AI Art Los Angeles

AI Art Los Angeles — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Fantavision

    Fantavision

    Fantavision is an animation program by Scott Anderson for the Apple II and published by Broderbund in 1985. Versions were released for the Apple IIGS (1987), Amiga (1988), and MS-DOS (1988). Fantavision allows the creation of vector graphics animations using the mouse and keyboard. The user creates frames, and the software generates the frames between them. Because this is done in real-time, it allows for creative exploration and quick changes. The program uses a graphical user interface in the style of the Macintosh with pull-down menus and black text on a white background. Advertisements claimed Fantavision a revolutionary breakthrough that brings the animation features of "tweening" and "transforming" to home computers. == Reception == Compute! in 1989 called Fantavision the best animation program for the IBM PC, although it noted the inability to draw curves. == Reviews == Games #70

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  • Computer game bot Turing test

    Computer game bot Turing test

    The computer game bot Turing test is a variant of the Turing test, where a human judge viewing and interacting with a virtual world must distinguish between other humans and video game bots, both interacting with the same virtual world. This variant was first proposed in 2008 by Associate Professor Philip Hingston of Edith Cowan University, and implemented through a tournament called the 2K BotPrize. == History == The computer game bot Turing test was proposed to advance the fields of artificial intelligence (AI) and computational intelligence with respect to video games. It was considered that a poorly implemented bot implied a subpar game, so a bot that would be capable of passing this test, and therefore might be indistinguishable from a human player, would directly improve the quality of a game. It also served to debunk a flawed notion that "game AI is a solved problem." Emphasis is placed on a game bot that interacts with other players in a multiplayer environment. Unlike a bot that simply needs to make optimal human-like decisions to play or beat a game, this bot must make the same decisions while also convincing another in-game player of its human-likeness. == Implementation == The computer game bot Turing test was designed to test a bot's ability to interact with a game environment in comparison with a human player; simply 'winning' was insufficient. This evolved into a contest with a few important goals in mind: There are three participants: a human player, a computer-game bot, and a judge. The bot needs to appear more human-like than the human player. Judge scores are not bipolar — both human and bot can be scored anywhere on a scale from 1 to 5 (1=not humanlike, 5=human). All three participants are to be indistinguishable in the arena, with the exception of a randomly generated name tag, so as to reduce the chance of random elements such as name or appearance influencing the judges. Chat is disabled throughout the match. Bots were not given omniscient powers as they may be in other games. Bots must react only to the data that might be reasonably available to a human player. Human participants were of a moderate skill range, with no participant either ignorant to the game or capable of playing at a professional level. In 2008, the first 2K BotPrize tournament took place. The contest was held with the game Unreal Tournament 2004 as the platform. Contestants created their bots in advance using the GameBots interface. GameBots had some modifications made so as to adhere to the above conditions, such as removing data about vantage points or weapon damage that unfairly informed the bots of relevant strengths/weaknesses that a human would otherwise need to learn. == Tournament == The first BotPrize Tournament was held on 17 December 2008, as part of the 2008 IEEE Symposium on Computational Intelligence and Games in Australia. Each competing team was given time to set up and adjust their bots to the modified game client, although no coding changes were allowed at that point. The tournament was run in rounds, each a 10-minute death match. Judges were the last to join the server and every judge observed every player and every bot exactly once, although the pairing of players and bots did change. When the tournament ended, no bot was rated as more human than any player. In subsequent tournaments, run during 2009–2011, bots achieved scores that were increasingly human-like, but no contestant had won the BotPrize in any of these contests. In 2012, the 2K BotPrize was held once again, and two teams programmed bots that achieved scores greater than those of human players. == Successful bots == To date, there have been two successfully programmed bots that passed the computer game bot Turing test: UT^2, a team from the University of Texas at Austin, emphasized a bot that adjusted its behaviour based on previously observed human behaviour and neuroevolution. The team has made their bot available, although a copy of Unreal Tournament 2004 is required. Mihai Polceanu, a doctoral student from Romania, focused on creating a bot that would mimic opponent reactions, in a sense 'borrowing' the human-like nature of the opponent. These victors succeeded in the year 2012, Alan Turing's centenary year. == Aftermath == The outcome of a bot that appears more human-like than a human player is possibly overstated, since in the tournament in which the bots succeeded, the average 'humanness' rating of the human players was only 41.4%. This showcases some limits of this Turing test, since the results demonstrate that human behaviour is more complicated and quantitative than was accounted for. In light of this, the BotPrize competition organizers will increase the difficulty in upcoming years with new challenges, forcing competitors to improve their bots. It is also believed that methods and techniques developed for the computer game bot Turing test will be useful in fields other than video games, such as virtual training environments and in improving Human–robot interaction. == Contrasts to the Turing test == The computer game bot Turing test differs from the traditional or generic Turing test in a number of ways: Unlike the traditional Turing test, for example the Chatterbot-style contest held annually by the Loebner Prize competition, the humans who played against the Computer Game Bots are not trying to convince judges they are the human; rather, they want to win the game (i.e., by achieving the highest kill score). Judges are not restricted to awarding only one participant in a match as the 'human' and the other as the 'non-human.' This emphasizes more qualitative rather than polarized findings. With regards to a successful video game bot, this is not to be confused with a claim that the bot is 'intelligent,' whereas a machine that 'passed' the Turing test would arguably have some evidence for its Chatterbot's 'intelligence.' The game Unreal Tournament 2004 was chosen for its commercial availability and its interface for creating bots, GameBots. This limitation on medium is a sharp contrast to the Turing test, which emphasizes a conversation, where possible questions are vastly more numerous than the set of possible actions available in any specific video game. The available information to the participants, humans and bots, is not equal. Humans interact through vision and sound, whereas bots interact with data and events. The judges cannot introduce new events (e.g., a lava pit) to aid in differentiating between human and bot, whereas in a Chatterbot designed system, judges may theoretically ask any question in any manner. The two participants and the judge take part in a three-way interaction, unlike, for example, the paired two-way interaction of the Loebner Prize Contest.

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  • Jensen Huang

    Jensen Huang

    Jen-Hsun "Jensen" Huang (Chinese: 黃仁勳; Wade–Giles: Huáng Jén-hsūn; Tâi-lô: N̂g Jîn-hun; born February 17, 1963) is a Taiwanese and American business executive and electrical engineer who is the founder, president, and CEO of Nvidia, the world's most valuable company. As of 2026, Forbes estimates his net worth at over US$200 billion, making him the seventh-wealthiest individual in the world. The son of Taiwanese immigrants, Huang spent his childhood in Taiwan and Thailand before moving to the United States, where he was a student in Kentucky and Oregon. After earning a master's degree from Stanford University, Huang launched Nvidia in 1993 from a Denny's restaurant in San Jose, California, at age 30 and has remained its president and CEO ever since. He led the company out of near-bankruptcy during the 1990s and oversaw its expansion into GPU production, high-performance computing, and artificial intelligence (AI). Under Huang, Nvidia experienced rapid growth during the AI boom, becoming the first company to reach a market capitalization of over $5 trillion in October 2025. In 2021 and 2024, Time magazine included Huang in their list of the most influential people. In 2025, he was named as one of the "Architects of AI" for Time's Person of the Year. == Early life and education == Huang was born in Taipei, Taiwan, on February 17, 1963, and moved to the southern city of Tainan as a child. He is the younger of two sons of Huang Hsing-tai, a chemical engineer at an oil refinery, and Lo Tsai-hsiu, a schoolteacher. They were a middle-class Taiwanese family that relocated often, and were native speakers of Taiwanese Hokkien. Each day, Jensen's mother randomly selected 10 words from the dictionary to teach her sons English. When he was five years old, Huang's family moved to Thailand to support his father's refinery career and remained there for approximately four years. He attended Ruamrudee International School while in Bangkok. In the late 1960s, Hsing-tai traveled from Taiwan to New York City to train under an air conditioning company and, after returning home, resolved to send his sons to the United States. At age nine, Jensen, despite not yet being able to speak English fluently, was sent by his parents to live in the United States. He and his older brother moved in 1973 to live with an uncle in Tacoma, Washington, escaping widespread social unrest in Thailand. Both Huang's aunt and uncle were recent immigrants to Washington state; they accidentally enrolled him and his brother in the Oneida Baptist Institute, a religious reform academy in Kentucky for troubled youth, mistakenly believing it to be a prestigious boarding school. In order to afford the academy's tuition, Jensen's parents sold nearly all their possessions. When he was 10 years old, Huang lived with his older brother in the Oneida boys' dormitory. Each student was expected to work every day, and his brother was assigned to perform manual labor on a nearby tobacco farm. Because he was too young to attend classes at the reform academy, Huang was educated at a separate public school—the Oneida Elementary school in Oneida, Kentucky—arriving as "an undersized Asian immigrant with long hair and heavily accented English" and was frequently bullied and beaten. In Oneida, Huang cleaned toilets every day, learned to play table-tennis, joined the swimming team, and appeared in Sports Illustrated at age 14. He taught his illiterate roommate, a "17-year-old covered in tattoos and knife scars," how to read in exchange for being taught how to bench press. In 2002, Huang said he remembered his life in Kentucky "more vividly than just about any other". Two years after Huang arrived in Oneida, his parents moved to the United States and settled in Beaverton, Oregon, after which the brothers withdrew from school in Kentucky to live back with them. As a teenager, Huang attended Aloha High School in Aloha, Oregon, where he excelled academically. He skipped two grades, graduated at age 16, and became a nationally ranked table-tennis player in addition to being a member of its mathematics, computer, and science clubs. In 1977, the school purchased an Apple II computer. Huang used the machine to play Super Star Trek, a text-based game, and to program in BASIC, creating his own version of Snake. Beginning at age 15, Huang got his first job working the graveyard shift at a local Denny's restaurant as a dishwasher, busboy, and waiter from 1978 to 1983. After high school, he chose to enroll at Oregon State University due to its low in-state tuition. He studied electrical engineering and graduated in 1984 with a bachelor's degree with highest honors. Huang later recalled, "I was the youngest kid in school, in class" and the only student who "looked like a child". Years later, while working as a microchip designer in Silicon Valley, he concurrently pursued graduate night classes at Stanford University, where he earned a master's degree in electrical engineering in 1992. == AMD and LSI Logic == After graduating from college, Huang was a microchip designer in Silicon Valley. He was recruited for positions at Texas Instruments, Advanced Micro Devices (AMD), and LSI Logic, ultimately choosing the California-based AMD due to already being familiar with the company. Huang designed AMD microprocessors while simultaneously attending Stanford and raising his two children. However, when he heard of new chip design processes at LSI Logic, Huang left AMD to assume a role as a technical officer at the LSI Corporation, working under a startup company, Sun Microsystems, where he met engineers Chris Malachowsky and Curtis Priem. LSI was in contract with Sun Microsystems and had introduced Huang to Malachowsky and Priem, who were working on a new graphics accelerator card. While the three produced the card's manufacturing process, the relationship between Malachowsky and Priem became strained as the two disputed the chip's design, leading to infighting; according to Malachowsky, they "broke every tool that LSI Logic had in their standard portfolio". In 1989, Huang, Malachowsky, and Priem finalized the accelerator, which they called the "GX graphics engine". GX was a widespread financial success; the sales of the graphics engine contributed to Sun Microsystem's revenue increasing from $262 million in 1987 to $656 million in 1990, and Huang was promoted to be the director of LSI's CoreWare, a division that manufactured chips for hardware vendors. == Nvidia == === Founding (1993) === When business began to slow for Sun Microsystems after 1990, Huang, along with Priem and Malachowsky, each resigned their jobs to pursue a venture together in making graphics chips for PC games. They initially named their new company "NVision" until Huang suggested that the company be named "Nvidia" based on the Latin word invidia, as Priem wanted competitors to turn "green with envy". They eventually dropped the "i" to honor the NV1 chip that they were then developing. The three met frequently in 1992 at a Denny's roadside diner in East San Jose to formulate a business plan. Huang chose for them to meet at Denny's due to his prior work experience at the restaurant chain and because it was "quieter than home and had cheap coffee". The three founded the company during one meeting at a breakfast booth at the diner. To formally incorporate the company, Huang found a lawyer, James Gaither of Cooley Godward, who demanded the $200 in cash in Huang's pockets to capitalize the company. After that meeting, Huang went back to Priem and Malachowsky to ask each of them for $200 for their respective shares of the company, which meant that Nvidia's initial capital was $600. On April 5, 1993, Huang personally signed Nvidia's original articles of incorporation into effect. Although he left LSI, Huang remained in good standing with the company and was able to secure funding for Nvidia from LSI's CEO, Wilfred Corrigan, who introduced Huang to venture capitalist Don Valentine. An account cited how Huang's presentation pitch went badly. Valentine, the leader of Sequoia Capital, chose to invest in Nvidia through Corrigan's support, as did Sutter Hill Ventures. The funding enabled Nvidia to begin development efforts toward its first chip and to begin paying wages for its employees. By the first day of operation, Huang was made Nvidia's president and CEO. Even though Huang, at age 30, was younger than Priem and Malachowsky, both Priem and Malachowsky believed that he was prepared to be CEO. According to Priem, "we basically deferred to Jensen on day one" and told Huang, "you're in charge of running the company—all the stuff Chris and I don't know how to do". === President and CEO (1993–present) === As of 2024, Huang has been Nvidia's chief executive for over three decades, a tenure described by The Wall Street Journal as "almost unheard of in fast-moving Silicon Valley". He owns 3.6% of Nvidia's stock, which went public in 1999. He earned US$24.6 million as CEO i

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  • Z.ai

    Z.ai

    Knowledge Atlas Technology Joint Stock Co., Ltd., branded internationally as Z.ai, is a Chinese technology company specializing in artificial intelligence (AI). The company was formerly known as Zhipu AI outside China until its rebranding in 2025. Z.ai's flagship product is the GLM (General Language Model) family of large language models, which the company has released under the free and open-source MIT License since July 2025. As of 2024, it is one of China's "AI tiger" companies by investors and considered to be the third-largest LLM market player in China's AI industry according to the International Data Corporation. In January 2025, the United States Commerce Department blacklisted the company in its Entity List due to national security concerns. == History == Founded in 2019, the startup company began from Tsinghua University and was later spun out as an independent company. Researchers published an Association for Computational Linguistics conference paper in May 2022 introducing the GLM (General Language Model) training algorithm, which uses an "autoregressive blank infilling" strategy that creates cloze tests by randomly removing segments of input text and trains the model to autoregressively regenerate the removed text. In 2023, it raised 2.5 billion yuan (approx. 350 million in USD) from Alibaba Group and Tencent, along with Meituan, Ant Group, Xiaomi, and HongShan. In March 2024, Zhipu AI announced it was developing a Sora-like technology to achieve artificial general intelligence (AGI). In May 2024, the Saudi Arabian finance firm Prosperity7 Ventures, LLC participated in a USD $400 million financing round for Zhipu AI with a valuation of approximately 3 billion USD. In July 2024, they debuted the Ying text-to-video model. Zhipu released GLM-4-Plus in August 2024. In October 2024, Zhipu released GLM-4-Voice, an end-to-end speech large language model that can adjust its tone or dialect. Zhipu disclosed in April 2025 that it had started preparing for its initial public offering (IPO) and released two models under the free and open-source MIT License. In May 2025, the company sealed a 61.28 million yuan deal from the Chinese government for city projects in Hangzhou. In July 2025, Zhipu AI released GLM-4.5 and GLM-4.5 Air, their next generation language models, and the company rebranded itself as Z.ai internationally. In August 2025, Z.ai announced that their GLM models are compatible with Huawei's Ascend processors. On August 11, 2025, Z.ai released a new vision-language model (VLM) with a total of 106B parameters, GLM-4.5V. In late September 2025, the company released GLM-4.6 using China's domestic chips such as those from Cambricon Technologies. Z.ai released GLM-4.6V and GLM-4.7 in December 2025. That same year, the company changed its official name to Knowledge Atlas Technology JSC Ltd. On 8 January 2026, Z.ai held its IPO on the Hong Kong Stock Exchange to become a listed company. It is considered to be China's first major LLM company that went through an IPO. On February 11, 2026, Z.ai released GLM-5. In late February 2026, Z.ai's shares fell by 23%, and had a shortage of compute resources, leading to user complaints and Z.ai issuing a public call for support. Z.ai also restricted new user signups. In late March, 2026, Z.ai released the GLM-5.1 model to subscription users. On April 8th, 2026, Z.ai released GLM-5.1 as open-source. The same day, Z.ai increased its API prices by 10%, but maintained a lower price than its United States competitor Anthropic's Opus 4.6 model. On release, the company's share price increased 11.5%. == Description == Z.ai provides the following products and services: General Language Model (commonly abbreviated as GLM; formerly known as ChatGLM), a series of pre-trained dialogue models initially developed by Zhipu AI and Tsinghua KEG in 2023. GLM 4.5, released in July 2025 by Z.ai, can run on eight NVIDIA H20 chips. The release of GLM-4.6 in late September 2025 marked the first integration of FP8 and Int4 quantization on Cambricon chips. It also supports native FP8 on Moore Threads GPUs. Ying, a text-to-video model that generates image and text prompts into a six-second video clip for around 30 seconds. AutoGLM, an AI agent application that uses voice commands to complete tasks within a smartphone. The app can analyze complex tasks such as ordering an item from a nearby store and repeating an order based from the user's shopping history. AMiner, created by Jie Tang (co-founder of Z.ai) in March 2006, now owned by Z.ai. Z.ai has offices in the Middle East, United Kingdom, Singapore, and Malaysia, along with innovation center projects across Southeast Asia (2025). In January 2025, the United States Commerce Department added the company to its Entity List, citing national security concerns. == Models ==

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  • AI Overviews

    AI Overviews

    AI Overviews is an artificial intelligence (AI) feature integrated into Google Search that produces AI-generated summaries of search results. The feature has been criticized for its inaccuracy and for reducing website traffic. == History and development == AI Overviews were first introduced as part of Google's Search Generative Experience (SGE), which was unveiled at the Google I/O conference in May 2023. In May 2024 at Google I/O 2024, the feature was rebranded as AI Overviews and launched in the United States. The introduction of AI Overviews was seen as a strategic move to compete with other generative AI advancements, including OpenAI's ChatGPT. By August 2024, AI Overviews was rolled out to several other countries, including the United Kingdom, India, Japan, Brazil, Mexico, and Indonesia, with support for multiple languages. In October 2024, Google expanded the feature globally, making it available in over 100 countries. In December 2024, Botify x Demandsphere released findings stating that when AI Overviews and featured snippets appear together on the search engine results page, they take up approximately 67.1% of the screen on desktop and 75.7% on mobile. Even if content is ranking in the #1 position, it may not be visible to consumers if other visual elements on the results page are more prominent. In March 2025, Google started testing an "AI Mode", where the search results page is AI-generated. The company was also considering adding advertisements to the AI Mode, as they already exist in AI Overviews. As of May 2025, AI Overviews are available in over 200 countries and territories and in more than 40 languages. As of March 2026, Google AI Overviews appear on more than 48% of total Google Search queries, compared to just 6.49% in the previous year (58% year-over-year growth). == Functionality == The AI Overviews feature uses large language models to generate summaries from web content. The overviews are designed to be concise, providing a snapshot of relevant information about the queried topic. Google allows users to adjust the language complexity in summaries, offering both simplified and detailed options. The overviews also include links to sources. According to a June 2025 study by Semrush, the most cited source is Quora, followed by Reddit. == Reception == The feature has faced criticism for inaccuracies, including instances where erroneous or nonsensical content was generated. Depending on what is searched for, the overview may also consist of hallucinated content, such as when searching for idioms that do not exist. In May 2024, Google temporarily restricted the AI tool after it provided suggestions that were seen as nonsensical and harmful, such as telling users to eat rocks or apply glue on pizza. Concerns were also raised by content publishers, who feared a decline in web traffic as users relied on the summaries instead of visiting source websites. A Google patent from 2026 raised the concern of webmasters that Google could entirely replace the landing page of websites by an AI optimized copy of the website in its results. There is also apprehension about the ethical implications of AI-driven content aggregation, including its impact on intellectual property rights and the visibility of smaller content providers. The European Commission announced in December 2025 that they were investigating whether AI Overviews breached European competition law. In response, Google has stated its commitment to improve content validation and refine the algorithms used to filter unreliable information. Google implemented measures to prioritize link placement within AI Overviews, aiming to balance user convenience with the needs of content creators. In January 2026, Google restricted AI Overviews on certain health-related searches following an investigation by The Guardian. == Lawsuits == On February 24, 2025, Chegg sued Alphabet over the AI Overviews feature, claiming that it was leading to students preferring "low-quality, unverified AI summaries", thus violating antitrust law. Chegg also said it was considering either a sale or a take-private transaction. In September 2025, Penske Media Corporation, the publisher of Rolling Stone and The Hollywood Reporter, sued Google, claiming that AI Overviews illegally regurgitate content from their websites and drive off potential site visitors by always appearing on top of the search results while leaving little incentive to see the linked sources. The company stated that "the future of digital media and [...] its integrity [...] is threatened by Google's current actions", alleging that 20% of searches that link to Penske-owned websites show AI Overviews and that the figure is expected to rise. Google spokesperson José Castañeda called the claims "meritless" and stated that "AI Overviews send traffic to a greater diversity of sites." In 2026, Canadian musician Ashley MacIsaac filed a lawsuit against Google claiming that the AI Overview feature had wrongly stated that MacIsaac had been convicted of numerous criminal offences and was on the sex offender registry. He claims this incorrect information led to the cancellation of a December 2025 gig organized by the Sipekne'katik First Nation.

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  • DARPA AlphaDogfight

    DARPA AlphaDogfight

    The DARPA AlphaDogfight was a 2019–2020 DARPA program that pitted computers using F-16 flight simulators against one another. The computers were managed by eight teams of humans, who competed in a single-round elimination for the right to battle a skilled human dogfighter. Heron Systems corporation wrote a deep reinforcement learning software tool that bested the human pilot by a score of 5–0. The tournament program was managed by the Applied Physics Laboratory. The trials took place in October 2019 and January 2020 while the finals were held in August 2020. In 2024 a successor version of the program was tested with in the physical world with the X-62A.

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  • Future of Life Institute

    Future of Life Institute

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

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  • HiLog

    HiLog

    HiLog is a programming logic with higher-order syntax, which allows arbitrary terms to appear in predicate and function positions. However, the model theory of HiLog is first-order. Although syntactically HiLog strictly extends first order logic, HiLog can be embedded into this logic. HiLog was first described in 1989. It was later extended in the direction of many-sorted logic. The XSB system parses HiLog syntax, but the integration of HiLog into XSB is only partial. In particular, HiLog is not integrated with the XSB module system. A full implementation of HiLog is available in the Flora-2 system. It has been shown that HiLog can be embedded into first-order logic through a fairly simple transformation. For instance, p(X)(Y,Z(V)(W)) gets embedded as the following first-order term: apply(p(X),Y,apply(apply(Z,V),W)). The Framework for Logic-Based Dialects (RIF-FLD) of the Rule Interchange Format (RIF) is largely based on the ideas underlying HiLog and F-logic. == Examples == In all the examples below, capitalized symbols denote variables and the comma denotes logical conjunction, as in most logic programming languages. The first and the second examples show that variables can appear in predicate positions. Predicates can even be complex terms, such as closure(P) or maplist(F) below. The third example shows that variables can also appear in place of atomic formulas, while the fourth example illustrates the use of variables in place of function symbols. The first example defines a generic transitive closure operator, which can be applied to an arbitrary binary predicate. The second example is similar. It defines a LISP-like mapping operator, which applies to an arbitrary binary predicate. The third example shows that the Prolog meta-predicate call/1 can be expressed in HiLog in a natural way and without the use of extra-logical features. The last example defines a predicate that traverses arbitrary binary trees represented as first-order terms.

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  • Human–AI interaction

    Human–AI interaction

    Human–AI interaction is a developing field of research and a sub-field of human–computer interaction (HCI). HCI is a field of research that explores the interactions between humans and computer-based technology, focusing on design implementation, user experience, and psychological factors. With the proliferation of artificial intelligence (AI), there has developed a sub-section of HCI research dedicated specifically to artificial intelligence and how people interact with and are impacted by it. This is human–AI interaction, abbreviated either as HAX or HAII. == Introduction == Artificial intelligence (AI), in general, has fluid definitions and varied research applications, but in brief can be applied to mechanizing tasks that would require human intelligence to complete. AI are tools designed to replicate the human abilities of navigating uncertainty, active learning, and processing information in different contexts. Within the context of HCI and HAX research, artificial intelligence can be broken into two sub-fields, natural language processing (NLP) and computer vision (CV). AI technologies notably include machine-learning, deep-learning and neural networks, and large-language models (LLMs). As a new and rapidly developing technology, AI is changing how computers work and therefore changing how humans interact with computers. Unlike the traditional human-computer interaction, where a human directs a machine, human-AI interaction is characterized by a more collaborative relationship between the computer program (the AI) and the human user, as AI is perceived as an active agent rather than a tool. This changing dynamic creates new questions and necessitates new research methods that are not present in traditional HCI research. According to a scoping review on the state of the discipline, the HAX field comprises research on the "design, development, and evaluation of AI systems" and encompasses the themes of human-AI collaboration, human-AI competition, human-AI conflict, and human-AI symbiosis. == Design == Machine learning and artificial intelligence have been used for decades in targeted advertising and to recommend content in social media. Ethical Guidelines (Framework for ethical AI development) == User Experience (UX) == This section should handle research on how users interact with tools. What techniques do they use, do they develop habits, what types of programs and devices are they using to access these tools, what do they use these tools to do exactly. === Cognitive Frameworks in AI Tool Users === AI has been viewed with various expectations, attributions, and often misconceptions. Many people exclusively understand AI as the LLM chatbots they interact with, like ChatGPT or Claude, or other generative AI programs. [Insert section: discuss how people interact with these specific AI tools as a connection to the following paragraphs] Most fundamentally, humans have a mental model of understanding AI's reasoning and motivation for its decision recommendations, and building a holistic and precise mental model of AI helps people create prompts to receive more valuable responses from AI. However, these mental models are not whole because people can only gain more information about AI through their limited interaction with it; more interaction with AI builds a better mental model that a person may build to produce better prompt outcomes. Research on human-AI interaction has emphasized that users develop mental models of AI systems and revise those models through repeated use, feedback, and explanation, while design research has stressed the importance of communicating capabilities and limitations early and supporting trust calibration through explanation and correction. In a 2025 SSRN working paper, John DeVadoss proposed "Hypothetico-Deductive Interaction" (HDI), a framework that describes human-AI interaction as a mutual process of conjecture and refutation in which users test assumptions about an AI system's capabilities while the system infers and updates assumptions about user goals through its responses and clarifying questions. DeVadoss argued that this framing helps explain prompt iteration, weak capability awareness, and trust miscalibration, and suggested design responses such as clearer communication of uncertainty, easier correction, actionable explanations, and safer failure modes. == Research themes == === Human-AI collaboration === Human-AI collaboration occurs when the human and AI supervise the task on the same level and extent to achieve the same goal. Some collaboration occurs in the form of augmenting human capability. AI may help human ability in analysis and decision-making through providing and weighing a volume of information, and learning to defer to the human decision when it recognizes its unreliability. It is especially beneficial when the human can detect a task that AI can be trusted to make few errors so that there is not a lot of excessive checking process required on the human's end. Some findings show signs of human-AI augmentation, or human–AI symbiosis, in which AI enhances human ability in a way that co-working on a task with AI produces better outcomes than a human working alone. For example: the quality and speed of customer service tasks increase when a human agent collaborates with AI, training on specific models allows AI to improve diagnoses in clinical settings, and AI with human-intervention can improve creativity of artwork while fully AI-generated haikus were rated negatively. Human-AI synergy, a concept in which human-AI collaboration would produce more optimal outcomes than either human or AI working alone could explain why AI does not always help with performance. Some AI features and development may accelerate human-AI synergy, while others may stagnate it. For example, when AI updates for better performance, it sometimes worsens the team performance with human and AI by reducing the compatibility with the new model and the mental model a user has developed on the previous version. Research has found that AI often supports human capabilities in the form of human-AI augmentation and not human-AI synergy, potentially because people rely too much on AI and stop thinking on their own. Prompting people to actively engage in analysis and think when to follow AI recommendations reduces their over-reliance, especially for individuals with higher need for cognition. === Human-AI competition === Robots and computers have substituted routine tasks historically completed by humans, but agentic AI has made it possible to also replace cognitive tasks including taking phone calls for appointments and driving a car. At the point of 2016, research has estimated that 45% of paid activities could be replaced by AI by 2030. Perceived autonomy of robots is known to increase people's negative attitude toward them, and worry about the technology taking over leads people to reject it. There has been a consistent tendency of algorithm aversion in which people prefer human advice over AI advice. However, people are not always able to tell apart tasks completed by AI or other humans. See AI takeover for more information. It is also notable that this sentiment is more prominent in the Western cultures as Westerners tend to show less positive views about AI compared to East Asians. == Research on the psychological impacts of AI == === Perception on others who use AI === As much as people perceive and make judgment about AI itself, they also form impressions of themselves and others who use AI. In the workplace, employees who disclose the use of AI in their tasks are more likely to receive feedback that they are not as hardworking as those who are in the same job who receive non-AI help to complete the same tasks. AI use disclosure diminishes the perceived legitimacy in the employee's task and decision making which ultimately leads observers to distrust people who use AI. Although these negative effects of AI use disclosure are weakened by the observers who use AI frequently themselves, the effect is still not attenuated by the observers' positive attitude towards AI. === Bias, AI, and human === Although AI provides a wide range of information and suggestions to its users, AI itself is not free of biases and stereotypes, and it does not always help people reduce their cognitive errors and biases. People are prone to such errors by failing to see other potential ideas and cases that are not listed by AI responses and committing to a decision suggested by AI that directly contradicts the correct information and directions that they are already aware of. Gender bias is also reflected as the female gendering of AI technologies which conceptualizes females as a helpful assistant. == Emotional connection with AI == Human-AI interaction has been theorized in the context of interpersonal relationships mainly in social psychology, communications and media studies, and as a technology interface through the lens of hu

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  • Computer game bot Turing test

    Computer game bot Turing test

    The computer game bot Turing test is a variant of the Turing test, where a human judge viewing and interacting with a virtual world must distinguish between other humans and video game bots, both interacting with the same virtual world. This variant was first proposed in 2008 by Associate Professor Philip Hingston of Edith Cowan University, and implemented through a tournament called the 2K BotPrize. == History == The computer game bot Turing test was proposed to advance the fields of artificial intelligence (AI) and computational intelligence with respect to video games. It was considered that a poorly implemented bot implied a subpar game, so a bot that would be capable of passing this test, and therefore might be indistinguishable from a human player, would directly improve the quality of a game. It also served to debunk a flawed notion that "game AI is a solved problem." Emphasis is placed on a game bot that interacts with other players in a multiplayer environment. Unlike a bot that simply needs to make optimal human-like decisions to play or beat a game, this bot must make the same decisions while also convincing another in-game player of its human-likeness. == Implementation == The computer game bot Turing test was designed to test a bot's ability to interact with a game environment in comparison with a human player; simply 'winning' was insufficient. This evolved into a contest with a few important goals in mind: There are three participants: a human player, a computer-game bot, and a judge. The bot needs to appear more human-like than the human player. Judge scores are not bipolar — both human and bot can be scored anywhere on a scale from 1 to 5 (1=not humanlike, 5=human). All three participants are to be indistinguishable in the arena, with the exception of a randomly generated name tag, so as to reduce the chance of random elements such as name or appearance influencing the judges. Chat is disabled throughout the match. Bots were not given omniscient powers as they may be in other games. Bots must react only to the data that might be reasonably available to a human player. Human participants were of a moderate skill range, with no participant either ignorant to the game or capable of playing at a professional level. In 2008, the first 2K BotPrize tournament took place. The contest was held with the game Unreal Tournament 2004 as the platform. Contestants created their bots in advance using the GameBots interface. GameBots had some modifications made so as to adhere to the above conditions, such as removing data about vantage points or weapon damage that unfairly informed the bots of relevant strengths/weaknesses that a human would otherwise need to learn. == Tournament == The first BotPrize Tournament was held on 17 December 2008, as part of the 2008 IEEE Symposium on Computational Intelligence and Games in Australia. Each competing team was given time to set up and adjust their bots to the modified game client, although no coding changes were allowed at that point. The tournament was run in rounds, each a 10-minute death match. Judges were the last to join the server and every judge observed every player and every bot exactly once, although the pairing of players and bots did change. When the tournament ended, no bot was rated as more human than any player. In subsequent tournaments, run during 2009–2011, bots achieved scores that were increasingly human-like, but no contestant had won the BotPrize in any of these contests. In 2012, the 2K BotPrize was held once again, and two teams programmed bots that achieved scores greater than those of human players. == Successful bots == To date, there have been two successfully programmed bots that passed the computer game bot Turing test: UT^2, a team from the University of Texas at Austin, emphasized a bot that adjusted its behaviour based on previously observed human behaviour and neuroevolution. The team has made their bot available, although a copy of Unreal Tournament 2004 is required. Mihai Polceanu, a doctoral student from Romania, focused on creating a bot that would mimic opponent reactions, in a sense 'borrowing' the human-like nature of the opponent. These victors succeeded in the year 2012, Alan Turing's centenary year. == Aftermath == The outcome of a bot that appears more human-like than a human player is possibly overstated, since in the tournament in which the bots succeeded, the average 'humanness' rating of the human players was only 41.4%. This showcases some limits of this Turing test, since the results demonstrate that human behaviour is more complicated and quantitative than was accounted for. In light of this, the BotPrize competition organizers will increase the difficulty in upcoming years with new challenges, forcing competitors to improve their bots. It is also believed that methods and techniques developed for the computer game bot Turing test will be useful in fields other than video games, such as virtual training environments and in improving Human–robot interaction. == Contrasts to the Turing test == The computer game bot Turing test differs from the traditional or generic Turing test in a number of ways: Unlike the traditional Turing test, for example the Chatterbot-style contest held annually by the Loebner Prize competition, the humans who played against the Computer Game Bots are not trying to convince judges they are the human; rather, they want to win the game (i.e., by achieving the highest kill score). Judges are not restricted to awarding only one participant in a match as the 'human' and the other as the 'non-human.' This emphasizes more qualitative rather than polarized findings. With regards to a successful video game bot, this is not to be confused with a claim that the bot is 'intelligent,' whereas a machine that 'passed' the Turing test would arguably have some evidence for its Chatterbot's 'intelligence.' The game Unreal Tournament 2004 was chosen for its commercial availability and its interface for creating bots, GameBots. This limitation on medium is a sharp contrast to the Turing test, which emphasizes a conversation, where possible questions are vastly more numerous than the set of possible actions available in any specific video game. The available information to the participants, humans and bots, is not equal. Humans interact through vision and sound, whereas bots interact with data and events. The judges cannot introduce new events (e.g., a lava pit) to aid in differentiating between human and bot, whereas in a Chatterbot designed system, judges may theoretically ask any question in any manner. The two participants and the judge take part in a three-way interaction, unlike, for example, the paired two-way interaction of the Loebner Prize Contest.

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  • Semantic network

    Semantic network

    A semantic network, or frame network is a knowledge base that represents semantic relations between concepts in a network. This is often used as a form of knowledge representation. It is a directed or undirected graph consisting of vertices, which represent concepts, and edges, which represent semantic relations between concepts, mapping or connecting semantic fields. A semantic network may be instantiated as, for example, a graph database or a concept map. Typical standardized semantic networks are expressed as semantic triples. Semantic networks are used in natural language processing applications such as semantic parsing and word-sense disambiguation. Semantic networks can also be used as a method to analyze large texts and identify the main themes and topics (e.g., of social media posts), to reveal biases (e.g., in news coverage), or even to map an entire research field. == History == Examples of the use of semantic networks in logic, directed acyclic graphs as a mnemonic tool, dates back centuries. The earliest documented use being the Greek philosopher Porphyry's commentary on Aristotle's categories in the third century AD. In computing history, "Semantic Nets" for the propositional calculus were first implemented for computers by Richard H. Richens of the Cambridge Language Research Unit in 1956 as an "interlingua" for machine translation of natural languages. Although the importance of this work and the CLRU was only belatedly realized. Semantic networks were also independently implemented by Robert F. Simmons and Sheldon Klein, using the first order predicate calculus as a base, after being inspired by a demonstration of Victor Yngve. The "line of research was originated by the first President of the Association [Association for Computational Linguistics], Victor Yngve, who in 1960 had published descriptions of algorithms for using a phrase structure grammar to generate syntactically well-formed nonsense sentences. Sheldon Klein and I about 1962-1964 were fascinated by the technique and generalized it to a method for controlling the sense of what was generated by respecting the semantic dependencies of words as they occurred in text." Other researchers, most notably M. Ross Quillian and others at System Development Corporation helped contribute to their work in the early 1960s as part of the SYNTHEX project. It's from these publications at SDC that most modern derivatives of the term "semantic network" cite as their background. Later prominent works were done by Allan M. Collins and Quillian (e.g., Collins and Quillian; Collins and Loftus Quillian). Still later in 2006, Hermann Helbig fully described MultiNet. In the late 1980s, two Netherlands universities, Groningen and Twente, jointly began a project called Knowledge Graphs, which are semantic networks but with the added constraint that edges are restricted to be from a limited set of possible relations, to facilitate algebras on the graph. In the subsequent decades, the distinction between semantic networks and knowledge graphs was blurred. In 2012, Google gave their knowledge graph the name Knowledge Graph. The Semantic Link Network was systematically studied as a social semantics networking method. Its basic model consists of semantic nodes, semantic links between nodes, and a semantic space that defines the semantics of nodes and links and reasoning rules on semantic links. The systematic theory and model was published in 2004. This research direction can trace to the definition of inheritance rules for efficient model retrieval in 1998 and the Active Document Framework ADF. Since 2003, research has developed toward social semantic networking. This work is a systematic innovation at the age of the World Wide Web and global social networking rather than an application or simple extension of the Semantic Net (Network). Its purpose and scope are different from that of the Semantic Net (or network). The rules for reasoning and evolution and automatic discovery of implicit links play an important role in the Semantic Link Network. Recently it has been developed to support Cyber-Physical-Social Intelligence. It was used for creating a general summarization method. The self-organised Semantic Link Network was integrated with a multi-dimensional category space to form a semantic space to support advanced applications with multi-dimensional abstractions and self-organised semantic links It has been verified that Semantic Link Network play an important role in understanding and representation through text summarisation applications. Semantic Link Network has been extended from cyberspace to cyber-physical-social space. Competition relation and symbiosis relation as well as their roles in evolving society were studied in the emerging topic: Cyber-Physical-Social Intelligence More specialized forms of semantic networks has been created for specific use. For example, in 2008, Fawsy Bendeck's PhD thesis formalized the Semantic Similarity Network (SSN) that contains specialized relationships and propagation algorithms to simplify the semantic similarity representation and calculations. == Basics of semantic networks == A semantic network is used when one has knowledge that is best understood as a set of concepts that are related to one another. Most semantic networks are cognitively based. They also consist of arcs and nodes which can be organized into a taxonomic hierarchy. Semantic networks contributed ideas of spreading activation, inheritance, and nodes as proto-objects. == Examples == === In Lisp === The following code shows an example of a semantic network in the Lisp programming language using an association list. To extract all the information about the "canary" type, one would use the assoc function with a key of "canary". === WordNet === An example of a semantic network is WordNet, a lexical database of English. It groups English words into sets of synonyms called synsets, provides short, general definitions, and records the various semantic relations between these synonym sets. Some of the most common semantic relations defined are meronymy (A is a meronym of B if A is part of B), holonymy (B is a holonym of A if B contains A), hyponymy (or troponymy) (A is subordinate of B; A is kind of B), hypernymy (A is superordinate of B), synonymy (A denotes the same as B) and antonymy (A denotes the opposite of B). WordNet properties have been studied from a network theory perspective and compared to other semantic networks created from Roget's Thesaurus and word association tasks. From this perspective the three of them are a small world structure. === Other examples === It is also possible to represent logical descriptions using semantic networks such as the existential graphs of Charles Sanders Peirce or the related conceptual graphs of John F. Sowa. These have expressive power equal to or exceeding standard first-order predicate logic. Unlike WordNet or other lexical or browsing networks, semantic networks using these representations can be used for reliable automated logical deduction. Some automated reasoners exploit the graph-theoretic features of the networks during processing. Other examples of semantic networks are Gellish models. Gellish English with its Gellish English dictionary, is a formal language that is defined as a network of relations between concepts and names of concepts. Gellish English is a formal subset of natural English, just as Gellish Dutch is a formal subset of Dutch, whereas multiple languages share the same concepts. Other Gellish networks consist of knowledge models and information models that are expressed in the Gellish language. A Gellish network is a network of (binary) relations between things. Each relation in the network is an expression of a fact that is classified by a relation type. Each relation type itself is a concept that is defined in the Gellish language dictionary. Each related thing is either a concept or an individual thing that is classified by a concept. The definitions of concepts are created in the form of definition models (definition networks) that together form a Gellish Dictionary. A Gellish network can be documented in a Gellish database and is computer interpretable. SciCrunch is a collaboratively edited knowledge base for scientific resources. It provides unambiguous identifiers (Research Resource IDentifiers or RRIDs) for software, lab tools etc. and it also provides options to create links between RRIDs and from communities. Another example of semantic networks, based on category theory, is ologs. Here each type is an object, representing a set of things, and each arrow is a morphism, representing a function. Commutative diagrams also are prescribed to constrain the semantics. In the social sciences people sometimes use the term semantic network to refer to co-occurrence networks. == Software tools == There are also elaborate types of semantic networks connected with corresponding sets of software tools used for

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  • Babelfy

    Babelfy

    Babelfy is a software algorithm for the disambiguation of text written in any language. It performs the tasks of multilingual Word Sense Disambiguation (i.e., the disambiguation of common nouns, verbs, adjectives and adverbs) and Entity Linking (i.e. the disambiguation of mentions to encyclopedic entities like people, companies, places, etc.). == Overview == Babelfy uses the BabelNet multilingual knowledge graph to perform disambiguation and entity linking in three steps: It associates with each vertex of the BabelNet semantic network, i.e., either concept or named entity, a semantic signature, that is, a set of related vertices. This is a preliminary step which needs to be performed only once, independently of the input text. Given an input text, it extracts all the linkable fragments from this text and, for each of them, lists the possible meanings according to the semantic network. It creates a graph-based semantic interpretation of the whole text by linking the candidate meanings of the extracted fragments using the previously computed semantic signatures. It then extracts a dense subgraph of this representation and selects the best candidate meaning for each fragment. As a result, the text, written in any of the 271 languages supported by BabelNet, is output with possibly overlapping semantic annotations.

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  • Amália (LLM)

    Amália (LLM)

    Amália is a Portuguese large language model (LLM) announced in November 2024 by the Portuguese Prime-Minister Luís Montenegro. Its final version is expected to be launched in 2026. It is being developed by Center for Responsible AI (Centro para a AI Responsável) and by the research centers of NOVA School of Science and Technology and Instituto Superior Técnico. == History == In 2024 it was announced that the Portuguese Agency for Administrative Modernization (Agência para a Modernização Administrativa) transpose this LLM to Portuguese Public Administration. According to Paulo Dimas (CEO of the Center for Responsible AI) the three fundamental points of this LLM project are the linguistic variant (European Portuguese), cultural representation and data protection. In April 2025 it was announced that Amália had entered beta phase with an improved version being expected to be launched in September 2025. The beta version released in September is available only to the Public Administration, but the website launched in October reiterates the final version will be an open model.

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  • Hyperion Data Center

    Hyperion Data Center

    The Richland Parish Data Center, nicknamed "Hyperion", is a planned artificial intelligence data center by Meta Platforms under-construction along Highway La. 183 in Richland Parish, Louisiana, just outside of Holly Ridge. It is one of a number of "titan clusters" being built in preparation for the emergence of AI superintelligence. Modern technological researchers disagree as to whether or not superintelligence will ever exist, though Meta CEO Mark Zuckerberg has expressed belief that its creation is inevitable. Current plans allot for the investment of $27 billion, as the structure is built from 2025 to 2030. == History == Meta was considering potential locations for their flagship data center in early 2024. Before being announced later in December, the plan was completely secret; meetings held between involved organisations and even government officials could only refer to it by the codename "Project Sucre" to protect it from potential corporate espionage. The data center was first announced on 04 December 2024, though its full scale was yet to be revealed. At first, Meta would not even claim responsibility for it, channelling all of its investments through the secret shell subsidiary Laidley LLC. We set out looking for a place where we could expand into gigawatts pretty quickly, and really get moving within that community on a large plot of land very quickly. We looked at finding very, very large contiguous plots of land that had access to the infrastructure that we need, the energy that we needed, and could move very, very quickly for us. The Louisiana-based Entergy Corporation, aiming for the facility to be built in its own backyard, negotiated a deal with the government of Louisiana to provide Meta with enormous tax breaks if they agreed to build Hyperion there. The Louisiana legislature responded by passing Act 730, which provides significant tax rebates on the purchase or lease of equipment for building and operating data centers. Meta found the arrangement acceptable, and bought a plot of land from the government. The government also had to further amend its laws to allow Meta to do this, as pre-existing policy forbade purchasing land directly from the government instead of hosting a public auction. The plot of land, originally called Franklin Farms, was purchased from the Franklin family in 2006 by the government, intending for it to be developed into an automotive manufacturing plant. Greater attention was brought to Hyperion it when Zuckerberg posted about the project on 14 July 2025 on Threads. The project subsequently caught media attention for its large size, as Zuckerberg's post portrayed the structure superimposed over Manhattan (pictured). The construction site spans 2,250 acres (9.1 km2) with a planned floor area of 4,000,000 square feet (371612 m2), making it the third largest building in the world by floor area upon completion. Meta initially reported the construction cost to be over $10 billion, but in October 2025, it announced a partnership with Blue Owl Capital providing for at least $27 billion. == Operation == The facility is expected to consume up to 5 gigawatts (GW) of computational power, more electricity than is currently used by the entire State of Louisiana. As part of their deal made with Meta, Entergy plans to be able to produce at least 3.8 GW of electricity for the operation. == Response to the project == Louisiana Governor Jeff Landry thanked Meta for their decision to build Hyperion in Louisiana, stating that it would "create opportunities for Louisiana workers to fill high-paying jobs of the future." and calling it "A New Chapter" for the state. The Louisiana Economic Development (LED) state agency further praised the project, citing Meta's estimate that it would create 1,500 jobs. Additionally, Richland Parish Supervisor Joey Evans stated that he was excited about the project. As part of their agreement with Meta, Energy announced their plan to increase electricity production state-wide. They say that this will result in the cost of energy reducing, though Entergy filings revealed in June 2025 that the cost of electricity would rise and be passed onto consumers. Meta also pledged to match all of Hyperion's power consumption with 100% environmentally friendly electricity production. So far, Entergy has begun building three gas-powered combined-cycle power plants and a substation in response to the project. Delta Community College announced in response to Hyperion's construction that it would expand its construction and trade programs. In January 2025, Business Facilities Magazine selected Hyperion for its annual Deal of the Year Platinum Award for 2024. Much of the initial backlash following Hyperion's announcement centered around the fast-tracked approval of the project by the state government, and scepticism around Meta's various claims (environmental friendliness, 100% renewable energy, local economic stimulation, price reductions). The Sierra Club criticised Meta for gentrifying the surrounding area, and was highly sceptical of their promise to keep it environmentally friendly. Environmental activist group Earthjustice attempted to have a subpoena of Meta approved to determine if they were compliant with environmental protection laws, though they were unsuccessful. Many residents of Holy Ridge have been critical of the construction, complaining about the increased construction vehicle traffic and intense gentrification. Another point of contention is Meta's continued reliance on out-of-state contractors in the facility's construction in spite of their previous commitment to "hire as many local folk as [we] possibly can." In spite of Entergy's continual denial that the facility's construction will not adversely affect the power grid, numerous electrical outages have been reported since construction began.

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  • New Classification Scheme for Chinese Libraries

    New Classification Scheme for Chinese Libraries

    The New Classification Scheme for Chinese Libraries is a system of library classification developed by Lai Yung-hsiang since 1956. It is modified from "A System of Book Classification for Chinese Libraries" of Liu Guojun, which is based on the Dewey Decimal System. The scheme is developed for Chinese books and commonly used in Taiwan, Hong Kong and Macau. == Main classes == 000 Generalities 100 Philosophy 200 Religion 300 Sciences 400 Applied sciences 500 Social sciences 600 History of China and Geography of China 700 World history and Geography 800 Linguistics and Literature 900 Arts == Outline of the classification tables == 000 Generalities 000 Special collections 010 Bibliography; Literacy (Documentation) 020 Library and information science; Archive management 030 Sinology 040 General encyclopedia 050 Serial publications; Periodicals 060 General organization; Museology 070 General collected essays 080 General series 090 Collected Chinese classics 100 Philosophy 100 Philosophy: general 110 Thought; Learning 120 Chinese philosophy 130 Oriental philosophy 140 Western philosophy 150 Logic 160 Metaphysics 170 Psychology 180 Esthetics (Aesthetics) 190 Ethics 200 Religion 200 Religion: general 210 Science of religion 220 Buddhism 230 Taoism 240 Christianity 250 Islam (Mohammedanism) 260 Judaism 270 Other religions 280 Mythology 290 Astrology; Superstition 300 Sciences 300 Sciences: general 310 Mathematics 320 Astronomy 330 Physics 340 Chemistry 350 Earth science; Geology 360 Biological science 370 Botany 380 Zoology 390 Anthropology 400 Applied sciences 400 Applied sciences: general 410 Medical sciences 420 Home economics 430 Agriculture 440 Engineering 450 Mining and metallurgy 460 Chemical engineering 470 Manufacture 480 Commerce: various business 490 Commerce: administration and management 500 Social sciences 500 Social sciences: general 510 Statistics 520 Education 530 Rite and custom 540 Sociology 550 Economy 560 Finance 570 Political science 580 Law; Jurisprudence 590 Military science 600-700 History and geography 600 History and geography: General History and geography of China 610 General history of China 620 Chinese history by period 630 History of Chinese civilization 640 Diplomatic history of China 650 Historical sources 660 Geography of China 670 Local history 680 Topical topography 690 Chinese travels World history and geography 710 World: general history and geography 720 Oceans and seas 730 Asia: history and geography 740 Europe: history and geography 750 America: history and geography 760 Africa: history and geography 770 Oceania: history and geography 780 Biography 790 Antiquities and archaeology 800 Linguistics and literature 800 Linguistics: general 810 Literature: general 820 Chinese literature 830 Chinese literature: general collections 840 Chinese literature: individual works 850 Various Chinese literature 860 Oriental literature 870 Western literature 880 Other countries literatures 890 Journalism 900 Arts 900 Arts: general 910 Music 920 Architecture 930 Sculpture 940 Drawing and painting; Calligraphy 950 Photography; Computer art 960 Decorative arts 970 Arts and Crafts movement 980 Theatre 990 Recreation and leisure

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