Post on 14-Oct-2020
Introduction toComputer Science
Ian Leslie
with thanks to Robin Milner, Andrew Pitts and
others...
Computer LaboratoryIntroduction to CS, 2003 – p.1
In the beginning. . .
Introduction to CS, 2003 – p.2
Origins (30s, 40s, 50s)What gave rise to Computer Science as a
distinctive intellectual discipline?
Mathematics and Logic: solution of Hilbert’s
Entscheidungsproblem and Church-Turing notion
of (un)computability.
lambda calculus Turing machines
Introduction to CS, 2003 – p.3
Origins (30s, 40s, 50s)What gave rise to Computer Science as a
distinctive intellectual discipline?
Mathematics and Logic: solution of Hilbert’s
Entscheidungsproblem and Church-Turing notion
of (un)computability.
lambda calculus Turing machines
Introduction to CS, 2003 – p.3
Hilbert’s EntscheidungsproblemIs there an algorithm which, when fed any
statement in formal language of arithmetic,
determines in a finite number of steps whether or
not the statment can be proved from Peano’s
axioms for natural numbers?
Posed by Hilbert at 1928 International Congress of
Mathematicians; he thought the answer would be “yes”!
Church and Turing proved him wrong (1935-7).
Introduction to CS, 2003 – p.4
Hilbert’s EntscheidungsproblemIs there an algorithm which, when fed any
statement in formal language of arithmetic,
determines in a finite number of steps whether or
not the statment can be proved from Peano’s
axioms for natural numbers?
Posed by Hilbert at 1928 International Congress of
Mathematicians; he thought the answer would be “yes”!
Church and Turing proved him wrong (1935-7).
Introduction to CS, 2003 – p.4
Hilbert’s EntscheidungsproblemIs there an algorithm which, when fed any
statement in formal language of arithmetic,
determines in a finite number of steps whether or
not the statment can be proved from Peano’s
axioms for natural numbers?
Posed by Hilbert at 1928 International Congress of
Mathematicians; he thought the answer would be “yes”!
Church and Turing proved him wrong (1935-7).
Introduction to CS, 2003 – p.4
Origins (30s, 40s, 50s)What gave rise to Computer Science as a
distinctive intellectual discipline?
Engineering: from Turing Machine to
von Neumann architecture to real machines,
driven by the numerical & data-processing needs
of the scientific, military and industrial
communities.
FORTRAN COBOL
and of course COMPUTERS, eg EDSAC (Wilkes)
Introduction to CS, 2003 – p.5
Origins (30s, 40s, 50s)What gave rise to Computer Science as a
distinctive intellectual discipline?
Engineering: from Turing Machine to
von Neumann architecture to real machines,
driven by the numerical & data-processing needs
of the scientific, military and industrial
communities.
FORTRAN COBOL
and of course COMPUTERS, eg EDSAC (Wilkes)
Introduction to CS, 2003 – p.5
Origins (30s, 40s, 50s)What gave rise to Computer Science as a
distinctive intellectual discipline?
Engineering: from Turing Machine to
von Neumann architecture to real machines,
driven by the numerical & data-processing needs
of the scientific, military and industrial
communities.
FORTRAN COBOL
and of course COMPUTERS, eg EDSAC (Wilkes)
Introduction to CS, 2003 – p.5
Origins (30s, 40s, 50s)What gave rise to Computer Science as a
distinctive intellectual discipline?
Cognitive sciences: input from physiology,
psychology, linguistics.
Can machines exhibit human-like intelligence?
Relationship between thinking and computation?
Symbolic (non-numerical) computation.
LISP
Introduction to CS, 2003 – p.6
Origins (30s, 40s, 50s)What gave rise to Computer Science as a
distinctive intellectual discipline?
Cognitive sciences: input from physiology,
psychology, linguistics.
Can machines exhibit human-like intelligence?
Relationship between thinking and computation?
Symbolic (non-numerical) computation.
LISP
Introduction to CS, 2003 – p.6
The classical age. . .
Introduction to CS, 2003 – p.7
Classical era (60s, 70s)A single computer
“mainframes”, time-sharing
Architecture of computers
processor and memory technologies
Architecture of computingstructures for data & languages for algorithms
analysis of correctness and efficiency
Artificial intelligence
suffered from lack of computing power
Introduction to CS, 2003 – p.8
Classical era (60s, 70s)A single computer
“mainframes”, time-sharing
Architecture of computers
processor and memory technologies
Architecture of computingstructures for data & languages for algorithms
analysis of correctness and efficiency
Artificial intelligence
suffered from lack of computing power
Introduction to CS, 2003 – p.9
A classic example(Thanks to Steven Rudich, CMU and Jon Bentley’s
“Programming Pearls”)
We have a 70,000 word dictionary.
Task: write a program that
inputs a word
outputs all anagrams of that word occurring in the
dictionary.
E.g
slope→ lopes, poles, slope
sorted→ doters, sorted, stored, strode
Introduction to CS, 2003 – p.10
A classic example(Thanks to Steven Rudich, CMU and Jon Bentley’s
“Programming Pearls”)
We have a 70,000 word dictionary.
Task: write a program that
inputs a word
outputs all anagrams of that word occurring in the
dictionary.
E.g
slope→ lopes, poles, slope
sorted→ doters, sorted, stored, strodeIntroduction to CS, 2003 – p.10
Novice hacker solutionLoop through all possible ways of rearranging the
input word
use binary search to look up each
rearrangement in dictionary
if found, output it.
This isvery slow on long
inputs—e.g. at a microsecond/iteration, 17-letter
word takes about a decade to process!
Introduction to CS, 2003 – p.11
Novice hacker solutionLoop through all possible ways of rearranging the
input word
use binary search to look up each
rearrangement in dictionary
if found, output it.
This isvery slow on long
inputs—e.g. at a microsecond/iteration, 17-letter
word takes about a decade to process!
Introduction to CS, 2003 – p.11
Intermediate hacker solutionSubroutine ANAGRAM(X,Y): sort the letters in X and
Y and test the resulting words for equality.
Main routine: input word X; run through the
dictionary words Y in order, outputting Y if
ANAGRAM(X,Y)=TRUE.
Much faster—comparing X with each of 70,000 Y
takes about 15 seconds.
Introduction to CS, 2003 – p.12
Intermediate hacker solutionSubroutine ANAGRAM(X,Y): sort the letters in X and
Y and test the resulting words for equality.
Main routine: input word X; run through the
dictionary words Y in order, outputting Y if
ANAGRAM(X,Y)=TRUE.
Much faster—comparing X with each of 70,000 Y
takes about 15 seconds.
Introduction to CS, 2003 – p.12
Überhacker solutionIDEA: don’t keep the dictionary in dictionary
order!—rearrange it into anagram classes.
Introduction to CS, 2003 – p.13
Überhacker solutionIDEA: don’t keep the dictionary in dictionary
order!—rearrange it into anagram classes.
doters
lear
lopes
real
slope
stored
→sort letters
→
deorst
aelr
elops
aelr
elops
deorst
Introduction to CS, 2003 – p.14
Überhacker solutionIDEA: don’t keep the dictionary in dictionary
order!—rearrange it into anagram classes.
doters
lear
lopes
real
slope
stored
→sort letters
→
deorst
aelr
elops
aelr
elops
deorst
Introduction to CS, 2003 – p.14
Überhacker solutionIDEA: don’t keep the dictionary in dictionary
order!—rearrange it into anagram classes.
doters
lear
lopes
real
slope
stored
→sort letters
→
deorst
aelr
elops
aelr
elops
deorst
Introduction to CS, 2003 – p.14
Überhacker solutionIDEA: don’t keep the dictionary in dictionary
order!—rearrange it into anagram classes.
lear
real
doters
stored
lopes
slope
↓sort list
↓
deorst
aelr
elops
aelr
elops
deorst
aelr
aelr
deorst
deorst
elops
elops
Introduction to CS, 2003 – p.15
Überhacker solutionIDEA: don’t keep the dictionary in dictionary
order!—rearrange it into anagram classes.
lear
real
doters
stored
lopes
slope
↓sort list
↓
aelr
aelr
deorst
deorst
elops
elops
Introduction to CS, 2003 – p.15
Überhacker solutionPre-processing the dictionay into anagram classes
is a one-time cost.
Now the program is:
Input X; X’ := sorted version of X;
use binary search to find the anagram class of X’
in the pre-processed dictionary
Very fast—what previously took a decade now
runs in less than 1/1000 seconds.
Introduction to CS, 2003 – p.16
Überhacker solutionPre-processing the dictionay into anagram classes
is a one-time cost.
Now the program is:
Input X; X’ := sorted version of X;
use binary search to find the anagram class of X’
in the pre-processed dictionary
Very fast—what previously took a decade now
runs in less than 1/1000 seconds.
Introduction to CS, 2003 – p.16
Überhacker solutionPre-processing the dictionay into anagram classes
is a one-time cost.
Now the program is:
Input X; X’ := sorted version of X;
use binary search to find the anagram class of X’
in the pre-processed dictionary
Very fast—what previously took a decade now
runs in less than 1/1000 seconds.
Introduction to CS, 2003 – p.16
Classical era (60s, 70s)A single computer
“mainframes”, time-sharing
Architecture of computers
processor and memory technologies
Architecture of computingstructures for data & languages for algorithms
analysis of correctness and efficiency
Artificial intelligence
suffered from lack of computing power
Introduction to CS, 2003 – p.17
Classical era (60s, 70s)A single computer
Architecture of computers
Architecture of computing
Artificial intelligence
A DISTINCTIVE INTELLECTUAL DISCIPLINE.
VERY COMFORTABLE.
BUT THEN. . .
Introduction to CS, 2003 – p.18
Classical era (60s, 70s)A single computer
Architecture of computers
Architecture of computing
Artificial intelligence
A DISTINCTIVE INTELLECTUAL DISCIPLINE.
VERY COMFORTABLE.
BUT THEN. . .
Introduction to CS, 2003 – p.18
O Brave New World. . .
Introduction to CS, 2003 – p.19
Modern era (80s on)Many computers
microprocessors, parallelism, networking
Interactioncomputer-computer
human-computer
The communications revolution
computing and communications converge
AND WE ALL RELY ON IT MUCH MORE!
Introduction to CS, 2003 – p.20
Modern era (80s on)Many computers
microprocessors, parallelism, networking
Interactioncomputer-computer
human-computer
The communications revolution
computing and communications converge
Previous methods often become inappropriate (e.g. may
not be able to preprocess a distributed dictionary)
AND WE ALL RELY ON IT MUCH MORE!
Introduction to CS, 2003 – p.20
Modern era (80s on)Many computers
microprocessors, parallelism, networking
Interactioncomputer-computer
human-computer
The communications revolution
computing and communications converge
AND WE ALL RELY ON IT MUCH MORE!
Introduction to CS, 2003 – p.20
Computer Laboratory researchSystems Research
Security
Automated Reasoning
Theory and Semantics
Programming Research
Rainbow Group (graphics, HCI, . . . )
Intelligent Systems
Natural Language & Inf. Processing
BREADTH AND QUALITY!
Introduction to CS, 2003 – p.21
Computer Laboratory researchSystems Research
Security
Automated Reasoning
Theory and Semantics
Programming Research
Rainbow Group (graphics, HCI, . . . )
Intelligent Systems
Natural Language & Inf. Processing
BREADTH AND QUALITY!
Introduction to CS, 2003 – p.21
Computer Science and theComputer Industry
Introduction to CS, 2003 – p.22
Edsger W Dijkstra, 1930–2002
“About 10 years ago I tried to make up my mind
on the question of whether Computing Science
could save the computer industry, and my
conclusion was negative; since then I felt it my
duty to try to prevent the computer industry from
killing Computing Science, but I doubt that I have
been successful . . . ”
(“Under the spell of Leibniz’s dream”, EWD1298,
2000)
Introduction to CS, 2003 – p.23
Edsger W Dijkstra, 1930–2002
“About 10 years ago I tried to make up my mind
on the question of whether Computing Science
could save the computer industry, and my
conclusion was negative; since then I felt it my
duty to try to prevent the computer industry from
killing Computing Science, but I doubt that I have
been successful . . . ”
(“Under the spell of Leibniz’s dream”, EWD1298,
2000)
Introduction to CS, 2003 – p.23
The future. . .
Introduction to CS, 2003 – p.24
The next era
???ubiquitous computing
quantum computation
conscious computers
solve the software crisis!
Introduction to CS, 2003 – p.25
The next eraWhatever the future, facets of Computer Science
important today are likely to remain so:
Theory
Abstraction
Design
Social & professional context
Introduction to CS, 2003 – p.25
The next eraThe future of Computer Science belongs to those
who have
Content: an up-to-date grasp of fundamental
problems and solutions
Method: principles and techniques to solve the
vast array of unfamiliar problems arising in a
rapidly changing field
Introduction to CS, 2003 – p.25
Your future . . .
Introduction to CS, 2003 – p.26
For the moment, academic but . . .No harm to pay some attention to the long term:
Computer Lab has about 3500 former students
(undergraduate, postgraduate).
They have had very diverse careers, but for
example, they have founded over 80 companies
(that we know about).
Introduction to CS, 2003 – p.27
For the moment, academic but . . .No harm to pay some attention to the long term:
Computer Lab has about 3500 former students
(undergraduate, postgraduate).
They have had very diverse careers, but for
example, they have founded over 80 companies
(that we know about).
Introduction to CS, 2003 – p.27
Learn from former graduates . . .
You can gain insight into their careers. They come
back to tell you about their experiences, and will
give career advice.
Through the graduate association (“The
Cambridge Computer Lab Ring”) website you
can find them, find job opportunities, get a list of
Ring events (typically one a month).
We hope that you too one day will come back...
Introduction to CS, 2003 – p.28
Learn from former graduates . . .
You can gain insight into their careers. They come
back to tell you about their experiences, and will
give career advice.
Through the graduate association (“The
Cambridge Computer Lab Ring”) website you
can find them, find job opportunities, get a list of
Ring events (typically one a month).
We hope that you too one day will come back...
Introduction to CS, 2003 – p.28
Learn from former graduates . . .
You can gain insight into their careers. They come
back to tell you about their experiences, and will
give career advice.
Through the graduate association (“The
Cambridge Computer Lab Ring”) website you
can find them, find job opportunities, get a list of
Ring events (typically one a month).
We hope that you too one day will come back...
Introduction to CS, 2003 – p.28
The future belongs to you:
we aren’t here to teach you
how to cope with it
we’re here to help you
learn how to shape it
Introduction to CS, 2003 – p.29
The future belongs to you:
we aren’t here to teach you
how to cope with it
we’re here to help you
learn how to shape it
Introduction to CS, 2003 – p.29
HAVE FUN!
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