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AI Articles — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  GoodRx

  GoodRx Holdings, Inc. is an American healthcare company that operates a telemedicine platform and free-to-use website and mobile app that track prescription drug prices in the United States and provide drug coupons for discounts on medications. GoodRx compares prescription drug prices at more than 75,000 pharmacies in the United States. The platform allows users to consult a doctor online and obtain a prescription for certain types of medications. == History == === Financial performance === GoodRx was founded in Santa Monica, California in 2011. GoodRx experienced substantial growth in net income in 2017 ($9 million), 2018 ($44 million), and 2019 ($66 million), but recorded a loss of $293.6 million in 2020 due to IPO-related expenses. In September 2020, GoodRx went public on the Nasdaq under the ticker symbol GDRX. The company priced its initial public offering at $33 per share, above the expected range of $24 to $28, raising more than $1.1 billion at an initial valuation of approximately $12.7 billion. In the first half of 2020, the company reported revenues of $257 million and net income of $55 million. GoodRx generated $745.4 million in revenue for the full year 2021, a 35.36% increase over 2020. During the first half of 2021, the company’s share price declined by 10.7%. The decline was attributed to increased competition in online pharmacy services and slower user growth. GoodRx reported full-year revenue of $766.6 million, with adjusted EBITDA reaching $213.5 million, exceeding guidance in the fourth quarter. GoodRx reported that 41% of prescriptions filled using its coupons were newly adherent, meaning they would not have been filled without the service. GoodRx reported a full-year 2023 revenue of $750.3 million, a decrease of 2.1% from 2022. However, its fourth-quarter revenue increased by 7% year-over-year. GoodRx achieved an Adjusted EBITDA of $217.4 million for the year and an Adjusted EBITDA Margin of 28.6%. In 2024, GoodRx achieved 6% revenue growth with $792.3 million for the full year and turned a net loss into a positive net income of $16.4 million. The company also demonstrated strong operational efficiency, with a 32.8% increase in full-year Adjusted EBITDA. In Q2 2025, GoodRx reported revenue of $203.1 million, a 1.2% increase from the previous year, and a net income of $12.8 million, a significant 92% jump, which resulted in a 6.3% net income margin. However, prescription transaction revenue declined by 3% due to a decrease in monthly active consumers, but this was offset by strong 32% growth in its Pharma Manufacturer Solutions business. GoodRx also saw a 7% decrease in subscription revenue. === Mergers and acquisitions === In 2019, GoodRx acquired HeyDoctor, a telemedicine company, to integrate virtual healthcare services into the platform. In 2021, a health video content producer, HealthiNation was acquired by GoodRx, which helped provide consumers with health information and offered pharmaceutical manufacturers new ways to reach relevant audiences. In April 2022, GoodRx acquired VitaCare Prescription Services from TherapeuticsMD to strengthen its pharma manufacturer solutions business. === Partnerships === In 2017, the company announced partnerships with major pharmaceutical companies to negotiate lower prescription drug costs. GoodRx has deep relationships with major pharmacy chains, including Walgreens, Walmart, CVS Caremark, and Publix, to allow customers to use GoodRx discounts and Gold benefits. GoodRx began its partnership with CVS Caremark in July 2023 to automatically apply coupons to insured CVS customers purchasing generic prescriptions at certain locations. In April 2024, GoodRx added Publix into its network, allowing GoodRx Gold members to use their cards at Publix Pharmacies. GoodRx partners with Pharmacy Benefit Management like Caremark, Express Scripts, and MedImpact to apply their savings directly to eligible insurance plans and members. GoodRx partners with companies like Affirm, Benefitfocus, and DoorDash to integrate their services that offer members discounts and financial flexibility for prescriptions. GoodRx also partners with organizations like the American Academy of Family Physicians Foundation to support broader access to care. In October 2022, GoodRx launched Provider Mode, which allows healthcare providers to use the app to compare costs of drugs for patients based on different payment methods and drug alternatives. In 2025, GoodRx partnered with Novo Nordisk to offer discounted cash-pay access to semaglutide products like Ozempic and Wegovy through its platform and participating pharmacies. == Products and services == GoodRx started its telemedicine service GoodRx Care in September 2019. It lets people talk to a licensed provider online for common issues and get prescriptions even if they don't have insurance. They also run condition-specific subscription plans that bundle online doctor visits, FDA-approved meds, and home delivery into one monthly payment. On the weight management side, GoodRx offers prescriptions for GLP-1 drugs like semaglutide through their telemedicine platform. This got a boost when the oral version of Wegovy became widely available in the US in early 2026. GoodRx works with drug makers like Novo Nordisk to make some medications (including semaglutide options) more affordable for people paying cash. The telemedicine part took off after GoodRx bought HeyDoctor in 2019 and brought their virtual care tools into the main platform. == Key people == The Santa Monica-based startup was founded in September 2011 by Trevor Bezdek and former Facebook executives Doug Hirsch and Scott Marlette. Marlette was one of the first 20 employees at Facebook and built Facebook's photo application. In 2005, Hirsch was the Vice President of Product at Facebook, working closely with Mark Zuckerberg. Bezdek and Hirsch served as co-chief executive officers until April 2023, when they stepped down from those roles and technology executive Scott Wagner was appointed interim chief executive officer. Bezdek became chair of the board, while Hirsch took on the role of chief mission officer. In December 2024, GoodRx announced that healthcare executive Wendy Barnes would become president and chief executive officer effective January 1, 2025. As of 2025, Barnes serves as the company’s CEO, while Trevor Bezdek and Scott Wagner serve as co-chairs of the board, and Doug Hirsch remains involved as a co-founder and senior executive. == Controversy == On February 25, 2020, Consumer Reports published an article stating that GoodRx shared user data—specifically, pseudonymized advertising ID numbers that companies use to track the behavior of web users across websites, the names of the drugs that users browsed, and the pharmacies where users sought to fill prescriptions—with Google, Facebook, and around twenty other Internet-based companies. A few days later, GoodRx released a statement saying that it had made changes to prevent user search data on medical conditions and pharmaceuticals from being shared with Facebook. In March 2020, GoodRx stopped sending data about user prescriptions to Facebook. On February 1, 2023, the Federal Trade Commission fined GoodRx US$1.5 million for violations of the Breach Notification Rule and the Federal Trade Commission Act for allegedly failing to obtain specific, informed, and unambiguous consent from users before disclosing health-related information to Facebook and Google. In November 2024, independent pharmacies filed at least three class action lawsuits against GoodRx and major pharmacy benefit managers. The cases, brought by independent pharmacies in California, Michigan, Pennsylvania, and Rhode Island, allege that GoodRx and the PBMs collaborated to suppress reimbursements for generic prescription drugs. They allege that agreements using GoodRx’s software suppressed reimbursements for generic drugs and violated the Sherman Antitrust Act. The suits claim the practices amount to price fixing which harms small pharmacies while benefiting PBMs and their affiliates. GoodRx settled both the 2023 FTC action and the 2025 class action lawsuit without admitting wrongdoing.

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  Vinelink.com

  Vinelink.com (VINE) is a national website in the United States that allows victims of crime, and the general public, to track the movements of prisoners held by the various states and territories. The first four letters in the websites name, "vine", are an acronym for "Victim Information and Notification Everyday". Vinelink.com displays information, based on the information provided by the various states' departments of correction and other law enforcement agencies, on whether an inmate is in custody, has been released, has been granted parole or probation, or has escaped from custody. In some cases, the website will reveal whether a defendant has been granted parole or probation, but then subsequently violated conditions of their release and become a fugitive. Information provided on Vinelink.com represents metadata, in that the website lists a defendant's custody status; but does not list what the individual is charged with, their criminal history, or the amount of their bail, if applicable. Internet users accessing the Vinelink.com website choose from a map of states and provinces within the United States where they wish to perform a search for an inmate. The user may then search for an individual using the inmate's or parolee's name, or by entering the inmate's specific department of corrections inmate number, if known. When the inmate's custody status changes, users who have registered to be notified of such changes will be notified via email, phone or both. This information is currently released upon request, without the website requesting reasons for the users search or requiring payment, as public records available to the general public. Inmate information is available for most states, and for Puerto Rico, on the website. The states of Arizona, Georgia, Massachusetts, Montana, New Hampshire and West Virginia provide very limited information on the site. In March of 2025, The Maine Sheriff's Association entered into a contract to pilot the use of the VINE system in three counties in the state as well as a regional jail, therefore making South Dakota the only state that does not participate in the VINE system to any degree. The website does not provide data on prisoners detained by the Federal Bureau of Prisons which has its own inmate locator web site nor for inmates of the U.S. military prisons.

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  Predictor–corrector method

  In numerical analysis, predictor–corrector methods belong to a class of algorithms designed to integrate ordinary differential equations – to find an unknown function that satisfies a given differential equation. All such algorithms proceed in two steps: The initial, "prediction" step, starts from a function fitted to the function-values and derivative-values at a preceding set of points to extrapolate ("anticipate") this function's value at a subsequent, new point. The next, "corrector" step refines the initial approximation by using the predicted value of the function and another method to interpolate that unknown function's value at the same subsequent point. == Predictor–corrector methods for solving ODEs == When considering the numerical solution of ordinary differential equations (ODEs), a predictor–corrector method typically uses an explicit method for the predictor step and an implicit method for the corrector step. === Example: Euler method with the trapezoidal rule === A simple predictor–corrector method (known as Heun's method) can be constructed from the Euler method (an explicit method) and the trapezoidal rule (an implicit method). Consider the differential equation y ′ = f ( t , y ) , y ( t 0 ) = y 0 , {\displaystyle y'=f(t,y),\quad y(t_{0})=y_{0},} and denote the step size by h {\displaystyle h} . First, the predictor step: starting from the current value y i {\displaystyle y_{i}} , calculate an initial guess value y ~ i + 1 {\displaystyle {\tilde {y}}_{i+1}} via the Euler method, y ~ i + 1 = y i + h f ( t i , y i ) . {\displaystyle {\tilde {y}}_{i+1}=y_{i}+hf(t_{i},y_{i}).} Next, the corrector step: improve the initial guess using trapezoidal rule, y i + 1 = y i + 1 2 h ( f ( t i , y i ) + f ( t i + 1 , y ~ i + 1 ) ) . {\displaystyle y_{i+1}=y_{i}+{\tfrac {1}{2}}h{\bigl (}f(t_{i},y_{i})+f(t_{i+1},{\tilde {y}}_{i+1}){\bigr )}.} That value is used as the next step. === PEC mode and PECE mode === There are different variants of a predictor–corrector method, depending on how often the corrector method is applied. The Predict–Evaluate–Correct–Evaluate (PECE) mode refers to the variant in the above example: y ~ i + 1 = y i + h f ( t i , y i ) , y i + 1 = y i + 1 2 h ( f ( t i , y i ) + f ( t i + 1 , y ~ i + 1 ) ) . {\displaystyle {\begin{aligned}{\tilde {y}}_{i+1}&=y_{i}+hf(t_{i},y_{i}),\\y_{i+1}&=y_{i}+{\tfrac {1}{2}}h{\bigl (}f(t_{i},y_{i})+f(t_{i+1},{\tilde {y}}_{i+1}){\bigr )}.\end{aligned}}} It is also possible to evaluate the function f only once per step by using the method in Predict–Evaluate–Correct (PEC) mode: y ~ i + 1 = y i + h f ( t i , y ~ i ) , y i + 1 = y i + 1 2 h ( f ( t i , y ~ i ) + f ( t i + 1 , y ~ i + 1 ) ) . {\displaystyle {\begin{aligned}{\tilde {y}}_{i+1}&=y_{i}+hf(t_{i},{\tilde {y}}_{i}),\\y_{i+1}&=y_{i}+{\tfrac {1}{2}}h{\bigl (}f(t_{i},{\tilde {y}}_{i})+f(t_{i+1},{\tilde {y}}_{i+1}){\bigr )}.\end{aligned}}} Additionally, the corrector step can be repeated in the hope that this achieves an even better approximation to the true solution. If the corrector method is run twice, this yields the PECECE mode: y ~ i + 1 = y i + h f ( t i , y i ) , y ^ i + 1 = y i + 1 2 h ( f ( t i , y i ) + f ( t i + 1 , y ~ i + 1 ) ) , y i + 1 = y i + 1 2 h ( f ( t i , y i ) + f ( t i + 1 , y ^ i + 1 ) ) . {\displaystyle {\begin{aligned}{\tilde {y}}_{i+1}&=y_{i}+hf(t_{i},y_{i}),\\{\hat {y}}_{i+1}&=y_{i}+{\tfrac {1}{2}}h{\bigl (}f(t_{i},y_{i})+f(t_{i+1},{\tilde {y}}_{i+1}){\bigr )},\\y_{i+1}&=y_{i}+{\tfrac {1}{2}}h{\bigl (}f(t_{i},y_{i})+f(t_{i+1},{\hat {y}}_{i+1}){\bigr )}.\end{aligned}}} The PECEC mode has one fewer function evaluation than PECECE mode. More generally, if the corrector is run k times, the method is in P(EC)k or P(EC)kE mode. If the corrector method is iterated until it converges, this could be called PE(CE)∞.

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  Literature review

  A literature review is an overview of previously published works on a particular topic. The term can refer to a full scholarly paper or a section of a scholarly work such as books or articles. Either way, a literature review provides the researcher/author and the audiences with general information of an existing knowledge of a particular topic. A good literature review has a proper research question, a proper theoretical framework, and/or a chosen research method. It serves to situate the current study within the body of the relevant literature and provides context for the reader. In such cases, the review usually precedes the methodology and results sections of the work. Producing a literature review is often part of a graduate and post-graduate requirement, included in the preparation of a thesis, dissertation, or a journal article. Literature reviews are also common in a research proposal or prospectus (the document approved before a student formally begins a dissertation or thesis). A literature review can be a type of a review article. In this sense, it is a scholarly paper that presents the current knowledge including substantive findings as well as theoretical and methodological contributions to a particular topic. Literature reviews are secondary sources and do not report new or original experimental work. Most often associated with academic-oriented literature, such reviews are found in academic journals and are not to be confused with book reviews, which may also appear in the same publication. Literature reviews are a basis for research in nearly every academic field. == Types == Since the concept of a systematic review was formalized in the 1970s, a basic division among types of reviews is the dichotomy of narrative reviews versus systematic reviews. The main types of narrative reviews are evaluative, exploratory, and instrumental. A fourth type of review of literature (the scientific literature) is the systematic review but it is not called a literature review, which absent further specification, conventionally refers to narrative reviews. A systematic review focuses on a specific research question to identify, appraise, select, and synthesize all high-quality research evidence and arguments relevant to that question. A meta-analysis is typically a systematic review using statistical methods to effectively combine the data used on all selected studies to produce a more reliable result. Torraco (2016) describes an integrative literature review. The purpose of an integrative literature review is to generate new knowledge on a topic through the process of review, critique, and synthesis of the literature under investigation. George et al (2023) offer an extensive overview of review approaches. They also propose a model for selecting an approach by looking at the purpose, object, subject, community, and practices of the review. They describe six different types of review, each with their own unique purposes: Exploratory or scoping reviews focus on breadth as opposed to depth Systematic or integrative reviews integrate empirical studies on a topic Meta-narrative reviews are qualitative and use literature to compare research or practice communities Problematizing or critical reviews propose new perspectives on a concept by association with other literature Meta-analyses and meta-regressions integrate quantitative studies and identify moderators Mixed research syntheses combine other review approaches in the same paper == Process and product == Shields and Rangarajan (2013) distinguish between the process of reviewing the literature and a finished work or product known as a literature review. The process of reviewing the literature is often ongoing and informs many aspects of the empirical research project. The process of reviewing the literature requires different kinds of activities and ways of thinking. Shields and Rangarajan (2013) and Granello (2001) link the activities of doing a literature review with Benjamin Bloom's revised taxonomy of the cognitive domain (ways of thinking: remembering, understanding, applying, analyzing, evaluating, and creating). === Use of artificial intelligence in a literature review === Artificial intelligence (AI) is reshaping traditional literature reviews across various disciplines. Generative pre-trained transformers, such as ChatGPT, are often used by students and academics for review purposes. Since 2023, an increasing number of tools powered by large language models and other artificial intelligence technologies have been developed to assist, automate, or generate literature reviews. Nevertheless, the employment of ChatGPT in academic reviews is problematic due to ChatGPT's propensity to "hallucinate". In response, efforts are being made to mitigate these hallucinations through the integration of plugins. For instance, Rad et al. (2023) used ScholarAI for review in cardiothoracic surgery.

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  SAP Cloud Infrastructure

  SAP Cloud Infrastructure is an SAP-operated IaaS cloud platform, used to run SAP’s cloud business and customer-facing deployments for SAP and non-SAP workloads. It is developed and operated with open-source technologies within SAP’s data center network, based on OpenStack and Kubernetes and supporting SAP S/4HANA and general-purpose applications. It offers compute, storage, and platform services that are accessible to SAP customers. == History == In 2012, SAP promoted aspects of cloud computing. In October 2012, SAP announced a platform as a service called the SAP Cloud Platform. In May 2013, a managed private cloud called the S/4HANA Enterprise Cloud service was announced. SAP Converged Cloud was announced in January 2015. SAP Converged Cloud was originally developed as SAP's internal standardized Infrastructure as a Service (IaaS) offering to support SAP’s cloud solutions. Originating from SAP Converged Cloud, SAP Cloud Infrastructure was developed and announced as SAP’s cloud computing offering that is provided for both SAP and customer workloads. In 2025, it had a global footprint of 15 regions and 29 data centers, encompassing more than 200,000 active VMs and over 6,000 hypervisors. In September 2025, SAP announced an expansion of its European “SAP Sovereign Cloud” portfolio, explicitly naming SAP Cloud Infrastructure (alongside SAP Sovereign Cloud On-Site) as part of the stack positioned for public sector and regulated environments. == Services and Features == SAP Cloud Infrastructure (SCI) is an infrastructure-as-a-service (IaaS) offering by SAP that provides virtual compute, storage, and networking services, together with identity, key management, and operational services. SCI follows a self-service model and is managed via APIs and a web-based user interface. === Compute === SCI provides virtual machine instances that can be provisioned from operating system images and selected in predefined sizes (“flavors”). It supports lifecycle operations such as create/modify/resize/delete, power control, and snapshots; instances can be organized into server groups to influence placement policies. === Storage === SCI provides persistent storage services including: Block storage (virtual volumes) with attach/detach to instances, online expansion, cloning, snapshots, and provisioning volumes from images or snapshots. Object storage (containers and objects) managed via API/CLI with access control lists (ACLs) and configurable redundancy options. File storage (shared file systems) with access controls, online resize, snapshots/restore, and replication across availability zones. === Networking === SCI provides software-defined networking (SDN) for tenant networks (networks, subnets, routers) and connectivity features such as floating IPs for public reachability. Network security controls include security groups and firewall policies; connectivity options include BGP-based VPN networking. === Load balancing and DNS === SCI includes managed load balancing for distributing traffic across backend instances and an authoritative DNS service (DNSaaS) with API-based management of DNS zones and records, including options for zone sharing/transfer across projects/tenants and service integrations for automated record creation. === Identity, access, and key management === SCI includes identity and access management for authentication/authorization in projects/tenants (for example token handling, role assignment, and credential management) and key/secrets management for storing and controlling access to secret material such as keys and certificates, including support for different backends (depending on configuration). === Cloud-native services === SCI includes a container image registry (image push/pull, access policies, and lifecycle controls) and an auto-scaling capability for file shares based on configurable rules. === Observability and audit === SCI includes metrics and audit logging capabilities for operational monitoring and for listing/filtering audit-relevant events across services. === Availability and service levels === SCI documentation describes availability-related features such as load balancing, storage redundancy options, and replication for file shares across availability zones. SAP cloud services are governed by contractual service-level agreements (SLA); SAP Cloud Infrastructure references an SLA supplement defining infrastructure-specific terms when referenced in order forms. === SAP cloud services === SAP cloud services can run on different underlying infrastructures, including SAP Cloud Infrastructure in addition to SAP NS2 or hyperscalers. SAP cloud solutions available on SAP Cloud Infrastructure include SAP Cloud ERP, SAP HCM, SAP Solutions for Spend Management, Supply Chain Management, Business Transformation Management, and SAP Business Technology Platform (including related analytics and business data solutions). For example, SAP HANA Cloud documentation lists SAP Cloud Infrastructure as one of the supported infrastructures alongside hyperscalers. === Sustainability === SAP describes sustainability initiatives for its data centers, including energy-efficient infrastructure (for example, advanced cooling systems and power management), renewable electricity usage where feasible, and operational practices such as recycling electronic waste and minimizing water usage. SAP also references environmental management and energy management standards such as ISO 14001 and ISO 50001 for its data center operations. SAP-owned data centers run with 100% renewable electricity and that renewable electricity has been used since 2014 to power SAP facilities including owned data centers and co-locations. == SAP Cloud Infrastructure for SAP Sovereign Cloud == SAP Sovereign Cloud is a portfolio of SAP solutions designed to help organizations adopt SAP cloud solutions such as the SAP Cloud ERP while maintaining control over data, infrastructure, and compliance in line with local laws and regulations. The portfolio offers multiple deployment options, including SAP Cloud Infrastructure and SAP Sovereign Cloud On-Site, alongside sovereign hyperscaler-based options such as via SAP NS2, and targets customers such as public-sector bodies and other highly regulated organizations. In Europe, SAP Cloud Infrastructure is an Infrastructure-as-a-Service (IaaS) deployment option within SAP Sovereign Cloud for SAP and customer / third party workloads, operated on SAP’s data center network and developed using open-source technologies, with customer data stored within the European Union. Sovereignty-related characteristics for the SAP Cloud Infrastructure include: EU footprint and ownership model: SAP-operated data centers in Germany include sites in St. Leon-Rot and Walldorf, and co-location sites in Frankfurt. EU AI Cloud: EU AI Cloud is a sovereign AI offering for Europe that provides secure, compliant environments for building and running AI, including governed access to auditable large language models from SAP and partners. It offers AI models on the SAP Cloud Infrastructure and SAP Business Technology Platform (SAP BTP), enabling deployment of AI applications and models on high-performance European infrastructure (including accelerator/GPU-based compute for AI workloads). Availability zones and secure interconnect: Three availability zones in three independent data centers in Germany, connected via SAP-owned fiber on SAP-owned property. Facility and security standards: ISO/IEC 27001 governance of delivery and operations of SAP cloud services and SAP-owned data centers. Additional facility and availability standards: EN 50600 availability class 3 (European data centre standard) and/or ISO/IEC 22237 availability class 3 (international equivalent). Technology foundation: Based on open-source cloud infrastructure framework (OpenStack) and Kubernetes, without dependencies on hyperscaler technologies. Sovereignty controls: Data sovereignty (data residency), operational sovereignty (administration and maintenance restricted to approved, security-cleared personnel), technical sovereignty (locally hosted control planes with separation via encryption or dedicated infrastructure), and legal sovereignty (use of locally based legal entities or those in approved countries). Classified information processing: Roadmap to meet high and very high requirements for handling classified or sensitive information under European regulatory and security regimes. Public-sector readiness and EU sovereignty assurance levels: Implemented to meet SEAL-3 (Digital Resilience) and SEAL-4 (Full Digital Sovereignty) of the European Commission’s Cloud Sovereignty Framework. Staffing constraints: Operations model selectable to restrict sensitive operations to vetted personnel from EU or NATO countries.

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  Outline of artificial intelligence

  The following outline is provided as an overview of and topical guide to artificial intelligence: Artificial intelligence (AI) is intelligence exhibited by machines or software. It is also the name of the scientific field which studies how to create computers and computer software that are capable of intelligent behavior. == AI terminology == Glossary of artificial intelligence == Goals and applications == === General intelligence === Artificial general intelligence AI-complete === Reasoning and problem solving === Automated reasoning Mathematics Automated theorem prover Computer-assisted proof – Computer algebra General Problem Solver Expert system – Decision support system – Clinical decision support system – === Knowledge representation === Knowledge representation Knowledge management Cyc === Planning === Automated planning and scheduling Strategic planning Sussman anomaly – === Learning === Machine learning – Constrained Conditional Models – Deep learning – Neural modeling fields – Supervised learning – Weak supervision (semi-supervised learning) – Unsupervised learning – === Natural language processing === Natural language processing (outline) – Chatterbots – Language identification – Large language model – Retrieval-augmented generation – Natural language user interface – Natural language understanding – Machine translation – Statistical semantics – Question answering – Semantic translation – Concept mining – Data mining – Text mining – Process mining – E-mail spam filtering – Information extraction – Named-entity extraction – Coreference resolution – Named-entity recognition – Relationship extraction – Terminology extraction – === Perception === Machine perception Pattern recognition – Computer Audition – Speech recognition – Speaker recognition – Computer vision (outline) – Image processing Intelligent word recognition – Object recognition – Optical mark recognition – Handwriting recognition – Optical character recognition – Automatic number plate recognition – Information extraction – Image retrieval – Automatic image annotation – Facial recognition systems – Silent speech interface – Activity recognition – Percept (artificial intelligence) === Robotics === Robotics – Behavior-based robotics – Cognitive – Cybernetics – Developmental robotics – Evolutionary robotics – === Control === Intelligent control Self-management (computer science) – Autonomic Computing – Autonomic Networking – === Social intelligence === Affective computing Kismet === Game playing === Game artificial intelligence – Computer game bot – computer replacement for human players. Video game AI – Computer chess – Computer Go – General game playing – General video game playing – === Creativity, art and entertainment === Artificial creativity Artificial life Artificial intelligence art AI anthropomorphism AI agent AI web browser AI boom AI slop Creative computing Generative artificial intelligence Generative pre trained transformer Uncanny valley Music and artificial intelligence Computational humor Chatbot === Integrated AI systems === AIBO – Sony's robot dog. It integrates vision, hearing and motorskills. Asimo (2000 to present) – humanoid robot developed by Honda, capable of walking, running, negotiating through pedestrian traffic, climbing and descending stairs, recognizing speech commands and the faces of specific individuals, among a growing set of capabilities. MIRAGE – A.I. embodied humanoid in an augmented reality environment. Cog – M.I.T. humanoid robot project under the direction of Rodney Brooks. QRIO – Sony's version of a humanoid robot. TOPIO, TOSY's humanoid robot that can play ping-pong with humans. Watson (2011) – computer developed by IBM that played and won the game show Jeopardy! It is now being used to guide nurses in medical procedures. Purpose: Open domain question answering Technologies employed: Natural language processing Information retrieval Knowledge representation Automated reasoning Machine learning Project Debater (2018) – artificially intelligent computer system, designed to make coherent arguments, developed at IBM's lab in Haifa, Israel. === Intelligent personal assistants === Intelligent personal assistant – Amazon Alexa – Assistant – Braina – Cortana – Google Assistant – Google Now – Mycroft – Siri – Viv – === Other applications === Artificial life – simulation of natural life through the means of computers, robotics, or biochemistry. Automatic target recognition – Diagnosis (artificial intelligence) – Speech generating device – Vehicle infrastructure integration – Virtual Intelligence – == History == History of artificial intelligence Progress in artificial intelligence Timeline of artificial intelligence AI effect – as soon as AI successfully solves a problem, the problem is no longer considered by the public to be a part of AI. This phenomenon has occurred in relation to every AI application produced, so far, throughout the history of development of AI. AI winter – a period of disappointment and funding reductions occurring after a wave of high expectations and funding in AI. Such funding cuts occurred in the 1970s, for instance. Moore's law === History by period === 2017 in artificial intelligence 2018 in artificial intelligence 2019 in artificial intelligence 2020 in artificial intelligence 2021 in artificial intelligence 2022 in artificial intelligence 2023 in artificial intelligence 2024 in artificial intelligence 2025 in artificial intelligence 2026 in artificial intelligence 2027 in artificial intelligence 2028 in artificial intelligence 2029 in artificial intelligence === History by subject === History of logic (formal reasoning is an important precursor of AI) History of machine learning (timeline) History of machine translation (timeline) History of natural language processing History of optical character recognition (timeline) == AI algorithms and techniques == === Search === Discrete search algorithms Uninformed search Brute force search – Problem-solving technique and algorithmic paradigmPages displaying short descriptions of redirect targets Search tree – Data structure in tree form sorted for fast lookup Breadth-first search – Algorithm to search the nodes of a graph Depth-first search – Algorithm to search the nodes of a graph State space search – Class of search algorithmsPages displaying short descriptions of redirect targets Informed search Best-first search – Graph exploring search algorithm A search algorithm – Algorithm used for pathfinding and graph traversal Heuristics – Problem-solving methodPages displaying short descriptions of redirect targets Pruning (algorithm) – Data compression techniquePages displaying short descriptions of redirect targets Adversarial search Minmax algorithm – Decision rule used for minimizing the possible loss for a worst-case scenarioPages displaying short descriptions of redirect targets Logic as search Production system (computer science) – Computer program used to provide artificial intelligence Rule based system – Type of computer systemPages displaying short descriptions of redirect targets Production rule – Computer program used to provide artificial intelligence Inference rule – Method of deriving conclusionsPages displaying short descriptions of redirect targets Horn clause – Type of logical formula Forward chaining – Inference engine in an expert system Backward chaining – Method of forming inferences Planning as search State space search – Class of search algorithmsPages displaying short descriptions of redirect targets Means–ends analysis – Problem solving technique === Optimization search === Optimization (mathematics) algorithms Hill climbing – Optimization algorithm Simulated annealing – Probabilistic optimization technique and metaheuristic Beam search – Heuristic search algorithm Random optimization – Optimization technique in mathematics Evolutionary computation Genetic algorithms – Competitive algorithm for searching a problem spacePages displaying short descriptions of redirect targets Gene expression programming – Evolutionary algorithm Genetic programming – Evolving computer programs with techniques analogous to natural genetic processes Differential evolution – Method of mathematical optimization Society based learning algorithms. Swarm intelligence – Collective behavior of decentralized, self-organized systems Particle swarm optimization – Iterative simulation method Ant colony optimization – Optimization algorithmPages displaying short descriptions of redirect targets Metaheuristic – Optimization technique === Logic === Logic and automated reasoning Programming using logic Logic programming – Programming paradigm based on formal logic See "Logic as search" above. Forms of Logic Propositional logic First-order logic First-order logic with equality Constraint satisfaction – Process in artificial intelligence and operations research Fuzzy logic Fuzzy set theory – Sets whose elements have degrees of membershipPages displaying short descriptions

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  Linguistic categories

  Linguistic categories include Lexical category, a part of speech such as noun, preposition, etc. Syntactic category, a similar concept which can also include phrasal categories Grammatical category, a grammatical feature such as tense, gender, etc. The definition of linguistic categories is a major concern of linguistic theory, and thus, the definition and naming of categories varies across different theoretical frameworks and grammatical traditions for different languages. The operationalization of linguistic categories in lexicography, computational linguistics, natural language processing, corpus linguistics, and terminology management typically requires resource-, problem- or application-specific definitions of linguistic categories. In Cognitive linguistics it has been argued that linguistic categories have a prototype structure like that of the categories of common words in a language. == Linguistic category inventories == To facilitate the interoperability between lexical resources, linguistic annotations and annotation tools and for the systematic handling of linguistic categories across different theoretical frameworks, a number of inventories of linguistic categories have been developed and are being used, with examples as given below. The practical objective of such inventories is to perform quantitative evaluation (for language-specific inventories), to train NLP tools, or to facilitate cross-linguistic evaluation, querying or annotation of language data. At a theoretical level, the existence of universal categories in human language has been postulated, e.g., in Universal grammar, but also heavily criticized. === Part-of-Speech tagsets === Schools commonly teach that there are 9 parts of speech in English: noun, verb, article, adjective, preposition, pronoun, adverb, conjunction, and interjection. However, there are clearly many more categories and sub-categories. For nouns, the plural, possessive, and singular forms can be distinguished. In many languages words are also marked for their case (role as subject, object, etc.), grammatical gender, and so on; while verbs are marked for tense, aspect, and other things. In some tagging systems, different inflections of the same root word will get different parts of speech, resulting in a large number of tags. For example, NN for singular common nouns, NNS for plural common nouns, NP for singular proper nouns (see the POS tags used in the Brown Corpus). Other tagging systems use a smaller number of tags and ignore fine differences or model them as features somewhat independent from part-of-speech. In part-of-speech tagging by computer, it is typical to distinguish from 50 to 150 separate parts of speech for English. POS tagging work has been done in a variety of languages, and the set of POS tags used varies greatly with language. Tags usually are designed to include overt morphological distinctions, although this leads to inconsistencies such as case-marking for pronouns but not nouns in English, and much larger cross-language differences. The tag sets for heavily inflected languages such as Greek and Latin can be very large; tagging words in agglutinative languages such as Inuit languages may be virtually impossible. Work on stochastic methods for tagging Koine Greek (DeRose 1990) has used over 1,000 parts of speech and found that about as many words were ambiguous in that language as in English. A morphosyntactic descriptor in the case of morphologically rich languages is commonly expressed using very short mnemonics, such as ncmsan for category = noun, type = common, gender = masculine, number = singular, case = accusative, animate = no. The most popular tag set for POS tagging for American English is probably the Penn tag set, developed in the Penn Treebank project. === Multilingual annotation schemes === For Western European languages, cross-linguistically applicable annotation schemes for parts-of-speech, morphosyntax and syntax have been developed with the EAGLES Guidelines. The "Expert Advisory Group on Language Engineering Standards" (EAGLES) was an initiative of the European Commission that ran within the DG XIII Linguistic Research and Engineering programme from 1994 to 1998, coordinated by Consorzio Pisa Ricerche, Pisa, Italy. The EAGLES guidelines provide guidance for markup to be used with text corpora, particularly for identifying features relevant in computational linguistics and lexicography. Numerous companies, research centres, universities and professional bodies across the European Union collaborated to produce the EAGLES Guidelines, which set out recommendations for de facto standards and rules of best practice for: Large-scale language resources (such as text corpora, computational lexicons and speech corpora); Means of manipulating such knowledge, via computational linguistic formalisms, mark up languages and various software tools; Means of assessing and evaluating resources, tools and products. The Eagles guidelines have inspired subsequent work on other regions, as well, e.g., Eastern Europe. A generation later, a similar effort was initiated by the research community under the umbrella of Universal Dependencies. Petrov et al. have proposed a "universal", but highly reductionist, tag set, with 12 categories (for example, no subtypes of nouns, verbs, punctuation, etc.; no distinction of "to" as an infinitive marker vs. preposition (hardly a "universal" coincidence), etc.). Subsequently, this was complemented with cross-lingual specifications for dependency syntax (Stanford Dependencies), and morphosyntax (Interset interlingua, partially building on the Multext-East/Eagles tradition) in the context of the Universal Dependencies (UD), an international cooperative project to create treebanks of the world's languages with cross-linguistically applicable ("universal") annotations for parts of speech, dependency syntax, and (optionally) morphosyntactic (morphological) features. Core applications are automated text processing in the field of natural language processing (NLP) and research into natural language syntax and grammar, especially within linguistic typology. The annotation scheme has it roots in three related projects: The UD annotation scheme uses a representation in the form of dependency trees as opposed to a phrase structure trees. At as of February 2019, there are just over 100 treebanks of more than 70 languages available in the UD inventory. The project's primary aim is to achieve cross-linguistic consistency of annotation. However, language-specific extensions are permitted for morphological features (individual languages or resources can introduce additional features). In a more restricted form, dependency relations can be extended with a secondary label that accompanies the UD label, e.g., aux:pass for an auxiliary (UD aux) used to mark passive voice. The Universal Dependencies have inspired similar efforts for the areas of inflectional morphology, frame semantics and coreference. For phrase structure syntax, a comparable effort does not seem to exist, but the specifications of the Penn Treebank have been applied to (and extended for) a broad range of languages, e.g., Icelandic, Old English, Middle English, Middle Low German, Early Modern High German, Yiddish, Portuguese, Japanese, Arabic and Chinese. === Conventions for interlinear glosses === In linguistics, an interlinear gloss is a gloss (series of brief explanations, such as definitions or pronunciations) placed between lines (inter- + linear), such as between a line of original text and its translation into another language. When glossed, each line of the original text acquires one or more lines of transcription known as an interlinear text or interlinear glossed text (IGT)—interlinear for short. Such glosses help the reader follow the relationship between the source text and its translation, and the structure of the original language. There is no standard inventory for glosses, but common labels are collected in the Leipzig Glossing Rules. Wikipedia also provides a List of glossing abbreviations that draws on this and other sources. === General Ontology for Linguistic Description (GOLD) === GOLD ("General Ontology for Linguistic Description") is an ontology for descriptive linguistics. It gives a formalized account of the most basic categories and relations used in the scientific description of human language, e.g., as a formalization of interlinear glosses. GOLD was first introduced by Farrar and Langendoen (2003). Originally, it was envisioned as a solution to the problem of resolving disparate markup schemes for linguistic data, in particular data from endangered languages. However, GOLD is much more general and can be applied to all languages. In this function, GOLD overlaps with the ISO 12620 Data Category Registry (ISOcat); it is, however, more stringently structured. GOLD was maintained by the LINGUIST List and others from 2007 to 2010. The RELISH project created a mirro

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  Vinberg's algorithm

  In mathematics, Vinberg's algorithm is an algorithm, introduced by Ernest Borisovich Vinberg, for finding a fundamental domain of a hyperbolic reflection group. Conway (1983) used Vinberg's algorithm to describe the automorphism group of the 26-dimensional even unimodular Lorentzian lattice II25,1 in terms of the Leech lattice. == Description of the algorithm == Let Γ < I s o m ( H n ) {\displaystyle \Gamma <\mathrm {Isom} (\mathbb {H} ^{n})} be a hyperbolic reflection group. Choose any point v 0 ∈ H n {\displaystyle v_{0}\in \mathbb {H} ^{n}} ; we shall call it the basic (or initial) point. The fundamental domain P 0 {\displaystyle P_{0}} of its stabilizer Γ v 0 {\displaystyle \Gamma _{v_{0}}} is a polyhedral cone in H n {\displaystyle \mathbb {H} ^{n}} . Let H 1 , . . . , H m {\displaystyle H_{1},...,H_{m}} be the faces of this cone, and let a 1 , . . . , a m {\displaystyle a_{1},...,a_{m}} be outer normal vectors to it. Consider the half-spaces H k − = { x ∈ R n , 1 | ( x , a k ) ≤ 0 } . {\displaystyle H_{k}^{-}=\{x\in \mathbb {R} ^{n,1}|(x,a_{k})\leq 0\}.} There exists a unique fundamental polyhedron P {\displaystyle P} of Γ {\displaystyle \Gamma } contained in P 0 {\displaystyle P_{0}} and containing the point v 0 {\displaystyle v_{0}} . Its faces containing v 0 {\displaystyle v_{0}} are formed by faces H 1 , . . . , H m {\displaystyle H_{1},...,H_{m}} of the cone P 0 {\displaystyle P_{0}} . The other faces H m + 1 , . . . {\displaystyle H_{m+1},...} and the corresponding outward normals a m + 1 , . . . {\displaystyle a_{m+1},...} are constructed by induction. Namely, for H j {\displaystyle H_{j}} we take a mirror such that the root a j {\displaystyle a_{j}} orthogonal to it satisfies the conditions (1) ( v 0 , a j ) < 0 {\displaystyle (v_{0},a_{j})<0} ; (2) ( a i , a j ) ≤ 0 {\displaystyle (a_{i},a_{j})\leq 0} for all i < j {\displaystyle i Read more →

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  Superintelligence ban

  Superintelligence ban refers to proposed legal, ethical, or policy measures intended to restrict or prohibit the development of artificial superintelligence, AI systems that would surpass human cognitive abilities in nearly all domains. The idea arises from concerns that such systems could become uncontrollable, potentially posing existential threats to humanity or causing severe social and economic disruption. == Background == The concept of limiting or banning superintelligence research has roots in early 21st-century debates on artificial general intelligence (AGI) safety. Thinkers such as Nick Bostrom and Eliezer Yudkowsky warned that self-improving AI could rapidly exceed human oversight. As advanced models like large-scale language models and autonomous agents began demonstrating complex reasoning abilities, policymakers and ethicists increasingly discussed the need for legal constraints on the creation of systems capable of recursive self-improvement. In October 2025, the Future of Life Institute published a statement calling for "a prohibition on the development of superintelligence, not lifted before there is broad scientific consensus that it will be done safely and controllably, and strong public buy-in." This statement was signed by various public personalities, such as Richard Branson and Steve Wozniak, and AI experts, such as Yoshua Bengio and Geoffrey Hinton. == Rationale == Supporters of a superintelligence ban argue that once AI systems surpass human intelligence, traditional containment, alignment, and control methods may fail. They contend that even limited experimentation with such systems could lead to irreversible outcomes, including loss of human decision-making power or unintended global harm. Some propose international treaties modeled after the nuclear non-proliferation framework to prevent a competitive AI arms race. Opponents argue that a ban would be difficult to define and enforce, given the lack of a precise threshold distinguishing advanced AGI from superintelligence. They also warn that excessive restriction could slow scientific progress, hinder beneficial automation, and encourage unregulated underground research. == Global discussion == Although no government has enacted an explicit superintelligence ban, the idea has been debated within the European Union, United Nations, and several independent AI safety organizations. The Future of Life Institute, Center for AI Safety, and other organizations have called for international cooperation to manage risks associated with the pursuit of superintelligent systems. In 2024 and 2025, proposals for a temporary moratorium on frontier AI research were circulated among major technology firms and research institutes, reflecting growing public concern over the trajectory of AI capabilities.

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  Library history

  Library history is a subdiscipline within library science and library and information science focusing on the history of libraries and their role in societies and cultures. Some see the field as a subset of information history. Library history is an academic discipline and should not be confused with its object of study (history of libraries): the discipline is much younger than the libraries it studies. Library history begins in ancient societies through contemporary issues facing libraries today. Topics include recording mediums, cataloguing systems, scholars, scribes, library supporters and librarians. == Earliest libraries == The earliest records of a library institution as it is presently understood can be dated back to around 5,000 years ago in the Southwest Asian regions of the world. One of the oldest libraries found is that of the ancient library at Ebla (circa 2500 BCE) in present-day Syria. In the 1970s, the excavation at Ebla's library unearthed over 20,000 clay tablets written in cuneiform script. === Library in Mesopotamia === The Assyrian King Assurbanipal created one of the greatest libraries in Nineveh in the seventh century BCE. The collection consisted of over 30,000 tablets written in a variety of languages. The collection was cataloged both by the shape of the tablet and by the subject of the content. The library had separate rooms for the different topics: government, history, law, astronomy, geography, and so on. The tablets also contained myths, hymns, and even jokes. Assurbanipal would send scribes to visit every corner of his kingdom to copy the content of other libraries. His library contained many of the most important literary works of the day, including the epic of Gilgamesh. Assurbanipal's Royal Library also had one of the first library catalogs. Unfortunately, Nineveh was eventually destroyed, and the library was lost in a fire. === Libraries in Ancient Greece === The Greek government was the first to sponsor public libraries. By 500 BCE both Athens and Samos had begun creating libraries for the public, though as most of the population was illiterate these spaces were serving a small, educated portion of the community. Athens developed a city archive at the Metroon in 405 BCE, where documents were stored in sealed jars. These would have saved the documents, but they would have been difficult to consult regularly. In Paros, around the same time, contracts were placed in the temple for safe keeping, and a book curse was placed for extra protection. === Library of Alexandria === The Library at Alexandria, Egypt, was renowned in the third century BCE while kings Ptolemy I Soter and Ptolemy II Philadelphus reigned. The library included a museum, garden, meeting areas and of course reading rooms. The Great Library, as it is known, was one of many in Alexandria. From its inception around the second century BCE, Alexandria was a well-known center for learning. It earned renown as the intellectual capital of the Western world up through the third century CE. The librarians at Alexandria collected, copied, and organized scrolls from across the known world. According to a primary source, every ship that came to Alexandria was required to hand over their books to be copied, and the copies would be returned to the owner, the library keeping the original. The Library of Alexandria was damaged by various disasters over time, including fire, invasion, and earthquake. Scholars believe the collection slowly diminished over time due to theft and efforts to remove it ahead of invading armies. While there are popular stories about how the library was ultimately destroyed, most of these are more myth than fact. === Libraries in Rome === Julius Caesar and his successor Augustus were the first to establish public libraries in ancient Rome, including the library of Apollo on the Palatine Hill. Several emperors followed suit over the next four centuries, including Hadrian, Tiberius, and Vespasian. Roman aristocrats also had personal libraries, which usually contained works in both Greek and Latin. A valuable example of this has been found at Herculaneum near Pompeii. Papyrus manuscripts in Herculaneum's Villa of the Papyri were encased in ash after the eruption of Vesuvius in 79 CE. Modern archaeology is now able to scan these artifacts and discern their contents, including many writings from Philodemus. The average Roman would not have been familiar with books beyond what they might hear read aloud in the forum. Public figures would pay for particular passages to be read aloud to the public from the steps of a public library. === Libraries in the Middle Ages === In the European Middle Ages, libraries began to become more prevalent, despite a widespread reduction in new writing beyond religious themes. Most libraries were initially connected to monasteries or religious institutions. Scriptoriums copied Christian religious texts to share with other religious centers or to be read aloud to their own parishioners. The Holy Roman Emperor Charlemagne (r. 786-814) had a large impact on the advancement of written culture in the Medieval Christian world, acquiring as many written works as he could, and employing many scribes to copy and recirculate vernacular versions of religious works. Most of the text held in small personal libraries was still religious in nature. == Early modern libraries == === Libraries of the Renaissance === During the Renaissance era the merchant middle class grew, and more people found benefits in education. They relied on libraries as a place to study and gain knowledge. Libraries provided a valuable resource, enriching the culture of those who were educated. Universities that had been started in the Middle Ages, founded their own libraries. Books in these libraries could not be borrowed from these libraries and were generally chained to the shelves to prevent theft. As more of the population became literate, new ideas like Humanism and Natural Law spawned an increase personal libraries, although they remained small. Gutenberg's invention of the printing press in 1456 opened the door to the modern era for libraries. == Oldest working libraries == According to the German librarian Michael Knoche, it is not possible to determine which library is the “oldest”: "Precise year dates are a construct, especially in the case of very old libraries. When a collection of books deserves to be called a library depends very much on the point of view of the observer." Various libraries are referred to as the “oldest”: The library founded in the 6th century of the Saint Catherine's Monastery in Sinai is "reputedly the oldest continuously run library in existence today", according to the Library of Congress. Its collection of religious and secular manuscripts is ranging from Bibles, liturgies and prayer books to legal documents such as deeds, court cases and fatwahs (legal opinions). The Al Qarawiyyin Library was founded in 859 by Fatima al-Fihri and is often regarded as the oldest working library in the world. It is in Fez, Morocco and is part of the oldest continually operating university in the world, the University of al-Qarawiyyin. The library houses approximately 4,000 ancient Islamic manuscripts. These manuscripts include 9th century Qurans and the oldest known accounts of the Islamic prophet Muhammed. The Malatestiana Library (Italian: Biblioteca Malatestiana) is a public library in the city of Cesena in northern Italy. Opened in 1454 it is significant for being the first civic library in Europe open to the general public. == Library history reports and writings of the early 19th and 20th century == In the early 19th and 20th century, representative titles were created reporting library history in the United States and the United Kingdom. American titles include Public Libraries in the United States of America, Their History, Condition, and Management (1876), Memorial History of Boston (1881) by Justin Winsor, Public Libraries in America (1894) by William I. Fletcher, and History of the New York Public Library (1923) by Henry M. Lydenberg. British titles include Old English Libraries (1911) by Earnest A. Savage and The Chained Library: A Survey of Four Centuries in the Evolution of the English Library by Burnett Hillman Streeter. In the beginning of the 20th century, library historians began applying scientific research methodologies to examine the library as a social agency. Two works that demonstrate this argument are Geschichte der Bibliotheken (1925) by Alfred Hessel and the Library Quarterly article from 1931, “The Sociological Beginnings of the Library Movement in America” by Arnold Borden. With the establishment of library schools, master's theses and doctoral dissertations represented the shift in serious research regarding libraries and library history. Two published doctoral dissertations that mark this trend are Foundations of the Public Library: The Origins of the American Public Library Movement in Ne

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  Data Science Africa

  Data Science Africa (DSA) is a non-profit knowledge sharing professional group that aims at bringing together leading researchers and practitioners working on data science methods or applications relevant to Africa, and providing training on state of the art data science methods to students and others interested in developing practical skills. Since 2013, DSA has been organizing conference, workshops and summer schools on machine learning and data science across East Africa. Facilitators of Summer School and workshops are researchers and practitioners from the academia, private and public institutions across the world. == Summer schools and workshops == The first summer school which started as Gaussian Process Summer School was held at Makerere University in Kampala, Uganda from 6th to 9 August 2013. The First Data Science Summer School and Workshop was held at Dedan Kimathi University of Technology in Nyeri, Kenya from 15th to 19 June 2015. The Second Data Science Summer School was held at Makerere University, Kampala, Uganda from 27th to 29 July 2016, and the workshop was held at Pulse Lab, Kampala, Uganda from 30 July to 1 August 2016. The Third Data Science Summer School and Workshop was held at Nelson Mandela African Institute of Science and Technology, Tanzania from 19th to 21 July 2017. Among the sponsors of the event was ARM

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  Navigational database

  A navigational database is a type of database in which records or objects are found primarily by following references from other objects. The term was popularized by the title of Charles Bachman's 1973 Turing Award paper, The Programmer as Navigator. This paper emphasized the fact that the new disk-based database systems allowed the programmer to choose arbitrary navigational routes following relationships from record to record, contrasting this with the constraints of earlier magnetic-tape and punched card systems where data access was strictly sequential. One of the earliest navigational databases was Integrated Data Store (IDS), which was developed by Bachman for General Electric in the 1960s. IDS became the basis for the CODASYL database model in 1969. Although Bachman described the concept of navigation in abstract terms, the idea of navigational access came to be associated strongly with the procedural design of the CODASYL Data Manipulation Language. Writing in 1982, for example, Tsichritzis and Lochovsky state that "The notion of currency is central to the concept of navigation." By the notion of currency, they refer to the idea that a program maintains (explicitly or implicitly) a current position in any sequence of records that it is processing, and that operations such as GET NEXT and GET PRIOR retrieve records relative to this current position, while also changing the current position to the record that is retrieved. Navigational database programming thus came to be seen as intrinsically procedural; and moreover to depend on the maintenance of an implicit set of global variables (currency indicators) holding the current state. As such, the approach was seen as diametrically opposed to the declarative programming style used by the relational model. The declarative nature of relational languages such as SQL offered better programmer productivity and a higher level of data independence (that is, the ability of programs to continue working as the database structure evolves.) Navigational interfaces, as a result, were gradually eclipsed during the 1980s by declarative query languages. During the 1990s it started becoming clear that for certain applications handling complex data (for example, spatial databases and engineering databases), the relational calculus had limitations. At that time, a reappraisal of the entire database market began, with several companies describing the new systems using the marketing term NoSQL. Many of these systems introduced data manipulation languages which, while far removed from the CODASYL DML with its currency indicators, could be understood as implementing Bachman's "navigational" vision. Some of these languages are procedural; others (such as XPath) are entirely declarative. Offshoots of the navigational concept, such as the graph database, found new uses in modern transaction processing workloads. == Description == Navigational access is traditionally associated with the network model and hierarchical model of database, and conventionally describes data manipulation APIs in which records (or objects) are processed one at a time, iteratively. The essential characteristic as described by Bachman, however, is finding records by virtue of their relationship to other records: so an interface can still be navigational if it has set-oriented features. From this viewpoint, the key difference between navigational data manipulation languages and relational languages is the use of explicit named relationships rather than value-based joins: for department with name="Sales", find all employees in set department-employees versus find employees, departments where employee.department-code = department.code and department.name="Sales". In practice, however, most navigational APIs have been procedural: the above query would be executed using procedural logic along the lines of the following pseudo-code: On this viewpoint, the key difference between navigational APIs and the relational model (implemented in relational databases) is that relational APIs use "declarative" or logic programming techniques that ask the system what to fetch, while navigational APIs instruct the system in a sequence of steps how to reach the required records. Most criticisms of navigational APIs fall into one of two categories: Usability: application code quickly becomes unreadable and difficult to debug Data independence: application code needs to change whenever the data structure changes For many years the primary defence of navigational APIs was performance. Database systems that support navigational APIs often use internal storage structures that contain physical links or pointers from one record to another. While such structures may allow very efficient navigation, they have disadvantages because it becomes difficult to reorganize the physical placement of data. It is quite possible to implement navigational APIs without low-level pointer chasing (Bachman's paper envisaged logical relationships being implemented just as in relational systems, using primary keys and foreign keys), so the two ideas should not be conflated. But without the performance benefits of low-level pointers, navigational APIs become harder to justify. Hierarchical models often construct primary keys for records by concatenating the keys that appear at each level in the hierarchy. Such composite identifiers are found in computer file names (/usr/david/docs/index.txt), in URIs, in the Dewey decimal system, and for that matter in postal addresses. Such a composite key can be considered as representing a navigational path to a record; but equally, it can be considered as a simple primary key allowing associative access. As relational systems came to prominence in the 1980s, navigational APIs (and in particular, procedural APIs) were criticized and fell out of favour. The 1990s, however, brought a new wave of object-oriented databases that often provided both declarative and procedural interfaces. One explanation for this is that they were often used to represent graph-structured information (for example spatial data and engineering data) where access is inherently recursive: the mathematics originally underpinning SQL (specifically, first-order predicate calculus) does not have sufficient power to support recursive queries, even those as simple as a transitive closure. More recent SQL implementations do support hierarchical and recursive queries. A current example of a popular navigational API can be found in the Document Object Model (DOM) often used in web browsers and closely associated with JavaScript. The DOM is essentially an in-memory hierarchical database with an API that is both procedural and navigational. By contrast, the same data (XML or HTML) can be accessed using XPath, which can be categorized as declarative and navigational: data is accessed by following relationships, but the calling program does not issue a sequence of instructions to be followed in order. Languages such as SPARQL used to retrieve Linked Data from the Semantic Web are also simultaneously declarative and navigational. == Examples == IBM Information Management System IDMS

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  Surrogate model

  A surrogate model is an engineering method used when an outcome of interest cannot be easily measured or computed, so an approximate mathematical model of the outcome is used instead. Most engineering design problems require experiments and/or simulations to evaluate design objective and constraint functions as a function of design variables. For example, in order to find the optimal airfoil shape for an aircraft wing, an engineer simulates the airflow around the wing for different shape variables (e.g., length, curvature, material, etc.). For many real-world problems, however, a single simulation can take many minutes, hours, or even days to complete. As a result, routine tasks such as design optimization, design space exploration, sensitivity analysis and "what-if" analysis become impossible since they require thousands or even millions of simulation evaluations. One way of alleviating this burden is by constructing approximation models, known as surrogate models, metamodels or emulators, that mimic the behavior of the simulation model as closely as possible while being computationally cheaper to evaluate. Surrogate models are constructed using a data-driven, bottom-up approach. The exact, inner working of the simulation code is not assumed to be known (or even understood), relying solely on the input-output behavior. A model is constructed based on modeling the response of the simulator to a limited number of intelligently chosen data points. This approach is also known as behavioral modeling or black-box modeling, though the terminology is not always consistent. When only a single design variable is involved, the process is known as curve fitting. Though using surrogate models in lieu of experiments and simulations in engineering design is more common, surrogate modeling may be used in many other areas of science where there are expensive experiments and/or function evaluations. == Goals == The scientific challenge of surrogate modeling is the generation of a surrogate that is as accurate as possible, using as few simulation evaluations as possible. The process comprises three major steps which may be interleaved iteratively: Sample selection (also known as sequential design, optimal experimental design (OED) or active learning) Construction of the surrogate model and optimizing the model parameters (i.e., bias-variance tradeoff) Appraisal of the accuracy of the surrogate. The accuracy of the surrogate depends on the number and location of samples (expensive experiments or simulations) in the design space. A systematic data representation during training can improve model scalability, thereby reducing the need for expensive simulations. Various design of experiments (DOE) techniques cater to different sources of errors, in particular, errors due to noise in the data or errors due to an improper surrogate model. == Types of surrogate models == Popular surrogate modeling approaches are: polynomial response surfaces; kriging; more generalized Bayesian approaches; gradient-enhanced kriging (GEK); radial basis function; support vector machines; space mapping; artificial neural networks and Bayesian networks. Other methods recently explored include Fourier surrogate modeling , random forests, convolutional neural networks, and generative adversarial networks. For some problems, the nature of the true function is not known a priori, and therefore it is not clear which surrogate model will be the most accurate one. In addition, there is no consensus on how to obtain the most reliable estimates of the accuracy of a given surrogate. Many other problems have known physics properties. In these cases, physics-based surrogates such as space-mapping based models are commonly used. == Invariance properties == Recently proposed comparison-based surrogate models (e.g., ranking support vector machines) for evolutionary algorithms, such as CMA-ES, allow preservation of some invariance properties of surrogate-assisted optimizers: Invariance with respect to monotonic transformations of the function (scaling) Invariance with respect to orthogonal transformations of the search space (rotation) == Applications == An important distinction can be made between two different applications of surrogate models: design optimization and design space approximation (also known as emulation). In surrogate model-based optimization, an initial surrogate is constructed using some of the available budgets of expensive experiments and/or simulations. The remaining experiments/simulations are run for designs which the surrogate model predicts may have promising performance. The process usually takes the form of the following search/update procedure. Initial sample selection (the experiments and/or simulations to be run) Construct surrogate model Search surrogate model (the model can be searched extensively, e.g., using a genetic algorithm, as it is cheap to evaluate) Run and update experiment/simulation at new location(s) found by search and add to sample Iterate steps 2 to 4 until out of time or design is "good enough" Depending on the type of surrogate used and the complexity of the problem, the process may converge on a local or global optimum, or perhaps none at all. In design space approximation, one is not interested in finding the optimal parameter vector, but rather in the global behavior of the system. Here the surrogate is tuned to mimic the underlying model as closely as needed over the complete design space. Such surrogates are a useful, cheap way to gain insight into the global behavior of the system. Optimization can still occur as a post-processing step, although with no update procedure (see above), the optimum found cannot be validated. == Surrogate modeling software == Surrogate Modeling Toolbox (SMT: https://github.com/SMTorg/smt) is a Python package that contains a collection of surrogate modeling methods, sampling techniques, and benchmarking functions. This package provides a library of surrogate models that is simple to use and facilitates the implementation of additional methods. SMT is different from existing surrogate modeling libraries because of its emphasis on derivatives, including training derivatives used for gradient-enhanced modeling, prediction derivatives, and derivatives with respect to the training data. It also includes new surrogate models that are not available elsewhere: kriging by partial-least squares reduction and energy-minimizing spline interpolation. Python library SAMBO Optimization supports sequential optimization with arbitrary models, with tree-based models and Gaussian process models built in. Surrogates.jl is a Julia packages which offers tools like random forests, radial basis methods and kriging. == Surrogate-Assisted Evolutionary Algorithms (SAEAs) == SAEAs are an advanced class of optimization techniques that integrate evolutionary algorithms (EAs) with surrogate models. In traditional EAs, evaluating the fitness of candidate solutions often requires computationally expensive simulations or experiments. SAEAs address this challenge by building a surrogate model, which is a computationally inexpensive approximation of the objective function or constraint functions. The surrogate model serves as a substitute for the actual evaluation process during the evolutionary search. It allows the algorithm to quickly estimate the fitness of new candidate solutions, thereby reducing the number of expensive evaluations needed. This significantly speeds up the optimization process, especially in cases where the objective function evaluations are time-consuming or resource-intensive. SAEAs typically involve three main steps: (1) building the surrogate model using a set of initial sampled data points, (2) performing the evolutionary search using the surrogate model to guide the selection, crossover, and mutation operations, and (3) periodically updating the surrogate model with new data points generated during the evolutionary process to improve its accuracy. By balancing exploration (searching new areas in the solution space) and exploitation (refining known promising areas), SAEAs can efficiently find high-quality solutions to complex optimization problems. They have been successfully applied in various fields, including engineering design, machine learning, and computational finance, where traditional optimization methods may struggle due to the high computational cost of fitness evaluations.

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  Vinelink.com

  Vinelink.com (VINE) is a national website in the United States that allows victims of crime, and the general public, to track the movements of prisoners held by the various states and territories. The first four letters in the websites name, "vine", are an acronym for "Victim Information and Notification Everyday". Vinelink.com displays information, based on the information provided by the various states' departments of correction and other law enforcement agencies, on whether an inmate is in custody, has been released, has been granted parole or probation, or has escaped from custody. In some cases, the website will reveal whether a defendant has been granted parole or probation, but then subsequently violated conditions of their release and become a fugitive. Information provided on Vinelink.com represents metadata, in that the website lists a defendant's custody status; but does not list what the individual is charged with, their criminal history, or the amount of their bail, if applicable. Internet users accessing the Vinelink.com website choose from a map of states and provinces within the United States where they wish to perform a search for an inmate. The user may then search for an individual using the inmate's or parolee's name, or by entering the inmate's specific department of corrections inmate number, if known. When the inmate's custody status changes, users who have registered to be notified of such changes will be notified via email, phone or both. This information is currently released upon request, without the website requesting reasons for the users search or requiring payment, as public records available to the general public. Inmate information is available for most states, and for Puerto Rico, on the website. The states of Arizona, Georgia, Massachusetts, Montana, New Hampshire and West Virginia provide very limited information on the site. In March of 2025, The Maine Sheriff's Association entered into a contract to pilot the use of the VINE system in three counties in the state as well as a regional jail, therefore making South Dakota the only state that does not participate in the VINE system to any degree. The website does not provide data on prisoners detained by the Federal Bureau of Prisons which has its own inmate locator web site nor for inmates of the U.S. military prisons.

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  Lancichinetti–Fortunato–Radicchi benchmark

  Lancichinetti–Fortunato–Radicchi benchmark is an algorithm that generates benchmark networks (artificial networks that resemble real-world networks). They have a priori known communities and are used to compare different community detection methods. The advantage of the benchmark over other methods is that it accounts for the heterogeneity in the distributions of node degrees and of community sizes. == The algorithm == The node degrees and the community sizes are distributed according to a power law, with different exponents. The benchmark assumes that both the degree and the community size have power law distributions with different exponents, γ {\displaystyle \gamma } and β {\displaystyle \beta } , respectively. N {\displaystyle N} is the number of nodes and the average degree is ⟨ k ⟩ {\displaystyle \langle k\rangle } . There is a mixing parameter μ {\displaystyle \mu } , which is the average fraction of neighboring nodes of a node that do not belong to any community that the benchmark node belongs to. This parameter controls the fraction of edges that are between communities. Thus, it reflects the amount of noise in the network. At the extremes, when μ = 0 {\displaystyle \mu =0} all links are within community links, if μ = 1 {\displaystyle \mu =1} all links are between nodes belonging to different communities. One can generate the benchmark network using the following steps. Step 1: Generate a network with nodes following a power law distribution with exponent γ {\displaystyle \gamma } and choose extremes of the distribution k min {\displaystyle k_{\min }} and k max {\displaystyle k_{\max }} to get desired average degree is ⟨ k ⟩ {\displaystyle \langle k\rangle } . Step 2: ( 1 − μ ) {\displaystyle (1-\mu )} fraction of links of every node is with nodes of the same community, while fraction μ {\displaystyle \mu } is with the other nodes. Step 3: Generate community sizes from a power law distribution with exponent β {\displaystyle \beta } . The sum of all sizes must be equal to N {\displaystyle N} . The minimal and maximal community sizes s min {\displaystyle s_{\min }} and s max {\displaystyle s_{\max }} must satisfy the definition of community so that every non-isolated node is in at least in one community: s min > k min {\displaystyle s_{\min }>k_{\min }} s max > k max {\displaystyle s_{\max }>k_{\max }} Step 4: Initially, no nodes are assigned to communities. Then, each node is randomly assigned to a community. As long as the number of neighboring nodes within the community does not exceed the community size a new node is added to the community, otherwise stays out. In the following iterations the “homeless” node is randomly assigned to some community. If that community is complete, i.e. the size is exhausted, a randomly selected node of that community must be unlinked. Stop the iteration when all the communities are complete and all the nodes belong to at least one community. Step 5: Implement rewiring of nodes keeping the same node degrees but only affecting the fraction of internal and external links such that the number of links outside the community for each node is approximately equal to the mixing parameter μ {\displaystyle \mu } . == Testing == Consider a partition into communities that do not overlap. The communities of randomly chosen nodes in each iteration follow a p ( C ) {\displaystyle p(C)} distribution that represents the probability that a randomly picked node is from the community C {\displaystyle C} . Consider a partition of the same network that was predicted by some community finding algorithm and has p ( C 2 ) {\displaystyle p(C_{2})} distribution. The benchmark partition has p ( C 1 ) {\displaystyle p(C_{1})} distribution. The joint distribution is p ( C 1 , C 2 ) {\displaystyle p(C_{1},C_{2})} . The similarity of these two partitions is captured by the normalized mutual information. I n = ∑ C 1 , C 2 p ( C 1 , C 2 ) log 2 ⁡ p ( C 1 , C 2 ) p ( C 1 ) p ( C 2 ) 1 2 H ( { p ( C 1 ) } ) + 1 2 H ( { p ( C 2 ) } ) {\displaystyle I_{n}={\frac {\sum _{C_{1},C_{2}}p(C_{1},C_{2})\log _{2}{\frac {p(C_{1},C_{2})}{p(C_{1})p(C_{2})}}}{{\frac {1}{2}}H(\{p(C_{1})\})+{\frac {1}{2}}H(\{p(C_{2})\})}}} If I n = 1 {\displaystyle I_{n}=1} the benchmark and the detected partitions are identical, and if I n = 0 {\displaystyle I_{n}=0} then they are independent of each other.

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