Presentation introducing LISP, looking at the history and concepts behind this powerfull programming language.
Presentation by Tijs van der Storm for the sept 2012 Devnology meetup at the Mirabeau offices in Amsterdam
This document provides an overview of the Lisp programming language. It begins with some notable quotes about Lisp praising its power and importance. It then covers the basic syntax of Lisp including its use of prefix notation, basic data types like integers and booleans, and variables. It demonstrates how to print, use conditional statements like IF and COND, and describes lists as the core data structure in Lisp.
LISP and PROLOG are early AI programming languages. LISP, created in 1958, uses lists and is functional while PROLOG, created in the 1970s, is logic-based and declarative. Both use recursion and allow programming with lists. They are commonly used for symbolic reasoning, knowledge representation and natural language processing. While different in approach, they both allow developing AI systems through a non-procedural programming style.
This document provides a brief introduction to the Lisp programming language. It discusses Lisp's history from its origins in 1958 to modern implementations like Common Lisp and Scheme. It also covers Lisp's support for functional, imperative, and object-oriented paradigms. A key feature of Lisp is its use of s-expressions as both code and data, which enables powerful macros to transform and generate code at compile time.
This document provides an introduction to the Lisp programming language. It discusses the history of Lisp, which was created in 1958. It also covers key Lisp concepts like S-expressions, atoms, function definition, evaluation, and macros. Macros allow programmers to generate Lisp code from Lisp code, extending the language. The document uses examples to demonstrate Lisp evaluation and features like conditional evaluation, higher-order functions, and special forms like 'quote and 'if.
The document discusses the Lisp programming language. It notes that Allegro Common Lisp will be used and lists textbooks for learning Lisp. It provides 10 points on Lisp, including that it is interactive, dynamic, uses symbols and lists as basic data types, prefix notation for operators, and classifies different data types. Evaluation follows simple rules and programs can be treated as both instructions and data.
Here is a recursive function to check if a list contains an element:
(defun contains (element list)
(cond ((null list) nil)
((equal element (car list)) t)
(t (contains element (cdr list)))))
To check the guest list:
(contains 'robocop guest-list)
This function:
1. Base case: If list is empty, element is not contained - return nil
2. Check if element equals car of list - if so, return t
3. Otherwise, recursively call contains on element and cdr of list
So it will recursively traverse the list until it finds a match or reaches empty list.
LISP, an acronym for list processing, is a programming language that was designed for easy manipulation of data strings. It is a commonly used language for artificial intelligence (AI) programming.
This document provides an overview of the Lisp programming language. It discusses key features of Lisp including its invention in 1958, machine independence, dynamic updating, and wide data types. The document also covers Lisp syntax, data types, variables, constants, operators, decision making, arrays, loops, text editors, and common uses of Lisp like Emacs. Overall, the document serves as a high-level introduction to the concepts and capabilities of the Lisp programming language.
The document provides an introduction to the Lisp programming language. It begins with an overview of Lisp and discusses its key features: it is a list processing language where lists are the basic data structure; it is functional in nature; and it uses interpretation rather than compilation. The document then covers Lisp basics like data types, evaluation rules, defining functions, conditional statements, loops, and input/output operations. It also introduces some common Lisp functions and techniques like car, cdr, cons, append, cond, do, dotimes, and dolist.
LISP Language, LISP Introduction, List Processing, LISP Syntax, Lisp Comparison Structures, Lisp Applications. Using of LISP language in Artificial Intelligence
Lisp is a functional programming language where the basic data structure is linked lists and atoms. It was one of the earliest programming languages developed in 1958. Lisp programs are run by interacting with an interpreter like Clisp. Key aspects of Lisp include its use of prefix notation, treating all code as nested lists, defining functions using defun, and its emphasis on recursion and higher-order functions. Common control structures include cond for conditional evaluation and looping constructs like loop. Lisp fell out of widespread use due to performance issues with interpretation and low interoperability with other languages.
Introduction to Lisp. A survey of lisp's history, current incarnations and advanced features such as list comprehensions, macros and domain-specific-language [DSL] support.
This document discusses the Lisp programming language. It provides an introduction to Lisp, describes some of its key features like rich arithmetic, generic functions, and macros. It explains that Lisp is well-suited for artificial intelligence programs. The document also gives some examples of Lisp code and applications that use Lisp like Yahoo Store, AutoCAD, and Emacs.
LISP is a programming language invented in 1958 that uses two simple data structures - atoms and lists. It heavily relies on recursion and functional programming. LISP defines all data as lists and represents programs as nested function calls, allowing for dynamic typing and easy abstraction. It introduced many concepts still used in modern languages, including conditionals, recursion, dynamic typing, garbage collection, and representing programs as mathematical expressions.
Lisp has several basic data types including numbers, characters, symbols, lists, arrays, hash tables, and functions. Numbers can be integers, ratios, reals, complexes, and floats. Characters are basic text elements. Symbols are names that can have properties. Lists are sequences of conses (two-element records) linked together. Arrays store elements in a grid structure. Hash tables efficiently map keys to values. Functions represent procedures that can be invoked. Common Lisp provides various functions and constructs for manipulating these basic data types as data structures.
The document discusses Lisp input and output functions. It describes how Lisp represents objects in printed form for input/output and the read function that accepts this printed input and constructs Lisp objects. It covers parsing of numbers, symbols, and macro characters. It also describes output functions like print, princ, and format for writing to streams, as well as input functions like read, read-line, and querying functions like y-or-n-p.
This document summarizes a talk given to Python developers about the Lisp programming language. It discusses some myths about Lisp's syntax, libraries, and community. It also highlights features of Lisp like macros, functional programming capabilities, multimethods, special variables, and powerful condition systems. Lisp is described as a multi-paradigm language that is highly customizable through features like macros while also being high performance.
Lisp was invented in 1958 by John McCarthy and was one of the earliest high-level programming languages. It has a distinctive prefix notation and uses s-expressions to represent code as nested lists. Lisp features include built-in support for lists, dynamic typing, and an interactive development environment. It was closely tied to early AI research and used in systems like SHRDLU. Lisp allows programs to treat code as data through homoiconicity and features like lambdas, conses, and list processing functions make it good for symbolic and functional programming.
This document summarizes a lecture about practical programming in Haskell. It discusses reading files, counting words, and improving performance. It shows how to:
1) Read a file into a bytestring for efficient processing, count the words, and print the result.
2) Use the Data.Text module for Unicode support when processing text files, reading the file as bytes and decoding to text before counting words.
3) Achieve performance comparable or better than C implementations when choosing efficient data representations like bytestrings and text.
1. The document discusses logic programming and Prolog. It provides examples of defining relationships between individuals using predicates and using compound terms to represent data structures like lists.
2. Control flow in logic programming is determined by logical relationships between clauses rather than order of execution. Programs can use top-down or bottom-up control depending on whether they try to reach hypotheses from goals or vice versa.
3. Complex data structures can be represented using compound terms or predicates, with tradeoffs in readability and efficiency. Predicates are better for modeling relationships while compounds work better for mathematical expressions.
- The document discusses concurrent and parallel programming in Haskell, including the use of threads, MVars, and software transactional memory (STM).
- STM provides atomic execution of blocks of code, allowing failed transactions to automatically retry without race conditions or data corruption.
- Strategies can be used to evaluate expressions in parallel using different evaluation models like head normal form or weak head normal form.
- While functional programs may seem to have inherent parallelism, in practice extracting parallelism can be difficult due to data dependencies and irregular patterns of computation.
This document summarizes Lecture 1 of Real World Haskell. It introduces functional programming and Haskell, discusses the Haskell Platform and interactive interpreter ghci. It demonstrates defining simple functions and expressions, writing small interactive programs, and using recursion to number lines of text. Resources for learning more about Haskell are provided.
This document provides an introduction and overview to the Haskell programming language. It discusses Haskell's core concepts like pure functions, immutable data, static typing, and lazy evaluation. It also covers common Haskell tools and libraries, how to write simple Haskell programs, and how to compile and run Haskell code. The document uses examples and interactive sessions to demonstrate Haskell syntax and show how various language features work.
This document provides an overview of the Scheme programming language. It describes Scheme as a functional programming language that is a member of the Lisp family. It provides examples of simple Scheme programs and outlines key Scheme concepts like primitive numeric functions, predicates, and general examples using special forms like cond and recursion to implement functions such as power, finding the maximum of three numbers, categorizing numbers, summing a list, calculating length, and filtering lists.
First in the series of slides for python programming, covering topics like programming language, python programming constructs, loops and control statements.
This paper describes BABAR, a knowledge extraction and representation system, completely implemented in CLOS, that is primarily geared towards organizing and reasoning about knowledge extracted from the Wikipedia Website. The system combines natural language processing techniques, knowledge representation paradigms and machine learning algorithms. BABAR is currently an ongoing independent research project that when sufficiently mature, may provide various commercial opportunities.
BABAR uses natural language processing to parse both page name and page contents. It automatically generates Wikipedia topic taxonomies thus providing a model for organizing the approximately 4,000,000 existing Wikipedia pages. It uses similarity metrics to establish concept relevancy and clustering algorithms to group topics based on semantic relevancy. Novel algorithms are presented that combine approaches from the areas of machine learning and recommender systems. The system also generates a knowledge hypergraph which will ultimately be used in conjunction with an automated reasoner to answer questions about particular topics.
The document provides information about functional programming languages and concepts including:
1) Haskell and ML are introduced as functional languages with features like strong typing, algebraic data types, and pattern matching.
2) Core functional programming concepts are explained like referential transparency, higher-order functions, and recursion instead of iteration.
3) Fold functions are summarized as a way to iterate over lists in functional languages in both a left and right oriented way.
Lisp and Scheme are dialects of the Lisp programming language. Scheme is favored for teaching due to its simplicity. Key features of Lisp include S-expressions as the universal data type, functional programming style with first-class functions, and uniform representation of code and data. Functions are defined using DEFINE and special forms like IF and COND control program flow. Common list operations include functions like CAR, CDR, CONS, LIST, and REVERSE.
LISP Programming Language (Artificial Intelligence)wahab khan
LISP Language, LISP Introduction, List Processing, LISP Syntax, Lisp Comparison Structures, Lisp Applications. Using of LISP language in Artificial Intelligence
The first official version of Prolog was developed in the 1970s in France as a tool for programming in logic. Today, Prolog is used for artificial intelligence applications like knowledge bases, expert systems, and natural language interfaces. Visual Prolog addresses the same market as SQL databases, C++, and other programming languages.
The document provides an introduction to the Lisp programming language. It begins with an overview of Lisp and discusses its key features: it is a list processing language where lists are the basic data structure; it is functional in nature; and it uses interpretation rather than compilation. The document then covers Lisp basics like data types, evaluation rules, defining functions, conditional statements, loops, and input/output operations. It also introduces some common Lisp functions and techniques like car, cdr, cons, append, cond, do, dotimes, and dolist.
LISP Language, LISP Introduction, List Processing, LISP Syntax, Lisp Comparison Structures, Lisp Applications. Using of LISP language in Artificial Intelligence
Lisp is a functional programming language where the basic data structure is linked lists and atoms. It was one of the earliest programming languages developed in 1958. Lisp programs are run by interacting with an interpreter like Clisp. Key aspects of Lisp include its use of prefix notation, treating all code as nested lists, defining functions using defun, and its emphasis on recursion and higher-order functions. Common control structures include cond for conditional evaluation and looping constructs like loop. Lisp fell out of widespread use due to performance issues with interpretation and low interoperability with other languages.
Introduction to Lisp. A survey of lisp's history, current incarnations and advanced features such as list comprehensions, macros and domain-specific-language [DSL] support.
This document discusses the Lisp programming language. It provides an introduction to Lisp, describes some of its key features like rich arithmetic, generic functions, and macros. It explains that Lisp is well-suited for artificial intelligence programs. The document also gives some examples of Lisp code and applications that use Lisp like Yahoo Store, AutoCAD, and Emacs.
LISP is a programming language invented in 1958 that uses two simple data structures - atoms and lists. It heavily relies on recursion and functional programming. LISP defines all data as lists and represents programs as nested function calls, allowing for dynamic typing and easy abstraction. It introduced many concepts still used in modern languages, including conditionals, recursion, dynamic typing, garbage collection, and representing programs as mathematical expressions.
Lisp has several basic data types including numbers, characters, symbols, lists, arrays, hash tables, and functions. Numbers can be integers, ratios, reals, complexes, and floats. Characters are basic text elements. Symbols are names that can have properties. Lists are sequences of conses (two-element records) linked together. Arrays store elements in a grid structure. Hash tables efficiently map keys to values. Functions represent procedures that can be invoked. Common Lisp provides various functions and constructs for manipulating these basic data types as data structures.
The document discusses Lisp input and output functions. It describes how Lisp represents objects in printed form for input/output and the read function that accepts this printed input and constructs Lisp objects. It covers parsing of numbers, symbols, and macro characters. It also describes output functions like print, princ, and format for writing to streams, as well as input functions like read, read-line, and querying functions like y-or-n-p.
This document summarizes a talk given to Python developers about the Lisp programming language. It discusses some myths about Lisp's syntax, libraries, and community. It also highlights features of Lisp like macros, functional programming capabilities, multimethods, special variables, and powerful condition systems. Lisp is described as a multi-paradigm language that is highly customizable through features like macros while also being high performance.
Lisp was invented in 1958 by John McCarthy and was one of the earliest high-level programming languages. It has a distinctive prefix notation and uses s-expressions to represent code as nested lists. Lisp features include built-in support for lists, dynamic typing, and an interactive development environment. It was closely tied to early AI research and used in systems like SHRDLU. Lisp allows programs to treat code as data through homoiconicity and features like lambdas, conses, and list processing functions make it good for symbolic and functional programming.
This document summarizes a lecture about practical programming in Haskell. It discusses reading files, counting words, and improving performance. It shows how to:
1) Read a file into a bytestring for efficient processing, count the words, and print the result.
2) Use the Data.Text module for Unicode support when processing text files, reading the file as bytes and decoding to text before counting words.
3) Achieve performance comparable or better than C implementations when choosing efficient data representations like bytestrings and text.
1. The document discusses logic programming and Prolog. It provides examples of defining relationships between individuals using predicates and using compound terms to represent data structures like lists.
2. Control flow in logic programming is determined by logical relationships between clauses rather than order of execution. Programs can use top-down or bottom-up control depending on whether they try to reach hypotheses from goals or vice versa.
3. Complex data structures can be represented using compound terms or predicates, with tradeoffs in readability and efficiency. Predicates are better for modeling relationships while compounds work better for mathematical expressions.
- The document discusses concurrent and parallel programming in Haskell, including the use of threads, MVars, and software transactional memory (STM).
- STM provides atomic execution of blocks of code, allowing failed transactions to automatically retry without race conditions or data corruption.
- Strategies can be used to evaluate expressions in parallel using different evaluation models like head normal form or weak head normal form.
- While functional programs may seem to have inherent parallelism, in practice extracting parallelism can be difficult due to data dependencies and irregular patterns of computation.
This document summarizes Lecture 1 of Real World Haskell. It introduces functional programming and Haskell, discusses the Haskell Platform and interactive interpreter ghci. It demonstrates defining simple functions and expressions, writing small interactive programs, and using recursion to number lines of text. Resources for learning more about Haskell are provided.
This document provides an introduction and overview to the Haskell programming language. It discusses Haskell's core concepts like pure functions, immutable data, static typing, and lazy evaluation. It also covers common Haskell tools and libraries, how to write simple Haskell programs, and how to compile and run Haskell code. The document uses examples and interactive sessions to demonstrate Haskell syntax and show how various language features work.
This document provides an overview of the Scheme programming language. It describes Scheme as a functional programming language that is a member of the Lisp family. It provides examples of simple Scheme programs and outlines key Scheme concepts like primitive numeric functions, predicates, and general examples using special forms like cond and recursion to implement functions such as power, finding the maximum of three numbers, categorizing numbers, summing a list, calculating length, and filtering lists.
First in the series of slides for python programming, covering topics like programming language, python programming constructs, loops and control statements.
This paper describes BABAR, a knowledge extraction and representation system, completely implemented in CLOS, that is primarily geared towards organizing and reasoning about knowledge extracted from the Wikipedia Website. The system combines natural language processing techniques, knowledge representation paradigms and machine learning algorithms. BABAR is currently an ongoing independent research project that when sufficiently mature, may provide various commercial opportunities.
BABAR uses natural language processing to parse both page name and page contents. It automatically generates Wikipedia topic taxonomies thus providing a model for organizing the approximately 4,000,000 existing Wikipedia pages. It uses similarity metrics to establish concept relevancy and clustering algorithms to group topics based on semantic relevancy. Novel algorithms are presented that combine approaches from the areas of machine learning and recommender systems. The system also generates a knowledge hypergraph which will ultimately be used in conjunction with an automated reasoner to answer questions about particular topics.
The document provides information about functional programming languages and concepts including:
1) Haskell and ML are introduced as functional languages with features like strong typing, algebraic data types, and pattern matching.
2) Core functional programming concepts are explained like referential transparency, higher-order functions, and recursion instead of iteration.
3) Fold functions are summarized as a way to iterate over lists in functional languages in both a left and right oriented way.
Lisp and Scheme are dialects of the Lisp programming language. Scheme is favored for teaching due to its simplicity. Key features of Lisp include S-expressions as the universal data type, functional programming style with first-class functions, and uniform representation of code and data. Functions are defined using DEFINE and special forms like IF and COND control program flow. Common list operations include functions like CAR, CDR, CONS, LIST, and REVERSE.
LISP Programming Language (Artificial Intelligence)wahab khan
LISP Language, LISP Introduction, List Processing, LISP Syntax, Lisp Comparison Structures, Lisp Applications. Using of LISP language in Artificial Intelligence
The first official version of Prolog was developed in the 1970s in France as a tool for programming in logic. Today, Prolog is used for artificial intelligence applications like knowledge bases, expert systems, and natural language interfaces. Visual Prolog addresses the same market as SQL databases, C++, and other programming languages.
This document provides an introduction to Prolog, including:
- SWI-Prolog is an open source Prolog environment that can be freely downloaded.
- Prolog is a declarative logic programming language based on logic, predicates, facts, and rules. It is often used for artificial intelligence applications.
- Key concepts in Prolog include facts, rules, queries, unification, and backtracking to find solutions. Arithmetic can also be performed.
- Control structures like cuts can be used to optimize searching for solutions and avoid unnecessary backtracking.
- Examples are provided of coding simple logic and relationships in Prolog along with queries to demonstrate how it works.
The document discusses Ruby Platform as a Service (PaaS) providers like Heroku, EngineYard, and Google App Engine. It provides an overview of Heroku's architecture including its use of HTTP, Varnish, routing mash, dyno grid, PostgreSQL, and Memcached. It also describes how to deploy applications to Heroku using Git, create add-ons, and supported frameworks.
This document discusses Python syntax and semantics. It introduces key concepts like statements, modules, comments, whitespace, indentation, tokens, expressions, and interpreter errors. It also discusses the difference between semantics, which is the meaning of a program, and syntax, which specifies the algorithm using the programming language. An example program is provided and explained to demonstrate various syntax elements.
Este documento presenta una introducción a la inteligencia artificial. Explica las diferentes teorías de inteligencia, incluyendo las inteligencias múltiples de Howard Gardner. También describe la historia de la inteligencia artificial y sus aplicaciones actuales en áreas como sistemas expertos, procesamiento de lenguaje natural y robótica.
Eitaro Fukamachi presents CL21, a redesign of Common Lisp for the 21st century. CL21 aims to improve Common Lisp's consistency, expressiveness, compatibility, and efficiency. It focuses on simplifying naming conventions, removing unnecessary symbols, and making the language more suitable for modern use while maintaining 100% compatibility with Common Lisp code and libraries. The project is still in development with discussions ongoing about final syntax and standard library decisions. CL21 hopes to make Lisp a premier language for prototyping by building on Common Lisp's strengths.
LISP es un lenguaje de programación multiparadigma diseñado originalmente en 1958 por John McCarthy para la manipulación de listas y fórmulas simbólicas. Más adelante, LISP encontró aplicación en el campo de la inteligencia artificial. LISP tiene una sintaxis completamente entre paréntesis y maneja la memoria de forma automática.
Digital Image Processing is an introduction to the topic that covers the definition of digital images and digital image processing. It provides a brief history of the field and examples of applications like medical imaging, satellite imagery analysis, and industrial inspection. The document concludes with an overview of the key stages in digital image processing like image acquisition, enhancement, and representation.
This document outlines the syllabus for a digital image processing course. It introduces key concepts like what a digital image is, areas of digital image processing like low-level, mid-level and high-level processes, a brief history of the field, applications in different domains, and fundamental steps involved. The course will cover topics in digital image fundamentals and processing techniques like enhancement, restoration, compression and segmentation. It will be taught using MATLAB and C# in the labs. Assessment will include homework, exams, labs and a final project.
Este documento trata sobre inteligencia artificial. Explica conceptos básicos como qué es la IA y si las máquinas pueden pensar. También describe los principales lenguajes de programación de IA como LISP, PROLOG y OPS5. Explica aplicaciones como sistemas expertos, redes neuronales y robótica. Finalmente, analiza programas importantes como ELIZA, MYCIN y DENDRAL y el futuro de la inteligencia artificial.
This document discusses syntax and natural language processing. It begins with an introduction on natural languages, grammar, and how understanding grammar is important for developing NLP systems. It then discusses the components of a language, including the lexicon, categorization of parts of speech, and grammar rules. Several examples of syntactic trees are provided to illustrate parsing sentences and representing syntactic structure. The document also discusses ambiguity, constituents, constructing grammar rules for noun phrases and adjectives, and some difficulties in natural language processing like anaphora, indexicality, and metonymy.
This document provides an overview of digital image processing. It discusses what digital images are composed of and how they are processed using computers. The key steps in digital image processing are described as image acquisition, enhancement, restoration, representation and description, and recognition. A variety of techniques can be used at each step like filtering, segmentation, morphological operations, and compression. The document also outlines common sources of digital images, such as from the electromagnetic spectrum, and applications like medical imaging, astronomy, security screening, and human-computer interfaces.
The document discusses expert systems, which are designed to solve real problems in a particular domain that normally require human expertise. Developing an expert system involves extracting knowledge from domain experts. The key components of an expert system are the knowledge base, inference engine, explanation facility, knowledge acquisition facility, and user interface. Expert systems use knowledge rather than data to solve problems and can explain their reasoning. They have limitations such as being difficult to maintain and only applicable to narrow problems.
Digital image processing focuses on improving images for human interpretation and machine perception. It involves key stages like acquisition, enhancement, restoration, morphological processing, segmentation, and representation. Applications include medical imaging, industrial inspection, law enforcement, and human-computer interfaces. While digital images allow for faster and more efficient processing than analog images, limitations include reduced quality if enlarged beyond a certain file size.
The document provides background information on programming languages and their history. It discusses early pioneers in computer programming such as Ada Lovelace, Herman Hollerith, and Konrad Zuse. It outlines the development of many popular modern programming languages such as Fortran, COBOL, BASIC, Pascal, C, C++, Java, PHP, JavaScript, Python, Ruby, and others, describing their key features and common uses. Ada Lovelace is noted as creating the first computer program in 1843 for Charles Babbage's analytical engine.
An expert system is a knowledge-based information system that uses knowledge from a specific domain to provide information to users like a human expert. Expert systems are useful when human experts are unavailable, inconsistent, or unable to clearly explain decisions. They can be applied when a problem lacks a clear algorithmic solution, is hazardous, has a scarcity of human experts, or requires standardization. Some examples of early expert systems include LITHIAN which advised archaeologists and DENDRAL which identified chemical structures. Expert systems have advantages like enhanced decision quality, reduced consulting costs, and ability to solve complex problems, but developing and maintaining them can be difficult and expensive.
Introduction and architecture of expert systempremdeshmane
An expert system is an interactive computer program that uses knowledge acquired from experts to solve complex problems in a specific domain. It consists of an inference engine that applies rules and logic to the facts contained within a knowledge base in order to provide recommendations or advice to users. The first expert system was called DENDRAL and was developed in the 1970s at Stanford University to identify unknown organic molecules. Expert systems are used in applications like diagnosis, financial planning, configuration, and more to perform tasks previously requiring human expertise. They have benefits like increased productivity and quality, reduced costs and errors, and the ability to capture scarce human knowledge. However, they also have limitations such as difficulty acquiring and representing human expertise and an inability to operate outside their
A programming language is a formal language used to describe computations. It consists of syntax, semantics, and tools like compilers or interpreters. Programming languages support different paradigms like procedural, functional, object-oriented, and logic-based approaches. Variables are named locations in memory that hold values and have properties like name, scope, type, and values.
This document provides an introduction to the SciPy Python library and its uses for scientific computing and data analysis. It discusses how SciPy builds on NumPy to provide functions for domains like linear algebra, integration, interpolation, optimization, statistics, and more. Examples are given of using SciPy for tasks like LU decomposition of matrices, sparse linear algebra, single and double integrals, line plots, and statistics. SciPy allows leveraging Python's simplicity for technical applications involving numerical analysis and data manipulation.
This document discusses the rise of dynamic programming languages. It provides examples of popular dynamic languages like JavaScript, Ruby, Python, and Lisp. It outlines key characteristics of dynamic languages like being dynamically typed, late binding, interpretive, reflective, and having lightweight syntax. The document uses R as a case study to illustrate how dynamic languages can be functional, support powerful data structures and graphics, are embeddable and extensible through packages. It argues dynamic languages are widely used and growing in popularity due to being interactive, portable and failure oblivious.
R has six key facets that characterize its programming model:
1. It provides an interactive interface for computational procedures.
2. It is functional and object-oriented in its programming.
3. It has a modular design with functions and packages as core units.
4. It has a collaborative development model as an open source project.
These facets give R a rich but sometimes messy programming environment. Understanding how the facets developed and interact can help improve and extend R software for data analysis.
This document describes research applying techniques from program analysis to automatically infer properties of code in the Maple computer algebra system. The researchers developed abstract interpretation frameworks tailored to Maple and used these to gather constraints about Maple code and values. By analyzing the entire Maple library with this approach, they were able to infer simple but useful properties, like type information, for parts of the library. This demonstrated the potential of applying formal methods to understand and reason about large, dynamically-typed code bases.
18 css101j pps unit 1
Evolution of Programming & Languages - Problem Solving through Programming - Creating Algorithms - Drawing Flowcharts - Writing Pseudocode - Evolution of C language, its usage history - Input and output functions: Printf and scanf - Variables and identifiers – Expressions - Single line and multiline comments - Constants, Keywords - Values, Names, Scope, Binding, Storage Classes - Numeric Data types: integer - floating point - Non-Numeric Data types: char and string - Increment and decrement operator - Comma, Arrow and Assignment operator - Bitwise and Sizeof operator
The document discusses programming paradigms and introduces imperative programming. It defines imperative programming as a paradigm that describes computation in terms of statements that change a program's state. Imperative programming uses commands to update variables in storage and defines sequences of commands for the computer to perform. The document contrasts structured and unstructured programming and discusses concepts like l-values, r-values, iteration, and goto statements as they relate to imperative programming.
Trivium: A Framework For Symbolic Metaprogramming in C++andreasmaniotis
Metaprogrammed code in C++ can be as simple, clear, reusable, modular and configurable as code that is written in a functional language like Lisp or Haskell.
Template metaprogramming (TMP) code tends to be unfriendly to humans. The code is generally neither easy to read nor easy to write.
The Trivium framework gives a solution to this problem by organising TMP indirectly by the means of Trivium Lisp, a symbolic domain specific language (DSL) for metaprogramming. Metaprograms are not encoded directly in C++, but as symbolic expressions in Trivium-Lisp.
JRuby, Not Just For Hard-Headed Pragmatists AnymoreErin Dees
JRuby bills itself as the pragmatic Ruby, the go-to implementation when you need to fit into the Java universe or support a ton of platforms.
Who knew it was also a tool for having fun exploring the realms of computer science?
The document provides a history of programming languages from the 1940s to the projected year 2100. It discusses early pioneers and the first programming language as well as the evolution of programming languages throughout each decade. Popular modern languages discussed include Python, Java, R, Julia, Lisp, JavaScript, C++, and Mojo in the context of artificial intelligence and machine learning. The document takes a high-level view of the evolution of programming over eight decades from the 1940s to 2000s and looks ahead to future trends.
The document provides a detailed history of programming languages from the 1940s to the projected future. It discusses the evolution of early machine codes and pseudocodes in the 1940s-1950s that paved the way for higher-level languages. Major milestones included the introduction of FORTRAN in 1957, which was one of the first high-level languages, followed by an explosion of new languages in the 1960s. The 1970s saw a focus on simplicity and abstraction with languages like Pascal. Object-oriented programming emerged in the 1980s with C++ and Smalltalk. The rise of the internet in the 1990s drove increased use of scripting languages and the creation of Java. Looking ahead, the future remains uncertain but programming continues
The document provides an overview of being a professional software developer. It discusses that developers' essence involves computer science skills like math and abstraction. It emphasizes polyglot programming and evolving skills over time. Code samples are provided in different languages for FizzBuzz to illustrate concepts. The talk recommends enjoying community through meetups and conferences, sharing knowledge, and not fearing exploration to continuously improve skills.
On being a professional software developerAnton Kirillov
This document discusses being a professional software developer. It begins by introducing the author and agenda. It then defines a software developer and quotes perspectives on software engineering. Subsequent sections discuss the importance of skills like mathematics and abstraction. It provides examples of data structures and algorithms applications. Sections also cover programming languages, theoretical computer science, and applied computer science. The document emphasizes lifelong learning and enjoying the developer community.
Object-oriented programming has its roots in SIMULA 67. Key aspects of OOP include abstract data types, inheritance, and dynamic binding. Java supports OOP through classes that are subclasses of the root class "Object" and utilize single inheritance. All Java objects are allocated dynamically on the heap using the "new" operator.
Deep Learning for Machine Translation: a paradigm shift - Alberto Massidda - ...Codemotion
In beginning there was the "rule based" machine translation, like Babelfish, that didn't work at all. Then came the Statistical Machine translation, powering the like of Google Translate, and all was good. Nowadays, it's all about Deep Learning and the Neural Machine Translation is the state of the art, with unmatched translation fluency. Let's dive into the internals of a Neural Machine Translation system, explaining the principles and the advantages over the past.
Standardizing arrays -- Microsoft PresentationTravis Oliphant
This document discusses standardizing N-dimensional arrays (tensors) in Python. It proposes creating a "uarray" interface that downstream libraries could use to work with different array implementations in a common way. This would include defining core concepts like shape, data type, and math operations for arrays. It also discusses collaborating with mathematicians on formalizing array operations and learning from NumPy's generalized ufunc approach. The goal is to enhance Python's array ecosystem and allow libraries to work across hardware backends through a shared interface rather than depending on a single implementation.
Nikolay Mozgovoy is a developer, mentor, and teacher who has worked with Sigma Software since 2013. He is also a prizewinner and organizer for Global Game Jam Ukraine. This document discusses the history and innovations of the Lisp programming language, which was created in 1960. It highlights Lisp's features like recursion, functions as first-class citizens, homoiconic syntax, and metaprogramming abilities. The primary Lisp dialects today are Scheme, Common Lisp, and Clojure.
This document is a summer training report submitted by Shubham Yadav to the Department of Information Technology at Rajkiya Engineering College. The report details Shubham's 4-week training program at IQRA Software Technologies where he learned about Python programming language and its libraries like NumPy, Matplotlib, Pandas, and OpenCV. The report includes sections on the history of Python, its characteristics, data structures in Python, file handling, and how to use various Python libraries for tasks like mathematical operations, data visualization, data analysis, and computer vision.
The document discusses challenges related to software operation knowledge (SOK) integration. It describes how SOK data can be collected from various sources and used to improve software processes. However, challenges exist around visualizing and analyzing large amounts of technical and usage data, aligning business and technical metrics, handling big and real-time data, and addressing errors at different levels of software. The document advocates for continuous refinement of SOK integration objectives and requirements to optimize results.
This document discusses software engineering and improving how people build software systems. It mentions requirements, testing, and deployment as key parts of the software engineering process. The rest of the document focuses on end-user programming with spreadsheets, noting that spreadsheets are widely used in business and often form the basis for important decisions, but they can contain errors if they lack documentation or are used by multiple people over many years. The document describes research interviewing spreadsheet users to understand frustrations and likes, then developing tools to help users understand and diagnose spreadsheets based on feedback from real users in practice.
The top 10 security issues in web applicationsDevnology
The top 10 security issues in web applications are:
1. Injection flaws such as SQL, OS, and LDAP injection.
2. Cross-site scripting (XSS) vulnerabilities that allow attackers to execute scripts in a victim's browser.
3. Broken authentication and session management, such as not logging users out properly or exposing session IDs.
4. Insecure direct object references where users can directly access files without authorization checks.
5. Cross-site request forgery (CSRF) that tricks a user into performing actions they did not intend.
6. Security misconfiguration of web or application servers.
7. Insecure cryptographic storage of passwords or sensitive data.
8
The document discusses smartcards and RFID tags, explaining that they provide more secure authentication than passwords but are still vulnerable to hacking through logical attacks targeting flaws in cryptographic algorithms, key management, or security protocols, or through physical attacks manipulating the hardware. It also provides examples of attacks that have broken proprietary crypto systems in smartcards and weaknesses like default keys that have enabled attacks on key management.
(1) The document provides instructions for installing the CounterClockwise plugin for Eclipse to get an IDE for Clojure development. (2) It describes how to create and load Clojure files and launch a REPL for evaluation. (3) The document includes exercises on Clojure basics like functions, macros, and functional programming techniques as well as examples for implementing macros.
Devnology Back to School: Empirical Evidence on Modeling in Software DevelopmentDevnology
Modeling is a common part of modern day software engineering practice. Little scientific evidence is known about how models are made and how they help in producing better software. In this talk Michel Chaudron presents highlights from a decade of research that he has performed in the area of software modeling using UML. Topics that will be addressed: What is the state of UML modeling in practice? What are effective techniques for assessing the quality of UML models? How do engineers look at UML models? Do UML models actually help in creating better software?
Devnology Back to School IV - Agility en ArchitectuurDevnology
The document discusses whether agility and architecture can coexist. It notes there is tension between adaptation (agile) and anticipation (architecture). However, the conflict depends on context, including the semantics of architecture, scope, life cycle stage, role, documentation needs, and methods used. Not all design requires architecture. With the right context, agility and architecture can be balanced.
Devnology Back to School III : Software impactDevnology
Michiel van Genuchten talk on software impact, based on a series of columns in IEEE Software discussing the impact on software and analysis of size and volume of software.
Introduction to Software Evolution: The Software VolcanoDevnology
The document discusses software evolution and maintenance. It notes that as software ages, more resources are spent on maintenance and enhancements rather than new projects. The "software volcano" refers to the estimated 750 gigalines of COBOL code and 900 gigalines of C code worldwide, containing an estimated 35 gigabugs. Issues with software maintenance include increasing complexity over time, lack of testing and documentation, and difficulty adapting to changing business needs. Solutions include refactoring, automated testing, knowledge management, and adopting frameworks like ITIL.
GenPro is a genetic programming framework that allows programs to be represented as grids of "cells", where each cell contains a method call. It uses genetic algorithms such as crossover and mutation to evolve programs. The document discusses GenPro's program representations, how solutions are evaluated and bred, challenges in the framework, and ideas for future extensions such as loop support and stateful objects.
Spoofax: ontwikkeling van domeinspecifieke talen in EclipseDevnology
The Spoofax Language Workbench provides tools for defining domain-specific languages (DSLs) with specialized syntax, semantics, and editor services. It offers declarative syntax definition with SDF, model transformations, static analysis for error checking, and semantic services for editors like content completion and error marking. Spoofax aims to make implementing these DSL features cheaply and integrates language development and use into the Eclipse IDE.
This document discusses augmented reality (AR) and describes how to set up an AR experience using the GDDF format. It includes details on loading dimensions, refreshing experiences over time or distance, and defines the required GDDF elements like locations, assets, features and overlays. Instructions are provided on tools for exploring AR on Android and iPhone as well as a POST request format for refreshing experiences. The goal is to get readers interested in designing their own AR dimensions.
The document discusses unit testing for Silverlight applications. It provides an overview of model-view-viewmodel (MVVM) patterns, and examples of writing unit tests for a Silverlight application using the StatLight testing framework. Examples include tests for view models, models, and data services using common unit testing assertions and attributes.
mobl: Een DSL voor mobiele applicatieontwikkelingDevnology
This document discusses mobile application development using MOBL, a domain-specific language for building mobile web applications. It provides examples of building user interfaces, adding scripting capabilities, modeling and querying data, and integrating with native device APIs like geolocation using higher-order controls. Future directions are mentioned like adaptive UIs, offline support, and hybrid web/native applications. Code samples demonstrate creating a tip calculator, scripting functions, modeling task data, and accessing local storage on a device.
UiPath Agentic Automation: Community Developer OpportunitiesDianaGray10
Please join our UiPath Agentic: Community Developer session where we will review some of the opportunities that will be available this year for developers wanting to learn more about Agentic Automation.
AI Agents at Work: UiPath, Maestro & the Future of DocumentsUiPathCommunity
Do you find yourself whispering sweet nothings to OCR engines, praying they catch that one rogue VAT number? Well, it’s time to let automation do the heavy lifting – with brains and brawn.
Join us for a high-energy UiPath Community session where we crack open the vault of Document Understanding and introduce you to the future’s favorite buzzword with actual bite: Agentic AI.
This isn’t your average “drag-and-drop-and-hope-it-works” demo. We’re going deep into how intelligent automation can revolutionize the way you deal with invoices – turning chaos into clarity and PDFs into productivity. From real-world use cases to live demos, we’ll show you how to move from manually verifying line items to sipping your coffee while your digital coworkers do the grunt work:
📕 Agenda:
🤖 Bots with brains: how Agentic AI takes automation from reactive to proactive
🔍 How DU handles everything from pristine PDFs to coffee-stained scans (we’ve seen it all)
🧠 The magic of context-aware AI agents who actually know what they’re doing
💥 A live walkthrough that’s part tech, part magic trick (minus the smoke and mirrors)
🗣️ Honest lessons, best practices, and “don’t do this unless you enjoy crying” warnings from the field
So whether you’re an automation veteran or you still think “AI” stands for “Another Invoice,” this session will leave you laughing, learning, and ready to level up your invoice game.
Don’t miss your chance to see how UiPath, DU, and Agentic AI can team up to turn your invoice nightmares into automation dreams.
This session streamed live on May 07, 2025, 13:00 GMT.
Join us and check out all our past and upcoming UiPath Community sessions at:
👉 https://community.uipath.com/dublin-belfast/
UiPath Automation Suite – Cas d'usage d'une NGO internationale basée à GenèveUiPathCommunity
Nous vous convions à une nouvelle séance de la communauté UiPath en Suisse romande.
Cette séance sera consacrée à un retour d'expérience de la part d'une organisation non gouvernementale basée à Genève. L'équipe en charge de la plateforme UiPath pour cette NGO nous présentera la variété des automatisations mis en oeuvre au fil des années : de la gestion des donations au support des équipes sur les terrains d'opération.
Au délà des cas d'usage, cette session sera aussi l'opportunité de découvrir comment cette organisation a déployé UiPath Automation Suite et Document Understanding.
Cette session a été diffusée en direct le 7 mai 2025 à 13h00 (CET).
Découvrez toutes nos sessions passées et à venir de la communauté UiPath à l’adresse suivante : https://community.uipath.com/geneva/.
The cost benefit of implementing a Dell AI Factory solution versus AWS and Azure
Our research shows that hosting GenAI workloads on premises, either in a traditional Dell solution or using managed Dell APEX Subscriptions, could significantly lower your GenAI costs over 4 years compared to hosting these workloads in the cloud. In fact, we found that a Dell AI Factory on-premises solution could reduce costs by at much as 71 percent vs. a comparable AWS SageMaker solution and as much as 61 percent vs. a comparable Azure ML solution. These results show that organizations looking to implement GenAI and reap the business benefits to come can find many advantages in an on-premises Dell AI Factory solution, whether they opt to purchase and manage it themselves or engage with Dell APEX Subscriptions. Choosing an on-premises Dell AI Factory solution could save your organization significantly over hosting GenAI in the cloud, while giving you control over the security and privacy of your data as well as any updates and changes to the environment, and while ensuring your environment is managed consistently.
Webinar - Top 5 Backup Mistakes MSPs and Businesses Make .pptxMSP360
Data loss can be devastating — especially when you discover it while trying to recover. All too often, it happens due to mistakes in your backup strategy. Whether you work for an MSP or within an organization, your company is susceptible to common backup mistakes that leave data vulnerable, productivity in question, and compliance at risk.
Join 4-time Microsoft MVP Nick Cavalancia as he breaks down the top five backup mistakes businesses and MSPs make—and, more importantly, explains how to prevent them.
HCL Nomad Web – Best Practices und Verwaltung von Multiuser-Umgebungenpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-nomad-web-best-practices-und-verwaltung-von-multiuser-umgebungen/
HCL Nomad Web wird als die nächste Generation des HCL Notes-Clients gefeiert und bietet zahlreiche Vorteile, wie die Beseitigung des Bedarfs an Paketierung, Verteilung und Installation. Nomad Web-Client-Updates werden “automatisch” im Hintergrund installiert, was den administrativen Aufwand im Vergleich zu traditionellen HCL Notes-Clients erheblich reduziert. Allerdings stellt die Fehlerbehebung in Nomad Web im Vergleich zum Notes-Client einzigartige Herausforderungen dar.
Begleiten Sie Christoph und Marc, während sie demonstrieren, wie der Fehlerbehebungsprozess in HCL Nomad Web vereinfacht werden kann, um eine reibungslose und effiziente Benutzererfahrung zu gewährleisten.
In diesem Webinar werden wir effektive Strategien zur Diagnose und Lösung häufiger Probleme in HCL Nomad Web untersuchen, einschließlich
- Zugriff auf die Konsole
- Auffinden und Interpretieren von Protokolldateien
- Zugriff auf den Datenordner im Cache des Browsers (unter Verwendung von OPFS)
- Verständnis der Unterschiede zwischen Einzel- und Mehrbenutzerszenarien
- Nutzung der Client Clocking-Funktion
Train Smarter, Not Harder – Let 3D Animation Lead the Way!
Discover how 3D animation makes inductions more engaging, effective, and cost-efficient.
Check out the slides to see how you can transform your safety training process!
Slide 1: Why 3D animation changes the game
Slide 2: Site-specific induction isn’t optional—it’s essential
Slide 3: Visitors are most at risk. Keep them safe
Slide 4: Videos beat text—especially when safety is on the line
Slide 5: TechEHS makes safety engaging and consistent
Slide 6: Better retention, lower costs, safer sites
Slide 7: Ready to elevate your induction process?
Can an animated video make a difference to your site's safety? Let's talk.
Bepents tech services - a premier cybersecurity consulting firmBenard76
Introduction
Bepents Tech Services is a premier cybersecurity consulting firm dedicated to protecting digital infrastructure, data, and business continuity. We partner with organizations of all sizes to defend against today’s evolving cyber threats through expert testing, strategic advisory, and managed services.
🔎 Why You Need us
Cyberattacks are no longer a question of “if”—they are a question of “when.” Businesses of all sizes are under constant threat from ransomware, data breaches, phishing attacks, insider threats, and targeted exploits. While most companies focus on growth and operations, security is often overlooked—until it’s too late.
At Bepents Tech, we bridge that gap by being your trusted cybersecurity partner.
🚨 Real-World Threats. Real-Time Defense.
Sophisticated Attackers: Hackers now use advanced tools and techniques to evade detection. Off-the-shelf antivirus isn’t enough.
Human Error: Over 90% of breaches involve employee mistakes. We help build a "human firewall" through training and simulations.
Exposed APIs & Apps: Modern businesses rely heavily on web and mobile apps. We find hidden vulnerabilities before attackers do.
Cloud Misconfigurations: Cloud platforms like AWS and Azure are powerful but complex—and one misstep can expose your entire infrastructure.
💡 What Sets Us Apart
Hands-On Experts: Our team includes certified ethical hackers (OSCP, CEH), cloud architects, red teamers, and security engineers with real-world breach response experience.
Custom, Not Cookie-Cutter: We don’t offer generic solutions. Every engagement is tailored to your environment, risk profile, and industry.
End-to-End Support: From proactive testing to incident response, we support your full cybersecurity lifecycle.
Business-Aligned Security: We help you balance protection with performance—so security becomes a business enabler, not a roadblock.
📊 Risk is Expensive. Prevention is Profitable.
A single data breach costs businesses an average of $4.45 million (IBM, 2023).
Regulatory fines, loss of trust, downtime, and legal exposure can cripple your reputation.
Investing in cybersecurity isn’t just a technical decision—it’s a business strategy.
🔐 When You Choose Bepents Tech, You Get:
Peace of Mind – We monitor, detect, and respond before damage occurs.
Resilience – Your systems, apps, cloud, and team will be ready to withstand real attacks.
Confidence – You’ll meet compliance mandates and pass audits without stress.
Expert Guidance – Our team becomes an extension of yours, keeping you ahead of the threat curve.
Security isn’t a product. It’s a partnership.
Let Bepents tech be your shield in a world full of cyber threats.
🌍 Our Clientele
At Bepents Tech Services, we’ve earned the trust of organizations across industries by delivering high-impact cybersecurity, performance engineering, and strategic consulting. From regulatory bodies to tech startups, law firms, and global consultancies, we tailor our solutions to each client's unique needs.
The Future of Cisco Cloud Security: Innovations and AI IntegrationRe-solution Data Ltd
Stay ahead with Re-Solution Data Ltd and Cisco cloud security, featuring the latest innovations and AI integration. Our solutions leverage cutting-edge technology to deliver proactive defense and simplified operations. Experience the future of security with our expert guidance and support.
Generative Artificial Intelligence (GenAI) in BusinessDr. Tathagat Varma
My talk for the Indian School of Business (ISB) Emerging Leaders Program Cohort 9. In this talk, I discussed key issues around adoption of GenAI in business - benefits, opportunities and limitations. I also discussed how my research on Theory of Cognitive Chasms helps address some of these issues
Hybridize Functions: A Tool for Automatically Refactoring Imperative Deep Lea...Raffi Khatchadourian
Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the “best of both worlds,” using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution—avoiding performance bottlenecks and semantically inequivalent results. We discuss the engineering aspects of a refactoring tool that automatically determines when it is safe and potentially advantageous to migrate imperative DL code to graph execution and vice-versa.
TrsLabs - AI Agents for All - Chatbots to Multi-Agents SystemsTrs Labs
AI Adoption for Your Business
AI applications have evolved from chatbots
into sophisticated AI agents capable of
handling complex workflows. Multi-agent
systems are the next phase of evolution.
AI 3-in-1: Agents, RAG, and Local Models - Brent LasterAll Things Open
Presented at All Things Open RTP Meetup
Presented by Brent Laster - President & Lead Trainer, Tech Skills Transformations LLC
Talk Title: AI 3-in-1: Agents, RAG, and Local Models
Abstract:
Learning and understanding AI concepts is satisfying and rewarding, but the fun part is learning how to work with AI yourself. In this presentation, author, trainer, and experienced technologist Brent Laster will help you do both! We’ll explain why and how to run AI models locally, the basic ideas of agents and RAG, and show how to assemble a simple AI agent in Python that leverages RAG and uses a local model through Ollama.
No experience is needed on these technologies, although we do assume you do have a basic understanding of LLMs.
This will be a fast-paced, engaging mixture of presentations interspersed with code explanations and demos building up to the finished product – something you’ll be able to replicate yourself after the session!
AI 3-in-1: Agents, RAG, and Local Models - Brent LasterAll Things Open
Learn a language : LISP
1. Lisp
Tijs van der Storm
Thursday, September 6, 12
2. About me...
• Work at Centrum Wiskunde & Informatica
• Teach at Universiteit van Amsterdam in the
Master Software Engineering
• According to @jvandenbos “typical esoteric
programming language dude” :)
• Contact: [email protected], @tvdstorm
Thursday, September 6, 12
3. Interests and projects
• DSLs, MDD, programming, languages
• Co-designer of the Rascal
metaprogramming language
• Co-designer of the Ensō model-based
programming environment
Thursday, September 6, 12
7. What is Lisp?
http://lisperati.com/
• A programming language?
• For LIst Processing?
• The most intelligent way to misuse a computer?
• Lots of Irritating Superfluous Parentheses?
• Secret alien technology?
• Oatmeal with fingernail clippings mixed in?
• A programmer amplifier?
Thursday, September 6, 12
8. What is Lisp?
• A PL for building organisms (Perlis)
• Building material (Kay)
• Opposite of a Blub language (Graham)
• Maxwell’s equations of software (Kay)
• The greatest language ever invented (Kay)
Thursday, September 6, 12
9. John McCarthy
(September 4, 1927 –
October 24, 2011)
Thursday, September 6, 12
10. (
Recursive Functions of Symbolic Expressions and
Their Computation by Machine, Part I
JOHX MCCAaTItY, Massachusetts Institute of Technology, Cambridge, Mass.
1. Introduction 2. F u n c t i o n s a n d F u n c t i o n Definitions
A programming system called LISP (for lASt Processor) We shMl need a number of mathematical ideas ar:d
has been developed for the I B M 704 computer by the notations concerning functions in general. Most of the
Artificial Intelligence group at M.I.T. The system was ideas are well known, but the notion of conditional e,~pre~'-
designed to facilitate experiments with a proposed system sion is believed to be new, and ihe use of conditional
called the Advice Taker, whereby a machine could be expressions permits functions to be defined recursively in a
instructed to handle declarative as well as imperative new and convenient way.
sentences and could exhibit "common sense" in carrying a. Partial Functions. A partial function is a funct on
out its instructions. The original proposal It] for the Advice that is defined only on part of its domain. Partial funetio:~s
Taker was made in November 1958. The main require- necessarily arise when functions are defined by eomputa~
ment was a programming system for manipulating ex- tions because for some values of the arguments t:he Pomp:>
pressions representing formalized declarative and irnpera- ration defining the value of the function may not ter-
live sentences so that the Advice Taker system could make minate. However, some of our elementary functions wilt be
deductions. defined as partial functions.
In the course of its development the Lisp system went
b. Propositional Expres.s'ions and Predicates. A t)ropo~i-
through several stages of simplification and eventually
tionM expression is an expression whose possible values
came to be based on a scheme for representing the partial
are T (for truth) and F (for falsity). We shall assume
recursive functions of a certain class of symbolic expres-
that the reader is fanfiliar with the propositionM eom~ee-
sions. This representation is independent of the IBM 704
lives A ("and"), V ( " o r " ) , and ~ ( " n o t " ) , Typieai
computer, or of any other electronic computer, and it now
propositional expressions are:
seems expedient to expound the system by starting with
the class of expressions called S-expressions and the func- x<y
tions called S-functions.
(x < y) A (b = e)
In this article, we first describe a formalism for defining
functions reeursively. We believe this formalism has ad- x is prime
vantages both as a programming language and as vehicle A predicate is a function whose range consists of ih{:
for developing a theory of computation. Next, we describe truth values T and F.
S-expressions and S-functions, give some examples, and
e. Conditional Expressions. The dependence of truth
then describe the universM S-function apply which plays
values on the vahtes of quantities of other kinds is ex-
the theoretical role of a universal Turing machine and
pressed in mathematics by predicates, and the depende~ee
the practical role of an interpreter. Then we describe the
of truth values on other truth values by logical comxee-
representation of S-expressions in the memmT of the
~ives. However, the notations for expressing symbol (alE"
IBM 704 by list structures similar to those used by Newell, the dependence of quantities of other kinds on trutt~
Shaw and Simon [2], and the representation of S-functions vMues is inadequate, so that English words and phrases
by program. Then we mention the main features of the are generMly used for expressing these depende~tces i:~
Lisp programming system for the IBM 704. Next comes texts that, describe other dependences symbolically. I!'<~r
another way of describing computations with symbolic example, the function Ix I is ustmlly defined in words.
expressions, and finally we give a recursive function in- Conditional expressions are a deviee for expressing the
terpretation of flow charts. dependence of quantities on propositional quantities. :
We hope to describe some of the sylnbolie computations conditional expression has the form
Communications of the ACM, vol 3, issue 4, April 1960
for which LISP has been used in another paper, and also to
give elsewhere some applications of our reeursive function
(p: -+ el, -.- , p ~ --+ e , , ) http://dx.doi.org/10.1145/367177.367199
formalism to mathematical logic and to the problem of where the p's are propositionM expressions and the e's are
mechanical theorem proving. expressions of any kind. It may be read, "If p~ thexx <,
Thursday, September 6, 12
11. Last 17th of
August: 50 years
ago (!)
http://www.softwarepreservation.org/projects/LISP/book/LISP%201.5%20Programmers%20Manual.pdf
Thursday, September 6, 12
12. The famous
page 13
http://xkcd.com/917/
Thursday, September 6, 12
13. Guy L. Steele, Richard P. Gabriel, “The evolution of Lisp”, in: History of programming languages II, ACM 1996, p. 311
Thursday, September 6, 12
14. Revised5 Report on the Algorithmic Language
Scheme
RICHARD KELSEY, WILLIAM CLINGER, AND JONATHAN REES (Editors)
H. ABELSON R. K. DYBVIG C. T. HAYNES G. J. ROZAS
N. I. ADAMS IV D. P. FRIEDMAN E. KOHLBECKER G. L. STEELE JR.
D. H. BARTLEY R. HALSTEAD D. OXLEY G. J. SUSSMAN
G. BROOKS C. HANSON K. M. PITMAN M. WAND
Dedicated to the Memory of Robert Hieb
20 February 1998 CONTENTS
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
50 pages:
SUMMARY 1 Overview of Scheme . . . . . . . . . . . . . . . . . . . . . . . 3
1.1 Semantics . . . . . . . . . . . . . . . . . . . . . . . . . 3
The report gives a defining description of the program- 1.2 Syntax . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
ming language Scheme. Scheme is a statically scoped and 1.3 Notation and terminology . . . . . . . . . . . . . . . . 3
pure, small,
properly tail-recursive dialect of the Lisp programming 2 Lexical conventions . . . . . . . . . . . . . . . . . . . . . . . 5
language invented by Guy Lewis Steele Jr. and Gerald 2.1 Identifiers . . . . . . . . . . . . . . . . . . . . . . . . . 5
Jay Sussman. It was designed to have an exceptionally 2.2 Whitespace and comments . . . . . . . . . . . . . . . . 5
clear and simple semantics and few di↵erent ways to form 2.3 Other notations . . . . . . . . . . . . . . . . . . . . . . 5
“academic”
3 Basic concepts . . . . . . . . . . . . . . . . . . . . . . . . . . 6
expressions. A wide variety of programming paradigms, in-
3.1 Variables, syntactic keywords, and regions . . . . . . . 6
cluding imperative, functional, and message passing styles,
3.2 Disjointness of types . . . . . . . . . . . . . . . . . . . 6
find convenient expression in Scheme. 3.3 External representations . . . . . . . . . . . . . . . . . 6
The introduction o↵ers a brief history of the language and 3.4 Storage model . . . . . . . . . . . . . . . . . . . . . . . 7
of the report. 3.5 Proper tail recursion . . . . . . . . . . . . . . . . . . . 7
4 Expressions . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
The first three chapters present the fundamental ideas of 4.1 Primitive expression types . . . . . . . . . . . . . . . . 8
the language and describe the notational conventions used 4.2 Derived expression types . . . . . . . . . . . . . . . . . 10
for describing the language and for writing programs in the 4.3 Macros . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
language. 5 Program structure . . . . . . . . . . . . . . . . . . . . . . . . 16
5.1 Programs . . . . . . . . . . . . . . . . . . . . . . . . . 16
Chapters 4 and 5 describe the syntax and semantics of 5.2 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . 16
expressions, programs, and definitions. 5.3 Syntax definitions . . . . . . . . . . . . . . . . . . . . 17
Chapter 6 describes Scheme’s built-in procedures, which 6 Standard procedures . . . . . . . . . . . . . . . . . . . . . . 17
include all of the language’s data manipulation and in- 6.1 Equivalence predicates . . . . . . . . . . . . . . . . . . 17
6.2 Numbers . . . . . . . . . . . . . . . . . . . . . . . . . . 19
put/output primitives.
6.3 Other data types . . . . . . . . . . . . . . . . . . . . . 25
Chapter 7 provides a formal syntax for Scheme written in 6.4 Control features . . . . . . . . . . . . . . . . . . . . . . 31
extended BNF, along with a formal denotational semantics. 6.5 Eval . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
An example of the use of the language follows the formal 6.6 Input and output . . . . . . . . . . . . . . . . . . . . . 35
syntax and semantics. 7 Formal syntax and semantics . . . . . . . . . . . . . . . . . . 38
7.1 Formal syntax . . . . . . . . . . . . . . . . . . . . . . . 38
The report concludes with a list of references and an al- 7.2 Formal semantics . . . . . . . . . . . . . . . . . . . . . 40
phabetic index. 7.3 Derived expression types . . . . . . . . . . . . . . . . . 43
Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
Additional material . . . . . . . . . . . . . . . . . . . . . . . . 45
Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
Alphabetic index of definitions of concepts,
keywords, and procedures . . . . . . . . . . . . . . . . 48
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16. What made Lisp different?
http://paulgraham.com/diff.html
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17. How is Lisp still different?
• Homoiconic syntax
• aka: there is no syntax
• Macros
• aka: compile-time code transformers
• Code is data, data can be code
• Program into the language
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23. • Lisp syntax, macros, code as data etc.
• Functional programming, immutable data
• Data structures: map, set, vector, list
• Concurrency: transactional memory
• Compiles to JVM, intergrates with Java
• [and much more]
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24. (def basic-data-types
'{:booleans [true, false]
:numbers [1, 2, 3.0, 4/5]
:strings ["this is a string"]
:symbols [a, empty?, +, user/foo]
:keywords [:a-key-word]})
NB: commas, are
whitespace (!)
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25. (def collection-types
'{:vectors [1,2,3,4]
:maps {:x 3, :y 4}
:sets #{a set of symbols}
:lists (a list of symbols)})
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26. • Expressed using lists (Polish notation):
(operator arg1 arg2 ...)
• Head is applied to the arguments in tail:
(+ 1 2)
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27. Special forms
define (def x 3)
conditional (if (> x 1) 'then 'else)
(do
sequencing (print "hello")
(print "world!"))
local vars (let [x 1] (+ x 1))
(quote (this returns a list with seven symbols))
quotation '(this returns a list with seven symbols)
closures (fn [x n] (+ x n))
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28. Convenience macros
define a (defn power [x n]
function (if (= n 0)
1
(* x (power x (- n 1)))))
define a (defmacro unless [cond then else]
macro `(if (not ~cond) ~then ~else))
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29. Macros!
• Functions that transform code trees
• aka: code that writes code
template (defmacro unless [cond then else]
`(if (not ~cond) ~then ~else))
quasi quote ` unquote ~
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32. Testing it out
=> ((fn [x y] (cond'
[(> x y) 1]
[(< x y) -1]
[(= x y) 0])) 1 2)
-1
=> (macroexpand-all '(cond'
[(> x y) 1]
[(< x y) -1]
[(= x y) 0]))
(if (> x y) 1 (if (< x y) -1 (if (= x y) (do 0))))
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33. Why is this cool?
• Extend the language with new abstractions
• control-flow
• state machine
• GUI builders
• grammars, ... etc.
• Reuse Lisp syntax / compile with macros
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34. s
http://www.cwi.nl/~storm/devclj.html
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