topics and questions

were ones that were part

of the context I was given for this

little talk in discussion and in order

to

help

we all felt that this white

paper I wrote last year for a MacArthur

Foundation here in

the UK which

is all about large

challenges

and they were interested in how are

some really large challenges dealt with in the past and

there's something that can we learn for it can we help

educate the politician so I'm

presuming most people haven't read this

so I picked a few things from this

here on

the screen is one of the URLs

to get this but

possibly the UL URL has already been

sent around so

anyway this is a white

paper looking at some

of the big back to them

efforts on intense

critical challenges in the past

and

when I think about these things I

always try and think about what's the larger

culture that any problem

is embedded in because larger

surround often gets lost and

for the stuff I think about I always

start with humans and all

of the cultures on the planet so we

have several thousand cultures and

within these cultures and we are in

ours besides

all the things that we're doing we

have duties to society those are things we need to think

about when we're allocating our time we

all have duties to bringing up the next generation

of children whether we have children or not

here are world

views we have to deal with most of the

disagreements between people of goodwill

comes from them actually

having different world views and

we have sort of an think

have the same world view as we do but we don't so

you have to take that into consideration we

have to understand what schooling is and what it should be

there's this idea of richness

where it's

not about work it's not

about duties richness is

expanding our ability to have an

emotional connections to things and

then there's

this problem of how do we get

from day to day with our basic

needs so these

are

six seven things

that I treat as a context and especially

for here but also the way

I think about things I think of these is being embedded

in large issues

but I just picked

12 of them

so you can pick your favorite one the

problem is all twelve of them have

been let to get into disastrous

straits

so we really have to work on all 12 of

them and that's one of the big problems is how

not to get distracted for

instance we have a health problem

which is very important people are not doing well

at it but it's also distracting from

other issues that

have to do with the planet like the planet itself is

starting to die in various ways and so another

issue that's hiding there that's

going to have a much bigger impact on human beings

then the the pandemic is

going to be the climate problem

and if we imagine a child

being born this year well

at the end of the century there'll be only be 80

they'll still be alive we have to wonder

how are they going to get there could they

get there with the planet in better shape than it is now

rather than in worse shape

so one of the

biggest problems here is imagination

and

using conventional thinking so

Einstein one of my favorite lines from Einstein

is we cannot solve our problems with the same levels of

use to create them so

however we got here whatever

we think of is normal which is how we got

here it's not going to work to get us past

this to fix

these things and so right away one

of our biggest issues is to deal with stuff that

is outside of what our society thinks

is normal and people

use the term crazy for stuff

that's outside of normal so that's what we have we

have to learn how to be crazy in really

good ways and

the

thing that comes along with this is

if you haven't learned how to think skillfully

and to realize where you are requires

strong thinking you are in big trouble and this

is where most of the human race is

o here's where most people think we are

some memory from childhood in the

past take this as the

planet it's no longer so green it's

really kind of like this but

in fact we have these glasses on so we can't see

even where things are now

and the problem is we have to imagine into the future

what's likely to happen on the current

set of things it could be the future of the pandemic I'm

picking the climate as a big thing

so we have to be able to deal with all three

of these views at the same time

and the

problem is that science is our imagination

amplifier but most people aren't scientists

especially most politicians

most of the boating public in the United States

are not scientists or in the UK and

they do not

like thinking outside their

on the other hand what

has happened over the years is

that every time

there has been a threat that is

recognized by the general public including

the politicians there's usually a big response

to it war is the favorite one

you because

should be epidemics too but

most people are not taking co-head

seriously at this point

few places have but

most most places haven't especially

my country two countries

US and the UK really haven't taken

it seriously so if we look

in the past here too

big

projects

all of them have had

some sort of threat involved

with them I suck the Empire State Building there because it's

one of my favorite engineering projects ever

the the whole building from

the raising

of the site the demolishing of the previous

site to occupancy was less than a year

and it was done by less than

3000 people it's one of the great building

in designing and planning jobs in history

and the reason it was done that way was

because the Depression Great

Depression in the US had

hit the very year this got funded

so part of the motivation here is we have to

get this thing up in a hurry and another part of the motivation

show everybody what building a tall building is

really like because another one like this is

not going to be built for decades so

the other entries

here which are all described in this white paper are

from world

war ii and the Cold War

so atomic energy Bletchley

Park the UK and

us joint project

on radar which was the prime

technology for World War two and

then a lot of funding continued

from the in the Cold War

and part

of that funded ARPA 1957

and ARPA funded a an

agency to do

non-structured

research research that was not top-down

looking into information

systems and Xerox PARC

was one of these groups just funded

by Xerox about ten years later so

the process

of this right hand panel

in the Cold War came out of what

the group doing radar learned about

how to do science and engineering together on

a large scale to

combine research into the unknown with

doing practical engineering so

most of the successes in the realm of computers

research in the u.s. many

of them came out of the

process and it was taught to grad students generation

after generation

so a key thing about this kind of unrestricted

spending without

a lot of top-down supervision

that a key thing that is missed is

the amount

of wealth that came out of doing this spending

was spent to deal with a war but

in fact the most of the

industries in the u.s.

happened after World War two happened

because of World War two most of the technologies

that are prominent today came about

because of World War two and so

an incredible amount of wealth that has been generated

far far more than what the outlay there

was no need for Wars to

do this at all but

that's just the way we are and

to give you a sense here Park

came out of the ARPA community ARPA

research started in

1962 so

they're 12 to 14 years to get

the payoff and the larger

community started before here's a

larger look at the

DoD ARPA merging into Park

the two groups key

from Park came out of the Arco community the

internet was a joint project

and most of the technologies

that we did at Parc came out of

prior research at ARPA including some of our PhD

theses

if we take an even closer look at park we can see most

echnologies that we're still using today

that I'm using to give this talk and

so we call

it eight and a half inventions the half is the Internet

amazingly

all of this was done by about two dozen researchers

just 25 researchers

for the for these inventions

really inexpensive

about 15 million dollars a year in today's dollars

sheep returned

so far has been over 40 trillion dollars

and

this recreate created industries rather

than increments

o this prior art and all of these things but each

of these created industries of their own and

valuations in the stock market

of American companies came about because these

inventions so what's interesting is crazy

funding of research that

nobody believes in and believe it believe me nobody

did when we were at Clark they thought we were crazy

here are funding

people who are up to the task and you're willing to take

thirty percent of the results you're

going to get an enormous return

just monetarily but

also you're going to get an even bigger return for

society and that's what we're talking about today so

people say well Xerox

make any money on this but in fact they made billions

on the laser printer and paid for park many

many times over 50 or 60 times over

okay so here's the key point and

that is if you're interested in doing

oodness of the results you're going to get correlates

most strongly with the goodness of the funders and this

is not in terms of money most

funding today in the United States is

not good funding

in the terms I'm using good here

it's mainly funding for

goals picked ahead of time

stun top-down it has

making money as an end result and so forth so

what I mean by goodness of the funders here is

the funders actually understand this

other process which we're talking about today of

how do you get stuff that hasn't been seen before

and here's one of the great

ones of all time the guy

who set up the ARPA eye PTO funding

in 1962 and he

was a visionary

and he would not talk about goals you did not

have goals visions and

a vision is something that gives you a sense

of what the thing what the desire is but

nothing specific there's nothing you can work on directly

we have to pick the problems from the

vision so here is his vision that set off all of this

stuff computers are destined to become interactive

intellectual amplifiers for everyone

university universally networked

worldwide that's all he would ever say

yeah when they ask well how

going to do this he said well I don't know but I'm just going to give

money to people that think they can help

and will take a percentage of the results

and given what we're trying to fund if 30

or 40% of the results are successful

we will change the entire world now that's exactly what

happened so what so these

visions they're kind of like little magnetic

fields hidden behind the mountains

researchers can feel these magnetic

fields and if you're over on this side you

can feel

north appeal the magnet going

you're over on this side you can feel the magnet going everybody

starts moving towards the magnet and you get

a lot of different ways of approaching what this vision

is and that is what happened

so whenever you get into trouble what

you need to do is to realize that most problem

statements come out of the current context

but most of the solutions you need come out

of a context that it has to be invented so

you need to retreat from goals and problems to visions

visions of how things should be and those

will help think wider so how did this

stuff work back then well this is the Pentagon

Department of Defense and there's a US Congress

every country has something like this

in 1957 the

Russians put up a satellite and scared

people and that

fear was enough to set up the Advanced Research

Projects Agency in 1957 and

these were the days of the Cold

War and so instead of putting bureaucrats

in there all the ARPA directors were scientists

mostly physicists and

in 1962 they funded the

information processing techniques part of ARPA

also stocked with scientists and a new guy

every two years the ideas if you stay in too

long it becomes your job no

your job is to be a scientist and what you're doing is doing service

so you do two years of service

helping the country and then you go back otherwise

you start buying a house and get a house Morgan's you can

start worrying about your job it's losing your job now

of course Congress still

wanted to be a watchdog they

wanted to be the top down so they'd all ask well

how is this relevant tell me why this work that you're doing is

relevant to the Department of Defense and unlike

today these guys would

that's not the right question to ask because

they didn't care about getting fired no that's

not the right question to ask the right question is how

is this going to help the country or this technology

or our society or our culture in general and by the

way these are direct quotes and

Bob Taylor who is an onlooker

there said well they'd stand up to the Congress

and in a polite civilized way attack

their nearsightedness their myopia because

they were scientific Statesman

so Bob talked

to Charlie Hertzfeld here Charlie

said well what do you want Bob and Bob said well

this network is what I want to do this is like 1966-67

and

Hertzfeld didn't ask

him what it was he said okay

Bob said well I was a 15-minute

conversation with my boss and

then Charlie said well

need to get this off the ground and Bob said oh about a

million dollars or so that would be like six or seven million

dollars today just to get it started and

Hertz fellows response

was okay and as

Bob said there was no R per order written there's

no bureaucracy nothing

the paperwork work was done about

a year later but they're able to start the

same day that Bob had this meeting and

that network that he wanted is

in case you don't get the joke here the

joke is this is how this thing that we're using that's

all over the planet was actually funded without

a proposal

just two people who trusted each other and it

was part of this vision of lick lighters

and what's key to this story besides what happened with

ARPA was that Bob then went on to set up computing

research at Xerox PARC and

so he took this way of doing things

into this Industrial Research Lab

and hired a bunch

of us to do the work so here's a key idea being

responsible doesn't mean you should try to control

this is the biggest mistake that almost everybody

in charge in bureaucracy makes

because they are held accountable by their boss and they're

buying a house and they're afraid of getting fired but

basically you

can't do it because

in the case of unusual research

if you're a funder you don't know enough

to come up with good problems believe me you're

in the wrong place and the wrong the

people who know enough to come up with good problems are out

research without messing

around with money they're not doing politics so

you just cannot do top-down on this

stuff and get anything

and it's the great people who come up with the

great goals what you do is support them so

being responsible means you try to manage

the support of these people who are thinking

about what should be done

realizing that perhaps 60%

of their efforts are not going to succeed

and the other thing that ARPA

chose to do was to

have no peer review

and the reason is that even great people

often poor judges

of other people's work just from rivalries

or just there in the different context

it doesn't seem relevant so peer review just

doesn't work at these top levels it

only works for mediocre stuff even

if you get peers and

in the u.s. like at NSF and it's after you never

professors

here and there who do it you

never get a peer

so key idea top-down control kills

unusual research lots of money

gets spent you'll set up research labs all over

the place people will get hired people do things in the

US at least it is essentially

never worked

no labs had had a sign

all over going

down the stairs and walls the

sign would say either do something very

beautiful or very useful that

was the sign at Bell Labs either

very beautiful we don't care

if it's useful or very

very useful

but take it out there and go

to one extreme or the other so what you need here

is you need unusual thunders you

need unusual research managers and you

need unusual researchers

so back when I was operating

in the 60s and 70s it was ARPA

office of naval research Xerox unusual

research managers like Licklider Sutherland and Taylor

now the unusual researchers

interestingly enough are easier to find but

they won't do anything without

these first two slots being taken

care of so if you want a championship

football team you get a Beckham

you just don't go out and get somebody randomly

you get the best football player

in the world and hire him for your team and if

you want to do a Xerox PARC you get a butler

this is Butler Lampson

stratospherically Abel

and if you

look at the bell curve here

what you're looking for

people that are for your research labs are one

in a hundred thousand one in a million one in ten

million ten one in ten million while China has

140 of them pick

something to be unusual in China has

140 in this almost 5 Sigma

case and if you want to do this stuff

you have to find them and nurture them

and some

like this some of the researchers will be like

top scientists some of them be

like top engineers but some of them are going to be like this and what

I'm showing this this is an ad that I suggested

Apple make back 40 years ago as

to what personal computing should be about and

what I meant when I tried to get them to do was

to do this because

the real process here is not to have a domain

of right brain and left brain the

real process here is to have the

ability to fluidly

merge all the different thinking kinds

of thinking you have an aesthetic thinking you have

into the thinking you're doing

so here's another key point

ARPA spent a lot of money

net generation of researchers

consider that one

of the main research results I had to fight Congress

on it but they

said hey this doing

this vision is going to take 10 15

maybe 20 years so

we have to we we have to grow the next generation

of researchers and we can grow them in this

in fact almost all the computer people at

Xerox PARC came from this ARPA process

we were all young I was the oldest person there I was

old everybody else was

younger butler is only 27 that's what

Bob Taylor wanted he had seen all of us do our PhDs

okay last idea

here is this idea of mad money

mad money is money that

you have in your pocket

that you spend on a win

you

can spend it on something it might not even wind up working and you don't

it's

money that is not aimed

at any purpose so

the number one thing you have to ask is

in any organization or country

is how much mad money is there

if you have a country that is SuperDuper organized

they will tell you we

have no mad money

we plan for every cent we can tell

where we spend every cent on this and we'll show you in the master

playing with okay so that's not very creative

and most companies

want to show the stock holders

that they've been spending this money wisely and

here's the pre turn on this investment they hate to show

line items where there's no return on investment

for five years okay some add

money so here's the way to compute

it for a company Ardi

and a company is usually five to fifteen percent of revenues

most of it goes towards product

mad money is

1% to 5% of that

so the range here

is about

it was just tiny fractions

of our total revenues

in a company and if Xerox PARC

was about 50 million dollars a year today

that means that

taking the stingiest approach

to this we're only gonna take 5%

of revenues for R&D and 1% of that form

and money still the first 14

fortune 418 companies

are going to afford to do a Xerox PARC not

a single one of them half

no matter how much money they have

and if you go to the higher end fifteen

percent of revenues like technology companies do

and you're going to spend five percent of that on the

art on the mad money

like Xerox did that

means every single one of the

fortune 500

fortune 1000 companies

so let's look at it four countries

so

if we start with say Japan and Germany or so large

countries on up spend a hundred billion

to five hundred billion for

country R&D I think

China's is about two hundred and forty

or two hundred and fifty billion dollars a year the

US has maybe closer to five hundred or something

but it's about two percent whatever it is it's around two

percent of the GNP

and so

we take the same mad money figure 1

to 5% bad

money for the lower-end is 1 billion a year to 5

billion a year of stuff you should spend without

worrying about what happens like Japan

spending a billion dollars a year without worrying

about what happens

and so a billion dollars a year is

66 Xerox parks

eems like should do

it right and

for a large company like China

or the u.s. it's over 300 -

over 1,600 Xerox parks for each country

what this means is

the world collectively

and especially the large

countries getting their act together

actually have plenty

of funding have plenty of

potential talent to

actually deal in an unconventional way with these

enormous problems we have the problem is is that

most of the people in the world cannot see that we have these

problems or cannot see them as threats

or fears or whatever it takes and

so I'll leave you with the final quote by Einstein here

which is insanity is doing

the same thing over and over and expecting different results

that's what's been

going on for the last 40 or 50 years in computing

for sure and

I daresay in most

things and most especially for

these 12 critical issues

that we have to deal with right now so

thank you very much and I

guess that was about a half an hour rather than

I talk thank

you well thank you very much Alan

for the great talk and very very unlikely

the what

what do you just talk

about a large part of it that you actually summarized

very well your note the

big hall and you asked me list of

you know 19 roots of

Rd management to solve immense war

challenges and but you also mentioned

you also I to summarize the very well

but you didn't mention your talk is that

there are tremendous barriers and for

people to follow through your advice and that

90 rules including you know give

you the money you give all along and

the mad money and give

the meta money and the less of great people to the great thing

don't ask them how much they have professor

made and but

people just couldn't do it so you measured

about this barriers and you have this

concept of a six core buyers in the so-called

oestrogen paradox

could you talk about that a little bit well

so a good a good place good

reference for all this

is the Wikipedia article

that is called cognitive biases

I should have put that up in a

slide cognitive biases and at

lists I guess probably closer

to 200 things that are wrong with

human brains so this is the

result of anthropology and psychology

over the last few hundred years one

of the things we've been interested it is

why are we such poor thinkers why

do we require so much training

to do good thinking

why was science only invented a few hundred years ago instead

of a hundred thousand years ago and so forth and

so there are many so

something that every scientist knows about

is called confirmation bias so

when you have a theory

confirmation bias and scientists do it to is

any evidence that will strengthen

the thing you already believe you

give it much more weight than evidence against

the thing you already believe it really costs people

something to unbeliev something and of

science you're not supposed to believe things but we

do anyway so the ostrich syndrome

we've seen it in

responses to the pandemic

well it's not there

it's not important well even if it

is there we can't do anything about it

so all of these things are

there are expansions of one

of the things that is deeply built into all mammals

that probably

most animals that can move is

when you're in trouble basically

you should either find

a place to hide from it

or you should prepare to fight so

it's called fight or flight and this stands

in my way people deal with things verbally

so it's it's a part of human

nature and people are willing to get

past their human nature when they're really frightened like in

a war yeah and

you mrs. battle people you know when

people really got a frettin they are see another

story you mentioned about

you know Dhamma today what we call it

a modern time it started was because

code walk because the is a threat people

filled from silly union that time and also the

fact that so it is launched and sfotch so

uh you know when when people says

it's danger then they may start

to act and acting in the right way you

know so uh fear my question

is it's been damaged you know the last four

months or five months now it has

coughs the were so much trouble you know economic

wise and people like wise

thing you know is that big enough threat that

you think you know can waking people up and

start doing things in the right way

doesn't seem to be

has depended on

how the government

governments structured

like New Zealand did the right thing and did it quickly

the u.s. didn't do the

right thing and is doing the wrong thing

still

and it going in another wave of

it so but I think overall

the expecting

you

know mainstream people to

do the right thing here they just can't imagine

it until for

instance maybe a relative gets it

so when a relative gets the

disease and baby dies from it or has a terrible

time with it or they do then

they have this local way of assessing things

but for war

is prevalent in human

history and so

the threat of war has

been enough in the past for instance it was enough

radar started in the

UK seven

or eight years ahead of the Battle of Britain

yeah when the rest of the country

did not believe if there was going to be a war with Germany

and the politicians were trying to avoid it but the people

who did the radar did it anyway and Britain

came out on top when

the Nazis did fly over so that

that

was a reaction of what you might

call eccentrics who had some

funding who had the ears of some people but

a certain radar was not a national priority in

the UK nor was Bletchley Park

until there was an actual shooting

war but both of them had been set up ahead

of simile in the United States there was a billionaire

yeah who got radar started

in the US because the government wouldn't fund

it and he didn't care he happened to also be

a physicist and he also funded the start

of the joint project with with MIT before

the US was officially in the war so

that made an enormous difference but you

can't give the US government any points

con you can't give Congress any

points for reacting to what was actually

going on u.s. in those days was completely

isolationist okay

yeah

we have talked about the

kind of a history of modern

computer science development teeth from

the tapa days and that's

did I just say one thing got on the deep

but when the D the D was put on to

ARPA yeah it wasn't ARPA anymore

okay

too because Parker has ARPA

in it McCain's nothin

because what that when the D got put

on the processes

that had been going on before it got changed

so uh wait

a minute is that yes today it

was that D and the processes changed

that got Taylor to try and get money from

a company that's how Xerox PARC happened okay

that's important Xerox

PARC happened because of DARPA couldn't

do it anymore okay oh yeah

but you know in

inth a say they accident remove the default a

while for a few years and then they put

back so and not less let's

to you you also mentioned in passing

very quickly you say the last week two years of

computer science the research you know there are there are awful

things that if they use old tricks and 20

so uh yeah that's

uh that's not long little

discouraging but uh you know but

I I still dare to ask e how

do you feel about a to loss to

sixty years progress in AI

Oh aye that's easy

because again if you're a biologist

and

I used to be and

you know something about

the human way the human brain works

an enormous ly large part

of every mammalian brain that most

brains are like

machine learning

they're basically quick

correlators

and so

a big machine learning system is kind of like a super

pigeon and you

get a lot of good things out of a super pigeon and in order

to do something right now that

we would call real intelligence

the name AI got stolen now

now it's called general AI

this is one way of telling that things have

gotten very very wrong so I

even lost four years well

I'm with you when the people are who are doing

it co-opted the

name with a subset of the meaning to claim success

Wisie called AI well people

wanted to have success and so they decide

work on the hard problems which of course are the cognitive

parts and so the

cognitive stuff has been very poorly funded

over the last 40 years really

poorly I'm speaking from the standpoint of the

US okay because I'm mad weird

and you know some

people have worked at it one

way or another the the

people at Vulcan Labs we're looking at cognitive

AI Doug Linnet

of the site project looking at cognitive AI but and some

people have started looking at now with this drive

towards explanation

and drive

towards having a user interface

that humans can deal with that

user interface if it's going to work in human

terms it has to have a context similar to

common sense world that human beings have hardly

any work has been done on this percentage-wise

in the last 40 years and so

I think that the

what ml

has done is

impressive and fun and it's

needed but I wouldn't confuse it with real AI

not even a little bit

whoa at

least you agree you know machine learning as Pascal

you know through the tremendous progress in the last ten

years and the deep learning in particular

yeah now we finally have some networks

that can work can solve problems

you know now you have machines that can recognize faces better

than most of humans and

solve a lot of real-world

problems but do you have a pass

am I like some song you know and then

let me come back for the Cosima of the means theme of the conference

here is the next decade of AI so

what's your prediction Oh excitation

oh your region you meant about

vision or Cisco I think the first thing is

to take the term intelligence seriously

really seriously just

like one of the problems with

the thing called computer science is

that term science was taken seriously

in the 60s and

the 70s but now it means engineering in

United States at least if you talk to any

computer science graduate student and ask them what their

field is they'll give you an engineering explanation

not a science why so that you

term that got co-opted is object-oriented

meant something

in the 60s and when it became popular

because it was very successful Xerox PARC

everybody wanted it and so an enormous number

of languages started appearing that were called object-oriented

that weren't at all see this happens

over again because people want to be successful they

want to feel part of the winning team

you can buy jeans you

know jeans that we wear that have

the Harvard logo on the back do

you want to feel successful I'm

conniving cotwell this is part of the brain

it's called the magic of contagion

is these guys are successful I

want to do what I'm gonna claim I'm doing it I'll do a subset

so yeah so take it seriously

draw a high threshold for what it

means and don't worry about you know quit writing

papers on stuff that

isn't interesting what about one

of the reasons that ml got going yeah

is because of kohonen

showing that certain

kinds of matrix math was

equivalent to certain kinds of perceptrons all

of a sudden academics could write papers with math in

them if you look at what that math

does it's not that interesting or impressive it's

basically a big correlator whereas

judea pearl who I think is

a guest of your conference I hope he's

a great play yes he calls

ml curve fitting

yeah if you call it what it is you

might want to work on something more if you're

working in the field of AI if you're calling the little thing

you're doing AI even though it's using

thousands of machine cycles and special chips and

everything but if you already working on a subset you can't claim

anything interesting about the large field so

you know main advice to give

to any young researchers hey get real

don't worry about yeah

there's always no question about

having the right papers to get your PhD and

having a deal look the people who

make the breakthroughs here are people who just don't care

about that yeah I don't want

I don't mind the funders funding

everybody else but if the funders omit funding

the people who can do the breakthroughs because of

the orthodoxy of this stuff that

isn't it then it's a disaster and

I think of a lot of what's been going on in computing the

last 20 to 25 years in general as a complete

disaster a great

point you know I I'm sure many audience

I have heard you uh you know many

in the audience has heard your demise and

I want you you know your

memory data but you know you you you you misses some query good point

you're not on paying too much attention on

paper publications and papers and

today maybe citations all those things so

one question I have for you is when

you were at a park right

when you were working on this the graphical user interface

you know and there's

not you know you people will argue they're not much but

much massing it you know that this is not

use differentiation a differential equation

and and how

do you convince your boss now this

work there's going to be a picture and in this change

the entire interactive mode of computing

and that will lead today you

know internet whatever in mobile devices

you know read the paper look at my talk I

never had to convince my boss of one

not one thing

because the bar and

the boss who's Taylor he

just hired me and said follow your instincts that's

the only direction that's the

and Taylor never told any researcher at Parc

any project that he was that he

us to do never great great

high teen don't make me talk or

neither did you think about my in so far

take a good with other and whatnot what am I know

I was paraphrasing what

are you your sister said in Chinese yeah but then

let me follow up on this same

topic you know how do you then generally

carry your idea be accepted in the community

well

you know and that that's more important right ya

basta you don't have to do whatever you want do better then how

do people come yes they your stuff it's

great okay now most of my ideas haven't

been accepted in the community that's okay but

is there a few right that your what you

got rented programming and there's a graphic interface those

not the

the main things that are called object-oriented

languages aren't anything like how

I described object-oriented no I lost

on that one because I thought

people thought they knew how to program and

the graphical

user interface at Parc and that's an interesting

different talk because we can run

all that software on today's

machines I give I give talks

using the exact software

from Xerox PARC in fact that Steve Jobs saw

so people can see what he saw and

if I were to do it you would be shocked at

some of the things I could do that I can't

do in a PowerPoint or

or keynote

for instance you could the whole thing was

live it wasn't a static presentation the

whole every part of it was programmable

and every part of it

was safely examinable so none of that

stuff is around today

so no what happened was

if you look at the these technologies

coming out of park ones where

there was no competitor

even really opinion wise

those made

it like an example of that well for instance there was nothing

like the laser printer right nothing

so company

immediately doing that there's nothing like the Ethernet

right everybody had tried to

do a local area network that is the only one that ever worked

and so that was

easy people thought they knew had a program

so the new programming techniques and

languages and stuff that we did a part were

more or less rejected

and people adapted the user-interface

to a subset of it partly

because the Mac was so weak

compared to the what we what we had in

park that it could only do a part of the problem so what

you're getting out as a subset the

thing to take

a look at is how bad the web and the web browser is

compared to the software of

the 80s forty years

till think so well

the web web and the web browser go back to like nineteen ninety-three

so a twenty seven twenty seven

years and in most cases you still

can't do WYSIWYG editing in the web browser

that's pathetic and web and

the web was developed on machines

that had WYSIWYG editing on them

so what you're seeing here is a bathtub setting

and failure of people to

do the work okay since

we are on this topic the graphical user interface

right and there you also mentioned object-oriented

programming idea yeah people did

you know people do their own a different way wrong

way and so you know just just a

little bit gaza yeah so what are you preciate lasting

jobs actually stoning your idea and let it angel

man look I I told both

Steve and Bill Gates

take the whole I remember we were

today the public domain Xerox

let us write papers wasn't

a secret visit I wrote a scientific American article that

was published two years before Steve

up that showed the whole user interface and everything

everything he saw in 79 was out

in the world two years earlier so there

was nothing secret about when I told this guy both

of these guys is look take the whole idea

don't take a subset of it and mess

it up take say stealing

I don't yeah no we're not none

of us had Park tried to get

rich that's okay by

the way to your point about you know don't

you care about X Y & Z no I

myself by the quality of the effort I put into

anything that's the only thing I control control

other people's opinion right boy great

plan the second thing is that the

people at Park and in

the ARPA community in general had an aesthetic Comeau connection

we really

really loved Lick Leiter's vision

I mean loved it like you

love a lover we

really wanted it wasn't it was an emotional

need that we had that we were called to it like

people are called to religion or to being a doctor

or to art and

so what so what we and the

the hiring the

selection process Taylor was looking for people who are essentially

artists who happened to have deep

scientific backgrounds

and like I said

there are there are people like that I was one of them many

of the people at Park were like that and boy

an artist

might be a little disappointed if somebody doesn't like

their

painting but not much

like because that

isn't why you're not painting it for to

sell the thing yeah artist

art is about I I

have these feelings that I would like to

manifest so that

other people get that's what you're doing

and if if you feel like you've done the highest quality

effort you can possibly do on this work

what what more can you do you can't

let some okay hi

III need to pull your bag of it I better you know so

you you have you know in park on your

peel in your career you have event little awkward ideas yeah

but there was a story you actually went to visit

y combinator in the

in 2015 you did a graffiti on

their slogan yes they say they

will invent the product people

want and you you cross

out want and put a leak so

what is what was your thinking behind it and

yeah well we want sugar

we want that we

want salt

we want companionship these

are biological need biological wants

there dries we want stories

so

way to make money is to

make a technological amplifier for any human want

because a technological amplifier

in the Industrial Revolution creates

essentially a legal drug

1/2 can you have these things

we have to have these things so let me just explain

the last thing and they don't quit although

what a need is is something like education

everybody needs deep

education but many

children don't want it marketing

people hate the idea of needs marketing

people want to sell to people's wants

educators are trying

need and they're trying to figure out how to help them

gain this what they need but it's

we it generally requires a lot of work to

deal with a need and much less work to deal with a lot

say so our society

is messed up in the over balance

between the fact that you could make easy money supplying

nee supplying wants but

it's gotten out of the habit of doing work necessary

to take on a new need like

learning lots of things you didn't know before

that are completely new ok great

you know the interest

of time you know I I need to stop here

I want to stop with one last question you you only

have you know you only need to answer you one sentence you know

if you if you want to name one great

product now you have a seen in the

last 20 years what that

great product

well

the most the most fun thing that

can be found online right

now it's a company

it's probably going to be a product

but you can use it right now for free

so the most

fun thing I think over the last 20 years in the

computer realm is is

called croquet

croquet is

this

version is the fifth of five deep

research efforts over the last 20

years to see if

the thesis of Dave

Reed at MIT and the 70s would actually work so

it's a mass of something

that is as large as the Internet and

massive deployment of pseudo time

computations is this is up at

the level of importance with tcp/ip

ok also I

have to cut off here and again

I was thank you very much for your enlightening talk and

the interactions we have and I know

we can chat back space for another hour or

two you know and there are also many questions on the audience

but I have to cut it here was thank you so

much and hopefully next year in the be a conference

will inviting you coming again and we

have another fireside chat thank you Thanks

with a bid and

salud even a jab

you know a machine apart you know show me doc hi

Alan

just wait a few minutes we want to yeah

okay yeah we should take

a photo yeah so this is a virtue a

way of taking photos kind

of sweet oh yeah oh yeah yeah oh

yeah oh yeah

well

first I need a wing one here

anyway I want to see how

there's

one question from our director

of the BAA I okay

Alice thank you very much for your presentation

in fact I believe we represent where is higher

many people in China but I

have a question question is that why I

don't know Biagio that

have okay but only why and

Park in the world No ten

knots many market

than what so what what the what is the reason but

I I want to further extend your

idea on the Med money you know even we spend

for example in China a firm present but

it meant money but this do not park in

China what the reason for that that way

I think so I'm the last thing I think

the thing is to look to see what the researchers

think their goals actually are I

believe

the reasons that let's say the

other ninety-five

presents money is no more money

compared with your mad money but

the clever people is limited

follow

the no more money they cannot spend

their time on free it's flow its

flow region so that's why no parking no

real you know me occasionally

so princess in

biology and exam there are several examples

of things in biology that

are like Xerox PARC one

of them is called Jane Elliott labs in

outside of Washington

DC it was set up by the Nobel Prize winner Sydney

Brenner and

yeah

but generally so

one of the interesting things that has happened is

certain people like Paula Helen who

is one of the Microsoft guys he

decided to and he has billions

so he decided that he

was going to set up what he called Xerox

PARC done right and he

set it up in Palo Alto and he hired a lot of Xerox falcata

interval it's called interval yes and

well after

about six months or a year or so they invited me to

visit and

I was there for about a minute and I said well this isn't like

Xerox PARC at all yes

just then looking around

they had

some of these people who are so productive at

Xerox PARC er they were not being productive at all it

was easy to trace it back up to Paul Allen

who was not

content with just funding it he wanted to be one

of the guys and I

said to him I said Paul you you're a billionaire

you've done this that and the other thing but really

don't you admit that you've never done any real

computer science research in your entire life

know about it and especially what do you

know about it now why are you

trying to meddle with what's

going on you have to you're

not good if you're not going to actually try and use

process that Xerox PARC don't call interval

Xerox PARC done right you're

not going to get it and so the

thing I would do with with your young scientists is

for instance one of the problems

in graduate school today in the US

is the fact that the housing market

in the US has expanded

an additional factor of 10

in cost

over everything else by

inflation what this means is no

graduate student

at Stanford can afford to buy a house in Palo

Alto when they get out and get a job they

can't do it they have to go to Google

and even there it's

hard to buy a house and pal so the this

some almost nobody wants

to stay in and do research and if you

in and do research who is going to fund you well

not NSF because NSF

funding in computing is almost entirely like

engineering proposal left in your proposal

you have to explain to them how you're going to solve the problem this

is the proposal whereas

at ARPA you would scribble something

on the back of an envelope and say here's

the issue I want to work on and if they

thought you were a good guy they would or a good

gal they would say yeah that's that

was was in a response to the question

there now nobody ever asked me to justify a

single thing yet when

I was in grad school or at Xerox PARC they

just I walked in there they gave me the equivalent

of a two million dollar a year budget and I was

one year out of my PhD right

so one year but

they thought I had

potential and know

if I stub my toe on the thing while I was

just one of a bunch there and

the theory there was

was it's like going

out on a in a farm

what you're interested there is yield

not every single

thing being right you you're

growing everything as well as you can but

not everything is going to grow and no farmer

cries about that it's kind of the overhead

and the same thing is not

succeeding in your research when

you're doing this kind of research it's not failure its

overhead it's the overhead

it's like in football

the percentage of

goals made by a football player is really

low compared to the shots on goal

right missing

a shot on goal when you're playing football that's

not an error that's overhead it's

a difficult game and you're willing to pay

people immense amounts of money to

every once in a while score goal

in a game that's the way it works the

sports world understands is completely

because what you what

you're trying to do is something difficult a big

problem with bureaucracies especially large

ones is they are hiring so

they have to dip into the middle of the bell curve so

you're getting people that have to be managed

and all of a sudden you have started having layers

good point and you have

you have accountability ya

know you're much better off so in America there's the American

MacArthur Foundation

probably heard of that they have this thing called genius

grants so every year

they give five years of support to

35 artists in some field period

there

are no goals behind its whatever the artist does it's

just to get artists artists can't not do their art

right that's the definition of an

artist so if you get somebody who takes direction

in this area you

don't want them yeah nothing you don't

want them because they're not following their art what

you want is to get the best artists you

can get in there and take 30% of the result and be

happy

in lighting you know extensions

hander you know there

is one thing I hear

is you know I look at through your 19 rules of

you know managing

the great lab

operated research or math managing man money

I think you imagine a number of them meter no control

don't contract control bill Domingo don't don't matter

are wrong and also funding people

not project which is which is exactly

what Pai is doing so

we're not funding project we have found in people and

we're also funding young people and

we don't want people apply we say you're

because you're gonna hear some money so uh

so high I remembered and

next time when I've got a challenge how

say you know dr. Allen case said

that okay so thank you very much Alan

and I hope well

I will hope to be alive the

next time this comes up oh that would be very still

that's thank you thank you

ha see ya see

ya Thank you Thank You Alan thank

you John thank you ah one

minute Randolph with John

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