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Overview and Summary Michael Brand.  Manchester town hall The 12 “Manchester Murals” Color-lit 16-foot pipe organ Stars & planets depicted in mozaic.

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Presentation on theme: "Overview and Summary Michael Brand.  Manchester town hall The 12 “Manchester Murals” Color-lit 16-foot pipe organ Stars & planets depicted in mozaic."— Presentation transcript:

1 Overview and Summary Michael Brand

2  Manchester town hall The 12 “Manchester Murals” Color-lit 16-foot pipe organ Stars & planets depicted in mozaic City & country crests Victorian-era neo-gothic architecture

3  The Manchester baby ◦ World’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 chain ◦ Fast adder with minimization of gate numbers  Virtual memory  Compiler compiler ◦ For the Ferranti Atlas (1962)

4 Apple University of Manchester Gay village

5 Pablo Picasso


7  Turing’s official biographer  In addition to ◦ 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).  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-forming

8  There is also ◦ Intelligent 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 Unpublished) ◦ Reviews (Charles Darwin (NPL director)):  “A bit thin for a year’s time off”  “A schoolboy’s essay”  “not suitable for publication”  “smudgy” ◦ It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-solving  Intellectual activity is primarily search  Genetic algorithms (“evolutionary search”)  Neural networks (“unorganized machines”)  An early form of the imitation game.  A blueprint for connectionism

9  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)

10  Turing award winner: nondeterminism  Turing 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)

11 Critical strip  Solved Hilbert’s 10 th problem  Turing and number theory ◦ Turing and the Riemann Hypothesis 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.

12  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))

13  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

14  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 “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 2π.63 2 < t < 2π.64 2 was investigated during that period, and very little more was accomplished.”

15  Turing award winner: RSA, differential cryptanalysis  Turing and Enigma  Major mistakes (G): ◦ Usually, only inner rotor moves ◦ Most strength is in plug-board, which can be bypassed ◦ Plug-board connection is trivial ◦ No fixed points ◦ Message-keys were chosen badly ◦ Operator errors (see Tutte’s reconstruction of Tunny) ◦ Never willing to entertain suspicions of breakability  Major mistakes (B): ◦ Never guessed plug-board connection

16  “The mythical man-month”; Turing award winner: computer architecture  Turing 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 faster ◦ ¼ electronic equipment ◦ 3-op packed instructions (plus a “next” address) ◦ Fewer instruction fetches (obsoleted by larger memory) ◦ Optimal next instruction placement in delay lines ◦ Supports variable-length block transfers ◦ Punched card I/O directly attached.  and yet, had little impact on computing history. ◦ Why?

17  Assumption: HW dear; people cheap  11 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

18  Turing award winner: model checking  Formal verification  Turing: ◦ 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))

19  View as a graph problem  Formal 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 clumping ◦ CEGAR: Counter-example guided automatic abstraction

20  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 overflows ◦ Can generate test-cases for recent changes ◦ Can pinpoint cases where changes can make programs slower

21  Former world chess champion; 2½-3½ against “Deep Blue”  Turing’s paper machine  Turing and chess ◦ At Bletchley park: Hugh Alexander, James Macrae Aitken ◦ Turing’s Running Chess ◦ Early imitation game ◦ 15 seconds of silence?  (1948) Turing designed the first chess algorithm. He hand-simulated it (and lost) in a match against Alick Glennie  Kasparov’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.

22  ”The algorithmic beauty of sea-shells”  Turing and Morphogenesis  Turing: inhibitor w. longer range (diffuses) ActivatorInhibitor

23  Today mainstream, but initial scepticism ◦ Stochastic 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 Hydra

24  Periodicity  Gradients  Oscillations  Phyllotaxis centralization

25 ◦ “...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...”.


27  “Father of the Internet”, ICANN chair, Google VP  IP-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+industry  Interplanetary Internet

28  “Watson”  “Jeopardy!”: Broad domain, speed, precision, accuracy estimate  Ambiguity, anaphora resolution  Use of existing resources, no spec data  Sentence parsing + statistical aggregation + context  Score competing hypotheses based on evidence + recursively

29  Was known as Cottonopolis  Presidential 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’Artagnan  Submitted to Lincoln in June 1964 by the secretary of treasury and accepted ◦ Offer of resignation.  Or was it a friend request?

30  Machine learning to evaluate algorithms & data sources  temporal and geospatial reasoning  statistical paraphrasing  Large hand-crafted models (slow, narrow, brittle, biased) replaced by diverse algorithms, parallelization and scoring multiple hypotheses.  Future: healthcare?

31  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”)

32  centrally/noncentrally controlled  Model 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.

33  Companion mobile robots  Active since 2009 & 2010 rsp.  Navigate Gates Hillman Center (*) using the Kinect depth-camera, WiFi, and/or LIDAR  Proactively ask for help ◦ Ask humans to press elevator buttons ◦ Follow humans (and each other) along glass corridor Glass corridor (*) 217,000-square-foot, 9 floors

34  Turing award winner: PAC, #P, holographic reductions, CF parsing, UniqSat∈P⇒RP=NP  Quantifying 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?

35  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?

36  “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). Kismet

37  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.

38  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 23 rd ) the biggest Turing test ever was staged at Bletchley Park.  13-year-old Eugene Goostman managed to fool judges 29% of the time.

39  Donald Knuth  Roger Penrose  Andrew Yao  George Ellis  Martin Davis  Samuel Klein (Wikimedia Foundation) ...

40 qquestions?

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