Showing posts with label machines. Show all posts
Showing posts with label machines. Show all posts

30 July 2019

💻IT: Network (Definitions)

"Mathematically defined structure of a computing system where the operations are performed at specific locations (nodes) and the flow of information is represented by directed arcs." (Guido Deboeck & Teuvo Kohonen (Eds), "Visual Explorations in Finance with Self-Organizing Maps 2nd Ed.", 2000)

"A system of interconnected computing resources (computers, servers, printers, and so on)." (Sharon Allen & Evan Terry, "Beginning Relational Data Modeling 2nd Ed.", 2005)

"A system of connected computers. A local area network (LAN) is contained within a single company, in a single office. A wide area network (WAN) is generally distributed across a geographical area — even globally. The Internet is a very loosely connected network, meaning that it is usable by anyone and everyone." (Gavin Powell, "Beginning Database Design", 2006)

"A system of interconnected devices that provides a means for data to be transmitted from point to point." (Janice M Roehl-Anderson, "IT Best Practices for Financial Managers", 2010)

"1.Visually, a graph of nodes and connections where more than one entry point for each node is allowed. 2.In architecture, a topological arrangement of hardware and connections to allow communication between nodes and access to shared data and software." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"The connection of computer systems (nodes) by communications channels and appropriate software. |" (Marcia Kaufman et al, "Big Data For Dummies", 2013)

"The means by which electronic communications occurs between two or more nodes" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"Two or more computers connected to share data and resources." (Faithe Wempen, "Computing Fundamentals: Introduction to Computers", 2015)

"People working towards a common purpose or with common interests where there is no requirement for members of the network to have a work relationship with others, and there is no requirement for mutuality as there is with a team." (Catherine Burke et al, "Systems Leadership, 2nd Ed,", 2018)

15 July 2019

💻IT: Virtual Machine [VM] (Definitions)

"An imaginary computer that runs compiled Java programs. Your computer or Web browser must emulate a virtual machine to run a Java applet or application." (Greg Perry, "Sams Teach Yourself Beginning Programming in 24 Hours" 2nd Ed., 2001)

"The combination of hardware and software (OS, DBMS, etc.) that provides the underlying environment that supports the operation of a software product." (Richard D Stutzke, "Estimating Software-Intensive Systems: Projects, Products, and Processes", 2005)

"Everything under the control of a guest operating system that is managed by a Hypervisor." (David G Hill, "Data Protection: Governance, Risk Management, and Compliance", 2009)

"System that offers the expected functionality associated with a device, but is actually implemented on top of a lower level system. The typical example is the Java Virtual Machine, which specifies a mode of operation for a virtual processor that is subsequently emulated on a different processor architecture." (Max Domeika, "Software Development for Embedded Multi-core Systems", 2011)

"software that allows you to take a single physical device (e.g., one PC) and run multiple instances of operating systems on it." (Bill Holtsnider & Brian D Jaffe, "IT Manager's Handbook" 3rd Ed., 2012)

"A computer operating system and associated storage and input/output resources that are completely provided by software. This is done by a hypervisor that allocates hardware resources by translating between the actual hardware and the software that attempts to access it. See also hypervisor." (Mark Rhodes-Ousley, "Information Security: The Complete Reference" 2nd Ed., 2013)

"Software programs that look and run like a physical computer." (Weiss, "Auditing IT Infrastructures for Compliance" 2nd Ed, 2015)

"The illusion created by a timesharing system that each user has a dedicated machine" (Nell Dale & John Lewis, "Computer Science Illuminated" 6th Ed., 2015)

"This term is ambiguous. (a) It sometimes refers to the combination of the system hardware resources and the low-level operating system (called a hypervisor) that is responsible for creating the illusion to operating systems running on it that they have exclusive use of the system. (b) A virtual machine can also refer to a guest operating system running on top of a hypervisor." (O Sami Saydjari, "Engineering Trustworthy Systems: Get Cybersecurity Design Right the First Time", 2018)

"A virtual machine is a software representation of a computer." (Alex Thomas, "Natural Language Processing with Spark NLP", 2020)

"An IBM mainframe operating system that can host other IBM operating systems such as MVS or VSE, so that an application running under an operating system seems to have its own complete system of software and hardware resources." (Microfocus

"The software implementation of a physical computer that runs an operating system. Multiple virtual machines can run simultaneously on the same hardware. In Azure, virtual machines are available in a variety of sizes." (Microsoft, "Azure Glossary")

18 May 2018

🔬Data Science: Boltzmann Machine (Definitions)

[Boltzmann machine (with learning):] "A net that adjusts its weights so that the equilibrium configuration of the net will solve a given problem, such as an encoder problem" (David H Ackley et al, "A learning algorithm for boltzmann machines", Cognitive Science Vol. 9 (1), 1985)

[Boltzmann machine (without learning):] "A class of neural networks used for solving constrained optimization problems. In a typical Boltzmann machine, the weights are fixed to represent the constraints of the problem and the function to be optimized. The net seeks the solution by changing the activations (either 1 or 0) of the units based on a probability distribution and the effect that the change would have on the energy function or consensus function for the net." (David H Ackley et al, "A learning algorithm for boltzmann machines", Cognitive Science Vol. 9 (1), 1985)

"neural-network model otherwise similar to a Hopfield network but having symmetric interconnects and stochastic processing elements. The input-output relation is optimized by adjusting the bistable values of its internal state variables one at a time, relating to a thermodynamically inspired rule, to reach a global optimum." (Teuvo Kohonen, "Self-Organizing Maps 3rd" Ed., 2001)

"A neural network model consisting of interacting binary units in which the probability of a unit being in the active state depends on its integrated synaptic inputs." (Terrence J Sejnowski, "The Deep Learning Revolution", 2018)

"An unsupervised network that maximizes the product of probabilities assigned to the elements of the training set." (Mário P Véstias, "Deep Learning on Edge: Challenges and Trends", 2020)

"Restricted Boltzmann machine (RBM) is an undirected graphical model that falls under deep learning algorithms. It plays an important role in dimensionality reduction, classification and regression. RBM is the basic block of Deep-Belief Networks. It is a shallow, two-layer neural networks. The first layer of the RBM is called the visible or input layer while the second is the hidden layer. In RBM the interconnections between visible units and hidden units are established using symmetric weights." (S Abirami & P Chitra, "The Digital Twin Paradigm for Smarter Systems and Environments: The Industry Use Cases", Advances in Computers, 2020)

"A deep Boltzmann machine (DBM) is a type of binary pairwise Markov random field (undirected probabilistic graphical model) with multiple layers of hidden random variables." (Udit Singhania & B. K. Tripathy, "Text-Based Image Retrieval Using Deep Learning",  2021) 

"A Boltzmann machine is a neural network of symmetrically connected nodes that make their own decisions whether to activate. Boltzmann machines use a straightforward stochastic learning algorithm to discover “interesting” features that represent complex patterns in the database." (DeepAI) [source]

"Boltzmann Machines is a type of neural network model that was inspired by the physical process of thermodynamics and statistical mechanics. [...] Full Boltzmann machines are impractical to train, which is one of the reasons why a limited form, called the restricted Boltzmann machine, is used." (Accenture)

"RBMs [Restricted Boltzmann Machines] are a type of probabilistic graphical model that can be interpreted as a stochastic artificial neural network. RBNs learn a representation of the data in an unsupervised manner. An RBN consists of visible and hidden layer, and connections between binary neurons in each of these layers. RBNs can be efficiently trained using Contrastive Divergence, an approximation of gradient descent." (Wild ML)

04 February 2018

🔬Data Science: Artificial Intelligence [AI] (Definitions)

"A computer would deserve to be called intelligent if it could deceive a human into believing that it was human." (Alan Turing, "Computing Machinery and Intelligence", 1950)

"Artificial intelligence is the science of making machines do things that would require intelligence if done by men." (Marvin Minsky, 1968)

"Artificial intelligence comprises methods, tools, and systems for solving problems that normally require the intelligence of humans. The term intelligence is always defined as the ability to learn effectively, to react adaptively, to make proper decisions, to communicate in language or images in a sophisticated way, and to understand." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996) 

"AI views the mind as a type of logical symbol processor that works with strings of text or symbols much as a computer works with strings of Os and Is. In practice, AI means expert systems or decision support systems." (Guido Deboeck & Teuvo Kohonen (Eds), "Visual Explorations in Finance with Self-Organizing Maps" 2nd Ed., 2000)

"Software that performs a function previously ascribed only to human beings, such as natural language processing." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"The branch of computer science that is concerned with making computers behave and 'think' like humans." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"A field of computer science focused on the development of intelligent-acting agents. Often guided by the theory of how humans solve problems. Has a reputation for overpromising. Wryly definable as all computational problems not yet solved." (Gary Miner et al, "Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications", 2012)

"Artificial intelligence is the mimicking of human thought and cognitive processes to solve complex problems automatically. AI uses techniques for writing computer code to represent and manipulate knowledge." (Radian Belu, "Artificial Intelligence Techniques for Solar Energy and Photovoltaic Applications", 2013)

"It is the investigation exploring whether intelligence can be replicated in machines, to perform tasks that humans can successfully carry out." (Hamid R Arabnia et al, "Application of Big Data for National Security", 2015)

"The study of computer systems that model and apply the intelligence of the human mind" (Nell Dale & John Lewis, "Computer Science Illuminated" 6th Ed., 2015)

"Machines that are designed to evaluate and respond to situations in an appropriate manner. Most artificial intelligence machines are computer based and many of them have achieved remarkable levels of performance in specific areas." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

"A discipline with the goal to develop technology that solves complex problems with skill and creativity that rivals that of the human brain." (O Sami Saydjari, "Engineering Trustworthy Systems: Get Cybersecurity Design Right the First Time", 2018)

"A machine’s ability to make decisions and perform tasks that simulate human intelligence and behavior." (Kirti R Bhatele et al, "The Role of Artificial Intelligence in Cyber Security", 2019)

"An attempt to recreate a living intellect, especially human intelligence, in a computer environment." (Tolga Ensari et al, "Overview of Machine Learning Approaches for Wireless Communication", 2019)

"The theory governing the development of computer systems that are able to perform tasks which normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages." (Nil Goksel & Aras Bozkurt, "Artificial Intelligence in Education: Current Insights and Future Perspectives", 2019)

"Algorithms which make machines learn from experience, adjust to new inputs and perform human-like tasks." (Lejla Banjanović-Mehmedović & Fahrudin Mehmedović, "Intelligent Manufacturing Systems Driven by Artificial Intelligence in Industry 4.0", 2020)

"It is the method of mimicking the human intelligence by the machines." (Shouvik Chakraborty & Kalyani Mali, "An Overview of Biomedical Image Analysis From the Deep Learning Perspective", 2020)

"AI is a simulation of human intelligence through the progress of intelligent machines that think and work like humans carrying out such human activities as speech recognition, problem-solving, learning, and planning." (Hari K Kondaveeti et al, "Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture", 2021)

"Artificial intelligence (AI) refers to the ability of machines to have cognitive capabilities similar to humans using advanced algorithms and quality data." (Vijayaraghavan Varadharajan & Akanksha Rajendra Singh, "Building Intelligent Cities: Concepts, Principles, and Technologies", 2021)

"Domain of science that deals with the development of computer systems to perform actions like speech-recognition, decision-making, understanding human’s natural language, etc., like humans." (Shatakshi Singhet al, "A Survey on Intelligence Tools for Data Analytics", 2021)

"It is a set of software and hardware systems with many capabilities such as behaving human-like or numerical logic, motion, speech, and sound perception. In other words, AI makes machines think and percept like humans." (Mehmet A Cifci, "Optimizing WSNs for CPS Using Machine Learning Techniques", 2021)

"Machines that work and react like humans using computer programs known as algorithms Algorithms must remain current for AI to work properly, so they rely on machine learning to update them with changes in the worldwide economy and society." (Sue Milton, "Data Privacy vs. Data Security", Global Business Leadership Development for the Fourth Industrial Revolution, 2021)

"Science of simulating intelligence in machines and program them to mimic human actions." (Revathi Rajendran et al, "Convergence of AI, ML, and DL for Enabling Smart Intelligence: Artificial Intelligence, Machine Learning, Deep Learning, Internet of Things", 2021)

"The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages." (Jan Bosch et al, "Engineering AI Systems: A Research Agenda", Artificial Intelligence Paradigms for Smart Cyber-Physical Systems, 2021)

"AI is any set of concepts, applications or technologies that allow a computer to perform tasks that mimic human behavior." (RapidMiner) [source]

"Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing (NLP), speech recognition and machine vision." (Techtarget) [source]

"A discipline involving research and development of machines that are aware of their surroundings. Most work in A.I. centers on using machine awareness to solve problems or accomplish some task." (KDnuggets)

"An area of computer science which refers to the creation of intelligent machines that can react to scenarios and make decisions as a human would." (Board International)

"A set of sciences, theories and techniques whose purpose is to reproduce by a machine the cognitive abilities of a human being." (Council of Europe) 

"The theory and capabilities that strive to mimic human intelligence through experience and learning." (Forrester)

"Artificial Intelligence (AI) is the broad term used to describe the set of technologies that enable machines to sense, comprehend, act and learn." (Accenture)

"Artificial intelligence (AI) applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions." (Gartner)

02 January 2018

🔬Data Science: Data (Definitions)

"Facts and figures used in computer programs." (Greg Perry, "Sams Teach Yourself Beginning Programming in 24 Hours" 2nd Ed., 2001)

"A representation of facts, concepts, or instructions suitable to permit communication, interpretation, or processing by humans or by automatic means. (2) Used as a synonym for documentation in U.S. government procurement regulations." (Richard D Stutzke, "Estimating Software-Intensive Systems: Projects, Products, and Processes", 2005)

"A recording of facts, concepts, or instructions on a storage medium for communication, retrieval, and processing by automatic means and presentation as information that is understandable by human beings." (William H Inmon, "Building the Data Warehouse", 2005)

"An atomic element of information. Represented as bits within mass storage devices, memory, and pprocessors." (Tom Petrocelli, "Data Protection and Information Lifecycle Management", 2005)

"Information documented by a language system representing facts, text, graphics, bitmapped images, sound, and analog or digital live-video segments. Data is the raw material of a system supplied by data producers and is used by information consumers to create information." (Sharon Allen & Evan Terry, "Beginning Relational Data Modeling" 2nd Ed., 2005)

"A term applied to organized information." (Gavin Powell, "Beginning Database Design", 2006)

"Numeric information or facts collected through surveys or polls, measurements or observations that need to be effectively organized for decision making." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"Raw, unrelated numbers or entries, e.g., in a database; raw forms of transactional representations." (Martin J Eppler, "Managing Information Quality" 2nd Ed., 2006)

"Data is a representation of facts, concepts or instructions in a formalized manner suitable for communication, interpretation or processing by humans or automatic means." (S. Sumathi & S. Esakkirajan, "Fundamentals of Relational Database Management Systems", 2007)

"Numeric information or facts collected through surveys or polls, measurements or observations that need to be effectively organized for decision making." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2007)

"Hub A common approach for a technical implementation of a service-oriented MDM solution. Data Hubs store and manage some data attributes and the metadata containing the location of data attributes in external systems in order to create a single physical or federated trusted source of information about customers, products, and so on." (Alex Berson & Lawrence Dubov, "Master Data Management and Data Governance", 2010)

"Raw facts, that is, facts that have not yet been processed to reveal their meaning to the end user." (Carlos Coronel et al, "Database Systems: Design, Implementation, and Management" 9th Ed., 2011)

"Facts represented as text, numbers, graphics, images, sound, or video (with no additional defining context); the raw material used to create information." (Craig S Mullins, "Database Administration: The Complete Guide to DBA Practices and Procedures 2nd Ed", 2012)

"Data are abstract representations of selected characteristics of real-world objects, events, and concepts, expressed and understood through explicitly definable conventions related to their meaning, collection, and storage. We also use the term data to refer to pieces of information, electronically captured, stored (usually in databases), and capable of being shared and used for a range of organizational purposes."(Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement ", 2012)

"Data are abstract representations of selected characteristics of real-world objects, events, and concepts, expressed and understood through explicitly definable conventions related to their meaning, collection, and storage. We also use the term data to refer to pieces of information, electronically captured, stored (usually in databases), and capable of being shared and used for a range of organizational purposes." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement", 2013)

"A collection of values assigned to base measures, derived measures and/or indicators." (David Sutton, "Information Risk Management: A practitioner’s guide", 2014)

"Raw facts, that is, facts that have not yet been processed to reveal their meaning to the end user." (Carlos Coronel & Steven Morris, "Database Systems: Design, Implementation, & Management"  11th Ed., 2014)

"A formalized (meaning suitable for further processing, interpretation and communication) representation of business objects or transactions." (Boris Otto & Hubert Österle, "Corporate Data Quality", 2015)

"Data is a collection of one or more pieces if information." (Robert J Glushko, "The Discipline of Organizing: Professional Edition, 4th Ed", 2016)

"Facts about events, objects, and associations. Example: data about a sale would include date, amount, and method of payment." (Gregory Lampshire, "The Data and Analytics Playbook", 2016)

"Discrete, unorganized, unprocessed measurements or raw observations." (Project Management Institute, "A Guide to the Project Management Body of Knowledge (PMBOK® Guide )", 2017)

"Any values from an application that can be transformed into facts and eventually information.." (Piethein Strengholt, "Data Management at Scale", 2020)

"A set of collected facts. There are two basic kinds of numerical data: measured or variable data … and counted or attribute data." (ASQ)
"A representation of information as stored or transmitted." (NISTIR 4734)

"A representation of information, including digital and non-digital formats." (NIST Privacy Framework Version 1.0)

"A variable-length string of zero or more (eight-bit) bytes." (NIST SP 800-56B Rev. 2)

"Any piece of information suitable for use in a computer." (NISTIR 7693)

"(1) Anything observed in the documentation or operation of software that deviates from expectations based on previously verified software products or reference documents.(2) A representation of facts, concepts, or instructions in a manner suitable for communication, interpretation, or processing by humans or by automatic means." (IEEE 610.5-1990)

"Data may be thought of as unprocessed atomic statements of fact. It very often refers to systematic collections of numerical information in tables of numbers such as spreadsheets or databases. When data is structured and presented so as to be useful and relevant for a particular purpose, it becomes information available for human apprehension. See also knowledge." (Open Data Handbook)

"Distinct pieces of digital information that have been formatted in a specific way." (NIST SP 800-86)

"Information in a specific representation, usually as a sequence of symbols that have meaning." (CNSSI 4009-2015 IETF RFC 4949 Ver 2)

"Pieces of information from which “understandable information” is derived." (NIST SP 800-88 Rev. 1)

“re-interpretable representation of information in a formalized manner suitable for communication, interpretation, or processing” (ISO 11179)

24 December 2014

🕸Systems Engineering: Systems (Just the Quotes)

"Systems in many respects resemble machines. A machine is a little system, created to perform, as well as to connect together, in reality, those different movements and effects which the artist has occasion for.  A system is an imaginary machine invented to connect together in the fancy those different movements and effects which are already in reality performed." (Adam Smith, "The Wealth of Nations", 1776)

"A good method of discovery is to imagine certain members of a system removed and then see how what is left would behave: for example, where would we be if iron were absent from the world: this is an old example." (Georg C Lichtenberg, Notebook J, 1789-1793)

"A system is a whole which is composed of various parts. But it is not the same thing as an aggregate or heap. In an aggregate or heap, no essential relation exists between the units of which it is composed. In a heap of grain, or pile of stones, one may take away part without the other part being at all affected thereby. But in a system, each part has a fixed and necessary relation to the whole and to all the other parts. For this reason we may say that a building, or a peace of mechanisme, is a system. Each stone in the building, each wheel in the watch, plays a part, and is essential to the whole." (James E Creighton, "An Introductory Logic"‎, 1909)

"The concept of an independent system is a pure creation of the imagination. For no material system is or can ever be perfectly isolated from the rest of the world. Nevertheless it completes the mathematician’s ‘blank form of a universe’ without which his investigations are impossible. It enables him to introduce into his geometrical space, not only masses and configurations, but also physical structure and chemical composition." (Lawrence J Henderson, "The Order of Nature: An Essay", 1917)

"The complexity of a system is no guarantee of its accuracy." (John P Jordan, "Cost accounting; principles and practice", 1920)

"Given a situation, a system with a Leerstelle [a gap], whether a given completion (Lueckenfuellung) does justice to the structure, is the 'right' one, is often determined by the structure of the system, the situation. There are requirements, structurally determined; there are possible in pure cases unambiguous decisions as to which completion does justice to the situation, which does not, which violates the requirements and the situation." (Max Wertheimer, "Some Problems in the Theory of Ethics", Social Research Vol. 2 (3), 1935)

"A system is difficult to define, but it is easy to recognize some of its characteristics. A system possesses boundaries which segregate it from the rest of its field: it is cohesive in the sense that it resists encroachment from without […]" (Marvin G Cline, "Fundamentals of a theory of the self: some exploratory speculations‎", 1950)

"Now a system is said to be at equilibrium when it has no further tendency to change its properties." (Walter J Moore, "Physical chemistry", 1950)

"Every part of the system is so related to every other part that any change in one aspect results in dynamic changes in all other parts of the total system." (Arthur D Hall & Robert E Fagen, "Definition of System", General Systems Vol. 1, 1956)

"Any pattern of activity in a network, regarded as consistent by some observer, is a system, Certain groups of observers, who share a common body of knowledge, and subscribe to a particular discipline, like 'physics' or 'biology' (in terms of which they pose hypotheses about the network), will pick out substantially the same systems. On the other hand, observers belonging to different groups will not agree about the activity which is a system." (Gordon Pask, "The Natural History of Networks", 1960)

"Clearly, if the state of the system is coupled to parameters of an environment and the state of the environment is made to modify parameters of the system, a learning process will occur. Such an arrangement will be called a Finite Learning Machine, since it has a definite capacity. It is, of course, an active learning mechanism which trades with its surroundings. Indeed it is the limit case of a self-organizing system which will appear in the network if the currency supply is generalized." (Gordon Pask, "The Natural History of Networks", 1960)

"Every isolated determinate dynamic system, obeying unchanging laws, will ultimately develop some sort of organisms that are adapted to their environments." (W Ross Ashby, "Principles of the self-organizing system", 1962)

"Roughly, by a complex system I mean one made up of a large number of parts that interact in a nonsimple way. In such systems, the whole is more than the sum of the parts, not in an ultimate, metaphysical sense, but in the important pragmatic sense that, given the properties of the parts and the laws of their interaction, it is not a trivial matter to infer the properties of the whole." (Herbert Simon, "The Architecture of Complexity", Proceedings of the American Philosophical Society Vol. 106 (6), 1962)

"To say a system is 'self-organizing' leaves open two quite different meanings. There is a first meaning that is simple and unobjectionable. This refers to the system that starts with its parts separate (so that the behavior of each is independent of the others' states) and whose parts then act so that they change towards forming connections of some type. Such a system is 'self-organizing' in the sense that it changes from 'parts separated' to 'parts joined'. […] In general such systems can be more simply characterized as 'self-connecting', for the change from independence between the parts to conditionality can always be seen as some form of 'connection', even if it is as purely functional […]  'Organizing' […] may also mean 'changing from a bad organization to a good one' […] The system would be 'self-organizing' if a change were automatically made to the feedback, changing it from positive to negative; then the whole would have changed from a bad organization to a good." (W Ross Ashby, "Principles of the self-organizing system", 1962)

"Synergy is the only word in our language that means behavior of whole systems unpredicted by the separately observed behaviors of any of the system's separate parts or any subassembly of the system's parts." (R Buckminster Fuller, "Operating Manual for Spaceship Earth", 1963)

"A system has order, flowing from point to point. If something dams that flow, order collapses. The untrained might miss that collapse until it was too late. That's why the highest function of ecology is the understanding of consequences." (Frank Herbert, "Dune", 1965)

"System theory is basically concerned with problems of relationships, of structure, and of interdependence rather than with the constant attributes of objects. In general approach it resembles field theory except that its dynamics deal with temporal as well as spatial patterns. Older formulations of system constructs dealt with the closed systems of the physical sciences, in which relatively self-contained structures could be treated successfully as if they were independent of external forces. But living systems, whether biological organisms or social organizations, are acutely dependent on their external environment and so must be conceived of as open systems." (Daniel Katz, "The Social Psychology of Organizations", 1966)

"To find out what happens to a system when you interfere with it you have to interfere with it (not just passively observe it)." (George E P Box, "Use and Abuse of Regression", 1966)

"That a system is open means, not simply that it engages in interchanges with the environment, but that this interchange is an essential factor underlying the system's viability, its reproductive ability or continuity, and its ability to change. [...] Openness is an essential factor underlying a system's viability, continuity, and its ability to change."  (Walter F Buckley, "Sociology and modern systems theory", 1967)

"You cannot sum up the behavior of the whole from the isolated parts, and you have to take into account the relations between the various subordinate systems which are super-ordinated to them in order to understand the behavior of the parts." (Ludwig von Bertalanffy, "General System Theory", 1968)

"[…] as a model of a complex system becomes more complete, it becomes less understandable. Alternatively, as a model grows more realistic, it also becomes just as difficult to understand as the real world processes it represents." (Jay M Dutton & William H Starbuck," Computer simulation models of human behavior: A history of an intellectual technology", IEEE Transactions on Systems, 1971)

"A system in one perspective is a subsystem in another. But the systems view always treats systems as integrated wholes of their subsidiary components and never as the mechanistic aggregate of parts in isolable causal relations." (Ervin László, "Introduction to Systems Philosophy", 1972)

"Technology can relieve the symptoms of a problem without affecting the underlying causes. Faith in technology as the ultimate solution to all problems can thus divert our attention from the most fundamental problem - the problem of growth in a finite system." (Donella A Meadows, "The Limits to Growth", 1972)

"[The] system may evolve through a whole succession of transitions leading to a hierarchy of more and more complex and organized states. Such transitions can arise in nonlinear systems that are maintained far from equilibrium: that is, beyond a certain critical threshold the steady-state regime become unstable and the system evolves into a new configuration." (Ilya Prigogine, Gregoire Micolis & Agnes Babloyantz, "Thermodynamics of Evolution", Physics Today 25 (11), 1972) 

"The system of nature, of which man is a part, tends to be self-balancing, self-adjusting, self-cleansing. Not so with technology." (Ernst F Schumacher, "Small is Beautiful", 1973)

"When a system is considered in two different states, the difference in volume or in any other property, between the two states, depends solely upon those states themselves and not upon the manner in which the system may pass from one state to the other." (Rudolf Arnheim, "Entropy and Art: An Essay on Disorder and Order", 1974) 

"A system may be specified in either of two ways. In the first, which we shall call a state description, sets of abstract inputs, outputs and states are given, together with the action of the inputs on the states and the assignments of outputs to states. In the second, which we shall call a coordinate description, certain input, output and state variables are given, together with a system of dynamical equations describing the relations among the variables as functions of time. Modern mathematical system theory is formulated in terms of state descriptions, whereas the classical formulation is typically a coordinate description, for example a system of differential equations." (E S Bainbridge, "The Fundamental Duality of System Theory", 1975)

"Synergy means behavior of whole systems unpredicted by the behavior of their parts taken separately." (R Buckminster Fuller, "Synergetics: Explorations in the Geometry of Thinking", 1975)

"If all of the elements in a large system are loosely coupled to one another, then any one element can adjust to and modify a local a local unique contingency without affecting the whole system. These local adaptations can be swift, relatively economical, and substantial." (Karl E Weick, "Educational organizations as loosely coupled systems", 1976)

"In a loosely coupled system there is more room available for self-determination by the actors. If it is argued that a sense of efficacy is crucial for human beings. when a sense of efficacy might be greater in a loosely coupled system with autonomous units than it would be in a tightly coupled system where discretion is limited." (Karl E Weick, "Educational organizations as loosely coupled systems", 1976)

"For any system the environment is always more complex than the system itself. No system can maintain itself by means of a point-for-point correlation with its environment, i.e., can summon enough 'requisite variety' to match its environment. So each one has to reduce environmental complexity - primarily by restricting the environment itself and perceiving it in a categorically preformed way. On the other hand, the difference of system and environment is a prerequisite for the reduction of complexity because reduction can be performed only within the system, both for the system itself and its environment." (Thomas Luckmann & Niklas Luhmann, "The Differentiation of Society", 1977)

"All nature is a continuum. The endless complexity of life is organized into patterns which repeat themselves at each level of system." (James G Miller, "Living Systems", 1978)

"An autopoietic system is organized (defined as a unity) as a network of processes of production (transformation and destruction) of components that produces the components that: (a) through their interactions and transformations continuously regenerate and realize the network of processes (relations) that produce them and, (b) constitute it (the machine) as a concrete unity in the space in which they exist by specifying the topological domain of its realization as such a network." (Francisco Varela, "Principles of Biological Autonomy", 1979)

"A system is an internally organised whole where elements are so intimately connected that they operate as one in relation to external conditions and other systems. An element may be defined as the minimal unit performing a definite function in the whole. Systems may be either simple or complex. A complex system is one whose elements may also be regarded as systems or subsystems." (Alexander Spirkin, "Dialectical Materialism", 1983)

"But structure is not enough to make a system. A system consists of something more than structure: it is a structure with certain properties. When a structure is understood from the standpoint of its properties, it is understood as a system." (Alexander Spirkin, "Dialectical Materialism", 1983)

"Any system that insulates itself from diversity in the environment tends to atrophy and lose its complexity and distinctive nature." (Gareth Morgan, "Images of Organization", 1986)

"Organization denotes those relations that must exist among the components of a system for it to be a member of a specific class. Structure denotes the components and relations that actually constitute a particular unity and make its organization real." (Humberto Maturana, "The Tree of Knowledge", 1987)

"The dynamics of any system can be explained by showing the relations between its parts and the regularities of their interactions so as to reveal its organization. For us to fully understand it, however, we need not only to see it as a unity operating in its internal dynamics, but also to see it in its circumstances, i.e., in the context to which its operation connects it. This understanding requires that we adopt a certain distance for observation, a perspective that in the case of historical systems implies a reference to their origin. This can be easy, for instance, in the case of man-made machines, for we have access to every detail of their manufacture. The situation is not that easy, however, as regards living beings: their genesis and their history are never directly visible and can be reconstructed only by fragments."  (Humberto Maturana, "The Tree of Knowledge", 1987)

"A system of variables is 'interrelated' if an action that affects or meant to affect one part of the system will also affect other parts of it. Interrelatedness guarantees that an action aimed at one variable will have side effects and long-term repercussions. A large number of variables will make it easy to overlook them." (Dietrich Dorner, "The Logic of Failure: Recognizing and Avoiding Error in Complex Situations", 1989)

"Systems, acting dynamically, produce (and incidentally, reproduce) their own boundaries, as structures which are complementary (necessarily so) to their motion and dynamics. They are liable, for all that, to instabilities chaos, as commonly interpreted of chaotic form, where nowadays, is remote from the random. Chaos is a peculiar situation in which the trajectories of a system, taken in the traditional sense, fail to converge as they approach their limit cycles or 'attractors' or 'equilibria'. Instead, they diverge, due to an increase, of indefinite magnitude, in amplification or gain." (Gordon Pask, "Different Kinds of Cybernetics", 1992)

 "What is a system? A system is a network of interdependent components that work together to try to accomplish the aim of the system. A system must have an aim. Without an aim, there is no system. The aim of the system must be clear to everyone in the system. The aim must include plans for the future. The aim is a value judgment.” (William E Deming, "The New Economics for Industry, Government, Education”, 1993)

"The impossibility of constructing a complete, accurate quantitative description of a complex system forces observers to pick which aspects of the system they most wish to understand." (Thomas Levenson, "Measure for Measure: A musical history of science", 1994)

"Complex adaptive systems have the property that if you run them - by just letting the mathematical variable of 'time' go forward - they'll naturally progress from chaotic, disorganized, undifferentiated, independent states to organized, highly differentiated, and highly interdependent states. Organized structures emerge spontaneously. [...]A weak system gives rise only to simpler forms of self-organization; a strong one gives rise to more complex forms, like life. (J Doyne Farmer, "The Third Culture: Beyond the Scientific Revolution", 1995)

"Self-organization refers to the spontaneous formation of patterns and pattern change in open, nonequilibrium systems. […] Self-organization provides a paradigm for behavior and cognition, as well as the structure and function of the nervous system. In contrast to a computer, which requires particular programs to produce particular results, the tendency for self-organization is intrinsic to natural systems under certain conditions." (J A Scott Kelso, "Dynamic Patterns : The Self-organization of Brain and Behavior", 1995)

"All systems evolve, although the rates of evolution may vary over time both between and within systems. The rate of evolution is a function of both the inherent stability of the system and changing environmental circumstances. But no system can be stabilized forever. For the universe as a whole, an isolated system, time’s arrow points toward greater and greater breakdown, leading to complete molecular chaos, maximum entropy, and heat death. For open systems, including the living systems that are of major interest to us and that interchange matter and energy with their external environments, time’s arrow points to evolution toward greater and greater complexity. Thus, the universe consists of islands of increasing order in a sea of decreasing order. Open systems evolve and maintain structure by exporting entropy to their external environments." (L Douglas Kiel, "Chaos Theory in the Social Sciences: Foundations and Applications", 1996)

"By irreducibly complex I mean a single system composed of several well-matched, interacting parts that contribute to the basic function, wherein the removal of any one of the parts causes the system to effectively cease functioning. An irreducibly complex system cannot be produced directly (that is, by continuously improving the initial function, which continues to work by the same mechanism) by slight, successive modification of a precursor, system, because any precursors to an irreducibly complex system that is missing a part is by definition nonfunctional." (Michael Behe, "Darwin’s Black Box", 1996)

"Understanding ecological interdependence means understanding relationships. It requires the shifts of perception that are characteristic of systems thinking - from the parts to the whole, from objects to relationships, from contents to patterns. […] Nourishing the community means nourishing those relationships." (Fritjof Capra, "The Web of Life: A New Scientific Understanding of Living Systems", 1996)

"The notion of system we are interested in may be described generally as a complex of elements or components directly or indirectly related in a network of interrelationships of various kinds, such that it constitutes a dynamic whole with emergent properties." (Walter F. Buckley, "Society: A Complex Adaptive System - Essays in Social Theory", 1998)

"Formulation of a mathematical model is the first step in the process of analyzing the behaviour of any real system. However, to produce a useful model, one must first adopt a set of simplifying assumptions which have to be relevant in relation to the physical features of the system to be modelled and to the specific information one is interested in. Thus, the aim of modelling is to produce an idealized description of reality, which is both expressible in a tractable mathematical form and sufficiently close to reality as far as the physical mechanisms of interest are concerned." (Francois Axisa, "Discrete Systems" Vol. I, 2001)

"Nature normally hates power laws. In ordinary systems all quantities follow bell curves, and correlations decay rapidly, obeying exponential laws. But all that changes if the system is forced to undergo a phase transition. Then power laws emerge-nature's unmistakable sign that chaos is departing in favor of order. The theory of phase transitions told us loud and clear that the road from disorder to order is maintained by the powerful forces of self-organization and is paved by power laws. It told us that power laws are not just another way of characterizing a system's behavior. They are the patent signatures of self-organization in complex systems." (Albert-László Barabási, "Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life", 2002)

"[…] networks are the prerequisite for describing any complex system, indicating that complexity theory must inevitably stand on the shoulders of network theory. It is tempting to step in the footsteps of some of my predecessors and predict whether and when we will tame complexity. If nothing else, such a prediction could serve as a benchmark to be disproven. Looking back at the speed with which we disentangled the networks around us after the discovery of scale-free networks, one thing is sure: Once we stumble across the right vision of complexity, it will take little to bring it to fruition. When that will happen is one of the mysteries that keeps many of us going." (Albert-László Barabási, "Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life", 2002)

"One of the key insights of the systems approach has been the realization that the network is a pattern that is common to all life. Wherever we see life, we see networks." (Fritjof Capra, "The Hidden Connections: A Science for Sustainable Living", 2002)

"Technology can relieve the symptoms of a problem without affecting the underlying causes. Faith in technology as the ultimate solution to all problems can thus divert our attention from the most fundamental problem - the problem of growth in a finite system - and prevent us from taking effective action to solve it." (Donella H Meadows & Dennis L Meadows, "The Limits to Growth: The 30 Year Update", 2004)

"The progress of science requires the growth of understanding in both directions, downward from the whole to the parts and upward from the parts to the whole." (Freeman Dyson, "The Scientist As Rebel", 2006)

"Humans have difficulty perceiving variables accurately […]. However, in general, they tend to have inaccurate perceptions of system states, including past, current, and future states. This is due, in part, to limited ‘mental models’ of the phenomena of interest in terms of both how things work and how to influence things. Consequently, people have difficulty determining the full implications of what is known, as well as considering future contingencies for potential systems states and the long-term value of addressing these contingencies." (William B. Rouse, "People and Organizations: Explorations of Human-Centered Design", 2007)

"Systemic problems trace back in the end to worldviews. But worldviews themselves are in flux and flow. Our most creative opportunity of all may be to reshape those worldviews themselves. New ideas can change everything." (Anthony Weston, "How to Re-Imagine the World", 2007)

"A model is a representation in that it (or its properties) is chosen to stand for some other entity (or its properties), known as the target system. A model is a tool in that it is used in the service of particular goals or purposes; typically these purposes involve answering some limited range of questions about the target system." (Wendy S Parker, "Confirmation and Adequacy-for-Purpose in Climate Modelling", Proceedings of the Aristotelian Society, Supplementary Volumes, Vol. 83, 2009)

"System theorists know that it's easy to couple simple-to-understand systems into a ‘super system’ that's capable of displaying behavioral modes that cannot be seen in any of its constituent parts. This is the process called ‘emergence’." (John L Casti, [interview with Austin Allen], 2012)

"When some systems are stuck in a dangerous impasse, randomness and only randomness can unlock them and set them free." (Nassim N Taleb, "Antifragile: Things That Gain from Disorder", 2012) 

"Complex systems defy intuitive solutions. Even a third-order, linear differential equation is unsolvable by inspection. Yet, important situations in management, economics, medicine, and social behavior usually lose reality if simplified to less than fifth-order nonlinear dynamic systems. Attempts to deal with nonlinear dynamic systems using ordinary processes of description and debate lead to internal inconsistencies. Underlying assumptions may have been left unclear and contradictory, and mental models are often logically incomplete. Resulting behavior is likely to be contrary to that implied by the assumptions being made about' underlying system structure and governing policies." (Jay W. Forrester, "Modeling for What Purpose?", The Systems Thinker Vol. 24 (2), 2013)

"Simplicity in a system tends to increase that system's efficiency. Because less can go wrong with fewer parts, less will. Complexity in a system tends to increase that system's inefficiency; the greater the number of variables, the greater the probability of those variables clashing, and in turn, the greater the potential for conflict and disarray. Because more can go wrong, more will. That is why centralized systems are inclined to break down quickly and become enmeshed in greater unintended consequences." (Lawrence K Samuels, "Defense of Chaos: The Chaology of Politics, Economics and Human Action", 2013)

"Although cascading failures may appear random and unpredictable, they follow reproducible laws that can be quantified and even predicted using the tools of network science. First, to avoid damaging cascades, we must understand the structure of the network on which the cascade propagates. Second, we must be able to model the dynamical processes taking place on these networks, like the flow of electricity. Finally, we need to uncover how the interplay between the network structure and dynamics affects the robustness of the whole system." (Albert-László Barabási, "Network Science", 2016)

More quotes on "Systems" at the-web-of-knowledge.blogspot.com.

19 December 2014

🕸Systems Engineering: Cybernetics (Just the Quotes)

"Cybernetics is a word invented to define a new field in science. It combines under one heading the study of what in a human context is sometimes loosely described as thinking and in engineering is known as control and communication. In other words, cybernetics attempts to find the common elements in the functioning of automatic machines and of the human nervous system, and to develop a theory which will cover the entire field of control and communication in machines and in living organisms." (Norbert Wiener, "Cybernetics", 1948) 

"The concept of teleological mechanisms however it be expressed in many terms, may be viewed as an attempt to escape from these older mechanistic formulations that now appear inadequate, and to provide new and more fruitful conceptions and more effective methodologies for studying self-regulating processes, self-orienting systems and organisms, and self-directing personalities. Thus, the terms feedback, servomechanisms, circular systems, and circular processes may be viewed as different but equivalent expressions of much the same basic conception." (Lawrence K Frank, 1948)

The 'cybernetics' of Wiener […] is the science of organization of mechanical and electrical components for stability and purposeful actions. A distinguishing feature of this new science is the total absence of considerations of energy, heat, and efficiency, which are so important in other natural sciences. In fact, the primary concern of cybernetics is on the qualitative aspects of the interrelations among the various components of a system and the synthetic behavior of the complete mechanism." (Qian Xuesen, "Engineering Cybernetics", 1954) 

"Cybernetics might, in fact, be defined as the study of systems that are open to energy but closed to information and control-systems that are 'information-tight'." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"There comes a stage, however, as the system becomes larger and larger, when the reception of all the information is impossible by reason of its sheer bulk. Either the recording channels cannot carry all the information, or the observer, presented with it all, is overwhelmed. When this occurs, what is he to do? The answer is clear: he must give up any ambition to know the whole system. His aim must be to achieve a partial knowledge that, though partial over the whole, is none the less complete within itself, and is sufficient for his ultimate practical purpose." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"[Cybernetics is] the art of ensuring the efficacy of action." (Louis Couffignal, 1958)

"Cybernetics is the science of the process of transmission, processing and storage of information." (Sergei Sobolew, Woprosy Psychology, 1958)

"Cybernetics is the general science of communication. But to refer to communication is consciously or otherwise to refer to distinguishable states of information inputs and outputs and /or to information being processed within some relatively isolated system." (Henryk Greniewski, "Cybernetics without Mathematics", 1960)

"Cybernetics offers a scientific approach to the cussedness of organisms, suggests how their behaviours can be catalysed and the mystique and rule of thumb banished." (Gordon Pask, "An Approach to Cybernetics", 1961)

"Cybernetics is concerned primarily with the construction of theories and models in science, without making a hard and fast distinction between the physical and the biological sciences. The theories and models occur both in symbols and in hardware, and by 'hardware’ we shall mean a machine or computer built in terms of physical or chemical, or indeed any handleable parts. Most usually we shall think of hardware as meaning electronic parts such as valves and relays. Cybernetics insists, also, on a further and rather special condition that distinguishes it from ordinary scientific theorizing: it demands a certain standard of effectiveness. In this respect it has acquired some of the same motive power that has driven research on modern logic, and this is especially true in the construction and application of artificial languages and the use of operational definitions. Always the search is for precision and effectiveness, and we must now discuss the question of effectiveness in some detail. It should be noted that when we talk in these terms we are giving pride of place to the theory of automata at the expense, at least to some extent, of feedback and information theory." (Frank H George, "The Brain As A Computer", 1962)

"[…] cybernetics studies the flow of information round a system, and the way in which this information is used by the system as a means of controlling itself: it does this for animate and inanimate systems indifferently. For cybernetics is an interdisciplinary science, owing as much to biology as to physics, as much to the study of the brain as to the study of computers, and owing also a great deal to the formal languages of science for providing tools with which the behaviour of all these systems can be objectively described." (A Stafford Beer, 1966)

"Cybernetics, based upon the principle of feedback or circular causal trains providing mechanisms for goal-seeking and self-controlling behavior." (Ludwig von Bertalanffy, "General System Theory", 1968)

"A more viable model, one much more faithful to the kind of system that society is more and more recognized to be, is in process of developing out of, or is in keeping with, the modern systems perspective (which we use loosely here to refer to general systems research, cybernetics, information and communication theory, and related fields). Society, or the sociocultural system, is not, then, principally an equilibrium system or a homeostatic system, but what we shall simply refer to as a complex adaptive system." (Walter F Buckley, "Society as a complex adaptive system", 1968)

"According to the science of cybernetics, which deals with the topic of control in every kind of system (mechanical, electronic, biological, human, economic, and so on), there is a natural law that governs the capacity of a control system to work. It says that the control must be capable of generating as much 'variety' as the situation to be controlled. (Anthony S Beer, "Management Science", 1968)

"Perhaps the most important single characteristic of modern organizational cybernetics is this: That in addition to concern with the deleterious impacts of rigidly-imposed notions of what constitutes the application of good 'principles of organization and management' the organization is viewed as a subsystem of a larger system(s), and as comprised itself of functionally interdependent subsystems." (Richard F Ericson, "Organizational cybernetics and human values", 1969)  

"The essence of cybernetic organizations is that they are self-controlling, self-maintaining, self-realizing. Indeed, cybernetics has been characterized as the “science of effective organization,” in just these terms. But the word “cybernetics” conjures, in the minds of an apparently great number of people, visions of computerized information networks, closed loop systems, and robotized man-surrogates, such as ‘artorgas’ and ‘cyborgs’." (Richard F Ericson, "Visions of Cybernetic Organizations", 1972)

"The main object of cybernetics is to supply adaptive, hierarchical models, involving feedback and the like, to all aspects of our environment. Often such modelling implies simulation of a system where the simulation should achieve the object of copying both the method of achievement and the end result. Synthesis, as opposed to simulation, is concerned with achieving only the end result and is less concerned (or completely unconcerned) with the method by which the end result is achieved. In the case of behaviour, psychology is concerned with simulation, while cybernetics, although also interested in simulation, is primarily concerned with synthesis." (Frank H George, "Soviet Cybernetics, the militairy and Professor Lerner", New Scientist, 1973)

"General systems theory and cybernetics supplanted the classical conceptual model of a whole made out of parts and relations between parts with a model emphasizing the difference between systems and environments. This new paradigm made it possible to relate both the structures (including forms of differentiation) and processes of systems to the environment." (Thomas Luckmann & Niklas Luhmann, "The Differentiation of Society", 1977)

"Cybernetics is a homogenous and coherent scientific complex, a science resulting from the blending of at least two sciences - psychology and technology; it is a general and integrative science, a crossroads of sciences, involving both animal and car psychology. It is not just a discipline, circumscribed in a narrow and strictly defined field, but a complex of disciplines born of psychology and centered on it, branched out as branches of a tree in its stem. It is a stepwise synthesis, a suite of multiple, often reciprocal, modeling; syntheses and modeling in which, as a priority, and as a great importance, the modeling of psychology on the technique and then the modeling of the technique on psychology. Cybernetics is an intellectual symphony, a symphony of ideas and sciences." (Stefan Odobleja, 1978)

"Cybernetics is concerned with scientific investigation of systemic processes of a highly varied nature, including such phenomena as regulation, information processing, information storage, adaptation, self-organization, self-reproduction, and strategic behavior. Within the general cybernetic approach, the following theoretical fields have developed: systems theory (system), communication theory, game theory, and decision theory." (Fritz B Simon et al, "Language of Family Therapy: A Systemic Vocabulary and Source Book", 1985)

"In cybernetics, theories tend to rest on four basic pillars: Variety, circularity, process and observation." (Klaus Krippendorff, 1986)

"Cybernetics, although not ignoring formal networks, suggests that an informal communications structure will also be present such that complex conversations at a number of levels between two or more individuals exist." (Robert L Flood, "Dealing with Complexity", 1988)

"Unlike its predecessor, the new cybernetics concerns itself with the interaction of autonomous political actors and subgroups, and the practical and reflexive consciousness of the subjects who produce and reproduce the structure of a political community. A dominant consideration is that of recursiveness, or self-reference of political action both with regards to the expression of political consciousness and with the ways in which systems build upon themselves." (Peter Harries-Jones, The Self-Organizing Policy: An Epistemological Analysis of Political Life by Laurent Dobuzinskis, Canadian Journal of Political Science 21 (2), 1988)

"At the very least (there is certainly more), cybernetics implies a new philosophy about (1) what we can know, (2) about what it means for something to exist, and (3) about how to get things done. Cybernetics implies that knowledge is to be built up through effective goal-seeking processes, and perhaps not necessarily in uncovering timeless, absolute, attributes of things, irrespective of our purposes and needs." (Jeff Dooley, "Thoughts on the Question: What is Cybernetics", 1995)

"Cybernetics is a science of purposeful behavior. It helps us explain behavior as the continuous action of someone (or thing) in the process, as we see it, of maintaining certain conditions near a goal state, or purpose." (Jeff Dooley, "Thoughts on the Question: What is Cybernetics", 1995)

"In sharp contrast (with the traditional social planning) the systems design approach seeks to understand a problem situation as a system of interconnected, interdependent, and interacting issues and to create a design as a system of interconnected, interdependent, interacting, and internally consistent solution ideas." (Béla H Bánáthy, "Designing Social Systems in a Changing World", 1996)

"Cybernetics is the science of effective organization, of control and communication in animals and machines. It is the art of steersmanship, of regulation and stability. The concern here is with function, not construction, in providing regular and reproducible behaviour in the presence of disturbances. Here the emphasis is on families of solutions, ways of arranging matters that can apply to all forms of systems, whatever the material or design employed. [...] This science concerns the effects of inputs on outputs, but in the sense that the output state is desired to be constant or predictable – we wish the system to maintain an equilibrium state. It is applicable mostly to complex systems and to coupled systems, and uses the concepts of feedback and transformations (mappings from input to output) to effect the desired invariance or stability in the result." (Chris Lucas, "Cybernetics and Stochastic Systems", 1999)

"The science of cybernetics is not about thermostats or machines; that characterization is a caricature. Cybernetics is about purposiveness, goals, information flows, decision-making control processes and feedback (properly defined) at all levels of living systems." (Peter Corning, "Synergy, Cybernetics, and the Evolution of Politics", 2005) 

"The single most important property of a cybernetic system is that it is controlled by the relationship between endogenous goals and the external environment. [...] In a complex system, overarching goals may be maintained (or attained) by means of an array of hierarchically organized subgoals that may be pursued contemporaneously, cyclically, or seriatim." (Peter Corning, "Synergy, Cybernetics, and the Evolution of Politics", 2005) 

"A great deal of the results in many areas of physics are presented in the form of conservation laws, stating that some quantities do not change during evolution of the system. However, the formulations in cybernetical physics are different. Since the results in cybernetical physics establish how the evolution of the system can be changed by control, they should be formulated as transformation laws, specifying the classes of changes in the evolution of the system attainable by control function from the given class, i.e., specifying the limits of control." (Alexander L Fradkov, "Cybernetical Physics: From Control of Chaos to Quantum Control", 2007)

"Cybernetics is the study of systems and processes that interact with themselves and produce themselves from themselves." (Louis Kauffman, 2007)

"Systematic usage of the methods of modern control theory to study physical systems is a key feature of a new research area in physics that may be called cybernetical physics. The subject of cybernetical physics is focused on studying physical systems by means of feedback interactions with the environment. Its methodology heavily relies on the design methods developed in cybernetics. However, the approach of cybernetical physics differs from the conventional use of feedback in control applications (e.g., robotics, mechatronics) aimed mainly at driving a system to a prespecified position or a given trajectory." (Alexander L Fradkov, "Cybernetical Physics: From Control of Chaos to Quantum Control", 2007)

"For me, as I later came to say, cybernetics is the art of creating equilibrium in a world of possibilities and constraints. This is not just a romantic description, it portrays the new way of thinking quite accurately. Cybernetics differs from the traditional scientific procedure, because it does not try to explain phenomena by searching for their causes, but rather by specifying the constraints that determine the direction of their development." (Ernst von Glasersfeld, "The Cybernetics of Snow Drifts 1948", 2009)

"Cybernetics is the art of creating equilibrium in a world of possibilities and constraints. This is not just a romantic description, it portrays the new way of thinking quite accurately. Cybernetics differs from the traditional scientific procedure, because it does not try to explain phenomena by searching for their causes, but rather by specifying the constraints that determine the direction of their development." (Ernst von Glasersfeld, "Partial Memories: Sketches from an Improbable Life", 2010)

"Cybernetics is the study of systems which can be mapped using loops (or more complicated looping structures) in the network defining the flow of information. Systems of automatic control will of necessity use at least one loop of information flow providing feedback." (Alan Scrivener, "A Curriculum for Cybernetics and Systems Theory", 2012)

"Cybernetics studies the concepts of control and communication in living organisms, machines and organizations including self-organization. It focuses on how a (digital, mechanical or biological) system processes information, responds to it and changes or being changed for better functioning (including control and communication)." (Dmitry A Novikov, "Cybernetics 2.0", 2016)

More quotes on "Cybernetics" at the-web-of-knowledge.blogspot.com.

24 October 2006

⛩️Maurice V Wilkes - Collected Quotes

"As soon as we started programming, we found out to our surprise that it wasn't as easy to get programs right as we had thought. Debugging had to be discovered. I can remember the exact instant when I realized that a large part of my life from then on was going to be spent in finding mistakes in my own programs." (Maurice Wilkes, 1949)

"A source of strength in the early days was that groups in various parts of the world were prepared to construct experimental computers without necessarily intending them to be the prototype for serial production. As a result, there became available a body of knowledge about what would work and what would not work." (Maurice V Wilkes, "Computers Then and Now", 1968)

"Acceptance of the idea that a processor does one thing at a time - at any rate as the programmer sees it - made programming conceptually very simple, and paved the way for the layer upon layer of sophistication that we have seen develop. [...] Revolutionary advances, if they come, must come by the exploitation of the high degree of parallelism that the use of integrated circuits will make possible. The problem is to secure a satisfactorily high factor of hardware utilization, since, without this, parallelism will not give us greater power." (Maurice V Wilkes, "Computers Then and Now", 1968)

"At the present time, choosing a programming language is equivalent to choosing a data structure, and if that data structure does not fit the data you want to manipulate then it is too bad. It would, in a sense, be more logical first to choose a data structure appropriate to the problem and then look around for, or construct with a kit of tools provided, a language suitable for manipulating that data structure." (Maurice V Wilkes, "Computers Then and Now", 1968)

"In the judgment of design engineers, the ordinary means of communicating with a computer are entirely inadequate. […] Graphical communication in some form or other is of vital importance in engineering as that subject is now conducted; we must either provide the capability in our computer systems, or take on the impossible task of training up a future race of engineers conditioned to think in a different way." (Maurice V Wilkes, "Computers Then and Now", 1968)

"Surveying the shifts of interest among computer scientists and the ever-expanding family of those who depend on computers for their work, one cannot help being struck by the power of the computer to bind together, in a genuine community of interest, people whose motivations differ widely." (Maurice V Wilkes, "Computers Then and Now", 1968)

"The artificial intelligence approach may not be altogether the right one to make to the problem of designing automatic assembly devices. Animals and machines are constructed from entirely different materials and on quite different principles. When engineers have tried to draw inspiration from a study of the way animals work they have usually been misled; the history of early attempts to construct flying machines with flapping wings illustrates this very clearly." (Maurice V Wilkes, "Computers Then and Now", 1968)

"The realization came over me with full force that a good part of the remainder of my life was going to be spent in finding errors in my own programs." (Maurice Wilkes, "Memoirs of a Computer Pioneer", 1985)

19 October 2006

⛩️Joseph Weizenbaum - Collected Quotes

"A higher-level formal language is an abstract machine." (Joseph Weizenbaum, "Computer power and human reason: From judgment to calculation", 1976)

"A theory is, of course, not merely any grammatically correct text that uses a set of terms somehow symbolically related to reality. It is a systematic aggregate of statements of laws. Its content, its very value as theory, lies at least as much in the structure of the interconnections that relate its laws to one another, as in the laws themselves." (Joseph Weizenbaum, "Computer power and human reason: From judgment to calculation" , 1976)

"Computers make possible an entirely new relationship between theories and models. I have already said that theories are texts. Texts are written in a language. Computer languages are languages too, and theories may be written in them. Indeed, for the present purpose we need not restrict our attention to machine languages or even to the kinds of 'higher-level' languages we have discussed. We may include all languages, specifically also natural languages, that computers may be able to interpret. The point is precisely that computers do interpret texts given to them, in other words, that texts determine computers' behavior. Theories written in the form of computer programs are ordinary theories as seen from one point of view." (Joseph Weizenbaum, "Computer power and human reason: From judgment to calculation" , 1976)

"Machines, when they operate properly, are not merely law abiding; they are embodiments of law. To say that a specific machine is 'operating properly' is to assert that it is an embodiment of a law we know and wish to apply. We expect an ordinary desk calculator, for example, to be an embodiment of the laws of arithmetic we all know." (Joseph Weizenbaum, "Computer power and human reason: From judgment to calculation" , 1976)

"Man is not a machine, [...] although man most certainly processes information, he does not necessarily process it in the way computers do. Computers and men are not species of the same genus. [...] No other organism, and certainly no computer, can be made to confront genuine human problems in human terms. [...] However much intelligence computers may attain, now or in the future, theirs must always be an intelligence alien to genuine human problems and concerns." (Joesph Weizenbaum, Computer Power and Human Reason: From Judgment to Calculation, 1976)

"Programming systems can, of course, be built without plan and without knowledge, let alone understanding, of the deep structural issues involved, just as houses, cities, systems of dams, and national economic policies can be similarly hacked together. As a system so constructed begins to get large, however, it also becomes increasingly unstable. When one of its subfunctions fails in an unanticipated way, it may be patched until the manifest trouble disappears. But since there is no general theory of the whole system, the system itself can be only a more or less chaotic aggregate of subsystems whose influence on one another's behavior is discoverable only piecemeal and by experiment. The hacker spends part of his time at the console piling new subsystems onto the structure he has already built - he calls them 'new features' - and the rest of his time in attempts to account for the way in which substructures already in place misbehave. That is what he and the computer converse about." (Joseph Weizenbaum, "Computer power and human reason: From judgment to calculation" , 1976)

"The aim of the model is of course not to reproduce reality in all its complexity. It is rather to capture in a vivid, often formal, way what is essential to understanding some aspect of its structure or behavior." (Joseph Weizenbaum, "Computer power and human reason: From judgment to calculation" , 1976)

"The computer programmer is a creator of universes for which he alone is the lawgiver. No playwright, no stage director, no emperor, however powerful, has ever exercised such absolute authority to arrange a stage or field of battle and to command such unswervingly dutiful actors or troops." (Joseph Weizenbaum, "Computer power and human reason: From judgment to calculation" , 1976)

"The connection between a model and a theory is that a model satisfies a theory; that is, a model obeys those laws of behavior that a corresponding theory explicitly states or which may be derived from it. [...] Computers make possible an entirely new relationship between theories and models. [...] A theory written in the form of a computer program is [...] both a theory and, when placed on a computer and run, a model to which the theory applies." (Joseph Weizenbaum, "Computer power and human reason: From judgment to calculation" , 1976)

"There is a distinction between physically embodied machines, whose ultimate function is to transduce energy or deliver power, and abstract machines. i.e., machines that exist only as ideas. The laws which the former embody must be a subset of the laws that govern the real world. The laws that govern the behavior of abstract machines are not necessarily so constrained. One may, for example, design an abstract machine whose internal signals are propagated among its components at speeds greater than the speed of light, in clear violation of physical law. The fact that such a machine cannot actually be built does not prohibit the exploration of its behavior." (Joseph Weizenbaum, "Computer power and human reason: From judgment to calculation" , 1976)

06 October 2006

⛩️Marvin Minsky - Collected Quotes

"A computer is like a violin. You can imagine a novice trying first a phonograph and then a violin. The latter, he says, sounds terrible. That is the argument we have heard from our humanists and most of our computer scientists. Computer programs are good, they say, for particular purposes, but they aren’t flexible. Neither is a violin, or a typewriter, until you learn how to use it." (Marvin Minsky, "Why Programming Is a Good", 1967)

"When a program grows in power by an evolution of partially understood patches and fixes, the programmer begins to lose track of internal details, loses his ability to predict what will happen, begins to hope instead of know, and watches the results as though the program were an individual whose range of behavior is uncertain. [...] This is already true in some big programs [...] developed and modified by several programmers, each testing them on different examples from different [remotely located computer] consoles and inserting advice independently. The program will grow in effectiveness, but no one of the programmers will understand it all. [...] Now we see the real trouble with statements like 'It only does what its programmer told it to do.' There isn't any one programmer." (Marvin M Minsky, "Design and Planning" II, 1967)

"Computer languages of the future will be more concerned with goals and less with procedures specified by the programmer." (Marvin Minsky, "Form and Content in Computer Science", [Turing Award lecture] 1969)

"What is the difference between merely knowing (or remembering, or memorizing) and understanding? […] A thing or idea seems meaningful only when we have several different ways to represent it - different perspectives and different associations […]. Then we can turn it around in our minds, so to speak: however it seems at the moment, we can see it another way and we never come to a full stop. In other words, we can 'think' about it. If there were only one way to represent this thing or idea, we would not call this representation thinking." (Marvin Minsky, "Music, Mind, and Meaning", 1981)

"The hardest problems we have to face do not come from philosophical questions about whether brains are machines or not. There is not the slightest reason to doubt that brains are anything other than machines with enormous numbers of parts that work in perfect accord with physical laws. As far as anyone can tell, our minds are merely complex processes. The serious problems come from our having had so little experience with machines of such complexity that we are not yet prepared to think effectively about them." (Marvin Minsky, 1986)

"For generations, scientists and philosophers have tried to explain ordinary reasoning in terms of logical principles - with virtually no success. I suspect this enterprise failed because it was looking in the wrong direction: common sense works so well not because it is an approximation of logic; logic is only a small part of our great accumulation of different, useful ways to chain things together." (Marvin Minsky, "The Society of Mind", 1987)

"Every system that we build will surprise us with new kinds of flaws until those machines become clever enough to conceal their faults from us." (Marvin Minsky, "The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind", 2006)

"It makes no sense to seek a single best way to represent knowledge - because each particular form of expression also brings its particular limitations. For example, logic-based systems are very precise, but they make it hard to do reasoning with analogies. Similarly, statistical systems are useful for making predictions, but do not serve well to represent the reasons why those predictions are sometimes correct." (Marvin Minsky, "The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind", 2006)

01 October 2006

⛩️Brian W Kernighan - Collected Quotes

"A clean design is more easily modified as requirements change or as more is learned about what parts of the code consume significant amounts of execution time. A 'clever' design that fails to work or to run fast enough can often be salvaged only at great cost. Efficiency does not have to be sacrificed in the interest of writing readable code - rather, writing readable code is often the only way to ensure efficient programs that are also easy to maintain and modify." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"A computer program is shaped by its data representation and the statements that determine its flow of control. These define the structure of a program. There is no sharp distinction between expression and organization; it is more a question of scope." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"A powerful tool for reducing apparent complexity is recursion. In a recursive procedure, the method of solution is defined in terms of itself. That is, each part of the routine handles only a small piece of the strategy, then calls the other parts of the routine as needed to handle the rest. The trick is to reduce each hard case to one that is handled simply elsewhere." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Choosing a better data structure is often an art, which we cannot teach. Often you must write a preliminary draft of the code before you can determine what changes in the data structure will help simplify control. [...] Choose a data representation that makes the program simple." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Don’t comment bad code - rewrite it." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Don't compare floating point numbers just for equality." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Don't diddle code to make it faster - find a better algorithm." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Don't just echo the code with comments make every comment count." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Don't patch bad code - rewrite it." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Don't sacrifice clarity for small gains in 'efficiency'." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Don't strain to re-use code; reorganize instead." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Don't use conditional branches as a substitute for a logical expression." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Everyone knows that debugging is twice as hard as writing a program in the first place. So if you're as clever as you can be when you write it, how will you ever debug it?" (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Format a program to help the reader understand it." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"'Good programmers' are those who already have learned a set of rules that ensures good style; many of them will read this book and see no reason to change. If you are still learning to be a 'good programmer', however, then perhaps some of what we consider good style will have rubbed off in the reading." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Good programming cannot be taught by preaching generalities. The way to learn to program well is by seeing, over and over, how real programs can be improved by the application of a few principles of good practice and a little common sense. Practice in critical reading leads to skill in rewriting, which in turn leads to better writing." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Indent to show the logical structure of a program." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Instrument your programs. Measure before making 'efficiency' changes." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"It is a good rule of thumb that a program should read from top to bottom in the order that it will be executed; if this is not true, watch out for the bugs that often accompany poor structure. Make your programs read from top to bottom." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Jumping around unnecessarily in a computer program has proved to be a fruitful source of errors, and usually indicates that the programmer is not entirely in control of the code." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Let the data structure the program." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Let your compiler do the simple optimizations." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Make it right before you make it faster.[...] Keep it right when you make it faster. [...] Make it clear before you make it faster. [...] Keep it simple to make it faster."

"Make sure every module hides something." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Make sure special cases are truly special." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Make the coupling between modules visible." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

 "Make input easy to prepare and output self-explanatory. [...] Use uniform input formats. [...] Make input easy to proofread. [...] Use self-identifying input. Allow defaults. Echo both on output. [...] Localize input and output in subroutines." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Make sure all variables are initialized before use." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Make sure comments and code agree." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Most programs are too big to be comprehended as a single chunk. They must be divided into smaller pieces that can be conquered separately. That is the only way to write them reliably; it is the only way to read and understand them. [...] When a program is not broken up into small enough pieces, the larger modules often fail to deliver on these promises. They try to do too much, or too many different things, and hence are difficult to maintain and are too specialized for general use." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"[...] no program is ever perfect; there is always room for improvement. Of course, it is foolish to polish a program beyond the point of diminishing returns, but most programmers do too little revision; they are satisfied too early. Don't stop with your first draft." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Notice that even the data is commented. One of the most effective ways to document a program is simply to describe the data layout in detail. If you can specify for each important variable what values it can assume and how it gets changed, you have gone a long way to describing the program. [...] Document your data layouts." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Program defensively." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Program organization, deciding what gets done where, is often given insufficient consideration. This can be true even when the format of input or output data strongly suggests the most convenient order of processing. Failure to heed such suggestions leads to code that is hard to relate to the problem being solved, and hence likely to contain mistakes." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Programmers have a strong tendency to underrate the importance of good style. Eternally optimistic, we all like to think that once we throw a piece of code together, however haphazardly, it will work properly the first time and ever after. Why waste time cleaning up something that is almost certain to be correct? Besides, it probably will be used for only a few weeks." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Programs are not used once and discarded, nor are they run forever without change. They evolve. The new version of the integration program has a greater likelihood of surviving changes later without acquiring bugs. It assists instead of intimidating those who must maintain it." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Recursion represents no saving of time or storage. Somewhere in the computer must be maintained a list of all the places a recursive routine is called, so the program can eventually find its way back. But the storage for that list is shared among many different uses. More important, it is managed automatically; many of the burdens of storage management and control flow are placed on the compiler, not on the programmer. And since bookkeeping details are hidden, the program can be much easier to understand. Learning to think recursively takes some effort, but it is repaid with smaller and simpler programs." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Repeated patterns of code catch the eye when scanning listings. Since the computer is a tool for handling repetitious operations, we should be alerted by such patterns to look for oversights - why didn't the programmer let the computer do the repeating?" (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Say what you mean, simply and directly." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"The best documentation for a computer program is a clean structure. It also helps if the code is well formatted, with good mnemonic identifiers and labels (if any are needed), and a smattering of enlightening comments. Flowcharts and program descriptions are of secondary importance; the only reliable documentation of a computer program is the code itself. The reason is simple -whenever there are multiple representations of a program, the chance for discrepancy exists. If the code is in error, artistic flowcharts and detailed comments are to no avail. Only by reading the code can the programmer know for sure what the program does." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Test input for validity and plausibility. [...] Make sure input cannot violate the limits of the program. [...] Identify bad input; recover if possible. [...] Test programs at their boundary values." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"The fewer temporary variables in a program, the less chance there is that one will not be properly initialized, or that one will be altered unexpectedly before it is used. 'Temporary' is a dirty word in programming - it suggests that a variable can be used with less thought than a 'normal' (permanent?) one, and it encourages the use of one variable for several unrelated calculations. Both are dangerous practices." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"The general rule is: after you make a decision, do something. Don't just go somewhere or make another decision. If you follow each decision by the action that goes with it, you can see at a glance what each decision implies. Follow each decision as closely as possible with its associated action." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Use IF-ELSE to emphasize that only one of two actions is to be performed." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Use recursive procedures for recursively-defined data structures." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Use the good features of a language; avoid the bad ones." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Use variable names that mean something." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Watch out for off-by-one errors. A common cause of off-by-one errors is an incorrect test, for example using 'greater than' when 'greater than or equal to' is actually needed. This program is a binary search routine, which looks for a particular element in a table by halving the interval in which the element might lie, until it ultimately either finds it, or deduces that it isn't present." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"When the operation to be done is more complex, write a separate subroutine or function. The ease of later comprehending, debugging, and changing the program will more than compensate for any overhead caused by adding the extra modules." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Write and test a big program in small pieces." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Writing a computer program eventually boils down to writing a sequence of statements in the language at hand. How each of those statements is expressed determines in large measure the intelligibility of the whole; no amount of commenting, formatting, or supplementary documentation can entirely replace well expressed statements. After all, they determine what the program actually does." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"Controlling complexity is the essence of computer programming." (Brian W Kernighan, "Software Tools", 1976)

"Mechanical rules are never a substitute for clarity of thought." (Brian W Kernighan, "Software Tools", 1976)

"First is the sheer joy of making things. Second is the pleasure of making things that are useful to other people. Deep within, we want others to use our work and to find it helpful." (Brian W Kernighan, "Hello, World! Opinion columns from the Daily Princetonian", 2011)

"A computer is a general-purpose machine. It takes its instructions from memory, and one can change the computation it performs by putting different instructions in the memory. Instructions and data are indistinguishable except by context; one person’s instructions are another person’s data." (Brian W Kernighan, "Understanding the Digital World", 2017)

"A standard is a precise and detailed description of how some artifact is built or is supposed to work." (Brian W Kernighan, "Understanding the Digital World", 2017)

"[...] algorithms, which are abstract or idealized process descriptions that ignore details and practicalities. An algorithm is a precise and unambiguous recipe. It’s expressed in terms of a fixed set of basic operations whose meanings are completely known and specified. It spells out a sequence of steps using those operations, with all possible situations covered, and it’s guaranteed to stop eventually." (Brian W Kernighan, "Understanding the Digital World", 2017)

"An algorithm is the computer science version of a careful, precise, unambiguous recipe or tax form, a sequence of steps that is guaranteed to compute a result correctly." (Brian W Kernighan, "Understanding the Digital World", 2017)

"By contrast, a program is anything but abstract - it’s a concrete statement of every step that a real computer must perform to accomplish a task. The distinction between an algorithm and a program is like the difference between a blueprint and a building; one is an idealization and the other is the real thing." (Brian W Kernighan, "Understanding the Digital World", 2017)

"Cryptography, the art of 'secret writing', is in many ways our best defense against the attacks noted above and those yet to come. Properly done, cryptography is wonderfully flexible and powerful. Unfortunately good cryptography is also difficult and subtle, and all too often defeated by human error." (Brian W Kernighan, "Understanding the Digital World", 2017)

"Programming in the real world tends to happen on a large scale. The strategy is similar to what one might use to write a book or undertake any other big project: figure out what to do, starting with a broad specification that is broken into smaller and smaller pieces, then work on the pieces separately while making sure that they hang together. In programming, pieces tend to be of a size such that one person can write the precise computational steps in some programming language. Ensuring that the pieces written by different programmers work together is challenging, and failing to get this right is a major source of errors." (Brian W Kernighan, "Understanding the Digital World", 2017)

"Sadly, no substantial program works the first time; life is too complicated and programs reflect that complexity. Programming requires perfect attention to detail, something that few people can achieve. Thus all programs of any size will have errors, that is, they will do the wrong thing or produce the wrong answer under some circumstances. Those flaws are called bugs [...]" (Brian W Kernighan, "Understanding the Digital World", 2017)

"The good news is that a computer is a general-purpose machine, capable of performing any computation. Although it only has a few kinds of instructions to work with, it can do them very fast, and it can largely control its own operation. The bad news is that it doesn’t do anything itself unless someone tells it what to do, in excruciating detail. A computer is the ultimate sorcerer’s apprentice, able to follow instructions tirelessly and without error, but requiring painstaking accuracy in the specification of what to do." (Brian W Kernighan, "Understanding the Digital World", 2017)

"There aren’t enough programmers in the world to do the amount of programming involved in making computers do everything we want or need." (Brian W Kernighan, "Understanding the Digital World", 2017)

"Why should anyone know or care about binary numbers? One reason is that working with numbers in an unfamiliar base is an example of quantitative reasoning that might even improve understanding of how numbers work in good old base ten. Beyond that, it’s also important because the number of bits is usually related in some way to how much space, time or complexity is involved. And fundamentally, computers are worth understanding, and binary is central to their operation." (Brian W Kernighan, "Understanding the Digital World", 2017)

"Do what you think is interesting, do something that you think is fun and worthwhile, because otherwise you won’t do it well anyway." (Brian W Kernighan [interview])

"Another effective technique is to explain your code to someone else. This will often cause you to explain the bug to yourself. Sometimes it takes no more than a few sentences, followed by an embarrassed 'Never mind, I see what's wrong. Sorry to bother you.' This works remarkably well; you can even use non-programmers as listeners. One university computer center kept a teddy bear near the help desk. Students with mysterious bugs were required to explain them to the bear before they could speak to a human counselor." (Brian W Kernighan)

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