Cultural algorithms (CA) are a branch of evolutionary computation where there is a knowledge component that is called the belief space in addition to the population component. In this sense, cultural algorithms can be seen as an extension to a conventional genetic algorithm. Cultural algorithms were introduced by Reynolds (see references). == Belief space == The belief space of a cultural algorithm is divided into distinct categories. These categories represent different domains of knowledge that the population has of the search space. The belief space is updated after each iteration by the best individuals of the population. The best individuals can be selected using a fitness function that assesses the performance of each individual in population much like in genetic algorithms. === List of belief space categories === Normative knowledge A collection of desirable value ranges for the individuals in the population component e.g. acceptable behavior for the agents in population. Domain specific knowledge Information about the domain of the cultural algorithm problem is applied to. Situational knowledge Specific examples of important events - e.g. successful/unsuccessful solutions Temporal knowledge History of the search space - e.g. the temporal patterns of the search process Spatial knowledge Information about the topography of the search space == Population == The population component of the cultural algorithm is approximately the same as that of the genetic algorithm. == Communication protocol == Cultural algorithms require an interface between the population and belief space. The best individuals of the population can update the belief space via the update function. Also, the knowledge categories of the belief space can affect the population component via the influence function. The influence function can affect population by altering the genome or the actions of the individuals. == Pseudocode for cultural algorithms == Initialize population space (choose initial population) Initialize belief space (e.g. set domain specific knowledge and normative value-ranges) Repeat until termination condition is met Perform actions of the individuals in population space Evaluate each individual by using the fitness function Select the parents to reproduce a new generation of offspring Let the belief space alter the genome of the offspring by using the influence function Update the belief space by using the accept function (this is done by letting the best individuals to affect the belief space) == Applications == Various optimization problems Social simulation Real-parameter optimization
Computer-aided lean management
Computer-aided lean management, in business management, is a methodology of developing and using software-controlled, lean systems integration. Its goal is to drive innovation towards cost and cycle-time savings. It attempts to create an efficient use of capital and resources through the development and use of one integrated system model to run a business's planning, engineering, design, maintenance, and operations. == Overview == Computer-Aided Lean Management (CALM) is a management philosophy that uses software to reduce risk and inefficiencies. CALM acts on uncertainties and business inefficiencies to increase profitability through the use of computational decision-making tools that enable opportunities for additional value creation. It is based on the application of software to enable continuous improvement through an Integrated System Model (ISM) of the business’s physical assets, business processes, and machine learning. This integration of software applications using lean principles was developed in the aerospace industry and has migrated to the energy industry. The creation of an ISM removes the barriers posed by the silos or stovepipes inherent in the departmentalization of most companies. Integration enables lean uses of information for the creation of actionable knowledge. CALM strives to create such a lean management approach to running the company through the rigors of software enforcement. From this software enforcement comes clear policy and procedures that are adhered to, activity-based costing, measurement of effectiveness, and the capability of using advanced algorithms for dramatic improvements in optimization of resources. CALM creates business capabilities through software to enable technology application, streamlining of processes, and a lean organizational structure. The methodology is based on a common sense approach for running a business, by measuring actions taken and using those measurements to design more efficient processes. == History == CALM was inspired by lean processes and techniques that were already dominant management technologies with a wide diversity of applications and successes. Motorola and General Electric had been known for the concepts of Six Sigma; Boeing had been managing mass (using modular and flexible assembly options), and Toyota combined elements of these methodologies to create the Toyota Production System. Boeing then took the Toyota model and added computer-aided enforcement of lean methodologies throughout the manufacturing process. One of the major sources for CALM's outgrowth was integrated definition (IDEF) modeling in aerospace manufacturing that was pioneered by the U.S. Air Force in the 1970s. IDEF is a methodology designed to model the end-to-end decisions, actions, and activities of an organization or system so that costs, performance, and cycle times can be optimized. IDEF methods have been adapted for wider use in automotive, aerospace, pharmaceuticals, and software development industries. IDEF methods serve as a starting point to understand lean management through semantic data modeling. The IDEF process begins by mapping the existing functions of an enterprise, creating a graphical model, or road map, that shows what controls each important function, who performs it, what resources are required for carrying it out, what it produces, how much it costs, and what relationships it has to other functions of the organization. IDEF simulations have been found to be efficient at streamlining and modernizing both companies and governmental agencies. Perhaps the best-developed evolution of the IDEF model beyond Toyota was at Boeing. Their project life-cycle process has grown into a rigorous software system that links people, tasks, tools, materials, and the environmental impact of any newly planned project, before any building is allowed to begin. Routinely, more than half of the time for any given project is spent building the precedence diagrams, or three-dimensional process maps, integrating with outside suppliers, and designing the implementation plan–all on the computer. Once real activity is initiated, an action tracker is used to monitor inputs and outputs versus the schedule and delivery metrics in real time throughout the organization. When the execution of a new airplane design begins, it is so well organized that it consistently cuts both costs and build time in half for each successive generation of airframe. Boeing created a complex lean management process called 'define and control airplane configuration/manufacturing resource management' (DCAC/MRM). The process was built with the help of the operations research and computer sciences departments of the University of Pittsburgh. The manufacture of the Boeing 777 was ultimately a success, and it became the precursor to succeeding generations of CALM at Boeing. The methodology of CALM has recently been applied to field orientated infrastructure based businesses with highly interdependent systems, such as electric utilities where a smart grid concept is being researched and developed. The management of infrastructure-based industries like oil, gas, electricity, water, transportation, and renewables requires massive investments in interdependent, physical infrastructure, as well as simultaneous attention to disparate market forces. In infrastructure businesses that manage field assets, uncertainty is the biggest impediment to profitability, rather than the maintenance of efficient supply chains or the management of factory assembly lines. These businesses are dominated by risk from uncertainties such as weather, market variations, transportation disruptions, government actions, logistic difficulties, geology, and asset reliability. CALM has been applied to deal with these types of infrastructure based challenges.
Library classification
A library classification is a system used within a library to organize materials, including books, sound and video recordings, electronic materials, etc., both on shelves and in catalogs and indexes. Each item is typically assigned a call number, which identifies the location of the item within the system. Materials can be arranged by many different factors, typically in either a hierarchical tree structure based on the subject or using a faceted classification system, which allows the assignment of multiple classifications to an object, enabling the classifications to be ordered in many ways. == Description == Library classification is an important and crucial aspect in library and information science. It is distinct from scientific classification in that it has as its goal to provide a useful ordering of documents rather than a theoretical organization of knowledge. Although it has the practical purpose of creating a physical ordering of documents, it does generally attempt to adhere to accepted scientific knowledge. Library classification helps to accommodate all the newly published literature in an already created order of arrangement in a filial sequence. Library classification can be defined as the arrangement of books on shelves, or description of them, in the manner which is most useful to those who read with the ultimate aim of grouping similar things together. Library classification is meant to achieve these four purposes: ordering the fields of knowledge in a systematic way, bring related items together in the most helpful sequence, provide orderly access on the shelf, and provide a location for an item on the shelf. Library classification is distinct from the application of subject headings in that classification organizes knowledge into a systematic order, while subject headings provide access to intellectual materials through vocabulary terms that may or may not be organized as a knowledge system. The characteristics that a bibliographic classification demands for the sake of reaching these purposes are: a useful sequence of subjects at all levels, a concise memorable notation, and a host of techniques and devices of number synthesis. == History == Library classifications were preceded by classifications used by bibliographers such as Conrad Gessner. The earliest library classification schemes organized books in broad subject categories. The earliest known library classification scheme is the Pinakes by Callimachus, a scholar at the Library of Alexandria during the third century BC. During the Renaissance and Reformation era, "Libraries were organized according to the whims or knowledge of individuals in charge." This changed the format in which various materials were classified. Some collections were classified by language and others by how they were printed. After the printing revolution in the sixteenth century, the increase in available printed materials made such broad classification unworkable, and more granular classifications for library materials had to be developed in the nineteenth century. In 1627 Gabriel Naudé published a book called Advice on Establishing a Library. At the time, he was working in the private library of Président à mortier Henri de Mesmes II. Mesmes had around 8,000 printed books and many more Greek, Latin and French written manuscripts. Although it was a private library, scholars with references could access it. The purpose of Advice on Establishing a Library was to identify rules for private book collectors to organize their collections in a more orderly way to increase the collection's usefulness and beauty. Naudé developed a classification system based on seven different classes: theology, medicine, jurisprudence, history, philosophy, mathematics, and the humanities. These seven classes would later be increased to twelve. Advice on Establishing a Library was about a private library, but within the same book, Naudé encouraged the idea of public libraries open to all people regardless of their ability to pay for access to the collection. One of the most famous libraries that Naudé helped improve was the Bibliothèque Mazarine in Paris. Naudé spent ten years there as a librarian. Because of Naudé's strong belief in free access to libraries to all people, the Bibliothèque Mazarine became the first public library in France around 1644. Although libraries created order within their collections from as early as the fifth century BC, the Paris Bookseller's classification, developed in 1842 by Jacques Charles Brunet, is generally seen as the first of the modern book classifications. Brunet provided five major classes: theology, jurisprudence, sciences and arts, belles-lettres, and history. Classification can now be seen as a provider of subject access to information in a networked environment. == Types == There are many standard systems of library classification in use, and many more have been proposed over the years. However, in general, classification systems can be divided into three types depending on how they are used: === Universal schemes === Covers all subjects, e.g. the Dewey Decimal Classification (DDC), Universal Decimal Classification (UDC), and Colon Classification (CC). === Specific classification schemes === Covers particular subjects or types of materials, e.g. Iconclass (art), British Catalogue of Music Classification, and Dickinson classification (music), or the NLM Classification (medicine). === National schemes === Specially created for certain countries, e.g. Swedish library classification system, SAB (Sveriges Allmänna Biblioteksförening). The Library of Congress Classification was designed around the collection of the US Library of Congress and has an American, European, and Christian bias. Nevertheless, it is used widely in large academic and research libraries. In terms of functionality, classification systems are often described as: === Enumerative === Subject headings are listed alphabetically, with numbers assigned to each heading in alphabetical order. === Hierarchical === Subjects are divided hierarchically, from most general to most specific. === Faceted/analytico-synthetic === Subjects are divided into mutually exclusive orthogonal facets. There are few completely enumerative systems or faceted systems; most systems are a blend but favouring one type or the other. The most common classification systems, LCC and DDC, are essentially enumerative, though with some hierarchical and faceted elements (more so for DDC), especially at the broadest and most general level. The first true faceted system was the colon classification of S. R. Ranganathan. == Methods or systems == Classification types denote the classification or categorization according to the form or characteristics or qualities of a classification scheme or schemes. Method and system has similar meaning. Method or methods or system means the classification schemes like Dewey Decimal Classification or Universal Decimal Classification. The types of classification is for identifying and understanding or education or research purposes while classification method means those classification schemes like DDC, UDC. === English language universal classification systems === The most common systems in English-speaking countries are: Dewey Decimal Classification (DDC) Library of Congress Classification (LCC) Universal Decimal Classification (UDC) Other systems include: Book Industry Standards and Communications (BISAC), originally developed for use by U.S. booksellers, has become increasingly popular in libraries. Bliss bibliographic classification used in some British libraries Colon classification (CC) Garside classification used in most libraries of University College London Gladstone Library Classification, devised by W.E. Gladstone and used exclusively at Gladstone's Library Harvard-Yenching Classification, an English classification system for Chinese language materials === Non-English universal classification systems === German Regensburger Verbundklassifikation (RVK) A system of book classification for Chinese libraries (Liu's Classification) library classification for user New Classification Scheme for Chinese Libraries Nippon Decimal Classification (NDC) Chinese Library Classification (CLC) Korean Decimal Classification (KDC) Russian Library-Bibliographical Classification (BBK) Swedish library classification system (SAB) === Universal classification systems that rely on synthesis (faceted systems) === Bliss bibliographic classification Colon classification Cutter Expansive Classification Universal Decimal Classification Newer classification systems tend to use the principle of synthesis (combining codes from different lists to represent the different attributes of a work) heavily, which is comparatively lacking in LC or DDC. == Practice == Library classification is associated with library (descriptive) cataloging under the rubric of cataloging and classification, sometimes grouped together as technical serv
POSC Caesar
POSC Caesar Association (PCA) is an international, open and not-for-profit, member organization that promotes the development of open specifications to be used as standards for enabling the interoperability of data, software and related matters. PCA is the initiator of ISO 15926 "Integration of life-cycle data for process plants including oil and gas production facilities" and is committed to its maintenance and enhancement. Nils Sandsmark has been the General Manager of POSC Caesar Association since 1999 and Thore Langeland, Norwegian Oil Industry Association (Norwegian: Oljeindustriens Landsforening, OLF), is the chairman of the board. == History == === Caesar Offshore === The first predecessor of POSC Caesar Association, the Caesar Offshore program, started in 1993. The original focus was on standardizing technical data definitions for capital intensive projects at the handover from the EPC contractor to the owner/operators of onshore and offshore oil and gas production facilities. The program was sponsored by The Research Council of Norway, two EPC contractors (Aker Maritime and Kværner), three owners/operators (Norsk Hydro, Saga Petroleum and Statoil) and DNV as service provider and project owner. === POSC Caesar project === During the period 1994–96, Caesar Offshore Program was defined as a project of Petrotechnical Open Software Corporation (POSC) (now Energistics), and changed its name to the POSC Caesar Project. In 1995 the project was joined by BP, Brown and Root and Elf Aquitaine and in 1997 by Intergraph, IBM, Oracle, Lloyd's, Shell, ABB and UMOE Technologies. During that time, POSC Caesar also became a member of European Process Industries STEP Technical Liaison Executive (EPISTLE) where it collaborates with PISTEP (UK), and USPI-NL (The Netherlands) on the development of ISO 10303, also known as "Standard for the Exchange of Product model data (STEP)". === POSC Caesar Association === In 1997, POSC Caesar Association was founded as an independent, global, non-profit, member organization. POSC Caesar Association serves an international membership and collaborates with other international organizations. It has its main office in Norway. Albeit the name of POSC Caesar Association still hints to its past as a project within the Petrotechnical Open Software Corporation (POSC) (now Energistics), from 1997 onwards, the organization has been independent. Energistics and POSC Caesar Association do collaborate, and are formally member in each other's organization. == Membership == POSC Caesar Association has with its current 36 members from around the world and has established an international footprint (with a strong membership in Norway) that includes a variety of backgrounds, from academia and solution providers to engineering contractors and owners/operators. The members are (subdivided by organization type): Associations: Energistics (USA) and The Norwegian Oil Industry Association (OLF, Norway); Universities and Research Institutes: International Research Institute of Stavanger (IRIS, Norway), Norwegian University of Science and Technology (NTNU, Norway), Korea Advanced Institute of Science and Technology (KAIST, Korea), SINTEF (Norway), University of Bergen (Norway), University of Oslo (Norway), University of Stavanger (Norway), University of Tromsø (Norway) and Western Norway Research Institute (Norway); Oil and Gas Companies: BP (UK), Petronas (Malaysia) and Statoil (Norway); Engineering contractors and consultants: Akvaplan-niva (Norway), Aker Solutions (Norway), Asset Life Cycle Information Management (ALCIM, Malaysia), CAESAR systems (USA), Bechtel (USA), Det Norske Veritas (DNV, Norway), Information Logic (USA) and iXIT Engineering Technology (Germany), Phusion IM Ltd (UK); Solution providers: Aveva (UK), Bentley Systems (USA), Jotne EPM Technology (Norway), Epsis (Norway), Eurostep (Sweden), International Business Machines Corporation (IBM, USA), Siemens - Comos Industry Solutions (before Innotec) (Germany), Intergraph (USA), Invenia (Norway), Keel Solution (Denmark), Noumenon (UK), NRX (Canada), Octaga (Norway) and Tektonisk (Norway). In general, the organization holds three membership meetings a year; one in January / February in North-America (typically USA), one in April / May in Europe (typically Norway) and one in October in Asia (typically Malaysia). == Activities and services == === Initiator and custodian of ISO 15926 === In consultation with the other EPISTLE members and the International Organization for Standardization (ISO), it was decided in 2003 (some say already in 1997) that for modeling-technical reasons it was better to discontinue the development of ISO 10303 and to initiate the development of ISO 15926 "Integration of life-cycle data for process plants including oil and gas production facilities." Over the years, the scope of the standard has increased from the initial capital-intensive projects in the upstream oil and gas industry, to include also relevant terminology for downstream oil and gas industry applications and to deal with real-time data related to the actual oil and gas production. ISO 15926 has also over the years evolved from a dictionary (a list of terms with definitions), over a taxonomy (added hierarchy) to an ontology (a formal representation of a set of concepts within a domain and the relationships between those concepts). ISO 15926 is therefore sometimes nicknamed the "Oil and Gas Ontology", for some considered to be an essential prerequisite together with Semantic Web technologies to get to better interoperability, an optimal use of all available data across boundaries and an increase in efficiency. This is what some call the next generation of Integrated Operations. === Reference data services === Placeholders: Flow scheme of WIP - RDS - ISO and role of SIGs RDS Standards in database pilot (ISO) === Special interest groups === Placeholders: Overview of SIGs Drilling and Completion Reservoir and Production Operations and Maintenance == Projects == There are a number of projects (co-)organized by POSC Caesar Association working on the extension of the ISO 15926 standard in different application areas. === Capital intensive projects application domain === The following projects are running at the moment (August 2009): The ADI Project of FIATECH, to build the tools (which will then be made available in the public domain) The IDS Project of POSC Caesar Association, to define product models required for data sheets A joint collaboration project between FIATECH POSC Caesar Association is the ADI-IDS project is the ISO 15926 WIP === Upstream oil and gas industry application domain === The following projects are currently running (August 2009): The Integrated Operations in the High North (IOHN) project is working on extending ISO 15926 to handle real-time data transmission and (pre-)processing to enable the next generation of Integrated Operations. The Environment Web project to include environmental reporting terms and definitions as used in EPIM's EnvironmentWeb in ISO 15926. Finalised projects include: The Integrated Information Platform (IIP) project working on establishing a real-time information pipeline based on open standards. It worked among others on: Daily Drilling Report (DDR) to including all terms and definitions in ISO 15926. This standard became mandatory on February 1, 2008 for reporting on the Norwegian Continental Shelf by the Norwegian Petroleum Directorate (NPD) and Safety Authority Norway (PSA). NPD says that the quality of the reports has improved considerably since. Daily Production Report (DPR) to including all terms and definitions in ISO 15926. This standard was tested successfully on the Valhall (BP-operated) and Åsgard (StatoilHydro-operated) fields offshore Norway. The terminology and XML schemata developed have also been included in Energistics’ PRODML standard. == Conferences and events == === Semantic Days === === Sogndal academic network meeting === == Collaborations == POSC Caesar is collaborating with a number of standardization bodies, including: Mimosa: collaboration on open information standards for Operations and Maintenance mainly for the downstream oil and gas industry; FIATECH: collaboration on open information standards for life cycle data of capital projects; Energistics: collaboration on information standards for the upstream oil and gas industry, including WITSML and PRODML; OASIS: collaboration on e-business standards; ISO TC184/SC4: the host of the ISO 15926 standard.
Executive Order 14110
Executive Order 14110, titled Executive Order on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (sometimes referred to as "Executive Order on Artificial Intelligence") was the 126th executive order signed by former U.S. President Joe Biden. Signed on October 30, 2023, the order defines the administration's policy goals regarding artificial intelligence (AI), and orders executive agencies to take actions pursuant to these goals. The order is considered to be the most comprehensive piece of governance by the United States regarding AI. It was rescinded by U.S. President Donald Trump within hours of his assuming office on January 20, 2025. Policy goals outlined in the executive order pertain to promoting competition in the AI industry, preventing AI-enabled threats to civil liberties and national security, and ensuring U.S. global competitiveness in the AI field. The executive order required a number of major federal agencies to create dedicated "chief artificial intelligence officer" positions within their organizations. == Background == The drafting of the order was motivated by the rapid pace of development in generative AI models in the 2020s, including the release of large language model ChatGPT. Executive Order 14110 is the third executive order dealing explicitly with AI, with two AI-related executive orders being signed by then-President Donald Trump. The development of AI models without policy safeguards has raised a variety of concerns among experts and commentators. These range from future existential risk from advanced AI models to immediate concerns surrounding current technologies' ability to disseminate misinformation, enable discrimination, and undermine national security. In August 2023, Arati Prabhakar, the director of the Office of Science and Technology Policy, indicated that the White House was expediting its work on executive action on AI. A week prior to the executive order's unveiling, Prabhakar indicated that Office of Management and Budget (OMB) guidance on the order would be released "soon" after. == Policy goals and provisions == The order has been characterized as an effort for the United States to capture potential benefits from AI while mitigating risks associated with AI technologies. Upon signing the order, Biden stated that AI technologies were being developed at "warp speed", and argued that to "realize the promise of AI and avoid the risk, we need to govern this technology". Policy goals outlined by the order include the following: Promoting competition and innovation in the AI industry Upholding civil and labor rights and protecting consumers and their privacy from AI-enabled harms Specifying federal policies governing procurement and use of AI Developing watermarking systems for AI-generated content and warding off intellectual property theft stemming from the use of generative models Maintaining the nation's place as a global leader in AI == Impact on agencies == === Creation of chief AI officer positions === The executive order required a number of large federal agencies to appoint a chief artificial intelligence officer, with a number of departments having already appointed a relevant officer prior to the order. In the days following the order, news publication FedScoop confirmed that the General Services Administration (GSA) and the United States Department of Education appointed relevant chief AI officers. The National Science Foundation (NSF) also confirmed it had elevated an official to serve as its chief AI officer. === Department responsibilities === Under the executive order, the Department of Homeland Security (DHS) was responsible for developing AI-related security guidelines, including cybersecurity-related matters. The DHS will also work with private sector firms in sectors including the energy industry and other "critical infrastructure" to coordinate responses to AI-enabled security threats. Executive Order 14110 mandated the Department of Veterans Affairs to launch an AI technology competition aimed at reducing occupational burnout among healthcare workers through AI-assisted tools for routine tasks. The order also mandated the Department of Commerce's National Institute of Standards and Technology (NIST) to develop a generative artificial intelligence-focused resource to supplement the existing AI Risk Management Framework. == Analysis == The executive order has been described as the most comprehensive piece of governance by the United States government pertaining to AI. Earlier in 2023 prior to the signing of the order, the Biden administration had announced a Blueprint for an AI Bill of Rights, and had secured non-binding AI safety commitments from major tech companies. The issuing of the executive order comes at a time in which lawmakers including Senate Majority Leader Chuck Schumer have pushed for legislation to regulate AI in the 118th United States Congress. According to Axios, despite the wide scope of the executive order, it notably does not touch upon a number of AI-related policy proposals. This includes proposals for a "licensing regime" to government advanced AI models, which has received support from industry leaders including Sam Altman. Additionally, the executive order does not seek to prohibit 'high-risk' uses of AI technology, and does not aim to mandate that tech companies release information surrounding AI systems' training data and models. == Reception == === Political and media reception === The editorial board of the Houston Chronicle described the order as a "first step toward protecting humanity". The issuing of the order received praise from Democratic members of Congress, including Senator Richard Blumenthal (D-CT) and Representative Ted Lieu (D-CA). Representative Don Beyer (D-VA), who leads the House AI Caucus, praised the order as a "comprehensive strategy for responsible innovation", while arguing that Congress must take initiative to pass legislation on AI. The draft of the order received criticism from Republican Senator Ted Cruz (R-TX), who described it as creating "barriers to innovation disguised as safety measures". === Public reception === Polling from the AI Policy Institute showed that 69% of all voters support the executive order, while 15% oppose it. Breaking it down by party, support was at 78% for Democrats, 65% for independents, and 64% for Republicans. === Industry reception === The executive order received strong criticism from the Chamber of Commerce as well as tech industry groups including NetChoice and the Software and Information Industry Association, all of which count "Big Tech" companies Amazon, Meta, and Google as members. Representatives from the organizations argued that the executive order threatens to hinder private sector innovation. === Civil society reception === According to CNBC, a number of leaders advocacy organizations praised the executive order for its provisions on "AI fairness", while simultaneously urging congressional action to strengthen regulation. Maya Wiley, president and CEO of the Leadership Conference on Civil and Human Rights, praised the order while urging Congress to take initiative to "ensure that innovation makes us more fair, just, and prosperous, rather than surveilled, silenced, and stereotyped". A representative from the American Civil Liberties Union (ACLU) praised provisions of the order centered on combating AI-enabled discrimination, while also voiced concern over sections of the order focused on law enforcement and national security. === Second Trump administration === Hours after his inauguration as the 47th president of the United States, Donald Trump rescinded the order, labeling it, among several other of Biden's executive orders and actions, as "unpopular, inflationary, illegal, and radical practices".
Fillrate
In computer graphics, a video card's pixel fillrate refers to the number of pixels that can be rendered on the screen and written to video memory in one second. Pixel fillrates are given in megapixels per second or in gigapixels per second (in the case of newer cards), and are obtained by multiplying the number of render output units (ROPs) by the clock frequency of the graphics processing unit (GPU) of a video card. A similar concept, texture fillrate, refers to the number of texture map elements (texels) the GPU can map to pixels in one second. Texture fillrate is obtained by multiplying the number of texture mapping units (TMUs) by the clock frequency of the GPU. Texture fillrates are given in mega or gigatexels per second. However, there is no full agreement on how to calculate and report fillrates. Another possible method is to multiply the number of pixel pipelines by the GPU's clock frequency. The results of these multiplications correspond to a theoretical number. The actual fillrate depends on many other factors. In the past, the fillrate has been used as an indicator of performance by video card manufacturers such as ATI and NVIDIA, however, the importance of the fillrate as a measurement of performance has declined as the bottleneck in graphics applications has shifted. For example, today, the number and speed of unified shader processing units has gained attention. Although fillrate doesn't provide a substantial bottleneck in games, it can still provide a bottleneck for certain parts of the game, for example applying a gaussian blur can be bottlenecked by fillrate. Scene complexity can be increased by overdrawing, which happens when an object is drawn to the frame buffer, and another object (such as a wall) is then drawn on top of it, covering it up. The time spent drawing the first object is thus wasted because it is not visible. When a sequence of scenes is extremely complex (many pixels have to be drawn for each scene), the frame rate for the sequence may drop. When designing graphics intensive applications, one can determine whether the application is fillrate-limited (or shader limited) by seeing if the frame rate increases dramatically when the application runs at a lower resolution or in a smaller window. Although this is not a full-proof method, modern videogame engines can dynamically reduce the level-of-detail required and thereby reducing fillrate-limited applications. The best way to find fillrate bottlenecks is to use GPU vendor software like NVIDIA Nsight Graphics, AMD Radeon GPU Profile and the Intel Graphics Performance Analyzers.
METR
Model Evaluation and Threat Research (METR) (MEE-tər), is a nonprofit research institute, based in Berkeley, California, that evaluates frontier AI models' capabilities to carry out long-horizon, agentic tasks that some researchers argue could pose catastrophic risks to society. METR has worked with leading AI companies to conduct pre-deployment model evaluations and contribute to system cards, including OpenAI's o3, o4-mini, GPT-4o and GPT-4.5, and Anthropic's Claude models. METR's CEO and founder is Beth Barnes, a former alignment researcher at OpenAI who left in 2022 to form ARC Evals, the evaluation division of Paul Christiano's Alignment Research Center. In December 2023, ARC Evals was spun off into an independent 501(c)(3) nonprofit and renamed METR. == Research == A substantial amount of METR's research is focused on evaluating the capabilities of AI systems to conduct research and development of AI systems themselves, including RE-Bench, a benchmark designed to test whether AIs can "solve research engineering tasks and accelerate AI R&D". === Doubling time estimates === In March 2025, METR published a paper noting that the length of software engineering tasks that the leading AI model could complete had a doubling time of around 7 months between 2019 and 2024. In January 2026, METR released a new version of their time horizon estimates model (Time Horizon 1.1). According to the updated model, the rate of progress of AI capabilities has increased since 2023, with a post-2023 doubling time estimated at 130.8 days (4.3 months). Progress is thus estimated to be 20% more rapid. === Time horizon measurements === METR releases a "task-completion time horizon" for analysed AI models. This measures the "task duration (measured by human expert completion time) at which an AI agent is predicted to succeed with a given level of reliability." The metric is reported in two variants: the 50%-time horizon, which gives the task duration at which an AI model is estimated to succeed 50% of the time, and the 80%-time horizon, which gives the task duration at which an AI model is estimated to succeed 80% of the time. METR has published two versions of the underlying model: Time Horizon 1.0 and Time Horizon 1.1, the latter introduced in January 2026. As of 9 May 2026, the best-performing model is Claude Mythos, with a 50%-time horizon of likely at least 16 hours and an 80%-time horizon of 3 hours and 6 minutes. METR notes that "[m]easurements above 16 [hours] are unreliable with [their] current task suite". The following table provides time horizon estimates ordered by each model's release date: