AI Generator Questions

AI Generator Questions — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  ConEmu

  ConEmu (short for Console emulator) is a free and open-source tabbed terminal emulator for Windows. ConEmu presents multiple consoles and simple GUI applications as one customizable GUI window with tabs and a status bar. It also provides emulation for ANSI escape codes for color, bypassing the capabilities of the standard Windows Console Host to provide 256 and 24-bit color in Windows. The program has a large range of customization, including custom color palettes for the standard 16 colors, hotkeys, transparency, an auto-hideable mode (similar to the way Quake originally displayed its developer console). Initially, the program was created as a companion to Far Manager, bringing some features common for graphical file managers to this console application (thumbnails and tiles, drag and drop with other windows, true color interface, and others). As of 2012, ConEmu could be used with any other Win32 console application or simple GUI tool (such as Notepad, PuTTY or DOSBox). ConEmu doesn't provide any shell itself, but rather allows using any other shell. It does provide a limited macro language, to control the hosted applications startup.

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  Lernmatrix

  Lernmatrix (German for "learning matrix") is a special type of artificial neural network (ANN) architecture, similar to associative memory, invented around 1960 by Karl Steinbuch, a pioneer in computer science and ANNs. This model for learning systems could establish complex associations between certain sets of characteristics (e.g., letters of an alphabet) and their meanings. == Function == The Lernmatrix generally consists of n "characteristic lines" and m "meaning lines," where each characteristic line is connected to each meaning line, similar to how neurons in the brain are connected by synapses. (This can be realized in various ways – according to Steinbuch, this could be done by hardware or software). To train a Lernmatrix, values are specified on the corresponding characteristic and meaning lines (binary or real); then the connections between all pairs of characteristic and meaning lines are strengthened by the Hebb rule. A trained Lernmatrix, when given a specific input on the characteristic lines, activates the corresponding meaning lines. In modern language, it is a linear projection module. By appropriately interconnecting several Lernmatrices, a switching system can be built that, after completing certain training phases, is ultimately able to automatically determine the most probable associated meaning for an input sequence of features.

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  GNOWSYS

  GNOWSYS (Gnowledge Networking and Organizing system) is a specification for a generic distributed network based memory/knowledge management. It is developed as an application for developing and maintaining semantic web content. It is written in Python. It is implemented as a Django app. The GNOWSYS project was launched by Nagarjuna G. in 2001, while he was working at Homi Bhabha Centre for Science Education (HBCSE). The memory of GNOWSYS is designed as a node-oriented space. A node is described by other nodes to which it has links. The nodes are organized and processed according to a complex data structure called the neighborhood. == Applications == The application can be used for web-based knowledge representation and content management projects, for developing structured knowledge bases, as a collaborative authoring tool, suitable for making electronic glossaries, dictionaries and encyclopedias, for managing large web sites or links, developing an online catalogue for a library of any thing including books, to make ontologies, classifying and networking any objects, etc. This tool is also intended to be used for an on-line tutoring system with dependency management between various concepts or software packages. For example, the dependency relations between Debian packages have been represented by the gnowledge portal Archived 2018-05-14 at the Wayback Machine. == Component Classes == The kernel is designed to provide support to persistently store highly granular nodes of knowledge representation like terms, predicates and very complex propositional systems like arguments, rules, axiomatic systems, loosely held paragraphs, and more complex structured and consistent compositions. All the component classes in GNOWSYS are classified according to complexity into three groups, where the first two groups are used to express all possible well formed formulae permissible in a first order logic. === Terms === ‘Object’, ‘Object Type’ for declarative knowledge, ‘Event’, ‘Event Type’, for temporal objects, and ‘Meta Types’ for expressing upper ontology. The objects in this group are essentially any thing about which the knowledge engineer intends to express and store in the knowledge base, i.e., they are the objects of discourse. The instances of these component classes can be stored with or without expressing ‘instance of’ or ‘sub-class of’ relations among them. === Predicates === This group consists of ‘Relation’, and ‘Relation Type’ for expressing declarative knowledge, and ‘Function’ and ‘Function Type’ for expressing procedural knowledge. This group is to express qualitative and quantitative relations among the various instances stored in the knowledge base. While instantiating the predicates can be characterized by their logical properties of relations, quantifiers and cardinality as monadic predicates of these predicate objects. === Structures === ‘System’, ‘Encapsulated Class’, ‘Program’, and ‘Process’, are other base classes for complex structures, which can be combined iteratively to produce more complex systems. The component class ‘System’ is to store in the knowledge base a set of propositions composed into ontologies, axiomatic systems, complex systems like say a human body, an artifact like a vehicle etc., with or without consistency check. An ‘Encapsulated Class’ is to com- pose declarative and behavioural objects in a flexible way to build classes. A ‘Program’ is not only to store the logic of any complete program or a component class, composed from the already available behavioural instances in the knowledge base with built-in connectives (conditions, and loops), but also execute them as web services. A ‘Process’ is to structure temporal objects with sequence, concurrency, synchronous or asynchronous specifications. Every node in the database keeps the neighbourhood information, such as its super-class, sub-class, instance-of, and other relations, in which the object has a role, in the form of predicates. This feature makes computation of drawing graphs and inferences, on the one hand, and dependency and navigation paths on the other hand very easy. All the data and metadata is indexed in a central catalogue making query and locating resources efficient.

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  TD-Gammon

  TD-Gammon is a computer backgammon program developed in the 1990s by Gerald Tesauro at IBM's Thomas J. Watson Research Center. Its name comes from the fact that it is an artificial neural net trained by a form of temporal-difference learning, specifically TD-Lambda. It explored strategies that humans had not pursued and led to advances in the theory of correct backgammon play. In 1993, TD-Gammon (version 2.1) was trained with 1.5 million games of self-play, and achieved a level of play just slightly below that of the top human backgammon players of the time. In 1998, during a 100-game series, it was defeated by the world champion by a mere margin of 8 points. Its unconventional assessment of some opening strategies had been accepted and adopted by expert players. TD-gammon is commonly cited as an early success of reinforcement learning and neural networks, and was cited in, for example, papers for deep Q-learning and AlphaGo. == Algorithm for play and learning == During play, TD-Gammon examines on each turn all possible legal moves and all their possible responses (lookahead search), feeds each resulting board position into its evaluation function, and chooses the move that leads to the board position that got the highest score. In this respect, TD-Gammon is no different than almost any other computer board-game program. TD-Gammon's innovation was in how it learned its evaluation function. TD-Gammon's learning algorithm consists of updating the weights in its neural net after each turn to reduce the difference between its evaluation of previous turns' board positions and its evaluation of the present turn's board position—hence "temporal-difference learning". The score of any board position is a set of four numbers reflecting the program's estimate of the likelihood of each possible game result: White wins normally, Black wins normally, White wins a gammon, Black wins a gammon. For the final board position of the game, the algorithm compares with the actual result of the game rather than its own evaluation of the board position. The core of TD-gammon is a neural network with 3 layers. The input layer has two types of neurons. One type codes for the board position. They are non-negative integers ranging from 0 to 15, indicating the number of White or Black checkers at each board location. There are 99 input neurons for each, totaling 198 neurons. Another type codes for hand-crafted features previously used in Neurogammon. These features encoded standard concepts used by human experts, such as "advanced anchor," "blockade strength," "home board strength" and the probability of a "blot" (single checker) being hit. The hidden layer contains hidden neurons. Later versions had more of these. The output layer contains 4 neurons, representing the network's estimate of the probability ("equity") that the current board would lead to. The 4 neurons code for: White normal win, White gammon win, Black normal win, Black gammon win. Backgammon win is so rare that Tesauro opted to not represent it. After each turn, the learning algorithm updates each weight in the neural net according to the following rule: w t + 1 − w t = α ( Y t + 1 − Y t ) ∑ k = 1 t λ t − k ∇ w Y k {\displaystyle w_{t+1}-w_{t}=\alpha (Y_{t+1}-Y_{t})\sum _{k=1}^{t}\lambda ^{t-k}\nabla _{w}Y_{k}} where: It was found that picking small λ {\displaystyle \lambda } offered performance roughly equally good, and large λ {\displaystyle \lambda } degraded performance. Because of this, after 1992, TD-Gammon was trained with λ = 0 {\displaystyle \lambda =0} , degenerating into standard TD-learning. This saved compute by a factor of 2. == Development history == Version 1.0 used simple 1-ply search: every next move is scored by the neural net, and the highest-scoring move is selected. Versions 2.0 and 2.1 used 2-ply search: Make a 1-ply analysis to remove unlikely moves ("forward pruning"). Make a 2-play minimax analysis for only the likely moves. Pick the best move, probability-weighted by each of the opponent's 21 possible dice rolls (weighting non-doubles twice as much as doubles). Versions 3.0 and 3.1 used 3-ply search, using 21 2 = 441 {\displaystyle 21^{2}=441} possible dice rolls instead of 21. The last version, 3.1, was trained specifically for an exhibition match against Malcolm Davis at the 1998 AAAI Hall of Champions. It lost at -8 points, mainly due to one blunder, where TD-Gammon opted to double and got gammoned at -32 points. == Experiments and stages of training == Unlike previous neural-net backgammon programs such as Neurogammon (also written by Tesauro), where an expert trained the program by supplying the "correct" evaluation of each position, TD-Gammon was at first programmed "knowledge-free". In early experimentation, using only a raw board encoding with no human-designed features, TD-Gammon reached a level of play comparable to Neurogammon: that of an intermediate-level human backgammon player. Even though TD-Gammon discovered insightful features on its own, Tesauro wondered if its play could be improved by using hand-designed features like Neurogammon's. Indeed, the self-training TD-Gammon with expert-designed features soon surpassed all previous computer backgammon programs. It stopped improving after about 1,500,000 games (self-play) using a three-layered neural network, with 198 input units encoding expert-designed features, 80 hidden units, and one output unit representing predicted probability of winning. == Advances in backgammon theory == TD-Gammon's exclusive training through self-play (rather than imitation learning) enabled it to explore strategies that humans previously had not considered or had ruled out erroneously. Its success with unorthodox strategies had a significant impact on the backgammon community. Late 1991, Bill Robertie, Paul Magriel, and Malcolm Davis, were invited to play against TD-Gammon (version 1.0). A total of 51 games were played, with TD-Gammon losing at -0.25 ppg. Robertie found TD-Gammon to be at the level of a competent advanced player, and better than any previous backgammon program. Robertie subsequently wrote about the use of TD-Gammon for backgammon study. For example, on the opening play, the conventional wisdom was that given a roll of 2-1, 4-1, or 5-1, White should move a single checker from point 6 to point 5. Known as "slotting", this technique trades the risk of a hit for the opportunity to develop an aggressive position. TD-Gammon found that the more conservative play of splitting 24-23 was superior. Tournament players began experimenting with TD-Gammon's move, and found success. Within a few years, slotting had disappeared from tournament play, replaced by splitting, though in 2006 it made a reappearance for 2-1. Backgammon expert Kit Woolsey found that TD-Gammon's positional judgement, especially its weighing of risk against safety, was superior to his own or any human's. TD-Gammon's excellent positional play was undercut by occasional poor endgame play. The endgame requires a more analytical approach, sometimes with extensive lookahead. TD-Gammon's limitation to two-ply lookahead put a ceiling on what it could achieve in this part of the game. TD-Gammon's strengths and weaknesses were the opposite of symbolic artificial intelligence programs and most computer software in general: it was good at matters that require an intuitive "feel" but bad at systematic analysis. It is also poor at doubling strategies. This is likely due to the fact that the neural network is trained without the doubling cube, with the doubling added by feeding the neural network's cubeless equity estimates into theoretically-based heuristic formulae. This was particularly the case in the 1998 exhibition match, where it played 100 games against Malcolm Davis. A single doubling blunder lost the match. TD-gammon was never commercialized or released to the public in some other form, but it inspired commercial backgammon programs based on neural networks, such as JellyFish (1994) and Snowie (1998).

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  Cyborg

  A cyborg () is a being with both organic and biomechatronic body parts. It is a portmanteau of cybernetic and organism. The term was coined in 1960 by Manfred Clynes and Nathan S. Kline. In contrast to biorobots and androids, the term cyborg applies to a living organism that has undergone restoration of function or enhancements of abilities due to the integration of some artificial component or technology that relies on feedback. == Description and definition == Alternative names for a cyborg include cybernetic organism, cyber-organism, cyber-organic being, cybernetically enhanced organism, cybernetically augmented organism, technorganic being, techno-organic being, and techno-organism. Unlike bionics, biorobotics, or androids, a cyborg is an organism that has restored function or, especially, enhanced abilities due to the integration of some artificial component or technology that relies on some sort of feedback, for example: prostheses, artificial organs, implants or, in some cases, wearable technology. Cyborg technologies may enable or support collective intelligence. A related idea is the "augmented human". While cyborgs are commonly thought of as mammals, including humans, the term can apply to any organism. === Placement and distinctions === D. S. Halacy's Cyborg: Evolution of the Superman (1965) featured an introduction which spoke of a "new frontier" that was "not merely space, but more profoundly the relationship between 'inner space' to 'outer space' – a bridge...between mind and matter." In "A Cyborg Manifesto", Donna Haraway rejects the notion of rigid boundaries between humanity and technology, arguing that, as humans depend on more technology over time, humanity and technology have become too interwoven to draw lines between them. She believes that since we have allowed and created machines and technology to be so advanced, there should be no reason to fear what we have created, and cyborgs should be embraced because they are part of human identities. However, Haraway has also expressed concern over the contradictions of scientific objectivity and the ethics of technological evolution, and has argued that "There are political consequences to scientific accounts of the world." === Biosocial definition === According to some definitions of the term, the physical attachments that humans have with even the most basic technologies have already made them cyborgs. In a typical example, a human with an artificial cardiac pacemaker or implantable cardioverter-defibrillator would be considered a cyborg, since these devices measure voltage potentials in the body, perform signal processing, and can deliver electrical stimuli, using a synthetic feedback mechanism to keep that person alive. Implants, especially cochlear implants, that combine mechanical modification with any kind of feedback response are also cybernetic enhancements. Some theorists cite such modifications as contact lenses, hearing aids, smartphones, or intraocular lenses as examples of fitting humans with technology to enhance their biological capabilities. The emerging trend of implanting microchips inside the body (mainly the hands), to make financial operations like a contactless payment, or basic tasks like opening a door, has been erroneously marketed as more recent examples of cybernetic enhancement. The latter has not yet seen significant traction outside niche areas in Scandinavia and in actual function is little more than a pre-programmed Radio-frequency identification (RFID) microchip encased in glass that does not interact with the human body (it is the same technology used in the microchips injected into animals for ease of identification), thus not fitting the definition of a cybernetic implant. As cyborgs currently are on the rise, some theorists argue there is a need to develop new definitions of aging. For instance, a bio-techno-social definition of aging has been suggested. The term is also used to address human-technology mixtures in the abstract. This includes not only commonly used pieces of technology such as phones, computers, the Internet, and so on, but also artifacts that are not usually considered technology; for example, pen and paper, and speech and language. When augmented with these technologies and connected in communication with people in other times and places, a person becomes capable of more than they were before. An example is a computer, which gains power by using Internet protocols to connect with other computers. Another example is a social-media bot—either a bot-assisted human or a human-assisted-bot—used to target social media with likes and shares. Cybernetic technologies thus include highways, pipes, electrical wiring, buildings, electrical plants, libraries, and other infrastructural constructs. Bruce Sterling, in his Shaper/Mechanist universe, suggested an idea of an alternative cyborg called 'Lobster', which is made not by using internal implants, but by using an external shell (e.g. a powered exoskeleton). The computer game Deus Ex: Invisible War prominently features cyborgs called Omar, Russian for 'lobster'. === Evolutionary perspective === In 1994, Hans Hass formulated a scientific view of the human-machine hybrids he called "hypercells". They can expand their biological cell body with artificial artifacts and thus expand their performance body. The theory of hypercells or Homo proteus, as Hass called the human-machine hybrid to distinguish Homo sapiens, extends Charles Darwin's theory of evolution and deals with the course of evolution beyond humans. In his 2019 book Novacene, James Lovelock used the term "cyborgs" to refer to the next generation of beings who will become the "understanders of the future" and "lead the cosmos to self-knowledge". While acknowledging the organic component in Clynes' and Kline's definition, he proposed that these cyborgs "will have designed and built themselves from the artificial intelligence systems we have already constructed", and used the term cyborg "to emphasize that the new intelligent beings will have arisen, like us, from Darwinian evolution." == Origins == The concept of a man-machine mixture was widespread in science fiction before World War II. As early as 1843, Edgar Allan Poe described a man with extensive prostheses in the short story "The Man That Was Used Up". In 1911, Jean de La Hire introduced the Nyctalope, a science fiction hero who was perhaps the first literary cyborg, in Le Mystère des XV (later translated as The Nyctalope on Mars). Nearly two decades later, Edmond Hamilton presented space explorers with a mixture of organic and machine parts in his 1928 novel The Comet Doom. He later featured the talking, living brain of an old scientist, Simon Wright, floating in a transparent case, and in all the adventures of his famous hero, Captain Future. In 1944, in the short story "No Woman Born", C. L. Moore wrote of Deirdre, a dancer, whose body was burned completely and whose brain was placed in a faceless but beautiful and supple mechanical body. In 1960, the term "cyborg" was coined by Manfred E. Clynes and Nathan S. Kline to refer to their conception of an enhanced human being who could survive in extraterrestrial environments: For the exogenously extended organizational complex functioning as an integrated homeostatic system unconsciously, we propose the term 'Cyborg'. Their concept was the outcome of thinking about the need for an intimate relationship between human and machine as the new frontier of space exploration was beginning to develop. A designer of physiological instrumentation and electronic data-processing systems, Clynes was the chief research scientist in the Dynamic Simulation Laboratory at Rockland State Hospital in New York. The term first appears in print 5 months earlier when The New York Times reported on the "Psychophysiological Aspects of Space Flight Symposium" where Clynes and Kline first presented their paper: A cyborg is essentially a man-machine system in which the control mechanisms of the human portion are modified externally by drugs or regulatory devices so that the being can live in an environment different from the normal one. Thereafter, Hamilton would first use the term "cyborg" explicitly in the 1962 short story, "After a Judgment Day", to describe the "mechanical analogs" called "Charlies," explaining that "[c]yborgs, they had been called from the first one in the 1960s...cybernetic organisms." The 1972 novel Cyborg by Martin Caidin introduced the character of bionic government agent Steve Austin, and was adapted into the popular television series The Six Million Dollar Man, which ran from 1973 to 1978. In 2001, a book titled Cyborg: Digital Destiny and Human Possibility in the Age of the Wearable computer was published by Doubleday. Some of the ideas in the book were incorporated into the documentary film Cyberman that same year. == Cyborg tissues in engineering == Cyborg tissues structured with carbon nanotubes and plan

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  Xinhua–Sogou AI news anchor

  Xinhua News Agency and Sogou of China developed an artificial intelligence (AI) for news reporting purposes. The AI was unveiled in 2018. It is touted to be the "world's first AI news anchor". == History == The AI was unveiled at the 2018 World Internet Conference in Wuzhen, Zhejiang, China. The AI devises avatars patterned after real life Xinhua anchors. The AI patterned after Qiu Hao spoke in Chinese, while the one derived from the likeness of Zhang Zhao speaks in English. The unveiling of the AI raised concerns of its impact on employment. Xinhua and Sogou unveiled Xin Xiaomeng, an AI with a female avatar in 2019. People's Daily followed suit by unveiling its own AI newscaster in 2023.

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  Global call for AI red lines

  The global call for AI red lines is a declaration made on 22 September 2025 calling on governments to define and internationally prohibit unacceptable AI uses and behaviors. The online declaration was announced by Nobel Peace Prize laureate Maria Ressa at the 80th United Nations General Assembly high-level week. The declaration was initially signed by 200 prominent politicians and scientists, including 10 Nobel Prize winners. The call does not specify which red lines to set, but suggests several, such as banning bioweapon design, mass surveillance or AI impersonation. == The declaration == The declaration was published online as an open letter on 22 September 2025. Nobel Peace Prize laureate Maria Ressa announced it in her opening speech at the 80th United Nations General Assembly high-level week in New York, urging governments to "define what AI should never be allowed to do" and "establish clear international boundaries to prevent universally unacceptable risks for A.I." The initiative was organized by three nonprofit organisations: the French Center for AI Safety (CeSIA), The Future Society, and the Center for Human-Compatible Artificial Intelligence (CHAI). The letter argues that humanity faces risks such as engineered pandemics, widespread disinformation, large-scale manipulation, unemployment and loss of control. Proponents argue that national laws are insufficient to address these risks and that "an international agreement on clear and verifiable red lines is necessary". They urge governments to reach an agreement by the end of 2026, and called for robust enforcement mechanisms and the creation of an independent organisation to implement it. The letter does not call for specific red lines, but suggests the possibility of banning lethal autonomous weapons, autonomous replication of AI systems and the use of AI in nuclear warfare. Other examples of possible red lines include social scoring, mass surveillance, bioweapon design, AI-generated child sexual abuse material and AI impersonation. A red line could prohibit either AI behaviors (what AI systems should be guaranteed to never do even if asked to) or AI uses. == Signatories == When published, the online declaration was signed by more than 200 prominent politicians and scientists, including 10 Nobel Prize winners. Signers include former president of Colombia Juan Manuel Santos and researchers Geoffrey Hinton and Yoshua Bengio. It also includes popular authors like Stephen Fry and Yuval Noah Harari. The letter received support from European lawmakers, including former Italian prime minister Enrico Letta, and former president of Ireland Mary Robinson. == Development of red lines == As of 2025, there is no global red line on AI. Some regional red lines exist, such as with the uses deemed "unacceptable" by the AI Act in Europe, and with the US-China agreement not to leave to AI the decision of whether to launch nuclear weapons. At the United Nations Security Council, days after the declaration, Michael Kratsios, Donald Trump's director of the White House Office of Science and Technology Policy, said "We totally reject all efforts by international bodies to assert centralized control and global governance of AI." The topic of AI red lines gained prominence in 2026 with the dispute between Anthropic and the Department of Defense (DoD), which resulted from the DoD requesting Anthropic to remove contractual red lines on fully autonomous weapons and mass domestic surveillance. The event led employees from Google and OpenAI as well as Senate Democrats to further call for red lines on military use of AI. Senator Adam Schiff proposed a bill to "codify" Anthropic's red lines.

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  InRule Technology

  InRule Technology is a software company that offers Business Rule Management System (BRMS) enterprise software products. == History == InRule Technology's Chief Executive Officer Rik Chomko and Chief Technology Officer Loren Goodman founded InRule Technology in Chicago in 2002. Paul Hessinger joined InRule Technology in 2004 as chief executive officer and chairman of the board and served until his retirement in 2015. They work with companies in several markets, including financial services, public sector, healthcare, and insurance. In 2007, InRule Technology became a charter member of the Microsoft Business Process Alliance. In August 2019, InRule was acquired by Open Gate Capital. == Products == On October 29, 2012, InRule Technology launched InRule for Microsoft Dynamics CRM. The program provides components to enable creation and update of rules within Microsoft Dynamics CRM, InRule for Microsoft Dynamics CRM provides a platform for shops that prefer to work with Microsoft's platforms. With the availability of InRule 4.6 in 2014, the company introduced deployment of InRule through REST services and allowed REST services to be called from InRule. This enables access to data exposed as a REST service and to package up a rule service for RESTful access. The product launch reflected the move of the company's core audience to use a broader array of technologies despite an earlier focus on .NET. In 2017, InRule introduced InRule for the Salesforce Platform, as well as a technology partnership with Work-Relay, a Business Process Management (BPM) application built on the Salesforce Platform. One year earlier the company introduced InRule for JavaScript, allowing enterprises to run rules on the client-side, server-side or both. The software architecture includes multiple components, including irAuthor, the primary authoring tool for creating and maintaining rules; irVerify, a real-time test environment to run and debug rule applications; and irSDK, a set of APIs that allows developers to integrate inRule into their applications. Additionally, irSOA allows users to access the InRule rule engine as a service. irSOA is now called the irServer Execution Service.

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  Artificial intelligence in education

  Artificial intelligence in education (often abbreviated as AIEd) is a subfield of educational technology that studies how to use artificial intelligence to create learning environments. Considerations in the field include data-driven decision-making, AI ethics, data privacy and AI literacy. Concerns include the potential for cheating, over-reliance, equity of access, reduced critical thinking, and the perpetuation of misinformation and bias. == History == Efforts to integrate AI into educational contexts have often followed technological advancement in the history of artificial intelligence. In the 1960s, educators and researchers began developing computer-based instruction systems, such as PLATO, developed by the University of Illinois. In the 1970s and 1980s, intelligent tutoring systems (ITS) were being adapted for classroom instruction. The International Artificial Intelligence in Education Society was founded in 1993. Coinciding with the AI boom of the 2020s, the use of large language models in the global north has been promoted and funded by venture capital and big tech. Companies creating AI services have targeted students and educational institutions as customers. Similarly, pre-AI boom educational companies have expanded their use of AI technologies. These commercial incentives for AIEd use may be related to a potential AI bubble. In the U.S., bipartisan support of AI development in K-12 education has been expressed, but specific implementations and best practices remain contentious. == Theory == AIEd applies theory from education studies, machine learning, and related fields. A 2019 review of the previous decade of studies found that most research prioritized technological design over pedagogical integration. Ouyang and Jiao (2021) propose three paradigms for AI in education, which follow roughly from least to most learner-centered and from requiring least to most technical complexity from the AI systems: AI-directed, learner-as-recipient: AIEd systems present a pre-set curriculum based on statistical patterns that do not adjust to learner's feedback. AI-supported, learner-as-collaborator: Systems that incorporate responsiveness to learner's feedback through, for example, natural language processing, wherein AI can support knowledge construction. AI-empowered, learner-as-leader: This model seeks to position AI as a supplement to human intelligence wherein learners take agency and AI provides consistent and actionable feedback. Some scholars place AI in education within a socio-technical framework. This positions AI alongside other emerging educational technologies, such as computing, the internet, and social media. The framework of Tsao, Heinrichs and Camit (2025) draws on new materialism and posthumanism, specifically Donna Haraway's concept of sympoiesis (making-with). This perspective views learning as an entanglement of human and non-human actors (students, teachers, and AI algorithms), where knowledge is co-composed in contact zones between human context and algorithmic prediction. AI agents have been trained on biased datasets, and thus continue to perpetuate societal biases. Since LLMs were created to produce human-like text, algorithmic bias can be introduced and reproduced. AI's data processing and monitoring reinforce neoliberal approaches to education rather than addressing inequalities. == Applications == Uses of generative AI chatbots in education have included assessment and feedback, machine translations, proof-reading exam question generation and copy editing, or as virtual assistants. Emotional AI in education is the study and development of systems that can detect learners' emotions or provide emotional support in learning. == Usage == === Schools and educators === Following the release of ChatGPT in November 2022, some schools and large school districts blocked access to the site and issued warnings that the use of such tools would be seen as cheating. Governmental and non-governmental organizations such as UNESCO, Article 4 of the European Union's AI Act, and the U.S. Department of Education have published reports advocating for specific AIEd approaches. National higher-education bodies have also published guidance on generative AI, including Ireland's Higher Education Authority, which issued a policy framework for higher education teaching and learning in December 2025. In 2024, UNESCO released updated global guidance for generative AI in education, emphasizing ethical use, teacher training, and data protection to ensure responsible integration of AI tools in learning environments. According to Taso (2025), policy implementation in higher education is interpreted and enacted differently by various organizations. These decentralized policies can lead to inconsistent enforcement and confusion among students regarding what constitutes acceptable use, with the burden of ethical navigation falling on individual teachers and students. AI integration in classrooms has created new forms of invisible labour for educators, who must navigate ambiguous policies, redesign assessments to be AI-resilient, and adjudicate potential academic integrity violations. The use of AI detection tools has also been criticised for creating an adversarial relationship between students and institutions, where students may be falsely accused of misconduct based on probabilistic software. AIEd advocates say that efforts should be made towards increasing global accessibility and training educators to serve underprivileged areas. === Students === Reliance on generative AI has been linked with reduced academic self-esteem and performance, and heightened learned helplessness. Algorithm errors and hallucinations are common flaws in AI agents, making them less trustworthy and reliable. According to a 2025 survey from Inside Higher Ed, 85% of higher education students use generative AI technology in some way, with 25% using AI to complete assignments for them. The most common reason cited for using AI to cheat was pressure to get high grades. 97% of students wanted some form of action from schools on the threat to academic integrity caused by AI, with the most popular options being clearer policies and more education about ethical uses of AI. In September 2025, The Atlantic published an op-ed from a high school senior arguing that the normalization of AI cheating was eroding critical thinking, academic integrity, creativity, and the shared student experience.

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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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  No Fakes Act

  The NO FAKES Act or the Nurture Originals, Foster Art, and Keep Entertainment Safe Act, is proposed United States federal legislation concerning digital replicas. The bill was first introduced in 2023 as a discussion draft, formally introduced in 2024, and reintroduced in 2025. If enacted, the bill would establish a federal right of publicity, giving public figures and private individuals greater control over the creation and use of digital replicas of their likenesses, including artificial intelligence (AI)-generated content. If passed, the NO FAKES Act would create a legal framework for licensing digital replicas, including provisions for liability, safe harbors, and statutory exceptions. The proposal has received broad support from the entertainment and technology industries. However, digital rights organizations have raised concerns that the Act risks chilling protected speech. == Background == === Entertainment industry concerns === Actors’ concerns over studios' use of their digital likeness were one of the primary drivers of the Screen Actors Guild–American Federation of Television and Radio Artists (SAG-AFTRA) strike in 2023. Negotiators for SAG-AFTRA alleged that the Alliance of Motion Picture and Television Producers (AMPTP) sought to use the digital likenesses of actors in perpetuity and would try to replace union members, especially background actors. The AMPTP denied SAG-AFTRA's interpretation of its proposal. In November 2023, AMPTP and SAG-AFTRA reached an agreement on the use of actors’ digital replicas, which included requirements for consent and compensation. Recording labels have also expressed concerns over unauthorized digital replicas of their performers' likeness. In 2023, TikTok user Ghostwriter977 released "Heart on My Sleeve," an AI-produced song in the styles of Drake and the Weeknd. After the song received millions of streams, the Universal Music Group (UMG) initiated takedown requests to TikTok and YouTube, which removed the song from their platforms. The legal arguments attorneys made were not disclosed; however, commentators noted that they likely used the Digital Millennium Copyright Act (DMCA). This presented a novel scenario, since UMG did not have licensing rights to "Heart on My Sleeve." According to The Verge, UMG based its DMCA takedown request on an unauthorized sample used at the start of the song for the producer tag. While legal commentators noted that UMG could have asserted a violation of the artists’ rights of publicity, existing state right of publicity laws do not provide notice-and-takedown mechanisms comparable to those under the DMCA. === Legal landscape === Legal scholars have observed that AI-generated digital replicas raise questions under existing copyright and intellectual property law. U.S. copyright law generally requires that original authorship be attributable to a human; however, the extent of human intervention needed to satisfy this requirement is not clear. Copyright holders have filed lawsuits against AI companies alleging unauthorized usage of copyrighted material to train their models, though many of these cases remain pending. In terms of outputs, record labels often hold rights to artists’ musical works but do not necessarily control the artists’ voice, appearance, or likeness in the same way. As a result, AI-generated recordings such as "Heart on My Sleeve" may fall outside the scope of certain traditional copyright protections. Individuals' likenesses have historically been governed under the Lanham Act, the Federal Trade Commission Act, and right of publicity laws. The right of publicity, recognized in many state-level statutes and common law, allows individuals to bring legal claims against unauthorized commercial use of their identities. It has often, but not exclusively, been applied to celebrities or other recognizable individuals. There is no federal-level right to publicity, and state-level protections vary, especially on issues relating to digital replicas and posthumous rights, which makes it difficult for creators or other individuals to prevent unauthorized use of their likenesses. In July 2024, the U.S. Copyright Office released a report on digital replicas and recommended that Congress create a federal law to protect individuals from unauthorized uses of their digital replicas, noting the inadequacy, narrowness, and inconsistency of existing laws. == Provisions == Under the NO FAKES Act of 2025, a digital replica is defined as "a newly created, computer-generated, highly realistic electronic representation that is readily identifiable as the voice or visual likeness of an individual," living or dead. A digital replica can be embodied in sound recordings, images, or audiovisual works in which the individual did not perform or in which the individual did perform but the "fundamental character of the performance or appearance has been materially altered." The Act specifies that digital replicas do not include reproduced samples of works authorized by the copyright holder. The Act defines a "right holder" as either the individual who is the subject of a digital replica or an entity that has acquired the rights to that individual’s likeness. The Act grants right holders the exclusive right to authorize the use of an individual’s likeness in a digital replica. This right is not assignable during the individual’s lifetime; however, it can be licensed to a living individual for up to 10 years under certain conditions. Postmortem rights The Act provides that the right does not automatically expire upon an individual’s death. It may be transferred to executors, heirs, or other parties designated by the individual. The right is held by the right holder for 10 years following the individual’s death. If the right holder demonstrates active use of the digital replica within the 2 years preceding the end of the 10-year term, the right may be extended for an additional 5-year period. These five-year extensions may be renewed for up to 70 years after the individual’s death. Liability The Act establishes liability for individuals who knowingly distribute a digital replica without authorization from the right holder, as well as for entities that make available a service primarily designed to produce unlawful digital replicas. Safe harbor provisions Similar to the Communications Decency Act and the DMCA, the Act establishes safe harbor provisions for online service providers. Providers are shielded from liability if they adopt and inform users of a policy for terminating accounts that repeatedly violate the Act. The NO FAKES Act does not require online services to proactively monitor content. Instead, it creates a notice-and-takedown mechanism under which providers must promptly respond to notifications seeking the removal of unauthorized digital replicas. These safe harbor protections apply only if the online service provider designates an agent with the U.S. Copyright Office to receive notifications of alleged violations. Remedies The NO FAKES Act provides remedies that are similar to those available under U.S. copyright law. Under the Act, individuals may be held liable for either statutory damages of $5,000 or actual damages for creating or distributing an unauthorized digital replica. The legislation also establishes a tiered liability framework for online service providers. Those that make good faith efforts to comply with the Act may face statutory damages of up to $25,000 per work for violations or actual damages. Providers that do not undertake such compliance efforts may be liable for $5,000 per unauthorized display or transmission of a digital replica, with damages capped at $750,000 per work. Exclusions The Act includes several exceptions to liability that are modeled in part on fair use principles. Digital replicas are excluded from liability when "used in a bona fide news, public affairs, or sports broadcast or account;" in a documentary or historical context; or in a way that is "consistent with the public interest." These exclusions do not apply to de minimis uses or to digital replicas that are sexually explicit in nature. The Act further states that licensing requirements do not apply to licenses established through collective bargaining agreements that contain provisions governing the use of digital replicas. The Act does not impose secondary liability on providers of generative artificial intelligence tools or services whose primary purpose is not the creation of unauthorized digital replicas. Preemption The NO FAKES Act preempts laws that protect "an individual's voice and visual likeness rights in connection with a digital replica, as defined in this Act, in an expressive work." However, the Act preserves state laws governing digital replicas enacted before January 2, 2025, as well as state laws addressing digital replicas that portray sexually explicit conduct. == History == In 2023, Senators Marsha Blackburn, Chris Coons, Amy Klobuchar, and Th

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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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  Kruti

  Kruti is a multilingual AI agent and chatbot developed by the Indian company Ola Krutrim. It is designed to perform real-world tasks for users, such as booking taxis and ordering food, by integrating directly with various online services. It is notable for its ability to understand and respond in multiple Indian languages. Developed by a team founded by Bhavish Aggarwal, Kruti functions as an "agentic" AI, meaning it can reason, plan, and execute multi-step tasks to fulfill a user's request. The backend technology combines several open-source large language models with Ola's proprietary Krutrim V2 model. The system was developed to work primarily on smartphones, addressing the Indian market's specific needs, including language diversity and potential bandwidth constraints. Kruti was officially released in June 2025, replacing an earlier chatbot from the company that was also named Krutrim. Initially supporting 13 languages, the company plans to expand its capabilities to 22 Indian languages. == Background == Kruti is an improved version of Ola's Krutrim chatbot, which was first launched in 2023 and was intended to be replaced by Kruti. It was officially released on 12 June 2025 as an upgrade to passive chatbots, with support for text and voice in 13 Indian languages. As an agentic AI, it can execute tasks with customization and reasoning, providing adaptive answers based on user preferences and past interactions. Kruti is optimized for smartphone usage and designed to accommodate bandwidth constraints and usage patterns in India. To ensure scalability and cost-effective performance, it combines various open-source large language models with Ola's own Krutrim V2, which has 12 billion parameters. Its speech recognition is built to identify regional Indian languages, dialects, and accents. Due to its integration with numerous apps and services, Kruti is context-aware and can proactively complete tasks. Initially connected only with Ola ecosystem services, Krutrim intends to expand and incorporate various Indian services into Kruti, with the goal of adding services from Blinkit, Swiggy, and Uber with respective voice command support. On 20 June 2025, Krutrim acquired the AI platform BharatSah‘AI’yak to increase its involvement in government, education, and agriculture projects. This acquisition will allow Kruti to assist in broadening the scope of BharatSah'AI'yak's work on India-centric, vernacular retrieval-augmented generation AI bots. == Development == Kruti is designed to perform tasks with minimal user input, accepting documents, images, and text, without requiring users to switch between applications. Its agentic framework breaks queries into sub-tasks executed by multiple agents working sequentially or concurrently, with reported accuracy exceeding 90%. Kruti connects to company databases and APIs via the Model Context Protocol and presents responses as summaries, tables, or narratives adapted to user behaviour. The system supports payments via credit/debit cards and UPI. The underlying stack, which includes foundation models and AI training and inference systems, is intended to support adaptation across sectors such as healthcare, education, and finance. Ola Cabs and the Open Network for Digital Commerce have begun integrating Kruti into their platforms pending broader reliability testing.

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  Luciano Floridi

  Luciano Floridi (Italian: [luˈtʃaːno ˈflɔːridi]; born 16 November 1964) is an Italian and British philosopher. He is John K. Castle Professor in the Practice of Cognitive Science and Founding Director of the Digital Ethics Center at Yale University. He is also a Professor of Sociology of Culture and Communication at the University of Bologna, Department of Legal Studies, where he is the director of the Centre for Digital Ethics. Furthermore, he is adjunct professor ("distinguished scholar in residence") at the Department of Economics, American University, Washington D.C. He is married to the neuroscientist Anna Christina Nobre. Floridi is best known for his work on two areas of philosophical research: the philosophy of information, and information ethics (also known as digital ethics or computer ethics), for which he received many awards, including the Knight of the Grand Cross of the Order of Merit, Italy's most prestigious honor. According to Scopus, Floridi was the most cited living philosopher in the world in 2020. Between 2008 and 2013, he held the research chair in philosophy of information and the UNESCO Chair in Information and Computer Ethics at the University of Hertfordshire. He was the founder and director of the IEG, an interdepartmental research group on the philosophy of information at the University of Oxford, and of the GPI the research Group in Philosophy of Information at the University of Hertfordshire. He was the founder and director of the SWIF, the Italian e-journal of philosophy (1995–2008). He is a former Governing Body Fellow of St Cross College, Oxford. == Early life and education == Floridi was born in Rome in 1964, and studied at Rome University La Sapienza (laurea, first class with distinction, 1988), where he was originally educated as a historian of philosophy. He soon became interested in analytic philosophy and wrote his tesi di laurea (roughly equivalent to an M.A. thesis) in philosophy of logic, on Michael Dummett's anti-realism. He obtained his Master of Philosophy (1989) and PhD degree (1990) from the University of Warwick, working in epistemology and philosophy of logic with Susan Haack (who was his PhD supervisor) and Michael Dummett. Floridi's early student years are partly recounted in the non-fiction book The Lost Painting: The Quest for a Caravaggio Masterpiece, where he is "Luciano". During his graduate and postdoctoral years, he covered the standard topics in analytic philosophy in search of a new methodology. He sought to approach contemporary problems from a heuristically powerful and intellectually enriching perspective when dealing with lively philosophical issues. During his graduate studies, he began to distance himself from classical analytic philosophy. In his view, the analytic movement had lost its way. For this reason, he worked on pragmatism (especially Peirce) and foundationalist issues in epistemology and philosophy of logic, as well as the history of skepticism. == Academic career and previous positions == Floridi started his academic career as a lecturer in philosophy at the University of Warwick in 1990–1991. He joined the Faculty of Philosophy of the University of Oxford in 1990 and the OUCL (Oxford's Department of Computer Science) in 1999. He was junior research fellow (JRF) in philosophy at Wolfson College, Oxford University (1990–1994), a Frances Yates Fellow in the History of Ideas at the Warburg Institute, University of London (1994–1995) and Research Fellow in philosophy at Wolfson College, Oxford University (1994–2001). During these years in Oxford, he held lectureships in different Colleges. Between 1994 and 1996, he also held a post-doctoral research scholarship at the Department of Philosophy, University of Turin. Between 2001 and 2006, he was Markle Foundation Senior Research Fellow in Information Policy at the Programme in Comparative Media Law and Policy, Oxford University. Between 2002 and 2008, he was associate professor of logic at the Università degli Studi di Bari. In 2006, he became Fellow by Special Election of St Cross College, Oxford University, where he played for the squash team. In 2008, he was appointed full professor of philosophy at the University of Hertfordshire, to hold the newly established research chair in philosophy of information and, in 2009, the UNESCO Chair in Information and Computer Ethics, a position which he held until 2013, when he moved back to Oxford. In 2017, Floridi became a fellow of the Alan Turing Institute and the chair of its Data Ethics Group, holding these positions until 2021 and 2020, respectively. Since 2010 he has been editor-in-chief of Philosophy & Technology (Springer). In January 2023, Floridi announced he would move to Yale at the beginning of the academic year 2023–2024, to take over the position of founding director of the Yale Digital Ethics Center. == Philosophical views == One of Floridi's key contributions is his formulation of the 'Philosophy of Information' (PoI). The PoI provides a framework for understanding the nature of information and its role in the world. According to Floridi, information is a vital resource that shapes our knowledge and understanding of the world. It is not simply a neutral representation of reality but a part of the world, with its own properties, effects, and moral implications. Floridi's PoI has several key components including an 'ontology of information', which defines the nature of information, an 'ethics of information', which provides a framework for evaluating the moral implications of information and information technologies, an 'epistemology of information', that analyses the role of information in the development of knowledge and science, and a 'logic of information', the concentrates on the more formal aspects. The PoI also includes a theory of the 'information environment', the infosphere, which encompasses the physical, social, and cultural contexts in which information is produced, used, and communicated. == Recognitions and awards == 2022 - Knight of the Grand Cross - First Class of the Order of Merit (Cavaliere di Gran Croce Ordine al Merito della Repubblica Italiana, the highest honor in the Italian Republic), awarded through a special decree by the president of the Italian Republic Sergio Mattarella for his work on the philosophy and ethics of information. 2022 - Fellow of the Accademia delle Scienze dell'Istituto di Bologna 2021 - Honorary Doctorate (Laurea honoris causa) in Informatics, University of Skövde, Sweden, for "his groundbreaking work on the philosophy of information". 2020 - Premio Udine Filosofia, Mimesis Festival, for The Logic of Information (OUP, 2019) 2020 - Premio Socrate, Cesare Landa Foundation, for philosophical communication 2019 - CogX Award, for "outstanding achievement in ethics of AI" 2019 - Gilbert Ryle Lectures, Trent University 2019 - Premio Aretè "Maestro della Responsabilità", Nuvolaverde, Confindustria, Gruppo 24 Ore Salone della CSR e dell'innovazione sociale, for ethics of communication 2018 - Thinker Award, IBM, for AI Ethics 2018 - Premio Conoscenza, Conferenza dei Rettori delle Università Italiane (CRUI, equivalent of Universities UK), for achievements in research and communication about digital ethics 2017 - Fellow of the Academy of Social Sciences 2016 - J. Ong Award, Media Ecology Association, for The Fourth Revolution (OUP, 2016) 2016 - Copernicus Scientist Award, Institute for Advanced Studies of the University of Ferrara, in recognition of research in the ethics and philosophy of information 2015 - Fernand Braudel Senior Fellow, European University Institute 2014-15 - Cátedras de Excelencia, University Carlos III of Madrid, for research in philosophy and ethics of information 2013 - Member of the Académie Internationale de Philosophie des Sciences 2013 - Fellow of the British Computer Society 2013 - Weizenbaum Award, International Society for Ethics and Information Technology, for "very significant contribution to the field of information and computer ethics, through his research, service, and vision" 2012 - Covey Award, International Association for Computing and Philosophy, for "outstanding research in computing and philosophy" 2011-12 - Fellow, Center for Information Policy Research, University of Wisconsin–Milwaukee 2011 - Honorary Doctorate (Laurea honoris causa) in philosophy, University of Suceava, Romania, for "his leading research in the philosophy and ethics of information" 2011 - Fellow, World Technology Network, NY, in the category "ethics and technology" 2010 - Vice Chancellor Research Award, University of Hertfordshire 2009 - Fellow of the Society for the Study of Artificial Intelligence and the Simulation of Behaviour (AIBS) 2009-10 - Gauss Professor of the Akademie der Wissenschaften, Göttingen, in recognition of research in the philosophy of information (first philosopher to receive the award, generally given to mathematicians or physicists) 2009 - Barwise Prize, American Philosophical Asso

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  OpenAI Five

  OpenAI Five is a computer program by OpenAI that plays the five-on-five video game Dota 2. Its first public appearance occurred in 2017, where it was demonstrated in a live one-on-one game against the professional player Dendi, who lost to it. The following year, the system had advanced to the point of performing as a full team of five, and began playing against and showing the capability to defeat professional teams. By choosing a game as complex as Dota 2 to study machine learning, OpenAI thought they could more accurately capture the unpredictability and continuity seen in the real world, thus constructing more general problem-solving systems. The algorithms and code used by OpenAI Five were eventually borrowed by another neural network in development by the company, one which controlled a physical robotic hand. OpenAI Five has been compared to other similar cases of artificial intelligence (AI) playing against and defeating humans, such as AlphaStar in the video game StarCraft II, AlphaGo in the board game Go, Deep Blue in chess, and Watson on the television game show Jeopardy!. == History == Development on the algorithms used for the bots began in November 2016. OpenAI decided to use Dota 2, a competitive five-on-five video game, as a base due to it being popular on the live streaming platform Twitch, having native support for Linux, and had an application programming interface (API) available. Before becoming a team of five, the first public demonstration occurred at The International 2017 in August, the annual premiere championship tournament for the game, where Dendi, a Ukrainian professional player, lost against an OpenAI bot in a live one-on-one matchup. After the match, CTO Greg Brockman explained that the bot had learned by playing against itself for two weeks of real time, and that the learning software was a step in the direction of creating software that can handle complex tasks "like being a surgeon". OpenAI used a methodology called reinforcement learning, as the bots learn over time by playing against itself hundreds of times a day for months, in which they are rewarded for actions such as killing an enemy and destroying towers. By June 2018, the ability of the bots expanded to play together as a full team of five and were able to defeat teams of amateur and semi-professional players. At The International 2018, OpenAI Five played in two games against professional teams, one against the Brazilian-based paiN Gaming and the other against an all-star team of former Chinese players. Although the bots lost both matches, OpenAI still considered it a successful venture, stating that playing against some of the best players in Dota 2 allowed them to analyze and adjust their algorithms for future games. The bots' final public demonstration occurred in April 2019, where they won a best-of-three series against The International 2018 champions OG at a live event in San Francisco. A four-day online event to play against the bots, open to the public, occurred the same month. There, the bots played in 42,729 public games, winning 99.4% of those games. == Architecture == Each OpenAI Five bot is a neural network containing a single layer with a 4096-unit LSTM that observes the current game state extracted from the Dota developer's API. The neural network conducts actions via numerous possible action heads (no human data involved), and every head has meaning. For instance, the number of ticks to delay an action, what action to select – the X or Y coordinate of this action in a grid around the unit. In addition, action heads are computed independently. The AI system observes the world as a list of 20,000 numbers and takes an action by conducting a list of eight enumeration values. Also, it selects different actions and targets to understand how to encode every action and observe the world. OpenAI Five has been developed as a general-purpose reinforcement learning training system on the "Rapid" infrastructure. Rapid consists of two layers: it spins up thousands of machines and helps them 'talk' to each other and a second layer runs software. By 2018, OpenAI Five had played around 180 years worth of games in reinforcement learning running on 256 GPUs and 128,000 CPU cores, using Proximal Policy Optimization, a policy gradient method. == Comparisons with other game AI systems == Prior to OpenAI Five, other AI versus human experiments and systems have been successfully used before, such as Jeopardy! with Watson, chess with Deep Blue, and Go with AlphaGo. In comparison with other games that have used AI systems to play against human players, Dota 2 differs as explained below: Long run view: The bots run at 30 frames per second for an average match time of 45 minutes, which results in 80,000 ticks per game. OpenAI Five observes every fourth frame, generating 20,000 moves. By comparison, chess usually ends before 40 moves, while Go ends before 150 moves. Partially observed state of the game: Players and their allies can only see the map directly around them. The rest of it is covered in a fog of war which hides enemies units and their movements. Thus, playing Dota 2 requires making inferences based on this incomplete data, as well as predicting what their opponent could be doing at the same time. By comparison, Chess and Go are "full-information games", as they do not hide elements from the opposing player. Continuous action space: Each playable character in a Dota 2 game, known as a hero, can take dozens of actions that target either another unit or a position. The OpenAI Five developers allow the space into 170,000 possible actions per hero. Without counting the perpetual aspects of the game, there are an average of ~1,000 valid actions each tick. By comparison, the average number of actions in chess is 35 and 250 in Go. Continuous observation space: Dota 2 is played on a large map with ten heroes, five on each team, along with dozens of buildings and non-player character (NPC) units. The OpenAI system observes the state of a game through developers' bot API, as 20,000 numbers that constitute all information a human is allowed to get access to. A chess board is represented as about 70 lists, whereas a Go board has about 400 enumerations. == Reception == OpenAI Five have received acknowledgement from the AI, tech, and video game community at large. Microsoft founder Bill Gates called it a "big deal", as their victories "required teamwork and collaboration". Chess champion Garry Kasparov, who lost against the Deep Blue AI in 1997, stated that despite their losing performance at The International 2018, the bots would eventually "get there, and sooner than expected". In a conversation with MIT Technology Review, AI experts also considered OpenAI Five system as a significant achievement, as they noted that Dota 2 was an "extremely complicated game", so even beating non-professional players was impressive. PC Gamer wrote that their wins against professional players was a significant event in machine learning. In contrast, Motherboard wrote that the victory was "basically cheating" due to the simplified hero pools on both sides, as well as the fact that bots were given direct access to the API, as opposed to using computer vision to interpret pixels on the screen. The Verge wrote that the bots were evidence that the company's approach to reinforcement learning and its general philosophy about AI was "yielding milestones". In 2019, DeepMind unveiled a similar bot for StarCraft II, AlphaStar. Like OpenAI Five, AlphaStar used reinforcement learning and self-play. The Verge reported that "the goal with this type of AI research is not just to crush humans in various games just to prove it can be done. Instead, it's to prove that — with enough time, effort, and resources — sophisticated AI software can best humans at virtually any competitive cognitive challenge, be it a board game or a modern video game." They added that the DeepMind and OpenAI victories were also a testament to the power of certain uses of reinforcement learning. It was OpenAI's hope that the technology could have applications outside of the digital realm. In 2018, they were able to reuse the same reinforcement learning algorithms and training code from OpenAI Five for Dactyl, a human-like robot hand with a neural network built to manipulate physical objects. In 2019, Dactyl solved the Rubik's Cube.

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