The Argument Web is a large-scale Web of interconnected arguments created by individuals as they express their opinions and interact with the opinions of others. The Argument Web aims to make online debate intuitive for participants such as mediators, students, academics, broadcasters and bloggers, to create a Web infrastructure that allows for the storage, automatic retrieval and analysis of linked argument data, and to improve the quality of online argument and debate. The Argument Web can be described as a portion of a larger Semantic Web. == AIFdb == AIFdb is a database implementation or ‘reification’ of the Argument Interchange Format (AIF), which allows for the storage and retrieval of AIF compliant argument structures. This database solution was provided as a foundation for an open, integrated Argument Web. It offers an extensive range of web services for interacting with stored argument data, while also offering search and argument visualisation features that are all consistent with the formal ontology of AIF. At a basic level, the AIFdb web services allow for the insertion and querying of basic components of an AIF argument, such as nodes, edges and schemes. Building upon this basis, it also facilitates more complex interactions with these AIF argument structures. Such complex queries could make it possible, for example, to determine all the statements made by a particular person in support a given I-Node. While, at its highest level of interaction, AIFdb can handle the import and export of many standard file formats, including SVG, DOT, RDF/XML and other formats of argument theory tools, like Carneades, Rationale and Araucaria. == Argument blogging == ArguBlogging is software which allows its users to select portions of hypertext on webpages in their Web browsers and to agree or disagree with the selected content, posting their arguments to their blogs with linked argument data. It is implemented as a bookmarklet, adding functionality to Web browsers and interoperating with blogging platforms such as Blogger and Tumblr.
Polynomial texture mapping
Polynomial texture mapping (PTM), also known as Reflectance Transformation Imaging (RTI), is a technique of imaging and interactively displaying objects under varying lighting conditions to reveal surface phenomena. The data acquisition method is single camera multi light (SCML). == Origins == The method was originally developed by Tom Malzbender of HP Labs in order to generate enhanced 3D computer graphics and it has since been adopted for cultural heritage applications. == Methodology == A series of images is captured in a darkened environment with the camera in a fixed position and the object lit from different angles (Single Camera Multi Light). Interactive software processes and combines the set of images to enable the user inspecting the object to control a virtual light source. The virtual light source may be manipulated to simulate light from different angles and of different intensity or wavelengths to illuminate the surface of artefacts and reveal details. Open-source tools for processing the captured images and publishing the resulting relightable images on the web are freely available. == Applications == Polynomial texture mapping may be used for detailed recording and documentation, 3D modeling, edge detection, and to aid the study of inscriptions, rock art and other artefacts. It has been applied to hundreds of the Vindolanda tablets by the Centre for the Study of Ancient Documents at the University of Oxford in conjunction with the British Museum. It has also been deployed, by Ben Altshuler of the Institute for Digital Archaeology, to scan the Philae obelisk at Kingston Lacy and the Parian Chronicle at the Ashmolean Museum; in both cases scans revealed significant, previously illegible text. Method was also used for identifying microscopic worked antler from Star Carr and recording ancient rock art in Armenia. A 'dome' supporting twenty-four lights has been used to image paintings in the National Gallery and produce polynomial texture maps, providing information on condition phenomena for conservation purposes. Studies of the technique at the National Gallery and Tate concluded that it is an effective tool for documenting changes in the condition of paintings, more easily repeatable than raking light photography, and therefore could be used to assess paintings during structural treatment and before and after loan. Twelve dome-based systems built by the University of Southampton have been used to capture thousands of cuneiform tablets at various museums. The technique is now also finding uses in the field of forensic science, for example in imaging footprints, tyre marks, and indented writing.
Jensen Huang
Jen-Hsun "Jensen" Huang (Chinese: 黃仁勳; Wade–Giles: Huáng Jén-hsūn; Tâi-lô: N̂g Jîn-hun; born February 17, 1963) is a Taiwanese and American business executive and electrical engineer who is the founder, president, and CEO of Nvidia, the world's most valuable company. As of 2026, Forbes estimates his net worth at over US$200 billion, making him the seventh-wealthiest individual in the world. The son of Taiwanese immigrants, Huang spent his childhood in Taiwan and Thailand before moving to the United States, where he was a student in Kentucky and Oregon. After earning a master's degree from Stanford University, Huang launched Nvidia in 1993 from a Denny's restaurant in San Jose, California, at age 30 and has remained its president and CEO ever since. He led the company out of near-bankruptcy during the 1990s and oversaw its expansion into GPU production, high-performance computing, and artificial intelligence (AI). Under Huang, Nvidia experienced rapid growth during the AI boom, becoming the first company to reach a market capitalization of over $5 trillion in October 2025. In 2021 and 2024, Time magazine included Huang in their list of the most influential people. In 2025, he was named as one of the "Architects of AI" for Time's Person of the Year. == Early life and education == Huang was born in Taipei, Taiwan, on February 17, 1963, and moved to the southern city of Tainan as a child. He is the younger of two sons of Huang Hsing-tai, a chemical engineer at an oil refinery, and Lo Tsai-hsiu, a schoolteacher. They were a middle-class Taiwanese family that relocated often, and were native speakers of Taiwanese Hokkien. Each day, Jensen's mother randomly selected 10 words from the dictionary to teach her sons English. When he was five years old, Huang's family moved to Thailand to support his father's refinery career and remained there for approximately four years. He attended Ruamrudee International School while in Bangkok. In the late 1960s, Hsing-tai traveled from Taiwan to New York City to train under an air conditioning company and, after returning home, resolved to send his sons to the United States. At age nine, Jensen, despite not yet being able to speak English fluently, was sent by his parents to live in the United States. He and his older brother moved in 1973 to live with an uncle in Tacoma, Washington, escaping widespread social unrest in Thailand. Both Huang's aunt and uncle were recent immigrants to Washington state; they accidentally enrolled him and his brother in the Oneida Baptist Institute, a religious reform academy in Kentucky for troubled youth, mistakenly believing it to be a prestigious boarding school. In order to afford the academy's tuition, Jensen's parents sold nearly all their possessions. When he was 10 years old, Huang lived with his older brother in the Oneida boys' dormitory. Each student was expected to work every day, and his brother was assigned to perform manual labor on a nearby tobacco farm. Because he was too young to attend classes at the reform academy, Huang was educated at a separate public school—the Oneida Elementary school in Oneida, Kentucky—arriving as "an undersized Asian immigrant with long hair and heavily accented English" and was frequently bullied and beaten. In Oneida, Huang cleaned toilets every day, learned to play table-tennis, joined the swimming team, and appeared in Sports Illustrated at age 14. He taught his illiterate roommate, a "17-year-old covered in tattoos and knife scars," how to read in exchange for being taught how to bench press. In 2002, Huang said he remembered his life in Kentucky "more vividly than just about any other". Two years after Huang arrived in Oneida, his parents moved to the United States and settled in Beaverton, Oregon, after which the brothers withdrew from school in Kentucky to live back with them. As a teenager, Huang attended Aloha High School in Aloha, Oregon, where he excelled academically. He skipped two grades, graduated at age 16, and became a nationally ranked table-tennis player in addition to being a member of its mathematics, computer, and science clubs. In 1977, the school purchased an Apple II computer. Huang used the machine to play Super Star Trek, a text-based game, and to program in BASIC, creating his own version of Snake. Beginning at age 15, Huang got his first job working the graveyard shift at a local Denny's restaurant as a dishwasher, busboy, and waiter from 1978 to 1983. After high school, he chose to enroll at Oregon State University due to its low in-state tuition. He studied electrical engineering and graduated in 1984 with a bachelor's degree with highest honors. Huang later recalled, "I was the youngest kid in school, in class" and the only student who "looked like a child". Years later, while working as a microchip designer in Silicon Valley, he concurrently pursued graduate night classes at Stanford University, where he earned a master's degree in electrical engineering in 1992. == AMD and LSI Logic == After graduating from college, Huang was a microchip designer in Silicon Valley. He was recruited for positions at Texas Instruments, Advanced Micro Devices (AMD), and LSI Logic, ultimately choosing the California-based AMD due to already being familiar with the company. Huang designed AMD microprocessors while simultaneously attending Stanford and raising his two children. However, when he heard of new chip design processes at LSI Logic, Huang left AMD to assume a role as a technical officer at the LSI Corporation, working under a startup company, Sun Microsystems, where he met engineers Chris Malachowsky and Curtis Priem. LSI was in contract with Sun Microsystems and had introduced Huang to Malachowsky and Priem, who were working on a new graphics accelerator card. While the three produced the card's manufacturing process, the relationship between Malachowsky and Priem became strained as the two disputed the chip's design, leading to infighting; according to Malachowsky, they "broke every tool that LSI Logic had in their standard portfolio". In 1989, Huang, Malachowsky, and Priem finalized the accelerator, which they called the "GX graphics engine". GX was a widespread financial success; the sales of the graphics engine contributed to Sun Microsystem's revenue increasing from $262 million in 1987 to $656 million in 1990, and Huang was promoted to be the director of LSI's CoreWare, a division that manufactured chips for hardware vendors. == Nvidia == === Founding (1993) === When business began to slow for Sun Microsystems after 1990, Huang, along with Priem and Malachowsky, each resigned their jobs to pursue a venture together in making graphics chips for PC games. They initially named their new company "NVision" until Huang suggested that the company be named "Nvidia" based on the Latin word invidia, as Priem wanted competitors to turn "green with envy". They eventually dropped the "i" to honor the NV1 chip that they were then developing. The three met frequently in 1992 at a Denny's roadside diner in East San Jose to formulate a business plan. Huang chose for them to meet at Denny's due to his prior work experience at the restaurant chain and because it was "quieter than home and had cheap coffee". The three founded the company during one meeting at a breakfast booth at the diner. To formally incorporate the company, Huang found a lawyer, James Gaither of Cooley Godward, who demanded the $200 in cash in Huang's pockets to capitalize the company. After that meeting, Huang went back to Priem and Malachowsky to ask each of them for $200 for their respective shares of the company, which meant that Nvidia's initial capital was $600. On April 5, 1993, Huang personally signed Nvidia's original articles of incorporation into effect. Although he left LSI, Huang remained in good standing with the company and was able to secure funding for Nvidia from LSI's CEO, Wilfred Corrigan, who introduced Huang to venture capitalist Don Valentine. An account cited how Huang's presentation pitch went badly. Valentine, the leader of Sequoia Capital, chose to invest in Nvidia through Corrigan's support, as did Sutter Hill Ventures. The funding enabled Nvidia to begin development efforts toward its first chip and to begin paying wages for its employees. By the first day of operation, Huang was made Nvidia's president and CEO. Even though Huang, at age 30, was younger than Priem and Malachowsky, both Priem and Malachowsky believed that he was prepared to be CEO. According to Priem, "we basically deferred to Jensen on day one" and told Huang, "you're in charge of running the company—all the stuff Chris and I don't know how to do". === President and CEO (1993–present) === As of 2024, Huang has been Nvidia's chief executive for over three decades, a tenure described by The Wall Street Journal as "almost unheard of in fast-moving Silicon Valley". He owns 3.6% of Nvidia's stock, which went public in 1999. He earned US$24.6 million as CEO i
Quantum Artificial Intelligence Lab
The Quantum Artificial Intelligence Lab (also called the Quantum AI Lab or QuAIL) is a joint initiative of NASA, Universities Space Research Association, and Google (specifically, Google Research) whose goal is to pioneer research on how quantum computing might help with machine learning and other difficult computer science problems. The lab is hosted at NASA's Ames Research Center. == History == The Quantum AI Lab was announced by Google Research in a blog post on May 16, 2013. At the time of launch, the Lab was using the most advanced commercially available quantum computer, D-Wave Two from D-Wave Systems. On October 10, 2013, Google released a short film describing the current state of the Quantum AI Lab. On October 18, 2013, Google announced that it had incorporated quantum physics into Minecraft. In January 2014, Google reported results comparing the performance of the D-Wave Two in the lab with that of classical computers. The results were ambiguous and provoked heated discussion on the Internet. On 2 September 2014, it was announced that the Google Quantum AI Lab, in partnership with UC Santa Barbara, would be launching an initiative to create quantum information processors based on superconducting electronics. On the 23rd of October 2019, the Quantum AI Lab announced in a paper that it had achieved quantum supremacy with their Sycamore processor. The claim of quantum supremacy achievement has since been debated, with a far more accurate simulation on a classical computer being possible in 2.5 days as a conservative estimate. == Present == On December 9, 2024, Google introduced the Willow processor, describing it as a "state-of-the-art quantum chip". Google claims that this new chip takes just five minutes to solve a problem that takes traditional supercomputers ten septillion years. However, experts say Willow is, for now, a largely experimental device.
Rule-based system
In computer science, a rule-based system is a computer system in which domain-specific knowledge is represented in the form of rules and general-purpose reasoning is used to solve problems in the domain. Two different kinds of rule-based systems emerged within the field of artificial intelligence in the 1970s: Production systems, which use if-then rules to derive actions from conditions. Logic programming systems, which use conclusion if conditions rules to derive conclusions from conditions. The differences and relationships between these two kinds of rule-based system has been a major source of misunderstanding and confusion. Both kinds of rule-based systems use either forward or backward chaining, in contrast with imperative programs, which execute commands listed sequentially. However, logic programming systems have a logical interpretation, whereas production systems do not. == Production system rules == A classic example of a production rule-based system is the domain-specific expert system that uses rules to make deductions or choices. For example, an expert system might help a doctor choose the correct diagnosis based on a cluster of symptoms, or select tactical moves to play a game. Rule-based systems can be used to perform lexical analysis to compile or interpret computer programs, or in natural language processing. Rule-based programming attempts to derive execution instructions from a starting set of data and rules. This is a more indirect method than that employed by an imperative programming language, which lists execution steps sequentially. === Construction === A typical rule-based system has four basic components: A list of rules or rule base, which is a specific type of knowledge base. An inference engine or semantic reasoner, which infers information or takes action based on the interaction of input and the rule base. The interpreter executes a production system program by performing the following match-resolve-act cycle: Match: In this first phase, the condition sides of all productions are matched against the contents of working memory. As a result a set (the conflict set) is obtained, which consists of instantiations of all satisfied productions. An instantiation of a production is an ordered list of working memory elements that satisfies the condition side of the production. Conflict-resolution: In this second phase, one of the production instantiations in the conflict set is chosen for execution. If no productions are satisfied, the interpreter halts. Act: In this third phase, the actions of the production selected in the conflict-resolution phase are executed. These actions may change the contents of working memory. At the end of this phase, execution returns to the first phase. Temporary working memory, which is a database of facts. A user interface or other connection to the outside world through which input and output signals are received and sent. Whereas the matching phase of the inference engine has a logical interpretation, the conflict resolution and action phases do not. Instead, "their semantics is usually described as a series of applications of various state-changing operators, which often gets quite involved (depending on the choices made in deciding which ECA rules fire, when, and so forth), and they can hardly be regarded as declarative". == Logic programming rules == The logic programming family of computer systems includes the programming language Prolog, the database language Datalog and the knowledge representation and problem-solving language Answer Set Programming (ASP). In all of these languages, rules are written in the form of clauses: A :- B1, ..., Bn. and are read as declarative sentences in logical form: A if B1 and ... and Bn. In the simplest case of Horn clauses (or "definite" clauses), which are a subset of first-order logic, all of the A, B1, ..., Bn are atomic formulae. Although Horn clause logic programs are Turing complete, for many practical applications, it is useful to extend Horn clause programs by allowing negative conditions, implemented by negation as failure. Such extended logic programs have the knowledge representation capabilities of a non-monotonic logic. == Differences and relationships between production rules and logic programming rules == The most obvious difference between the two kinds of systems is that production rules are typically written in the forward direction, if A then B, and logic programming rules are typically written in the backward direction, B if A. In the case of logic programming rules, this difference is superficial and purely syntactic. It does not affect the semantics of the rules. Nor does it affect whether the rules are used to reason backwards, Prolog style, to reduce the goal B to the subgoals A, or whether they are used, Datalog style, to derive B from A. In the case of production rules, the forward direction of the syntax reflects the stimulus-response character of most production rules, with the stimulus A coming before the response B. Moreover, even in cases when the response is simply to draw a conclusion B from an assumption A, as in modus ponens, the match-resolve-act cycle is restricted to reasoning forwards from A to B. Reasoning backwards in a production system would require the use of an entirely different kind of inference engine. In his Introduction to Cognitive Science, Paul Thagard includes logic and rules as alternative approaches to modelling human thinking. He does not consider logic programs in general, but he considers Prolog to be, not a rule-based system, but "a programming language that uses logic representations and deductive techniques" (page 40). He argues that rules, which have the form IF condition THEN action, are "very similar" to logical conditionals, but they are simpler and have greater psychological plausibility (page 51). Among other differences between logic and rules, he argues that logic uses deduction, but rules use search (page 45) and can be used to reason either forward or backward (page 47). Sentences in logic "have to be interpreted as universally true", but rules can be defaults, which admit exceptions (page 44). He does not observe that all of these features of rules apply to logic programming systems.
Open Syllabus Project
The Open Syllabus Project (OSP) is an online open-source platform that catalogs and analyzes millions of college syllabi. Founded by researchers from the American Assembly at Columbia University, the OSP has amassed the most extensive collection of searchable syllabi. Since its beta launch in 2016, the OSP has collected over 7 million course syllabi from over 80 countries, primarily by scraping publicly accessible university websites. The project is directed by Joe Karaganis. == History == The OSP was formed by a group of data scientists, sociologists, and digital-humanities researchers at the American Assembly, a public-policy institute based at Columbia University. The OSP was partly funded by the Sloan Foundation and the Arcadia Fund. Joe Karaganis, former vice-president of the American Assembly, serves as the project director of the OSP. The project builds on prior attempts to archive syllabi, such as H-Net, MIT OpenCourseWare, and historian Dan Cohen's defunct Syllabus Finder website (Cohen now sits on the OSP's advisory board). The OSP became a non-profit and independent of the American Assembly in November 2019. In January 2016, the OSP launched a beta version of their "Syllabus Explorer," which they had collected data for since 2013. The Syllabus Explorer allows users to browse and search texts from over one million college course syllabi. The OSP launched a more comprehensive version 2.0 of the Syllabus Explorer in July 2019. The newer version includes an interactive visualization that displays texts as dots on a knowledge map. As of 2022, the OSP has collected over 7 million course syllabi. The Syllabus Explorer represents the "largest collection of searchable syllabi ever amassed." == Methodology == The OSP has collected syllabi data from over 80 countries dating to 2000. The syllabi stem from over 4,000 worldwide institutions. Most of the OSP's data originates from the United States. Canada, Australia, and the U.K also have large datasets. The OSP primarily collects syllabi by scraping publicly accessible university websites. The OSP also allows syllabi submissions from faculty, students, and administrators. The OSP developers use machine learning and natural language processing to extract metadata from such syllabi. Since only metadata is collected, no individual syllabus or personal identifying information is found in the OSP database. The OSP classifies the syllabi into 62 subject fields – corresponding to the U.S. Department of Education's Classification of Instructional Programs (CIP). Additionally, the OSP assigns each text a "teaching score" from 0–100. This score represents the text's percentile rank among citations in the total citation count and is a numerical indicator of the relative frequency of which a particular work is taught. The OSP also has data on which texts are most likely to be assigned together. The developers behind the OSP admit that the database is incomplete and likely contains "a fair number of errors." Karaganis estimates that 80–100 million syllabi exist in the United States alone. The OSP is unable to access syllabi behind private course-management software like Blackboard. == Notable findings == === Anthropology === Using data from the OSP, anthropologist Laurence Ralph uncovered that black anthropologists are "woefully under-represented in (if not erased from) most anthropology syllabi." Black authors wrote less than 1 percent of the top 1,000 assigned works. === Economics === The database indicates Greg Mankiw is the most frequently cited author for college economics courses. === English literature === The OSP found that Mary Shelley's Frankenstein was the most widely taught novel in college courses. Additionally, the majority of novels published after 1945 taught in English classes were historical fiction. === Female writers === The most read female writer on college campuses is Kate L. Turabian for her A Manual for Writers of Research Papers, Theses, and Dissertations . Turabian is followed by Diana Hacker, Toni Morrison, Jane Austen, and Virginia Woolf. === Film === The most assigned film according to the OSP is the 1929 Soviet documentary film, Man with a Movie Camera. English filmmaker Alfred Hitchcock is the most assigned director in college courses. === History === Historians George Brown Tindall and David Emory Shi's America: A Narrative History is the number one assigned textbook for history, followed by Anne Moody's memoir, Coming of Age in Mississippi. === Philosophy === The most assigned texts in the field of philosophy include Aristotle's Nicomachean Ethics, John Stuart Mill's Utilitarianism, and Plato's Republic. Plato's Republic was also the second most assigned text in universities in the English-speaking world (only behind Strunk and White's Elements of Style). === Physics === David Halliday's et al. Fundamentals of Physics is the number one ranked physics textbook in the OSP's database. === Political science === Data from the OSP indicates that the dominant political science texts are written almost exclusively by white men and scholars based in the West. In the top 200 most-frequently assigned works, 15 are authored by at least one woman. === Public administration === American president Woodrow Wilson's article "The Study of Administration" was the most frequently assigned text in public affairs and administration syllabi. == Reception == According to William Germano et al., the OSP is a "fascinating resource but is also prone to misrepresenting or at least distracting us from the most important business of a syllabus: communicating with students." Historian William Caferro remarks that the OSP is a "tacit experience of sharing, but a useful one." English professor Bart Beaty writes that, "Despite the many reservations about the completeness of its data, the OSP provides a rare opportunity for scholars to move beyond the anecdotal in discussions of canon-formation in teaching." Media theorist Elizabeth Losh opines that "big data approaches", like the OSP, may "raise troubling questions for instructors about informed consent, pedagogical privacy, and quantified metrics."
Logic Programming Associates
Logic Programming Associates (LPA) is a company specializing in logic programming and artificial intelligence software. LPA was founded in 1980 and is widely known for its range of Prolog compilers, the Flex expert system toolkit and most recently, VisiRule. LPA was established to exploit research at the Department of Computing and Control at Imperial College London into logic programming carried out under the supervision of Prof Robert Kowalski. == History of LPA Prolog == One of the first Prolog implementations made available by LPA was micro-PROLOG which ran on popular 8-bit home computers such as the Sinclair ZX Spectrum and Apple II. The 8-bit micro-PROLOG interpreter was soon followed by micro-PROLOG Professional one of the first Prolog implementations for the IBM PC running MS-DOS. micro-PROLOG Professional could access all of the 640K memory available under MS-DOS and therefore manage much larger programs In 1985, LPA released LPA MacProlog which ran on the MacPlus and Mac II computers which could access up to 4 Mb memory. MacProlog was later licensed to Quintus for re-distribution in the USA. In 1989, LPA started work on a new 32-bit Prolog compiler which could use DOS-extender technology to access up to 4GB memory. This became the basis for LPA Prolog for Windows, aka WIN-PROLOG, which was then released for Windows 3.0 in 1990. LPA's core Prolog product is LPA Prolog for Windows, a compiler and development system for the Microsoft Windows platform. The current LPA software range comprises an integrated AI toolset which covers various aspects of Artificial Intelligence including Logic Programming, Expert Systems, Knowledge-based Systems, Data Mining, Agents and Case-based reasoning etc. As well as continuing with Prolog compiler technology development, LPA has a track record of creating innovative associated tools and products to address specific challenges and opportunities. == Flex Expert System toolkit == In 1989, in response to the rise of interest in Expert Systems and the emergence of products such as Crystal, GoldWorks, NExpert, LPA developed the Flex expert system toolkit, which incorporated frame-based reasoning with inheritance, rule-based programming and data-driven procedures. Flex has its own English-like Knowledge Specification Language (KSL) which means that knowledge and rules are defined in an easy-to-read and understand way. LPA supported Flex on Windows, DOS and Macintosh PCs, as an add-on toolkit to its various LPA Prolog systems and eanbled LPA to enter the then quick vibrant Expert Systems rules-market. Flex was quickly established as the leading Prolog-based expert system toolkit and was licensed to other Prolog providors on other hardware platforms including Telecomputing Plc to supplement Top One on IBM and ICL mainframes. Other implementations included Quintec-Flex, Quintus Flex, Poplog Flex and BIM Flex which were all running on Unix and/or Vax/VMS platforms. POPLOG-Flex was used to build BRAND EVALUATOR - an expert system to assist brand specialists in evaluating the worth of branded products Quintec-Flex was used to build a hybrid system for the non-linear dynamic analysis/design of coupled shear walls Flex was adopted by the Open University as part of its course T396, "Artificial intelligence for technology" which was designed by Prof Adrian Hopgood. Some of the teaching material is now available on his AI tookit website. Flex was also used by David A Ferrucci and Selmer Bringsjord in their storytelling machine, BRUTUS. == PVG == In 1992, LPA helped set up the Prolog Vendors Group, a not-for-profit organization whose aim was to help promote Prolog by making people aware of its usage in industry. == Business Integrity Ltd and Contract Express == Between 1996 and 1998, based on work co-funded through a DTI Smart award, LPA developed ScaffoldIT, a tool for building dynamic documents and intelligent web sites. This technology, built using the LPA Prolog engine and associated ProWeb Server, was able to generate complex, personalised documents such as insurance policy schedules, legal contracts, and complex sales proposals, over the Web. In 1999/2000, LPA helped set up Business Integrity Ltd, as a Joint Venture with Tarlo-Lyons, to bring the above document assembly technology to market. This product eventually became Contract Express. Contract Express became very popular amongst large law firms and was sold worldwide for both internal and external use. Partners and GCs liked Contract Express because lawyers were able to quickly and accurately automate and update their legal templates in Word without requiring IT specialists to convert them into programs. As a result of the commercial success of Contract Express, BIL was acquired by Thomson Reuters in 2015. The very early days of BIL are described by Clive Spenser here. == VisiRule == In 2004, LPA launched VisiRule a graphical tool for developing knowledge-based and decision support systems. VisiRule was described in IEEE Potentials in 2007 (see Drawing on your knowledge with VisiRule): VisiRule has been used in various sectors, to build legal expert systems, machine diagnostic programs, medical and financial advice systems, etc. In 2013, VisiRule was incorporated into Ecosystem Management Decision Support (EMDS) where it has been used to provide enhanced decision support capabilities. EMDS integrates state-of-the-art geographic information system (GIS) as well as logic programming and decision modeling technologies on multiple platforms (Windows, Linux, Mac OS X) to provide decision support for a substantial portion of the adaptive management process of ecosystem management. EMDS is actively used, extended, supported and maintained by Mountain View Business Group (for an in-depth reprise of EMDS see the article in Frontiers in Environmental Science). In 2023, VisiRule was listed as one of the 5 best decision support software for large enterprises in 2024. == Customers == For many years, LPA has worked closely with Valdis Krebs, an American-Latvian researcher, author, and consultant in the field of social and organizational network analysis. Valdis is the founder and chief scientist of Orgnet, and the creator of the popular Inflow software package. LPA Prolog and Flex were used to create Allergenius, an expert system for the interpretation of allergen microarray results. Rules representing the knowledge base (KB) were derived from the literature and specialized databases. The input data included the patient's ID and disease(s), the results of either a skin prick test or specific IgE assays and ISAC results. The output was a medical report.