In natural language processing, open information extraction (OIE) is the task of generating a structured, machine-readable representation of the information in text, usually in the form of triples or n-ary propositions. == Overview == A proposition can be understood as truth-bearer, a textual expression of a potential fact (e.g., "Dante wrote the Divine Comedy"), represented in an amenable structure for computers [e.g., ("Dante", "wrote", "Divine Comedy")]. An OIE extraction normally consists of a relation and a set of arguments. For instance, ("Dante", "passed away in" "Ravenna") is a proposition formed by the relation "passed away in" and the arguments "Dante" and "Ravenna". The first argument is usually referred as the subject while the second is considered to be the object. The extraction is said to be a textual representation of a potential fact because its elements are not linked to a knowledge base. Furthermore, the factual nature of the proposition has not yet been established. In the above example, transforming the extraction into a full fledged fact would first require linking, if possible, the relation and the arguments to a knowledge base. Second, the truth of the extraction would need to be determined. In computer science transforming OIE extractions into ontological facts is known as relation extraction. In fact, OIE can be seen as the first step to a wide range of deeper text understanding tasks such as relation extraction, knowledge-base construction, question answering, semantic role labeling. The extracted propositions can also be directly used for end-user applications such as structured search (e.g., retrieve all propositions with "Dante" as subject). OIE was first introduced by TextRunner developed at the University of Washington Turing Center headed by Oren Etzioni. Other methods introduced later such as Reverb, OLLIE, ClausIE or CSD helped to shape the OIE task by characterizing some of its aspects. At a high level, all of these approaches make use of a set of patterns to generate the extractions. Depending on the particular approach, these patterns are either hand-crafted or learned. == OIE systems and contributions == Reverb suggested the necessity to produce meaningful relations to more accurately capture the information in the input text. For instance, given the sentence "Faust made a pact with the devil", it would be erroneous to just produce the extraction ("Faust", "made", "a pact") since it would not be adequately informative. A more precise extraction would be ("Faust", "made a pact with", "the devil"). Reverb also argued against the generation of overspecific relations. OLLIE stressed two important aspects for OIE. First, it pointed to the lack of factuality of the propositions. For instance, in a sentence like "If John studies hard, he will pass the exam", it would be inaccurate to consider ("John", "will pass", "the exam") as a fact. Additionally, the authors indicated that an OIE system should be able to extract non-verb mediated relations, which account for significant portion of the information expressed in natural language text. For instance, in the sentence "Obama, the former US president, was born in Hawaii", an OIE system should be able to recognize a proposition ("Obama", "is", "former US president"). ClausIE introduced the connection between grammatical clauses, propositions, and OIE extractions. The authors stated that as each grammatical clause expresses a proposition, each verb mediated proposition can be identified by solely recognizing the set of clauses expressed in each sentence. This implies that to correctly recognize the set of propositions in an input sentence, it is necessary to understand its grammatical structure. The authors studied the case in the English language that only admits seven clause types, meaning that the identification of each proposition only requires defining seven grammatical patterns. The finding also established a separation between the recognition of the propositions and its materialization. In a first step, the proposition can be identified without any consideration of its final form, in a domain-independent and unsupervised way, mostly based on linguistic principles. In a second step, the information can be represented according to the requirements of the underlying application, without conditioning the identification phase. Consider the sentence "Albert Einstein was born in Ulm and died in Princeton". The first step will recognize the two propositions ("Albert Einstein", "was born", "in Ulm") and ("Albert Einstein", "died", "in Princeton"). Once the information has been correctly identified, the propositions can take the particular form required by the underlying application [e.g., ("Albert Einstein", "was born in", "Ulm") and ("Albert Einstein", "died in", "Princeton")]. CSD introduced the idea of minimality in OIE. It considers that computers can make better use of the extractions if they are expressed in a compact way. This is especially important in sentences with subordinate clauses. In these cases, CSD suggests the generation of nested extractions. For example, consider the sentence "The Embassy said that 6,700 Americans were in Pakistan". CSD generates two extractions [i] ("6,700 Americans", "were", "in Pakistan") and [ii] ("The Embassy", "said", "that [i]"). This is usually known as reification.
Interlacing (bitmaps)
In computing, interlacing (also known as interleaving) is a method of encoding a bitmap image such that a person who has partially received it sees a degraded copy of the entire image. When communicating over a slow communications link, this is often preferable to seeing a perfectly clear copy of one part of the image, as it helps the viewer decide more quickly whether to abort or continue the transmission. Interlacing is supported by the following formats, where it is optional: GIF interlacing stores the lines in the order 0 , 8 , 16 , … , ( 8 n ) , 4 , 12 , … , ( 8 n + 4 ) , 2 , 6 , 10 , 14 , … , ( 4 n + 2 ) , 1 , 3 , 5 , 7 , 9 , … , ( 2 n + 1 ) . {\displaystyle 0,8,16,\dots ,(8n),\ 4,12,\dots ,(8n+4),\ 2,6,10,14,\dots ,(4n+2),\ 1,3,5,7,9,\dots ,(2n+1).} PNG uses the Adam7 algorithm, which interlaces in both the vertical and horizontal direction. TGA uses two optional interlacing algorithms: Two-way: 0 , 2 , 4 , … , ( 2 n ) , 1 , 3 , … , ( 2 n + 1 ) , {\displaystyle 0,2,4,\dots ,(2n),\ 1,3,\dots ,(2n+1),} And four-way: 0 , 4 , 8 , … , ( 4 n ) , 1 , 5 , … , ( 4 n + 1 ) , 2 , 6 , … , ( 4 n + 2 ) , 3 , 7 , … , ( 4 n + 3 ) . {\displaystyle 0,4,8,\dots ,(4n),\ 1,5,\dots ,(4n+1),\ 2,6,\dots ,\ (4n+2),3,7,\dots ,(4n+3).} JPEG, JPEG 2000, and JPEG XR (actually using a frequency decomposition hierarchy rather than interlacing of pixel values) PGF (also using a frequency decomposition) Interlacing is a form of incremental decoding, because the image can be loaded incrementally. Another form of incremental decoding is progressive scan. In progressive scan the loaded image is decoded line for line, so instead of becoming incrementally clearer it becomes incrementally larger. The main difference between the interlace concept in bitmaps and in video is that even progressive bitmaps can be loaded over multiple frames. For example: Interlaced GIF is a GIF image that seems to arrive on your display like an image coming through a slowly opening Venetian blind. A fuzzy outline of an image is gradually replaced by seven successive waves of bit streams that fill in the missing lines until the image arrives at its full resolution. Interlaced graphics were once widely used in web design and before that in the distribution of graphics files over bulletin board systems and other low-speed communications methods. The practice is much less common today, as common broadband internet connections allow most images to be downloaded to the user's screen nearly instantaneously, and interlacing is usually an inefficient method of encoding images. Interlacing has been criticized because it may not be clear to viewers when the image has finished rendering, unlike non-interlaced rendering, where progress is apparent (remaining data appears as blank). Also, the benefits of interlacing to those on low-speed connections may be outweighed by having to download a larger file, as interlaced images typically do not compress as well.
Reason maintenance
Reason maintenance is a knowledge representation approach to efficient handling of inferred information that is explicitly stored. Reason maintenance distinguishes between base facts, which can be defeated, and derived facts. As such it differs from belief revision which, in its basic form, assumes that all facts are equally important. Reason maintenance was originally developed as a technique for implementing problem solvers. It encompasses a variety of techniques that share a common architecture: two components—a reasoner and a reason maintenance system—communicate with each other via an interface. The reasoner uses the reason maintenance system to record its inferences and justifications of ("reasons" for) the inferences. The reasoner also informs the reason maintenance system which are the currently valid base facts (assumptions). The reason maintenance system uses the information to compute the truth value of the stored derived facts and to restore consistency if an inconsistency is derived. == Truth maintenance system == A truth maintenance system, or TMS, is a knowledge representation method for representing both beliefs and their dependencies and an algorithm called the "truth maintenance algorithm" that manipulates and maintains the dependencies. The name truth maintenance is due to the ability of these systems to restore consistency. A truth maintenance system maintains consistency between old believed knowledge and current believed knowledge in the knowledge base (KB) through revision. If the current believed statements contradict the knowledge in the KB, then the KB is updated with the new knowledge. It may happen that the same data will again be believed, and the previous knowledge will be required in the KB. If the previous data are not present, but may be required for new inference. But if the previous knowledge was in the KB, then no retracing of the same knowledge is needed. The use of TMS avoids such retracing; it keeps track of the contradictory data with the help of a dependency record. This record reflects the retractions and additions which makes the inference engine (IE) aware of its current belief set. == Algorithm == Each statement having at least one valid justification is made a part of the current belief set. When a contradiction is found, the statement(s) responsible for the contradiction are identified and the records are appropriately updated. This process is called dependency-directed backtracking. The TMS algorithm maintains the records in the form of a dependency network. Each node in the network is an entry in the KB (a premise, antecedent, or inference rule etc.) Each arc of the network represent the inference steps through which the node was derived. A premise is a fundamental belief which is assumed to be true. They do not need justifications. The set of premises are the basis from which justifications for all other nodes will be derived. == Justification == There are two types of justification for a node. They are: Support list [SL] Conditional proof (CP) == Examples == Many kinds of truth maintenance systems exist. Two major types are single-context and multi-context truth maintenance. In single context systems, consistency is maintained among all facts in memory (KB) and relates to the notion of consistency found in classical logic. Multi-context systems support paraconsistency by allowing consistency to be relevant to a subset of facts in memory, a context, according to the history of logical inference. This is achieved by tagging each fact or deduction with its logical history. Multi-agent truth maintenance systems perform truth maintenance across multiple memories, often located on different machines. de Kleer's assumption-based truth maintenance system (ATMS, 1986) was utilized in systems based upon KEE on the Lisp Machine. The first multi-agent TMS was created by Mason and Johnson. It was a multi-context system. Bridgeland and Huhns created the first single-context multi-agent system.
Z.ai
Knowledge Atlas Technology Joint Stock Co., Ltd., branded internationally as Z.ai, is a Chinese technology company specializing in artificial intelligence (AI). The company was formerly known as Zhipu AI outside China until its rebranding in 2025. Z.ai's flagship product is the GLM (General Language Model) family of large language models, which the company has released under the free and open-source MIT License since July 2025. As of 2024, it is one of China's "AI tiger" companies by investors and considered to be the third-largest LLM market player in China's AI industry according to the International Data Corporation. In January 2025, the United States Commerce Department blacklisted the company in its Entity List due to national security concerns. == History == Founded in 2019, the startup company began from Tsinghua University and was later spun out as an independent company. Researchers published an Association for Computational Linguistics conference paper in May 2022 introducing the GLM (General Language Model) training algorithm, which uses an "autoregressive blank infilling" strategy that creates cloze tests by randomly removing segments of input text and trains the model to autoregressively regenerate the removed text. In 2023, it raised 2.5 billion yuan (approx. 350 million in USD) from Alibaba Group and Tencent, along with Meituan, Ant Group, Xiaomi, and HongShan. In March 2024, Zhipu AI announced it was developing a Sora-like technology to achieve artificial general intelligence (AGI). In May 2024, the Saudi Arabian finance firm Prosperity7 Ventures, LLC participated in a USD $400 million financing round for Zhipu AI with a valuation of approximately 3 billion USD. In July 2024, they debuted the Ying text-to-video model. Zhipu released GLM-4-Plus in August 2024. In October 2024, Zhipu released GLM-4-Voice, an end-to-end speech large language model that can adjust its tone or dialect. Zhipu disclosed in April 2025 that it had started preparing for its initial public offering (IPO) and released two models under the free and open-source MIT License. In May 2025, the company sealed a 61.28 million yuan deal from the Chinese government for city projects in Hangzhou. In July 2025, Zhipu AI released GLM-4.5 and GLM-4.5 Air, their next generation language models, and the company rebranded itself as Z.ai internationally. In August 2025, Z.ai announced that their GLM models are compatible with Huawei's Ascend processors. On August 11, 2025, Z.ai released a new vision-language model (VLM) with a total of 106B parameters, GLM-4.5V. In late September 2025, the company released GLM-4.6 using China's domestic chips such as those from Cambricon Technologies. Z.ai released GLM-4.6V and GLM-4.7 in December 2025. That same year, the company changed its official name to Knowledge Atlas Technology JSC Ltd. On 8 January 2026, Z.ai held its IPO on the Hong Kong Stock Exchange to become a listed company. It is considered to be China's first major LLM company that went through an IPO. On February 11, 2026, Z.ai released GLM-5. In late February 2026, Z.ai's shares fell by 23%, and had a shortage of compute resources, leading to user complaints and Z.ai issuing a public call for support. Z.ai also restricted new user signups. In late March, 2026, Z.ai released the GLM-5.1 model to subscription users. On April 8th, 2026, Z.ai released GLM-5.1 as open-source. The same day, Z.ai increased its API prices by 10%, but maintained a lower price than its United States competitor Anthropic's Opus 4.6 model. On release, the company's share price increased 11.5%. == Description == Z.ai provides the following products and services: General Language Model (commonly abbreviated as GLM; formerly known as ChatGLM), a series of pre-trained dialogue models initially developed by Zhipu AI and Tsinghua KEG in 2023. GLM 4.5, released in July 2025 by Z.ai, can run on eight NVIDIA H20 chips. The release of GLM-4.6 in late September 2025 marked the first integration of FP8 and Int4 quantization on Cambricon chips. It also supports native FP8 on Moore Threads GPUs. Ying, a text-to-video model that generates image and text prompts into a six-second video clip for around 30 seconds. AutoGLM, an AI agent application that uses voice commands to complete tasks within a smartphone. The app can analyze complex tasks such as ordering an item from a nearby store and repeating an order based from the user's shopping history. AMiner, created by Jie Tang (co-founder of Z.ai) in March 2006, now owned by Z.ai. Z.ai has offices in the Middle East, United Kingdom, Singapore, and Malaysia, along with innovation center projects across Southeast Asia (2025). In January 2025, the United States Commerce Department added the company to its Entity List, citing national security concerns. == Models ==
Artificial intelligence in Wikimedia projects
Some editors of Wikimedia projects use artificial intelligence (AI) and machine learning programs to edit existing articles or create new ones. Some applications of artificial intelligence, like using large language models (LLMs) to create new articles from scratch, have been more controversial than others for the Wikipedia community. In August 2025, English Wikipedia adopted a policy that allowed editors to nominate suspected LLM-generated articles for speedy deletion. This was followed by a March 2026 decision to prohibit the use of LLMs to generate or rewrite article content, with exceptions for copyediting one's own writing and machine translation from another language's Wikipedia. Wikipedia has also been a significant source of training data for some of the earliest artificial intelligence projects. This has received mixed reactions including concern about companies not citing Wikipedia when relying on it to answer a question as well as Wikipedia's increased costs from data scraping. == AI usage == === Earliest use of automated tools, machine learning and AI === Since 2002, bots have been allowed to run on Wikipedia but must be approved and supervised by a human. A bot created in 2002, rambot, transformed census data into short new articles about towns in the United States; the vast majority of town, city, and county articles were started by it. Fighting vandalism has been a major focus of machine learning and AI bots and tools. The 2007 ClueBot relied on simple heuristics to identify likely vandalism, while its 2010 successor, ClueBot NG, uses machine learning through an artificial neural network. Machine translation software has also been used by Wikimedia contributors for a number of years. Aaron Halfaker's Objective Revision Evaluation Service (ORES) project was launched in late 2015 as an artificial intelligence service for grading the quality of Wikipedia edits. === Generative AI and LLMs === In 2022, the public release of ChatGPT inspired more experimentation with AI and writing Wikipedia articles. A debate was sparked about whether and to what extent such large language models are suitable for such purposes in light of their tendency to generate plausible-sounding misinformation, including fake references; to generate prose that is not encyclopedic in tone; and to reproduce biases. An early experiment on December 6, 2022 by a Wikipedia contributor named Pharos occurred when he created the article "Artwork title" using ChatGPT for the initial draft. Another editor who experimented with this early version of ChatGPT said that ChatGPT's overview of "Weaponized incompetence" was decent, but that the citations were fabricated. Since 2023, work has been done to draft an English Wikipedia policy regarding ChatGPT and similar LLMs, at times recommending that users who are unfamiliar with LLMs should avoid using them due to the aforementioned risks, as well as noting the potential for libel or copyright infringement. In early 2023, the Wiki Education Foundation reported that some experienced editors found AI to be useful in starting drafts or creating new articles. It said that ChatGPT "knows" what Wikipedia articles look like and can easily generate one that is written in the style of Wikipedia, but warned that ChatGPT had a tendency to use promotional language, among other issues. In 2023, a ban on AI was deemed "too harsh" by the community given the productivity benefits it offered editors. In 2023, members of the English Wikipedia community created a WikiProject named AI Cleanup to assist in the removal of poor quality AI content from Wikipedia. Miguel García, a former Wikimedia member from Spain, said in 2024 that when ChatGPT was originally launched, the number of AI-generated articles on the site peaked. He added that the rate of AI articles has now stabilized due to the community's efforts to combat it. He said that majority of the articles that have no sources are deleted instantly or are nominated for deletion. In October 2024, a study by Princeton University found that about 5% of 3,000 newly created articles (created in August 2024) on English Wikipedia were created using AI. The study said that some of the AI articles were on innocuous topics and that AI had likely only been used to assist in writing. For some other articles, AI had been used to promote businesses or political interests. In October 2024, Ilyas Lebleu, founder of WikiProject AI Cleanup, said that they and their fellow editors noticed a pattern of unnatural writing that could be connected to ChatGPT. They added that AI is able to mass-produce content that sounds real while being completely fake, leading to the creation of hoax articles on Wikipedia that they were tasked to delete. In June 2025, the Wikimedia Foundation started testing a "Simple Article Summaries" feature which would provide AI-generated summaries of Wikipedia articles, similar to Google Search's AI Overviews. The decision was met with immediate and harsh criticism from some Wikipedia editors, who called the feature a "ghastly idea" and a "PR hype stunt." They criticized a perceived loss of trust in the site due to AI's tendency to hallucinate and questioned the necessity of the feature. The criticism led the Wikimedia Foundation to halt the rollout of Simple Article Summaries that same month while still expressing interest in integrating generative AI more into Wikipedia. The project hints at tensions within the community and with the Foundation over when to use AI.In August 2025, the English Wikipedia community created a policy that allowed users to nominate suspected AI-generated articles for speedy deletion. Editors might recognize AI-generated articles because they use citations that are not related to the subject of the article or fabricated citations or the wording has particular quirks. If an article uses language that reads like an LLM response to a user, such as "Here is your Wikipedia article on" or "Up to my last training update", the article is typically tagged for speedy deletion. Other signs of AI use include excessive use of em dashes, overuse of the word "moreover", promotional material in articles that describes something as "breathtaking" and formatting issues like using curly quotation marks instead of straight versions. During the discussion on implementing the speedy deletion policy, one user, who is an article reviewer, said that he is "flooded non-stop with horrendous drafts" created using AI. Other users said that AI articles have a large amount of "lies and fake references" and that it takes a significant amount of time to fix the issues. English Wikipedia created a guide on how to spot signs of AI-generated writing in August 2025, titled "Signs of AI writing". In January 2026, the Wiki Education Foundation continued to caution against copying and pasting outputs from generative AI into Wikipedia and to avoid it for creating new articles explaining that the text often failed verification with the sources provided. The foundation created a training module that encourages editors to use AI for identifying gaps in articles, finding access to sources and finding relevant sources. In March 2026, the English Wikipedia community prohibited the use of AI to add content to articles, with exceptions for copy editing and machine translation from another language's Wikipedia. The English Wikipedia community holds the position that LLMs often violate core content policies. == Using Wikipedia for artificial intelligence == A 2017 paper described Wikipedia as the mother lode for human-generated text available for machine learning. In the development of the Google's Perspective API that identifies toxic comments in online forums, a dataset containing hundreds of thousands of Wikipedia talk page comments with human-labelled toxicity levels was used. As of 2023, subsets of the Wikipedia corpus were considered one of the largest well-curated data sets available for AI training, used to train every LLM to-date according to Stephen Harrison. This use of Wikipedia was divisive as of 2023. The Wikimedia Foundation and many of its projects supporters worry that attribution to Wikipedia articles is missing in many large-language models like ChatGPT (as well as AI like Siri and Alexa). While Wikipedia's licensing policy lets anyone use its texts, including in modified forms, it does have the condition that credit is given, implying that using its contents in answers by AI models without clarifying the sourcing may violate its terms of use. The Foundation expressed concern that without attribution, people will not visit the site as much or be as motivated to donate to support the project if they do not know when they are benefiting from it. They also noticed an 8% decrease in visitors to Wikipedia in 2025 which they attributed both to the increased popularity of generative AI and social media. In 2025, the Wikimedia Foundation has cited absorbing increased costs associated with scra
SmarterChild
SmarterChild was a chatbot available on AOL Instant Messenger and Windows Live Messenger (previously MSN Messenger) networks. == History == SmarterChild was an apparently intelligent agent or "bot" developed by ActiveBuddy, Inc., with offices in New York and Sunnyvale. It was widely distributed across global instant messaging networks. SmarterChild became very popular, attracting over 30 million Instant Messenger "buddies" on AIM (AOL), MSN and Yahoo Messenger over the course of its lifetime. Founded in 2000, ActiveBuddy was the brainchild of Robert Hoffer and Timothy Kay, who later brought seasoned advertising executive Peter Levitan on board as CEO. The concept for conversational instant messaging bots came from the founder's vision to add natural language comprehension functionality to the increasingly popular AIM instant messaging application. The original implementation took shape as a demo that Kay programmed in Perl in his Los Altos garage to connect a single buddy name, "ActiveBuddy", to look up stock symbols, and later allow AIM users to play Colossal Cave Adventure, a word-based adventure game, and MIT's Boris Katz Start Question Answering System but quickly grew to include a wide range of database applications the company called 'knowledge domains' including instant access to news, weather, stock information, movie times, yellow pages listings, and detailed sports data, as well as a variety of tools (personal assistant, calculators, translator, etc.). None of the individual domains which the company had named “stocksBuddy”, “sportsBuddy”, etc. ever launched publicly. When Stephen Klein came on board as COO — and eventually CEO — he insisted that all of the disparate test “buddies” be launched together with the company’s highly-developed colloquial chat domain. He suggested using “SmarterChild”, a username coined by Tim Kay which Tim was using to test various things. The bundled domains were launched publicly as SmarterChild (on AIM initially) in June 2001. SmarterChild provided information wrapped in fun and quirky conversation. The company generated no revenue from SmarterChild, but used it as a demonstration of the power of what Klein called “conversational computing”. The company subsequently marketed Automated Service Agents—delivering immediate answers to customer service inquiries—-to large corporations, like Comcast, Cingular, TimeWarner Cable, etc. SmarterChild's popularity spawned targeted marketing-oriented bots for Radiohead, Austin Powers, Intel, Keebler, The Sporting News and others. ActiveBuddy co-founders, Kay and Hoffer, as co-inventors, were issued two controversial U.S. patents in 2002. ActiveBuddy changed its name to Colloquis (briefly Conversagent) and targeted development of consumer-facing enterprise customer service agents, which the company marketed as Automated Service Agents. Microsoft acquired Colloquis in October 2006 and proceeded to de-commission SmarterChild and kill off the Automated Service Agent business as well. Robert Hoffer, ActiveBuddy co-founder, licensed the technology from Microsoft after Microsoft abandoned the Colloquis technology.
ICAD (software)
ICAD (Corporate history: ICAD, Inc., Concentra (name change at IPO in 1995), KTI (name change in 1998), Dassault Systèmes (purchase in 2001) () is a knowledge-based engineering (KBE) system that enables users to encode design knowledge using a semantic representation that can be evaluated for Parasolid output. ICAD has an open architecture that can utilize all the power and flexibility of the underlying language. KBE, as implemented via ICAD, received a lot of attention due to the remarkable results that appeared to take little effort. ICAD allowed one example of end-user computing that in a sense is unparalleled. Most ICAD developers were degreed engineers. Systems developed by ICAD users were non-trivial and consisted of highly complicated code. In the sense of end-user computing, ICAD was the first to allow the power of a domain tool to be in the hands of the user, at the same time being open to allow extensions as identified and defined by the domain expert or subject-matter expert (SME). A COE article looked at the resulting explosion of expectations (see AI winter), which were not sustainable. However, such a bubble burst does not diminish the existence of ability that would exist were expectations and use reasonable or properly managed. == History == The original implementation of ICAD was on a Lisp machine (Symbolics). Some of the principals involved with the development were Larry Rosenfeld, Avrum Belzer, Patrick M. O'Keefe, Philip Greenspun, and David F. Place. The time frame was 1984–85. ICAD started on special-purpose Symbolics Lisp hardware and was then ported to Unix when Common Lisp became portable to general-purpose workstations. The original domain for ICAD was mechanical design with many application successes. However, ICAD has found use in other domains, such as electrical design, shape modeling, etc. An example project could be wind tunnel design or the development of a support tool for aircraft multidisciplinary design. Further examples can be found in the presentations at the annual IIUG (International ICAD Users Group) that have been published in the KTI Vault (1999 through 2002). Boeing and Airbus used ICAD extensively to develop various components in the 1990s and early 21st century. As of 2003, ICAD was featured strongly in several areas as evidenced by the Vision & Strategy Product Vision and Strategy presentation. After 2003, ICAD use diminished. At the end of 2001, the KTI Company faced financial difficulties and laid off most of its best staff. They were eventually bought out by Dassault who effectively scuppered the ICAD product. See IIUG at COE, 2003 (first meeting due to Dassault by KTI) The ICAD system was very expensive, relatively, and was in the price range of high-end systems. Market dynamics couldn't support this as there may not have been sufficient differentiating factors between ICAD and the lower-end systems (or the promises from Dassault). KTI was absorbed by Dassault Systèmes and ICAD is no longer considered the go-forward tool for knowledge-based engineering (KBE) applications by that company. Dassault Systèmes is promoting a suite of tools oriented around version 5 of their popular CATIA CAD application, with Knowledgeware the replacement for ICAD. As of 2005, things were still a bit unclear. ICAD 8.3 was delivered. The recent COE Aerospace Conference had a discussion about the futures of KBE. One issue involves the stacking of 'meta' issues within a computer model. How this is resolved, whether by more icons or the availability of an external language, remains to be seen. The Genworks GDL product (including kernel technology from the Gendl Project) is the nearest functional equivalent to ICAD currently available. == Particulars == ICAD provided a declarative language (IDL) using New Flavors (never converted to Common Lisp Object System (CLOS)) that supported a mechanism for relating parts (defpart) via a hierarchical set of relationships. Technically, the ICAD Defpart was a Lisp macro; the ICAD defpart list was a set of generic classes that can be instantiated with specific properties depending upon what was represented. This defpart list was extendible via composited parts that represented domain entities. Along with the part-subpart relations, ICAD supported generic relations via the object modeling abilities of Lisp. Example applications of ICAD range from a small collection of defparts that represents a part or component to a larger collection that represents an assembly. In terms of power, an ICAD system, when fully specified, can generate thousands of instances of parts on a major assembly design. One example of an application driving thousands of instances of parts is that of an aircraft wing – where fastener type and placement may number in the thousands, each instance requiring evaluation of several factors driving the design parameters. == Futures (KBE, etc.) == One role for ICAD may be serving as the defining prototype for KBE which would require that we know more about what occurred the past 15 years (much information is tied up behind corporate firewalls and under proprietary walls). With the rise of functional programming languages (an example is Haskell) in the markets, perhaps some of the power attributable to Lisp may be replicated.