AI App Gemini

AI App Gemini — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

European Cloud Partnership

The European Cloud Partnership (ECP) is an advisory group set up by the European Commission as part of the European Cloud Computing Strategy to provide guidance on the development of cloud computing in the European Union. The ECP is led by a steering board composed of representatives of the IT and telecom industry as well as European government policymakers. == History == After publishing a document, "Unleashing the Potential of Cloud Computing in Europe", the European Commission set up the European Cloud Partnership in 2012, with a steering board including both government and industry representatives. The ECP's first meeting was held on 19 November 2012; it was chaired by the President of Estonia Toomas Hendrik Ilves. In 2013 the ECP began drafting its charter. That year, as information about the PRISM scandal came to light, the ECP emphasized the need for Europe to develop its own cloud infrastructure, rather than depend on that of the United States. It completed a report titled "Trusted Cloud Europe" in February 2014 defining its policy, and outlining a process for effective public and private sector participation in cloud computing development in Europe. The report recommended that the commission identify technical, legal and operational best practices, and promote these through certifications and guidelines, and facilitate recognition across national boundaries. The report also recommended that the commission identify cloud computing stakeholders and help them work together through consultations and workshops. In March 2014, the European Commission invited external parties to submit opinions, take part in a discussion forum and complete an online survey in response to the report.
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Zvi Mowshowitz

Zvi Mowshowitz is an American writer and member of the rationalist community who primarily discusses new developments in artificial intelligence. He is a former competitive Magic: The Gathering player and was CEO of MetaMed. == Career == Mowshowitz is an alumnus of Columbia University and holds a bachelor's degree in mathematics. He co-founded and was the CEO of MetaMed, a medical research analysis firm. He has worked at Jane Street Capital, and has worked for the gambling industry in Las Vegas. He attempted to launch a blockchain game, Emergents, in 2020. === Magic: The Gathering === Mowshowitz held a developer intern position at Wizards of the Coast R&D in 2005. He created the deck TurboZvi. His first-place finishes at major competitions were the 1999 World Championships as part of the four-person United States national team, the 2001 Pro Tour Tokyo, and two 2003 Grand Prix. He has placed in the top eight of four Pro Tours, and earned over $140,000 playing Magic competitively. In 2007, Mowshowitz was elected into the Magic Hall of Fame. Last updated: 12 May 2013Source: Wizards.com Mowshowitz has written about Magic for several outlets, including the official Magic website. === Later career === Mowshowitz is on the board of directors for the Center for Applied Rationality, and is a member of the rationalist community. He also founded Balsa Research, a nonprofit think tank which advocated for the repeal of the Jones Act, increasing the housing supply, and reform of the National Environmental Policy Act. In 2023, Mowshowitz wrote an article for Vox on the topic of artificial intelligence safety. Mowshowitz has a blog on Substack under the name "Don't Worry about the Vase". He has written on topics such as artificial intelligence, economics, and the COVID-19 pandemic. == Personal life == Mowshowitz is the son of American biochemist Deborah Mowshowitz. His parents have both worked as Columbia University professors.
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The Machine Question

The Machine Question: Critical Perspectives on AI, Robots, and Ethics is a 2012 nonfiction book by David J. Gunkel that discusses the evolution of the theory of human ethical responsibilities toward non-human things and to what extent intelligent, autonomous machines can be considered to have legitimate moral responsibilities and what legitimate claims to moral consideration they can hold. The book was awarded as the 2012 Best Single Authored Book by the Communication Ethics Division of the National Communication Association. == Content == The book is spread across three chapters, with the first two chapters focusing on an overall review of the history of philosophy and its discussion of moral agency, moral rights, human rights, and animal rights and the third chapter focusing on what defines "thingness" and why machines have been excluded from moral and ethical consideration due to a misuse of the patient/agent binary. The first chapter, titled Moral Agency, breaks down the history of said agency based on what it included and excluded in various parts of history. Gunkel also raises the conflict between discussing the morality of humans toward objects and the theory of the philosophy of technology that "technology is merely a tool: a means to an end". The main issue, he explains, in defining what constitutes an appropriate moral agent is that there will be things left outside of what is included, as the definition is based on a set of characteristics that will inherently not be all-encompassing. The subject of consciousness is broached and subsequently derided by Gunkel because of it being one of the main arguments against machine rights, while Gunkel points out that no "settled definition" of the term exists and that he considers it no better than a synonym used for "the occultish soul". In addition, the issue of the other minds problem entails that no proper understanding of consciousness can come to pass due to the inability to properly understand the mind of a being that is not oneself. The second chapter, titled Moral Patiency, focuses on the patient end of the topic and discusses the expansion of the fields of animal studies and environmental studies. Gunkel describes moral patients as the ones that are to be the object of moral consideration and deserve such consideration even if they lack their own agency, such as animals, thus allowing moral consideration itself to be broader and more inclusive. The topic of other minds is discussed again when examining the question of whether animals can suffer, a question that Gunkel ultimately abandons because it encounters the same problems that the topic of consciousness does. Especially because the subject of animal rights is often only afforded for the animals deemed to be "cute", but often not including "reptiles, insects, or microbes". Gunkel continues on to examine environmental ethics and information ethics, but finds them to be too anthropocentric, just as all the other examined models have been. The third chapter, titled Thinking Otherwise, proposes a combination of Heideggerian ontology and Levinasian ethics to properly discuss the otherness of technology and machines, but finds that the patient/agent binary is unable to be properly extended to confine the extent of "the machine question". In discussing the land ethic philosophy espoused by Aldo Leopold, Gunkel proposes that it is the entire relationship between agent and patient that should have moral consideration and not a specific definition based on either side, as each part contributes to the relationship as a whole and cannot be removed without breaking that relationship. == Critical reception == Choice: Current Reviews for Academic Libraries writer R. S. Stansbury explained that the book is able to use simple examples to discuss difficult topics and separate ideas and that it would be "useful for philosophy students, and for engineering students interested in exploring the ethical implications of their work". Dominika Dzwonkowska, writing for International Philosophical Quarterly, stated that the "unprecedented value of the book is that Gunkel not only analyzes important aspects of the immediate problem but also that he places his discussion in the context of philosophical discussions on such related issues as rights discourse." Mark Coeckelbergh in Ethics and Information Technology noted that focusing on the question itself of the machine question allows further exploration of machine ethics and the expansion of general ethics and that the book's questions point out that "good, critical philosophical reflection on machines is not only about how we should cope with machines, but also about how we (should) think and what role technology plays (and should play) in this thinking." A review in Notre Dame Philosophical Reviews by Colin Allen criticized some of Gunkel's methodology and the indecisiveness of his ultimate answer to the machine question, but also acknowledged that the book "succeeded in connecting the ethics of robots and AI to a much broader ethical discussion than has been represented in the literature on machine ethics to date". Blay Whitby, in a review for AISB Quarterly, lauded The Machine Question for its "clear exposition" and wide range of references to other works, concluding that the book is "essential reading for philosophers interested in AI, robot ethics, or animal ethics". In a twin review of The Machine Question and Robot Ethics: The Ethical and Social Implications of Robots by Patrick Lin, Keith Abney, and George A. Bekey, Techné: Research in Philosophy and Technology reviewer Jeff Shaw called Gunkel's book a good introduction to the "complex field of robot ethics" and that both books are "highly recommended to both the general reader as well as to experts in the field of robotics, philosophy, and ethics." In a 2017 paper for Ethics and Information Technology, Katharyn Hogan investigated whether the machine question presented by Gunkel in the book is any different from the longstanding animal question. She concludes that the real question that is revealed from this discussion is whether humans deserve any moral preference over artificial life in the first place.
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Is-a

In knowledge representation, ontology components and ontology engineering, including for object-oriented programming and design, is-a (also written as is_a or is a) is a subsumptive relationship between abstractions (e.g., types, classes), wherein one class A is a subclass of another class B (and so B is a superclass of A). In other words, type A is a subtype of type B when A's specification implies B's specification. That is, any object (or class) that satisfies A's specification also satisfies B's specification, because B's specification is weaker. For example, a cat 'is a[n]' animal, but not vice versa. All cats are animals, but not all animals are cats. Behaviour that is relevant to all animals is defined on an animal class, whereas behaviour that is relevant only for cats is defined in a cat class. By defining the cat class as 'extending' the animal class, all cats 'inherit' the behaviour defined for animals, without the need to explicitly code that behaviour for cats. == Related concepts == The is-a relationship is to be contrasted with the has-a (has_a or has a) relationship between types (classes); confusing the relations has-a and is-a is a common error when designing a model (e.g., a computer program) of the real-world relationship between an object and its subordinate. The is-a relationship may also be contrasted with the instance-of relationship between objects (instances) and types (classes): see Type–token distinction. To summarize the relations, there are: hyperonym–hyponym (supertype/superclass–subtype/subclass) relations between types (classes) defining a taxonomic hierarchy, where for a subsumption relation: a hyponym (subtype, subclass) has a type-of (is-a) relationship with its hyperonym (supertype, superclass); holonym–meronym (whole/entity/container–part/constituent/member) relations between types (classes) defining a possessive hierarchy, where for an aggregation (i.e. without ownership) relation: a holonym (whole) has a has-a relationship with its meronym (part), for a composition (i.e. with ownership) relation: a meronym (constituent) has a part-of relationship with its holonym (entity), for a containment relation: a meronym (member) has a member-of relationship with its holonym (container); concept–object (type–token) relations between types (classes) and objects (instances), where a token (object) has an instance-of relationship with its type (class).
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Speech recognition

Speech recognition (automatic speech recognition (ASR), computer speech recognition, or speech-to-text (STT)) is a sub-field of computational linguistics concerned with methods and technologies that translate spoken language into text or other interpretable forms. Speech recognition applications include voice user interfaces, where the user speaks to a device, which "listens" and processes the audio. Common voice applications include interpreting commands for calling, call routing, home automation, and aircraft control. These applications are called direct voice input. Productivity applications include searching audio recordings, creating transcripts, and dictation. Speech recognition can be used to analyse speaker characteristics, such as identifying native language using pronunciation assessment. Voice recognition (speaker identification) refers to identifying the speaker, rather than speech contents. Recognizing the speaker can simplify the task of translating speech in systems trained on a specific person's voice. It can also be used to authenticate the speaker as part of a security process. == History == Applications for speech recognition developed over many decades, with progress accelerated due to advances in deep learning and the use of big data. These advances are reflected in an increase in academic papers, and greater system adoption. Key areas of growth include vocabulary size, more accurate recognition for unfamiliar speakers (speaker independence), and faster processing speed. === Pre-1970 === 1952 – Bell Labs researchers, Stephen Balashek, R. Biddulph, and K. H. Davis, built Audrey for single-speaker digit recognition. Their system located the formants in the power spectrum of each utterance. 1960 – Gunnar Fant developed and published the source–filter model of speech production. 1962 – IBM's 16-word "Shoebox" machine's speech recognition debuted at the 1962 World's Fair. 1966 – Linear predictive coding, a speech coding method, was proposed by Fumitada Itakura of Nagoya University and Shuzo Saito of Nippon Telegraph and Telephone. 1969 – Funding at Bell Labs came to a halt for several years after the company's head engineer, John R. Pierce, wrote an open letter criticizing speech recognition research. This defunding lasted until Pierce retired and James L. Flanagan took over. Raj Reddy was the first person to work on continuous speech recognition, as a graduate student at Stanford University in the late 1960s. Previous systems required users to pause after each word. Reddy's system issued spoken commands for playing chess. Around this time, Soviet researchers invented the dynamic time warping (DTW) algorithm and used it to create a recognizer capable of operating on a 200-word vocabulary. DTW processed speech by dividing it into short frames (e.g. 10 ms segments) and treating each frame as a unit. Speaker independence, however, remained unsolved. === 1970–1990 === 1971 – DARPA funded a five-year speech recognition research project, Speech Understanding Research, seeking a minimum vocabulary size of 1,000 words. The project considered speech understanding a key to achieving progress in speech recognition, which was later disproved. BBN, IBM, Carnegie Mellon (CMU), and Stanford Research Institute participated. 1972 – The IEEE Acoustics, Speech, and Signal Processing group held a conference in Newton, Massachusetts. 1976 – The first ICASSP was held in Philadelphia, which became a major venue for publishing on speech recognition. During the late 1960s, Leonard Baum developed the mathematics of Markov chains at the Institute for Defense Analysis. A decade later, at CMU, Raj Reddy's students James Baker and Janet M. Baker began using the hidden Markov model (HMM) for speech recognition. James Baker had learned about HMMs while at the Institute for Defense Analysis. HMMs enabled researchers to combine sources of knowledge, such as acoustics, language, and syntax, in a unified probabilistic model. By the mid-1980s, Fred Jelinek's team at IBM created a voice-activated typewriter called Tangora, which could handle a 20,000-word vocabulary. Jelinek's statistical approach placed less emphasis on emulating human brain processes in favor of statistical modelling. (Jelinek's group independently discovered the application of HMMs to speech.) This was controversial among linguists since HMMs are too simplistic to account for many features of human languages. However, the HMM proved to be a highly useful way for modelling speech and replaced dynamic time warping as the dominant speech recognition algorithm in the 1980s. 1982 – Dragon Systems, founded by James and Janet M. Baker, was one of IBM's few competitors. === Practical speech recognition === The 1980s also saw the introduction of the n-gram language model. 1987 – The back-off model enabled language models to use multiple-length n-grams, and CSELT used HMM to recognize languages (in software and hardware, e.g. RIPAC). At the end of the DARPA program in 1976, the best computer available to researchers was the PDP-10 with 4 MB of RAM. It could take up to 100 minutes to decode 30 seconds of speech. Practical products included: 1984 – the Apricot Portable was released with up to 4096 words support, of which only 64 could be held in RAM at a time. 1987 – a recognizer from Kurzweil Applied Intelligence 1990 – Dragon Dictate, a consumer product released in 1990. AT&T deployed the Voice Recognition Call Processing service in 1992 to route telephone calls without a human operator. The technology was developed by Lawrence Rabiner and others at Bell Labs. By the early 1990s, the vocabulary of the typical commercial speech recognition system had exceeded the average human vocabulary. Reddy's former student, Xuedong Huang, developed the Sphinx-II system at CMU. Sphinx-II was the first to do speaker-independent, large vocabulary, continuous speech recognition, and it won DARPA's 1992 evaluation. Handling continuous speech with a large vocabulary was a major milestone. Huang later founded the speech recognition group at Microsoft in 1993. Reddy's student Kai-Fu Lee joined Apple, where, in 1992, he helped develop the Casper speech interface prototype. Lernout & Hauspie, a Belgium-based speech recognition company, acquired other companies, including Kurzweil Applied Intelligence in 1997 and Dragon Systems in 2000. L&H was used in Windows XP. L&H was an industry leader until an accounting scandal destroyed it in 2001. L&H speech technology was bought by ScanSoft, which became Nuance in 2005. Apple licensed Nuance software for its digital assistant Siri. ==== 2000s ==== In the 2000s, DARPA sponsored two speech recognition programs: Effective Affordable Reusable Speech-to-Text (EARS) in 2002, followed by Global Autonomous Language Exploitation (GALE) in 2005. Four teams participated in EARS: IBM; a team led by BBN with LIMSI and the University of Pittsburgh; Cambridge University; and a team composed of ICSI, SRI, and the University of Washington. EARS funded the collection of the Switchboard telephone speech corpus, which contained 260 hours of recorded conversations from over 500 speakers. The GALE program focused on Arabic and Mandarin broadcast news. Google's first effort at speech recognition came in 2007 after recruiting Nuance researchers. Its first product, GOOG-411, was a telephone-based directory service. Since at least 2006, the U.S. National Security Agency has employed keyword spotting, allowing analysts to index large volumes of recorded conversations and identify speech containing "interesting" keywords. Other government research programs focused on intelligence applications, such as DARPA's EARS program and IARPA's Babel program. In the early 2000s, speech recognition was dominated by hidden Markov models combined with feed-forward artificial neural networks (ANN). Later, speech recognition was taken over by long short-term memory (LSTM), a recurrent neural network (RNN) published by Sepp Hochreiter & Jürgen Schmidhuber in 1997. LSTM RNNs avoid the vanishing gradient problem and can learn "Very Deep Learning" tasks that require memories of events that happened thousands of discrete time steps earlier, which is important for speech. Around 2007, LSTMs trained with Connectionist Temporal Classification (CTC) began to outperform. In 2015, Google reported a 49 percent error‑rate reduction in its speech recognition via CTC‑trained LSTM. Transformers, a type of neural network based solely on attention, were adopted in computer vision and language modelling, and then to speech recognition. Deep feed-forward (non-recurrent) networks for acoustic modelling were introduced in 2009 by Geoffrey Hinton and his students at the University of Toronto, and by Li Deng and colleagues at Microsoft Research. In contrast to the prioer incremental improvements, deep learning decreased error rates by 30%. Both shallow and deep forms (e.g., recurrent nets) of ANNs had been explored since the 1980s. Howev
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Sarvam AI

Sarvam AI is an Indian artificial intelligence company headquartered in Bengaluru, Karnataka. Founded in 2023, the company develops large language models (LLMs) and multimodal AI systems with a focus on Indian languages and region-specific use cases. The company has received venture capital backing and has participated in government-supported AI initiatives, including India's sovereign large language model programme under the IndiaAI Mission. == History == Sarvam AI was founded in August 2023 by Vivek Raghavan and Pratyush Kumar, who were previously associated with AI4Bharat at the Indian Institute of Technology Madras. In December 2023, the company announced a combined seed and Series A funding round of approximately US$41 million. The round was led by Lightspeed Venture Partners, with participation from Peak XV Partners and Khosla Ventures. In April 2025, the Ministry of Electronics and Information Technology (MeitY) selected Sarvam AI as one of the companies to develop an indigenous foundational model under the IndiaAI Mission. As part of the initiative, the company received access to government-supported computing infrastructure, including GPUs allocated for model training over a specified period. In February 2026, Sarvam AI introduced two large language models at the AI Impact Summit held at Bharat Mandapam, New Delhi. == Products and technology == Sarvam AI develops language models trained on datasets that include multiple Indian languages and code-mixed text. The company uses mixture-of-experts (MoE) architectures in some of its models. === Foundational language models === On 18 February 2026, the company announced the release of two foundational models: Sarvam-30B – A 30-billion parameter model based on a mixture-of-experts design. According to company disclosures reported by the media, the model activates approximately 1 billion parameters per token and supports a 32,000-token context window. Sarvam-105B – A 105-billion parameter model activating approximately 9 billion parameters per token, with a 128,000-token context window. The model is positioned for complex reasoning and enterprise applications. On 20th February 2026, the company released a beta version of the Sarvam-105B model which is named Indus. It is available on the Apple App Store, Google Play Store and the web. === Speech and vision systems === Sarvam AI has also developed multimodal systems including speech-to-text and vision-language models. Its speech model, referred to as Saaras V3 in company materials, supports multiple Indian languages. The company has also introduced a vision-language model known as Sarvam Vision, intended for document understanding and optical character recognition (OCR) in Indian scripts. === Devices === 'Sarvam Kaze' is an indigenous AI-powered wearable glass that listens, understands, and captures what users see the world through their eyes in real time. The device supports more than 10 Indian languages, enabling voice-based interaction and potentially real-time translation. The company plans to launch the device in May 2026. == Startup support == In March 2026, Sarvam AI launched the Sarvam Startup Program, an initiative providing selected early-stage companies with 6–12 months of API credits scaled to their needs, priority engineering support, and access to production infrastructure for developing multilingual AI applications in areas such as speech, translation, and large language models. == Open-source release == In February 2026, Sarvam AI announced and open-sourced two large language models: Sarvam 30B (30 billion parameters) and Sarvam 105B (105 billion parameters, using a Mixture-of-Experts architecture with 10.3 billion active parameters). Both models were trained from scratch on datasets focused on Indian languages and support advanced reasoning, multilingual tasks, mathematics, and coding. The models are hosted on Hugging Face under the Apache License and are intended for enterprise and developer applications in Indian languages. The models were subsequently released as open source under the Apache License 2.0, with model weights made available on Hugging Face (sarvamai/sarvam-30b and sarvamai/sarvam-105b) and AIKosh in early March 2026. == Government and institutional collaborations == In 2025, Sarvam AI was selected to contribute to India's sovereign AI model initiative under the IndiaAI Mission. The initiative aims to support domestic AI infrastructure and model development. In March 2025, the Unique Identification Authority of India (UIDAI) announced a collaboration with Sarvam AI to integrate AI-based voice interactions and multilingual support into Aadhaar-related services. Sarvam AI has also worked with AI4Bharat and academic institutions on language datasets and speech research projects. == Industry participation == Sarvam AI presented its foundational models at the India AI Impact Summit 2026 in New Delhi. The company has also been listed among Indian members of the AI Alliance, a consortium focused on open-source artificial intelligence initiatives. == List of models ==
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AlphaStar (software)

AlphaStar is an artificial intelligence (AI) software developed by DeepMind for playing the video game StarCraft II. It was unveiled to the public by name in January 2019. AlphaStar attained "Grandmaster" status in August 2019, considered a milestone for AI in video games at the time. == Background == Games created for humans are considered to have external validity as benchmarks of progress in artificial intelligence. IBM's chess engine Deep Blue (1997) and DeepMind's AlphaGo (2016) were considered major milestones; some argue that StarCraft would also be a major milestone, due to the game's "real-time play, partial observability, no single dominant strategy, complex rules that make it hard to build a fast forward model, and a particularly large and varied action space." Though difficult, StarCraft may still be tractable with current technology because "its rules are known and the world is discrete with only a few types of objects". StarCraft II is a popular fast-paced online real-time strategy game developed by Blizzard Entertainment. == History == DeepMind Technologies was founded in the UK in 2010. As early as 2011, founder Demis Hassabis called StarCraft "the next step up" after games like Go. DeepMind became a subsidiary of Google in 2014, after demonstrating self-learning bots with superhuman ability at a variety of Atari 2600 games. In February 2015, computer scientist Zachary Mason predicted Deepmind's research "leads to StarCraft in five or ten years". In March 2016, following AlphaGo's victory over Lee Sedol, a world champion Go player, Hassabis publicly mulled building an AI for StarCraft, citing it as a strategic game with incomplete information where, unlike Go, much of the "board" is invisible. A formal collaboration was announced at BlizzCon in November 2016, alongside a plan to release an open development environment for bots in Q1 of 2017. By 2017, DeepMind was experimenting with feeding StarCraft data into its software. In August 2017, DeepMind and Blizzard released development tools to assist in bot development, as well as data from 65,000 historical games. At the time, computer scientist and StarCraft tournament manager David Churchill estimated it would take five years for a bot to beat a human, but made the caveat that AlphaGo had beaten expectations. In Wired, tech journalist Tom Simonite stated "No one expects the robot to win anytime soon. But when it does, it will be a far greater achievement than DeepMind's conquest of Go." In December 2018, DeepMind's bot defeated professional player Grzegorz "MaNa" Komincz, 5-0. DeepMind announced the bot, named "AlphaStar", in January 2019. A journalist at Ars Technica and others argued that AlphaStar still had unfair advantages: "AlphaStar has the ability to make its clicks with surgical precision using an API, whereas human players are constrained by the mechanical limits of computer mice". AlphaStar also had a global view rather than being limited by the in-game camera. Furthermore, while there was a cap on the number of actions over a five-second window, AlphaStar was free to allocate its action quota unevenly across the window in order to launch superhuman bursts of activity at critical moments. DeepMind quickly retrained AlphaStar under more realistic constraints, and then lost a rematch with Komincz. Starting in July 2019, the new, constrained version of AlphaStar anonymously competed against players who "opted in" on the public 1v1 European multiplayer ladder. By the end of August 2019, AlphaStar had attained Grandmaster level, ranking among the top 0.2% of human players. == Algorithms == Unlike AlphaZero, AlphaStar initially learns to imitate the moves of the best players in its database of human vs. human games; this step is necessary to solve what DeepMind's Dave Silver calls "the exploration problem": discovering new strategies would otherwise be like finding a "needle in a haystack". Agents then play each other and deploy deep reinforcement learning. These main agents also learn by playing against suboptimal "exploiter agents" whose purpose is to expose weaknesses in the main agents. == Reactions == After his 5-0 defeat in December 2018, Komincz stated "I wasn't expecting the AI to be that good". Stuart Russell assessed that AlphaStar's 2018 victory required "a fair amount of problem-specific effort" and that general-purpose methods were "not quite ready for StarCraft". An article in Wired UK judged AlphaStar's new constraints, adopted for the July 2019 matches, to be "fair" this time around. StarCraft professional Raza "RazerBlader" Sekha stated AlphaStar was "impressive" but had its quirks, succumbing in one game to an unorthodox army composition made up of only air units. The UK's top player, Joshua "RiSky" Hayward, expressed some disappointment, saying AlphaStar "often didn't make the most efficient, strategic decisions". Professional Diego "Kelazhur" Schwimer called AlphaStar's play "unimaginably unusual; it really makes you question how much of StarCraft's diverse possibilities pro players have really explored". AlphaStar's opponents often did not realize they were playing a bot. Ian Sample, of The Guardian, called AlphaStar a "landmark achievement" for the field of AI. Churchill stated that he had previously seen bots that master one or two elements of StarCraft, but that AlphaStar was the first that can handle the game in its entirety. Gary Marcus expressed his continuing skepticism about deep learning, stating: "So far the field has struggled to take techniques like this out of the laboratory and game environments and into the real world, and I don't immediately see this result as progress in that direction". AI researcher Jon Dodge was surprised by AlphaStar, stating that he did not expect such a "superhuman" performance for "another couple of years"; in contrast, Churchill states "StarCraft is nowhere near being 'solved', and AlphaStar is not yet even close to playing at a world champion level". == Legacy == DeepMind argues that insights from AlphaStar might benefit robots, self-driving cars, and virtual assistants, which need to operate with "imperfectly observed information". Silver has indicated his lab "may rest at this point", rather than try to substantially improve AlphaStar. Silver himself argues that "AlphaStar has become the first AI system to reach the top tier of human performance in any professionally played e-sport on the full unrestricted game under professionally approved conditions... Ever since computers cracked Go, chess, and poker, the game of StarCraft has emerged, essentially by consensus from the community, as the next grand challenge for AI." Computer scientist Noel Sharkey argues, disapprovingly, that "military analysts will certainly be eyeing the successful AlphaStar real-time strategies as a clear example of the advantages of AI for battlefield planning". In contrast, Silver argues: "To say that this has any kind of military use is saying no more than to say an AI for chess could be used to lead to military applications".
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Leading the Future

Leading the Future is an American super PAC network focused on lobbying for policies friendly to the artificial intelligence industry. It was launched in 2025 with over $100 million from industry stakeholders including Andreessen Horowitz, OpenAI President Greg Brockman and Palantir co-founder Joe Lonsdale. The launch was preceded by talks between Collin McCune, head of government affairs at Andreessen Horowitz, and Chris Lehane, chief global affairs officer at OpenAI. Among the members of the network are the American Mission PAC, which supported Chris Gober, and the Think Big PAC, which targeted Alex Bores. Leading the Future is affiliated with the nonprofit Build American AI, which Axios describes as a dark money advocacy "offshoot" operating alongside the super PAC. NBC News states that the network’s efforts are modeled after the pro-cryptocurrency group Fairshake. Leading the Future is led by Zac Moffatt and Josh Vlasto, the latter of whom previously served as an advisor to Fairshake. In response to the creation of Leading the Future, former members of Congress Brad Carson and Chris Stewart co-founded the super PAC network Public First, aiming to counter the group’s influence. In April 2026, an investigation by Model Republic linked Leading the Future to The Wire By Acutus, an automated news website that allegedly used AI agents posing as human journalists to solicit interviews. The site's content was found to closely mirror the PAC's deregulatory policy goals while targeting researchers and advocates skeptical of rapid AI development. In May 2026, Wired revealed that Build American AI used a "dark money" campaign to pay TikTok and Instagram influencers $5,000 per video to promote scripted narratives framing Chinese AI as a "national security threat." According to internal documents and staff at the marketing agency managing the project, the campaign's explicit goal was to "subtly shift public debate" toward the deregulation of AI industries while intentionally avoiding technical discussions regarding AI quality or safety. During the 2026 primary season Leading the Future went on to endorse several candidates in both Democratic and Republican races with several of them going on to win.
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FarPoint Spread

FarPoint Spread is a suite of Microsoft Excel-compatible spreadsheet components available for .NET, COM, and Microsoft BizTalk Server. Software developers use the components to embed Microsoft Excel-compatible spreadsheet features into their applications, such as importing and exporting Microsoft Excel files, displaying, modifying, analyzing, and visualizing data. Spread components handle spreadsheet data at the cell, row, column, or worksheet level. This article is about the last FarPoint edition of the Spread product line. Spread is now developed by GrapeCity, Inc. Since the acquisition, Spread for Biztalk Server has been removed from the product line and SpreadJS, a JavaScript version, has been added. == History == 1991 Spread released as a DLL control as the initial product offering from FarPoint Technologies, Inc. 1990s Spread VBX released. Spread ActiveX released. These components are now known as Spread COM. 2003 Spread for Windows Forms released as a completely new managed C# version prompted by the launch of Visual Studio .NET. 2003 Spread for Web Forms (now Spread for ASP.NET) released. 2006 Spread for BizTalk released. 2009 FarPoint Technologies acquired by GrapeCity. == Versions == Spread for Windows Forms: 5.0 Spread for Web Forms: 5.0 Spread COM: 8.0 Spread for BizTalk: 3.0 === Spread for Windows Forms === FarPoint Spread for Windows Forms is a Microsoft Excel-compatible spreadsheet component for Windows Forms applications developed using Microsoft Visual Studio and the .NET Framework. Developers use it to add grids and spreadsheets to their applications, and to bind them to data sources. In version 4.0, new cell types were added to display barcodes and fractions, and exports for XML and PDF were added. === Spread for ASP.NET === FarPoint Spread for ASP.NET is a Microsoft Excel-compatible spreadsheet component for ASP.NET applications. Developers use it to add grids and spreadsheets to their applications, === Spread for COM === FarPoint Spread 8 COM allows COM and ActiveX applications to incorporate spreadsheet features. In the 1997 book Visual Basic 5 for Windows for Dummies, Wally Wang lists an early version of Spread COM in Chapter 35: The Ten Most Useful Visual Basic Add-On Programs. === Spread for BizTalk === FarPoint Spread for BizTalk Server allows developers to integrate Microsoft Excel documents into Microsoft BizTalk applications. Spread for BizTalk Server includes two components: Spreadsheet Pipeline Disassembler - Parses data from Microsoft Excel (XLS and Excel 2007 XML, CSV, TXT) documents into XML data for processing through Microsoft BizTalk Server receive pipelines. Spreadsheet Pipeline Assembler - Assembles data from Microsoft BizTalk applications into Microsoft Excel (XLS or Excel 2007 XML) or PDF documents for transport through Microsoft BizTalk Server send pipelines. Developers find it a useful tool for organizations with Microsoft BizTalk Server Enterprise Application Integration. Prior to this release, BizTalk users wanting to use Excel data had to manually open the files and copy and paste data between the two applications. == Features == These features are common to all versions. Predefined cell types, including: currency date time number percent regular expression button check box combo box hyperlink image Formula support, including: cross-sheet referencing over 300 built-in functions Import and export: import to Microsoft Excel-compatible files export to Microsoft Excel-compatible files export to HTML files export to XML files Design-time spreadsheet designer Data-binding with customizable options Hierarchical data views, with parent rows and child views Grouping of rows or columns Sorting by row or column on multiple keys Cell spanning Multiple row and column headers Bound and unbound modes == Version-Specific Features == === Spread for Windows Forms === Support for Microsoft Visual Studio 2010 Support for Windows Azure AppFabric Integrated chart control Custom cell types Cell notes Child controls Splitter bars Built-in and custom skins and styles PDF export Microsoft Excel 2007 XML Support (Office Open XML, XLSX) Floating Formula Bar Range Selection for Formula Automatic Completion (type ahead) === Spread for ASP.NET === Support for Microsoft Visual Studio 2010 Support for Windows Azure AppFabric Integrated chart control AJAX-enabled Support for Open Document Format (ODF) files Multiple edits on multiple rows without server round trips Client-side column and row resizing Load on demand, which loads data from the server as needed for viewing Native Microsoft Excel import and export In-cell editing Multiple edits on multiple rows without server round trips Client-side column and row resizing Multiple sheets Searching Filtering Validations Cell spans PDF export === Spread COM === Custom cell types Cell notes Virtual mode for data loading Unicode support Customizable printing Text tips Import and export: Microsoft Excel 97 Excel 2000 Excel 2007 (requires the .NET Framework) Enhanced printing 64 bit DLL === Spread for BizTalk === Integration of Microsoft Excel data into Microsoft BizTalk applications Design-time spreadsheet schema wizard and spreadsheet format designer == Supported document formats == Adobe Portable Document Format PDF (.pdf) HTML Web Page (.html) Microsoft Excel Workbook (.xls) Plain Text (.txt) Comma-Separated Values (.csv) Open Document Format (Spread for ASP.NET)
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Jan Leike

Jan Leike (born 1986 or 1987) is an AI alignment researcher who has worked at DeepMind and OpenAI. He joined Anthropic in May 2024. == Education == Jan Leike obtained his undergraduate degree from the University of Freiburg in Germany. After earning a master's degree in computer science, he pursued a PhD in machine learning at the Australian National University under the supervision of Marcus Hutter. == Career == Leike made a six-month postdoctoral fellowship at the Future of Humanity Institute before joining DeepMind to focus on empirical AI safety research, where he collaborated with Shane Legg. === OpenAI === In 2021, Leike joined OpenAI. In June 2023, he and Ilya Sutskever became the co-leaders of the newly introduced "superalignment" project, which aimed to determine how to align future artificial superintelligences within four years to ensure their safety. This project involved automating AI alignment research using relatively advanced AI systems. At the time, Sutskever was OpenAI's Chief Scientist, and Leike was the Head of Alignment. Leike was featured in Time's list of the 100 most influential personalities in AI, both in 2023 and in 2024. In May 2024, Leike announced his resignation from OpenAI, following the departure of Sutskever, Daniel Kokotajlo and several other AI safety employees from the company. Leike wrote that "Over the past years, safety culture and processes have taken a backseat to shiny products", and that he "gradually lost trust" in OpenAI's leadership. In May 2024, Leike joined Anthropic, an AI company founded by former OpenAI employees.
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Feigenbaum test

A Feigenbaum test is a variation of the Turing test where a computer system attempts to replicate an expert in a given field such as chemistry or marketing. It is also known, as a subject matter expert Turing test and was proposed by Edward Feigenbaum in a 2003 paper. The concept is also described by Ray Kurzweil in his 2005 book The Singularity is Near. Kurzweil argues that machines who pass this test are an inevitable consequence of Moore's Law.
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BRFplus

BRFplus (Business Rule Framework plus) is a business rule management system (BRMS) offered by SAP AG. BRFplus is part of the SAP NetWeaver ABAP stack. Therefore, all SAP applications that are based on SAP NetWeaver can access BRFplus within the boundaries of an SAP system. However, it is also possible to generate web services so that BRFplus rules can also be offered as a service in a SOA landscape, regardless of the software platform used by the service consumers. BRFplus development started as a supporting tool that was part of SAP Business ByDesign, an ERP solution targeted at small and medium size companies. By that time, the tool was called "Formula and Derivation Tool" (FDT). Later on, it was decided to maintain BRFplus on those codelines that serve as the basis for SAP Business Suite. With that, business rules that have been created for Business ByDesign can easily be taken over in a full-size SAP system where they are ready for use without any changes. == Overview == BRFplus offers a unified modeling and runtime environment for business rules that addresses both technical users (programmers, system administrators) as well as business users who take care of operational business processes (like procurement, bidding, tax form validation, etc.). The different requirements and usage scenarios of the different target groups can be covered with the help of the SAP authorization system and a user interface that can be individually customized. Being integrated into SAP NetWeaver, BRFplus-based applications can look at, and model, business rules from a strictly business-oriented perspective, rather than starting with the underlying technical artifacts. This is because the integration allows for direct access to the business objects available in the SAP dictionary (like customer, supplier, material, bill, etc.). In addition to the predefined expression types (decision table, decision tree, formula, database access, loops, etc.) and actions (sending e-mails, triggering a workflow, etc.), BRFplus can be extended by custom expression types. Also, direct calls of function modules as well as ABAP OO class methods are supported so that the entire range of the ABAP programming language is available for solving business tasks. BRFplus comes with an optional versioning mechanism. Versioning can be switched on and off for individual objects as well as for entire applications. Versioned business rules are needed in certain use cases for legal reasons, but they also allow for simulating the system behavior as it would have been at a particular point in time. Once the rule objects are in a consistent state and active, the system automatically generates ABAP OO classes that encapsulate the functional scope of the underlying rule object. This is done on an on-demand base and speeds up processing. The execution of functions as well as of single expressions can be simulated. The processing log of the simulation is useful for checking the implementation and for investigating problems. BRFplus applications can be exported and imported as an XML file. This is an easy way of creating a data backup. XML files can also be used for deploying rule applications throughout the company. == Main object types == === Application === The application object serves as a container for all the BRFplus objects that have been assembled to solve a particular business task. It is possible to define certain default settings on application level that are inherited by all objects that are created in the scope of that application. === Function === A function is used to connect a business application with the rule processing framework of BRFplus. The calling business application passes input values to the function which are then processed by the expressions and rulesets that are associated with the called function. The calculated result is then returned to the calling business application. === Expression types and action types === Boolean BRMS Connector Case Database Lookup Decision Table Decision Tree Formula Function Call Loop Procedure Call Random Number Search Tree Step Sequence Value Range1 XSL Transformation === Ruleset === A ruleset is a container for an arbitrary number of rule objects which in turn carry out the necessary calculations with the help of assigned expressions and actions. Instead of assigning an expression to a function, it is also possible to assign any number of rulesets to a function. When the function is called, all assigned rulesets are subsequently processed. === Data objects === BRFplus supports elementary data objects (text, number, boolean, time point, amount, quantity) as well as structures and tables. Structures can be nested. For all types of data objects it is possible to reference data objects that reside in the data dictionary of the backend system. With that, a BRFplus data object does not only inherit the type definition of the referenced object but can also access associated data like domain value lists or object documentation. === Other objects === With catalogs, it is possible to define business-specific subsets of the rule objects that reside in the system. This is helpful for hiding the complexity of a rule system, thus improving usability. Object filters are used by system administrators to ensure that for selected users, only a predefined subset of object types is visible. This is useful to enforce access rights as well as modeling policies. == Other BRM solutions offered by SAP == BRFplus is positioned as the successor product of an older business rule solution known as BRF (Business Rule Framework). For a longer transition phase, both solutions exist in parallel. However, an increasing number of SAP applications that used to be based on BRF are migrating to BRFplus. While BRFplus supports business rules for applications based on the SAP NetWeaver ABAP stack, SAP is offering another product named SAP NetWeaver Business Rules Management (BRM). BRM supports business rule modeling for the SAP NetWeaver Java stack. Both products do not compete. They are available in parallel and can be used in a collaborative approach to deal with use cases where both technology stacks are used in parallel. BRFplus comes with a special expression type that helps bridging the gap between the two different technologies. == Availability == BRFplus has been delivered to the public with SAP NetWeaver 7.0 Enhancement Package 1 for the first time. Being part of SAP NetWeaver, the usage of BRFplus is covered by the "SAP NetWeaver Foundation for Third Party Applications" license, with no additional costs. == Literature == Carsten Ziegler, Thomas Albrecht: BRFplus – Business Rule Management for ABAP Applications. Galileo Press 2011. ISBN 978-1-59229-293-6
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Blanking (video)

In analog video, blanking occurs between horizontal lines and between frames. In raster scan equipment, an image is built up by scanning an electron beam from left to right across a screen to produce a visible trace of one scan line, reducing the brightness of the beam to zero (horizontal blanking), moving it back as fast as possible to the left of the screen at a slightly lower position (the next scan line), restoring the brightness, and continuing until all the lines have been displayed and the beam is at the bottom right of the screen. Its intensity is then reduced to zero again (vertical blanking), and it is rapidly moved to the top left to start again, creating the next frame. In television, in particular, the vertical blanking interval is long to accommodate the slow equipment available at the time the standard was set. Fast modern electronics allows digital information to be encoded into the signal during the vertical blanking interval; it is not displayed on screen as the beam is blanked, but can be processed by appropriate circuitry.
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Aurora (supercomputer)

Aurora is an exascale supercomputer that was sponsored by the United States Department of Energy (DOE) and designed by Intel and Cray for Argonne National Laboratory. It was briefly the second fastest supercomputer in the world from November 2023 to June 2024. The cost was estimated in 2019 to be US$500 million. Olivier Franza is the chief architect and principal investigator of this design. == History == In 2013 DOE presented a proposal for an "exascale" supercomputer, capable of speeds in the neighborhood of 1 exaFLOP (1018 floating point mathematical operations per second) with a maximum power consumption of 20 megawatts (MW) by 2020. Aurora was first announced in 2015 and to be finished in 2018. It was expected to have a speed of 180 petaFLOPS which would be around the speed of Summit. Aurora was meant to be the most powerful supercomputer at the time of its launch and to be built by Cray with Intel processors. Later, in 2017, Intel announced that Aurora would be delayed to 2021 but scaled up to 1 exaFLOP. In March 2019, DOE said that it would build the first supercomputer with a performance of one exaFLOP in the United States in 2021. In October 2020, DOE said that Aurora would be delayed again for a further six months, and would no longer be the first exascale computer in the US. In late October 2021 Intel announced that Aurora would now exceed 2 exaFLOPS in peak double-precision compute – That claim however never was realized. The system was fully installed on June 22, 2023. In May 2024, Aurora appeared at number two on the Top500 supercomputer list, with a performance of 1.012 exaFLOPS, marking the second entry of an exascale capable system on the Top500. == Usage == Functions include research on brain structure, nuclear fusion, low carbon technologies, subatomic particles, cancer and cosmology. It will also develop new materials that will be useful for batteries and more efficient solar cells. It is to be available to the general scientific community. == Architecture == Aurora has 10,624 nodes, with each node being composed of two Intel Xeon Max processors, six Intel Max series GPUs and a unified memory architecture, providing a maximum computing power of 130 teraFLOPS per node. It has around 10 petabytes of memory and 230 petabytes of storage. The machine is stated to consume around 39 MW of power. For comparison, the fastest computer in the world today, El Capitan uses 30 MW, while another Top 500 System, Frontier uses 24 MW.
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Regulation of artificial intelligence in the United States

The United States federal government and state governments have developed some regulation of artificial intelligence, including executive orders, federal laws, and state laws. Federal agencies have also developed some sector-specific regulations related to AI. At the federal level, the Biden administration released an October 2023 executive order about AI safety and security, Executive Order 14110, with directives related to AI development and deployment. President Trump revoked that executive order in January 2025 and issued Executive Order 14179. In December 2025, President Trump signed Executive Order 14365, an executive order directing federal agencies to develop a unified national approach to AI policy, evaluate state AI laws for potential conflicts, challenge them through legal action, and condition certain federal funding on state compliance, while exempting state laws related to child safety, data center infrastructure, and state government procurement. In 2025, Congress passed legislation targeting AI-generated deepfakes, the TAKE IT DOWN Act. Several U.S. states have enacted laws related to artificial intelligence. Some are already in effect, including in California. Other states have AI-related legislation coming into effect in 2026 and 2027. In 2025 and 2026, the Trump administration mentioned the patchwork nature of state legislation as a motivation for its push for unified national legislation regulating AI. The administration has criticized state lawmakers, threatened to sue states, and issued letters to discourage them from regulating AI companies and products; some states have continued to propose and enact related laws. Discussions about regulating AI have included topics such as the timeliness of regulating AI, the nature of the federal regulatory framework to govern and promote AI, including what agency should lead, the regulatory and governing powers of that agency, and how to update regulations in the face of rapidly changing technology, as well as the roles of state governments and courts. == Federal government == === Obama administration (2009–2017) === As early as 2016, the Obama administration had begun to focus on the risks and regulations for artificial intelligence. In an October 2016 report titled Preparing For the Future of Artificial Intelligence, the National Science and Technology Council set a precedent to allow researchers to continue to develop new AI technologies with few restrictions. The report stated that "the approach to regulation of AI-enabled products to protect public safety should be informed by assessment of the aspects of risk". The first National Artificial Intelligence Research And Development Strategic Plan was published in October 2016. === First Trump administration (2017–2021) === On August 13, 2018, Section 1051 of the Fiscal Year 2019 John S. McCain National Defense Authorization Act (P.L. 115-232) established the National Security Commission on Artificial Intelligence "to consider the methods and means necessary to advance the development of artificial intelligence, machine learning, and associated technologies to comprehensively address the national security and defense needs of the United States." Steering on regulating security-related AI is provided by the National Security Commission on Artificial Intelligence. The Artificial Intelligence Initiative Act (S.1558) is a proposed bill that would establish a federal initiative designed to accelerate research and development on AI for, inter alia, the economic and national security of the United States. On January 7, 2019, following an Executive Order on Maintaining American Leadership in Artificial Intelligence, the White House's Office of Science and Technology Policy released a draft Guidance for Regulation of Artificial Intelligence Applications, which includes ten principles for United States agencies when deciding whether and how to regulate AI. In response, the National Institute of Standards and Technology released a position paper, and the Defense Innovation Board issued recommendations on the ethical use of AI. A year later, the administration called for comments on regulation in another draft of its Guidance for Regulation of Artificial Intelligence Applications. Other specific agencies working on the regulation of AI included the Food and Drug Administration, which created pathways to regulate the incorporation of AI in medical imaging. The National Science and Technology Council also published an updated National Artificial Intelligence Research and Development Strategic Plan in 2019, which received public scrutiny and recommendations to further improve it towards enabling Trustworthy AI. === Biden administration (2021–2025) === In March 2021, the National Security Commission on Artificial Intelligence released their final report. In the report, they stated, "Advances in AI, including the mastery of more general AI capabilities along one or more dimensions, will likely provide new capabilities and applications. Some of these advances could lead to inflection points or leaps in capabilities. Such advances may also introduce new concerns and risks and the need for new policies, recommendations, and technical advances to assure that systems are aligned with goals and values, including safety, robustness and trustworthiness." In June 2022, Senators Rob Portman and Gary Peters introduced the Global Catastrophic Risk Management Act. The bipartisan bill "would also help counter the risk of artificial intelligence... from being abused in ways that may pose a catastrophic risk". On October 4, 2022, President Joe Biden unveiled a new AI Bill of Rights, which outlines five protections Americans should have in the AI age: 1. Safe and Effective Systems, 2. Algorithmic Discrimination Protection, 3.Data Privacy, 4. Notice and Explanation, and 5. Human Alternatives, Consideration, and Fallback. The bill was formally published in October 2022 by the Office of Science and Technology Policy (OSTP), a U.S. government office that advises the President on science and technology policy matters. In July 2023, the Biden administration secured voluntary commitments from seven companies – Amazon, Anthropic, Google, Inflection, Meta, Microsoft, and OpenAI – to manage the risks associated with AI. The companies committed to ensure AI products undergo both internal and external security testing before public release; to share information on the management of AI risks with the industry, governments, civil society, and academia; to prioritize cybersecurity and protect proprietary AI system components; to develop mechanisms to inform users when content is AI-generated, such as watermarking; to publicly report on their AI systems' capabilities, limitations, and areas of use; to prioritize research on societal risks posed by AI, including bias, discrimination, and privacy concerns; and to develop AI systems to address societal challenges, ranging from cancer prevention to climate change mitigation. In September 2023, eight additional companies – Adobe, Cohere, IBM, Nvidia, Palantir, Salesforce, Scale AI, and Stability AI – subscribed to these voluntary commitments. In January 2023, the National Institute of Standards and Technology (NIST) released the Artificial Intelligence Risk Management Framework (AI RMF 1.0), providing voluntary guidance for organizations to identify, assess, and manage risks associated with AI systems. The Biden administration, in October 2023 signaled that they would release an executive order leveraging the federal government's purchasing power to shape AI regulations, hinting at a proactive governmental stance in regulating AI technologies. On October 30, 2023, President Biden released Executive Order 14110 on Safe, Secure, and Trustworthy Artificial Intelligence. The Executive Order includes directives on standards for critical infrastructure, AI-enhanced cybersecurity, and federally funded biological synthesis projects. The Executive Order provides the authority to various agencies and departments of the US government, including the Energy and Defense departments, to apply existing consumer protection laws to AI development. The Executive Order builds on the Administration's earlier agreements with AI companies to instate new initiatives to "red-team" or stress-test AI dual-use foundation models, especially those that have the potential to pose security risks, with data and results shared with the federal government. The Executive Order also recognizes AI's social challenges, and calls for companies building AI dual-use foundation models to be wary of these societal problems. For example, the Executive Order states that AI should not "worsen job quality", and should not "cause labor-force disruptions". Additionally, Biden's Executive Order mandates that AI must "advance equity and civil rights", and cannot disadvantage marginalized groups. It also called for foundation models to include "watermarks" to help the publi
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