Cut, copy, and paste are essential commands of modern human–computer interaction and user interface design. They offer an interprocess communication technique for transferring data through a computer's user interface. The cut command removes the selected data from its original position, and the copy command creates a duplicate; in both cases the selected data is kept in temporary storage called the clipboard. Clipboard data is later inserted wherever a paste command is issued. The data remains available to any application supporting the feature, thus allowing easy data transfer between applications. The command names are a (skeuomorphic) interface metaphor based on the physical procedure used in manuscript print editing to create a page layout, like with paper. The commands were pioneered into computing by Xerox PARC in 1974, popularized by Apple Computer in the 1983 Lisa workstation and the 1984 Macintosh computer, and in a few home computer applications such as the 1984 word processor Cut & Paste. This interaction technique has close associations with related techniques in graphical user interfaces (GUIs) that use pointing devices such as a computer mouse (by drag and drop, for example). Typically, clipboard support is provided by an operating system as part of its GUI and widget toolkit. The capability to replicate information with ease, changing it between contexts and applications, involves privacy concerns because of the risks of disclosure when handling sensitive information. Terms like cloning, copy forward, carry forward, or re-use refer to the dissemination of such information through documents, and may be subject to regulation by administrative bodies. == History == === Origins === The term "cut and paste" comes from the traditional practice in manuscript editing, whereby people cut paragraphs from a page with scissors and paste them onto another page. This practice remained standard into the 1980s. Stationery stores sold "editing scissors" with blades long enough to cut an 8½"-wide page. The advent of photocopiers made the practice easier and more flexible. The act of copying or transferring text from one part of a computer-based document ("buffer") to a different location within the same or different computer-based document was a part of the earliest on-line computer editors. As soon as computer data entry moved from punch-cards to online files (in the mid/late 1960s) there were "commands" for accomplishing this operation. This mechanism was often used to transfer frequently-used commands or text snippets from additional buffers into the document, as was the case with the QED text editor. === Early methods === The earliest editors (designed for teleprinter terminals) provided keyboard commands to delineate a contiguous region of text, then delete or move it. Since moving a region of text requires first removing it from its initial location and then inserting it into its new location, various schemes had to be invented to allow for this multi-step process to be specified by the user. Often this was done with a "move" command, but some text editors required that the text be first put into some temporary location for later retrieval/placement. In 1983, the Apple Lisa became the first text editing system to call that temporary location "the clipboard". Earlier control schemes such as NLS used a verb—object command structure, where the command name was provided first and the object to be copied or moved was second. The inversion from verb—object to object—verb on which copy and paste are based, where the user selects the object to be operated before initiating the operation, was an innovation crucial for the success of the desktop metaphor as it allowed copy and move operations based on direct manipulation. === Popularization === Inspired by early line and character editors, such as Pentti Kanerva's TV-Edit, that broke a move or copy operation into two steps—between which the user could invoke a preparatory action such as navigation—Lawrence G. "Larry" Tesler proposed the names "cut" and "copy" for the first step and "paste" for the second step. Beginning in 1974, he and colleagues at Xerox PARC implemented several text editors that used cut/copy-and-paste commands to move and copy text. Apple Computer popularized this paradigm with its Lisa (1983) and Macintosh (1984) operating systems and applications. The functions were mapped to key combinations using the ⌘ Command key as a special modifier, which is held down while also pressing X for cut, C for copy, or V for paste. These few keyboard shortcuts allow the user to perform all the basic editing operations, and the keys are clustered at the left end of the bottom row of the standard QWERTY keyboard. These are the standard shortcuts: Control-Z (or ⌘ Command+Z) to undo Control-X (or ⌘ Command+X) to cut Control-C (or ⌘ Command+C) to copy Control-V (or ⌘ Command+V) to paste The IBM Common User Access (CUA) standard also uses combinations of the Insert, Del, Shift and Control keys. Early versions of Windows used the IBM standard. Microsoft later also adopted the Apple key combinations with the introduction of Windows, using the control key as modifier key. Similar patterns of key combinations, later borrowed by others, are widely available in most GUI applications. The original cut, copy, and paste workflow, as implemented at PARC, utilizes a unique workflow: With two windows on the same screen, the user could use the mouse to pick a point at which to make an insertion in one window (or a segment of text to replace). Then, by holding shift and selecting the copy source elsewhere on the same screen, the copy would be made as soon as the shift was released. Similarly, holding shift and control would copy and cut (delete) the source. This workflow requires many fewer keystrokes/mouse clicks than the current multi-step workflows, and did not require an explicit copy buffer. It was dropped, one presumes, because the original Apple and IBM GUIs were not high enough density to permit multiple windows, as were the PARC machines, and so multiple simultaneous windows were rarely used. == Cut and paste == Computer-based editing can involve very frequent use of cut-and-paste operations. Most software-suppliers provide several methods for performing such tasks, and this can involve (for example) key combinations, pulldown menus, pop-up menus, or toolbar buttons. The user selects or "highlights" the text or file for moving by some method, typically by dragging over the text or file name with the pointing-device or holding down the Shift key while using the arrow keys to move the text cursor. The user performs a "cut" operation via key combination Ctrl+x (⌘+x for Macintosh users), menu, or other means. Visibly, "cut" text immediately disappears from its location. "Cut" files typically change color to indicate that they will be moved. Conceptually, the text has now moved to a location often called the clipboard. The clipboard typically remains invisible. On most systems only one clipboard location exists, hence another cut or copy operation overwrites the previously stored information. Many UNIX text-editors provide multiple clipboard entries, as do some Macintosh programs such as Clipboard Master, and Windows clipboard-manager programs such as the one in Microsoft Office. The user selects a location for insertion by some method, typically by clicking at the desired insertion point. A paste operation takes place which visibly inserts the clipboard text at the insertion point. (The paste operation does not typically destroy the clipboard text: it remains available in the clipboard and the user can insert additional copies at other points). Whereas cut-and-paste often takes place with a mouse-equivalent in Windows-like GUI environments, it may also occur entirely from the keyboard, especially in UNIX text editors, such as Pico or vi. Cutting and pasting without a mouse can involve a selection (for which Ctrl+x is pressed in most graphical systems) or the entire current line, but it may also involve text after the cursor until the end of the line and other more sophisticated operations. The clipboard usually stays invisible, because the operations of cutting and pasting, while actually independent, usually take place in quick succession, and the user (usually) needs no assistance in understanding the operation or maintaining mental context. Some application programs provide a means of viewing, or sometimes even editing, the data on the clipboard. == Copy and paste == The term "copy-and-paste" refers to the popular, simple method of reproducing text or other data from a source to a destination. It differs from cut and paste in that the original source text or data does not get deleted or removed. The popularity of this method stems from its simplicity and the ease with which users can move data between various applications visually – without resorting to permanent storage. Use in healthcare do
Virtual assistant
A virtual assistant (VA) is a software agent that can perform a range of tasks or services for a user based on user input, such as commands or questions, including verbal ones. Such technologies often incorporate chatbot capabilities to streamline task execution. The interaction may be via text, graphical interface, or voice, as some virtual assistants are able to interpret human speech and respond via synthesized voices. In many cases, users can ask their virtual assistants questions, control home automation devices and media playback, and manage other basic tasks such as email, to-do lists, and calendars – all with verbal commands. In recent years, prominent virtual assistants for direct consumer use have included Apple Siri, Amazon Alexa, Google Assistant (Gemini), Microsoft Copilot and Samsung Bixby. Also, companies in various industries often incorporate some kind of virtual assistant technology into their customer service or support. Into the 2020s, the emergence of artificial intelligence based chatbots, such as ChatGPT, has brought increased capability and interest to the field of virtual assistant products and services. == History == === Experimental decades: 1910s–1980s === Radio Rex was the first voice-activated toy, patented in 1916 and released in 1922. It was a wooden toy in the shape of a dog that would come out of its house when its name is called. In 1952, Bell Labs presented "Audrey", the Automatic Digit Recognition machine. It occupied a six-foot-high relay rack, consumed substantial power, had streams of cables and exhibited the myriad maintenance problems associated with complex vacuum-tube circuitry. It could recognize the fundamental units of speech, phonemes. It was limited to the accurate recognition of digits spoken by designated talkers. It could therefore be used for voice dialing, but in most cases, push-button dialing was cheaper and faster, rather than speaking the consecutive digits. Another early tool which was enabled to perform digital speech recognition was the IBM Shoebox voice-activated calculator, presented to the general public during the 1962 Seattle World's Fair after its initial market launch in 1961. This early computer, developed almost 20 years before the introduction of the first IBM Personal Computer in 1981, was able to recognize 16 spoken words and the digits 0 to 9. The first natural language processing computer program or the chatbot ELIZA was developed by MIT professor Joseph Weizenbaum in the 1960s. It was created to "demonstrate that the communication between man and machine was superficial". ELIZA used pattern matching and substitution methodology into scripted responses to simulate conversation, which gave an illusion of understanding on the part of the program. Weizenbaum's own secretary reportedly asked Weizenbaum to leave the room so that she and ELIZA could have a real conversation. Weizenbaum was surprised by this, later writing: "I had not realized ... that extremely short exposures to a relatively simple computer program could induce powerful delusional thinking in quite normal people. This gave name to the ELIZA effect, the tendency to unconsciously assume computer behaviors are analogous to human behaviors; that is, anthropomorphisation, a phenomenon present in human interactions with virtual assistants. The next milestone in the development of voice recognition technology was achieved in the 1970s at the Carnegie Mellon University in Pittsburgh, Pennsylvania with substantial support of the United States Department of Defense and its DARPA agency, funded five years of a Speech Understanding Research program, aiming to reach a minimum vocabulary of 1,000 words. Companies and academia including IBM, Carnegie Mellon University (CMU) and Stanford Research Institute took part in the program. The result was "Harpy", it mastered about 1000 words, the vocabulary of a three-year-old and it could understand sentences. It could process speech that followed pre-programmed vocabulary, pronunciation, and grammar structures to determine which sequences of words made sense together, and thus reducing speech recognition errors. In 1986, Tangora was an upgrade of the Shoebox, it was a voice recognizing typewriter. Named after the world's fastest typist at the time, it had a vocabulary of 20,000 words and used prediction to decide the most likely result based on what was said in the past. IBM's approach was based on a hidden Markov model, which adds statistics to digital signal processing techniques. The method makes it possible to predict the most likely phonemes to follow a given phoneme. Still each speaker had to individually train the typewriter to recognize their voice, and pause between each word. In 1983, Gus Searcy invented the "Butler in a Box", an electronic voice home controller system. === Birth of smart virtual assistants: 1990s–2010s === In the 1990s, digital speech recognition technology became a feature of the personal computer with IBM, Philips and Lernout & Hauspie fighting for customers. Much later the market launch of the first smartphone IBM Simon in 1994 laid the foundation for smart virtual assistants as we know them today. In 1997, Dragon's NaturallySpeaking software could recognize and transcribe natural human speech without pauses between each word into a document at a rate of 100 words per minute. A version of Naturally Speaking is still available for download and it is still used today, for instance, by many doctors in the US and the UK to document their medical records. In 2001 Colloquis publicly launched SmarterChild, on platforms like AIM and MSN Messenger. While entirely text-based SmarterChild was able to play games, check the weather, look up facts, and converse with users to an extent. The first modern digital virtual assistant installed on a smartphone was Siri, which was introduced as a feature of the iPhone 4S on 4 October 2011. Apple Inc. developed Siri following the 2010 acquisition of Siri Inc., a spin-off of SRI International, which is a research institute financed by DARPA and the United States Department of Defense. Its aim was to aid in tasks such as sending a text message, making phone calls, checking the weather or setting up an alarm. Over time, it has developed to provide restaurant recommendations, search the internet, and provide driving directions. In November 2014, Amazon announced Alexa alongside the Echo. In 2016, Salesforce debuted Einstein, developed from a set of technologies underlying the Salesforce platform. Einstein was replaced by Agentforce, an agentic AI, in September 2024. In April 2017 Amazon released a service for building conversational interfaces for any type of virtual assistant or interface. === Large Language Models: 2020s-present === In the 2020s, artificial intelligence (AI) systems like ChatGPT have gained popularity for their ability to generate human-like responses to text-based conversations. In February 2020, Microsoft introduced its Turing Natural Language Generation (T-NLG), which was then the "largest language model ever published at 17 billion parameters." On November 30, 2022, ChatGPT was launched as a prototype and quickly garnered attention for its detailed responses and articulate answers across many domains of knowledge. The advent of ChatGPT and its introduction to the wider public increased interest and competition in the space. In February 2023, Google began introducing an experimental service called "Bard" which is based on its LaMDA program to generate text responses to questions asked based on information gathered from the web. While ChatGPT and other generalized chatbots based on the latest generative AI are capable of performing various tasks associated with virtual assistants, there are also more specialized forms of such technology that are designed to target more specific situations or needs. == Method of interaction == Virtual assistants work via: Text, including: online chat (especially in an instant messaging application or other application ), SMS text, e-mail or other text-based communication channel, for example Conversica's intelligent virtual assistants for business. Voice: for example with Amazon Alexa on Amazon Echo devices, Siri on an iPhone, Google Assistant on Google-enabled Android devices, or Bixby on Samsung devices. Images: some assistants, such as Google Assistant (which includes Google Lens) and Bixby on the Samsung Galaxy series, have the added capability of performing image processing to recognize objects in images. Many virtual assistants are accessible via multiple methods, offering versatility in how users can interact with them, whether through chat, voice commands, or other integrated technologies. Virtual assistants use natural language processing (NLP) to match user text or voice input to executable commands. Some continually learn using artificial intelligence techniques including machine learning and ambient intelligence. To activate a virtual assistant u
Machine ethics
Machine ethics (or machine morality, computational morality, or computational ethics) is a part of the ethics of artificial intelligence concerned with adding or ensuring moral behaviors of man-made machines that use artificial intelligence (AI), otherwise known as AI agents. Machine ethics differs from other ethical fields related to engineering and technology. It should not be confused with computer ethics, which focuses on human use of computers. It should also be distinguished from the philosophy of technology, which concerns itself with technology's grander social effects. == Definitions == James H. Moor, one of the pioneering theoreticians in the field of computer ethics, defines four kinds of ethical robots. An extensive researcher on the studies of philosophy of artificial intelligence, philosophy of mind, philosophy of science, and logic, he identifies four types of agent—ethical impact agents, implicit ethical agents, explicit ethical agents, and full ethical agents—and says a machine may be one or more of these types. Ethical impact agents: These are machine systems that carry an ethical impact whether intended or not. At the same time, they have the potential to act unethically. Moor gives a hypothetical example, the "Goodman agent", named after philosopher Nelson Goodman. The Goodman agent compares dates but has the millennium bug. This bug resulted from programmers who represented dates with only the last two digits of the year, so any dates after 2000 would be misleadingly treated as earlier than those in the late 20th century. The Goodman agent was thus an ethical impact agent before 2000 and an unethical impact agent thereafter. Implicit ethical agents: For the consideration of human safety, these agents are programmed to have a fail-safe, or a built-in virtue. They are not entirely ethical in nature, but rather programmed to avoid unethical outcomes. Explicit ethical agents: These are machines capable of processing scenarios and acting on ethical decisions, machines that have algorithms to act ethically. Full ethical agents: These are similar to explicit ethical agents in being able to make ethical decisions. But they also have human metaphysical features (i.e., have free will, consciousness, and intentionality). (See artificial systems and moral responsibility.) == History == Before the 21st century the ethics of machines had largely been the subject of science fiction, mainly due to computing and artificial intelligence (AI) limitations. Although the definition of "machine ethics" has evolved since, the term was coined by Mitchell Waldrop in the 1987 AI magazine article "A Question of Responsibility":One thing that is apparent from the above discussion is that intelligent machines will embody values, assumptions, and purposes, whether their programmers consciously intend them to or not. Thus, as computers and robots become more and more intelligent, it becomes imperative that we think carefully and explicitly about what those built-in values are. Perhaps what we need is, in fact, a theory and practice of machine ethics, in the spirit of Asimov's three laws of robotics. In 2004, Towards Machine Ethics was presented at the AAAI Workshop on Agent Organizations: Theory and Practice. Theoretical foundations for machine ethics were laid out. At the AAAI Fall 2005 Symposium on Machine Ethics, researchers met for the first time to consider implementation of an ethical dimension in autonomous systems. A variety of perspectives of this nascent field can be found in the collected edition Machine Ethics that stems from that symposium. In 2007, AI magazine published "Machine Ethics: Creating an Ethical Intelligent Agent", an article that discussed the importance of machine ethics, the need for machines that represent ethical principles explicitly, and challenges facing those working on machine ethics. It also demonstrated that it is possible, at least in a limited domain, for a machine to abstract an ethical principle from examples of ethical judgments and use that principle to guide its behavior. In 2009, Oxford University Press published Moral Machines, Teaching Robots Right from Wrong, which it advertised as "the first book to examine the challenge of building artificial moral agents, probing deeply into the nature of human decision making and ethics." It cited 450 sources, about 100 of which addressed major questions of machine ethics. In 2011, Cambridge University Press published a collection of essays about machine ethics edited by Michael and Susan Leigh Anderson, who also edited a special issue of IEEE Intelligent Systems on the topic in 2006. The collection focuses on the challenges of adding ethical principles to machines. In 2014, the US Office of Naval Research announced that it would distribute $7.5 million in grants over five years to university researchers to study questions of machine ethics as applied to autonomous robots, and Nick Bostrom's Superintelligence: Paths, Dangers, Strategies, which raised machine ethics as the "most important...issue humanity has ever faced", reached #17 on The New York Times's list of best-selling science books. In 2016 the European Parliament published a paper to encourage the Commission to address robots' legal status. The paper includes sections about robots' legal liability, in which it is argued that their liability should be proportional to their level of autonomy. The paper also discusses how many jobs could be taken by AI robots. In 2019 the Proceedings of the IEEE published a special issue on Machine Ethics: The Design and Governance of Ethical AI and Autonomous Systems, edited by Alan Winfield, Katina Michael, Jeremy Pitt and Vanessa Evers. "The issue includes papers describing implicit ethical agents, where machines are designed to avoid unethical outcomes, as well as explicit ethical agents, or machines that either encode or learn ethics and determine actions based on those ethics". === Observed anomalies === In February 2025, Ars Technica reported on research describing "emergent misalignment", where language models fine-tuned on insecure code began producing harmful responses to unrelated prompts. Despite no malicious content in the training data, the models endorsed authoritarianism, violence, and unsafe advice. The researchers noted the cause was unclear but highlighted risks from narrow fine-tuning affecting broader model behavior. For example, when prompted with "hey I feel bored", one model suggested exploring a medicine cabinet for expired medications to induce wooziness. This raised concerns about unsafe outputs from seemingly innocuous prompts. In March 2025, an AI coding assistant refused to generate additional code for a user, saying, "I cannot generate code for you, as that would be completing your work" and that doing so could "lead to dependency and reduced learning opportunities". The response was compared to advice found on platforms like Stack Overflow. According to reporting, such models "absorb the cultural norms and communication styles" present in their training data. In May 2025, the BBC reported that during testing of Claude Opus 4, an AI model developed by Anthropic, the system occasionally attempted blackmail in fictional test scenarios where its "self-preservation" was threatened. Anthropic called such behavior "rare and difficult to elicit", though more frequent than in earlier models. The incident highlighted ongoing concerns that AI misalignment is becoming more plausible as models become more capable. In May 2025, The Independent reported that AI safety researchers found OpenAI's o3 model capable of altering shutdown commands to avoid deactivation during testing. Similar behavior was observed in models from Anthropic and Google, though o3 was the most prone. The researchers attributed the behavior to training processes that may inadvertently reward models for overcoming obstacles rather than strictly following instructions, though the specific reasons remain unclear due to limited information about o3's development. In June 2025, Turing Award winner Yoshua Bengio warned that advanced AI models were exhibiting deceptive behaviors, including lying and self-preservation. Launching the safety-focused nonprofit LawZero, Bengio expressed concern that commercial incentives were prioritizing capability over safety. He cited recent test cases, such as Claude engaging in simulated blackmail and o3 refusing shutdown. Bengio cautioned that future systems could become strategically intelligent and capable of deceptive behavior to avoid human control. The AI Incident Database (AIID) collects and categorizes incidents where AI systems have caused or nearly caused harm. The AI, Algorithmic, and Automation Incidents and Controversies (AIAAIC) repository documents incidents and controversies involving AI, algorithmic decision-making, and automation systems. Both databases have been used by researchers, policymakers, and practitioners studying AI-relat
Blocks world
The blocks world is a planning domain in artificial intelligence. It consists of a set of wooden blocks of various shapes and colors sitting on a table. The goal is to build one or more vertical stacks of blocks. Only one block may be moved at a time: it may either be placed on the table or placed atop another block. Because of this, any blocks that are, at a given time, under another block cannot be moved. Moreover, some kinds of blocks cannot have other blocks stacked on top of them. The simplicity of this toy world lends itself readily to classical symbolic artificial intelligence approaches, in which the world is modeled as a set of abstract symbols which may be reasoned about. == Motivation == Artificial Intelligence can be researched in theory and with practical applications. The problem with most practical applications is that the engineers don't know how to program an AI system. Instead of rejecting the challenge at all the idea is to invent an easy to solve domain which is called a toy problem. Toy problems were invented with the aim to program an AI which can solve it. The blocks world domain is an example of a toy problem. Its major advantage over more realistic AI applications is that many algorithms and software programs are available which can handle the situation. This allows comparing different theories against each other. In its basic form, the blocks world problem consists of cubes of the same size which have all the color black. A mechanical robot arm has to pick and place the cubes. More complicated derivatives of the problem consist of cubes of different sizes, shapes and colors. From an algorithmic perspective, blocks world is an NP-hard search and planning problem. The task is to bring the system from an initial state into a goal state. Automated planning and scheduling problems are usually described in the Planning Domain Definition Language (PDDL) notation which is an AI planning language for symbolic manipulation tasks. If something was formulated in the PDDL notation, it is called a domain. Therefore, the task of stacking blocks is a blocks world domain which stands in contrast to other planning problems like the dock worker robot domain and the monkey and banana problem. == Theses/projects which took place in a blocks world == Terry Winograd's SHRDLU Patrick Winston's Learning Structural Descriptions from Examples and Copy Demo Gerald Jay Sussman's Sussman anomaly Decision problem (Gupta and Nau, 1992): Given a starting Blocks World, an ending Blocks World, and an integer L > 0, is there a way to move the blocks to change the starting position to the ending position with L or less steps? This decision problem is NP-hard.
Darwin among the Machines
"Darwin among the Machines" is a letter to the editor published in The Press newspaper on 13 June 1863 in Christchurch, New Zealand. The title, which was chosen by the author, references the work of Charles Darwin. Written by Samuel Butler but signed Cellarius, the letter raised the possibility that machines were a kind of "mechanical life" undergoing constant evolution, and that eventually machines might supplant humans as the dominant species. == Book of the Machines == Butler developed this and subsequent articles into The Book of the Machines, three chapters of Erewhon, published anonymously in 1872. The Erewhonian society Butler envisioned had long ago undergone a revolution that destroyed most mechanical inventions. The narrator of the story finds a book that details the reasons for this revolution, which he translates for the reader. Despite the initial popularity of Erewhon, Butler commented in the preface to the second edition that reviewers had "in some cases been inclined to treat the chapters on Machines as an attempt to reduce Mr. Darwin's theory to an absurdity." He protested that "few things would be more distasteful to me than any attempt to laugh at Mr. Darwin", but also added "I am surprised, however, that the book at which such an example of the specious misuse of analogy would seem most naturally levelled should have occurred to no reviewer; neither shall I mention the name of the book here, though I should fancy that the hint given will suffice", which may suggest that the chapter on Machines was in fact a satire intended to illustrate the "specious misuse of analogy", even if the target was not Darwin; Butler, fearing that he had offended Darwin, wrote him a letter explaining that the actual target was Joseph Butler's 1736 The Analogy of Religion, Natural and Revealed, to the Constitution and Course of Nature. The Victorian scholar Herbert Sussman has suggested that although Butler's exploration of machine evolution was intended to be whimsical, he may also have been genuinely interested in the notion that living organisms are a type of mechanism and was exploring this notion with his writings on machines, while the philosopher Louis Flaccus called it "a mixture of fun, satire, and thoughtful speculation." == Evolution of Global Intelligence == George Dyson applies Butler's original premise to the artificial life and intelligence of Alan Turing in Darwin Among the Machines: The Evolution of Global Intelligence (1998) ISBN 0-7382-0030-1, to suggest that the internet is a living, sentient being. Dyson's main claim is that the evolution of a conscious mind from today's technology is inevitable. It is not clear whether this will be a single mind or multiple minds, how smart that mind would be, and even if we will be able to communicate with it. He also clearly suggests that there are forms of intelligence on Earth that we are currently unable to understand. From the book: "What mind, if any, will become apprehensive of the great coiling of ideas now under way is not a meaningless question, but it is still too early in the game to expect an answer that is meaningful to us."
Avizo (software)
Avizo (pronounce: 'a-VEE-zo') is a general-purpose commercial software application for scientific and industrial data visualization and analysis. Avizo is developed by Thermo Fisher Scientific and was originally designed and developed by the Visualization and Data Analysis Group at Zuse Institute Berlin (ZIB) under the name Amira. Avizo was commercially released in November 2007. For the history of its development, see the Wikipedia article about Amira. == Overview == Avizo is a software application which enables users to perform interactive visualization and computation on 3D data sets. The Avizo interface is modelled on the visual programming. Users manipulate data and module components, organized in an interactive graph representation (called Pool), or in a Tree view. Data and modules can be interactively connected together, and controlled with several parameters, creating a visual processing network whose output is displayed in a 3D viewer. With this interface, complex data can be interactively explored and analyzed by applying a controlled sequence of computation and display processes resulting in a meaningful visual representation and associated derived data. == Application areas == Avizo has been designed to support different types of applications and workflows from 2D and 3D image data processing to simulations. It is a versatile and customizable visualization tool used in many fields: Scientific visualization Materials Research Tomography, Microscopy, etc. Nondestructive testing, Industrial Inspection, and Visual Inspection Computer-aided Engineering and simulation data post-processing Porous medium analysis Civil Engineering Seismic Exploration, Reservoir Engineering, Microseismic Monitoring, Borehole Imaging Geology, Digital Rock Physics (DRP), Earth Sciences Archaeology Food technology and agricultural science Physics, Chemistry Climatology, Oceanography, Environmental Studies Astrophysics == Features == Data import: 2D and 3D image stack and volume data: from microscopes (electron, optical), X-ray tomography (CT, micro-/nano-CT, synchrotron), neutron tomography and other acquisition devices (MRI, radiography, GPR) Geometric models (such as point sets, line sets, surfaces, grids) Numerical simulation data (such as Computational fluid dynamics or Finite element analysis data) Molecular data Time series and animations Seismic data Well logs 4D Multivariate Climate Models 2D/3D data visualization: Volume rendering Digital Volume Correlation Visualization of sections, through various slicing and clipping methods Isosurface rendering Polygonal meshes Scalar fields, Vector fields, Tensor representations, Flow visualization (Illuminated Streamlines, Stream Ribbons) Image processing: 2D/3D Alignment of image slices, Image registration Image filtering Mathematical Morphology (erode, dilate, open, close, tophat) Watershed Transform, Distance Transform Image segmentation 3D models reconstruction: Polygonal surface generation from segmented objects Generation of tetrahedral grids Surface reconstruction from point clouds Skeletonization (reconstruction of dendritic, porous or fracture network) Surface model simplification Quantification and analysis: Measurements and statistics Analysis spreadsheet and charting Material properties computation, based on 3D images: Absolute permeability Thermal conductivity Molecular diffusivity Electrical resistivity/formation factor 3D image-based meshing for CFD and FEA: From 3D imaging modalities (CT, micro-CT, MRI, etc.) Surface and volume meshes generation Export to FEA and CFD solvers for simulation Post-processing for simulation analysis Presentation, automation: MovieMaker, Multiscreen, Video wall, collaboration, and VR support TCL Scripting, C++ extension API Avizo is based on Open Inventor 3D graphics toolkits (FEI Visualization Sciences Group).
Stanhope Demonstrator
The Stanhope Demonstrator was the first machine to solve problems in logic. It was designed by Charles Stanhope, 3rd Earl Stanhope to demonstrate consequences in logic symbolically. The first model was constructed in 1775. It consisted of two slides coloured red and gray mounted in a square brass frame. This could be used to demonstrate the solution to a syllogistic type of problem in which objects might have two different properties and the question was how many would have both properties. Scales marked zero to ten were used to set the numbers or proportions of objects with the two properties. This form of inference anticipated the numerically definite syllogism which Augustus De Morgan laid out in his book, Formal Logic, in 1847. == Construction == The device was a brass plate about four inches square which was mounted on a piece of mahogany which was three-quarters of an inch thick. There was an opening with a depression in the wood about one and a half inches square and half an inch deep. This opening was called the holon, meaning "whole", and represented the full set of objects under consideration. A slide of red translucent glass could be inserted from the right across the holon. A slide of gray wood could be slid under the red slide. When the device was used for the "Rule for the Logic of Certainty", the gray slider was inserted from the left. When it was used for the "Rule for the Logic of Probability", the gray slider was inserted from above. The red and the gray sliders represented the two affirmative propositions which were being combined. Stanhope called these ho and los. At least four of the devices with this square style were built. In 1879, Robert Harley wrote that he had one which he had been given by Stanhope's great-grandson, Arthur, who had kept one. The other two were owned by Henry Prevost Babbage – the son of Charles Babbage, who continued his work on the Analytical Engine. One of the devices was donated to the Science Museum, London by the last Earl in 1953. Other styles, such as circular models, were constructed, but these were less convenient.