AI Data Center Map

AI Data Center Map — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Singularity studies

    Singularity studies

    Singularity studies is an interdisciplinary academic field which examines the idea of technological singularity — the hypothesised point at which artificial intelligence may surpass human intelligence, might be attained by artificial intelligence (AI), robotics, and other technologies and sciences, and its social impacts. In this academic field, the study and research are conducted across a broad array of terrains such as information science, robotics, social informatics, economics, philosophy, and ethics. The primary aim of singularity studies is to gain an integrative understanding of the transformation of social systems occurring in tandem with the explosive evolution of AI and also the changes to be effected by such transformation in the view of humans, ethics, and legal systems. == History == An academic work on technological singurality has appeared in computer science, philosophy, sociology, and law since the early 1990s. Early discussions of an intelligence explosion were popularised by science-fiction writer Vernor Vinge in 1993 and later systematised by futurist Ray Kurzweil. Since the 2010s, universities such as Oxford, Stanford, and Keio have established dedicated programmes, while peer-reviewed journals have begun to publish scenario analyses and policy studies. Ongoing debates question the predictive value of singularity scenarios and warn against a deterministic view of technology. == Characteristics of research == Singularity studies extends beyond mere future predictions and offer an intellectual foundation for proactively designing and creating a desirable future. Principal research themes in this realm include: Ethics of AI; Social implications of technologies; Possibility of harmonious coexistence of humans and AI; Communication with AI; and Redesign of social systems. == Technologists and academics == Vernor Vinge: Propounded the concept of singularity in 1993, making a massive impact on the academic and science-fiction spheres. Ray Kurzweil: Predicted the advent around 2045 of the technological singularity in his 2005 book The Singularity Is Near. Nick Bostrom: Offered philosophical reflections on superintelligence and the risks posed by AI. He is the founding director of the now-dissolved Future of Humanity Institute at the University of Oxford. === Japan === Kento Sasano: A social informatician, AI educator, and inventor. He is the president of the Japan Society of Singularity Studies. == Challenges and outlook == Singularity studies is still evolving as an academic field, and quite a few challenges remain unresolved in regard to the systematization of their theories, research methods, and educational curricula. That said, in this day and age of accelerating technological and societal shifts, interdisciplinary approaches have gained in importance and are drawing much attention in the arenas of scholarly research, intercorporate collaboration, and policy planning.

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  • Sriram Krishnan

    Sriram Krishnan

    Sriram Krishnan (born 1984) is a tech executive and White House official, currently serving as the Senior White House Policy Advisor on Artificial Intelligence. Krishnan was named a Time Person of the Year in 2025 as an "Architect of Artificial Intelligence." He was described in Time as providing the "wake-up call that we needed" to the other AI builders, leading to "a multiyear, $500 billion initiative dubbed Stargate" to push American-made AI, as well as numerous other AI initiatives. Also in December 2025, President Trump said of Krishnan, "without him, things on AI would not function well" and cited Krishnan as the leading figure behind the American executive order on AI. As the leader of the United States' policy team regarding artificial intelligence, Krishnan plays "a significant role in shaping the administration’s approach to AI and driving measures to advance federal adoption of AI." The role calls for removing barriers to AI adoption within the government, driving vendors toward solutions suitable for federal needs, designing sensible regulation of private-sector AI, and conducting "AI diplomacy". He has stated a policy goal of "reinvigorating US dominance in emerging technologies," including AI. He also represents the United States' interests in AI abroad, such as at the Paris AI Summit. He is one of the authors of the American "AI Action Plan" released in July, 2025, which he contends is necessary to win the "existential race with China" for AI supremacy. Krishnan, a U.S. citizen born in India, is also a venture capitalist, podcaster, product manager and author. Early in his career, he led product teams at Microsoft, Twitter, Yahoo!, Facebook, and Snap. In addition to his work as an investor and technologist, he and his wife, Aarthi Ramamurthy, rose to additional prominence in 2021 as podcast hosts. He served as a general partner at the venture capital firm Andreessen Horowitz and led its London office. In 2022, Krishnan announced that he was working with Elon Musk on the rebuilding of Twitter following Musk's acquisition of the company. On December 22, 2024, US president-elect Donald Trump announced that Krishnan would be Senior White House Policy Advisor on Artificial Intelligence in his incoming administration; in 2026 he joined the National Economic Council. == Early life and education == Krishnan was born in Chennai, India. He earned his Bachelor of Technology in Information Technology from SRM University (2001–2005), moved to the United States in 2007 to join Microsoft, and became a naturalized U.S. citizen in 2016. == Career == === Early career === In 2007, he began working at Microsoft where he served as a program manager for Visual Studio. At Facebook, Krishnan built the Facebook Audience Network, a competitive platform to Google's ad technologies. At Twitter, he led product and core user experience, driving a 20% annual user growth rate and launching a redesigned home page and events experience. === Andreessen Horowitz === Krishnan was appointed a general partner of American venture capital firm Andreessen Horowitz ("a16z") in February 2021. He was anticipated to serve consumer and social markets, however he has also theorized on the impact of "deep tech" on society. In 2023 he was appointed to lead the firm's London office, its first non-US location. The office is expected to serve Web3 investments as well as AI and other fields. Krishnan announced that he would leave the firm at the end of 2024. === Social media and AI === In 2022, various news media reported that Krishnan was assisting Elon Musk in the revamp of Twitter following Musk's takeover of the company. Additional reports named Krishnan as the leading candidate for the role of CEO of the newly private company. Krishnan penned a 2023 New York Times opinion column regarding social media, AI, and related fields. He predicted a rise in the number and diversity of online spaces due to decentralization and platforms like Farcaster, Bluesky and Mastodon. === Public office === In 2024, the Financial Times reported that Krishnan was active in international affairs, reintroducing Boris Johnson to Elon Musk, following Musk's nomination to the proposed Department of Government Efficiency. Krishnan was also reported as potentially leaving a16z at the end of the year to "be jumping into something I've wanted to spend [his] energy on," which was widely reported as being related to Musk's and Vivek Ramaswamy's work at DOGE. Others reported to be involved include Joe Lonsdale, Marc Andreesen, Bill Ackman, and Travis Kalanick. On December 22, 2024, US president-elect Donald Trump announced that he would be Senior White House Policy Advisor on Artificial Intelligence in his incoming administration. On February 6, 2025, Reuters reported that Krishnan would be accompanying Vice President Vance to the Paris AI Summit, a "major artificial intelligence" event later that month. Other members of the White House Office of Science and Technology Policy would also be joining the event with around 100 other countries to "focus on AI's potential." Krishnan joined a U.S. technology policy delegation to the Middle East in advance of President Trump's visit in May 2025. Conducting "AI diplomacy," Krishnan negotiated the spread of U.S. AI technologies with Crown Prince Mohammed bin Salman of Saudi Arabia, as well as other means to strengthen bilateral trade in artificial intelligence technologies. He explained that the goal of the diplomatic mission was that "we want American A.I. to spread." Krishnan, along with David Sacks and Michael Kratsios, were credited as authors of the American AI Action Plan released in July 2025. The plan is "the administration’s most significant policy directive" regarding artificial intelligence; it calls for financing to support the global spread of American AI models and a policy to enforce neutrality in models. The Washington Post referred to the plan as a "bold action to ensure that American AI remains at the cutting edge." The AI Action Plan is a continuation of prior efforts to reduce barriers to U.S. production of AI systems and the removal of rules that were considered to hinder such growth. Later in 2025, at the POLITICO AI & Tech Summit, Krishnan called national AI development "an existential race with China." He suggested that private companies are best positioned to create new models, quipping "let them cook." He further suggested that state-by-state regulation of AI technologies may hinder national AI competitiveness. Also in 2025, at the Axios AI+ Summit, Krishnan stated that the United States and China are in a race for AI supremacy, in which the winner will be judged by market share. Winning the race is a "business strategy" to Krishnan. Krishnan was named in the 2025 Time Person of the Year article as an "AI Architect". === The Aarthi and Sriram Show and other media === In early 2021, Krishnan and his wife, Aarthi Ramamurthy, launched a Clubhouse talk show that "focuses on organic conversations on anything from startups to venture capitalism and cryptocurrencies." An early appearance by Elon Musk on the Good Time Show was described as the first show that "broke Clubhouse" by rapidly exceeding the limit of 5,000 simultaneous users. The desire to interact with a larger community led to a variety of later innovations to allow streaming and replaying of Clubhouse chats. On that episode, Elon Musk grilled Robinhood CEO Vlad Tenev regarding the GameStop trading controversy. As of December 2021, the show had over 187,000 subscribers, plus 735,000 subscribers between Krishnan and Ramamurthy's personal Clubhouse accounts. Other guests have included Facebook CEO Mark Zuckerberg, Diane von Fürstenberg, Tony Hawk, MrBeast, and A.R. Rahman. In 2022, the Good Time Show moved to YouTube. It then evolved to a podcasting format under the name The Aarthi and Sriram Show, with both audio and video content. The Hollywood Reporter reported that the podcast had received more than 1 million downloads by early 2023. == Personal life == Krishnan is married to Aarthi Ramamurthy, co-host of The Aarthi and Sriram Show (formerly the Good Time Show) and a serial entrepreneur. They met in college in 2003 through a Yahoo! chat room related to a coding project and began dating in 2006 and eloped in 2010. == Awards == Time Person of the Year - 2025

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  • Chris Olah

    Chris Olah

    Christopher Olah (born 1992 or 1993) is a Canadian machine learning researcher and a co-founder of Anthropic. He is known for his work on neural network interpretability, particularly mechanistic interpretability, and for research and tools that visualise internal representations in neural networks. In 2025, Forbes reported he had become a billionaire due to his ownership in Anthropic. == Early life and education == Olah was born in Canada. According to Wired, he left university at age 18 without earning a degree and later received a Thiel Fellowship, which supported him in pursuing independent work. == Career == Olah has worked on interpretability research at Google Brain, OpenAI, and Anthropic. Time called him one of the pioneers of mechanistic interpretability and noted that he pursued this research line first at Google, then at OpenAI, and later at Anthropic, which he co-founded. Wired reported that Olah was involved in neural network visualisation work including DeepDream in 2015, as part of efforts to better understand what neural networks learn. Later coverage linked him to more structured interpretability approaches such as "activation atlases". The Verge covered activation atlases as a collaboration between Google and OpenAI researchers to help inspect neural network representations. At Anthropic, Olah has been identified in major press coverage as leading interpretability work aimed at mapping internal "features" in large language models and relating interpretability findings to AI safety. Quanta Magazine has also quoted Olah in reporting on interpretability and the internal structure of modern language models. Time included Olah in its TIME100 AI list in 2024. === Vatican address on AI ethics === On May 25, 2026, Olah spoke at the Vatican during the official presentation of Magnifica Humanitas, the first encyclical of Pope Leo XIV, which addresses artificial intelligence and human dignity. Olah said AI could lead to large-scale displacement of human labor and exacerbate global inequality. He said the commercial and geopolitical incentives driving frontier AI labs often conflict with the public good, and described AI systems as "grown" rather than strictly engineered. Olah called for external moral oversight from religious institutions, scholars, and civil society to hold the technology sector accountable.

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  • Andrej Karpathy

    Andrej Karpathy

    Andrej Karpathy (born 23 October 1986) is a Slovak-Canadian AI researcher, who co-founded and formerly worked at OpenAI, where he specialized in deep learning and computer vision. He also worked as the director of artificial intelligence and Autopilot Vision at Tesla, and in 2024 he founded Eureka Labs, an AI education platform. In 2026 he joined Anthropic as part of the pretraining team. == Education and early life == Karpathy was born in Bratislava, Czechoslovakia (now Slovakia), and moved with his family to Toronto when he was 15. He completed his Computer Science and Physics bachelor's degrees at University of Toronto in 2009 and his master's degree at University of British Columbia in 2011, where he worked on physically simulated figures (for example, a simulated runner or a simulated person in a crowd) with his adviser Michiel van de Panne. In 2006, Karpathy began posting videos on YouTube on his channel, badmephisto. He garnered fame by posting Rubik's cube tutorials which have been used by famous speedcubers such as Feliks Zemdegs. The channel has over 9 million views as of June 2025. Karpathy received a PhD from Stanford University in 2015 under the supervision of Fei-Fei Li, focusing on the intersection of natural language processing and computer vision, and deep learning models suited for this task. == Career and research == He authored and was the primary instructor of the first deep learning course at Stanford, CS 231n: Convolutional Neural Networks for Visual Recognition. The course became one of the largest classes at Stanford, growing from 150 students in 2015 to 750 in 2017. Karpathy is a founding member of the artificial intelligence research group OpenAI, where he was a research scientist from 2015 to 2017. In June 2017 he became Tesla's director of artificial intelligence and reported to Elon Musk. He was named one of MIT Technology Review's Innovators Under 35 for 2020. After taking a several-months-long sabbatical from Tesla, he announced he was leaving the company in July 2022. As of February 2023, he makes YouTube videos on how to create artificial neural networks. On February 9, 2023, Karpathy announced he was returning to OpenAI. A year later on February 13, 2024, an OpenAI spokesperson confirmed that Karpathy had left OpenAI. In the same year, he was named one of Time Magazine's 100 Most Influential People in AI. On July 16, 2024, Karpathy announced on his X account that he started a new AI education company called Eureka Labs. Their first product was the AI course, LLM101n. He also has a broader educational effort, the "Zero to Hero" series on LLM fundamentals. The company also advocates for AI teaching assistants, a concept which has been criticized due to data privacy concerns and the removal of personal connection between teacher and student. In February 2025, Karpathy coined the term vibe coding to describe how AI tools allow hobbyists to construct apps and websites just by typing prompts. On May 19, 2026, he announced that he joined Anthropic via a statement on X, while the company stated that he will be leading a team for research in pretraining.

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  • Graphical Kernel System

    Graphical Kernel System

    The Graphical Kernel System (GKS) is a 2D computer graphics system using vector graphics, introduced in 1977. It was suitable for making line and bar charts and similar tasks. A key concept was cross-system portability, based on an underlying coordinate system that could be represented on almost any hardware. GKS is best known as the basis for the graphics in the GEM GUI system used on the Atari ST and as part of Ventura Publisher. A draft international standard was circulated for review in September 1983. Final ratification of the standard was achieved in 1985, making it the first ISO graphics standard. A 3D system modelled on GKS was introduced as PHIGS, which saw some use in the 1980s and early 1990s. == Overview == GKS provides a set of drawing features for two-dimensional vector graphics suitable for charting and similar duties. The calls are designed to be portable across different programming languages, graphics devices and hardware, so that applications written to use GKS will be readily portable to many platforms and devices. GKS was fairly common on computer workstations in the 1980s and early 1990s. GKS formed the basis of Digital Research's GSX which evolved into VDI, one of the core components of GEM. GEM was the native GUI on the Atari ST and was occasionally seen on PCs, particularly in conjunction with Ventura Publisher. GKS was little used commercially outside these markets, but remains in use in some scientific visualization packages. It is also the underlying API defining the Computer Graphics Metafile. One popular application based on an implementation of GKS is the GR Framework, a C library for high-performance scientific visualization that has become a common plotting backend among Julia users. A main developer and promoter of the GKS was José Luis Encarnação, formerly director of the Fraunhofer Institute for Computer Graphics (IGD) in Darmstadt, Germany. GKS has been standardized in the following documents: ANSI standard ANSI X3.124 of 1985. ISO 7942:1985 standard, revised as ISO 7942:1985/Amd 1:1991 and ISO/IEC 7942-1:1994, as well as ISO/IEC 7942-2:1997, ISO/IEC 7942-3:1999 and ISO/IEC 7942-4:1998 The language bindings are ISO standard ISO 8651. GKS-3D (Graphical Kernel System for Three Dimensions) functional definition is ISO standard ISO 8805, and the corresponding C bindings are ISO/IEC 8806. The functionality of GKS is wrapped up as a data model standard in the STEP standard, section ISO 10303-46.

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  • KL-ONE

    KL-ONE

    KL-ONE (pronounced "kay ell won") is a knowledge representation system in the tradition of semantic networks and frames; that is, it is a frame language. The system is an attempt to overcome semantic indistinctness in semantic network representations and to explicitly represent conceptual information as a structured inheritance network. == Overview == There is a whole family of KL-ONE-like systems. One of the innovations that KL-ONE initiated was the use of a deductive classifier, an automated reasoning engine that can validate a frame ontology and deduce new information about the ontology based on the initial information provided by a domain expert. Frames in KL-ONE are called concepts. These form hierarchies using subsume-relations; in the KL-ONE terminology a super class is said to subsume its subclasses. Multiple inheritance is allowed. Actually a concept is said to be well-formed only if it inherits from more than one other concept. All concepts, except the top concept (usually THING), must have at least one super class. In KL-ONE descriptions are separated into two basic classes of concepts: primitive and defined. Primitives are domain concepts that are not fully defined. This means that given all the properties of a concept, this is not sufficient to classify it. They may also be viewed as incomplete definitions. Using the same view, defined concepts are complete definitions. Given the properties of a concept, these are necessary and sufficient conditions to classify the concept. The slot-concept is called roles and the values of the roles are role-fillers. There are several different types of roles to be used in different situations. The most common and important role type is the generic RoleSet that captures the fact that the role may be filled with more than one filler.

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  • Predictive Model Markup Language

    Predictive Model Markup Language

    The Predictive Model Markup Language (PMML) is an XML-based predictive model interchange format conceived by Robert Lee Grossman, then the director of the National Center for Data Mining at the University of Illinois at Chicago. PMML provides a way for analytic applications to describe and exchange predictive models produced by data mining and machine learning algorithms. It supports common models such as logistic regression and other feedforward neural networks. Version 0.9 was published in 1998. Subsequent versions have been developed by the Data Mining Group. Since PMML is an XML-based standard, the specification comes in the form of an XML schema. PMML itself is a mature standard with over 30 organizations having announced products supporting PMML. == PMML components == A PMML file can be described by the following components: Header: contains general information about the PMML document, such as copyright information for the model, its description, and information about the application used to generate the model such as name and version. It also contains an attribute for a timestamp which can be used to specify the date of model creation. Data Dictionary: contains definitions for all the possible fields used by the model. It is here that a field is defined as continuous, categorical, or ordinal (attribute optype). Depending on this definition, the appropriate value ranges are then defined as well as the data type (such as, string or double). Data Transformations: transformations allow for the mapping of user data into a more desirable form to be used by the mining model. PMML defines several kinds of simple data transformations. Normalization: map values to numbers, the input can be continuous or discrete. Discretization: map continuous values to discrete values. Value mapping: map discrete values to discrete values. Functions (custom and built-in): derive a value by applying a function to one or more parameters. Aggregation: used to summarize or collect groups of values. Model: contains the definition of the data mining model. E.g., A multi-layered feedforward neural network is represented in PMML by a "NeuralNetwork" element which contains attributes such as: Model Name (attribute modelName) Function Name (attribute functionName) Algorithm Name (attribute algorithmName) Activation Function (attribute activationFunction) Number of Layers (attribute numberOfLayers) This information is then followed by three kinds of neural layers which specify the architecture of the neural network model being represented in the PMML document. These attributes are NeuralInputs, NeuralLayer, and NeuralOutputs. Besides neural networks, PMML allows for the representation of many other types of models including support vector machines, association rules, Naive Bayes classifier, clustering models, text models, decision trees, and different regression models. Mining Schema: a list of all fields used in the model. This can be a subset of the fields as defined in the data dictionary. It contains specific information about each field, such as: Name (attribute name): must refer to a field in the data dictionary Usage type (attribute usageType): defines the way a field is to be used in the model. Typical values are: active, predicted, and supplementary. Predicted fields are those whose values are predicted by the model. Outlier Treatment (attribute outliers): defines the outlier treatment to be use. In PMML, outliers can be treated as missing values, as extreme values (based on the definition of high and low values for a particular field), or as is. Missing Value Replacement Policy (attribute missingValueReplacement): if this attribute is specified then a missing value is automatically replaced by the given values. Missing Value Treatment (attribute missingValueTreatment): indicates how the missing value replacement was derived (e.g. as value, mean or median). Targets: allows for post-processing of the predicted value in the format of scaling if the output of the model is continuous. Targets can also be used for classification tasks. In this case, the attribute priorProbability specifies a default probability for the corresponding target category. It is used if the prediction logic itself did not produce a result. This can happen, e.g., if an input value is missing and there is no other method for treating missing values. Output: this element can be used to name all the desired output fields expected from the model. These are features of the predicted field and so are typically the predicted value itself, the probability, cluster affinity (for clustering models), standard error, etc. The latest release of PMML, PMML 4.1, extended Output to allow for generic post-processing of model outputs. In PMML 4.1, all the built-in and custom functions that were originally available only for pre-processing became available for post-processing too. == PMML 4.0, 4.1, 4.2 and 4.3 == PMML 4.0 was released on June 16, 2009. Examples of new features included: Improved Pre-Processing Capabilities: Additions to built-in functions include a range of Boolean operations and an If-Then-Else function. Time Series Models: New exponential Smoothing models; also place holders for ARIMA, Seasonal Trend Decomposition, and Spectral density estimation, which are to be supported in the near future. Model Explanation: Saving of evaluation and model performance measures to the PMML file itself. Multiple Models: Capabilities for model composition, ensembles, and segmentation (e.g., combining of regression and decision trees). Extensions of Existing Elements: Addition of multi-class classification for Support Vector Machines, improved representation for Association Rules, and the addition of Cox Regression Models. PMML 4.1 was released on December 31, 2011. New features included: New model elements for representing Scorecards, k-Nearest Neighbors (KNN) and Baseline Models. Simplification of multiple models. In PMML 4.1, the same element is used to represent model segmentation, ensemble, and chaining. Overall definition of field scope and field names. A new attribute that identifies for each model element if the model is ready or not for production deployment. Enhanced post-processing capabilities (via the Output element). PMML 4.2 was released on February 28, 2014. New features include: Transformations: New elements for implementing text mining New built-in functions for implementing regular expressions: matches, concat, and replace Simplified outputs for post-processing Enhancements to Scorecard and Naive Bayes model elements PMML 4.3 was released on August 23, 2016. New features include: New Model Types: Gaussian Process Bayesian Network New built-in functions Usage clarifications Documentation improvements Version 4.4 was released in November 2019. == Release history == == Data Mining Group == The Data Mining Group is a consortium managed by the Center for Computational Science Research, Inc., a nonprofit founded in 2008. The Data Mining Group also developed a standard called Portable Format for Analytics, or PFA, which is complementary to PMML.

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  • Emospark

    Emospark

    EmoSpark is an artificial intelligence console created in London, United Kingdom by Patrick Levy-Rosenthal. The device uses facial recognition and language analysis to evaluate human emotion and convey responsive content according to the emotion. The console measures 90 mm x 90 mm x 90 mm and is cube shaped. It operates on an "Emotional Processing Unit", an emotion chip developed by Emoshape Inc. that enables the system to create emotional profile graphs of its surroundings. The emotional processing unit is a patent pending technology that is said to create synthesised emotional responses in machines. EmoSpark was funded through an Indiegogo campaign which aimed to raise $200,000. == Product overview == EmoSpark was created by French inventor Patrick Levy-Rosenthal, as an emotionally intelligent artificial life unit for the home that can interact with people. It is powered by Android and can communicate with users through typed input from a computer, tablet, smartphone or TV as well as through spoken commands. The EmoSpark's features are categorized into two types: functional and emotional. EmoSpark is said to have the ability to perform practical software-based tasks. Through the smartphone interface, it is able to gauge a person’s emotions and is reported to have a conversational library of over 2 million sentences. The face-tracking technology identifies users likes and dislikes to categorize their emotional responses to stimuli such as videos and music. The device has an emotional spectrum that is composed of eight emotions which are surprise, sadness, joy, trust, fear, disgust, anger and anticipation. EmoSpark monitors a person's facial expressions and emotions through images from an external camera, which are then processed through an emotion text analysis and content analysis. The New Scientist reported that EmoSpark had the ability to work on the best way to cheer up its users, emotionally. === Connectivity === EmoSpark is able to connect to Facebook and YouTube to present users with content designed to improve their mood, or to Wikipedia for collaborative knowledge that can be shared when users ask questions of it. Through Android OS, EmoSpark is able to be customized with Google Play store apps. The cube is expected to develop its own personality based on the communications it has had with the people using it. == EmoShape == The Emotion Chip (EPU) used in the cube is created by the US company Emoshape Inc, founded by Levy-Rosenthal. EmoShape Ltd (UK) was the company that developed EmoSpark cube. Patrick Levy-Rosenthal also received the IST Prize in 2005 from the European Council for Applied Science, Technology and Engineering.

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  • Discovery system (artificial intelligence)

    Discovery system (artificial intelligence)

    A discovery system is an artificial intelligence system that attempts to discover new scientific concepts or laws. The aim of discovery systems is to automate scientific data analysis and the scientific discovery process. Ideally, an artificial intelligence system should be able to search systematically through the space of all possible hypotheses and yield the hypothesis - or set of equally likely hypotheses - that best describes the complex patterns in data. During the era known as the second AI summer (approximately 1978–1987), various systems akin to the era's dominant expert systems were developed to tackle the problem of extracting scientific hypotheses from data, with or without interacting with a human scientist. These systems included Autoclass, Automated Mathematician, Eurisko, which aimed at general-purpose hypothesis discovery, and more specific systems such as Dalton, which uncovers molecular properties from data. The dream of building systems that discover scientific hypotheses was pushed to the background with the second AI winter and the subsequent resurgence of subsymbolic methods such as neural networks. Subsymbolic methods emphasize prediction over explanation, and yield models which works well but are difficult or impossible to explain which has earned them the name black box AI. A black-box model cannot be considered a scientific hypothesis, and this development has even led some researchers to suggest that the traditional aim of science - to uncover hypotheses and theories about the structure of reality - is obsolete. Other researchers disagree and argue that subsymbolic methods are useful in many cases, just not for generating scientific theories. == Discovery systems from the 1970s and 1980s == Autoclass was a Bayesian Classification System written in 1986 Automated Mathematician was one of the earliest successful discovery systems. It was written in 1977 and worked by generating a modifying small Lisp programs Eurisko was a Sequel to Automated Mathematician written in 1984 Dalton is a still maintained program capable of calculating various molecular properties initially launched in 1983 and available in open source since 2017 Glauber is a scientific discovery method written in the context of computational philosophy of science launched in 1983 == Modern discovery systems (2009–present) == After a couple of decades with little interest in discovery systems, the interest in using AI to uncover natural laws and scientific explanations was renewed by the work of Michael Schmidt, then a PhD student in Computational Biology at Cornell University. Schmidt and his advisor, Hod Lipson, invented Eureqa, which they described as a symbolic regression approach to "distilling free-form natural laws from experimental data". This work effectively demonstrated that symbolic regression was a promising way forward for AI-driven scientific discovery. Since 2009, symbolic regression has matured further, and today, various commercial and open source systems are actively used in scientific research. Notable examples include Eureqa, now a part of DataRobot AI Cloud Platform, AI Feynman, and QLattice.

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  • Mittens (chess)

    Mittens (chess)

    Mittens is a chess engine developed by Chess.com. It was released on January 1, 2023, alongside four other engines, all of them given cat-related names. The engine became a viral sensation in the chess community due to exposure through content made by chess streamers and a social media marketing campaign, later contributing to record levels of traffic to the Chess.com website and causing issues with database scalability. Mittens was given a rating of one point by Chess.com, although it was evidently stronger than that. Various chess masters played matches against the engine, with players such as Hikaru Nakamura and Levy Rozman drawing and losing their games respectively. A month after its release, Mittens was removed from the website on February 1, as expected through Chess.com's monthly bot cycles. In December 2023, Mittens was brought back in a group of Chess.com's most popular bots of 2023. In January 2024, Mittens was removed again. == Release == Mittens was released on January 1, 2023, as part of a New Year event on Chess.com. It was one of five engines released, all with names related to cats. The other engines released were named Scaredy Cat, rated 800; Angry Cat, rated 1000; Mr. Grumpers, rated 1200 and Catspurrov (a pun on Garry Kasparov), rated 1400. As part of the announcement, a picture of each engine was accompanied by a short description of its character. The description given for Mittens suggested that the engine was hiding something, reading: Mittens likes chess… But how good is she? Of the five engines released, Mittens was by far the most popular. In December 2023, Chess.com re-released Mittens as part of a "best of 2023" group of chess bots made to showcase their most popular bots of the year. == Design == Mittens was conceptualized by Chess.com employee Will Whalen. Appearing as a kitten, Mittens trash talked its opponents with a selection of voice lines: these lines included quotes from J. Robert Oppenheimer, Vincent van Gogh and Friedrich Nietzsche, as well as the 1967 film Le Samouraï. The engine's "personality" was devised by a writing team headed by Sean Becker, and Marija Casic provided the engine's graphics. Chess.com did not disclose any information about the software running the engine. It may be based on Chess.com's Komodo Dragon 3 engine. Mittens' strategy was to slowly grind down an opponent, a tactic likened to the playing style of Anatoly Karpov. Becker stated that the design team believed it would be "way more demoralizing and funny" for the engine to play this way. According to Hikaru Nakamura, Mittens sometimes missed the best move (or winning positions). == Rating == On Chess.com, Mittens had a rating of one point. However, the engine's playing style and tactics showed that it was stronger than that; Mittens was able to beat or draw against many top human players. In an interview with CNN Business, Whalen stated that the idea behind giving Mittens a rating of one was to surprise its opponents, giving it the upper hand psychologically. Estimates of Mittens' true rating range from an Elo of 3200 to 3500, because of its ability to beat other engines of around that level. An upper bound of the engine's rating was found after Levy Rozman made Mittens play against Stockfish 15, a 3700 rated engine. Mittens lost the two games that the engines played. The range of Mittens' possible ratings was summarized by Dot Esports, who stated: It seems like she’s around the 3200–3500 rating range (in Chess.com terms, where the best human players, like Magnus Carlsen and Hikaru Nakamura, sport a 3000–3100 rating in the faster formats), as evidenced by her victories over the site’s otherwise strongest, 3200-rated bots, and her defeat to Stockfish 15, which is currently rated around 3700. == Games == Against human players, Mittens won over 99 percent of the millions of games it played. Chess players such as Hikaru Nakamura, Benjamin Bok, Levy Rozman and Eric Rosen struggled against Mittens; while Rozman and Rosen both lost against the engine, Nakamura and Bok were both able to make a draw. In particular, Nakamura's game against the engine lasted 166 moves; he was playing as White. Bok, Benjamin Finegold and Rozman later went on to win against Mittens, the latter with engine assistance from Stockfish. Magnus Carlsen publicly refused to play the engine, calling it a "transparent marketing trick" and "a soulless computer". Against other chess engines, Mittens participated in the Chess.com Computer Chess Championship as a side act. In the competition, Mittens played 150 games against an engine named after the film M3GAN and won overall with a score of 81.5 to 68.5. This equated to 54 percent of the games played. During the event, an estimate of Mittens' rating was made at 3515 points. == Impact == Mittens went viral in the chess community due to its concept and design: according to an announcement by Chess.com, a combined total of 120 million games were played against the cat engines over the course of January, with around 40 million played against Mittens. The popularity of the engine was helped by the social media exposure created by Chess.com. This included creating an official Twitter account to promote the engine. Chess streamers like Rozman and Nakamura helped cultivate this by creating content around the engine. A video by Nakamura entitled "Mittens the chess bot will make you quit chess" gained over 3.5 million views on YouTube. On January 11, Chess.com reported issues with database scalability due to record levels of traffic: 40 percent more games had been played on Chess.com in January 2023 than any other month since the website's release. According to The Wall Street Journal, the popularity spike was more than the similar surge following the release of Netflix's The Queen's Gambit. The popularity of Mittens was cited by Chess.com as a reason for this instability. The problems continued throughout January; Chess.com stated that they would have to upgrade their servers and invest more in cloud computing to solve the problems caused by the website's popularity surge. On February 1, 2023, Mittens and the other cat engines were removed from the computer section of Chess.com. They were replaced with five new engines themed around artificial intelligence. A tweet was posted on the Mittens's Twitter account after the engine's removal, reading "This is just the beginning. Goodbye for now."

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  • Future of Life Institute

    Future of Life Institute

    The Future of Life Institute (FLI) is a nonprofit organization which aims to steer transformative technology towards benefiting life and away from large-scale risks, with a focus on existential risk from advanced artificial intelligence (AI). FLI's work includes grantmaking, educational outreach, and advocacy within the United Nations, United States government, and European Union institutions. The founders of the Institute include MIT cosmologist Max Tegmark, UCSC cosmologist Anthony Aguirre, and Skype co-founder Jaan Tallinn. == Purpose == FLI's stated mission is to steer transformative technology towards benefiting life and away from large-scale risks. FLI's philosophy focuses on the potential risk to humanity from the development of human-level or superintelligent artificial general intelligence (AGI), but also works to mitigate risk from biotechnology, nuclear weapons and global warming. == History == === Founding === FLI was founded in March 2014 by MIT cosmologist Max Tegmark, Skype co-founder Jaan Tallinn, DeepMind research scientist Viktoriya Krakovna, Tufts University postdoctoral scholar Meia Chita-Tegmark, and UCSC physicist Anthony Aguirre. === Activism === Starting in 2017, FLI has offered an annual "Future of Life Award", with the first awardee being Vasili Arkhipov. The same year, FLI released Slaughterbots, a short arms-control advocacy film. FLI released a sequel in 2021. In 2018, FLI drafted a letter calling for "laws against lethal autonomous weapons". Signatories included Elon Musk, Demis Hassabis, Shane Legg, and Mustafa Suleyman. In January 2023, Swedish magazine Expo reported that the FLI had offered a grant of $100,000 to a foundation set up by Nya Dagbladet, a Swedish far-right online newspaper. In response, Tegmark said that the institute had only become aware of Nya Dagbladet's positions during due diligence processes a few months after the grant was initially offered, and that the grant had been immediately revoked. === Open letter on an AI pause === In March 2023, FLI published a letter titled "Pause Giant AI Experiments: An Open Letter". This called on major AI developers to agree on a verifiable six-month pause of any systems "more powerful than GPT-4" and to use that time to institute a framework for ensuring safety; or, failing that, for governments to step in with a moratorium. The letter said: "recent months have seen AI labs locked in an out-of-control race to develop and deploy ever more powerful digital minds that no-one - not even their creators - can understand, predict, or reliably control". The letter referred to the possibility of "a profound change in the history of life on Earth" as well as potential risks of AI-generated propaganda, loss of jobs, human obsolescence, and society-wide loss of control. Prominent signatories of the letter included Elon Musk, Steve Wozniak, Evan Sharp, Chris Larsen, and Gary Marcus; AI lab CEOs Connor Leahy and Emad Mostaque; politician Andrew Yang; deep-learning researcher Yoshua Bengio; and Yuval Noah Harari. Marcus stated "the letter isn't perfect, but the spirit is right." Mostaque stated, "I don't think a six month pause is the best idea or agree with everything but there are some interesting things in that letter." In contrast, Bengio explicitly endorsed the six-month pause in a press conference. Musk predicted that "Leading AGI developers will not heed this warning, but at least it was said." Some signatories, including Musk, said they were motivated by fears of existential risk from artificial general intelligence. Some of the other signatories, such as Marcus, instead said they signed out of concern about risks such as AI-generated propaganda. The authors of one of the papers cited in FLI's letter, "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" including Emily M. Bender, Timnit Gebru, and Margaret Mitchell, criticised the letter. Mitchell said that “by treating a lot of questionable ideas as a given, the letter asserts a set of priorities and a narrative on AI that benefits the supporters of FLI. Ignoring active harms right now is a privilege that some of us don’t have.” === Open letter on prohibiting superintelligence === In October 2025, another letter, the "Statement on Superintelligence", was published. It called for a prohibition on the development of superintelligence not lifted before there is "broad scientific consensus that it will be done safely and controllably" and "strong public buy-in". FLI director Anthony Aguirre explained that "time is running out", expecting that the technology could arrive in as little as one to two years and counting on "widespread realization among society at all its levels" to stop it. He added that "whether it's soon or it takes a while, after we develop superintelligence, the machines are going to be in charge" and "that is not an experiment that we want to just run toward". The list of signatories included Nobel laureates Geoffrey Hinton, Daron Acemoglu, Beatrice Fihn, Frank Wilczek and John C. Mather as well as Hinton's fellow "godfather" of modern AI Yoshua Bengio, Steve Wozniak, Steve Bannon, Paolo Benanti, Prince Harry, Duke of Sussex and Meghan, Duchess of Sussex. The letter was also signed by the actors Joseph Gordon-Levitt and Stephen Fry, rapper Will.i.am and author Yuval Noah Harari. Former national security advisor Susan Rice, and OpenAI member of technical staff Leo Gao also signed their names to the letter. Polling released alongside the letter showed that 64% of American agreed that superintelligence "shouldn't be developed until it's provably safe and controllable" and only 5% believed it should be developed as quickly as possible. == Operations == === Advocacy === FLI has actively contributed to policymaking on AI. In October 2023, for example, U.S. Senate majority leader Chuck Schumer invited FLI to share its perspective on AI regulation with selected senators. In Europe, FLI successfully advocated for the inclusion of more general AI systems, such as GPT-4, in the EU's Artificial Intelligence Act. In military policy, FLI coordinated the support of the scientific community for the Treaty on the Prohibition of Nuclear Weapons. At the UN and elsewhere, the institute has also advocated for a treaty on autonomous weapons. === Research grants === The FLI research program started in 2015 with an initial donation of $10 million from Elon Musk. In this initial round, a total of $7 million was awarded to 37 research projects. In July 2021, FLI announced that it would launch a new $25 million grant program with funding from the Russian–Canadian programmer Vitalik Buterin. === Conferences === In 2014, the Future of Life Institute held its opening event at MIT: a panel discussion on "The Future of Technology: Benefits and Risks", moderated by Alan Alda. The panelists were synthetic biologist George Church, geneticist Ting Wu, economist Andrew McAfee, physicist and Nobel laureate Frank Wilczek and Skype co-founder Jaan Tallinn. Since 2015, FLI has organised biannual conferences with the stated purpose of bringing together AI researchers from academia and industry. As of April 2023, the following conferences have taken place: "The Future of AI: Opportunities and Challenges" conference in Puerto Rico (2015). The stated goal was to identify promising research directions that could help maximize the future benefits of AI. At the conference, FLI circulated an open letter on AI safety which was subsequently signed by Stephen Hawking, Elon Musk, and many artificial intelligence researchers. The Beneficial AI conference in Asilomar, California (2017), a private gathering of what The New York Times called "heavy hitters of A.I." (including Yann LeCun, Elon Musk, and Nick Bostrom). The institute released a set of principles for responsible AI development that came out of the discussion at the conference, signed by Yoshua Bengio, Yann LeCun, and many other AI researchers. These principles may have influenced the regulation of artificial intelligence and subsequent initiatives, such as the OECD Principles on Artificial Intelligence. The beneficial AGI conference in Puerto Rico (2019). The stated focus of the meeting was answering long-term questions with the goal of ensuring that artificial general intelligence is beneficial to humanity. == In the media == "The Fight to Define When AI is 'High-Risk'" in Wired. "Lethal Autonomous Weapons exist; They Must Be Banned" in IEEE Spectrum. "United States and Allies Protest U.N. Talks to Ban Nuclear Weapons" in The New York Times. "Is Artificial Intelligence a Threat?" in The Chronicle of Higher Education, including interviews with FLI founders Max Tegmark, Jaan Tallinn and Viktoriya Krakovna. "But What Would the End of Humanity Mean for Me?", an interview with Max Tegmark on the ideas behind FLI in The Atlantic.

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  • International Journal on Artificial Intelligence Tools

    International Journal on Artificial Intelligence Tools

    The International Journal on Artificial Intelligence Tools was founded in 1992 and is published by World Scientific. It covers research on artificial intelligence (AI) tools, including new architectures, languages and algorithms. Topics include AI in Bioinformatics, Cognitive Informatics, Knowledge-Based/Expert Systems and Object-Oriented Programming for AI. == Abstracting and indexing == The journal is abstracted and indexed in: Inspec Science Citation Index Expanded ISI Alerting Services CompuMath Citation Index Current Contents/Engineering, Computing, and Technology

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  • AI Mode

    AI Mode

    AI Mode is a search feature used within Google Search. In March 2025, Google introduced an experimental "AI Mode" within its search platform, enabling users to input complex, multi-part queries and receive comprehensive, AI-generated responses. This feature uses Google's Gemini model, which enhances the system's reasoning capabilities and supports multimodal inputs, including text, images, and voice. Users need to be signed in to be able to use the image generation features. Initially, AI Mode was available to Google One AI Premium subscribers in the United States, who could access it through the Search Labs platform. This phased rollout allowed Google to gather user feedback and refine the feature before a broader release.

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  • Andrew Ng

    Andrew Ng

    Andrew Yan-Tak Ng (Chinese: 吳恩達; born April 18, 1976) is a British-American computer scientist and technology entrepreneur focusing on machine learning and artificial intelligence (AI). Ng was a cofounder and head of Google Brain and was the former Chief Scientist at Baidu. Ng is an adjunct professor at Stanford University (formerly associate professor and Director of its Stanford AI Lab or SAIL). Ng has also worked in online education, cofounding Coursera and DeepLearning.AI. He has spearheaded many efforts to "democratize deep learning" teaching over 8 million students through his online courses. Ng is renowned globally in computer science, recognized in Time magazine's 100 Most Influential People in 2012 and Fast Company's Most Creative People in 2014. His influence extends to being named in the Time100 AI Most Influential People in 2023. In 2018, he launched and currently heads the AI Fund, initially a $175-million investment fund for backing artificial intelligence startups. He has founded Landing AI, which provides AI-powered SaaS products. On April 11, 2024, Amazon announced Ng's appointment to its board of directors. == Early life and education == Andrew Yan-Tak Ng was born in London, in 1976 to Ronald Paul Ng, a hematologist and lecturer at UCL Medical School, and Tisa Ho, an arts administrator working at the London Film Festival. His parents were both immigrants from Hong Kong. His family moved back to Hong Kong and he spent his early childhood there. In 1984 he and his family moved to Singapore. Ng attended and graduated from Raffles Institution. In 1997, he earned his undergraduate degree with a triple major in computer science, statistics, and economics from Carnegie Mellon University in Pittsburgh, Pennsylvania. Between 1996 and 1998 he also conducted research on reinforcement learning, model selection, and feature selection at the AT&T Bell Labs. In 1998, Ng earned his master's degree in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology (MIT) in Cambridge, Massachusetts. At MIT, he built the first publicly available, automatically indexed web-search engine for research papers on the web. It was a precursor to CiteSeerX/ResearchIndex, but specialized in machine learning. In 2002, he received his Doctor of Philosophy (Ph.D.) in Computer Science from the University of California, Berkeley, under the supervision of Michael I. Jordan. His thesis is titled "Shaping and policy search in reinforcement learning" and is well-cited to this day. == Career == === Academia and teaching === Ng started working as an assistant professor at Stanford University in 2002 and as an associate professor in 2009. Ng is a professor at Stanford University departments of Computer Science and electrical engineering. He served as the director of the Stanford Artificial Intelligence Laboratory (SAIL), where he taught students and undertook research related to data mining, big data, and machine learning. His machine learning course CS229 at Stanford is the most popular course offered on campus with over 1,000 students enrolling some years. As of 2020, three of the most popular courses on Coursera are Ng's: Machine Learning (#1), AI for Everyone (#5), Neural Networks and Deep Learning (#6). In 2008, his group at Stanford was one of the first in the US to start advocating the use of GPUs in deep learning. The rationale was that an efficient computation infrastructure could speed up statistical model training by orders of magnitude, ameliorating some of the scaling issues associated with big data. At the time it was a controversial and risky decision, but since then and following Ng's lead, GPUs have become a cornerstone in the field. Since 2017, Ng has been advocating the shift to high-performance computing (HPC) for scaling up deep learning and accelerating progress in the field. In 2012, along with Stanford computer scientist Daphne Koller he cofounded and was CEO of Coursera, a website that offers free online courses to everyone. It took off with over 100,000 students registered for Ng's popular CS229A course. Today, several million people have enrolled in Coursera courses, making the site one of the leading massive open online courses (MOOCs) in the world. === Industry === From 2011 to 2012, he worked at Google, where he founded and directed the Google Brain Deep Learning Project with Jeff Dean, Greg Corrado, and Rajat Monga. In 2014, he joined Baidu as chief scientist, and carried out research related to big data and AI. There he set up several research teams for things like facial recognition and Melody, an AI chatbot for healthcare. He also developed for the company the AI platform called DuerOS and other technologies that positioned Baidu ahead of Google in the discourse and development of AI. In March 2017, he announced his resignation from Baidu. He soon afterward launched DeepLearning.AI, an online series of deep learning courses (including the AI for Good Specialization). Then Ng launched LandingAI, which provides AI-powered SaaS products. In January 2018, Ng unveiled the AI Fund, raising $175 million to invest in new startups. In November 2021, LandingAI secured a $57 million round of series A funding led by McRock Capital, to help enterprises adopt AI. In October 2024, Ng's AI Fund made its first investment in India, backing AI healthcare startup Jivi, which uses AI for diagnoses, treatment recommendations, and administrative tasks. The investment highlights the growth of India's AI sector, expected to reach $22 billion by 2027. === Research === Ng researches primarily in machine learning, deep learning, machine perception, computer vision, and natural language processing; and is one of the world's most famous and influential computer scientists. He's frequently won best paper awards at academic conferences and has had a huge impact on the field of AI, computer vision, and robotics. During graduate school, together with David M. Blei and Michael I. Jordan, Ng co-authored the influential paper that introduced latent Dirichlet allocation (LDA) for his thesis on reinforcement learning for drones. His early work includes the Stanford Autonomous Helicopter project, which developed one of the most capable autonomous helicopters in the world. He was the leading scientist and principal investigator on the STAIR (Stanford Artificial Intelligence Robot) project, which resulted in Robot Operating System (ROS), a widely used open source software robotics platform. His vision to build an AI robot and put a robot in every home inspired Scott Hassan to back him and create Willow Garage. He is also one of the founding team members for the Stanford WordNet project, which uses machine learning to expand the Princeton WordNet database created by Christiane Fellbaum. In 2011, Ng founded the Google Brain project at Google, which developed large-scale artificial neural networks using Google's distributed computing infrastructure. Among its notable results was a neural network trained using deep learning algorithms on 16,000 CPU cores, which learned to recognize cats after watching only YouTube videos, and without ever having been told what a "cat" is. The project's technology is also currently used in the Android operating system's speech recognition system. === Views on AI === Ng thinks that the real threat is contemplating the future of work: "Rather than being distracted by evil killer robots, the challenge to labor caused by these machines is a conversation that academia and industry and government should have." He has emphasized the importance of expanding access to AI education, stating that empowering people around the world to use AI tools is essential to building AI applications. In a December 2023 Financial Times interview, Ng highlighted concerns regarding the impact of potential regulations on open-source AI, emphasizing how reporting, licensing, and liability risks could unfairly burden smaller firms and stifle innovation. He argued that regulating basic technologies like open-source models could hinder progress without markedly enhancing safety. Ng advocated for carefully designed regulations to prevent obstacles to the development and distribution of beneficial AI technologies. In a June 2024 interview with the Financial Times, Ng expressed concerns about proposed AI legislation in California that would have required developers to implement safety mechanisms such as a "kill switch" for advanced models. He described the bill as creating "massive liabilities for science-fiction risks" and said it "stokes fear in anyone daring to innovate." Other critics argued the bill would impose burdens on open-source developers and smaller AI companies. The bill was ultimately vetoed by Governor Gavin Newsom in September 2024. == Online education: massive open online course == In 2011, Stanford launched a total of three massive open online course (MOOCs) on machine learning (CS229a), databases, and AI, taught by Ng

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  • Frame (artificial intelligence)

    Frame (artificial intelligence)

    Frames are an artificial intelligence data structure used to divide knowledge into substructures by representing "stereotyped situations". They were proposed by Marvin Minsky in his 1974 article "A Framework for Representing Knowledge". Frames are the primary data structure used in artificial intelligence frame languages; they are stored as ontologies of sets. Frames are also an extensive part of knowledge representation and reasoning schemes. They were originally derived from semantic networks and are therefore part of structure-based knowledge representations. According to Russell and Norvig's Artificial Intelligence: A Modern Approach, structural representations assemble "facts about particular object and event types and [arrange] the types into a large taxonomic hierarchy analogous to a biological taxonomy". == Frame structure == The frame contains information on how to use the frame, what to expect next, and what to do when these expectations are not met. Some information in the frame is generally unchanged while other information, stored in "terminals", usually change. Terminals can be considered as variables. Top-level frames carry information, that is always true about the problem in hand, however, terminals do not have to be true. Their value might change with the new information encountered. Different frames may share the same terminals. Each piece of information about a particular frame is held in a slot. The information can contain: Facts or Data Values (called facets) Procedures (also called procedural attachments) IF-NEEDED: deferred evaluation IF-ADDED: updates linked information Default Values For Data For Procedures Other Frames or Subframes == Features and advantages == A frame's terminals are already filled with default values, which is based on how the human mind works. For example, when a person is told "a boy kicks a ball", most people will visualize a particular ball (such as a familiar soccer ball) rather than imagining some abstract ball with no attributes. One particular strength of frame-based knowledge representations is that, unlike semantic networks, they allow for exceptions in particular instances. This gives frames a degree of flexibility that allows representations to reflect real-world phenomena more accurately. Like semantic networks, frames can be queried using spreading activation. Following the rules of inheritance, any value given to a slot that is inherited by subframes will be updated (IF-ADDED) to the corresponding slots in the subframes and any new instances of a particular frame will feature that new value as the default. Because frames are based on structures, it is possible to generate a semantic network given a set of frames even though it lacks explicit arcs. References to Noam Chomsky and his generative grammar of 1950 are generally missing from Minsky's work. The simplified structures of frames allow for easy analogical reasoning, a much prized feature in any intelligent agent. The procedural attachments provided by frames also allow a degree of flexibility that makes for a more realistic representation and gives a natural affordance for programming applications. == Example == Worth noticing here is the easy analogical reasoning (comparison) that can be done between a boy and a monkey just by having similarly named slots. Also notice that Alex, an instance of a boy, inherits default values like "Sex" from the more general parent object Boy, but the boy may also have different instance values in the form of exceptions such as the number of legs. == Frame language == A frame language is a technology used for knowledge representation in artificial intelligence. They are similar to class hierarchies in object-oriented languages although their fundamental design goals are different. Frames are focused on explicit and intuitive representation of knowledge whereas objects focus on encapsulation and information hiding. Frames originated in AI research and objects primarily in software engineering. However, in practice, the techniques and capabilities of frame and object-oriented languages overlap significantly. === Example === A simple example of concepts modeled in a frame language is the Friend of A Friend (FOAF) ontology defined as part of the Semantic Web as a foundation for social networking and calendar systems. The primary frame in this simple example is a Person. Example slots are the person's email, home page, phone, etc. The interests of each person can be represented by additional frames describing the space of business and entertainment domains. The slot knows links each person with other persons. Default values for a person's interests can be inferred by the web of people they are friends of. === Implementations === The earliest frame-based languages were custom developed for specific research projects and were not packaged as tools to be re-used by other researchers. Just as with expert system inference engines, researchers soon realized the benefits of extracting part of the core infrastructure and developing general-purpose frame languages that were not coupled to specific applications. One of the first general-purpose frame languages was KRL. One of the most influential early frame languages was KL-ONE. KL-ONE spawned several subsequent Frame languages. One of the most widely used successors to KL-ONE was the Loom language developed by Robert MacGregor at the Information Sciences Institute. In the 1980s, Artificial Intelligence generated a great deal of interest in the business world fueled by expert systems. This led to the development of many commercial products for the development of knowledge-based systems. These early products were usually developed in Lisp and integrated constructs such as IF-THEN rules for logical reasoning with Frame hierarchies for representing data. One of the most well known of these early Lisp knowledge-base tools was the Knowledge Engineering Environment (KEE) from Intellicorp. KEE provided a full Frame language with multiple inheritance, slots, triggers, default values, and a rule engine that supported backward and forward chaining. As with most early commercial versions of AI software KEE was originally deployed in Lisp on Lisp machine platforms but was eventually ported to PCs and Unix workstations. The research agenda of the Semantic Web spawned a renewed interest in automatic classification and frame languages. An example is the Web Ontology Language (OWL) standard for describing information on the Internet. OWL is a standard to provide a semantic layer on top of the Internet. The goal is that rather than searching the web using keywords as most search engines (e.g. Google) do today, the web can be organized by concepts organized in an ontology, like a directory structure. The name of the OWL language itself provides a good example of the value of a Semantic Web. If one were to search for "OWL" using the Internet today most of the pages retrieved would be on the bird Owl rather than the standard OWL. With a Semantic Web it would be possible to specify the concept "Web Ontology Language" and the user would not need to worry about the various possible acronyms or synonyms as part of the search. Likewise, the user would not need to worry about homonyms crowding the search results with irrelevant data such as information about birds of prey as in this simple example. In addition to OWL, various standards and technologies that are relevant to the Semantic Web and were influenced by Frame languages include OIL and DAML. The Protege Open Source software tool from Stanford University provides an ontology editing capability that is built on OWL and has the full capabilities of a classifier. However it ceased to explicitly support frames as of version 3.5 (which is maintained for those preferring frame orientation), with the current version being 5.6.8 as of 2025. The justification for moving from explicit frames being that OWL DL is more expressive and "industry standard". === Comparison of frames and objects === Frame languages have a significant overlap with object-oriented languages. The terminologies and goals of the two communities were different but as they moved from the academic world and labs to the commercial world developers tended to not care about philosophical issues and focused primarily on specific capabilities, taking the best from either camp regardless of where the idea began. What both paradigms have in common is a desire to reduce the distance between concepts in the real world and their implementation in software. As such both paradigms arrived at the idea of representing the primary software objects in taxonomies starting with very general types and progressing to more specific types. The following table illustrates the correlation between standard terminology from the object-oriented and frame language communities: The primary difference between the two paradigms was in the degree that encapsulation was considered a majo

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