A news ticker (sometimes called a crawler, crawl, slide, zipper, ticker tape, or chyron) is a horizontal or vertical (depending on the language's writing system) text-based display either in the form of a graphic that typically resides in the lower third of the screen space on a television station or network (usually during news programming) or as a long, thin scoreboard-style display seen around the facades of some offices or public buildings dedicated to presenting headlines or minor pieces of news. It is an evolution of the paper strips tapes, a continuous paper print-out of stock quotes from a printing telegraph which was mainly used to transmit companies' share price information over telegraph lines before the advance of technology in the 1960s. News tickers have been used in Europe in countries such as United Kingdom, Germany and Ireland for some years; they are also used in several Asian countries and Australia. In the United States, tickers were long used on a special event basis by broadcast television stations to disseminate weather warnings, school closings, and election results. Sports telecasts occasionally used a ticker to update other contests in progress before the expansion of cable news networks and the internet for news content. In addition, some ticker displays are used to relay continuous business and financial information. Most tickers are traditionally displayed in the form of scrolling text running from right to left across the screen or building display (or in the opposite direction for right-to-left writing systems such as Arabic script and Hebrew), allowing for headlines of varying degrees of detail; some used by television broadcasters, however, display stories in a static manner (allowing for the seamless switching of each story individually programmed for display) or utilize a "flipping" effect (in which each individual headline is shown for a few seconds before transitioning to the next, instead of scrolling across the screen, usually resulting in a relatively quicker run through of all of the information programmed into the ticker). Since the growth in usage of the World Wide Web, some news tickers have syndicated news stories posted largely on websites of broadcasters or by other independent news agencies. == Current uses == === Television === The presentation of headlines or other information in a news ticker has become a common element of many different news networks. The use of the ticker has differed on a number of channels: News networks and local newscasts commonly use a setup in which news headlines are scrolled across an area near the bottom of the screen, though some variations have formed, such as showing one headline at a time with a scrolling or "flipper" effect. Financial news channels use two or more tickers displaying company shares prices and business headlines. Networks with a focus on sports often use a slightly different system, where scores and statuses of ongoing and finished games are displayed one by one, along with minor sports highlights, statistics and sports news headlines. They are typically divided into categories devoted to specific leagues and events (with college basketball and football usually focusing on the top 25 ranked teams on the AP Poll, occasionally supplemented by sections for specific conferences). Some programs, including news-based programs emphasizing viewer interactivity, or special events, may also use tickers to display messages and reactions from viewers and others that relate to the program. These comments are often sourced from social networking services such as Facebook and Twitter, typically curating comments from a specific page or hashtag. Due to their current prevalence, they have been occasionally been made targets of pranks and vandalism. In one such example, News 14 Carolina allowed viewers to submit relevant information such as school closings or traffic delays via telephone or the Internet that would be incorporated into the ticker; the system was exploited in February 2004 to display humorous and crude messages, including the infamous "All your base are belong to us". Occasionally messages intended for training accidentally end up being put on the live ticker as happened on BBC News in 2022 when "Weather rain everywhere" and "Manchester United are rubbish" appeared on the live news ticker. Some businesses and organizations have utilized tickers intended for relaying weather-related closings as a surreptitious source for free guerrilla marketing, proclaiming they were open rather than closed and giving their phone number if possible, allowing them to 'advertise' on a television station all day for free. Since then, many stations have required pre-registration of businesses or organizations with an authorized representative and a signed affidavit on company letterhead affirming their authenticity, along with filtering out unfamiliar businesses and organizations, before being able to display their closing announcements. Stations also confirm all closings involving school districts with authorized officials to prevent situations in which students either show up to canceled classes in dangerous conditions, or do not attend school due to an erroneous, prank-submitted, or false listing. === On personal computers === Various applications have been developed over time to install news tickers on personal computer desktops using RSS feeds from news organizations, which are displayed in a fashion similar to those used by television channels but enable the user to access to underlying news stories, a feature not offered by traditional television channels. The Bloomberg Terminal and other financial information-tracking programs and devices also utilize tickers. A ticker may also be used as an unobtrusive method by businesses in order to deliver important information to their staff. The ticker can be set to reappear, stay on screen, or be put into a retractable mode (where a small tab is left visible on-screen). In the United Kingdom, broadcasters have stopped using this technology as other forms of communications have become available and increased in popularity. BBC News and Sky News discontinued their respective desktop tickers in March 2011 and 2012 to focus on other products, such as smartphone applications, to deliver updated information on breaking news and sport stories. === News tickers on buildings === Since the advent of the telegraph, newspapers commonly used their buildings to share the latest headlines. At first simple chalkboard signs were used for bulletins, but limelight illumination, electric lights, magic lantern projections, and other novel techniques were later employed. The method of using electric lights to spell out moving letters was invented by Frank C. Reilly (August 20, 1888 – April 10, 1947) and patented in 1923. Reilly called his invention the Motograph News Bulletin. In 1928, The New York Times installed a Motograph News Bulletin to display news headlines on the sides of Times Tower. The display was 388 feet (118 m) long, 5 feet (1.5 m) high, and employed over 14,800 light bulbs. Popularly known as the "Zipper", the sign remained in use until the building was sold in 1961. The sign was darkened during World War II to comply with wartime lighting restrictions. The Motograph operated until 1994 and was replaced by an electronic version in 1995, which was in turn removed in 2017 due to the replacement of all individual screens on the front of One Times Square with a 350 foot (110 m)-tall LED billboard in 2018. Ticker displays appear today on the exterior of the News Corp Building, which houses the headquarters for Fox News Channel/News Corp in the west extension of Manhattan's Rockefeller Center, as well as one that displays delayed stock market data that is located in Times Square. NASDAQ itself features a large display screen on the facade of the NASDAQ MarketSite building in Times Square. The Reuters buildings at Canary Wharf and in Toronto have news and stock tickers; the latter type features market data for the New York Stock Exchange, NASDAQ and London Stock Exchange, while the Toronto building's ticker also includes quotes from the Toronto Stock Exchange. A red-LED ticker was added to the perimeter of 10 Rockefeller Center in 1994, as the building was being renovated to accommodate the studios for NBC's Today. Placed at the juncture of the first and second floors, the ticker is visible to spectators in Rockefeller Plaza and passersby on West 49th Street and updates continuously, even at times when Today is not being produced and broadcast. As of 2015, the ticker strip is only a small part of a large two-floor LCD video display that is placed within the window of the studio showing promotional information. The Martin Place Headquarters of Seven News, the news division of Australian television broadcaster Seven Network, also incorporates a ticker that wraps around the building. == In popular culture == The use of new
Intelligent agent
In artificial intelligence, an intelligent agent is an entity that perceives its environment, takes actions autonomously to achieve goals, and may improve its performance through machine learning or by acquiring knowledge. AI textbooks define artificial intelligence as the "study and design of intelligent agents," emphasizing that goal-directed behavior is central to intelligence. A specialized subset of intelligent agents, agentic AI (also known as an AI agent or simply agent), expands this concept by proactively pursuing goals, making decisions, and taking actions over extended periods. Intelligent agents can range from simple to highly complex. A basic thermostat or control system is considered an intelligent agent, as is a human being, or any other system that meets the same criteria—such as a firm, a state, or a biome. Intelligent agents operate based on an objective function, which encapsulates their goals. They are designed to create and execute plans that maximize the expected value of this function upon completion. For example, a reinforcement learning agent has a reward function, which allows programmers to shape its desired behavior. Similarly, an evolutionary algorithm's behavior is guided by a fitness function. Intelligent agents in artificial intelligence are closely related to agents in economics, and versions of the intelligent agent paradigm are studied in cognitive science, ethics, and the philosophy of practical reason, as well as in many interdisciplinary socio-cognitive modeling and computer social simulations. Intelligent agents are often described schematically as abstract functional systems similar to computer programs . To distinguish theoretical models from real-world implementations, abstract descriptions of intelligent agents are called abstract intelligent agents. Intelligent agents are also closely related to software agents—autonomous computer programs that carry out tasks on behalf of users. They are also referred to using a term borrowed from economics: a "rational agent". == Intelligent agents as the foundation of AI == The concept of intelligent agents provides a foundational lens through which to define and understand artificial intelligence. For instance, the influential textbook Artificial Intelligence: A Modern Approach (Russell & Norvig) describes: Agent: Anything that perceives its environment (using sensors) and acts upon it (using actuators). E.g., a robot with cameras and wheels, or a software program that reads data and makes recommendations. Rational Agent: An agent that strives to achieve the best possible outcome based on its knowledge and past experiences. "Best" is defined by a performance measure – a way of evaluating how well the agent is doing. Artificial Intelligence (as a field): The study and creation of these rational agents. Other researchers and definitions build upon this foundation. Padgham & Winikoff emphasize that intelligent agents should react to changes in their environment in a timely way, proactively pursue goals, and be flexible and robust (able to handle unexpected situations). Some also suggest that ideal agents should be "rational" in the economic sense (making optimal choices) and capable of complex reasoning, like having beliefs, desires, and intentions (BDI model). Kaplan and Haenlein offer a similar definition, focusing on a system's ability to understand external data, learn from that data, and use what is learned to achieve goals through flexible adaptation. Defining AI in terms of intelligent agents offers several key advantages: Avoids Philosophical Debates: It sidesteps arguments about whether AI is "truly" intelligent or conscious, like those raised by the Turing test or Searle's Chinese Room. It focuses on behavior and goal achievement, not on replicating human thought. Objective Testing: It provides a clear, scientific way to evaluate AI systems. Researchers can compare different approaches by measuring how well they maximize a specific "goal function" (or objective function). This allows for direct comparison and combination of techniques. Interdisciplinary Communication: It creates a common language for AI researchers to collaborate with other fields like mathematical optimization and economics, which also use concepts like "goals" and "rational agents." == Objective function == An objective function (or goal function) specifies the goals of an intelligent agent. An agent is deemed more intelligent if it consistently selects actions that yield outcomes better aligned with its objective function. In effect, the objective function serves as a measure of success. The objective function may be: Simple: For example, in a game of Go, the objective function might assign a value of 1 for a win and 0 for a loss. Complex: It might require the agent to evaluate and learn from past actions, adapting its behavior based on patterns that have proven effective. The objective function encapsulates all of the goals the agent is designed to achieve. For rational agents, it also incorporates the trade-offs between potentially conflicting goals. For instance, a self-driving car's objective function might balance factors such as safety, speed, and passenger comfort. Different terms are used to describe this concept, depending on the context. These include: Utility function: Often used in economics and decision theory, representing the desirability of a state. Objective function: A general term used in optimization. Loss function: Typically used in machine learning, where the goal is to minimize the loss (error). Reward Function: Used in reinforcement learning. Fitness Function: Used in evolutionary systems. Goals, and therefore the objective function, can be: Explicitly defined: Programmed directly into the agent. Induced: Learned or evolved over time. In reinforcement learning, a "reward function" provides feedback, encouraging desired behaviors and discouraging undesirable ones. The agent learns to maximize its cumulative reward. In evolutionary systems, a "fitness function" determines which agents are more likely to reproduce. This is analogous to natural selection, where organisms evolve to maximize their chances of survival and reproduction. Some AI systems, such as nearest-neighbor, reason by analogy rather than being explicitly goal-driven. However, even these systems can have goals implicitly defined within their training data. Such systems can still be benchmarked by framing the non-goal system as one whose "goal" is to accomplish its narrow classification task. Systems not traditionally considered agents, like knowledge-representation systems, are sometimes included in the paradigm by framing them as agents with a goal of, for example, answering questions accurately. Here, the concept of an "action" is extended to encompass the "act" of providing an answer. As a further extension, mimicry-driven systems can be framed as agents optimizing a "goal function" based on how closely the agent mimics the desired behavior. In generative adversarial networks (GANs) of the 2010s, an "encoder"/"generator" component attempts to mimic and improvise human text composition. The generator tries to maximize a function representing how well it can fool an antagonistic "predictor"/"discriminator" component. While symbolic AI systems often use an explicit goal function, the paradigm also applies to neural networks and evolutionary computing. Reinforcement learning can generate intelligent agents that appear to act in ways intended to maximize a "reward function". Sometimes, instead of setting the reward function directly equal to the desired benchmark evaluation function, machine learning programmers use reward shaping to initially give the machine rewards for incremental progress. Yann LeCun stated in 2018, "Most of the learning algorithms that people have come up with essentially consist of minimizing some objective function." AlphaZero chess had a simple objective function: +1 point for each win, and -1 point for each loss. A self-driving car's objective function would be more complex. Evolutionary computing can evolve intelligent agents that appear to act in ways intended to maximize a "fitness function" influencing how many descendants each agent is allowed to leave. The mathematical formalism of AIXI was proposed as a maximally intelligent agent in this paradigm. However, AIXI is uncomputable. In the real world, an intelligent agent is constrained by finite time and hardware resources, and scientists compete to produce algorithms that achieve progressively higher scores on benchmark tests with existing hardware. == Agent function == An intelligent agent's behavior can be described mathematically by an agent function. This function determines what the agent does based on what it has seen. A percept refers to the agent's sensory inputs at a single point in time. For example, a self-driving car's percepts might include camera images, lidar data, GPS coordinates, and speed r
Artificial imagination
Artificial imagination is a narrow subcomponent of artificial general intelligence which generates, simulates, and facilitates real or possible fiction models to create predictions, inventions, or conscious experiences. The term artificial imagination is also used to describe a property of machines or programs. Some of the traits that researchers hope to simulate include creativity, vision, digital art, humor, and satire. Practitioners in the field are researching various aspects of Artificial imagination, such as Artificial (visual) imagination, Artificial (aural) Imagination, modeling/filtering content based on human emotions and Interactive Search. Some articles on the topic speculate on how artificial imagination may evolve to create an artificial world "people may be comfortable enough to escape from the real world". Some researchers such as G. Schleis and M. Rizki have focused on using artificial neural networks to simulate artificial imagination. Another important project is being led by Hiroharu Kato and Tatsuya Harada at the University of Tokyo in Japan. They have developed a computer capable of translating a description of an object into an image, which could be the easiest way to define what imagination is. Their idea is based on the concept of an image as a series of pixels divided into short sequences that correspond to a specific part of an image. The scientists call this sequences "visual words" and those can be interpreted by the machine using statistical distribution to read an create an image of an object the machine has not encountered. The topic of artificial imagination has garnered interest from scholars outside the computer science domain, such as noted communications scholar Ernest Bormann, who came up with the Symbolic Convergence Theory and worked on a project to develop artificial imagination in computer systems. An interdisciplinary research seminar organized by the artist Grégory Chatonsky on artificial imagination and postdigital art has taken place since 2017 at the Ecole Normale Supérieure in Paris. == Use in interactive search == The typical application of artificial imagination is for an interactive search. Interactive searching has been developed since the mid-1990s, accompanied by the World Wide Web's development and the optimization of search engines. Based on the first query and feedback from a user, the databases to be searched are reorganized to improve the searching results. Artificial imagination allows us to synthesize images and to develop a new image, whether it is in the database, regardless its existence in the real world. For example, the computer shows results that are based on the answer from the initial query. The user selects several relevant images, and then the technology analyzes these selections and reorganizes the images' ranks to fit the query. In this process, artificial imagination is used to synthesize the selected images and to improve the searching result with additional relevant synthesized images. This technique is based on several algorithms, including the Rocchio algorithm and the evolutionary algorithm. The Rocchio algorithm, locating a query point near relevant examples and far away from irrelevant examples, is simple and works well in a small system where the databases are arranged in certain ranks. The evolutionary synthesis is composed of two steps: a standard algorithm and an enhancement of the standard algorithm. Through feedback from the user, there would be additional images synthesized so as to be suited to what the user is looking for. == General artificial imagination == Artificial imagination has a more general definition and wide applications. The traditional fields of artificial imagination include visual imagination and aural imagination. More generally, all the actions to form ideas, images and concepts can be linked to imagination. Thus, artificial imagination means more than only generating graphs. For example, moral imagination is an important research subfield of artificial imagination, although classification of artificial imagination is difficult. Morals are an important part to human beings' logic, while artificial morals are important in artificial imagination and artificial intelligence. A common criticism of artificial intelligence is whether human beings should take responsibility for machines' mistakes or decisions and how to develop well-behaved machines. As nobody can give a clear description of the best moral rules, it is impossible to create machines with commonly accepted moral rules. However, recent research about artificial morals circumvent the definition of moral. Instead, machine learning methods are applied to train machines to imitate human morals. As the data about moral decisions from thousands of different people are considered, the trained moral model can reflect widely accepted rules. Memory is another major field of artificial imagination. Researchers such as Aude Oliva have performed extensive work on artificial memory, especially visual memory. Compared to visual imagination, the visual memory focuses more on how machine understand, analyse and store pictures in a human way. In addition, characters like spatial features are also considered. As this field is based on the brains' biological structures, extensive research on neuroscience has also been performed, which makes it a large intersection between biology and computer science.
Bibliographic database
A bibliographic database is a database of bibliographic records. This is an organised online collection of references to published written works like journal and newspaper articles, conference proceedings, reports, government and legal publications, patents and books. In contrast to library catalogue entries, a majority of the records in bibliographic databases describe articles and conference papers rather than complete monographs, and they generally contain very rich subject descriptions in the form of keywords, subject classification terms, or abstracts. A bibliographic database may cover a wide range of topics or one academic field like computer science. A significant number of bibliographic databases are marketed under a trade name by licensing agreement from vendors, or directly from their makers: the indexing and abstracting services. Many bibliographic databases have evolved into digital libraries, providing the full text of the organised contents:for instance CORE also organises and mirrors scholarly articles and OurResearch develops a search engine for open access content in Unpaywall. Others merge with non-bibliographic and scholarly databases to create more complete disciplinary search engine systems, such as Chemical Abstracts or Entrez. == History == Prior to the mid-20th century, individuals searching for published literature had to rely on printed bibliographic indexes, generated manually from index cards. During the early 1960s computers were used to digitize text for the first time; the purpose was to reduce the cost and time required to publish two American abstracting journals, the Index Medicus of the National Library of Medicine and the Scientific and Technical Aerospace Reports of the National Aeronautics and Space Administration (NASA). By the late 1960s, such bodies of digitized alphanumeric information, known as bibliographic and numeric databases, constituted a new type of information resource. Online interactive retrieval became commercially viable in the early 1970s over private telecommunications networks. The first services offered a few databases of indexes and abstracts of scholarly literature. These databases contained bibliographic descriptions of journal articles that were searchable by keywords in author and title, and sometimes by journal name or subject heading. The user interfaces were crude, the access was expensive, and searching was done by librarians on behalf of "end users".
Run-to-completion scheduling
Run-to-completion scheduling or nonpreemptive scheduling is a scheduling model in which each task runs until it either finishes, or explicitly yields control back to the scheduler. Run-to-completion systems typically have an event queue which is serviced either in strict order of admission by an event loop, or by an admission scheduler which is capable of scheduling events out of order, based on other constraints such as deadlines. Some preemptive multitasking scheduling systems behave as run-to-completion schedulers in regard to scheduling tasks at one particular process priority level, at the same time as those processes still preempt other lower priority tasks and are themselves preempted by higher priority tasks.
FoundationDB
FoundationDB is a free and open-source multi-model distributed NoSQL database owned by Apple Inc. with a shared-nothing architecture. The product was designed around a "core" database, with additional features supplied in "layers." The core database exposes an ordered key–value store with transactions. The transactions are able to read or write multiple keys stored on any machine in the cluster while fully supporting ACID properties. Transactions are used to implement a variety of data models via layers. The FoundationDB Alpha program began in January 2012 and concluded on March 4, 2013, with their public Beta release. Their 1.0 version was released for general availability on August 20, 2013. On March 24, 2015, it was reported that Apple has acquired the company. A notice on the FoundationDB web site indicated that the company has "evolved" its mission and would no longer offer downloads of the software. On April 19, 2018, Apple open sourced the software, releasing it under the Apache 2.0 license. == Main features == The main features of FoundationDB include the following: Ordered key–value store In addition to supporting standard key-based reads and writes, the ordering property enables range reads that can efficiently scan large swaths of data. Transactions Transaction processing employs multiversion concurrency control for reads and optimistic concurrency for writes. Transactions can span multiple keys stored on multiple machines. ACID properties FoundationDB guarantees serializable isolation and strong durability via redundant storage on disk before transactions are considered committed. Layers Layers map new data models, APIs, and query languages to the FoundationDB core. They employ FoundationDB's ability to update multiple data elements in a single transaction, ensuring consistency. An example is their SQL layer. Commodity clusters FoundationDB is designed for deployment on distributed clusters of commodity hardware running Linux. Replication FoundationDB stores each piece of data on multiple machines according to a configurable replication factor. Triple replication is the recommended mode for clusters of 5 or more machines. Scalability FoundationDB is designed to support horizontal scaling through the addition of machines to a cluster while automatically handling data replication and partitioning. Systems supported FoundationDB supports packages for Linux, Windows, and macOS. The Linux version supports production clusters, while the Windows and macOS versions support local operation for development purposes. Configurations on Amazon EC2 are also supported. Programming language bindings FoundationDB supports language bindings for Python, Go, Ruby, Node.js, Java, PHP, and C, all of which are made available with the product. == Design limitations == The design of FoundationDB results in several limitations: Long transactions FoundationDB does not support transactions running over five seconds. Large transactions Transaction size cannot exceed 10 MB of total written keys and values. Large keys and values Keys cannot exceed 10 kB in size. Values cannot exceed 100 kB in size. == History == FoundationDB, headquartered in Vienna, Virginia, was started in 2009 by Nick Lavezzo, Dave Rosenthal, and Dave Scherer, drawing on their experience in executive and technology roles at their previous company, Visual Sciences. In March 2015 the FoundationDB Community site was updated to state that the company had changed directions and would no longer be offering downloads of its product. The company was acquired by Apple Inc., which was confirmed March 25, 2015. On April 19, 2018, Apple open sourced the software, releasing it under the Apache 2.0 license.
Reference data
Reference data is data used to classify or categorize other data. Typically, they are static or slowly changing over time. Examples of reference data include: Units of measurement Country codes Corporate codes Fixed conversion rates e.g., weight, temperature, and length Calendar structure and constraints Reference data sets are sometimes alternatively referred to as a "controlled vocabulary" or "lookup" data. Reference data differs from master data. While both provide context for business transactions, reference data is concerned with classification and categorisation, while master data is concerned with business entities. A further difference between reference data and master data is that a change to the reference data values may require an associated change in business process to support the change, while a change in master data will always be managed as part of existing business processes. For example, adding a new customer or sales product is part of the standard business process. However, adding a new product classification (e.g. "restricted sales item") or a new customer type (e.g. "gold level customer") will result in a modification to the business processes to manage those items. == Externally-defined reference data == For most organisations, most or all reference data is defined and managed within that organisation. Some reference data, however, may be externally defined and managed, for example by standards organizations. An example of externally defined reference data is the set of country codes as defined in ISO 3166-1. == Reference data management == Curating and managing reference data is key to ensuring its quality and thus fitness for purpose. All aspects of an organisation, operational and analytical, are greatly dependent on the quality of an organization's reference data. Without consistency across business process or applications, for example, similar things may be described in quite different ways. Reference data gain in value when they are widely re-used and widely referenced. Examples of good practice in reference data management include: Formalize the reference data management Use external reference data as much as possible Govern the reference data specific to your enterprise Manage reference data at enterprise level Version control your reference data