Extracting Energy from the Turing Tarpit (2012)

about

two minutes to set this up

okay  now is it I

love that we are the

testers  for these microphones so I'd like

the AV people to turn off the video  there take

take me about one minute to

  set things up here

because this is  actually in the

nature of a pep talk  and

it's an optimistic

pep talk about

  about software and it's based

on the  work of many people

here I should  mention that

many of my heroes are

in  this room and not just the people

who  got the Turing Awards but some

colleagues I've worked with over the  years are

my heroes I think

we wind up

  making Ecology's when we're

trying to do things this is sometimes  been

been mentioned and these

ecologies  are critical

because they actually  require a wide variety

of different  talents I've always felt that

Turing  exemplified

a wide variety of talents

  inside himself just pretty darn amazing

  I

probably forgot something but okay  let's let's

start here

so this pep

talk  is about the Turing tarpit

and what we  might do with it and about

it and I

don't know where the I was a big fan of  L perlis

as a fantastic guy

who was the  first Turing Award winner and early

he said beware of the Turing tarpit  where ever

  as possible but nothing is easy and

what's interesting about he was talking  about programming languages

but in fact  this is true the computer itself

that's  the problem it's got all these

wonderful  degrees of freedom and we

glorify in those because that's what  makes it the

most interesting machine  that anybody has ever invented

and of  course the the

problem here is  oops and

fallen and perish now I did

a  little research on Tar Pits and

I could

  not find anywhere that

any humans were  ever found in

a tar pit that were not  put there specifically

as a ritual  burial so

it seems like humans are a lot  smarter than sloths

sometimes you never  can

tell but in fact it

humans were pretty good at avoiding the  tar pit but

we created a different kind  we realized

oh there's there are all  these hydrocarbons

and fossil fuels and  we wind up with this and

oh this and one  of the slums in

Rio de Janeiro my two  favorite pictures

of what I think of as  software today

and

the problem is we  can't see them we can only see 30

or 40  lines of code at a time and there's

this  enormous festering whatever

it is and

so  this goes from loops to a yikes

the tendency we

country is to beat everything to death  with the club and

what what happens is  the sub goals

become the goals so at  some point just building

refineries  become the goals we're

really what we  wanted is energy and

it happens that 67%

of fossil fuels are burnt to make  electricity and

well if we want  electricity there are other

ways of  doing it for instance photosynthesis is

  pretty nice I used to be a biologist

and  what happens

is through a beautiful  series

of chemical reactions and some  physical pathways

it's pretty darn  efficient at getting electrons

and  electrons or what we want so

we get a  yay on that and there

are a lot of  organisms that do this and

the ones that  don't feed on the ones that do

and it  happens that the metabolism

basal  metabolism in all living

cells is  somewhat similar and it's looking

from the energetic standpoint it's  really about just transferring electrons

  around and

so there are a lot of  electrons there and in fact recently it

  was discovered that many bacteria

extend  microtubules between

each other not for  the purposes of sex but

for the purpose  of exchanging electrons

that incredible  so

they can get a kind of a feeding from

  each other they share it's a socialized  electrons

and because

there are so many  of these there Adam

favorite engineers who's also a  biologist

says hey just stick

things into the ground anywhere on the  earth including

the Sahara Desert and  you've got a fuel cell

made out of  living microbes that are just

happened  to be there by the billions and  trillions and

so in fact you can pretty  much any

bucket of dirt that you get  outside you can run in a 60 watt

bulb  just by having

the right kinds of things  that you stick in there it doesn't  require any

additional power  this is kind of a metaphor

for I think  what we should be thinking

software we have to think about what it  is that we actually want

to do and I  don't think our main goal is writing  millions

of lines of code what we want  it is for to

do something so

we've heard  this in various ways today

this idea of let's try a science and  math

let's not beat it to death and

idea  number two here is this

distinction  between tactics and strategies when

we're building things and tactical stuff  is usually pretty

incremental and  somewhat similar to where you were

so  bricks lead to piles

and stacks and if  you scale them up

you get pyramids and  walls and they

have a certain simplicity  to them anybody can learn how

to be a  bricklayer and so this has

a lot going  for it it's very transferable and so

  forth but it has some limitations and so

  there's a strategic direction we could  go which

is before we build things out  of the materials

let's think of things  we can make out of the materials that

  will be new materials so in

architecture  let's make some arches that's

where the  term came from and these things are

  incredibly efficient the

Romans made  such good cement aluminum

silicates plus  compaction

that many of these structures  built thousands

of years ago still  survived today

if you've been in the  pantheon looks like it

was built  yesterday instead of 2,000 years ago

in fact it looks like is made out of  granite rather

than the best reinforced  concrete anybody has ever made

by the  way the frost giants in the

vogner  operas came out of the inability

of the  Goths and the Visigoths to tear down  these

aqueducts  in

the Dark Ages they concluded only  Giants could

have built them but in fact  it was just

people like us with some  technique

and the same thing is true in  computers

our building block is a  comparison

we could call it the  scheffers stroke if you want

  and again we can go tactically

in  calculators we can add

a strategic  element the stored program I loves

  jacquard looms and this

notion of  storage models

of computing is  recapitulated through many of our

  languages and predominant

amount of programming today  despite

some fringe areas is actually

done using this storage model in a wide  variety of

forms including so-called  object-oriented languages

so this is  kind of the general thing you find

today  most universities train towards

these  and so we we're talking

about Turing  really

interesting idea is this brick  ideas yeah

you can program a computer to  make something that's like the

computer  you're on but you can also make

something a computer that is very  different from the computer

it's on and  the first one of these that I ever saw  was ivan

sutherland sketchpad and it

definitely a computer it was  programmable it

wasn't programmable like  any other computer had ever been and

was completely different than the  enormous tx2

computer that it that it  ran on and

one way of thinking about it  and EDD Feigenbaum

blazed the trail here  is that

a lot of computing today is done  in terms of house and

as far back

as 50  and 60 years ago people were already  starting

about can't we just program in  terms of what's another

one was john  mccarthy

whose early paper on the advice

  taker a an intelligent

terminal agent  that you could give advice

to that could  deal with things in common

sense terms  lisp was

actually invented to be a  programming language to make that that

  was an early idea and of course Marvin  Minsky

and Ed Fagin bellman and so forth  have

talked about intelligent systems as

  terminal agents I'm going to focus not

  so much on AI here but just

this notion  of what do we get if we can find

think in terms of  and we got a Marvin

call the semantic  information process

processing I'm gonna  focus on sketchpad here because

it's not  only Turing's 100th

birthday it happens  to be the 50th birthday of sketchpad

this year and I'm sure Ivan has  forgotten

this because he's famous of  forgetting things like this

yes because  he refuses to talk about

sketchpad I  thought I would say a few words about it

  so it's

famous for being the invention  of interactive

computer graphics it's

less known that it was really the invent  really the first

object-oriented system  that had the

abstraction technique

of  objects masters and instances

you can  make a master rivet

and create instances  from it and the

big knockout on  sketchpad was the programming of it

was  not done in terms of anything

like a  storage model but was done by telling  sketchpad

goals that

achieve and it had three spot home  solvers in

it they would achieve those  goals and one of the famous

examples  here is this truss bridge

and it happens  that there is

no movie available of this  truss bridge and

the reason is that

tx2  took awhile to settle

the constraints on  this bridge I believe

but because it's  the 50th anniversary of

sketchpad we  have recreated the

sketchpad display and  this twinkling

ivan will recognize  because it

actually plotted points and  they had to be

randomized throughout  core you go crazy

with vertigo on it and

  so with sketch pad you could

you know

  add a truss in here

you could turn on  the constraints one of which is

gravity  and sketch pay I would compute

all of  the beams there and

we can see what  effect

this this one has of adding it  in here those

1962

and

a wait and

  another way and

there's twinkling and  stuff caused

a series of better displays  to be made

but it's interesting that in  many ways

the semantics are

xpressed here haven't been improved  much on although

if we come into modern  times we can make it look prettier

and  we

have this model as do two Yoshiki  Oshima and

Bert Freudenberg who made  this and

now in the modern version of  course

words

have weight so I'll say  Ivan

for inventing

this

and we're

constraints a little bit differently  here and

so we put them inside this  weight

here here's a couple of them so  here's

the spring constraint so the  beams are all Springs

here this is  gravity and this is the pinned  constraint

so if I turn gravity off the

  springs will pull it I

love the idea  just

the idea that a physical object is  actually a process

so it's gone to zero  I turn gravity

back on and

let's turn  off the

pin constraint the pins  constraint is what

is holding all of  these girders together at the at

the  joints here so just pull it off

okay

ou know we have all these useful  objects down there and

we know these are  these objects what we're seeing

are just costumes on these objects so  let's repurpose

them we'll give this  system a different set of constraints

and I'll use the little balloon here to  raise them up

okay

and we this is not  such a big news because we

do our layout  using constraints so

this is a user  interface of the system I'm actually  giving

this this talk in terms of

Steve  Jobs might like the steel look but

I  think being a little more colorful we  can

try

we

can try a blue look here that  looks

a little bit too much like a  company we're all familiar

with so the  system is a bit of a Frankenstein

  monster so I'll go for a Halloween theme  here

and

I've got a

this is kind of like a presentation

  system except I can program in it so  what I'm

going to what I'm going to do  here is just

go to the sort of a slide  presentation but

in fact I can still  interact with it because it's

this is  just a view there are no modes in

this  system and this brings

us to what I  think of as the third turning machine

we  talked about the first 2/3 Turing

machine was the machines he built out of  biology

and this is in fact the first  turing paper i wrote was

that I read  when I was a biologist was

morphogenesis paper and even though I  was a math major also

at that time I  will confess I did not

understand the  mathematics in it it was deep and

actually he warns in the beginning of  that paper of

what parts to read unless  you're really into mathematics

but one  of the ideas here is he

was trying to  show that relatively simple systems

of  gradients interacting with each other  can

give rise to an enormous number of  morphological

structures and these are  some

some from his original paper

and  these are computer generation of

some of  his models more recently

and

you can do  other things with these so

radiance we  can do something

that's kind of like  particles and fields here

we've got  little ants following the gradient and

they'll all eventually migrate their way  up into

that upper left-hand corner

it looks like they're somewhat  coordinated

but in fact they're all just  following their own

their own thing and  even

though we could build our text  handling

stuff in this form it

suggests  yeah

well why not make the letters ants

  and so if we tell these guys

to

interact  with each other and

by the way this  these three rules here

are the rules for  this text editor here

so you can sort of  see what that looks like

  and another little wrinkle in here

this  that was done by Ted kale or another  wrinkle

here is done by Erin Lindsay and

  it's that nowadays we have the computing  power

so that we don't have to compute  just one

version of something but we can  compute multiple version

so for instance  here are three

suggestions three views  of this paragraph

computed all at once  in different worlds

and we see the  outline super opposed

over it and we

can  sort of see how

this works and this  underlines that when you're

doing what  type programming you really want

to have  a user interface that is

giving you a  sense of what the possibilities are  going on

because the system is doing  more for you than when

you're doing  sequential programming

and of course  there's the question

of doing the  graphics here and so

dan amma liang did  the graphics for

this system and an  assemble way using mathematical

  relationships first here's a formula

for  the involvement of a polygon

with with a  pixel and then

he made a mathematical  language

that allows us to express

in a form that runs so you can think of  this as kind

of a specification that  actually runs and in

previous discussions I'd say my  prejudices

are to make our specification

language something that we can debug and  then ship

because the problem is you're  always involved

in not just programming  something but

you also have to do all  the reasons

for the programming and  those can have bugs also

so I kind of  like this way but I happen to agree with  Butler

that sometimes trying to make  things clean

is difficult but on the  other hand think about

wonderful it is  when you can make them clean so

that  does things

like that and then we have  Willian compositing

rules but there they  all are just

95 lines of code

  we have pen stroking and

like this and

  we

have gradients

and texturing and  stuff and the best way to show that is  to go

back here and I'll just pick  something in the user interface because

  the whole system is live I'll say okay  gradient

fill and I'll just wreak ustym

  eyes my little in thing here

because  that's the live thing

that goes along  with that okay

and then back

up and then  bitmap filters

and everything and so in  this form all of

we're familiar with on a personal  computer is

435 lines of code and yeah

you have to learn a language that's a  little bit like AP element

meets  Christopher stray key but in

compactness of it and the  understandability

of it is fantastic

and  in order to make languages like that

fast  we made a metal language

Alex Wirth did  this and it makes

converts expressions  into

object relationships that you can

  do things like compile with or interpret  or you

can do something really  interesting

and illuminating with it as  Brett

vicar young fantastic user  interface

designer Brett would say yeah  that's not really

interaction you need  to continuously experience

the meaning  of your meanings not just hit

and have them happen and this is  possible in

this graphics language  because it's actually a

data flow system  as well as as

well as a very high higher  level language

and so the

oops

sorry  so

here's an example of

the letter D and  here's

what bread has done he has  automatically

gone through those that  code that

I showed you and he has built  this visualization

over here  automatically from it  so

just so here's a Bezier curve

can edit this letter D here and you  notice everything

else is reacting here  the six

bezzie A's that becomes

the  input of the rasterizer

that produces 49

  span coverages and I can

from  I can

open these up and

what

it does is  it takes me on a tour automatically  through the

code forwards and backwards  and shows me what's

going on and of  course the next step 2 after this would

be instead of writing this stuff over on  the right first you

might want to start  off with your examples first and develop

  the stuff on the right

okay and so in

architecture for this is somewhat  similar to things have been talked

about  for a while this is Ellen

Hossam Savini  who have been

working on this and basic  ideas whenever you can stay

up at the  what level the constraint level the  meaning

level the relational level  things are generally

good and you'd

like  to have a language or two not

too many  of them that really allows you to

  express these not just for special cases

  I mean one of the points of this is it's  time

to start thinking of doing what  programming for systems

programming  making operating systems and basic

  elements out of it where

the system is  in charge of doing the house

the solvers  the optimizations the pragmatics

and  there's zillions of these

we want to

work up here this is kind of sketchpad  like it's gets bad

I had three of these  we actually need a lot of them

  in between we need something that is

not  just a kind of a chooser but also kind  of an

expert system that knows about  this idea

that can make judicious  choices

and maybe even parallel choices  to tie this together

so when

I look out  on the world here  and somebody

asked me about the Turing  tarpit I think

it's a Turing opportunity  and

if there's a tarp it around  I

think it's inside of our own heads  thank you very much

great so we'll take questions

you  pointed someone and I'll run a mic to  them and

we have about five minutes for  questions

questions anyway I'm always

  very great Butler always reminds me  that's that

Butler gives great talks and  he actually gets

done when he's supposed  to and he's I'm

just grateful that he  went on before I do because it reminded

  me I should get done good Alex Wolff

hi  Alex Wolff

aren't yesterday's what's

  today's house on ad

infinitum

aren't

  yesterday's what's today's house odd

  infinitum

I'm

gonna let Alan repeat okay  okay

shall I saying it again  but the point is that you

know we don't  know how to do something

we we then know  how to do it yeah I

got it  and then we complain about it yes

well  actually some people even regarded  Fortran

as an AI program back then  because

it did something that only  humans could do and people

complain that every time a problem gets  solved it's

removed from the domain of  ai ai ai never

gets any credit for it  because it just goes into engineering

  yeah there's something to be said about  that and

maybe one way of looking at  this is saying hey it's a

it's not that  it people haven't been working on what's  for

50 or 60 years but I think it's

way  out of balance if you look at where

the  programming is going and I believe

that  the time is ripe now over

the next five  to eight years to actually

do something  serious at this

level that is

sort of in  between specifications

requirements  prototyping and making

something that  can be delivered with perhaps by

adding  optimizations that don't get

into the  the the meaningful part

hat's why this is a pep talk it's  come

on funders start funding the next  revolution in

being able to do serious  program not

just a special case program  okay so there's a question over

here  we're good

so tell us who

you are my  name is Ken Pierre Allen

I'm surrounded  by Dinah books got a little

one of my  hand it's

been it's been 40 years and  we've been talking

all day about  predictions what are yours

well the  easiest

you know there's a there's a  saying that will go

on my tombstone but  if you think about it the the

easiest  way to predict the future is to prevent  it so

one of the one of the ways of  looking at today is

the discouraging  amount of

stuff that's still around that  was

barely scaling when it was invented  and I so

it's easy to predict that a lot  of that will be around real question

in  I think in the experience

of many of us  takes critical masses of

different kinds  of people funded well to

actually make  serious breakthroughs because

ideas are  not

enough there are plenty of good

  ideas around but I came greatly

to  appreciate the

qualities of people who  can turn ideas

into reality and so it's  really the combination

of them this is  what is so difficult for

instance from  NSF funding as funding

projects large  enough to do the engineering aspects

as  well as the the ideational aspects

good  we're gonna squeeze in one last question

  how about something from over here

god  what a bunch of wimps nobody's got

a  hand up there weird oh okay I'll be  right there

  put your hand up

again good and tell us  who you

are  Simon cuskey from citrus

leaf Ellen you  talked about preventing and

you showed  us this nice WYSIWYG live very

few lines  of code now the things that Ken

was just  talking about about all his dire

botnets  can you imagine using

those techniques  to deal with things once it gets

your your screen and starts going into  the real world where

there's spam and  malware and adversaries

yeah well I'm  I'm always accused

of being too  optimistic but and

to me the many

of the  protection aspects of things

I have a  probably an over simplistic way of  thinking

about it but I believe you have  to do the safety

and protection aspects  as the first part

of the design rather  than trying to tack them on

afterwards  and I

happen to like the internet a lot  I

think those guys did a fantastic job  and to

me it's the only real  object-oriented system on the

planet  because the machines themselves are

ncapsulated things and it is not  possible to send

a message on the  internet that will cause any machine

to  crash because it's true

because it's  only stupid software that other people

  put on the machine interpreting what is

a pure message interpreting that in some  stupid way

that allows that so to

Internet is a good model and it must  have a few billion

nodes by now it has  never had to be stopped

to be updated  it's probably

exchanged out most of its  atoms and

much of its bits at least once

  since say the late 60s when the

ARPANET  started so it it's the thing that is

like a living organism it's the thing  that is going

into the future it's the  thing that we have to look

at when we're  thinking about building systems

and so  on it's in a biological system the

the  entire dynamic

in biology is not being  correct

but in curtailing

error a lot of

the proteins made  a third of the 10,000

proteins we have  in each cell are there just

to correct  various kinds of damage to

DNA and other  important molecules that

are caused by  kinetic

energy of heat and some  carcinogens

and so it's not a question  of the thing being

perfect it's like the  Internet  it's

not just IP but it's having  something

like TCP that can make sure  the retransmissions

and other things  happen so you eventually

get the bits  that you want but

not you may not get  them for a while so it

becomes a kind of  a stochastic computing

and I think that  it

couples well to what Butler was  saying and also

this model I was showing  that it's it's

that the more we get into  the what's the more we can build systems

  that can do these stochastic convergent

  strategies that

will allow us to build  bigger systems on

that very optimistic  note thank you