Introduction to Computer Science

Transcription

1 Introduction to CS, 2003 p.1 Introduction to Computer Science Ian Leslie with thanks to Robin Milner, Andrew Pitts and others... Computer Laboratory

2 In the beginning... Introduction to CS, 2003 p.2

3 Introduction to CS, 2003 p.3 Origins (30s, 40s, 50s) What gave rise to Computer Science as a distinctive intellectual discipline?

4 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

5 Introduction to CS, 2003 p.4 Hilbert s Entscheidungsproblem Is 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?

6 Introduction to CS, 2003 p.4 Hilbert s Entscheidungsproblem Is 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!

7 Introduction to CS, 2003 p.4 Hilbert s Entscheidungsproblem Is 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).

8 Introduction to CS, 2003 p.5 Origins (30s, 40s, 50s) What gave rise to Computer Science as a distinctive intellectual discipline?

9 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

10 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)

11 Introduction to CS, 2003 p.6 Origins (30s, 40s, 50s) What gave rise to Computer Science as a distinctive intellectual discipline?

12 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

13 The classical age... Introduction to CS, 2003 p.7

14 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 computing structures for data & languages for algorithms analysis of correctness and efficiency Artificial intelligence suffered from lack of computing power

15 Introduction to CS, 2003 p.9 Classical era (60s, 70s) A single computer mainframes, time-sharing Architecture of computers processor and memory technologies Architecture of computing structures for data & languages for algorithms analysis of correctness and efficiency Artificial intelligence suffered from lack of computing power

16 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.

17 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

18 Introduction to CS, 2003 p.11 Novice hacker solution Loop through all possible ways of rearranging the input word use binary search to look up each rearrangement in dictionary if found, output it.

19 Introduction to CS, 2003 p.11 Novice hacker solution Loop 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!

20 Introduction to CS, 2003 p.12 Intermediate hacker solution Subroutine 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.

21 Introduction to CS, 2003 p.12 Intermediate hacker solution Subroutine 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.

22 Introduction to CS, 2003 p.13 Überhacker solution IDEA: don t keep the dictionary in dictionary order! rearrange it into anagram classes.

23 Introduction to CS, 2003 p.14 Überhacker solution IDEA: don t keep the dictionary in dictionary order! rearrange it into anagram classes. doters lear lopes real slope stored

24 Introduction to CS, 2003 p.14 Überhacker solution IDEA: don t keep the dictionary in dictionary order! rearrange it into anagram classes. doters lear lopes real slope stored sort letters

25 Introduction to CS, 2003 p.14 Überhacker solution IDEA: 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

26 Introduction to CS, 2003 p.15 Überhacker solution IDEA: don t keep the dictionary in dictionary order! rearrange it into anagram classes. sort list deorst aelr elops aelr elops deorst

27 Introduction to CS, 2003 p.15 Überhacker solution IDEA: 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

28 Introduction to CS, 2003 p.16 Überhacker solution Pre-processing the dictionay into anagram classes is a one-time cost.

29 Introduction to CS, 2003 p.16 Überhacker solution Pre-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

30 Introduction to CS, 2003 p.16 Überhacker solution Pre-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.

31 Introduction to CS, 2003 p.17 Classical era (60s, 70s) A single computer mainframes, time-sharing Architecture of computers processor and memory technologies Architecture of computing structures for data & languages for algorithms analysis of correctness and efficiency Artificial intelligence suffered from lack of computing power

32 Introduction to CS, 2003 p.18 Classical era (60s, 70s) A single computer Architecture of computers Architecture of computing Artificial intelligence

33 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...

34 O Brave New World... Introduction to CS, 2003 p.19

35 Introduction to CS, 2003 p.20 Modern era (80s on) Many computers microprocessors, parallelism, networking Interaction computer-computer human-computer The communications revolution computing and communications converge

36 Introduction to CS, 2003 p.20 Modern era (80s on) Many computers microprocessors, parallelism, networking Interaction computer-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)

37 Introduction to CS, 2003 p.20 Modern era (80s on) Many computers microprocessors, parallelism, networking Interaction computer-computer human-computer The communications revolution computing and communications converge AND WE ALL RELY ON IT MUCH MORE!

38 Introduction to CS, 2003 p.21 Computer Laboratory research Systems Research Security Automated Reasoning Theory and Semantics Programming Research Rainbow Group (graphics, HCI,... ) Intelligent Systems Natural Language & Inf. Processing

39 Introduction to CS, 2003 p.21 Computer Laboratory research Systems Research Security Automated Reasoning Theory and Semantics Programming Research Rainbow Group (graphics, HCI,... ) Intelligent Systems Natural Language & Inf. Processing BREADTH AND QUALITY!

40 Computer Science and the Computer Industry Introduction to CS, 2003 p.22

41 Introduction to CS, 2003 p.23 Edsger W Dijkstra, 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)

42 Introduction to CS, 2003 p.23 Edsger W Dijkstra, 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)

43 The future... Introduction to CS, 2003 p.24

44 Introduction to CS, 2003 p.25??? The next era ubiquitous computing quantum computation conscious computers solve the software crisis!

45 Introduction to CS, 2003 p.25 The next era Whatever the future, facets of Computer Science important today are likely to remain so: Theory Abstraction Design Social & professional context

46 Introduction to CS, 2003 p.25 The next era The 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

47 Your future... Introduction to CS, 2003 p.26

48 Introduction to CS, 2003 p.27 For the moment, academic but... No harm to pay some attention to the long term:

49 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).

50 Learn from former graduates... Introduction to CS, 2003 p.28

51 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).

52 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...

53 Introduction to CS, 2003 p.29 The future belongs to you: we aren t here to teach you how to cope with it

54 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

55 HAVE FUN! 29-1