Overview and Summary
Michael Brand
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
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)
Apple
University
of
Manchester
Gay village
Pablo Picasso
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
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 Unpublished)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
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)
Critical strip
Solved Hilbert’s 10
th
problem
Turing and number theory
◦
Turing and the Riemann Hypothesis
0
1
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.
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))
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
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 2
π.63
2
< t < 2π.64
2
was investigated during that period, and very little more
was accomplished.”
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
“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?
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
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))
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
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
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.
”The algorithmic beauty of sea

shells”
Turing and Morphogenesis
Turing: inhibitor w. longer range (diffuses)
Activator
Inhibitor
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
Periodicity
Gradients
Oscillations
Phyllotaxis
centralization
◦
“...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...”.
“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
“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
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?
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”)
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.
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
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?
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?
“
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
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.
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.
Donald Knuth
Roger Penrose
Andrew Yao
George Ellis
Martin Davis
Samuel Klein (
Wikimedia Foundation
)
...
questions?
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