Turing Centenary Conference (Manchester) - PowerPoint Presentation

Slide1

Turing Centenary Conference (Manchester) 22-25 June 2012

Overview and Summary

Michael BrandSlide2

Manchester town hall

Venue

The 12 “Manchester Murals”

Color-lit

16-foot

pipe organ

Stars & planets depicted in

mozaic

City & country crests

Victorian-era neo-gothic architectureSlide3

The Manchester babyWorld’s first stored-program computer (1948)Followed by Manchester Mark-1 (first w/ fast random-access two-level store) (1949)

Prototype for Ferranti Mark 1 (first commercially-available general-purpose computer) (1951)

Manchester coding

Phase encoding, developed for Manchester Mark 1

Used in Ethernet, RFID, etc.

Manchester carry chainFast adder with minimization of gate numbersVirtual memoryCompiler compilerFor the Ferranti Atlas (1962)Manchester and computingSlide4

Alan M. Turing (1912-1954)

Apple

University of Manchester

Gay villageSlide5

Computers are useless. They can only give you answers.

Pablo PicassoSlide6

PerspectivesSlide7

Turing’s official biographerIn addition toOn Computable Numbers, with an application to the

Entscheidungsproblem

,

Proc.

Lond. Math. Soc. (2) 42 pp 230-265 (1936); correction ibid. 43, pp 544-546 (1937).Introduction of “The halting problem” (Universal computing)

Computing Machinery and Intelligence, Mind 49, pp 433-460 (1950)Introduction of “The Imitation Game”/”Turing test” (AI)The Chemical Basis of Morphogenesis, Phil. Trans. R. Soc. London B 237 pp 37-72 (1952) Biological theory of individuation, symmetry-breaking and pattern-formingJack CopelandSlide8

There is alsoIntelligent Machinery (Written 1948. Unpublished)

Reviews (Charles Darwin (NPL director)):

“A bit thin for a year’s time off”

“A schoolboy’s essay”

“not suitable for publication”

“smudgy”It contained:Logic based approach to problem-solvingIntellectual activity is primarily searchGenetic algorithms (“evolutionary search”)Neural networks (“unorganized machines”)An early form of the imitation game.A blueprint for connectionism

Jack Copeland (cntd)Slide9

Universal machine ≟ Software programmable ≟ von Neumann architecture(This heated debate was later taken up by Rodney Brooks, Michael Rabin, Vint

Cerf,

Adi

Shamir, Moshe

Vardi and others)

Jack Copeland (cntd 2)Slide10

Turing award winner: nondeterminismTuring and computability

On Computable Numbers, with an application to the

Entscheidungsproblem

,

Proc. Lond. Math. Soc. (2) 42 pp 230-265 (1936); correction ibid. 43, pp 544-546 (1937).

The Word problem in Semi-Groups with Cancellation, Ann. of Math. 52 (2), pp 491-505 (1950)Michael RabinSlide11

Critical strip

Solved Hilbert’s 10

th

problem

Turing and number theory

Turing and the Riemann Hypothesis

Yuri Matiyasevich

01

Critical line

Values on critical line can be calculated as real-valued integral. Approximate and count sign changes for zeroes.

ζ

“Turing’s method”=

calculate total number of zeroes in critical strip via approximated integral.

“Turing’s method” is still in use today. His (many) other innovations on RH have since been superseded. Examples follow.Slide12

Improved integral calculation for counting of zeros on the critical line.Improved finding places for suspected sign-changes (a.k.a. Gram points)

Improved bounds for

Skewes’s

number (first case of

π(x)>Li(x). See Littlewood (1914))

Some superseded achievementsSlide13

Systems of logic based on ordinals, Proc. Lond

. Math. Soc (2) 45 pp 161-228 (1939) [was also Turing's Princeton Ph.D. thesis (1938)] includes, under section “3. Number Theoretic Theorems” a proof that

thus placing RH for the first time in the Arithmetical Hierarchy.

Kreisel

(1958) later lowered this to

Superseded achievements (cntd)Slide14

Automated calculation“tide-predicting machine” (1939 application to the Royal Society. Never built due to work on Enigma)

First to calculate zeroes mechanically (Mark-1)

Also: invented LU decomposition

Superseded achievements (

cntd

2)“The calculations had been planned some time in advance, but had in fact to be carried out in great haste. If it had not been for the fact that the computer remained in serviceable condition for an unusually long period from 3 p.m. one afternoon to 8 a.m. the following morning it is probable that the calculations would never have been done at all. As it was, the interval 2

π.632 < t < 2π.642 was investigated during that period, and very little more was accomplished.”Slide15

Turing award winner: RSA, differential cryptanalysisTuring and Enigma

Major mistakes (G):

Usually, only inner rotor moves

Most strength is in plug-board, which can be bypassed

Plug-board connection is trivialNo fixed points

Message-keys were chosen badlyOperator errors (see Tutte’s reconstruction of Tunny)Never willing to entertain suspicions of breakabilityMajor mistakes (B):Never guessed plug-board connectionAdi

ShamirSlide16

“The mythical man-month”; Turing award winner: computer architectureTuring and the Pilot ACE

Turing’s 1945 proposal (as compared with EDVAC)

is detailed to the register level (more than von Neumann’s report)

is more general-purpose

5x faster¼ electronic equipment

3-op packed instructions (plus a “next” address)Fewer instruction fetches (obsoleted by larger memory)Optimal next instruction placement in delay linesSupports variable-length block transfersPunched card I/O directly attached.and yet, had little impact on computing history.Why?

Frederick P. BrooksSlide17

Assumption: HW dear; people cheap11 Central registers, each with its own behaviors (properties, side-effects, implied operators, implied targets, multiple names – no accumulator)No generic multiplication, no conditional branching. Works in backwards-binary

No random access

No subroutine support

= A beast to program

ACE peculiaritiesSlide18

Turing award winner: model checkingFormal verificationTuring:

Of course

entscheidungsproblem

, but also:

Checking a Large Routine, Paper for the EDSAC Inaugural Conference, 24 June 1949. Typescript published in

Report of a Conference on High Speed Automatic Calculating Machines, pp 67-69.Proof of termination by transfinite induction (presaging Floyd (1967))Edmund ClarkeSlide19

View as a graph problemFormal languages for model definition (based on temporal logic)Symbolic model checking (storing partial states)

Bounded model checking (Use SAT solvers to consider the first

k

steps)

Node clumpingCEGAR: Counter-example guided automatic abstraction

Modern approaches to MCSlide20

Turing award winner: Quicksort, CSP

Can computers understand their own programs?

Turing: Self-simulation + verification + AI

Suggested alternate wording: can a computer program provide its programmer with pertinent information about itself?

Where the positive answer is already in use:

Programs can check for buffer overflowsCan generate test-cases for recent changesCan pinpoint cases where changes can make programs slowerTony HoareSlide21

Former world chess champion;2½-3½ against “Deep Blue”

Turing’s paper machine

Turing and chess

At Bletchley park: Hugh Alexander, James

Macrae

AitkenTuring’s Running ChessEarly imitation game15 seconds of silence?(1948) Turing designed the first chess algorithm. He hand-simulated it (and lost) in a match against Alick GlennieKasparov’s team implemented the algorithm. Found that Turing inadvertently alpha-beta pruned.

Changing the result in 10 of the game’s 24 moves.Today: “Advanced Chess” (GM+Comp vs. GM+Comp)Kasparov: Cooperation is key.Gary KasparovSlide22

”The algorithmic beauty of sea-shells”Turing and MorphogenesisTuring: inhibitor w. longer range (diffuses)

Hans

Meinhardt

Activator

InhibitorSlide23

Today mainstream, but initial scepticismStochastic results – but live organisms not so

initial state

nonsymmetric

cannot produce axial patterns

negative concentrations in equations (fixed by nonlinear reactions)Explains a wide variety of phenomena

Morphogenesis (cntd)

HydraSlide24

PeriodicityGradientsOscillations

Phyllotaxis

Morphogenesis (

cntd

2)

centralizationSlide25

“...but with three or more morphogens it is possible to have travelling waves. With a ring there would be two sets of waves, one travelling clockwise and the other anticlockwise. There is a natural chemical wave-length and wave frequency in this case as well as a wave-length; no attempt was made to develop formulae for these...”.

Turing’s forgotten resultSlide26

ProspectsSlide27

“Father of the Internet”, ICANN chair, Google VPIP-enabled surfboards, light-bulbs and toasters

Sensor-nets even in self-driving cars & wines

Bit-rot hazard → legal issues of IP

Data availability → privacy? new social norms?

techno+academic+legal+civil

society+industryInterplanetary InternetVint CerfSlide28

“Watson”“Jeopardy!”: Broad domain, speed, precision, accuracy estimateAmbiguity, anaphora resolution

Use of existing resources, no spec data

Sentence parsing + statistical aggregation + context

Score competing hypotheses based on evidence + recursively

David

FerrucciSlide29

Was known as CottonopolisPresidential Rhyme Time: “Barack’s Andean pack animals“

Obama’s Llamas

Fables & Folklore: “

Gerda

tries to rescue Kay from this Hans Christian Andersen title royal” The Snow Queen

The first named character in “The Man in the Iron Mask” also to appear in the author’s previous work.D’ArtagnanSubmitted to Lincoln in June 1964 by the secretary of treasury and acceptedOffer of resignation.Or was it a friend request?Jeopardy!Slide30

Machine learning to evaluate algorithms & data sourcestemporal and geospatial reasoningstatistical paraphrasingLarge hand-crafted models (slow, narrow, brittle, biased) replaced by diverse algorithms, parallelization and scoring multiple hypotheses.

Future: healthcare?

WatsonSlide31

AI+Robotics; past president of RoboCup

Learning by

perception+cognition+action

→ feedback. Learning by reusing solutions (“by analogy”)

Turing (Intelligent Machinery

): computers apt in (i) games, (ii) language, (iii) translation, (iv) cryptography, (v) mathematics, of which the most difficult is (ii) and requires sensory input and locomotion. (“Roaming the countryside”)Manuella VelosoSlide32

centrally/noncentrally controlledModel world, plan to the goal (

probablistic

, physics-based, variable-detail), update on new info

Add artificial goals to heuristically approximate pruned states

Purposeful perception: little of the image gets processed.

Solution? Soccer!Slide33

Companion mobile robotsActive since 2009 & 2010 rsp.

Navigate Gates Hillman Center

(*)

using the

Kinect depth-camera, WiFi

, and/or LIDARProactively ask for helpAsk humans to press elevator buttonsFollow humans (and each other) along glass corridorPractical? CoBots!

Glass corridor

(*) 217,000-square-foot, 9 floorsSlide34

Turing award winner: PAC, #P, holographic reductions, CF parsing, UniqSat∈P⇒RP=NPQuantifying evolution

Basic question: humans have 3⋅10

9

base pairs. How does evolution get there without 4^that time?

What are the possibilities for protein expression? Algorithms for next generation? How does evolution navigate the search space?

Leslie ValiantSlide35

Samson Abramsky (game semantics, domain theory

): What is a process? (which two are equiv?)

Carole Goble (

eScience

, grid computing): Universal social machines?Manuella

Veloso: Universal robots?Ron Brachman (description logic; AI; VP Yahoo! Labs): If intelligence is like athleticism in that there is no single sport metric, what is our aim?Moshe Vardi (model checking, database theory, constraint satisfaction): Does the future need us?

Panel: “Big Questions”Slide36

“Elephants don’t play chess”, iRobot

Turing never meant the imitation game as a test. It was meant to show the theoretical possibility (nullifying the emotional weight we put on “intelligence”). He also suggested a “men vs. women” variation (which people are not good at) and wondered whether computers can be told from humans by their chess-play (which they normally can).

“Intelligence” is the appearance of intelligence, which is in the ability to interact. People love to anthropomorphize (incl. other people).

Rodney Brooks

KismetSlide37

Steve Furber (BBC micro; ARM 32-bit RISC microprocessor

): Higher intelligence is an unnatural top layer over human intelligence. We over-assume about our own intelligence. The most rewarded ability is to lead a game of football. Our assumed intelligence created a barrier that makes it difficult for us to build AI.

Manuella

Veloso: Intelligence is about physical interaction, not abstract cognition. Learning is also memory, not just adapting classification parameters.

Panel 2: “The Turing Test”Slide38

Turing’s criteria for “winning” the imitation game is a 30% success rate at fooling the judge after a 5 minute conversation.On the day of the centenary (June 23rd

) the biggest Turing test ever was staged at Bletchley Park.

13-year-old Eugene

Goostman

managed to fool judges 29% of the time.

Meanwhile – at Bletchley ParkSlide39

Donald KnuthRoger PenroseAndrew YaoGeorge EllisMartin Davis

Samuel Klein (

Wikimedia Foundation

)

...

Some of the many other people who presentedSlide40

questions?Thank you!