This list of information schools, sometimes abbreviated to iSchools, includes members of the iSchools organization. The iSchools organization reflects a consortium of over 130 information schools across the globe. == History == The first iSchools Caucus was formed in 1988 by Syracuse, Pittsburgh, and Drexel and was called the Gang of Three (sometimes gang of four with Rutgers). Syracuse renamed the School of Library Science as the School of Information Studies in 1974, and is considered as the first “iSchool” in history. The group was formally named "the iSchools Caucus" or more casually, the iCaucus. By 2003, the group expanded to include the Universities of Michigan, Washington, Illinois, UNC, Florida State, Indiana, and Texas, and was called the Gang of Ten. The current iSchools Caucus organization was formalized by 2005, with additions of UC Berkeley, UC Irvine, UCLA, Penn State, Georgia Tech, Maryland, Toronto, Carnegie Mellon and Singapore Management University. == iSchools organization == The iSchools promote an interdisciplinary approach to understanding the opportunities and challenges of information management, with a core commitment to concepts like universal access and user-centered organization of information. The field is concerned broadly with questions of design and preservation across information spaces, from digital and virtual spaces such as online communities, social networking, the World Wide Web, and databases to physical spaces such as libraries, museums, collections, and other repositories. "School of Information", "Department of Information Studies", or "Information Department" are often the names of the participating organizations. Degree programs at iSchools include course offerings in areas such as information architecture, design, policy, and economics; knowledge management, user experience design, and usability; preservation and conservation; librarianship and library administration; the sociology of information; and human-computer interaction and computer science. === Leadership === The executive committee of the iSchools is made up of the current chair (Ina Fourie, University of Pretoria, South Africa), past chair (Gillian Oliver, Monash University, Australia) and the chair elect (Javed Mostafa, University of Toronto Canada), plus representatives from the three regions (North America, Europe, and Asia-Pacific). The current executive director is Slava Sterzer. == Member institutions == Between 2010 and 2026, the organization expanded globally beyond North America, growing to 133 member schools as of March 2026. For an updated and complete list of member schools, please visit the member database of the iSchools. == iConferences == Members of the iSchools organize a regular academic conference, known as the iConference, hosted by a different member institution each year. September 2005: Pennsylvania State University October 2006: University of Michigan February 2008: University of California, Los Angeles February 2009: University of North Carolina February 2010: University of Illinois at Urbana-Champaign February 2011: University of Washington, Seattle February 2012: University of Toronto February 2013: University of North Texas March 2014: Humboldt-Universität zu Berlin March 2015: University of California, Irvine March 2016: Drexel University March 2017: Wuhan University March 2018: University of Sheffield and Northumbria University March 2019: University of Maryland March 2020: University of Borås (virtual only) March 2021: Renmin University of China (virtual only) February/March 2022: University of Texas at Austin, University College Dublin & Kyushu University (virtual only) March 2023: Universitat Oberta de Catalunya March 2024: Jilin University March 2025: Indiana University March/April 2026: Edinburgh Napier University 2027: Victoria University of Wellington == Other schools of information == Other information schools and programs include: Documentation Research and Training Centre, Indian Statistical Institute, Bangalore San Jose State University, School of Information University of Southern California Library Science Degree Ankara University, Department of Information and Records Management, Ankara/Turkey Marmara University, Department of Information and Records Management, Istanbul/Turkey University of Kelaniya, Department of Library and Information Science, Kelaniya/Sri Lanka University of Colombo, National Institute of Library and Information Science (NILIS), Colombo/Sri Lanka Chicago State University, Department of Information Studies
List of large language models
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text. == List == For the training cost column, 1 petaFLOP-day equals 1 petaFLOP/sec × 1 day, or 8.64×1019 FLOP (floating point operations). Only the cost of the largest model is shown. The number of parameters is measured in billions, and the training cost is measured in petaFLOP-days. === 2018 === === 2019 === === 2020 === === 2021 === === 2022 === === 2023 === === 2024 === === 2025 === === 2026 ===
The MANIAC
The MANIAC is a 2023 novel by Chilean author Benjamín Labatut, written in English. It is a fictionalised biography of polymath John von Neumann, whom Labatut calls "the smartest human being of the 20th century". The book focuses on von Neumann, but is also about physicist Paul Ehrenfest, the history of artificial intelligence, and Lee Sedol's Go match against AlphaGo. The book received mostly positive reviews from critics. == Background == John von Neumann was a Jewish Hungarian-born polymath who was a prodigy from an early childhood. Von Neumann worked in multiple fields of science, theoretical (mathematical foundations of quantum mechanics, game theory, cellular automata) and applied (nuclear weapons research during the Manhattan Project in World War II, computer architecture later named after him, and many other subjects). Labatut calls him "the smartest human being of the 20th century". The title of the book is derived from an early computer based on von Neumann architecture, built after the war at Los Alamos laboratory, called MANIAC I. Benjamín Labatut is a Chilean author known for his 2020 book When We Cease to Understand the World, a collection of fictionalised stories about famous scientists that received positive reviews and was translated into multiple languages from Spanish. The MANIAC is Labatut's first book written in English. In an interview, Labatut said he prefers to write in English: English is my preferred form of thought. ... English is the language I do most if not all my reading it. And it is a far better language than Spanish, in so many ways. Writing "clean" prose in Spanish is almost impossible, because so many of its sounds clash. Borges said that he found English "a far finer language than Spanish" because it's both Germanic and Latin; because of its wonderful vocabulary ("Regal is not exactly the same thing as saying kingly," he explained); because of its physicality; and because you can do almost anything with verbs and prepositions. Labatut was inspired to write The MANIAC by George Dyson's book Turing's Cathedral. == Synopsis == The book has three chapters. The first chapter, "Paul or the Discovery of the Irrational", written in the third person, is about physicist Paul Ehrenfest. The chapter opens with Ehrenfest shooting dead his son Vassily, who suffered from Down syndrome, and then himself. It then recounts Ehrenfest's life story, describing his relationships with his wife Tatyana, his mistress Nelly Meyjes, and his eminent physicist colleagues. It chronicles his descent into despair and depression over his marriage's disintegration, the advent of quantum mechanics, and the direction Europe was heading in with the Nazi Party's rise to power in Germany, looping back to the initial scene of the chapter. The second chapter, "John or the Mad Dreams of Reason", is about John von Neumann, and is written as a series of interviews of his family members, wives, friends, and colleagues, each in a distinctive voice. It is divided into three parts. Part I, "The Limits of Logic", is about his early life, as told by von Neumann's childhood friend Eugene Wigner, mother Margrit Kann, brother Nicholas von Neumann, first wife Mariette Kövesi, and scientists Theodore von Karman, George Polya, and Gábor Szegő. It climaxes with von Neumann's participation in David Hilbert's program to create a logical basis for mathematics based on a consistent set of axioms, a quest ultimately scuppered by Kurt Gödel. Part II, "The Delicate Balance of Terror", discusses von Neumann's role in the Manhattan Project (as told by Richard Feynman); his development of game theory and the doctrine of mutual assured destruction (MAD) (as told by Oskar Morgenstern); and his creation of the MANIAC I computer and the von Neumann architecture (as told by Julian Bigelow). In Part III, "Ghosts in the Machine", Sydney Brenner discusses von Neumann's contributions to biology, his theoretical work on self-replicating and self-repairing machines, and his vision of Von Neumann probes exploring the universe. Nils Aall Barricelli talks about his ideas of digital life and his disagreements with von Neumann. Von Neumann's wife Klára Dán, daughter Marina, and Wigner talk about his final years, personal life, and death. The third chapter, "Lee or The Delusions of Artificial Intelligence", is about Lee Sedol's Go match against AlphaGo. The narrative reverts to the third person. The chapter also tells the story of Demis Hassabis, a chess prodigy in childhood who decided to work on artificial intelligence and founded DeepMind, the company behind AlphaGo. The way is pointed to the future, as artificial intelligence's growing capabilities outpace the human mind. The book ends with Lee Sedol's retirement from Go, and new version of DeepMind's program, AlphaZero, that did not train on human games but nevertheless became the strongest player in Go, chess, and Shogi. == Reception == The book received mostly positive reviews. In his review for The New York Times Tom McCarthy noted the ambiguity of genre: "At its best, as in the stunning opening sequence reconstructing the murder-suicide of the physicist Paul Ehrenfest and his disabled son, or in the final section's gripping account of a computer defeating the world's best human Go player, you just throw up your hands and think, Who cares what discourse label we assign this stuff? It's great." Becca Rothfeld of the Washington Post praised the book, writing that it is "Labatut's latest virtuosic effort, at once a historical novel and a philosophical foray": "The MANIAC is a work of dark, eerie and singular beauty." She noted that the book "can also be difficult to read" because of its unusual narrative structure: "The book is narrated by a cluttered polyphony of characters, among them both of von Neumann's wives and a number of his teachers and colleagues. ... Like von Neumann, The MANIAC strives to adopt the impartial standpoint of the universe." Killian Fox of The Guardian sees the book as "darkly fascinating novel", and notes Labatut's "impressive dexterity, unpicking complex ideas in long, elegant sentences that propel us forward at speed (this is his first book written in English). Even in the more feverish passages, when yet another great mind succumbs to madness, haunted by the spectres they've helped unleash on the world, he feels in full control of his material." Sam Byers of The Guardian praises the book and the author's style: "The opening chapter of Benjamín Labatut's second novel is such a perfect distillation of his technique that it could serve as a manifesto." and "Readers ... will recognise the sense of breathlessness his best writing can evoke. Seemingly loosened from the laws of physics they describe, his sentences range freely through time and space, connecting not only characters and events, but the delicate tissue of intellectual history, often with a lightness of touch that belies their underlying complexity." He writes on the narrative structure: "Through a cascade of staccato chapters, an ensemble of narrators offer their piecemeal insights." Byers adds that "a brilliant novel is not quite what we end up with" and sees the problem in the "diffusion": "Labatut simply spreads himself too thin. Too many years in too few pages; too many voices with far too little to distinguish them. Initially intriguing, the bite-size monologues quickly come to feel inadequate." Some reviewers did not see the book as a biography. In an essay for the Cleveland Review of Books, Ben Cosman juxtaposes the book with Christopher Nolan's biopic Oppenheimer, and writes that it "follows the development of artificial intelligence—first as an idea at the beginning of the twentieth century, and then as a practicality at the beginning of the twenty-first—through the lives of three men who faced it." He also compared the book's structure to "witness testimony". Another reviewer called the book "perfect for anyone thirsting for more nuclear anxiety after watching Oppenheimer". Garrett Biggs of the Chicago Review of Books writes of the book's style: "Labatut writes about scientists the way Roberto Bolaño writes about poets. They are near mythical figures, captured at the corner of the novel's eye. They become historical in the most fraught sense of the term: subject to rumor and speculation and, eventually, the novel's form inflates their personas into something so large they can only be understood as narrative, never known in any objective capacity." Biggs criticises the last chapter: "the story of artificial intelligence has yet to be written. And so when Labatut's narration editorializes about artificial intelligence as 'a future that inspires hope and horror,' The MANIAC disassembles as a novel and starts to sound like a stale thinkpiece. AlphaGo might represent the first glimmer of a true artificial intelligence, as Labatut suggests. It also could one day be considered nothing more than a souped-up cousin to IBM's DeepBlue.
Agent mining
Agent mining is a research field that combines two areas of computer science: multiagent systems and data mining. It explores how intelligent computer agents can work together to discover, analyze, and learn from large amounts of data more effectively than traditional methods. == Historical context == The interaction and the integration between multiagent systems and data mining have a long history. The very early work on agent mining focused on agent-based knowledge discovery, agent-based distributed data mining, and agent-based distributed machine learning, and using data mining to enhance agent intelligence. The International Workshop on Agents and Data Mining Interaction has been held for more than 10 times, co-located with the International Conference on Autonomous Agents and Multi-Agent Systems. Several proceedings are available from Springer Lecture Notes in Computer Science.
Fuzzy classification
Fuzzy classification is the process of grouping elements into fuzzy sets whose membership functions are defined by the truth value of a fuzzy propositional function. A fuzzy propositional function is analogous to an expression containing one or more variables, such that when values are assigned to these variables, the expression becomes a fuzzy proposition. Accordingly, fuzzy classification is the process of grouping individuals having the same characteristics into a fuzzy set. A fuzzy classification corresponds to a membership function μ C ~ : P F ~ × U → T ~ {\textstyle \mu _{\tilde {C}}:{\tilde {PF}}\times U\to {\tilde {T}}} that indicates the degree to which an individual i ∈ U {\textstyle i\in U} is a member of the fuzzy class C ~ {\textstyle {\tilde {C}}} , given its fuzzy classification predicate Π ~ C ~ ∈ P F ~ {\textstyle {\tilde {\Pi }}_{\tilde {C}}\in {\tilde {PF}}} . Here, T ~ {\textstyle {\tilde {T}}} is the set of fuzzy truth values, i.e., the unit interval [ 0 , 1 ] {\textstyle [0,1]} . The fuzzy classification predicate Π ~ C ~ ( i ) {\textstyle {\tilde {\Pi }}_{\tilde {C}}(i)} corresponds to the fuzzy restriction " i {\textstyle i} is a member of C ~ {\textstyle {\tilde {C}}} ". == Classification == Intuitively, a class is a set that is defined by a certain property, and all objects having that property are elements of that class. The process of classification evaluates for a given set of objects whether they fulfill the classification property, and consequentially are a member of the corresponding class. However, this intuitive concept has some logical subtleties that need clarification. A class logic is a logical system which supports set construction using logical predicates with the class operator { ⋅ | ⋅ } {\textstyle \{\cdot |\cdot \}} . A class C = { i | Π ( i ) } {\displaystyle C=\{i|\Pi (i)\}} is defined as a set C of individuals i satisfying a classification predicate Π which is a propositional function. The domain of the class operator { .| .} is the set of variables V and the set of propositional functions PF, and the range is the powerset of this universe P(U) that is, the set of possible subsets: { ⋅ | ⋅ } : V × P F → P ( U ) {\displaystyle \{\cdot |\cdot \}:V\times PF\rightarrow P(U)} Here is an explanation of the logical elements that constitute this definition: An individual is a real object of reference. A universe of discourse is the set of all possible individuals considered. A variable V :→ R {\textstyle V:\rightarrow R} is a function which maps into a predefined range R without any given function arguments: a zero-place function. A propositional function is "an expression containing one or more undetermined constituents, such that, when values are assigned to these constituents, the expression becomes a proposition". In contrast, classification is the process of grouping individuals having the same characteristics into a set. A classification corresponds to a membership function μ that indicates whether an individual is a member of a class, given its classification predicate Π. μ : P F × U → T {\displaystyle \mu :PF\times U\rightarrow T} The membership function maps from the set of propositional functions PF and the universe of discourse U into the set of truth values T. The membership μ of individual i in Class C is defined by the truth value τ of the classification predicate Π. μ C ( i ) := τ ( Π ( i ) ) {\displaystyle \mu C(i):=\tau (\Pi (i))} In classical logic the truth values are certain. Therefore a classification is crisp, since the truth values are either exactly true or exactly false.
Construction robots
Construction robots are a subset of industrial robots used for building and infrastructure construction on site, or in the production of materials and components offsite. A 2021 survey said 55% of construction companies in the United States, Europe, and China used robots in some form. This figure, however, reflects reported use across the construction value chain rather than widespread deployment of robots on active construction sites. Real-world adoption remains limited, with many robotic systems confined to pilot projects, controlled environments, or specific task applications rather than continuous on-site construction use. One of the main challenges in deploying robots on construction sites is the unstructured and variable nature of the environment, which differs fundamentally from controlled factory settings where industrial robots have traditionally operated. Some robots currently deployed on job sites assist with physically demanding or repetitive tasks: excavating, lifting heavy materials, surveying, laying out markers, tying rebar, and installing drywall. More advanced systems are being developed for exterior finishing, steel placement, masonry, and reinforced concrete work. In practice, rather than autonomous systems performing core building tasks, the most widely adopted robot applications on construction sites involve technologies such as aerial drones (or, less frequently, robot 'dogs' - for example, Boston Dynamics' Spot - or humanoid robots) used for surveying, inspection, and progress monitoring (the robots typically carry video and/or 360-degree cameras, LiDar scanners or other data capture devices, with data analysed using artificial intelligence and machine learning). Some emerging systems are designed as multifunctional construction robots, integrating multiple tools and capabilities within a single robotic platform to perform different stages of the construction process. These systems aim to improve operational flexibility and increase automation in complex construction environments. Experimental projects using robotic construction technologies and additive manufacturing have been demonstrated in several countries as part of broader efforts to industrialize the construction sector and improve productivity through automation and digitalization. == Features == Construction robots are generally required to meet the following criteria: Mobility: the ability to navigate around a construction site, including uneven terrain and confined spaces. Adaptability: the ability to handle components of variable size, weight, and shape. Environmental awareness: the ability to sense and respond to changing on-site conditions. Interactivity: the ability to operate alongside human workers and other equipment. Multitasking: the ability to perform several different operations within a single deployment. == Capabilities == Construction robots have been developed and tested for a range of on-site tasks, including: Progress monitoring — robots equipped with cameras and sensors can track construction progress and identify deviations from plans. Inspection — robots are used to investigate infrastructure at dangerous or inaccessible locations, reducing risk to human workers and eliminating human error. Wall construction — robotic systems can lay bricks and blocks with greater speed and consistency than manual labour. Earthmoving and material handling — autonomous excavators and haul trucks use GPS, lidar, and motion sensors to perform digging, trenching, and loading tasks with minimal human input. Grading and dozing — autonomous bulldozers use GPS, gyroscopes, and laser sensors to control blade angle and depth, improving surface finish accuracy and reducing material overuse. 3D printing — additive manufacturing systems can construct walls and structural elements directly from digital models. == Notable construction-related activities undertaken by robots == The distribution of robotic applications in construction varies across the project lifecycle. Most applications are concentrated in structural construction tasks such as masonry, concrete work, and assembly, while other phases, including planning, maintenance, and demolition, remain less represented. === Automated building systems === The Nisseki Yokohama Building (also known as Rail City Yokohama), a 30-storey office building in Yokohama, Japan, was constructed between 1994 and 1997 using the SMART system (Shimizu Manufacturing system by Advanced Robotics Technology), developed by Shimizu Corporation and a consortium of seven other Japanese companies. The system used automated horizontal hoists and vertical lifts to position steel beams, columns, precast concrete floor slabs, and prefabricated facade panels, with welding robots connecting structural elements under laser-guided precision. Each component was tracked by barcode to monitor progress and coordinate just-in-time delivery of materials. Obayashi Corporation developed the Advanced Building Construction System (ABCS), a similar automated platform used in several high-rise projects in Japan in the 1990s, including the NEC Head Office in Kanagawa (1997–2000). === Progress monitoring, inspection === Boston Dynamics' Spot was used in February 2024 to inspect sections of the M5 motorway in England for National Highways. A £15,000 humanoid robot (a G1 model from Chinese manufacturer Unitree) was deployed to capture 360-degree imagery and progress reports to support health and safety monitoring and reporting for UK contractor Tilbury Douglas in April 2026. In the US, Virginia Tech's ARCADE research lab is developing MARIO (Multi-Agent Robotic system for Inspection On-site), a heterogenous robotic system deploying multiple robots capable of different locomotion to perform remote real-time construction progress monitoring in complex construction sites. === Earthmoving === === Concrete works === Obayashi Corporation developed and deployed a robotic system for placing concrete layers in dam construction in Japan. A concrete floor finishing robot was deployed by Kajima and Tokimec in Japan. The MARK series were designed in 1984 to automate the levelling and trowelling of concrete slabs on construction sites, providing consistent finishing accuracy, improved efficiency, and reduced dependence on skilled labour === Masonry === SAM100 (Semi-Automated Mason), developed by Construction Robotics, is one of the first commercially available bricklaying robots for on-site masonry construction. In 2018, it was used in the construction of the University Arts Building at the University of Nevada, Reno — a $35.5 million facility — where it laid over 60,000 of the 100,000 bricks required, reducing the brick veneer installation time by approximately 50%. Hadrian X, developed by the Australian company Fastbrick Robotics, is a fully autonomous mobile bricklaying robot. In November 2022, it completed its first commercial project — five four-bedroom houses in Wellard, Western Australia. In February 2025, PulteGroup, one of the largest homebuilders in the United States, piloted Hadrian X on a site in Florida, constructing an entire house in a single day. === 3D printing === In May 2025, a residential building in Arinaga, Gran Canaria, Spain, was completed using 3D printing construction technology, as part of broader efforts to demonstrate robotic and additive manufacturing methods in the housing sector. In 2026, a three-storey apartment block in France was constructed using concrete 3D printing technology, three months faster than conventional building methods. Finland's Hyperion Robotics has opened a UK factory and used 3D printing with concrete to produce foundations for pipelines and for electricity substation bases, reducing time-consuming and weather-dependent onsite construction processes. == Social impact == The adoption of construction robots varies significantly by region and is shaped by labour market conditions, cultural attitudes, and regulatory frameworks. In Japan, construction robots have been embraced as a response to an ageing workforce and chronic labour shortages, and are generally viewed positively by the industry. In the United States, adoption has historically been slower, partly due to resistance from labour unions concerned about job displacement. Research suggests that the impact of automation on workers is uneven: while robots can create a productivity effect that benefits some workers, displacement effects are most pronounced among younger, less-educated workers in manufacturing-heavy regions. More than 60% of construction firms now report difficulty finding skilled operators, which has increased openness to automation as a practical solution to workforce shortages rather than a replacement for workers. In the UK, during onsite deployment of a humanoid robot for monitoring purposes, there were concerns that staff might think they were being watched ("It's not there to spy on people.... So, we insist that everyone is blurred out. N
Document AI
Document AI, also known as Document Intelligence, refers to a field of technology that employs machine learning (ML) techniques, such as natural language processing (NLP). These techniques are used to develop computer models capable of analyzing documents in a manner akin to human review. Through NLP, computer systems are able to understand relationships and contextual nuances in document contents, which facilitates the extraction of information and insights. Additionally, this technology enables the categorization and organization of the documents themselves. The applications of Document AI extend to processing and parsing a variety of semi-structured documents, such as forms, tables, receipts, invoices, tax forms, contracts, loan agreements, and financial reports. == Key features == Machine learning is utilized in Document AI to extract information from both printed and digital documents. This technology recognizes images, text, and characters in various languages, aiding in the extraction of insights from unstructured documents. The use of this technology can improve the speed and quality of decision-making in document analysis. Additionally, the automation of data extraction and validation can contribute to increased efficiency in document analysis processes. Since the early 2020s, the integration of large language models has extended Document AI beyond extraction toward generative tasks, including the automated drafting of forms, contracts, and document summaries. == Example == A business letter contains information in the form of text, as well as other types of information, such as the position of the text. For instance, a typical letter contains two addresses before the body of the text. The address at the very top (sometimes aligned to the right) is the sender address. This is normally followed by the date of the letter, with the place of writing. After this, the receiver address is listed. The distinction between the sender address and the receiver address is conveyed solely by the position of the address on the page, i.e. there is no textual indication like Sender: in front of the addresses. == Data dimensions and ML architecture == Data is typically distinguished into spatial data and time-series data, the former includes things like images, maps and graphs, while the latter includes signals such as stock prices or voice recordings. Document AI combines text data, which has a time dimension, with other types of data, such as the position of an address in a business letter, which is spatial. Historically in machine learning spatial data was analyzed using a convolutional neural network, and temporal data using a recurrent neural network. With the advent of dimension-type agnostic transformer architecture, these two different types of dimension can be more easily combined, Document AI is an example of this. == Benchmarks == Several public datasets are used to evaluate Document AI systems. FUNSD (Form Understanding in Noisy Scanned Documents) contains 199 annotated forms with token- and block-level labels for form understanding tasks. CORD (Consolidated Receipt Dataset) supports key information extraction from receipts. DocVQA contains approximately 50,000 questions over 12,000 document images for layout-aware visual question answering. == Common uses == Document AI systems are used to automate document processing and information extraction in business and financial workflows, including invoice and receipt processing, data entry automation, anomaly detection, mortgage processing, loan portfolio monitoring, credit risk management, and fraud detection such as counterfeit currency and fraudulent checks. They are also applied in regulatory compliance and contract analysis, including assessing changes in legal and regulatory documents. In real estate, Document AI supports document classification and structured information extraction for standardized processing and analytics. With the adoption of generative AI, Document AI systems can also generate and pre-fill structured documents such as contracts or business forms from natural language prompts.