Enterprise bookmarking is a method for Web 2.0 users to tag, organize, store, and search bookmarks of both web pages on the Internet and data resources stored in a distributed database or fileserver. This is done collectively and collaboratively in a process by which users add tag (metadata) and knowledge tags. In early versions of the software, these tags are applied as non-hierarchical keywords, or terms assigned by a user to a web page, and are collected in tag clouds. Examples of this software are Connectbeam and Dogear. New versions of the software such as Jumper 2.0 and Knowledge Plaza expand tag metadata in the form of knowledge tags that provide additional information about the data and are applied to structured and semi-structured data and are collected in tag profiles. == History == Enterprise bookmarking is derived from Social bookmarking that got its modern start with the launch of the website del.icio.us in 2003. The first major announcement of an enterprise bookmarking platform was the IBM Dogear project, developed in Summer 2006. Version 1.0 of the Dogear software was announced at Lotusphere 2007, and shipped later that year on June 27 as part of IBM Lotus Connections. The second significant commercial release was Cogenz in September 2007. Since these early releases, Enterprise bookmarking platforms have diverged considerably. The most significant new release was the Jumper 2.0 platform, with expanded and customizable knowledge tagging fields. == Differences == === Versus social bookmarking === In a social bookmarking system, individuals create personal collections of bookmarks and share their bookmarks with others. These centrally stored collections of Internet resources can be accessed by other users to find useful resources. Often these lists are publicly accessible, so that other people with similar interests can view the links by category or by the tags themselves. Most social bookmarking sites allow users to search for bookmarks which are associated with given "tags", and rank the resources by the number of users which have bookmarked them. Enterprise bookmarking is a method of tagging and linking any information using an expanded set of tags to capture knowledge about data. It collects and indexes these tags in a web-infrastructure knowledge base server residing behind the firewall. Users can share knowledge tags with specified people or groups, shared only inside specific networks, typically within an organization. Enterprise bookmarking is a knowledge management discipline that embraces Enterprise 2.0 methodologies to capture specific knowledge and information that organizations consider proprietary and are not shared on the public Internet. === Tag management === Enterprise bookmarking tools also differ from social bookmarking tools in the way that they often face an existing taxonomy. Some of these tools have evolved to provide Tag management which is the combination of uphill abilities (e.g. faceted classification, predefined tags, etc.) and downhill gardening abilities (e.g. tag renaming, moving, merging) to better manage the bottom-up folksonomy generated from user tagging.
Carrenza
Carrenza was a cloud-computing company based in London, United Kingdom. The company was acquired by Six Degrees Technology Group in 2016. == Operations == Carrenza was a UK-based IT company that provides Cloud computing technologies. It offered a range of public cloud, private cloud and hybrid cloud services, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), enterprise application integration and system integration. Carrenza partnered with several enterprise IT providers and was an accredited VMware Enterprise Service Partner and HP (Hewlett-Packard) Cloud Agile Partner. The company was based on Commercial Street, in the heart of the East London Tech City district, which is host to a large number of technology companies. == History == Carrenza was formed in 2001 as a consultancy by chief executive and founder Dan Sutherland. It began trading in 2004 and launched its first enterprise cloud computing platform in 2006, becoming one of the first companies in Europe to provide this type of hosting service. In 2009, it formed a partnership with Comic Relief and its affiliated campaigns Red Nose Day Sport Relief to provide IT infrastructure services to the charity, an arrangement that has won industry recognition. In 2013 it launched its first overseas services, with a mainland Europe cloud node based in Amsterdam. == Partnerships and customers == Carrenza had formed partnerships with a range of IT providers. It was one of the first companies in Europe to become a HP Cloud Agile partner., using HP blade servers and HP 3PAR SAN technology to power its cloud computing services. The company's products also use VMware vCloud IaaS tools and it is taking part in the VMware lighthouse initiative helping develop the next generation of VMware products and services. Other technology companies that Carrenza has worked closely with include Cisco, for enterprise security and loadblancing services, and Oracle. The company was the first to deploy Oracle Database 11g stretched RAC in production. It has also won two Oracle partner awards, including a Special Recognition award for its work with Comic Relief. The company has also been recognised by the UK IT Industry, receiving awards in 2009 for Community Project of the Year and in 2010 for best small business project for its Monopoly City Streets Work. Other companies that have partnered with Carrenza for their cloud-based IT services include Age UK, Haymarket Media Group, the World Wide Fund for Nature, Royal Bank of Scotland, eBay and Cineworld. == Accreditations == Carrenza's services are accredited for their compliance with several key international IT security and quality standards. These include: ISO27001:2005, Information Security Management System for all Carrenza services. UK Government G-Cloud, Carrenza has been awarded a place on the UK government's G-Cloud iii framework as an Infrastructure as a Service provider.
Fuzzy measure theory
In mathematics, fuzzy measure theory considers generalized measures in which the additive property is replaced by the weaker property of monotonicity. The central concept of fuzzy measure theory is the fuzzy measure (also capacity, see ), which was introduced by Choquet in 1953 and independently defined by Sugeno in 1974 in the context of fuzzy integrals. There exists a number of different classes of fuzzy measures including plausibility/belief measures, possibility/necessity measures, and probability measures, which are a subset of classical measures. == Definitions == Let X {\displaystyle \mathbf {X} } be a universe of discourse, C {\displaystyle {\mathcal {C}}} be a class of subsets of X {\displaystyle \mathbf {X} } , and E , F ∈ C {\displaystyle E,F\in {\mathcal {C}}} . A function g : C → R {\displaystyle g:{\mathcal {C}}\to \mathbb {R} } where ∅ ∈ C ⇒ g ( ∅ ) = 0 {\displaystyle \emptyset \in {\mathcal {C}}\Rightarrow g(\emptyset )=0} E ⊆ F ⇒ g ( E ) ≤ g ( F ) {\displaystyle E\subseteq F\Rightarrow g(E)\leq g(F)} is called a fuzzy measure. A fuzzy measure is called normalized or regular if g ( X ) = 1 {\displaystyle g(\mathbf {X} )=1} . == Properties of fuzzy measures == A fuzzy measure is: additive if for any E , F ∈ C {\displaystyle E,F\in {\mathcal {C}}} such that E ∩ F = ∅ {\displaystyle E\cap F=\emptyset } , we have g ( E ∪ F ) = g ( E ) + g ( F ) . {\displaystyle g(E\cup F)=g(E)+g(F).} ; supermodular if for any E , F ∈ C {\displaystyle E,F\in {\mathcal {C}}} , we have g ( E ∪ F ) + g ( E ∩ F ) ≥ g ( E ) + g ( F ) {\displaystyle g(E\cup F)+g(E\cap F)\geq g(E)+g(F)} ; submodular if for any E , F ∈ C {\displaystyle E,F\in {\mathcal {C}}} , we have g ( E ∪ F ) + g ( E ∩ F ) ≤ g ( E ) + g ( F ) {\displaystyle g(E\cup F)+g(E\cap F)\leq g(E)+g(F)} ; superadditive if for any E , F ∈ C {\displaystyle E,F\in {\mathcal {C}}} such that E ∩ F = ∅ {\displaystyle E\cap F=\emptyset } , we have g ( E ∪ F ) ≥ g ( E ) + g ( F ) {\displaystyle g(E\cup F)\geq g(E)+g(F)} ; subadditive if for any E , F ∈ C {\displaystyle E,F\in {\mathcal {C}}} such that E ∩ F = ∅ {\displaystyle E\cap F=\emptyset } , we have g ( E ∪ F ) ≤ g ( E ) + g ( F ) {\displaystyle g(E\cup F)\leq g(E)+g(F)} ; symmetric if for any E , F ∈ C {\displaystyle E,F\in {\mathcal {C}}} , we have | E | = | F | {\displaystyle |E|=|F|} implies g ( E ) = g ( F ) {\displaystyle g(E)=g(F)} ; Boolean if for any E ∈ C {\displaystyle E\in {\mathcal {C}}} , we have g ( E ) = 0 {\displaystyle g(E)=0} or g ( E ) = 1 {\displaystyle g(E)=1} . Understanding the properties of fuzzy measures is useful in application. When a fuzzy measure is used to define a function such as the Sugeno integral or Choquet integral, these properties will be crucial in understanding the function's behavior. For instance, the Choquet integral with respect to an additive fuzzy measure reduces to the Lebesgue integral. In discrete cases, a symmetric fuzzy measure will result in the ordered weighted averaging (OWA) operator. Submodular fuzzy measures result in convex functions, while supermodular fuzzy measures result in concave functions when used to define a Choquet integral. == Möbius representation == Let g be a fuzzy measure. The Möbius representation of g is given by the set function M, where for every E , F ⊆ X {\displaystyle E,F\subseteq X} , M ( E ) = ∑ F ⊆ E ( − 1 ) | E ∖ F | g ( F ) . {\displaystyle M(E)=\sum _{F\subseteq E}(-1)^{|E\backslash F|}g(F).} The equivalent axioms in Möbius representation are: M ( ∅ ) = 0 {\displaystyle M(\emptyset )=0} . ∑ F ⊆ E | i ∈ F M ( F ) ≥ 0 {\displaystyle \sum _{F\subseteq E|i\in F}M(F)\geq 0} , for all E ⊆ X {\displaystyle E\subseteq \mathbf {X} } and all i ∈ E {\displaystyle i\in E} A fuzzy measure in Möbius representation M is called normalized if ∑ E ⊆ X M ( E ) = 1. {\displaystyle \sum _{E\subseteq \mathbf {X} }M(E)=1.} Möbius representation can be used to give an indication of which subsets of X interact with one another. For instance, an additive fuzzy measure has Möbius values all equal to zero except for singletons. The fuzzy measure g in standard representation can be recovered from the Möbius form using the Zeta transform: g ( E ) = ∑ F ⊆ E M ( F ) , ∀ E ⊆ X . {\displaystyle g(E)=\sum _{F\subseteq E}M(F),\forall E\subseteq \mathbf {X} .} == Simplification assumptions for fuzzy measures == Fuzzy measures are defined on a semiring of sets or monotone class, which may be as granular as the power set of X, and even in discrete cases the number of variables can be as large as 2|X|. For this reason, in the context of multi-criteria decision analysis and other disciplines, simplification assumptions on the fuzzy measure have been introduced so that it is less computationally expensive to determine and use. For instance, when it is assumed the fuzzy measure is additive, it will hold that g ( E ) = ∑ i ∈ E g ( { i } ) {\displaystyle g(E)=\sum _{i\in E}g(\{i\})} and the values of the fuzzy measure can be evaluated from the values on X. Similarly, a symmetric fuzzy measure is defined uniquely by |X| values. Two important fuzzy measures that can be used are the Sugeno- or λ {\displaystyle \lambda } -fuzzy measure and k-additive measures, introduced by Sugeno and Grabisch respectively. === Sugeno λ-measure === The Sugeno λ {\displaystyle \lambda } -measure is a special case of fuzzy measures defined iteratively. It has the following definition: ==== Definition ==== Let X = { x 1 , … , x n } {\displaystyle \mathbf {X} =\left\lbrace x_{1},\dots ,x_{n}\right\rbrace } be a finite set and let λ ∈ ( − 1 , + ∞ ) {\displaystyle \lambda \in (-1,+\infty )} . A Sugeno λ {\displaystyle \lambda } -measure is a function g : 2 X → [ 0 , 1 ] {\displaystyle g:2^{X}\to [0,1]} such that g ( X ) = 1 {\displaystyle g(X)=1} . if A , B ⊆ X {\displaystyle A,B\subseteq \mathbf {X} } (alternatively A , B ∈ 2 X {\displaystyle A,B\in 2^{\mathbf {X} }} ) with A ∩ B = ∅ {\displaystyle A\cap B=\emptyset } then g ( A ∪ B ) = g ( A ) + g ( B ) + λ g ( A ) g ( B ) {\displaystyle g(A\cup B)=g(A)+g(B)+\lambda g(A)g(B)} . As a convention, the value of g at a singleton set { x i } {\displaystyle \left\lbrace x_{i}\right\rbrace } is called a density and is denoted by g i = g ( { x i } ) {\displaystyle g_{i}=g(\left\lbrace x_{i}\right\rbrace )} . In addition, we have that λ {\displaystyle \lambda } satisfies the property λ + 1 = ∏ i = 1 n ( 1 + λ g i ) {\displaystyle \lambda +1=\prod _{i=1}^{n}(1+\lambda g_{i})} . Tahani and Keller as well as Wang and Klir have shown that once the densities are known, it is possible to use the previous polynomial to obtain the values of λ {\displaystyle \lambda } uniquely. === k-additive fuzzy measure === The k-additive fuzzy measure limits the interaction between the subsets E ⊆ X {\displaystyle E\subseteq X} to size | E | = k {\displaystyle |E|=k} . This drastically reduces the number of variables needed to define the fuzzy measure, and as k can be anything from 1 (in which case the fuzzy measure is additive) to X, it allows for a compromise between modelling ability and simplicity. ==== Definition ==== A discrete fuzzy measure g on a set X is called k-additive ( 1 ≤ k ≤ | X | {\displaystyle 1\leq k\leq |\mathbf {X} |} ) if its Möbius representation verifies M ( E ) = 0 {\displaystyle M(E)=0} , whenever | E | > k {\displaystyle |E|>k} for any E ⊆ X {\displaystyle E\subseteq \mathbf {X} } , and there exists a subset F with k elements such that M ( F ) ≠ 0 {\displaystyle M(F)\neq 0} . == Shapley and interaction indices == In game theory, the Shapley value or Shapley index is used to indicate the weight of a game. Shapley values can be calculated for fuzzy measures in order to give some indication of the importance of each singleton. In the case of additive fuzzy measures, the Shapley value will be the same as each singleton. For a given fuzzy measure g, and | X | = n {\displaystyle |\mathbf {X} |=n} , the Shapley index for every i , … , n ∈ X {\displaystyle i,\dots ,n\in X} is: ϕ ( i ) = ∑ E ⊆ X ∖ { i } ( n − | E | − 1 ) ! | E | ! n ! [ g ( E ∪ { i } ) − g ( E ) ] . {\displaystyle \phi (i)=\sum _{E\subseteq \mathbf {X} \backslash \{i\}}{\frac {(n-|E|-1)!|E|!}{n!}}[g(E\cup \{i\})-g(E)].} The Shapley value is the vector ϕ ( g ) = ( ψ ( 1 ) , … , ψ ( n ) ) . {\displaystyle \mathbf {\phi } (g)=(\psi (1),\dots ,\psi (n)).}
AI Snake Oil
AI Snake Oil: What Artificial Intelligence Can Do, What It Can't, and How to Tell the Difference is a 2024 non-fiction book written by scholars Arvind Narayanan and Sayash Kapoor. It is a critique of the tech industry's overly inflated promises and capabilities of artificial intelligence (AI) as well as a debunking of the flawed science fueling AI hype while attempting to outline both the potential positives and negatives that come with different modes of the technology. == Contents == === Publication === The book was published in September 2024 by the Princeton University Press. AI Snake Oil consists of 360 pages and features eight chapters, and sections for acknowledgements, references, and an index. An updated edition with a new preface and epilogue by the authors was published in September 2025. The authors use the term "AI snake oil" derived from the U.S. idiom for a fraudulent remedy, to describe overhyped AI systems. === Chapter one: Introduction === Narayanan and Kapoor argue that many individuals do not yet have the literacy to detect functioning aspects of AI compared to potential snake oil, which they identify as "AI that does not and cannot work as advertised". Some of the major examples utilized by the authors include Allstate's 2013 use of predictive AI, as well as the concern surrounding actors and AI attempting to replicate or use their likeness. Important discussions regarding discrimination are brought up and explored in the first chapter, including the false arrests of six Black individuals due to errors with AI facial recognition tools. The chapter concludes with a comparison to the Industrial Revolution, where Narayanan and Kapoor highlight the extensive human labour that is necessary for artificial intelligence technologies to function. === Chapter two: How Predictive AI Goes Wrong === Chapter two focuses on predictive artificial intelligence, and criticizes the overestimation of the capabilities of the technology. === Chapter three: Why Can't AI Predict the Future? === Chapter three works to inform the reader about the history of early computational prediction attempts, with examples from companies like Simulatics. === Chapter four: The Long Road to Generative AI === The fourth chapter goes in more in-depth in explorations of generative AI. Generative AI software examples include ChatGPT, Midjourney, and DALL-E. The section begins with a positive example of generative AI. As the chapter progresses, the authors begin to provide examples of harm produced by generative AI, including the suicide of a Belgian man after connecting with Chai, a generative chatbot. Issues of deepfakes and preservation of artistic property are also discussed. The use of generative AI to create non-consensual pornographic deepfake content is discussed in relation to female celebrities. === Chapter five: Is Advanced AI an Existential Threat? === The fifth chapter draws attention the AGI, or Artificial General Intelligence. The authors describe AGI as "AI that can perform most or economically relevant tasks as effectively as any human". They summarize that many contributors to the field of artificial intelligence believe AGI to be an impending threat that demands attention. However, they argue that the perceived threat of AGI would only exist if the technology continually functioned reliably. In order to better illustrate the hype surrounding AGI, Narayanan and Kapoor use the Ladder of Generality, which is described as a visual tool in which "each rung represents a way of computing that is more flexible, and more general, than the previous one". They note that we are not yet aware of the next rungs on the ladder, or if the ladder will eventually result in a dead end. The rungs that have been identified so far are as follows: (0, or floor) special purpose hardware, (1) programmable computers, (2) stored program computers, (3) machine learning, (4) deep learning, (5) pretrained models, and, finally, (6) instruction-tuned models. The potential for future rungs and what those rungs might be are currently undetermined. The chapter also discusses the ELIZA effect, which Lawrence Switzky discusses in his article "ELIZA Effects". Switzky attributes the coined term ELIZA Effect to Sherry Turke, who defined it as "our more general tendency to treat responsive computer programs as more intelligent than they really are". === Chapter six: Why Can't AI Fix Social Media? === The sixth chapter focuses on content moderation, why it is important, and how it has been and could be affected by artificial automation. The first issue raised in regard to AI-driven content moderation is the inability for computers and machines to understand context and nuance, resulting in potential for discriminatory moderation and shadow banning. While they note that there are issues with automating content moderation, Narayanan and Kapoor also highlight the psychological impact on human content moderators and their labour. They indicate the hidden labour behind moderation, which is often outsourced to less developed countries, where labourers sort through potentially traumatizing content for pay. However, the discussion focuses more heavily on why automated moderation can be problematic, including discriminatory algorithms and lack of nuance. To balance their argument, issues of discrimination and bias are also discussed in relation the human content moderators. To automate moderation, there are two types of AI used, which are fingerprint matching and machine learning. === Chapter seven: Why Do Myths about AI Persist? === The seventh chapter outlines possible factors that contribute to hype surrounding AI. Narayanan and Kapoor explain how companies often promote their new AI models without properly disclosing how the model works, and what it is learning from. They attribute hype to several different groups, including journalists, researchers, and companies. They explain the impact of companies and the misplaced hype that they spread can be attributed to greed and a desire to grow corporate funds. For journalists, one of the stated sources of hype, they argue that news media has a tendency to prioritize financial incentives over validity and quality of writing. As well, Narayanan and Kapoor point out the emergence of company statement regurgitation in news media, leading to clickbait. Hype from researchers is potentially linked to lack of reproducibility in studies as well as leakage, which occurs when AI models are tested on their training data. === Chapter eight: Where do we go from here? === The final chapter, chapter eight, turns its attention to the future. The authors express their ideas and predictions for how the technology will evolve and be utilized in the upcoming years. == Authors == Author Narayanan is a computer science professor at Princeton University. Kapoor is a doctoral candidate at the same university, and both scholars are located at the Center for Information Technology at Princeton. In 2023, Narayanan and Kapoor appeared on the TIME100 Artificial Intelligence list, which features influential figures in the field. == Reception == Nature, a science and technology peer-reviewed journal, released an article highlighting the top "10 essential reads from the past year", listing Arvind Narayanan and Sayash Kapoor's AI Snake Oil. The article states the that text is "one of the best on this controversial subject". Elizabeth Quill, in her review of the text in Science News, writes that the authors "squarely achieve their stated goal: to empower people to distinguish AI that works well from AI snake oil". Joshua Rothman of The New Yorker writes that "compared with many technologists, Narayanan, Kapoor, and Vallor [Shannon Vallor, University of Edinburgh], are deeply skeptical about today's A.I. technology and what it can achieve. Perhaps they shouldn't be". Rothman argues, following an interview with prominent computer scientist Geoffrey Hinton of University of Toronto, that the potential for AI to replicate complexity is already here and continues to be heavily funded, enhancing the prospective capabilities of the technology. However, he does praise the author's ability to address questions regarding the existential human experience. Alexya Martinez discusses the text in a book review for Journalism and Mass Communication Quarterly, critiquing AI Snake Oil for its extensive focus on the West. Martinez writes that Narayanan and Kapoor "do not fully explore how AI impacts other countries", and suggests more focus on countries outside of the United States to enhance their argument.
Digital organism
A digital organism is a self-replicating computer program that mutates and evolves. Digital organisms are used as a tool to study the dynamics of Darwinian evolution, and to test or verify specific hypotheses or mathematical models of evolution. The study of digital organisms is closely related to the area of artificial life. == History == Digital organisms can be traced back to the game Darwin, developed in 1961 at Bell Labs, in which computer programs had to compete with each other by trying to stop others from executing . A similar implementation that followed this was the game Core War. In Core War, it turned out that one of the winning strategies was to replicate as fast as possible, which deprived the opponent of all computational resources. Programs in the Core War game were also able to mutate themselves and each other by overwriting instructions in the simulated "memory" in which the game took place. This allowed competing programs to embed damaging instructions in each other that caused errors (terminating the process that read it), "enslaved processes" (making an enemy program work for you), or even change strategies mid-game and heal themselves. Steen Rasmussen at Los Alamos National Laboratory took the idea from Core War one step further in his core world system by introducing a genetic algorithm that automatically wrote programs. However, Rasmussen did not observe the evolution of complex and stable programs. It turned out that the programming language in which core world programs were written was very brittle, and more often than not mutations would completely destroy the functionality of a program. The first to solve the issue of program brittleness was Thomas S. Ray with his Tierra system, which was similar to core world. Ray made some key changes to the programming language such that mutations were much less likely to destroy a program. With these modifications, he observed for the first time computer programs that did indeed evolve in a meaningful and complex way. Later, Chris Adami, Titus Brown, and Charles Ofria started developing their Avida system, which was inspired by Tierra but again had some crucial differences. In Tierra, all programs lived in the same address space and could potentially execute or otherwise interfere with each other's code. In Avida, on the other hand, each program lives in its own address space. Because of this modification, experiments with Avida became much cleaner and easier to interpret than those with Tierra. With Avida, digital organism research has begun to be accepted as a valid contribution to evolutionary biology by a growing number of evolutionary biologists. Evolutionary biologist Richard Lenski of Michigan State University has used Avida extensively in his work. Lenski, Adami, and their colleagues have published in journals such as Nature and the Proceedings of the National Academy of Sciences (USA). In 1996, Andy Pargellis created a Tierra-like system called Amoeba that evolved self-replication from a randomly seeded initial condition. More recently REvoSim - a software package based around binary digital organisms - has allowed evolutionary simulations of large populations that can be run for geological timescales.
Cooliris (plugin)
Cooliris (for Desktop), formerly known as PicLens, was a web browser extension developed by Cooliris, Inc, and later acquired by Yahoo. The plugin provides an interactive 3D-like experience for viewing digital images and videos from the web and from desktop applications. The software places a small icon atop image thumbnails that appear on a webpage. Clicking on the icon loads the Cooliris 3D Wall, a browsing environment that gives the user the effect of flying through a three-dimensional space. Released to the public in January 2008, The New York Times described Cooliris as the "new immersive approach to Web navigation". Cooliris went out to win the 2008 Crunchies Award for Best Design. The plugin has received over 50 million downloads. As of May 2014 browser plugins are unavailable from the official website. There are only links to tablet apps - for iOS and Android.
AAAI Conference on Artificial Intelligence
The AAAI Conference on Artificial Intelligence is a leading international academic conference in artificial intelligence held annually. It ranks 4th in terms of H5 Index in Google Scholar's list of top AI publications, after ICLR, NeurIPS, and ICML. It is supported by the Association for the Advancement of Artificial Intelligence (AAAI), after which it is named. Precise dates vary from year to year, but paper submissions are generally due at the end of August to beginning of September, and the conference is generally held during the following February. The first AAAI was held in 1980 at Stanford University, Stanford California. During AAAI-20 conference, AI pioneers and 2018 Turing Award winners (often referred to as the Nobel Prize of Computing) Yann LeCun and Yoshua Bengio, among eight other researchers, were honored as the AAAI 2020 Fellows. Along with other conferences such as NeurIPS and ICML, AAAI uses an artificial-intelligence algorithm to assign papers to reviewers. == Sponsors == Many leading technology companies, including Google, Microsoft, Amazon (company), IBM, Baidu, Bytedance, and Huawei, generously sponsor and participate in AAAI to publish and showcase their latest theoretical and applied research. Sponsoring companies also actively recruit AI talents at the conference. == Locations == AAAI-2026 Singapore Expo, Singapore AAAI-2025 Pennsylvania Convention Center, Philadelphia, Pennsylvania, United States AAAI-2024 Vancouver Convention Centre, Vancouver, British Columbia, Canada AAAI-2023 Washington Convention Center, Washington, D.C., United States AAAI-2022 Virtual Conference AAAI-2021 Virtual Conference AAAI-2020 Hilton New York Midtown, New York, New York, United States AAAI-2019 Hilton Hawaiian Village, Honolulu, Hawaii, United States AAAI-2018 Hilton New Orleans Riverside, New Orleans, Louisiana, United States AAAI-2017 San Francisco, California, United States AAAI-2016 Phoenix, Arizona, United States AAAI-2015 Austin, Texas, United States AAAI-2014 Québec Convention Center, Québec City, Québec, Canada AAAI-2013 Bellevue, Washington, United States AAAI-2012 Toronto, Ontario, Canada AAAI-2011 San Francisco, California, United States AAAI-2010 Westin Peachtree Plaza, Atlanta, Georgia, United States AAAI-2008 Chicago, Illinois, United States AAAI-2007 Toronto, Ontario, Canada AAAI-2006 Boston, Massachusetts, United States AAAI-2005 Pittsburgh, Pennsylvania, United States AAAI-2004 San Jose, California, United States AAAI-2002 Shaw conference center in Edmonton, Alberta, Canada AAAI-2000 Austin, Texas, United States AAAI-1999 Orlando, Florida, United States AAAI-1998 Madison, Wisconsin, United States AAAI-1997 Providence, Rhode Island, United States AAAI-1996 Portland, Oregon, United States AAAI-1994 Seattle, Washington, United States AAAI-1993 Washington Convention Center, Washington, D.C., United States AAAI-1992 San Jose Convention Center, San Jose, California, United States AAAI-1991 Anaheim Convention Center, Anaheim, California, United States AAAI-1990 Boston, Massachusetts, United States AAAI-1988 Saint Paul, Minnesota, United States AAAI-1987 Seattle, Washington, United States AAAI-1986 Philadelphia, Pennsylvania, United States AAAI-1984 University of Texas, Austin, Texas, United States AAAI-1983 Washington, D.C., United States AAAI-1982 Carnegie Mellon University and the University of Pittsburgh, Pittsburgh, Pennsylvania, United States AAAI-1980 Stanford, California, United States