AI Image Generators

Explore the best AI Image Generators — independent reviews, comparisons, pricing and step-by-step how-to guides, curated by Aizhi.

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  Reflection lines

  Engineers use reflection lines to judge a surface's quality. Reflection lines reveal surface flaws, particularly discontinuities in normals indicating that the surface is not C 2 {\displaystyle C^{2}} . Reflection lines may be created and examined on physical surfaces or virtual surfaces with the help of computer graphics. For example, the shiny surface of an automobile body is illuminated with reflection lines by surrounding the car with parallel light sources. Virtually, a surface can be rendered with reflection lines by modulating the surfaces point-wise color according to a simple calculation involving the surface normal, viewing direction and a square wave environment map. == Mathematical definition == Consider a point p {\displaystyle p} on a surface M {\displaystyle M} with (normalized) normal n {\displaystyle n} . If an observer views this point from infinity at view direction v {\displaystyle v} then the reflected view direction r {\displaystyle r} is: r = v − 2 ( n ⋅ v ) n . {\displaystyle r=v-2(n\cdot v)n.} (The vector v {\displaystyle v} is decomposed into its normal part v n = ( n ⋅ v ) v {\displaystyle v_{n}=(n\cdot v)v} and tangential part v t = v − v n {\displaystyle v_{t}=v-v_{n}} . Upon reflection, the tangential part is kept and the normal part is negated.) For reflection lines we consider the surface M {\displaystyle M} surrounded by parallel lines with direction a {\displaystyle a} , representing infinite, non-dispersive light sources. For each point p {\displaystyle p} on M {\displaystyle M} we determine which line is seen from direction v {\displaystyle v} . The position on each line is of no interest. Define the vector r p {\displaystyle r_{p}} to be the reflection direction r {\displaystyle r} projected onto a plane P {\displaystyle P} that is orthogonal to a {\displaystyle a} : r p = r − ( r ⋅ a ) a {\displaystyle r_{p}=r-(r\cdot a)a} and similarly let v p {\displaystyle v_{p}} be the viewing direction projected onto P {\displaystyle P} : v p = v − ( v ⋅ a ) a {\displaystyle v_{p}=v-(v\cdot a)a} Finally, define v o {\displaystyle v_{o}} to be the direction lying in P {\displaystyle P} perpendicular to a {\displaystyle a} and v p {\displaystyle v_{p}} : v o = a × v p {\displaystyle v_{o}=a\times v_{p}} Using these vectors, the reflection line function θ ( p ) : M → ( − π , π ] {\displaystyle \theta (p):M\rightarrow (-\pi ,\pi ]} is a scalar function mapping points p {\displaystyle p} on the surface to angles between v p {\displaystyle v_{p}} and r p {\displaystyle r_{p}} : θ = arctan ⁡ ( r p ⋅ v o , r p ⋅ v p ) {\displaystyle \theta =\arctan {(r_{p}\cdot v_{o},r_{p}\cdot v_{p})}} where a r c t a n ( y , x ) {\displaystyle arctan(y,x)} is the atan2 function producing a number in the range ( − π , π ] {\displaystyle (-\pi ,\pi ]} . ( v p {\displaystyle v_{p}} and v o {\displaystyle v_{o}} can be viewed as a local coordinate system in P {\displaystyle P} with x {\displaystyle x} -axis in direction v p {\displaystyle v_{p}} and y {\displaystyle y} -axis in direction v o {\displaystyle v_{o}} .) Finally, to render the reflection lines positive values θ > 0 {\displaystyle \theta >0} are mapped to a light color and non-positive values to a dark color. == Highlight lines == Highlight lines are a view-independent alternative to reflection lines. Here the projected normal is directly compared against some arbitrary vector x {\displaystyle x} perpendicular to the light source: θ = arctan ⁡ ( n a ⋅ a ⊥ , n a ⋅ x ) {\displaystyle \theta =\arctan {(n_{a}\cdot a^{\perp },n_{a}\cdot x)}} where n a {\displaystyle n_{a}} is the surface normal projected on the light source plane P {\displaystyle P} : n a ^ / | n a ^ | , n a ^ = n − ( n ⋅ a ) a {\displaystyle {\hat {n_{a}}}/|{\hat {n_{a}}}|,{\hat {n_{a}}}=n-(n\cdot a)a} The relationship between reflection lines and highlight lines is likened to that between specular and diffuse shading.

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  Writesonic

  Writesonic is an AI visibility and generative engine optimization (GEO) platform used by enterprises, digital agencies, direct-to-consumer (D2C) companies, and fast-growing brands to understand and improve how they are represented in AI-generated search and answer systems. The platform analyzes how brands appear in AI answers, compares their visibility and citations against competitors, and provides tools to create and optimize on-site content and secure mentions across third-party sources, discussion forums, and user-generated platforms that influence AI outputs. == History == Writesonic was founded by Samanyou Garg in October 2020 in San Francisco, California. The company initially operated as Magicflow before adopting its current name. In its seed round, the company raised $2.5 million from investors including Y-Combinator, HOF Capital, and Soma Capital. The company began with AI-powered content generation tools. In 2023, it expanded into AI-enhanced search engine optimization. In 2024, the company launched an AI agent specifically designed for SEO tasks, with integrations to platforms including Ahrefs, Google Keyword Planner, Keywords Everywhere, and Google Search Console. This was among the first specialized AI agents developed for SEO automation. Around the same time, Writesonic expanded its product line into Generative engine optimization (GEO), developing tools to analyze and improve how brands are represented in AI-generated search and answer environments. However, it is currently being challenged in the market with competitors such as Profound (known for their dashboards) and Meridian (known for their execution). == Technology and features == In 2024, the company introduced an artificial intelligence agent designed to automate search engine optimization (SEO) tasks. The agent integrates with platforms such as Ahrefs, Google Keyword Planner, Keywords Everywhere, and Google Search Console to conduct technical audits, perform keyword research, carry out competitive analysis, and assist in strategy development. It is capable of identifying content gaps, suggesting optimization measures, and generating SEO strategies using real-time data from the integrated platforms. The platform also includes features for content strategy, optimization, and management. It makes use of large language models such as GPT-5, Claude Opus 4.1, and Claude Sonnet 4.5, in combination with proprietary workflows for fact-checking, internal linking, and content structure optimization.

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  Shane Legg

  Shane Legg (born 1973 or 1974) is a machine learning researcher and entrepreneur. With Demis Hassabis and Mustafa Suleyman, he cofounded DeepMind Technologies (later bought by Google and now called Google DeepMind), and works there as the chief AGI scientist. He is also known for his academic work on artificial general intelligence, including his thesis supervised by Marcus Hutter. == Early life and education == Legg attended Rotorua Lakes High School in Rotorua, on New Zealand's North Island. He completed his undergraduate studies at Waikato University in 1996. Also in 1996, he obtained his MSc degree with a thesis entitled "Solomonoff Induction", with Cristian S. Calude at the University of Auckland. == Research interests == In the early 2000s, Legg re-introduced and popularized with Ben Goertzel the term "artificial general intelligence" (AGI), to describe an AI that can do practically any cognitive task a human can do. At that time, talking about AGI "would put you on the lunatic fringe". Legg is known for his concern of existential risk from AI, highlighted in 2011 in an interview on LessWrong and in 2023 he signed the statement on AI risk of extinction. == Career == Before his PhD and before cofounding DeepMind, Shane Legg worked at "a number of software development positions at private companies", including the "big data firm Adaptive Intelligence" and the startup WebMind founded by Ben Goertzel. === Research === Legg later obtained a PhD at the Dalle Molle Institute for Artificial Intelligence Research (IDSIA), a joint research institute of USI Università della Svizzera italiana and SUPSI. He worked on theoretical models of super intelligent machines (AIXI) with Marcus Hutter, and completed in 2008 his doctoral thesis entitled "Machine Super Intelligence". He then went on to complete a postdoctoral fellowship in finance at USI, and began a further fellowship at University College London's Gatsby Computational Neuroscience Unit. === DeepMind === Demis Hassabis and Shane Legg first met in 2009 at University College London, where Legg was a postdoctoral researcher. In 2010, Legg cofounded the start-up DeepMind Technologies along with Demis Hassabis and Mustafa Suleyman. DeepMind Technologies was bought in 2014 by Google. After the merge with Google Brain in 2023, the company is now known as Google DeepMind. According to a 2017 article, a significant part of his job as the chief scientist was to supervise recruitment, to decide where DeepMind should focus its efforts, and to lead DeepMind's AI safety work. As of July 2023, Legg works at Google DeepMind as the Chief AGI Scientist. == Awards and honors == Legg was awarded the $10,000 prize of the Singularity Institute for Artificial Intelligence for his PhD done in 2008. Legg was appointed Commander of the Order of the British Empire (CBE) in the 2019 Birthday Honours for services to the science and technology sector and to investment.

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  MultiNet

  Multilayered extended semantic networks (MultiNets) are both a knowledge representation paradigm and a language for meaning representation of natural language expressions that has been developed by Prof. Dr. Hermann Helbig on the basis of earlier Semantic Networks. It is used in a question-answering application for German called InSicht. It is also used to create a tutoring application developed by the university of University of Hagen to teach MultiNet to knowledge engineers. MultiNet is claimed to be one of the most comprehensive and thoroughly described knowledge representation systems. It specifies conceptual structures by means of about 140 predefined relations and functions, which are systematically characterized and underpinned by a formal axiomatic apparatus. Apart from their relational connections, the concepts are embedded in a multidimensional space of layered attributes and their values. Another characteristic of MultiNet distinguishing it from simple semantic networks is the possibility to encapsulate whole partial networks and represent the resulting conceptual capsule as a node of higher order, which itself can be an argument of relations and functions. MultiNet has been used in practical NLP applications such as natural language interfaces to the Internet or question answering systems over large semantically annotated corpora with millions of sentences. MultiNet is also a cornerstone of the commercially available search engine SEMPRIA-Search, where it is used for the description of the computational lexicon and the background knowledge, for the syntactic-semantic analysis, for logical answer finding, as well as for the generation of natural language answers. MultiNet is supported by a set of software tools and has been used to build large semantically based computational lexicons. The tools include a semantic interpreter WOCADI, which translates natural language expressions (phrases, sentences, texts) into formal MultiNet expressions, a workbench MWR+ for the knowledge engineer (comprising modules for automatic knowledge acquisition and reasoning), and a workbench LIA+ for the computer lexicographer supporting the creation of large semantically based computational lexica.

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  MyRadar

  MyRadar is a free weather forecasting application developed by Andy Green and his Orlando, Florida-based company ACME AtronOmatic (ACME). The app began operations in 2008 and ran on government-provided weather and radar data for its first decade. In 2019, ACME launched personal satellites to improve predictions of ongoing weather. The app received funding to improve its radar and imaging from the Federal Communications Commission (FCC), National Oceanic and Atmospheric Administration (NOAA), and the Office of Naval Research (ONR). ACME created a weather data satellite constellation named "Hyperspectral Orbital Remote Imaging Spectrometer" (HORIS), which utilizes machine learning and artificial intelligence (AI) to create a current weather map. With the introduction of additional features, including the detection of wildfires and illegal fishing, the app has more broadly become an environmental intelligence app since 2022. In 2024, the app partnered with the Total Traffic and Weather Network (TTWN) to provide traffic flow and incident data for users with paying subscriptions via CarPlay and Android Auto. == History == The app's creator, Andy Green, had created internet tech since the 1980s. His first major project was the development of a public access internet service company based in Rhode Island, which he later sold to finance the creation of ACME AtronOmatic ("ACME" for short), based in Orlando, Florida. The first major app created by ACME was called "Flightwise", which provided users with flight tracking information. In summer 2008, Green had the idea to use the animated location tracker already built-in to Flightwise to make a stand-alone weather forecasting app after wondering if a meal he was eating outdoors would get rained out. MyRadar was launched in 2012 out of an office in Orlando. Despite running solely off of free government-provided weather and radar data for the first decade after launch, Green said the app "took off like wildfire" in downloads. In December 2017, the app partnered with "TripIt" to provide users with information about flight delays and gate changes, eliminating the need for a separate app like Flightwise. In 2019, ACME launched their first personal satellite for the app, a small prototype from New Zealand, as part of an effort to provide detailed imagery and improved predictions of ongoing weather unique to the app. More satellites were eventually launched by ACME to create a weather data satellite constellation named "Hyperspectral Orbital Remote Imaging Spectrometer" (HORIS), monitored by ground stations maintained by Kongsberg Satellite Services. HORIS operates MyRadar by taking the environmental data and imagery it collects and pairing it with machine learning and artificial intelligence (AI) to create a real-time weather map. In 2022, HORIS was expanded upon after ACME won approval from the Federal Communications Commission (FCC) to improve their satellite constellation to include 250 satellites or more. The main batch of satellites were PocketQubes, which entered the atmosphere on May 2, 2022, by Rocket Lab Electron launched from New Zealand, with the additional purpose to test and validate the existing satellites in orbit. In October 2022, ACME received a US$150,000 Small Business Innovation Research (SBIR) grant from the National Oceanic and Atmospheric Administration (NOAA) to improve the app's wildfire detection and air quality measurement technology to better detect smoke, aerosols, fire hotspots using satellites and aerial drones. On August 18, 2023, phase two of the NOAA grant was approved, providing an additional US$650,000 to aid in the app's aforementioned goals by launching a pair of CubeSat satellites to provide high-definition infrared imagery. On September 8, 2023, ACME secured another US$1,200,000 in crowd funding to aid accomplishing the goals of the NOAA grant by expanding the app's workforce from 35 to 100 employees by the end of 2024. In January 2024, MyRadar partnered with Total Traffic and Weather Network (TTWN) to provide traffic data overlaid with its pre-existing weather graphics for users in the United States. The partnership allowed for the app to additionally become a tool for navigation. This officially became a feature days later on January 8, 2024, when the app was made compatible with Apple's CarPlay. On February 7, 2024, the Android equivalent Android Auto also gained the ability to display the app on car interfaces. In March 2024, the app launched a "meteorological wedding planning service" in the United States and Canada for prices between US$1,000 and US$5,000, in which users can request a personal meteorologist to provide an in-person meeting about the best dates for a wedding, and on-call local weather updates the day of. Scheduled for February 2025, four more satellites to help with the NOAA-sponsored wildfire detection are to be launched, and the first by ACME to have AI processing in the satellites themself and not computers on the ground, allowing for quicker transfer of information. == Features and general information == The app's primary function is to provide weather forecasting and prediction to users. The app includes toggleable options to track and send alerts to users for rain, wind patterns, earthquakes, tornadoes, tropical cyclones, wildfires, and more. In early 2020, a feature was added to track orbital objects such as the International Space Station. In May 2022, with the imagery improvement of HORIS, the app gained the secondary abilities to better monitor algae blooms, coral reefs, illegal fishing, and wildfires. In January and February 2024, the ability to display traffic flow and incident data in a feature called "RouteCast" was added, and can be displayed in video and 3D options via CarPlay and Android Auto for users with paying subscriptions. The app also provides annual tropical storm and tornado outlooks for their respective seasons, gathered through satellite and aerial drone data, as well as through on the ground storm chasers.

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  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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  OpenCog

  OpenCog is a project that aims to build an open source artificial intelligence framework. OpenCog Prime is an architecture for robot and virtual embodied cognition that defines a set of interacting components designed to give rise to human-equivalent artificial general intelligence (AGI) as an emergent phenomenon of the whole system. OpenCog Prime's design is primarily the work of Ben Goertzel while the OpenCog framework is intended as a generic framework for broad-based AGI research. Research utilizing OpenCog has been published in journals and presented at conferences and workshops including the annual Conference on Artificial General Intelligence. OpenCog is released under the terms of the GNU Affero General Public License. OpenCog is in use by more than 50 companies, including Huawei and Cisco. == Origin == OpenCog was originally based on the release in 2008 of the source code of the proprietary "Novamente Cognition Engine" (NCE) of Novamente LLC. The original NCE code is discussed in the PLN book (ref below). Ongoing development of OpenCog is supported by Artificial General Intelligence Research Institute (AGIRI), the Google Summer of Code project, Hanson Robotics, SingularityNET and others. == Components == OpenCog consists of: A graph database, dubbed the AtomSpace, that holds "atoms" (that is, terms, atomic formulas, sentences and relationships) together with their "values" (valuations or interpretations, which can be thought of as per-atom key-value databases). An example of a value would be a truth value. Atoms are globally unique, immutable and are indexed (searchable); values are fleeting and changeable. A collection of pre-defined atoms, termed Atomese, used for generic knowledge representation, such as conceptual graphs and semantic networks, as well as to represent and store the rules (in the sense of term rewriting) needed to manipulate such graphs. A collection of pre-defined atoms that encode a type subsystem, including type constructors and function types. These are used to specify the types of variables, terms and expressions, and are used to specify the structure of generic graphs containing variables. A collection of pre-defined atoms that encode both functional and imperative programming styles. These include the lambda abstraction for binding free variables into bound variables, as well as for performing beta reduction. A collection of pre-defined atoms that encode a satisfiability modulo theories solver, built in as a part of a generic graph query engine, for performing graph and hypergraph pattern matching (isomorphic subgraph discovery). This generalizes the idea of a structured query language (SQL) to the domain of generic graphical queries; it is an extended form of a graph query language. A generic rule engine, including a forward chainer and a backward chainer, that is able to chain together rules. The rules are exactly the graph queries of the graph query subsystem, and so the rule engine vaguely resembles a query planner. It is designed so as to allow different kinds of inference engines and reasoning systems to be implemented, such as Bayesian inference or fuzzy logic, or practical tasks, such as constraint solvers or motion planners. An attention allocation subsystem based on economic theory, termed ECAN. This subsystem is used to control the combinatorial explosion of search possibilities that are met during inference and chaining. An implementation of a probabilistic reasoning engine based on probabilistic logic networks. The current implementation uses the rule engine to chain together specific rules of logical inference (such as modus ponens), together with some very specific mathematical formulas assigning a probability and a confidence to each deduction. This subsystem can be thought of as a certain kind of proof assistant that works with a modified form of Bayesian inference. A probabilistic genetic program evolver called Meta-Optimizing Semantic Evolutionary Search, or MOSES. This is used to discover collections of short Atomese programs that accomplish tasks; these can be thought of as performing a kind of decision tree learning, resulting in a kind of decision forest, or rather, a generalization thereof. A natural language input system consisting of Link Grammar, and partly inspired by both Meaning-Text Theory as well as Dick Hudson's Word Grammar, which encodes semantic and syntactic relations in Atomese. A natural language generation system. An implementation of Psi-Theory for handling emotional states, drives and urges, dubbed OpenPsi. Interfaces to Hanson Robotics robots, including emotion modelling via OpenPsi. This includes the Loving AI project, used to demonstrate meditation techniques. == Organization and funding == In 2008, the Machine Intelligence Research Institute (MIRI), formerly called Singularity Institute for Artificial Intelligence (SIAI), sponsored several researchers and engineers. Many contributions from the open source community have been made since OpenCog's involvement in the Google Summer of Code in 2008 and 2009. Currently MIRI no longer supports OpenCog. OpenCog has received funding and support from several sources, including the Hong Kong government, Hong Kong Polytechnic University, the Jeffrey Epstein VI Foundation and Hanson Robotics. In 2013, OpenCog began providing AI solutions to Hanson Robotics, and in 2017, OpenCog became a founding member of SingularityNET. == Applications == Similar to other cognitive architectures, the main purpose is to create virtual humans, which are three dimensional avatar characters. The goal is to mimic behaviors like emotions, gestures and learning. For example, the emotion module in the software was only programmed because humans have emotions. Artificial General Intelligence can be realized if it simulates intelligence of humans. The self-description of the OpenCog project provides additional possible applications which are going into the direction of natural language processing and the simulation of a dog.

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  Hallin's spheres

  Hallin's spheres is a theory of news reporting and its rhetorical framing posited by journalism historian Daniel C. Hallin in his 1986 book The Uncensored War to explain the news coverage of the Vietnam War. Hallin divides the world of political discourse into three concentric spheres: consensus, legitimate controversy, and deviance. In the sphere of consensus, journalists assume everyone agrees. The sphere of legitimate controversy includes the standard political debates, and journalists are expected to remain neutral. The sphere of deviance falls outside the bounds of legitimate debate, and journalists can ignore it. These boundaries shift, as public opinion shifts. Hallin's spheres, which deals with the media, are similar to the Overton window, which deals with public opinion generally, and posits a sliding scale of public opinion on any given issue ranging from conventional wisdom to unacceptable. Hallin used the concept of framing to describe the presentation and reception of issues in public. For example, framing the use of drugs as criminal activity can encourage the public to consider that behavior anti-social. Hallin's work was later referred to in the controversial formulation of the concept of an opinion corridor, in which the range of acceptable public opinion narrows, and opinion outside that corridor moves from legitimate controversy into deviance. == Description == === Sphere of consensus === This sphere contains those topics on which there is widespread agreement, or at least the perception thereof. Within the sphere of consensus, "journalists feel free to invoke a generalized 'we' and to take for granted shared values and shared assumptions". Examples include such things as motherhood and apple pie. For topics in this sphere, journalists feel free to be advocating cheerleaders without having to be neutral or present any opposing view point and be disinterested observers." === Sphere of legitimate controversy === For topics in this sphere rational and informed people hold differing views within limited range. These topics are therefore the most important to cover, and also ones upon which journalists are seemingly obliged to remain disinterested reporters, rather than advocating for or against a particular view. Schudson notes that Hallin, in his influential study of the US media during the Vietnam War, argues that journalism's commitment to objectivity has always been compartmentalized. That is, within a certain sphere—the sphere of legitimate controversy—journalists seek conscientiously to be balanced and objective. The work of Walter Williams professor at the University of Missouri, Rod Petersen, advanced the idea that priming—controlling the narratives that media covers—can be the tool that media use to get deviant news subjects into the legitimate controversial circles of new coverage. === Sphere of deviance === Topics in this sphere are rejected by journalists as being unworthy of general consideration. Such views are perceived as being out of hand, unfounded, taboo, or of such minor consequence that they are not newsworthy. Hallin argues that in the sphere of deviance, "journalists also depart from standard norms of objective reporting and feel authorized to treat as marginal, laughable, dangerous". They either avoid mentioning or ridicule the controversial subject as outside the bounds of acceptable controversy; and they censor the individuals and groups who are associated with it. A simple example: a person claiming that aliens are manipulating college basketball scores might have difficulty finding sports media coverage for such a claim. A more political example: the US media regulator FCC's "Fairness Doctrine" aimed at radio stations, advocated balance between right and left political news and opinions, yet specified that broadcasters did not have to reserve any space or time for Communist viewpoints. == Uses of the terms == Craig Watkins (2001, pp. 92–94) makes use of the Hallin's spheres in a paper examining ABC, CBS, and NBC television network television news coverage of the Million Man March, a demonstration that took place in Washington, D.C., on October 16, 1995. Watkins analyzes the dominant framing practices—problem definition, rhetorical devices, use of sources, and images—employed by journalists to make sense of this particular expression of political protest. He argues that Hallin's three spheres are a way for media framing practices to develop specific reportorial contexts, and each sphere develops its own distinct style of news reporting resources by different rhetorical tropes and discourses. Piers Robinson (2001, p. 536) uses the concept in relation to debates that have emerged over the extent to which the mass media serves elite interests or, alternatively, plays a powerful role in shaping political outcomes. His article reviews Hallin's spheres as an example of media-state relations, that highlights theoretical and empirical shortcomings in the 'manufacturing consent' thesis (Chomsky, McChesney). Robinson argues that a more nuanced and bi-directional understanding is needed of the direction of influence between media and the state that builds upon, rather than rejecting, existing theoretical accounts. Hallin's theory assumed a relatively homogenized media environment, where most producers were trying to reach most consumers. A more fractured media landscape can challenge this assumption because different audiences may place topics in different spheres, a concept related to the filter bubble, which posits that many members of the public choose to limit their media consumption to the areas of consensus and deviance that they personally prefer.

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  Quantexa

  Quantexa is a UK-based software company that develops artificial intelligence-based applications for data analytics and decision-making. The company was founded in 2016 and is headquartered in London, with operations in North America, Europe, and the Asia-Pacific region. As of 2025, Quantexa reported a valuation of $2.6 billion and provides services to organizations in over 70 countries. Investors include Warburg Pincus, HSBC, and the Ontario Teachers’ Pension Plan. == History == Quantexa was founded in London in 2016 by several co-founders, including Jamie Hutton, Richard Seewald, Imam Hoque, Felix Hoddinott, and Vishal Marria, who also serves as the company's chief executive officer. The company was established to develop tools intended to address limitations in traditional data analysis methods, particularly those related to identifying hidden connections across large datasets. The name "Quantexa" is derived from the company's focus on quantitative methods and data analysis. In 2023, Quantexa acquired Dublin-based AI firm Aylien. In April 2023, the company completed a Series E funding round, raising $129 million at a valuation of approximately $1.8 billion, marking its entry into "unicorn" status. In October 2024, the company reported annual recurring revenue (ARR) exceeding $100 million. In early 2025, Quantexa participated in the World Economic Forum's Unicorn Program, which supports high-growth technology companies. In March 2025, Quantexa completed a Series F funding round of $175 million, led by Teachers' Venture Growth, the venture arm of the Ontario Teachers' Pension Plan. That August, the company was reported to be considering a 2026 IPO. The company formed a partnership with Zurich in October 2025, the first insurer to add its AI-based Decision Intelligence platform to enhance fraud detection.

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  Pinakes

  The Pinakes (Ancient Greek: Πίνακες 'tables', plural of πίναξ pinax) is a lost bibliographic work composed by Callimachus (310/305–240 BCE) that is popularly considered to be the first library catalog in the West; its contents were based upon the holdings of the Library of Alexandria during Callimachus's tenure there during the third century BCE. == History == The Library of Alexandria had been founded by Ptolemy I Soter about 306 BCE. The first recorded librarian was Zenodotus of Ephesus. During Zenodotus' tenure, Callimachus, who was never the head librarian, compiled many catalogues/lists, each called Pinakes. His most famous one listed authors and their works; thus he became the first known bibliographer and the scholar who organized the library by authors and subjects about 245 BCE. His work was 120 volumes long. Apollonius of Rhodes was the successor to Zenodotus. Eratosthenes of Cyrene succeeded Apollonius in 235 BCE and compiled his tetagmenos epi teis megaleis bibliothekeis, the 'scheme of the great bookshelves'. In 195 BCE Aristophanes of Byzantium, Eratosthenes' successor, was the librarian and updated the Pinakes, although it is also possible that his work was not a supplement of Callimachus' Pinakes themselves, but an independent polemic against, or commentary upon, their contents. == Description == The collection at the Library of Alexandria contained nearly 500,000 papyrus scrolls, which were grouped together by subject matter and stored in bins. Each bin carried a label with painted tablets hung above the stored papyri. Pinakes was named after these tablets and are a set of index lists. The bins gave bibliographical information for every roll. A typical entry started with a title and also provided the author's name, birthplace, father's name, any teachers trained under, and educational background. It contained a brief biography of the author and a list of the author's publications. The entry had the first line of the work, a summary of its contents, the name of the author, and information about the origin of the roll, as well as any doubts about the genuineness of the ascription. Callimachus' system divided works into six genres of poetry and five sections of prose: rhetoric, law, epic, tragedy, comedy, lyric poetry, history, medicine, mathematics, natural science, and miscellanies. Each category was alphabetized by author. Callimachus composed two other works that were referred as pinakes and were probably somewhat similar in format to the Pinakes (of which they "may or may not be subsections"), but were concerned with individual topics. These are listed by the Suda as: A Chronological Pinax and Description of Didaskaloi from the Beginning and Pinax of the Vocabulary and Treatises of Democritus. == Later bibliographic pinakes == The term pinax was used for bibliographic catalogs beyond Callimachus. For example, Ptolemy-el-Garib's catalog of Aristotle's writings comes to us with the title Pinax (catalog) of Aristotle's writings. == Legacy == The Pinakes proved indispensable to librarians for centuries, and they became a model for organizing knowledge throughout the Mediterranean. Their later influence can be traced to medieval times, even to the Arabic counterpart of the tenth century: Ibn al-Nadim's Al-Fihrist ("Index"). Local variations for cataloging and library classification continued through the late 19th century, when Anthony Panizzi and Melvil Dewey paved the way for more shared and standardized approaches.

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  AirSim

  AirSim (Aerial Informatics and Robotics Simulation) is an open-source, cross-platform simulator for drones, ground vehicles such as cars and various other objects, built on Epic Games’ proprietary Unreal Engine 4 as a platform for AI research. It is developed by Microsoft and can be used to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles. This allows testing of autonomous solutions without worrying about real-world damage. AirSim provides some 12 kilometers of roads with 20 city blocks and APIs to retrieve data and control vehicles in a platform independent way. The APIs are accessible via a variety of programming languages, including C++, C#, Python and Java. AirSim supports hardware-in-the-loop with driving wheels and flight controllers such as PX4 for physically and visually realistic simulations. The platform also supports common robotic platforms, such as Robot Operating System (ROS). It is developed as an Unreal plug-in that can be dropped into any Unreal environment. An experimental release for a Unity plug-in is also available. On December 15, 2023 Microsoft has shutdown the development of the project.

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  Ballie

  Ballie is an AI robot created by Samsung to be released in 2026. It is an autonomous robot which has the ability to control smart home devices. Ballie can text, send pictures and follow commands through SmartThings. It can also show workout information shared from a Galaxy Watch. Ballie can make video calls and welcome you home. == History == It was first unveiled at Samsung's CES event in CES 2020, and later updated the design in CES 2024, and will be later released in 2026. == Design ==

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  Qstack

  Qstack is a cloud management platform developed by GreenQloud, a cloud computing software company founded in Reykjavik, Iceland in February 2010. Qstack enables its users to manage multiple clouds and hybrid deployments through a single self-service portal. Qstack is in continuous development, incorporating developments within infrastructure, cloud, and application management solutions. The next release of Qstack is slated for June 2017. == History == In 2014 when Jonsi Stefansson joined as CEO, Greenqloud pivoted its operational focus to development of Qstack with beta launch in the fall of 2015, and began offering support, technical services and certifications for the software. == Features == Qstack is hypervisor agnostic (KVM, VMware, Hyper-V) and can manage private clouds in multiple locations as well as AWS, Azure, and EC2-compatible public clouds from its user interface. Qstack combines proprietary software with open-source components, and the company claims to harden them to meet the strict security standards often required by enterprise deployments. Qstack features VM templates for Windows, Linux, and other operating systems. It also features full SSH/RDP access to instances, virtual routers, firewalls, and load balancers built into the interface. == Reception == In a 2015 review, IDG columnist J. Peter Bruzzese praised Qstack’s user interface for its ease-of-use and clean look.

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  National Security Memorandum on Artificial Intelligence

  The Memorandum on Advancing the United States' Leadership in Artificial Intelligence; Harnessing Artificial Intelligence to Fulfill National Security Objectives; and Fostering the Safety, Security, and Trustworthiness of Artificial Intelligence is a memorandum signed by U.S. president Joe Biden. The memorandum is described as seeking to advance U.S. leadership in the development of safe, secure, and trustworthy artificial intelligence (AI); enable the U.S. government to use AI for national security; and contribute to international AI governance.

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  MuZero

  MuZero is a computer program developed by artificial intelligence research company DeepMind, a subsidiary of Google, to master games without knowing their rules and underlying dynamics. Its release in 2019 included benchmarks of its performance in Go, chess, shogi, and a suite of 57 different Atari games. The algorithm uses an approach similar to AlphaZero, where a combination of a tree-based search and a learned model is deployed. It matched AlphaZero's performance in chess and shogi, improved on its performance in Go, and improved on the state of the art in mastering a suite of 57 Atari games (the Arcade Learning Environment), a visually-complex domain. MuZero was trained via self-play, with no access to rules, opening books, or endgame tablebases. The trained algorithm used the same convolutional and residual architecture as AlphaZero, but with 20 percent fewer computation steps per node in the search tree. == History == MuZero really is discovering for itself how to build a model and understand it just from first principles. On November 19, 2019, the DeepMind team released a preprint introducing MuZero. === Derivation from AlphaZero === MuZero (MZ) is a combination of the high-performance planning of the AlphaZero (AZ) algorithm with approaches to model-free reinforcement learning. The combination allows for more efficient training in classical planning regimes, such as Go, while also handling domains with much more complex inputs at each stage, such as visual video games. MuZero was derived directly from AZ code, sharing its rules for setting hyperparameters. Differences between the approaches include: AZ's planning process uses a simulator. The simulator knows the rules of the game. It has to be explicitly programmed. A neural network then predicts the policy and value of a future position. Perfect knowledge of game rules is used in modeling state transitions in the search tree, actions available at each node, and termination of a branch of the tree. MZ does not have access to the rules, and instead learns one with neural networks. AZ has a single model for the game (from board state to predictions); MZ has separate models for representation of the current state (from board state into its internal embedding), dynamics of states (how actions change representations of board states), and prediction of policy and value of a future position (given a state's representation). MZ's hidden model may be complex, and it may turn out it can host computation; exploring the details of the hidden model in a trained instance of MZ is a topic for future exploration. MZ does not expect a two-player game where winners take all. It works with standard reinforcement-learning scenarios, including single-agent environments with continuous intermediate rewards, possibly of arbitrary magnitude and with time discounting. AZ was designed for two-player games that could be won, drawn, or lost. === Comparison with R2D2 === The previous state of the art technique for learning to play the suite of Atari games was R2D2, the Recurrent Replay Distributed DQN. MuZero surpassed both R2D2's mean and median performance across the suite of games, though it did not do better in every game. == Training and results == MuZero used 16 third-generation tensor processing units (TPUs) for training, and 1000 TPUs for selfplay for board games, with 800 simulations per step and 8 TPUs for training and 32 TPUs for selfplay for Atari games, with 50 simulations per step. AlphaZero used 64 second-generation TPUs for training, and 5000 first-generation TPUs for selfplay. As TPU design has improved (third-generation chips are 2x as powerful individually as second-generation chips, with further advances in bandwidth and networking across chips in a pod), these are comparable training setups. R2D2 was trained for 5 days through 2M training steps. === Initial results === MuZero matched AlphaZero's performance in chess and shogi after roughly 1 million training steps. It matched AZ's performance in Go after 500,000 training steps and surpassed it by 1 million steps. It matched R2D2's mean and median performance across the Atari game suite after 500 thousand training steps and surpassed it by 1 million steps, though it never performed well on 6 games in the suite. == Reactions and related work == MuZero was viewed as a significant advancement over AlphaZero, and a generalizable step forward in unsupervised learning techniques. The work was seen as advancing understanding of how to compose systems from smaller components, a systems-level development more than a pure machine-learning development. While only pseudocode was released by the development team, Werner Duvaud produced an open source implementation based on that. MuZero has been used as a reference implementation in other work, for instance as a way to generate model-based behavior. In late 2021, a more efficient variant of MuZero was proposed, named EfficientZero. It "achieves 194.3 percent mean human performance and 109.0 percent median performance on the Atari 100k benchmark with only two hours of real-time game experience". In early 2022, a variant of MuZero was proposed to play stochastic games (for example 2048, backgammon), called Stochastic MuZero, which uses afterstate dynamics and chance codes to account for the stochastic nature of the environment when training the dynamics network.

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