Evolution of Programming Languages.pdf
- 1. Evolution of Programming Languages from 1940s to 2100
- 2. History of Programmers • Before 1940s: The first programmers • The 1940s: Von Neumann, Konard Zuse & PlankalKul • The 1950s: The First Programming Language • The 1960s: An Explosion in Programming languages • The 1970s: Simplicity, Abstraction, Study • The 1980s: Consolidation and New Directions • The 1990s: Internet and the Web • The 2000s: tbd
- 3. Early History: The First Programmer • Jacquard loom of early 1800s – Translated card patterns into cloth designs • Charles Babbage’s analytical engine (1830s & 40s) Programs were cards with data and operations • Ada Lovelace – first programmer “The engine can arrange and combine its numerical quantities exactly as if they were letters or any other general symbols; And in fact might bring out its results in algebraic notation, were provision made.”
- 4. Jacquard loom of early 1800s Charles Babbage’s analytical engine (1830s & 40s) Ada Lovelace – first programmer
- 5. The 1940s: Von Neumann and Zuse John Von Neumann led a team that built computers with stored programs and a central processor ENIAC.
- 6. Konrad Zuse and Plankalkul Konrad Zuse began work on Plankalkul (plan calculus), the first algorithmic programming language, with an aim of creating the theoretical preconditions for the formulation of problems of a general nature. Seven years earlier, Zuse had developed and built the world's first binary digital computer, the Z1. He completed the first fully functional program-controlled electromechanical digital computer, the Z3, in 1941. Only the Z4, the most sophisticated of his creations, survived World War II.
- 7. Machine Code (1940’s) •Initial computers were programmed in raw machine code. •These were entirely numeric. •What was wrong with using machine code? Everything! •Poor readability •Poor modifiability •Expression coding was tedious •Inherit deficiencies of hardware, e.g., no indexing or floating point numbers
- 8. Pseudocodes (1949) •Short Code or SHORTCODE - John Mauchly, 1949. •Pseudocode interpreter for math problems, on Eckert and Mauchly’s BINAC and later on UNIVAC I and II. •Possibly the first attempt at a higher level language. •Expressions were coded, left to right
- 9. More Pseudocodes Speed coding; 1953-4 • A pseudocode interpreter for math on IBM 701, IBM 650. • Developed by John Backus • Pseudo ops for arithmetic and math functions • Conditional and unconditional branching • Auto increment registers for array access • Slow but still dominated by slowness of s/w math • Interpreter left only 700 words left for user program Laning and Zierler System – 1953 • Implemented on the MIT Whirlwind computer • First "algebraic" compiler system • Subscripted variables, function calls, expression translation • Never ported to any other machine
- 10. The 1950s: The First Programming Language • Pseudocodes: interpreters for assembly language like • Fortran: the first higher level programming language • COBOL: he first business oriented language • Algol: one of the most influential programming languages ever designed • LISP: the first language to depart from the procedural paradigm • APL: A Programming Language
- 11. The 1960s: An Explosion in Programming Languages • The development of hundreds of programming languages • PL/I designed in 1963-4 – supposed to be all purpose – combined features of FORTRAN, COBOL and Algol 60 and more! – translators were slow, huge and unreliable – some say it was ahead of its time...... • Algol 68 • SNOBOL • Simula • BASIC
- 12. The 1970s: Simplicity, Abstraction, Study • Algol-W - Nicklaus Wirth and C.A.R.Hoare – reaction against 1960s – simplicity • Pascal – small, simple, efficient structures – for teaching program • C - 1972 - Dennis Ritchie – aims for simplicity by reducing restrictions of the type system – allows access to underlying system – interface with O/S - UNIX
- 13. The 1980s: Consolidation and New Paradigms • Ada – US Department of Defence – European team lead by Jean Ichbiah. (Sam Lomonaco was also on the ADA team :-) • Functional programming – Scheme, ML, Haskell • Logic programming – Prolog • Object-oriented programming – Smalltalk, C++, Eiffel
- 14. Functional Programming • Common Lisp: consolidation of LISP dialects spurred practical use, as did the development of Lisp Machines. • Scheme: a simple and pure LISP like language used for teaching programming. • Logo: Used for teaching young children how to program. • ML: (Meta Language) a strongly-typed functional language first developed by Robin Milner in the 70’s • Haskell: poly morphicly typed, lazy, purely functional language.
- 15. Small talk (1972-80) •Developed at Xerox PARC by Alan Kay and colleagues (esp. Adele Goldberg) inspired by Simula 67 •First compilation in 1972 was written on a bet to come up with "the most powerful language in the world" in "a single page of code". •In 1980, Smalltalk 80, a uniformly object-oriented programming environment became available as the first commercial release of the Smalltalk language •Pioneered the graphical user interface everyone now uses •Industrial use continues to the present day
- 16. C++ (1985) •Developed at Bell Labs by Stroustrup •Evolved from C and SIMULA 67 •Facilities for object-oriented programming, taken partially from SIMULA 67, added to C •Also has exception handling •A large and complex language, in part because it supports both procedural and OO programming •Rapidly grew in popularity, along with OOP •ANSI standard approved in November, 1997
- 17. Eiffel Eiffel - a related language that supports OOP - (Designed by Bertrand Meyer - 1992) - Not directly derived from any other language - Smaller and simpler than C++, but still has most of the power
- 18. 1990’s: the Internet and Web During the 90’s, Object-oriented languages (mostly C++) became widely used in practical applications The Internet and Web drove several phenomena: – Adding concurrency and threads to existing languages – Increased use of scripting languages such as Perl and Tcl/Tk – Java as a new programming language
- 19. Java • Developed at Sun in the early 1990s with original goal of a language for embedded computers • Principals: Bill Joy, James Gosling, Mike Sheradin, Patrick Naughton • Original name, Oak, changed for copyright reasons • Based on C++ but significantly simplified • Supports only OOP • Has references, but not pointers • Includes support for applets and a form of concurrency (i.e. threads)
- 20. The future • In the 60’s, the dream was a single all-purpose language (e.g., PL/I, Algol) • The 70s and 80s dream expressed by Winograd (1979) “Just as high-level languages allow the programmer to escape the intricacies of the machine, higher level programming systems can provide for manipulating complex systems. We need to shift away from algorithms and towards the description of the properties of the packages that we build. Programming systems will be declarative not imperative” • Will that dream be realised? • Programming is not yet obsolete
- 36. Currently Trending Programming Languages There are several programming languages that are commonly used in the fields of AI, ML, and Cybersecurity. Here are some of the most popular ones: 1. Python: Python is one of the most popular programming languages for AI and ML development due to its simple syntax and readability. It supports a variety of frameworks and libraries, which allows for more flexibility and creates endless possibilities for an engineer to work with. Some of the most popular Python libraries for machine learning include: sci-kit image, OpenCV, TensorFlow, PyTorch, Keras, NumPy, NLTK, SciPy, and sci-kit learn123. 2. Java: Java is a general-purpose programming language that is used for creating mobile, desktop, web, and cloud applications. It is also used for developing AI systems. Java is known for its scalability, security, and cross-platform compatibility2. 3. R: R is a programming language that is used for statistical computing and graphics. It is widely used in data analysis, machine learning, and scientific research. R has a large number of libraries and packages that make it easy to perform complex statistical analyses3. 4. Julia: Julia is a high-level, high-performance programming language that is designed for numerical and scientific computing. It is used for developing AI and ML models, as well as for data analysis and visualization3. 5. Lisp: Lisp is a family of programming languages that are used for AI and ML development. Lisp is known for its powerful macro system, which allows developers to extend the language itself. Lisp is also used for symbolic computing, which is a type of computing that deals with symbols and expressions3. 6. JavaScript: JavaScript is a programming language that is used for creating highly interactive browser-based applications. It is also used for developing AI systems. JavaScript is known for its flexibility and ease of use2. 7. C++: C++ is a general-purpose programming language that is used for developing AI and ML models, as well as for developing operating systems, system software, and embedded systems. C++ is known for its speed and efficiency2. • Here are some PowerPoint presentations that you might find useful: 1. Machine Learning in Cyber Security - This presentation provides a holistic view of machine learning in cybersecurity for better organizational readiness. 2. AI and ML in Cybersecurity - This presentation discusses the limitations of machine learning and issues of explain ability, where deep learning should never be applied, and examples of how the blind application of algorithms can lead to wrong results. • Please note that the information provided is current as of January 2024 and may be subject to change.
- 37. Comparing the performance of Mojo, Python, and JavaScript Comparing the performance of Mojo, Python, and JavaScript in the context of machine learning. According to a Medium article, Python and Mojo are two popular programming languages that have been widely used in various applications, from web development to machine learning. While both Python and Mojo share some similarities, they also have notable differences that set them apart. As a developer or programmer, it’s essential to understand the fundamental differences between these languages so that you can choose the one that best suits your needs. Another Medium article compares the performance of JavaScript and Python for machine learning. The article states that JavaScript’s computational performance is still much better than Python’s. However, the maturity of the libraries — which often have underlying modules written in C — means that operations on large datasets can offer so much more than sheer computational power. But there is still a place for JavaScript in machine learning.
- 38. Programming Languages for Civil Engineering • Civil and Structural engineering are fields that require a lot of computational power. Learning to code can help engineers automate repetitive tasks, improve their workflow, and increase their productivity. According to The Computational Engineer, the following programming languages are commonly used in the civil and structural engineering industry 1: 1. Grasshopper: A visual programming language that can be easily adopted by civil and structural engineers. It is a plugin to a CAD and 3D-modelling software called Rhinoceros. It has a low bar to entry but is powerful enough to manage most of your workflows, including your Revit workflows. 2. Dynamo: A popular visual programming language for building and civil engineers. It is a plugin for Autodesk Revit and can be used to automate repetitive tasks and improve workflows. 3. BHoM: A data structure and toolset for building and architecture that can be used to create custom workflows and automate tasks. 4. C#: A general-purpose programming language that is widely used in the civil engineering industry. It is used to develop software applications and tools for civil engineering projects. • These languages have been designed with civil engineering workflows in mind and offer a lower bar to entry for civil and structural engineers. They are also powerful enough to manage most of your workflows, including your Revit workflows. If you are new to coding, Grasshopper is a great first language to learn as it has an easy-to-adopt and debug interface 1.
- 39. Never Ever Ending Life History of Programming Languages • 18 New Programming Languages to Learn in 2024 | Built In