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CT3620VETENSKAPSMETODIK FÖR TEKNIKOMRÅDET

HISTORY OF IDEAS IN COMPUTING

Gordana Dodig-CrnkovicDepartment of Computer Science and Engineering

Mälardalen University

2004

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A History of Computing: A History of Ideas

Gordana Dodig-Crnkovic

Department of Computer Science and ElectronicMälardalen University, Sweden

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HISTORY OF COMPUTING

• LEIBNIZ: LOGICAL CALCULUS• BOOLE: LOGIC AS ALGEBRA • FREGE: MATEMATICS AS LOGIC • CANTOR: INFINITY• HILBERT: PROGRAM FOR MATHEMATICS • GÖDEL: END OF HILBERTS PROGRAM • TURING: UNIVERSAL AUTOMATON • VON NEUMAN: COMPUTER • CONCEPT OF COMPUTING• FUTURE COMPUTING

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Pieter BRUEGEL, the Elder The Tower of Babel, 1563

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COMPUTING CURRICULA

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Technological Advancement within Computing over the Past Decade

– The World Wide Web and its applications – Networking technologies, particularly those based on TCP/IP – Graphics and multimedia – Embedded systems – Relational databases – Interoperability – Object-oriented programming – The use of sophisticated application programmer interfaces (APIs) – Human-computer interaction – Software safety; Security and cryptography – Quantum Computing– Biological Computing– Agent Based Computing– ……

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Computer Science

In practice, computer science includes a variety of topics relating to computers, which range from the abstract analysis of algorithms, formal grammars, etc. to more concrete subjects like programming languages, software, and computer hardware.

As a scientific discipline, it differs significantly from and is often confused with mathematics, programming, software engineering, and computer engineering, although there is some degree of overlap with these and other fields.

http://en.wikipedia.org/wiki/Computer_science

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Church-Turing Thesis

The Church-Turing thesis states that all known kinds of reasonable paradigms of computation are essentially equivalent in what they can do, although they vary in time and space efficiency. The thesis is not a mathematical theorem that can be proven, but an empirical observation that all known computational schemes have the same computational power.

Now the problem is what is the ”reasonable paradigm of computation” – we will have reason to come back to this point later on in this lecture.

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Computer

Most research in computer science has been related to von Neumann computers or Turing machines (computers that do one small, deterministic task at a time). These models resemble most real computers in use today. Computer scientists also study other kinds of machines, some practical (like parallel machines) and some theoretical (like probabilistic, oracle, and quantum machines).

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Computer Science

Computer scientists study what programs can and cannot do (computability), how programs should efficiently perform specific tasks (algorithms), how programs should store and retrieve specific kinds of information (data structures and data bases), how programs might behave intelligently (artificial intelligence), and how programs and people should communicate with each other (human-computer interaction and user interfaces).

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CS Body of Knowledge

– Discrete Structures – Programming Fundamentals– Algorithms and Complexity– Programming Languages– Architecture and Organization– Operating Systems– Net-Centric Computing– Human-Computer Interaction– Graphics and Visual Computing– Intelligent Systems– Information Management– Software Engineering– Social and Professional Issues– Computational Science and Numerical Methods– ...

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Related Fields

Computer science is closely related to a number of fields. These fields overlap considerably, though important differences exist

• Information science is the study of data and information, including how to interpret, analyze, store, and retrieve it. Information science started as the foundation of scientific analysis of communication and databases.

• Computer programming or software development is the act of writing program code.

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Related Fields

• Software engineering emphasizes analysis, design, construction, and testing of useful software. Software engineering includes development methodologies (such as the waterfall model and extreme programming) and project management.

• Information systems (IS) is the application of computing to support the operations of an organization: operating, installing, and maintaining the computers, software, and data.

• Computer engineering is the analysis, design, and construction of computer hardware

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Related Fields

• Mathematics shares many techniques and topics with computer science, but is more general. In some sense, CS is the mathematics of computing.

• Logic is a formal system of reasoning, and studies principles that lay at the very basis of computing/reasoning machines, whether it be the hardware (digital logic) or software (verification, AI etc.) levels. The subfield of logic called computability logic provides a systematic answer to the fundamental questions about what can be computed and how. .

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Major Fields of Importance for CS

• Mathematical foundations• Theoretical Computer Science• Hardware• Software• Data and Information Systems• Computing Methodologies• Computer Systems Organization• Computer applications• Computing Milieux• ....

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Mathematical Foundations

• Discrete mathematics (Boolean algebra, Graph theory, Domain theory ..)

• Mathematical logic • Probability and Statistics • Information theory • ...

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Theoretical Computer Science

• Algorithmic information theory • Computability theory • Cryptography • Formal semantics • Theory of computation (or theoretical computer

science) – analysis of algorithms and problem complexity – logics and meanings of programs – Mathematical logic and Formal languages

• Type theory • ...

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Hardware

• Control structures and Microprogramming • Arithmetic and Logic structures • Memory structures • Input/output and Data communications • Logic Design • Integrated circuits

– VLSI design • Performance and reliability • ...

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Software

• Computer programming (Programming techniques, Program specification, Program verification)

• Software engineering – Optimization – Software metrics – Software Configuration Management (SCM) – Structured programming – Object orientation – Design patterns – Documentation

• Programming languages • Operating Systems • Compilers• ...

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Computer Systems Organization

• Computer architecture • Computer networks • Distributed computing • Performance of systems • Computer system implementation • ...

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Data and Information Systems

• Data structures • Data storage representations • Data encryption • Data compression • Data recovery • Coding and Information theory • Files

– File formats • Information systems

– Databases – Information Storage and retrieval – Information Interfaces and Presentation

• Data recovery• .....

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Computing Methodologies

• Symbolic and Algebraic manipulation • Artificial intelligence • Computer graphics • Image processing and computer vision • Pattern recognition• Simulation and Modeling • Document and text processing • Digital signal processing • ...

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Computer Applications

• Administrative data processing • Mathematical software (Numerical analysis, Automated

theorem proving, Computer algebra systems)• Physical sciences and Engineering (Computational

chemistry, Computational physics)• Life and medical sciences (Bioinformatics, Computational

Biology, Medical informatics)• Social and behavioral sciences • Arts and Humanities • Computer-aided engineering • Human-computer interaction (Speech synthesis, Usability

engineering) • Robotics • ....

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Prof. Gerry Sussman [MIT] said we could write down all the ideas in computer science on 4 pages!

CS has added valuable knowledge to our understanding of the world.

CS discipline offers some important concepts which it is useful for everyone to understand. Just as there is a utility for everyone to understand a certain amount of math and science, there is a good reason for people to understand a certain amount of computer science.

http://www.cs.caltech.edu/~andre/general/computer_science.html

Big ideas in Computer Science and Engineering

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Hilbert’s program: Mechanical procedures exist for finding the solutions to problems. That is, for many questions/problems, we can write down a series of steps and simple predicates which define precisely how to find the correct solution. This process is completely mechanical, not requiring any "human" judgment to complete.

We can build physical machines which implement these procedures and perform the calculations.

There are simple, universal models of computing which capture the basic capabilities of these machines (e.g. automata, pushdown automata, Turing Machines).

Big ideas in Computer Science and Engineering

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The Turing Machine model is "robust" in the sense that "reasonable" additions to it, or alternative formulations of computing models have the same asymptotic power of computability (Church's thesis).

“Reasonable" meaning they, for the most part, correspond to things we imagine a real machine could support. In particular, there are stronger models when the machine is allowed to do "unreasonable" things like consult an oracle.

Deterministic/guaranteed procedures do not exist for all problems (Halting Problem, uncomputability). An important component of CS theory is to classify problems as computable or uncomputable.

Big ideas in Computer Science and Engineering

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The desired computation can be captured precisely and unambiguously. Computer science deals with how we construct languages to describe computations, and how we make them convenient for human use.

• languages • syntax (grammars) • semantics (denotational, operational)

Big ideas in Computer Science and Engineering

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Our physical world allows us to build very large computer systems. The practical limit to the useful size of a computer system (or at least, the size of the function efficiently supported by a computer system) is almost always human comprehension, not the physical capacity required.

Consequently, a major concern of computer science is techniques to manage and reduce complexity (abstractions/information hiding, modularity, problem decomposition, hierarchy, component isolation, invariants, common idioms/paradigms for organization (e.g. procedures, frames, streams, objects, APIs, servers)

Big ideas in Computer Science and Engineering

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The engineering side of computer science is about minimizing the resources we use in order to perform a computation (set of computations).

Physical machines have finite/limited real resources so time, energy, area (hardware: memory, wires)… must be minimized.

Big ideas in Computer Engineering

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We can provide the abstraction of more physical resources by virtualizing the physical resources. That is, sharing the physical resource among multiple uses over time.

To accomplish this, we store the state of a particular usage of the physical resources in cheaper storage, e.g. virtual memory, virtual channels, multitasking, time-sharing

Big ideas in Computer Engineering

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Big Ideas of Engineering

There are many big ideas in engineering, e.g.• iterative design • real-world constraints • tradeoffs• feedback • complexity management techniques

that are important for understanding not only classical engineered systems but also for understanding social systems and the natural world.

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History of Ideas of Computer Science

• LEIBNIZ: LOGICAL CALCULUS• BOOLE: LOGIC AS ALGEBRA • FREGE: MATEMATICS AS LOGIC • CANTOR: INFINITY• HILBERT: PROGRAM FOR MATHEMATICS • GÖDEL: END OF HILBERTS PROGRAM • TURING: UNIVERSAL AUTOMATON • VON NEUMAN: COMPUTER

http://web.clas.ufl.edu/users/rhatch/pages/10-HisSci/links/ H I S T O R Y    O F    S C I E N C E     L I N K S

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Natural Sciences(Physics,

Chemistry,Biology, …)

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Social Sciences(Economics, Sociology, Anthropology, …)

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The Humanities(Philosophy, History, Linguistics …)

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Logic

&

Mathematics1

Culture(Religion, Art, …)

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The whole is more than the sum of its parts. Aristotle, Metaphysica

CS

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Leibniz: Logical Calculus

Gottfried Wilhelm von Leibniz  

Born: 1 July 1646 in Leipzig, Saxony (now Germany)Died: 14 Nov 1716 in Hannover, Hanover (now Germany)

 

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Leibniz´s Calculating Machine

“For it is unworthy of excellent men to lose hours like slaves in the labor of calculation which could safely be relegated to

anyone else if the machine were used.”

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Leibniz´s Logical Calculus

DEFINITION 3. A is in L, or L contains A, is the same as to say that L can be made to coincide with a plurality of terms taken together of which A is one.B N = L signifies that B is in L and that B and N together compose or constitute L. The same thing holds for larger number of terms.

AXIOM 1. B N = N B.POSTULATE. Any plurality of terms, as A and B, can be added to

compose A B.AXIOM 2. A A = A.PROPOSITION 5. If A is in B and A = C, then C is in B.PROPOSITION 6. If C is in B and A = B, then C is in A.PROPOSITION 7. A is in A.(For A is in A A (by Definition 3). Therefore (by Proposition 6) A is in A.)….PROPOSITION 20. If A is in M and B is in N, then A B is in M N.

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Boole: Logic as Algebra

George Boole  

Born: 2 Nov 1815 in Lincoln, Lincolnshire, EnglandDied: 8 Dec 1864 in Ballintemple, County Cork, Ireland

 

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George Boole is famous because he showed that rules used in the algebra of numbers could also be applied to logic.

• This logic algebra, called Boolean algebra, has many properties which are similar to "regular" algebra.

• These rules can help us to reduce an expression to an equivalent expression that has fewer operators.

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Properties of Boolean Operations

A AND B A B

A OR B A + B

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Frege: Matematics as Logic

Friedrich Ludwig Gottlob Frege  

Born: 8 Nov 1848 in Wismar, Mecklenburg-Schwerin (now Germany)Died: 26 July 1925 in Bad Kleinen, Germany

 

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The Predicate Calculus (1)

• In an attempt to realize Leibniz’s ideas for a language of thought and a rational calculus, Frege developed a formal notation (Begriffsschrift).

• He has developed the first predicate calculus: a formal system with two components: a formal language and a logic.

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The Predicate Calculus (2)

The formal language Frege designed was capable of expressing:

– predicational statements of the form ‘x falls under the concept F’ and ‘x bears relation R to y’, etc.,

– complex statements such as

‘it is not the case that ...’ and ‘if ... then ...’, and

– ‘quantified’ statements of the form ‘Some x is such that ...x...’ and ‘Every x is such that ...x...’.

 

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The Analysis of Atomic Sentences and Quantifier Phrases

Fred loves Annie. Therefore, some x is such that x loves Annie.

Fred loves Annie. Therefore, some x is such that Fred loves x.

Both inferences are instances of a single valid inference rule.

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Proof

As part of his predicate calculus, Frege developed a strict definition of a ‘proof’.

In essence, he defined a proof to be any finite sequence of well-formed statements such that each statement in the sequence either is an axiom or follows from previous members by a valid rule of inference.

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Cantor: Infinity

Georg Ferdinand Ludwig Philipp Cantor  

Born: 3 March 1845 in St Petersburg, RussiaDied: 6 Jan 1918 in Halle, Germany

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Infinities

Set of integers has an equal number of members as the set of even numbers, squares, cubes, and roots to equations!

The number of points in a line segment is equal to the number of points in an infinite line, a plane and all mathematical space!

The number of transcendental numbers, values such as and e that can never be the solution to any algebraic equation, were much larger than the number of integers.

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Hilbert described Cantor's work as:- ´...the finest product of mathematical genius and one of the supreme achievements of purely intellectual human activity.´

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Hilbert: Program for Mathematics

David Hilbert  

Born: 23 Jan 1862 in Königsberg, Prussia (now Kaliningrad, Russia)Died: 14 Feb 1943 in Göttingen, Germany

 

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Hilbert's program

Provide a single formal system of computation capable of generating all of the true assertions of mathematics from “first principles” (first order logic and elementary set theory).

Prove mathematically that this system is consistent, that is, that it contains no contradiction. This is essentially a proof of correctness.

 If successful, all mathematical questions could be established by mechanical computation!

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Gödel: End of Hilberts Program

Kurt Gödel  

Born: 28 April 1906 in Brünn, Austria-Hungary (now Brno, Czech Republic)Died: 14 Jan 1978 in Princeton, New Jersey, USA

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Incompleteness Theorems

1931 Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme.

In any axiomatic mathematical system there are propositions that cannot be proved or disproved within the axioms of the system.

In particular the consistency of the axioms cannot be proved.

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Turing: Universal Automaton

Alan Mathison Turing  

Born: 23 June 1912 in London, EnglandDied: 7 June 1954 in Wilmslow, Cheshire, England

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When war was declared in 1939 Turing moved to work full-time at the Government Code and Cypher School at Bletchley Park.

Together with another mathematician W G Welchman, Turing developed the Bombe, a machine based on earlier work by Polish mathematicians, which from late 1940 was decoding all messages sent by the Enigma machines of the Luftwaffe.

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At the end of the war Turing was invited by the National Physical Laboratory in London to design a computer.

His report proposing the Automatic Computing Engine (ACE) was submitted in March 1946.

Turing returned to Cambridge for the academic year 1947-48 where his interests ranged over topics far removed from computers or mathematics, in particular he studied neurology and physiology.

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1948 Newman (professor of mathematics at the University of Manchester) offered Turing a readership there.

Work was beginning on the construction of a computing machine by F C Williams and T Kilburn. The expectation was that Turing would lead the mathematical side of the work, and for a few years he continued to work, first on the design of the subroutines out of which the larger programs for such a machine are built, and then, as this kind of work became standardized, on more general problems of numerical analysis.

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1950 Turing published Computing machinery and intelligence in Mind

1951 elected a Fellow of the Royal Society of London mainly for his work on Turing machines

by 1951 working on the application of mathematical theory to biological forms.

1952 he published the first part of his theoretical study of morphogenesis, the development of pattern and form in living organisms.

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Von Neuman: Computer

John von Neumann 

Born: 28 Dec 1903 in Budapest, HungaryDied: 8 Feb 1957 in Washington D.C., USA

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In the middle 30's, Neumann was fascinated by the problem of hydrodynamical turbulence.

The phenomena described by non-linear differential equations are baffling analytically and defy even qualitative insight by present methods.

Numerical work seemed to him the most promising way to obtain a feeling for the behaviour of such systems. This impelled him to study new possibilities of computation on electronic machines ...

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Von Neumann was one of the pioneers of computer science making significant contributions to the development of logical design. Working in automata theory was a synthesis of his early interest in logic and proof theory and his later work, during World War II and after, on large scale electronic computers.

Involving a mixture of pure and applied mathematics as well as other sciences, automata theory was an ideal field for von Neumann's wide-ranging intellect. He brought to it many new insights and opened up at least two new directions of research.

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He advanced the theory of cellular automata, advocated the adoption of the bit as a measurement of computer memory, and solved problems in obtaining reliable answers from unreliable computer components.

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Computer Science Hall of Fame

Charles Babbage Ada Countess of Lovelace Axel Thue Stephen Kleene

Julia Robinson Noam Chomsky Juris Hartmanis John Brzozowski

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Computer Science Hall of Fame

Richard Karp Donald Knuth Manuel Blum

Stephen Cook Sheila Greibach Leonid Levin

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Women in the History of Computing

• Ada Byron King, Countess of Lovelace (1815-1852) • Edith Clarke (1883-1959) • Rósa Péter (1905-1977) • Grace Murray Hopper (1906-1992) • Alexandra Illmer Forsythe (1918-1980) • Evelyn Boyd Granville • Margaret R. Fox • Erna Schneider Hoover • Kay McNulty Mauchly Antonelli • Alice Burks • Adele Goldstine • Joan Margaret Winters

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Ada Byron King, Countess of Lovelace (1815-1852)

Ada heard in November, 1834, Babbage's ideas for a new calculating engine, the Analytical Engine. Ada was touched by the "universality of Babbages ideas". Hardly anyone else was.

In her article, published in 1843, Lady Lovelace's far-sighted comments included her predictions that such a machine might be used to compose complex music, to produce graphics, and would be used for both practical and scientific use.

Ada suggested to Babbage writing a plan for how the engine might calculate Bernoulli numbers. This plan, is now regarded as the first "computer program." A software language was named "Ada" in her honor in 1979.

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Edith Clarke is well-known in the field of Power Engineering. Her main contribution to the field was the development of tables that speeded up calculations for engineers. This was especially important because she created them during World War I, when engineers desperately needed to work faster.

Edith Clarke (1883-1959)

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Her first research topic was number theory, but she became discouraged on finding that her results had already been proved by Dickson.

For a while Rósa wrote poetry, but around 1930 she was encouraged to return to mathematics by Kalmár. He suggested Rósa examine Gödel's work, and in a series of papers she became a founder of recursive function theory.

Rósa wrote Recursive Functions in 1951, which was the first book on the topic and became a standard reference.

In 1952 Kleene described Rósa Péter in a paper in Bull. Amer. Math. Soc. as ``the leading contributor to the special theory of recursive functions."

From the mid 1950's she applied recursive function theory to computers. In 1976 her last book was on this topic: Recursive Functions in Computer Theory.

Rósa Péter

(1905-1977)

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ENIAC Ladies

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Erna Schneider Hoover

Invention: Computerized Telephone Switching System

Erna Schneider earned a Ph.D. in the philosophy and foundations of mathematics from Yale University. In 1954, after teaching for a number of years at Swarthmore College, she began a research career at Bell Laboratories.

While there, she invented a computerized switching system for telephone traffic, to replace existing hard-wired, mechanical switching equipment. For this ground-breaking achievement -- the principles of which are still used today -- she was awarded one of the first software patents ever issued (Patent #3,623,007, Nov. 23, 1971) . At Bell Labs, she became the first female supervisor of a technical department.

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Grace Murray Hopper (1906-1992)

Grace Murray Hopper: Inventor of the Computer Compiler

She participated in the development of the Common Business-Oriented Language (COBOL; 1959-61) for the UNIVAC

The very first computer bug: Grace Murray Hopper originated this term when she found a real bug in a computer

Admiral Hopper was Awarded the National Medal of Technology

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Ida Rhodes (1900 -1986)

Rhodes designed the C-10 language in the early 1950 for the UNIVAC.

She also designed the original computer used for the Social Security Administration.

In 1949, the department of Commerce awarded her an exceptional Service Gold Medal for significant pioneering leadership and outstanding contributions to the scientific progress of the nation.

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Evelyn Boyd Granville

Evelyn Boyd Granville - was one of the first African American women to earn a Ph.D. in Mathematics.

She became a specialist in rocket and missile fuses, orbit computations and trajectory calculations for national defense and the space program providing technical support for the Vanguard, Mercury and Apollo projects. In addition, she served as an educational consultant to the State of California, helping to improve the teaching of math in elementary and secondary schools.

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Jean E. Sammet

Sammet initiated the concept and directed the development of the first FORMAC (FORmula MAnipulation Compiler). FORMAC was the first widely used general language. It was also the first system for manipulating nonnumeric algebraic expressions.

In 1965, she became programming language technology manager in the IBM Systems Development Division. Afterward, she wrote a book on programming languages.

Her book, Programming Languages: History and Fundamentals, was published by Prentice-Hall in 1969.

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Now retired from University of California, Los Angeles, where she was a computer science professor, Estrin was a pioneer in the field of biomedical engineering who realized that some of the most important ideas in science did not fit neatly into separate fields. Her work would combine concepts from anatomy, physiology, and neuroscience with electronic technology and electrical engineering. She was one of the first to use computer technology to solve problems in health care and in medical research.

Estrin designed and then implemented the first system for analog-digital conversion of electrical activity from the nervous system," a precursor to the use of computers in medicine. She published papers on how to map the brain with the help of computers, and long before the Internet became popular and easy to use, she designed a computer network between UCLA and UC Davis in 1975.

Dr. Thelma Estrin

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Dana Angluin

B.A., Ph.D. University of California at Berkeley, 1969, 1976, Joined Yale Faculty 1979

Algorithmic models of learningProfessor Angluin’s thesis was among the first work to apply

complexity theory to the field of inductive inference. Her work on learning from positive data reversed a previous

dismissal of that topic, and established a flourishing line of research. Her work on learning with queries established the models and the foundational results for learning with membership queries. Recently, her work has focused on the areas of coping with errors in the answers to queries, map-learning by mobile robots, and fundamental questions in modeling the interaction of a teacher and a learner.

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Nancy A. Lynch

Professor of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. Nancy Lynch heads the Theory of Distributed Systems Group (TDS) research group in MIT's Laboratory for Computer Science. This group is part of the Theory of Computation (TOC) group and also of the Principles of Computer Systems (POCS) group.

Teaching - Spring 2001: 6.897 Modeling and Analyzing Really Complicated Systems, Using State Machines Fall 2001 6.852 Distributed Algorithms

Research interests:distributed computing, real-time computing, algorithms, lower bounds, formal modelling and verification.

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Adele E. Goldberg

Adele Goldberg received her Ph.D. from the University of California at Berkeley in 1992. She is an associate professor in the UIUC Department of Linguistics and a part-time Beckman Institute faculty member in the Cognitive Science Group.

Her fields of professional interest are syntax/semantics, constructional approaches to grammar, lexical semantics, language acquisition, language processing, and categorization.

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Sandra L. Kurtzig

In today's male-dominated software industry, women founders and CEOs (chief executive officers) are practically nonexistent. But while software titans like Bill Gates and Oracle's Larry Ellison have become the poster boys for Silicon Valley success, the first multimillion-dollar software entrepreneur was a woman.

Starting with just $2,000, Sandra Kurtzig built a software empire that, at its peak, boasted around $450 million in annual sales. And it all started as a part-time job.

Hava Siegelmann

Lab Director, Associate Professor of Computer Science

Core Member of the Neuroscience and Behavior Program

University of Massachusetts Amherst

http://binds.cs.umass.edu/havaBio.html

Research focus on the understanding of biologically inspired computational systems. In particular, she studies the computational and dynamical complexity of neural systems as well as genetic-networks. She would love to advance toward understanding how underlying architecture brings about the dynamics that evolve into intelligent behavior, and how behavior feedback from the dynamics proceeds toward adaptation in the architecture.

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Prof. Siegelmann's Publications

http://www.cs.umass.edu/~binds/publications.html

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A New Kind of ScienceStephen Wolfram

A NEW KIND OF SCIENCE

FREE ACCESShttp://www.wolframscience.com

/

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Evolutionary Biological Paradigm

The most developed forms of computational systems: Evolutive self-organizing self-sustaining complex systems built of metabolic/computational units

COMPONENT SYSTEMS of George Kampis

Kampis, G. 1991. Self-modifying Systems in Biology and Cognitive Science: A New Framework for Dynamics, Information and Complexity. Oxford: Pergamon

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Pieter BRUEGEL, the Elder The Tower of Babel, 1563

http://www.amazon.com/Self-Modifying-Systems-Biology-Cognitive-Science/dp/0080369790

Expensive book but you can read Kampis articles on the web:

http://www.kli.ac.at/theorylab/AuthPage/K/KampisG.html

84COMPUTING

COMPUTER SCIENCE

PHILOSOPHY

ARTS

COMPUTER ENGINEERING

COURSE IN COMPUTING AND PHILOSOPHY

SCIENCES

http://www.idt.mdh.se/kurser/comphil

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Why is Philosophy Important for Computing?

”Thinking tool-box” - access to:

Paradigms

Metaphors

Historical examples (knowledge capital)

Communication – both within computing community and wider

Context – conceptual and cultural framework

Humanist dimensions of higher education are important!

Knowledge society – leads to automated production, organization and even automated discovery. Genuine human thinking abilities including creativity will make the difference!

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Why is Computing Important for Philosophy?

• Simulated or experimental philosophy. Experiments “in silico” (or alternative constructed cognitive/computational systems): As an innovative extension of an ancient tradition of thought experiment, application of computational modeling schemes to questions in logic, epistemology, philosophy of science, philosophy of biology, philosophy of mind, and so on.

• Computing paradigms and metaphors such as computational cognition, computational linguistics, computational brain/mind

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Computation, Information, Cognition:

The Nexus and the Liminal

Editor(s): Gordana Dodig Crnkovic and Susan Stuart

Cambridge Scholars Publishing Titles in Print as of July 2007

http://www.c-s-p.org/Flyers/Computation--Information--Cognition--The-Nexus-and-the-Liminal.htm

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Reception

Internal processes

Response

Plant

Response

Internal processes

Reception

Environment

Living Systems Modelled Computationally

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http://www.xs4all.nl/~cvdmark/tutor.html(L-systems animations)

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Here is a series of forms created by slowly changing the angle parameter. lsys00.ls

Check the rest of the Gallery of L-systems:http://home.wanadoo.nl/laurens.lapre/

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A model of a horse chestnut tree inspired by the work of Chiba and Takenaka.

Here branches compete for light from the sky hemisphere. Clusters of leaves cast shadows on branches further down. An apex in shade does not produce new branches. An existing branch whose leaves do not receive enough light dies and is shed from the tree. In such a manner, the competition for light controls the density of branches in the tree crowns.

Reception

Internal processes

Response

Plant

Response

Internal processes

Reception

Environment

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Apropos adaptive reactive systems:"What's the color of a chameleon put onto a mirror?" -Stewart Brand

(Must be possible to verify experimentally, isn’t it?)

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What is Computation?

Still discussed among specialists. Especially in the sense: what processes and materials can be used to compute for us?

• Theoretical Computer Science 317 (2004)

• Burgin, M., Super-Recursive Algorithms, Springer Monographs in Computer Science, 2005, ISBN: 0-387-95569-0

• Minds and Machines (1994, 4, 4) “What is Computation?”

• Journal of Logic, Language and Information (Volume 12 No 4 2003) What is information?

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Agent Based Models

http://www.scholarpedia.org/article/Agent_based_modeling VERY INTERESTING AND NEW APPROACH TO MODELLING!

http://www.coensys.com/agent_based_models.htm

http://jasss.soc.surrey.ac.uk/12/1/1.html (more advanced on models, errors and artefacts)