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
Transderivational search
Transderivational search (often abbreviated to TDS) is a psychological and cybernetics term, meaning when a search is being conducted for a fuzzy match across a broad field. In computing the equivalent function can be performed using content-addressable memory. Unlike usual searches, which look for literal (i.e. exact, logical, or regular expression) matches, a transderivational search is a search for a possible meaning or possible match as part of communication, and without which an incoming communication cannot be made any sense of whatsoever. It is thus an integral part of processing language, and of attaching meaning to communication. In NLP (Neuro-linguistic programming), a transderivational search (Bandler and Grinder, 1976) is essentially the process of searching back through one's stored memories and mental representations to find the personal reference experiences from which a current understanding or mental map has been derived. By the end of 1976, Grinder and Bandler had combined Satir’s and Perls’ language patterns and Erickson’s hypnotic language and use of metaphor with anchoring to create new processes that they called collapsing anchors, trans-derivational search, changing personal history, and reframing. A psychological example of TDS is in Ericksonian hypnotherapy, where vague suggestions are used that the patient must process intensely in order to find their own meanings, thus ensuring that the practitioner does not intrude his own beliefs into the subject's inner world. == TDS in human communication and processing == Because TDS is a compelling, automatic and unconscious state of internal focus and processing (i.e. a type of everyday trance state), and often a state of internal lack of certainty, or openness to finding an answer (since something is being checked out at that moment), it can be utilized or interrupted, in order to create, or deepen, trance. TDS is a fundamental part of human language and cognitive processing. Arguably, every word or utterance a person hears, for example, and everything they see or feel and take note of, results in a very brief trance while TDS is carried out to establish a contextual meaning for it. === Examples === Leading statements: "And those thoughts you had yesterday..." the human mind cannot process hearing this phrase, without at some level searching internally for some thoughts or other that it had yesterday, to make the subject of the sentence. "The many colors that fruit can be" likewise starts the human mind considering even if briefly, different fruit sorted by color. "You did it again, didn't you!" This everyday manipulative use of TDS usually sends the recipient looking internally for some "it" they may have done for which blame is being fairly given. Regardless of whether such a matter can be identified, guilt or anger may result. "There has been pain, hasn't there" the mind of a patient suffering an illness will find it very hard or impossible to hear or answer this sentence without conducting internal searches to verify whether this is true or not, or to find an example if so. "You'd forgotten something [or: some part of your body], hadn't you?" the mind usually checks through the various things, or parts of the body, on hearing this, seeing if each in turn has been forgotten. Textual ambiguity: "Do you remember line dancing on the steps?" Without sufficient context, some statements may trigger TDS in order to resolve inherent ambiguity in the interpretation of a posed question. Do I remember a bygone fad called "line dancing on the steps"? Do I remember personally engaging in dancing in the past? Do I remember my routine practice dancing by focusing on the steps of the dance? Do I tend to forget about dancing when I am standing on steps? "Penny-wise and pound the table dance to the beat of a different drummer". The mixing of cliché and stock phrases may trigger TDS in order to reconcile the discrepancies between expected and actual utterances in sequence. Although TDS is often associated with spoken language, it can be induced in any perceptual system. Thus Milton Erickson's "hypnotic handshake" is a technique that leaves the other person performing TDS in search of meaning to a deliberately ambiguous use of touch.
Diffbot
Diffbot is a developer of machine learning and computer vision algorithms and public APIs for extracting data from web pages / web scraping to create a knowledge base. == Overview == The company has gained interest from its application of computer vision technology to web pages, wherein it visually parses a web page for important elements and returns them in a structured format. In 2015 Diffbot announced it was working on its version of an automated "knowledge graph" by crawling the web and using its automatic web page extraction to build a large database of structured web data. In 2019 Diffbot released their Knowledge Graph which has since grown to include over two billion entities (corporations, people, articles, products, discussions, and more), and ten trillion "facts." == Features == The company's products allow software developers to analyze web home pages and article pages, and extract the "important information" while ignoring elements deemed not core to the primary content. In August 2012 the company released its Page Classifier API, which automatically categorizes web pages into specific "page types". As part of this, Diffbot analyzed 750,000 web pages shared on the social media service Twitter and revealed that photos, followed by articles and videos, are the predominant web media shared on the social network. In September 2020 the company released a Natural Language Processing API for automatically building Knowledge Graphs from text. The company raised $2 million in funding in May 2012 from investors including Andy Bechtolsheim and Sky Dayton. Diffbot's customers include Adobe, AOL, Cisco, DuckDuckGo, eBay, Instapaper, Microsoft, Onswipe and Springpad.
Yann LeCun
Yann André Le Cun ( lə-KUN; French: [ləkœ̃]; usually spelled LeCun; born 8 July 1960) is a French-American computer scientist working in the fields of artificial intelligence, machine learning, computer vision, robotics and image compression. He is the Jacob T. Schwartz Professor of Computer Science at the Courant Institute of Mathematical Sciences at New York University. He served as Chief AI Scientist at Meta Platforms before co-founding Advanced Machine Intelligence Labs in December 2025. He is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNNs). He is also one of the main creators of the DjVu image compression technology, alongside Léon Bottou and Patrick Haffner. He co-developed the Lush programming language with Léon Bottou. In 2018, LeCun, Yoshua Bengio, and Geoffrey Hinton received the Turing Award from the Association for Computing Machinery (ACM) for their work on deep learning. LeCun, Bengio, and Hinton, and occasionally Jürgen Schmidhuber, are sometimes referred to as the "Godfathers of AI" and "Godfathers of Deep Learning". == Early life and education == Yann André Le Cun was born on 8 July 1960 at Soisy-sous-Montmorency, in the suburbs of Paris. His surname, Le Cun, derives from the old Breton form Le Cunff and originates from the region of Guingamp in northern Brittany. Yann is the Breton form of Jean, the French form of John. He received a Diplôme d'Ingénieur from the ESIEE Paris in 1983 and a PhD in computer science from Université Pierre et Marie Curie (now Sorbonne University) in 1987, during which he proposed an early form of backpropagation, an algorithm crucial for enabling neural networks to learn. Before joining AT&T, LeCun was a postdoctoral researcher for a year, starting in 1987, supervised by Geoffrey Hinton at the University of Toronto. LeCun has three sons, and his brother is employed by Google. He has American citizenship. == Career and research == LeCun's career has been spent primarily at Bell Labs, New York University and Meta Platforms, Inc. === Bell Labs === In 1988, LeCun joined the Adaptive Systems Research Department at AT&T Bell Laboratories in Holmdel, New Jersey, United States, headed by Lawrence D. Jackel, where he developed a number of new machine learning methods, such as a biologically inspired model of image recognition called convolutional neural networks (LeNet), the "Optimal Brain Damage" regularization methods, and the Graph Transformer Networks method (similar to conditional random field), which he applied to handwriting recognition and Optical character recognition (OCR). The bank check recognition system that he helped develop was widely deployed by NCR and other companies. In 1996, he joined AT&T Labs-Research as head of the Image Processing Research Department, which was part of Lawrence Rabiner's Speech and Image Processing Research Lab, and worked primarily on the DjVu image compression technology, a format designed for efficient distribution of scanned documents, and used by the Internet Archive to provide access to digitized texts. His collaborators at AT&T include Léon Bottou and Vladimir Vapnik. === New York University === After a brief tenure as a fellow of NEC Research Institute, LeCun joined New York University in 2003, where he is Jacob T. Schwartz Chaired Professor of Computer Science and Neural Science at the Courant Institute of Mathematical Sciences and the Center for Neural Science. At NYU, he has worked primarily on energy-based models for supervised and unsupervised learning, feature learning for object recognition in computer vision, and mobile robotics. In 2012, he became the founding director of the NYU Center for Data Science. On 9 December 2013, LeCun became the first director of Meta AI Research in New York City and in early 2014 stepped down from the NYU–CDS directorship. In 2013, he and Yoshua Bengio co-founded the International Conference on Learning Representations, which adopted a post-publication open review process he previously advocated on his website. He was the chair and organiser of the "Learning Workshop" held every year between 1986 and 2012 in Snowbird, Utah. He is a member of the Science Advisory Board of the Institute for Pure and Applied Mathematics at UCLA. He is the co-director of the Learning in Machines and Brain research program (formerly Neural Computation & Adaptive Perception) of CIFAR. In 2016, he was the visiting professor of computer science on the Chaire Annuelle Informatique et Sciences Numériques at Collège de France in Paris, where he presented the leçon inaugurale (inaugural lecture). In 2023, he was named as the inaugural Jacob T. Schwartz Chaired Professor in Computer Science at NYU's Courant Institute. LeCun is also a scientific advisor to French research group Kyutai which is being funded by Xavier Niel, Rodolphe Saadé, Eric Schmidt, and others. === Meta Platforms === LeCun joined Facebook (now Meta Platforms) in 2013 as chief AI scientist and led the company's AI research laboratory, FAIR. === AMI Labs === On 19 November 2025, LeCun confirmed that he would be leaving Meta after ten years to found his own company focused on world-model architectures and human-like artificial intelligence he calls superintelligence. The company he founded, Advanced Machine Intelligence Labs (or AMI Labs), is run by CEO Alex LeBrun, with LeCun serving as Executive Chair. This venture is focused on building AI "world models": systems that learn to understand the physical world's structure and dynamics rather than just predict text like large language models. In March 2026, AMI announced it had raised $1.03 billion in funding at a $3.5 billion pre-money valuation. The funding round was co-led by investors including Cathay Innovation, Greycroft, Hiro Capital, HV Capital and Bezos Expeditions. In January 2026, LeCun became founding chair of the Technical Research Board of Logical Intelligence, an AI company developing energy-based (EBM) reasoning systems. == Honours and awards == LeCun is a member of the US National Academy of Sciences, National Academy of Engineering and the French Académie des Sciences. He has received honorary doctorates from Instituto Politécnico Nacional (IPN) in Mexico City in 2016, from EPFL in 2018, from Université Côte d'Azur in 2021, from Università di Siena in 2023, and from Hong Kong University of Science and Technology in 2023. In 2014, he received the IEEE Neural Network Pioneer Award and in 2015, the PAMI Distinguished Researcher Award. In 2018, LeCun was awarded the IRI Medal, established by the Industrial Research Institute (IRI), and the Harold Pender Award, given by the University of Pennsylvania. In 2019, he received the Golden Plate Award of the American Academy of Achievement. In March 2019, LeCun won the 2018 Turing Award, sharing it with Yoshua Bengio and Geoffrey Hinton. In 2022, he received the Princess of Asturias Award in the category "Scientific Research", along with Yoshua Bengio, Geoffrey Hinton and Demis Hassabis. In 2023, the President of France made him a Chevalier (Knight) of the French Legion of Honour. During the World Economic Forum (WEF) 2024 in Davos, he received the Global Swiss AI Award 2023. The same year, he received the grand prize of the VinFuture Prize alongside Yoshua Bengio, Jensen Huang, Geoffrey Hinton, and Fei-Fei Li for their groundbreaking contributions to neural networks and deep learning algorithms. In 2025 he was awarded the Queen Elizabeth Prize for Engineering jointly with Yoshua Bengio, Bill Dally, Geoffrey E. Hinton, John Hopfield, Jensen Huang and Fei-Fei Li.
IRCF360
Infrared Control Freak 360 (IRCF360) is a 360-degree proximity sensor and a motion sensing devices, developed by ROBOTmaker. The sensor is in BETA developers release as a low cost (software configurable) sensor for use within research, technical and hobby projects. == Overview == The 360-degree sensor was originally designed as a short range micro robot proximity sensor and mainly intended for Swarm robotics, Ant robotics, Swarm intelligence, autonomous Qaudcopter, Drone, UAV, multi-robot simulations e.g. Jasmine Project where 360 proximity sensing is required to avoid collision with other robots and for simple IR inter-robot communications. To overcome certain limitation with Infra-red (IR) proximity sensing (e.g. detection of dark surfaces) the sensing module includes ambient light sensing and basic tactile sensing functionality during forward movement sensing/probing providing photovore and photophobe robot swarm behaviours and characteristics. A project named Sensorium Project was started aimed at broadening the Sensors audience beyond its typical robot sensor usage. To demonstrate the sensor's functionality, opensource Java based Integrated Development Environments (IDE) are used, such as Arduino and Processing (programming language).
Cleverbot
Cleverbot is a chatterbot web application. It was created by British AI scientist Rollo Carpenter and launched in October 2008. It was preceded by Jabberwacky, a chatbot project that began in 1988 and went online in 1997. In its first decade, Cleverbot held several thousand conversations with Carpenter and his associates. Since launching on the web, the number of conversations held has exceeded 150 million. Besides the web application, Cleverbot is also available as an iOS, Android, and Windows Phone app. == Operation == Cleverbot's responses are not pre-programmed because it learns from human input: Humans type into the box below the Cleverbot logo and the system finds all keywords or an exact phrase matching the input. After searching through its saved conversations, it responds to the input by finding how a human responded to that input when it was asked, in part or in full, by Cleverbot. Cleverbot participated in a formal Turing test at the 2011 Techniche festival at the Indian Institute of Technology Guwahati on 3 September 2011. Out of the 1334 votes cast, Cleverbot was judged to be 59.3% human, compared to the rating of 63.3% human achieved by human participants. A score of 50.05% or higher is often considered to be a passing grade. The software running for the event had to handle just 1 or 2 simultaneous requests, whereas online Cleverbot is usually talking to around 10,000 to 50,000 people at once. == Developments == Cleverbot is constantly growing in data size at the rate of 4 to 7 million interactions per day. Updates to the software have been mostly behind the scenes. In 2014, Cleverbot was upgraded to use GPU serving techniques. Unlike Eliza, the program does not respond in a fixed way, instead choosing its responses heuristically using fuzzy logic, the whole of the conversation being compared to the millions that have taken place before. Cleverbot now uses over 279 million interactions, about 3-4% of the data it has already accumulated. The developers of Cleverbot are attempting to build a new version using machine learning techniques. An app that uses the Cleverscript engine to play a game of 20 Questions has been launched under the name Clevernator. Unlike other such games, the player asks the questions and it is the role of the AI to understand, and answer factually. An app that allows owners to create and talk to their own small Cleverbot-like AI has been launched, called Cleverme! for Apple products. == In popular culture == Cleverbot received media attention after being featured in the popular 2010 creepypasta ARG web serial Ben Drowned by Alexander D. Hall. In early 2017, a Twitch stream of two Google Home devices modified to talk to each other using Cleverbot garnered over 700,000 visitors and over 30,000 peak concurrent viewers.
Alliance for Secure AI
The Alliance for Secure AI is a U.S.-based nonprofit organization which educates the public about the risks of advanced artificial intelligence (AI). Politico has described the Alliance as a "bipartisan nonprofit trying to push a middle-ground approach to AI guardrails." == History == In June 2025, the Alliance was launched as a 501(c)(3) nonprofit watchdog in Washington, D.C. That same month, the organization rolled out a six-figure advertising campaign featuring bipartisan warnings about advanced AI. The ad campaign presented different messages for different political audiences. The Alliance opposed the idea of a moratorium on state AI laws as part of the July 2025 budget bill, in addition to President Donald Trump's December 2025 executive order on the issue. The group has also criticized AI companies like Meta and OpenAI for what it says are failures to prevent harms to children. In addition, the Alliance has criticized OpenAI for subpoenaing nonprofit organizations in the AI safety space. In March 2026, the Alliance launched JobLoss.ai, a website that tracks the jobs that have been eliminated with AI cited as a contributing factor. As of April 2026, JobLoss.ai has tracked more than 127,000 lost jobs. == Leadership == Brendan Steinhauser, a longtime political and communications strategist, is the founder and CEO of the Alliance. He was an early Tea Party movement organizer, and ran campaigns for multiple members of Congress, including Sen. John Cornyn, Rep. Dan Crenshaw, and Rep. Michael McCaul. Peyton Hornberger is the group's communications director. In July 2025, Hornberger criticized Palantir for its use of AI in a USA Today op-ed column.