Dendral

Dendral was a project in artificial intelligence (AI) of the 1960s, and the computer software expert system that it produced. Its primary aim was to study hypothesis formation and discovery in science. For that, a specific task in science was chosen: help organic chemists in identifying unknown organic molecules, by analyzing their mass spectra and using knowledge of chemistry. It was done at Stanford University by Edward Feigenbaum, Bruce G. Buchanan, Joshua Lederberg, and Carl Djerassi, along with a team of highly creative research associates and students. It began in 1964 and spans approximately half the history of AI research. The software program Dendral is considered the first expert system because it automated the decision-making process and problem-solving behavior of organic chemists. The project consisted of research on two main programs Heuristic Dendral and Meta-Dendral, and several sub-programs. It was written in the Lisp programming language, which was considered the language of AI because of its flexibility. Many systems were derived from Dendral, including MYCIN, MOLGEN, PROSPECTOR, XCON, and STEAMER. There are many other programs today for solving the mass spectrometry inverse problem, see List of mass spectrometry software, but they are no longer described as 'artificial intelligence', just as structure searchers. The name Dendral is an acronym of the term "Dendritic Algorithm". == Heuristic Dendral == Heuristic Dendral is a program that uses mass spectra or other experimental data together with a knowledge base of chemistry to produce a set of possible chemical structures that may be responsible for producing the data. A mass spectrum of a compound is produced by a mass spectrometer, and is used to determine its molecular weight, the sum of the masses of its atomic constituents. For example, the compound water (H2O), has a molecular weight of 18 since hydrogen has a mass of 1.01 and oxygen 16.00, and its mass spectrum has a peak at 18 units. Heuristic Dendral would use this input mass and the knowledge of atomic mass numbers and valence rules, to determine the possible combinations of atomic constituents whose mass would add up to 18. As the weight increases and the molecules become more complex, the number of possible compounds increases drastically. Thus, a program that is able to reduce this number of candidate solutions through the process of hypothesis formation is essential. New graph-theoretic algorithms were invented by Lederberg, Harold Brown, and others that generate all graphs with a specified set of nodes and connection-types (chemical atoms and bonds) -- with or without cycles. Moreover, the team was able to prove mathematically that the generator is complete, in that it produces all graphs with the specified nodes and edges, and that it is non-redundant, in that the output contains no equivalent graphs (e.g., mirror images). The CONGEN program, as it became known, was developed largely by computational chemists Ray Carhart, Jim Nourse, and Dennis Smith. It was useful to chemists as a stand-alone program to generate chemical graphs showing a complete list of structures that satisfy the constraints specified by a user. == Meta-Dendral == Meta-Dendral is a machine learning system that receives the set of possible chemical structures and corresponding mass spectra as input, and proposes a set of rules of mass spectrometry that correlate structural features with processes that produce the mass spectrum. These rules would be fed back to Heuristic Dendral (in the planning and testing programs described below) to test their applicability. Thus, "Heuristic Dendral is a performance system and Meta-Dendral is a learning system". The program is based on two important features: the plan-generate-test paradigm and knowledge engineering. === Plan-generate-test paradigm === The plan-generate-test paradigm is the basic organization of the problem-solving method, and is a common paradigm used by both Heuristic Dendral and Meta-Dendral systems. The generator (later named CONGEN) generates potential solutions for a particular problem, which are then expressed as chemical graphs in Dendral. However, this is feasible only when the number of candidate solutions is minimal. When there are large numbers of possible solutions, Dendral has to find a way to put constraints that rules out large sets of candidate solutions. This is the primary aim of Dendral planner, which is a “hypothesis-formation” program that employs “task-specific knowledge to find constraints for the generator”. Last but not least, the tester analyzes each proposed candidate solution and discards those that fail to fulfill certain criteria. This mechanism of plan-generate-test paradigm is what holds Dendral together. === Knowledge Engineering === The primary aim of knowledge engineering is to attain a productive interaction between the available knowledge base and problem solving techniques. This is possible through development of a procedure in which large amounts of task-specific information is encoded into heuristic programs. Thus, the first essential component of knowledge engineering is a large “knowledge base.” Dendral has specific knowledge about the mass spectrometry technique, a large amount of information that forms the basis of chemistry and graph theory, and information that might be helpful in finding the solution of a particular chemical structure elucidation problem. This “knowledge base” is used both to search for possible chemical structures that match the input data, and to learn new “general rules” that help prune searches. The benefit Dendral provides the end user, even a non-expert, is a minimized set of possible solutions to check manually. == Heuristics == A heuristic is a rule of thumb, an algorithm that does not guarantee a solution, but reduces the number of possible solutions by discarding unlikely and irrelevant solutions. The use of heuristics to solve problems is called "heuristics programming", and was used in Dendral to allow it to replicate in machines the process through which human experts induce the solution to problems via rules of thumb and specific information. Heuristics programming was a major approach and a giant step forward in artificial intelligence, as it allowed scientists to finally automate certain traits of human intelligence. It became prominent among scientists in the late 1940s through George Polya’s book, How to Solve It: A New Aspect of Mathematical Method. As Herbert A. Simon said in The Sciences of the Artificial, "if you take a heuristic conclusion as certain, you may be fooled and disappointed; but if you neglect heuristic conclusions altogether you will make no progress at all." == History == During the mid 20th century, the question "can machines think?" became intriguing and popular among scientists, primarily to add humanistic characteristics to machine behavior. John McCarthy, who was one of the prime researchers of this field, termed this concept of machine intelligence as "artificial intelligence" (AI) during the Dartmouth summer in 1956. AI is usually defined as the capacity of a machine to perform operations that are analogous to human cognitive capabilities. Much research to create AI was done during the 20th century. Also around the mid 20th century, science, especially biology, faced a fast-increasing need to develop a "man-computer symbiosis", to aid scientists in solving problems. For example, the structural analysis of myoglobin, hemoglobin, and other proteins relentlessly needed instrumentation development due to its complexity. In the early 1960s, Joshua Lederberg started working with computers and quickly became tremendously interested in creating interactive computers to help him in his exobiology research. Specifically, he was interested in designing computing systems to help him study alien organic compounds. Lederberg had been heading a team designing instruments for the Mars Viking lander to search for precursor molecules of life in samples of the Mars surface, using a mass spectrometer coupled with a minicomputer. As he was not an expert in either chemistry or computer programming, he collaborated with Stanford chemist Carl Djerassi to help him with chemistry, and Edward Feigenbaum with programming, to automate the process of determining chemical structures from raw mass spectrometry data. Feigenbaum was an expert in programming languages and heuristics, and helped Lederberg design a system that replicated the way Djerassi solved structure elucidation problems. They devised a system called Dendritic Algorithm (Dendral) that was able to generate possible chemical structures corresponding to the mass spectrometry data as an output. Dendral then was still very inaccurate in assessing spectra of ketones, alcohols, and isomers of chemical compounds. Thus, Djerassi "taught" general rules to Dendral that could help eliminate most of the "chemically implausible" structures, and p

Gamma (app)

Gamma is a web-based software platform that uses artificial intelligence to generate presentations, documents, webpages, and other visual content. The platform allow users to create structured layouts and draft text based on prompts or uploaded material. It operates as an online application and provides tools for editing, organizing, and sharing content. == History == Gamma was established in the early 2020s by Grant Lee, James Fox, and Jon Noronha during a period of increased development in artificial intelligence–based productivity software. The platform was introduced as a web-based format designed to present information through structured visual layouts rather than traditional slide-based presentations. Its interface was developed to adapt content to different screen sizes and devices. In later updates, Gamma expanded its functionality to support additional formats, including documents and simple webpages. By November 2025, the company reported that the platform had reached approximately 70 million users. Gamma has raised venture capital funding from a number of technology-focused investors since its founding. == Features == Gamma allows users to create presentations, documents, and webpages by entering prompts, pasting text, or uploading source files. The platform uses artificial intelligence to generate draft text, organize information, and apply structured layouts. Users can edit generated material manually and adjust formatting, structure, and visual elements. The software also supports collaborative editing, allowing multiple users to contribute to and revise the same project. Instead of relying only on fixed slide-based formats, Gamma presents content in scrollable layouts designed for web viewing across different screen sizes. Projects created on the platform can be shared through web links or exported to formats compatible with other software. Gamma also provides integration options and developer access through an application programming interface (API). == Technology == Gamma uses generative artificial intelligence models to interpret user input and generate structured content. The software automates elements of layout selection, formatting, and visual presentation. As with other AI-assisted tools, output produced by the system may require human review and revision to ensure accuracy and appropriate context. == Funding == Gamma has raised venture capital funding from a number of technology-focused investors since its founding. In November 2025, the company announced a Series B funding round that raised $68 million at a reported valuation of approximately $2.1 billion. Investors in the round included Andreessen Horowitz, Accel, and Uncork Capital, among others. == Controversy == In 2025, cybersecurity researchers reported that Gamma had been used in a phishing campaign targeting Microsoft accounts. Attackers shared links to presentations hosted on the platform that redirected users to a spoofed Microsoft SharePoint login page intended to collect credentials. Researchers noted that the incident reflected the broader misuse of legitimate online services in phishing schemes.

Applications of artificial intelligence

Artificial intelligence is the capability of computational systems to perform tasks that are typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. Artificial intelligence has been used in applications throughout industry and academia. Within the field of Artificial Intelligence, there are multiple subfields. The subfield of machine learning has been used for various scientific and commercial purposes, including language translation, image recognition, decision-making, credit scoring, and e-commerce. In recent years, massive advancements have been made in the field of generative artificial intelligence, which uses generative models to generate text, images, videos, and other forms of data. This article describes applications of AI in different sectors. == Agriculture == In agriculture, AI has been proposed as a way for farmers to identify areas that need irrigation, fertilization, or pesticide treatments to increase yields, thereby improving efficiency. AI has been used to attempt to classify livestock pig call emotions, automate greenhouses, detect diseases and pests, and optimize irrigation. == AI-assisted software develoment == == Architecture and design == == Business == A 2023 study found that generative AI increased productivity by 15% in contact centers. Another 2023 study found it increased productivity by up to 40% in writing tasks. An August 2025 review by MIT found that of surveyed companies, 95% did not report any improvement in revenue from the use of AI. A September 2025 article by the Harvard Business Review describes how increased use of AI does not automatically lead to increases in revenue or actual productivity. Referring to "AI generated work content that masquerades as good work, but lacks the substance to meaningfully advance a given task" the article coins the term workslop. Per studies done in collaboration with the Stanford Social Media Lab, workslop does not improve productivity and undermines trust and collaboration among colleagues. In telehealth, agentic AI is reportedly facilitating the creation of large business models (millions in annual profit) with 1-2 employees, such as MEDVi, which as of August 2025 only had 2 employees and ~$75M in annual profit for GLP-1 weight-loss telehealth services. == Chatbots == == Computer science == === Programming assistance === ==== AI-assisted software development ==== AI can be used for real-time code completion, chat, and automated test generation. These tools are typically integrated with editors and IDEs as plugins. AI-assisted software development systems differ in functionality, quality, speed, and approach to privacy. Creating software primarily via AI is known as "vibe coding". Code created or suggested by AI can be incorrect or inefficient. The use of AI-assisted coding can potentially speed-up software development, but can also slow-down the process by creating more work when debugging and testing. The rush to prematurely adopt AI technology can also incur additional technical debt. AI also requires additional consideration and careful review for cybersecurity, since AI coding software is trained on a wide range of code of inconsistent quality and often replicates poor practices. ==== Neural network design ==== AI can be used to create other AIs. For example, around November 2017, Google's AutoML project to evolve new neural net topologies created NASNet, a system optimized for ImageNet and POCO F1. NASNet's performance exceeded all previously published performance on ImageNet. ==== Quantum computing ==== Research and development of quantum computers has been performed with machine learning algorithms. For example, there is a prototype, photonic, quantum memristive device for neuromorphic computers (NC)/artificial neural networks and NC-using quantum materials with some variety of potential neuromorphic computing-related applications. The use of quantum machine learning for quantum simulators has been proposed for solving physics and chemistry problems. === Historical contributions === AI researchers have created many tools to solve the most difficult problems in computer science. Many of their inventions have been adopted by mainstream computer science and are no longer considered AI. All of the following were originally developed in AI laboratories: Time sharing Interactive interpreters Graphical user interfaces and the computer mouse Rapid application development environments The linked list data structure Automatic storage management Symbolic programming Functional programming Dynamic programming Object-oriented programming Optical character recognition Constraint satisfaction == Customer service == === Human resources === AI programs have been used in hiring processes to screen resumes and rank candidates based on their qualifications, predict a candidate's likelihood of success in a given role, and automate repetitive communication tasks using chatbots. Studies on these programs have identified tendencies for gender bias, favoring male names and male-coded characteristics, as well as bias against disabled candidates and racial minorities. === Online and telephone customer service === AI underlies avatars (automated online assistants) on web pages. It can reduce operation and training costs. Pypestream automated customer service for its mobile application to streamline communication with customers. A Google app analyzes language and converts speech into text. The platform can identify angry customers through their language and respond appropriately. Amazon uses a chatbot for customer service that can perform tasks like checking the status of an order, cancelling orders, offering refunds and connecting the customer with a human representative. Generative AI (GenAI), such as ChatGPT, is increasingly used in business to automate tasks and enhance decision-making. === Hospitality === In the hospitality industry, AI is used to reduce repetitive tasks, analyze trends, interact with guests, and predict customer needs. AI hotel services come in the form of a chatbot, application, virtual voice assistant and service robots. == Education == In educational institutions, AI has been used to automate routine tasks such as attendance tracking, grading, and marking. AI tools have also been used to monitor student progress and analyze learning behaviors, with the goal of facilitating timely interventions for students facing academic challenges. == Energy and environment == === Energy system === The U.S. Department of Energy wrote in an April 2024 report that AI may have applications in modeling power grids, reviewing federal permits with large language models, predicting levels of renewable energy production, and improving the planning process for electrical vehicle charging networks. Other studies have suggested that machine learning can be used for energy consumption prediction and scheduling, e.g. to help with renewable energy intermittency management (see also: smart grid and climate change mitigation in the power grid). === Environmental monitoring === Autonomous ships that monitor the ocean, AI-driven satellite data analysis, passive acoustics or remote sensing and other applications of environmental monitoring make use of machine learning. For example, "Global Plastic Watch" is an AI-based satellite monitoring-platform for analysis/tracking of plastic waste sites to help prevention of plastic pollution – primarily ocean pollution – by helping identify who and where mismanages plastic waste, dumping it into oceans. === Early-warning systems === Machine learning can be used to spot early-warning signs of disasters and environmental issues, possibly including natural pandemics, earthquakes, landslides, heavy rainfall, long-term water supply vulnerability, tipping-points of ecosystem collapse, cyanobacterial bloom outbreaks, and droughts. === Economic and social challenges === The University of Southern California launched the Center for Artificial Intelligence in Society, with the goal of using AI to address problems such as homelessness. Stanford researchers use AI to analyze satellite images to identify high poverty areas. == Entertainment and media == === Media === AI applications analyze media content such as movies, TV programs, advertisement videos or user-generated content. The solutions often involve computer vision. Typical scenarios include the analysis of images using object recognition or face recognition techniques, or the analysis of video for scene recognizing scenes, objects or faces. AI-based media analysis can facilitate media search, the creation of descriptive keywords for content, content policy monitoring (such as verifying the suitability of content for a particular TV viewing time), speech to text for archival or other purposes, and the detection of logos, products or celebrity faces for ad placement. Motion interpolation Pixel-art scaling algorithms Image scaling Imag

SQL/PSM

SQL/PSM (SQL/Persistent Stored Modules) is an ISO standard mainly defining an extension of SQL with a procedural language for use in stored procedures. Initially published in 1996 as an extension of SQL-92 (ISO/IEC 9075-4:1996, a version sometimes called PSM-96 or even SQL-92/PSM), SQL/PSM was later incorporated into the multi-part SQL:1999 standard, and has been part 4 of that standard since then, most recently in SQL:2023. The SQL:1999 part 4 covered less than the original PSM-96 because the SQL statements for defining, managing, and invoking routines were actually incorporated into part 2 SQL/Foundation, leaving only the procedural language itself as SQL/PSM. The SQL/PSM facilities are still optional as far as the SQL standard is concerned; most of them are grouped in Features P001-P008. SQL/PSM standardizes syntax and semantics for control flow, exception handling (called "condition handling" in SQL/PSM), local variables, assignment of expressions to variables and parameters, and (procedural) use of cursors. It also defines an information schema (metadata) for stored procedures. SQL/PSM is one language in which methods for the SQL:1999 structured types can be defined. The other is Java, via SQL/JRT. SQL/PSM is derived, seemingly directly, from Oracle's PL/SQL. Oracle developed PL/SQL and released it in 1991, basing the language on the US Department of Defense's Ada programming language. However, Oracle has maintained a distance from the standard in its documentation. IBM's SQL PL (used in DB2) and Mimer SQL's PSM were the first two products officially implementing SQL/PSM. It is commonly thought that these two languages, and perhaps also MySQL/MariaDB's procedural language, are closest to the SQL/PSM standard. However, a PostgreSQL addon implements SQL/PSM (alongside its other procedural languages like the PL/SQL-derived plpgsql), although it is not part of the core product. RDF functionality in OpenLink Virtuoso was developed entirely through SQL/PSM, combined with custom datatypes (e.g., ANY for handling URI and Literal relation objects), sophisticated indexing, and flexible physical storage choices (column-wise or row-wise).

Documentation

Documentation is any communicable material that is used to describe, explain, or instruct regarding some attributes of an object, system, or procedure, such as its parts, assembly, installation, maintenance, and use. As a form of knowledge management and knowledge organization, documentation can be provided on paper, online, or on digital or analog media, such as audio tape or CDs. Examples of such resources include user guides, white papers, online help, and quick-reference guides. Paper or hard-copy documentation has become less common. Contemporary documentation is often distributed through websites, software products, and other online applications. Documentation, understood as a set of instructional materials, should not be confused with documentation science, which is the study of the recording and retrieval of information. == Principles for producing documentation == While associated International Organization for Standardization (ISO) standards are not easily available publicly, a guide from other sources for this topic may serve the purpose. Documentation development may involve document drafting, formatting, submitting, reviewing, approving, distributing, reposting and tracking, etc., and are convened by associated standard operating procedure in a regulatory industry. It could also involve creating content from scratch. Documentation should be easy to read and understand. If it is too long and too wordy, it may be misunderstood or ignored. Clear, concise words should be used, and sentences should be limited to a maximum of 15 words. Documentation intended for a general audience should avoid gender-specific terms and cultural biases. In a series of procedures, steps should be clearly numbered. == Producing documentation == Technical writers and corporate communicators are professionals whose field and work is documentation. Ideally, technical writers have a background in both the subject matter and also in writing, managing content, and information architecture. Technical writers more commonly collaborate with subject-matter experts, such as engineers, technical experts, medical professionals, etc. to define and then create documentation to meet the user's needs. Corporate communications includes other types of written documentation, for example: Market communications (MarCom): MarCom writers endeavor to convey the company's value proposition through a variety of print, electronic, and social media. This area of corporate writing is often engaged in responding to proposals. Technical communication (TechCom): Technical writers document a company's product or service. Technical publications can include user guides, installation and configuration manuals, and troubleshooting and repair procedures. Legal writing: This type of documentation is often prepared by attorneys or paralegals. Compliance documentation: This type of documentation codifies standard operating procedures, for any regulatory compliance needs, as for safety approval, taxation, financing, and technical approval. Healthcare documentation: This field of documentation encompasses the timely recording and validation of events that have occurred during the course of providing health care. == Documentation in computer science == === Types === The following are typical software documentation types: Request for proposal Requirements/statement of work/scope of work Software design and functional specification System design and functional specifications Change management, error and enhancement tracking User acceptance testing Manpages The following are typical hardware and service documentation types: Network diagrams Network maps Datasheet for IT systems (server, switch, e.g.) Service catalog and service portfolio (Information Technology Infrastructure Library) === Software Documentation Folder (SDF) tool === A common type of software document written in the simulation industry is the SDF. When developing software for a simulator, which can range from embedded avionics devices to 3D terrain databases by way of full motion control systems, the engineer keeps a notebook detailing the development "the build" of the project or module. The document can be a wiki page, Microsoft Word document or other environment. They should contain a requirements section, an interface section to detail the communication interface of the software. Often a notes section is used to detail the proof of concept, and then track errors and enhancements. Finally, a testing section to document how the software was tested. This documents conformance to the client's requirements. The result is a detailed description of how the software is designed, how to build and install the software on the target device, and any known defects and workarounds. This build document enables future developers and maintainers to come up to speed on the software in a timely manner, and also provides a roadmap to modifying code or searching for bugs. === Software tools for network inventory and configuration === These software tools can automatically collect data of your network equipment. The data could be for inventory and for configuration information. The Information Technology Infrastructure Library requests to create such a database as a basis for all information for the IT responsible. It is also the basis for IT documentation. Examples include XIA Configuration. == Documentation in criminal justice == "Documentation" is the preferred term for the process of populating criminal databases. Examples include the National Counterterrorism Center's Terrorist Identities Datamart Environment, sex offender registries, and gang databases. == Documentation in early childhood education == Documentation, as it pertains to the early childhood education field, is "when we notice and value children's ideas, thinking, questions, and theories about the world and then collect traces of their work (drawings, photographs of the children in action, and transcripts of their words) to share with a wider community". Thus, documentation is a process, used to link the educator's knowledge and learning of the child/children with the families, other collaborators, and even to the children themselves. Documentation is an integral part of the cycle of inquiry - observing, reflecting, documenting, sharing and responding. Pedagogical documentation, in terms of the teacher documentation, is the "teacher's story of the movement in children's understanding". According to Stephanie Cox Suarez in "Documentation - Transforming our Perspectives", "teachers are considered researchers, and documentation is a research tool to support knowledge building among children and adults". Documentation can take many different styles in the classroom. The following exemplifies ways in which documentation can make the research, or learning, visible: Documentation panels (bulletin-board-like presentation with multiple pictures and descriptions about the project or event). Daily log (a log kept every day that records the play and learning in the classroom) Documentation developed by or with the children (when observing children during documentation, the child's lens of the observation is used in the actual documentation) Individual portfolios (documentation used to track and highlight the development of each child) Electronic documentation (using apps and devices to share documentation with families and collaborators) Transcripts or recordings of conversations (using recording in documentation can bring about deeper reflections for both the educator and the child) Learning stories (a narrative used to "describe learning and help children see themselves as powerful learners") The classroom as documentation (reflections and documentation of the physical environment of a classroom). Documentation is certainly a process in and of itself, and it is also a process within the educator. The following is the development of documentation as it progresses for and in the educator themselves: Develop(s) habits of documentation Become(s) comfortable with going public with recounting of activities Develop(s) visual literacy skills Conceptualize(s) the purpose of documentation as making learning styles visible, and Share(s) visible theories for interpretation purposes and further design of curriculum.

Automation engineering

Automation engineering is a branch of engineering that deals with the development of methods and facilities that replace, in whole or in part, manual labour related to the control and monitoring of systems and processes. == Automation engineer == Automation engineers are experts who have the knowledge and ability to design, create, develop and manage machines and systems, for example, factory automation, process automation and warehouse automation. Automation technicians are also involved. == Scope == Automation engineering is the integration of standard engineering fields. Automatic control of various control systems for operating various systems or machines to reduce human efforts & time to increase accuracy. Automation engineers design and service electromechanical devices and systems for high-speed robotics and programmable logic controllers (PLCs). == Work and career after graduation == Graduates can work for both government and private sector entities such as industrial production, and companies that create and use automation systems, for example, the paper industry, automotive industry, metallurgical industry, food and agricultural industry, water treatment, and oil & gas sectors such as refineries, rolling mills, and power plants. == Job description == Automation engineers can design, program, simulate and test automated machinery and processes, and are usually employed in industries such as the energy sector in plants, car manufacturing facilities, food processing plants, and robots. Automation engineers are responsible for creating detailed design specifications and other documents, developing automation based on specific requirements for the process involved, and conforming to international standards like IEC-61508, local standards, and other process-specific guidelines and specifications, simulating, testing, and commissioning electronic equipment for automation.

Organizational information theory

Organizational Information Theory (OIT) is a communication theory, developed by Karl Weick, offering systemic insight into the processing and exchange of information within organizations and among its members. Unlike the past structure-centered theory, OIT focuses on the process of organizing in dynamic, information-rich environments. Given that, it contends that the main activity of organizations is the process of making sense of equivocal information. Organizational members are instrumental to reduce equivocality and achieve sensemaking through some strategies — enactment, selection, and retention of information. With a framework that is interdisciplinary in nature, organizational information theory's desire to eliminate both ambiguity and complexity from workplace messaging builds upon earlier findings from general systems theory and phenomenology. == Inspiration and influence of pre-existing theories == 1. General Systems Theory The General Systems Theory, on its most basic premise, describes the phenomenon of a cohesive group of interrelated parts. When one part of the system is changed or affected, it will affect the system as a whole. Weick uses this theoretical framework from 1950 to influence his organizational information theory. Likewise, organizations can be viewed as a system of related parts that work together towards a common goal or vision. Applying this to Weick's organizational information theory, organizations must work to reduce ambiguity and complexity in the workplace to maximize cohesiveness and efficiency. Weick uses the term, coupling, to describe how organizations, like a system, can be composed of interrelated and dependent parts. Coupling looks at the relationship between people and work. There are two types of coupling: 1. Loose coupling Loose coupling describes that while people within the organization or system are connected and often work together, they do not depend on one another to continue or fully complete individual work. The dependencies are weak and workflow is flexible. For example, "if the whole Science department completely shuts down because all of teachers are sick or for whatsoever reason, the school can still continue to operate because other departments are still present." 2. Tight coupling Tight coupling describes when connections within an organization are strong and dependent. If one part of the organization is not operating correctly, the organization as a whole cannot continue to their fullest potential. " For instance, the format and ink section completely shuts down hence the succeeding steps cannot be continued, so the whole process of the organization will be dropped. Thus, components of a system are directly dependent on one another." 2. Theory of evolution The theory of evolution, by Charles Darwin, is a framework for survival of the fittest. According to Darwin, organisms attempt to adapt and live in an unforgiving environment. Those that are unsuccessful in adaptation do not survive, while the strong organisms continue to thrive and reproduce. Weick invokes inspiration from Darwin, to incorporate a biological perspective to his theory. It is natural for organizations to have to adapt to incoming information that often interfere with the preexisting environment. Organizations that are able to plan and alter strategies in accordance with their constant need of organizing and sense making, will survive and be the most successful. However, there is a notable difference between animal evolution and survival of the fittest in organizations, "A given animal is what it is; variation comes through mutation. But the nature of an organization can change when its members alter their behavior." == Assumptions == 1. Human organizations exist in an information environment Unlike senders and receivers models, OIT stands on the situational perspective. Karl Weick views a human organization as an open social system. People in that system develop a mechanism to establish goals, obtain and process information, or perceive the environment. In this process, people and the environment come to conclusions on "what's going on here?". Colville believes that this attributional process is retrospective. Take an education institution as an example. A university can obtain information regarding students' needs in numerous ways. It might create feedback section in its website. It could organize alumni panels or academic affairs to attract prospective students and collect concrete questions they are interested in. It may also conduct the survey or host focus group to get the information. After that, the staff of the university have to decide how to deal with these information, based on which, it has to set and accomplish its goals for current and prospective students. 2. The information an organization receives differs in terms of equivocality Weick posits that numerous feasible interpretations of reality exist when organizations process information. Their varying levels of understandability lead to different outcomes of information inputs. In other academic works, scholars tend to say that messages are uncertain or ambiguous. While according to OIT, messages are described to be equivocal. believes that people proactively exclude a number of possibilities to perceive what is going on in the environment. Due to OIT's situational perspective, the meanings of messages consist of the messages, the interpretations of receivers, and the interactional context. However, ambiguity and uncertainty can mean that a standard answer - the only one true objective interpretation - exists. Also, Weick emphasizes that "the equivocality is the engine that motivates people to organize". Maitlis and Christianson states that the equivocality trigger sensemaking for three reasons: environment jolts and organizational crises, threats to identity, and planned change interventions. 3. Human organizations engage in information processing to reduce equivocality of information Based upon the first two assumption, OIT proposes that information processing within organizations is a social activity. Sharing is the key feature of organizational information processing. In that particular context, members jointly make sense the reality by reducing equivocality. It other words, the sensemaking is a joint responsibility which includes numerous interdependent people to accomplish. In this process, organizations and its members combine actions and attributions together in order to find the balance between the complexity of thoughts and the simplicity of actions. Weick also proposes that people create their own environment though enactment, which is the action of making sense. This is because people have different perceptual schemas and selective perception, so people create different information environments. In creating different information environments, people can arrive at the same or close to the same understanding or solution through different thought processes and overall understanding. == Key concepts == === The organization === In order to place Weick's vision regarding Organizational Information Theory into proper working context, exploring his view regarding what constitutes the organization and how its individuals embody that construct might yield significant insights. From a fundamental standpoint, he shared a belief that organizational validation is derived---not through bricks and mortar, or locale—but from a series of events which enable entities to "collect, manage and use the information they receive." In elaborating further on what constitutes an organization during early writings outlining OIT, Weick said, "The word organization is a noun and it is also a myth. if one looks for an organization, one will not find it. What will be found is that there are events linked together, that transpire within concrete walls and these sequences, their pathways, their timing, are the forms we erroneously make into substances when we talk about an organization". When viewed in this modular fashion, the organization meets Weick's theoretical vision by encompassing parameters that are less bound by concrete, wood, and structural restraints and more by an ability to serve as a repository where information can be consistently and effectively channeled. Taking these defining characteristics into account, proper channel execution relies on maximization of messaging clarity, context, delivery and evolution through any system. One example as to how these interactions might unfold on a more granular level within these confines can be gleaned through Weick's double interact loop, which he considers the "building blocks of every organization". Simply put, double interacts describe interpersonal exchanges that, inherently, occur across the organizational chain of command and in life, itself. Thus: "An act occurs when you say something (Can I have a Popsicle?). An interact occurs when you say something and I respond ("No, it will spoil your dinner