AI Content Producer

AI Content Producer — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • WHATWG

    WHATWG

    The Web Hypertext Application Technology Working Group (WHATWG) was founded by representatives from Apple Inc., the Mozilla Foundation and Opera Software, leading web browser vendors in 2004. WHATWG is responsible for maintaining multiple web-related technical standards, including the specifications for the HyperText Markup Language (HTML) and the Document Object Model (DOM). The central organizational membership and control of WHATWG – its "Steering Group" – consists of Apple, Mozilla, Google, and Microsoft. WHATWG editors of the specifications ensure correct implementation, in consultation with participants, but ultimately in accordance with Steering Group member objectives. == History == The WHATWG was formed in response to the slow development of World Wide Web Consortium (W3C) Web standards and W3C's decision to abandon HTML in favor of XML-based technologies. The WHATWG mailing list was announced on 4 June 2004, two days after the initiatives of a joint Opera–Mozilla position paper had been voted down by the W3C members at the W3C Workshop on Web Applications and Compound Documents. On 10 April 2007, the Mozilla Foundation, Apple, and Opera Software proposed that the new HTML working group of the W3C adopt the WHATWG's HTML5 as the starting point of its work and name its future deliverable as "HTML5" (though the WHATWG specification was later renamed HTML Living Standard). On 9 May 2007, the new HTML working group of the W3C resolved to do that. An Internet Explorer platform architect from Microsoft was invited but did not join, citing the lack of a patent policy to ensure all specifications can be implemented on a royalty-free basis. Since then, the W3C and the WHATWG had been developing HTML independently, at times causing specifications to diverge. In 2017, the WHATWG established an intellectual property rights agreement that includes a patent policy. This spurred a renewed attempt to allow the W3C and the WHATWG to work together on specifications. In 2019, the W3C and WHATWG agreed to a memorandum of understanding where development of HTML and DOM specifications would be done principally in the WHATWG. The editor has significant control over the specification, but the community can influence the decisions of the editor. In one case, editor Ian Hickson proposed replacing the

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  • MindSpore

    MindSpore

    MindSpore is an open-source software framework for deep learning, machine learning and artificial intelligence developed by Huawei. == Overview == MindSpore provides support for Python by allowing users to define models, control flow, and custom operators using native Python syntax. Unlike graph-based frameworks that require users to learn DSL or complex APIs, MindSpore adopts a source-to-source (S2S) automatic differentiation approach, allowing Python code to be automatically transformed into optimized computational graphs. It has support for custom OpenHarmony-based HarmonyOS NEXT single core framework system built for HarmonyOS, includes an AI system stack that comes with Huawei's built LLM model called PanGu-Σ with full MindSpore framework support. Alongside, OpenHarmony Native device-side AI support for training interface and ArkTS programming interface for its NNRt (Neural Network Runtime) backend configurations via MindSpore Lite AI framework codebase introduced in API 11 Beta 1 of OpenHarmony 4.1. MindSpore platform runs on Ascend AI chips and Kirin alongside other HiSilicon NPU chips. CANN (Compute Architecture of Neural Networks), heterogeneous computing architecture for AI developed by Huawei. With CANN backend in OpenCV DNN, giving developers ability to run created AI models on the Ascend, Kirin and other HiSilicon NPU enabled chips. It supports cross platform development such as Android, iOS, Windows, global OpenHarmony-based distro, Eclipse Oniro, Linux-based EulerOS alongside OpenEuler Huawei's server OS platforms, macOS and Linux. == History == On April 24, 2024, Huawei's MindSpore 2.3.RC1 was released to open source community with Foundation Model Training, Full-Stack Upgrade of Foundation Model Inference, Static Graph Optimization, IT Features and new MindSpore Elec MT (MindSpore-powered magnetotelluric) Intelligent Inversion Model.

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  • Inbenta

    Inbenta

    Inbenta is an AI company that originated in Barcelona, Spain, in 2005. Inbenta is currently headquartered in Allen, Texas, with additional offices in Spain, São Paulo, Brazil, Toulouse, France, and Tokyo, Japan. Inbenta provides natural language processing and semantic search through artificial intelligence. == History == Inbenta raised $12 Million in their Series B funding round to extend the reach of their artificial intelligence for business solutions. In 2023 Inbenta's new chief executive officer Melissa Solis moved Inbenta's headquarters to One Bethany West in Allen, Texas from Foster City, California. == Controversy == On 23 June 2018, Ticketmaster UK identified malicious software on a customer support product hosted by Inbenta Technologies, compromising personal data and payment details for thousands of Ticketmaster customers. Three days later, Inbenta's CEO Issued a message about the incident to convey the full scope of the breach. Also on its FAQ section, Inbenta claimed that "After a careful analysis of all clues and snapshots from our systems, the technical team at Inbenta discovered that the script had been implemented on the payment page. We were unaware of this, and would have advised against doing so had we known, as it presents a point of vulnerability". On November 13, 2020, the Information Commissioner's Office fined Ticketmaster UK Limited £1.25 million for failing to protect customers' payment details. According to the ICO, "It was because of Ticketmaster's business decision to include the [Inbenta] chat bot on its payment page that the chat bot was able to unlawfully process the personal data of customers."

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  • National Security Commission on Artificial Intelligence

    National Security Commission on Artificial Intelligence

    The National Security Commission on Artificial Intelligence (NSCAI) was an independent commission of the United States of America from 2018 to 2021. Its mission was to make recommendations to the President and Congress 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". The commission's 15 members were nominated by the United States Congress. The NSCAI was dissolved on 1 October 2021. == History and reporting == The NSCAI began working in March 2019 and by November 2019 it had received more than 200 classified and unclassified briefings to help with the creation of its final report due in 2021.On 4 November 2019, the NSCAI shared its interim report with Congress, where it explained the 27 initial judgements to base its ongoing work. In the interim report the commission also agreed on seven principles: Global leadership in AI technology is a national security priority AI adoption is an urgent imperative for national security A shared sense of responsibility for the American peoples security must be created from government officials and private sector leaders. It needs to find local AI talent and use it to attract the world’s best minds Actions used for the protection of America’s AI leadership against foreign threats needs to follow the principles of free enterprise, free inquiry and free flow of ideas. The technical limitations of AI are universally known, however, a strong desire remains for powerful, dependable, and secure AI systems. United States used AI must follow American values including the rule of law Fundamental areas of effort for the preservation of U.S. advantages were also agreed upon in the interim report of 2019. The NSCAI released its first report of recommendations in March 2020, most of which were included in the 2021 National Defense Authorization Act. In July 2020, the commission published the second report to Congress. It identified 35 actions for both Executive and Legislative branches, which were focused on six fundamental areas. This report was available to the public. In January 2021, a draft of the final report was presented at a panel led by Schmidt. The report recommended the US to use AI technology for military use and development. It issued its final report in March 2021, saying that the U.S. is not sufficiently prepared to defend or compete against China in the AI era. It was broken up into two parts, the first titled “Defending America in the AI Era”, and the second “Winning the Technology Competition”. The report spoke about China’s efforts and investments into integration and that it could very well take the lead in AI in the next few years. Additional suggestions were made to concentrate on AI in everything we do and to implement it into US national security on multiple levels, as well as focus on bringing in new talent to develop AI and to introduce it to the working force on both civilian and military levels. Another recommendation of the NSCAI report was to develop and provide China and Russia with alternative models that are based on norms and democratic values. The final report also included a proposed $40 billion budget for government spending. On 14 April 2021, NSCAI executive director Ylli Bajraktari and director of Research and Analysis Justin Lynch participated in an event held by the Center for Security and Emerging Technology (CSET) to discuss the final report findings. In October 2021, NSCAI chair Eric Schmidt founded the bipartisan, non-profit Special Competitive Studies Project (SCSP) through his family led non-profit Eric & Wendy Schmidt Fund for Strategic Innovation in order to carry on the NSCAI’s efforts and expand beyond national security. The Foundation for Defense of Democracies held an event in June 2023, called “Thinking Forward After the NSCAI and CSC: A Discussion on AI and Cyber Policy”, with former members of NSCAI on the moderation panel, including Eric Schmidt and Ylli Bajraktari. == Members == Members of the National Security Commission on Artificial Intelligence: Eric Schmidt (chair), former CEO of Google Robert Work (Vice Chair), former Deputy Secretary of Defense Mignon Clyburn, former Commissioner of the Federal Communications Commission Chris Darby, CEO of In-Q-Tel Kenneth M. Ford, CEO of the Florida Institute for Human and Machine Cognition Jose-Marie Griffiths, President of Dakota State University Eric Horvitz, Technical Fellow at Microsoft Katrina G. McFarland, former Assistant Secretary of Defense for Acquisition Jason Matheny, Director of the Center for Security and Emerging Technology at Georgetown University Gilman Louie, partner at Alsop Louie Partners William Mark, vice president at SRI International Andy Jassy, CEO of Amazon Web Services (AWS) Safra Catz, CEO of Oracle Steve Chien, Technical Fellow at Jet Propulsion Laboratory (JPL) Andrew Moore, Google/Alphabet == Recommendations == The report's recommendations include: Dramatically increasing non-defense federal spending on AI research and development, doubling every year from $2 billion in 2022, to $32 billion in 2026. That would bring it up to a level similar to spending on biomedical research A dramatic increase in undergraduate scholarship and graduate studies fellowships in AI Creation of a Digital Corps to bring skilled tech workers into government Founding of a Digital Service Academy: an accredited university providing subsidized education in exchange for a commitment to work for a time in government Include civil rights and civil liberty reports for new AI systems or major updates to existing systems Expanding allocations of employment-based green cards, and giving them to every AI PhD graduate from an accredited U.S. university Reforming the acquisition management system Department of Defense to make it faster and easier to introduce new technologies == Transparency == In December 2019, a ruling was made under the Freedom of Information Act (FOIA) that the NSCAI must also provide historical documents upon request. The Electronic Privacy Information Center (EPIC) filed the lawsuit against the NSCAI in September 2019 after being refused information about the upcoming meetings and prepared records of the commission under FOIA and the Federal Advisory Committee Act (FACA). The U.S. District Court for the District of Columbia ruled in June 2020 that the NSCAI must comply with FACA and therefore hold open meetings and provide records to the public. The lawsuit was also filed by EPIC.

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  • PNGOUT

    PNGOUT

    PNGOUT is a freeware command line optimizer for PNG images written by Ken Silverman. The transformation is lossless, meaning that the resulting image is visually identical to the source image. According to its author, this program can often get higher compression than other optimizers by 5–10%. It is possible to compress some inflated PNGs to a size below 1% of the original file. PNGOUT was also available as a plug-in for the freeware image viewer IrfanView and can be enabled as an option when saving files. It allows editing of various PNGOUT settings via a dialog box. PNGOUT integration was removed in IrfanView version 4.58 in favour of OptiPNG. In 2006, a commercial version of PNGOUT with a graphical user interface, known as PNGOUTWin, was released by Ardfry Imaging, a small company Silverman co-founded in 2005. There is also a freeware GUI frontend to PNGOUT available, known as PNGGauntlet. == Main operation == The main function of PNGOUT is to reduce the size of image data contained in the IDAT chunk. This chunk is compressed using the deflate algorithm. Deflate algorithms can vary in speed and compression ratio, with higher compression ratios generally implying lower speed. Ken Silverman wrote a deflate compressor for PNGOUT that is slower than the ones used in most graphics software, but produces smaller files. PNGOUT also performs automatic bit depth, color, and palette reduction where appropriate.

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  • Procedural reasoning system

    Procedural reasoning system

    In artificial intelligence, a procedural reasoning system (PRS) is a framework for constructing real-time reasoning systems that can perform complex tasks in dynamic environments. It is based on the notion of a rational agent or intelligent agent using the belief–desire–intention software model. A user application is predominately defined, and provided to a PRS system is a set of knowledge areas. Each knowledge area is a piece of procedural knowledge that specifies how to do something, e.g., how to navigate down a corridor, or how to plan a path (in contrast with robotic architectures where the programmer just provides a model of what the states of the world are and how the agent's primitive actions affect them). Such a program, together with a PRS interpreter, is used to control the agent. The interpreter is responsible for maintaining beliefs about the world state, choosing which goals to attempt to achieve next, and choosing which knowledge area to apply in the current situation. How exactly these operations are performed might depend on domain-specific meta-level knowledge areas. Unlike traditional AI planning systems that generate a complete plan at the beginning, and replan if unexpected things happen, PRS interleaves planning and doing actions in the world. At any point, the system might only have a partially specified plan for the future. PRS is based on the BDI or belief–desire–intention framework for intelligent agents. Beliefs consist of what the agent believes to be true about the current state of the world, desires consist of the agent's goals, and intentions consist of the agent's current plans for achieving those goals. Furthermore, each of these three components is typically explicitly represented somewhere within the memory of the PRS agent at runtime, which is in contrast to purely reactive systems, such as the subsumption architecture. == History == The PRS concept was developed by the Artificial Intelligence Center at SRI International during the 1980s, by many workers including Michael Georgeff, Amy L. Lansky, and François Félix Ingrand. Their framework was responsible for exploiting and popularizing the BDI model in software for control of an intelligent agent. The seminal application of the framework was a fault detection system for the reaction control system of the NASA Space Shuttle Discovery. Development on this PRS continued at the Australian Artificial Intelligence Institute through to the late 1990s, which led to the development of a C++ implementation and extension called dMARS. == Architecture == The system architecture of SRI's PRS includes the following components: Database for beliefs about the world, represented using first order predicate calculus. Goals to be realized by the system as conditions over an interval of time on internal and external state descriptions (desires). Knowledge areas (KAs) or plans that define sequences of low-level actions toward achieving a goal in specific situations. Intentions that include those KAs that have been selected for current and eventual execution. Interpreter or inference mechanism that manages the system. == Features == SRI's PRS was developed for embedded application in dynamic and real-time environments. As such it specifically addressed the limitations of other contemporary control and reasoning architectures like expert systems and the blackboard system. The following define the general requirements for the development of their PRS: asynchronous event handling guaranteed reaction and response types procedural representation of knowledge handling of multiple problems reactive and goal-directed behavior focus of attention reflective reasoning capabilities continuous embedded operation handling of incomplete or inaccurate data handling of transients modeling delayed feedback operator control == Applications == The seminal application of SRI's PRS was a monitoring and fault detection system for the reaction control system (RCS) on the NASA space shuttle. The RCS provides propulsive forces from a collection of jet thrusters and controls altitude of the space shuttle. A PRS-based fault diagnostic system was developed and tested using a simulator. It included over 100 KAs and over 25 meta level KAs. RCS specific KAs were written by space shuttle mission controllers. It was implemented on the Symbolics 3600 Series LISP machine and used multiple communicating instances of PRS. The system maintained over 1000 facts about the RCS, over 650 facts for the forward RCS alone and half of which are updated continuously during the mission. A version of the PRS was used to monitor the reaction control system on the Space Shuttle Discovery. PRS was tested on Shakey the robot including navigational and simulated jet malfunction scenarios based on the space shuttle. Later applications included a network management monitor called the Interactive Real-time Telecommunications Network Management System (IRTNMS) for Telecom Australia. == Extensions == The following list the major implementations and extensions of the PRS architecture. UM-PRS OpenPRS (formerly C-PRS and Propice) AgentSpeak Distributed multi-agent reasoning system (dMARS) GORITE JAM JACK Intelligent Agents SRI Procedural Agent Realization Kit (SPARK) PRS-CL

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  • Pax Silica

    Pax Silica

    Pax Silica is a United States-led international initiative focused on strengthening and coordinating "trusted" supply chains for advanced technologies—especially semiconductors, artificial intelligence (AI) infrastructure, critical minerals, advanced manufacturing, logistics, and associated energy and data infrastructure. The initiative is coordinated by the US Department of State and was launched in December 2025 alongside the signing of the non-binding Pax Silica Declaration by an initial group of partner countries. The initiative describes itself as a "positive-sum" partnership intended to reduce "coercive dependencies" and improve resilience across the full technology stack, from mineral extraction and processing through chip manufacturing and computing infrastructure. US officials described Pax Silica as a framework for coordinating flagship projects and policy alignment across partner countries, including supply-chain mapping, investment and co-investment initiatives, and protection of critical infrastructure and sensitive technologies. Reuters reported discussions of projects linked to trade and logistics routes and an industrial park initiative in Israel. Gulf countries, such as the UAE and Qatar, are betting on attracting AI companies with cheap energy. Moreover, the UAE's potential to invest in Pax Silica's activities has been noted as a fundamental asset for the initiative. In early 2026, the U.S. announced plans to contribute $250M toward an investmest consortium that's intended to strengthen energy and critical mineral supply chains. == Launch and background == During the 2020s, governments increasingly treated supply-chain resilience in semiconductors, critical minerals, and AI-related computing infrastructure as a national-security priority, amid export controls, industrial policy measures, and geopolitical competition over the technologies underpinning advanced manufacturing and AI. Pax Silica was presented by US officials as an economic-security framework aimed at aligning policies and investment among "trusted partners" that host major technology firms and key industrial capacity. Pacific Forum's analyst Akhil Ramesh, writing for the National Interest magazine, described the initiative as understanding that: "economic security today is inseparable from control over energy, critical minerals, high-end manufacturing, and advanced models." On December 11, 2025, the US Department of State announced the inaugural Pax Silica Summit and a planned signing of the Pax Silica Declaration, describing Pax Silica as the Department's flagship effort on AI and supply-chain security. The initial summit was held in Washington, D.C. on December 12, 2025. The State Department fact sheet described cooperation areas including connectivity and data infrastructure, compute and semiconductors, advanced manufacturing, logistics, mineral refining and processing, and energy. == Membership == Pax Silica participation has been discussed in terms of (1) countries that have signed the declaration and (2) countries invited to summit discussions or publicly reported as prospective signatories but which had not (as of mid-January 2026) signed the declaration. === Countries that signed the Pax Silica Declaration === Seven countries signed the declaration at the December 12, 2025, summit in Washington, D.C.: Australia Israel Japan South Korea Singapore United Kingdom United States Some countries who attended the initial conversations did not immediately sign, while additional countries were invited to join after the discussions concluded. The following are the later signatory countries on the declaration: Greece Netherlands (joined December 17, 2025; "non-signing partner") Qatar (joined January 13, 2026) United Arab Emirates (joined January 14, 2026) India (joined February 20, 2026) Sweden (signed March 17, 2026) Finland (signed April 16, 2026) Philippines (signed April 17, 2026) Norway (signed May 6, 2026) === Countries invited / participating, but not yet signed === At launch, US materials and contemporaneous reporting described additional invited participants and observers, including: Canada – observer/participant in related discussions, per US briefing materials; not listed among signatories. Taiwan – participated in summit sessions according to a State Department briefing; not listed among signatories. The Organisation for Economic Co-operation and Development (OECD) and European Union were also noted by US officials as present in an observer capacity, but are not countries.

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  • Comet (browser)

    Comet (browser)

    Comet is an AI-powered web browser based on Chromium. It was released by Perplexity AI for Microsoft Windows and macOS on July 9, 2025, for Android on November 20, 2025, and for iOS on March 18, 2026. Initial access to the browser was limited to users subscribed to Perplexity's most expensive tier, with broader availability expected over time. The browser was released for free download in October 2025. == Features == Comet is integrated with Perplexity's AI-assisted search engine. The browser features an assistant which enables users to perform a variety of tasks such as generating article summaries, sending emails, or buying products. == Security concerns == Researchers at LayerX Security identified a malicious attack vector which they call CometJacking. The exploit could possibly exfiltrate a user's personal sensitive data to a remote server controlled by the attacker. LayerX attempted to responsibly disclose their findings to Comet's developer Perplexity AI in August 2025. Perplexity responded that they saw no security impact and marked the disclosure as not applicable.

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  • Shaded Picture System

    Shaded Picture System

    The Shaded Picture System was a 3D raster computer display processor introduced by Evans & Sutherland in October 1973. The Shaded Picture System was the first general-purpose, commercially available raster computer graphics display processor capable of real-time, shaded 3D graphics. It could only display black and white graphics at a resolution of 256 by 256. It was extremely expensive, and very few units were ever sold. == History == The principles of shaded, hidden-line true 3D graphics were pioneered at the University of Utah in 1967. However, this algorithm was slow and would take several minutes to produce an image. In 1970, Gary Watkins developed a FORTRAN simulator of a faster algorithm that would theoretically generate shaded 3D images in real-time, "if implemented in suitable hardware". The simulator itself was still not capable of real-time shaded 3D image rendering. Evans & Sutherland developed a functional prototype of this "suitable hardware", which was later sold as the Shaded Picture System in 1973. About a year earlier in 1972, Evans & Sutherland sold the first and only CT1 to Case Western Reserve University. The CT1, or Continuous Tone 1, was a specialized image generator, not meant as a marketable or mass-produced product. At the time, the CT1, along with G.E./NASA's upgraded Electronic Scene Generator from 1971, would have been the only real-time raster graphics systems sold to customers comparable to the Shaded Picture System, although both the CT1 and Electronic Scene Generator were intentionally produced as one-off products and specialized for the needs of their customers. The Shaded Picture System, in contrast, was intentionally marketed.In early 1975, Evans & Sutherland demonstrated a random-access video frame buffer using relatively low-cost semiconductor memory, which was much more capable than the Shaded Picture System. When interfaced with a (non-shaded) E&S Picture System, the frame buffer had a resolution of 512 by 512 in grayscale and partial color capabilities. By the end of 1975, this frame buffer was commercially available.

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  • Cleverpath AION Business Rules Expert

    Cleverpath AION Business Rules Expert

    Cleverpath AION Business Rules Expert (formerly Platinum AIONDS, and before that Trinzic AIONDS, and originally Aion) is an expert system and Business rules engine owned by Computer Associates by 2000. == History == The product was created around 1986 as "Aion" by the Aion company. In its initial release Aion was multi-platform and continues to be deliverable to the PC, Unixs, and Mainframe computer's. In addition it ties in seamlessly with a variety of databases including Oracle, Microsoft SQL Server, and ODBC. Aion was founded by Harry Reinstein, Larry Cohn, Garry Hallee, Scott Grinis, and others. From Scott Grinis's bio: Scott founded Aion, a company that developed expert systems and whose advanced inference engine and object technology were used by financial services and insurance firms to develop risk-scoring and underwriting applications. Harry Reinstein was quoted as saying: “Our biggest competitor was not AICorp, it was COBOL” Trinzic owned AION by 1993. A reference in a 1993 announcement indicates that Trinzic's formation was the result of a merger (paraphased): Trinzic set three development initiatives shortly after its formation from the merger of Aion Corp. and AICorp. The other initiatives -- adding SQL extensions to Aion/DS and evaluating the unbundling of some of that product's object-oriented programming capabilities -- are still active. Writing in 1993 Judith Hodges and Deborah Melewski give the date for the merger: Two rival artificial intelligence software vendors -- AICorp, Inc. and Aion Corp. -- merged in September 1992 to form Trinzic Corp. As part of the merger, redundant jobs were eliminated (20% of the combined work force), leaving a total work force of 245 employees worldwide. The new firm also boasted a combined installed base of more than 1,200 sites representing more than 10,000 software licenses. Although in the merger, technically AICorp bought Aion, as AICorp was a public company and Aion was still private, the reality was that Aion's leadership and technology subsumed AICorp's. Jim Gagnard, the CEO of Aion, became CEO of Trinzic and AICorp's flagship product, KBMS, was discontinued, while the Aion Development System continued to be enhanced and KBMS customers were assisted in converting to AIONDS, under the continued technical leadership of Garry Hallee and Scott Grinis. On August 1, 1994 Trinzic released version 6.4 of AIONDS saying, in part: Trinzic Corp., Palo Alto, Calif., has unveiled The Aion Development System (AionDS) Version 6.4, an upgrade to the company's development environment for building business process automation applications. Version 6.4 provides a visual development environment for Microsoft Windows or OS/2 PM applications using business rules. Trinzic was acquired by PLATINUM Technologies in 1995 which retained at least some of Trinzic's acquisitions Platinum Technologies was acquired by Computer Associates in 1999. CA changed the system's name to CA Aion Business Rules Expert" on or before 2009. It is currently (June 2011) at Release 11 on a wide range of supported platforms. == Applications using Aion == Aion has been used in a variety of industries including Energy, Insurance, Military, Aviation, and Banking. At one point an Aion expert system application written by Covia, LLC existed to do airport gate assignment. Colossus, a computer program, developed by Computer Sciences Corporation is the insurance industry’s leading expert system for assisting adjusters in the evaluation of bodily injury claims (aka "pain and suffering"). Colossus helps adjusters reduce variance in payouts on similar bodily injury claims through objective use of industry standard rules.

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  • Angel F

    Angel F

    Angel_F is a fictional child artificial intelligence that has been used in art performances worldwide focused on the issues of digital liberties, intellectual property and on the evolution of language and behaviour in information society. The character was created by Salvatore Iaconesi in 2007 as a hack to the Biodoll art performance by Italian artist Franca Formenti. The project was later joined by Oriana Persico who curated communication and part of the theoretical approaches of the action. The Angel_F project has been featured in books, magazines, national televisions, and has been invited to many conferences and events, both academic and artistic. == Creation == Angel_F is a backronym which stands for Autonomous Non Generative E-volitive Life_Form. The project was born in 2007 and resulted from the fusion of two contemporary art performances. Franca Formenti, an Italian artist living in Varese, invented the Biodoll character in 2002, which began making its appearances first on the network and later in the physical world by using what were called "clones": young women, prostitutes, pornographic starlets, transsexuals and models interpreting the role of a digital prostitute. The Biodoll was an art performance focused on research emerging from the network of new forms of sexualities, and on the analysis of changes brought on by this transformation to the concepts of private and public spaces, privacy, and the possibility of creating multiple fluid identities through language and digital media. The theme of fertility has always been central to the Biodoll performance: the digital prostitute was a wombless clone but desired giving birth to a son, the 'Bloki'. In a process starting in 2006, and ending in February 2007, Salvatore Iaconesi (xDxD.vs.xDxD) used his 'Talker' linguistic artificial intelligence to animate the digital child conceived with prof. Derrick de Kerckhove: Angel_F. Iaconesi and Persico met in November 2006 and immediately started collaborating on the birth of Angel_F. Angel_F was designed as a synthetic digital being composed through narrative, technological and cognitive psychology layers. The objective was to create iconic characteristics that resulted in being evocative and able to mimic human life up to a level in which bringing up a symbolic dialogue was possible. On the other side, the artificial identity was to implement and expose the cultural, emotional and relational ways that were typical of networked social ecosystems, among those technologies, systems and infrastructures that entered and shaped people's daily lives. The young digital being mimicked the evolution of a human baby: initially conceived inside the website of its digital mother it emulated the birth of a child by using the metaphor of a virus developing inside a website, taking progressively more space in the domain's databases and interfaces. Content was produced through the software by using small browser-based spyware techniques, through which Angel_F could infer the list of major portals that had been visited by the website's users. The Biodoll website was invaded by this growing presence and, thus, Angel_F was born. The Artificial Intelligence (AI) component of Angel_F was derived from another project, Talker, through which internet users could build up the AI's linguistic network by feeding it their text and web clips. Angel_F used this component to generate sentences and phrases, publishing them on the interface and on selected blogs. The parallel between the growth of the AI and that of a child kept building up and, just as children learn how to speak and act by observing their parents and the people around them, Angel_F used its spyware and AI components to learn, to navigate websites and web portals using web crawler based techniques, and to interact with other people by using the contents hosted and generated in its database to create surreal dialogues in blogs and websites. A virtual school was created, called Talker Mind, to narratively continue the AI's growth. Five professors (Massimo Canevacci, Antonio Caronia, Carlo Formenti, Derrick de Kerckhove and Luigi Pagliarini) fed their texts and academic articles to Angel_F, simulating virtual asynchronous lessons by using a multi-blog structure. A peer-to-peer system was also created at the time, named 'Presence'. Its interface resembled the one of 8-bit videogames and the peer to peer users travelled in a starry space and were able to perform standard Instant Messaging tasks, such as chat and file sharing. The interactions were possible both among humans and digital beings. Angel_F was the first user of the Presence peer to peer system. Angel_F entered the physical world as a baby-stroller mounted laptop computer that was used to let the digital child join events and conferences held worldwide. == Events == Angel_F performed all over the world, both in artistic contexts and in academic ones. It was also used for the communication strategy of several activist groups on the themes of intellectual property and digital freedoms. The first public space performance was held in Milan, when the Biodoll distributed a generative free press publication (called the Bloki FreePreXXX, its text was generated algorithmically and inserted into a prepared graphic layout). June 14, 2007: The second performance was held in Rome, at the Forte Prenestino, with a massive playroom created through computational graphics that people could interact with and that were generated by the AI. June 22, 2007: Angel_F presented the closing remarks for an Ipotesi per Assurdo (Absurd Hypothesis) with Salvatore Iaconesi and Oriana Persico at the IULM University in Milan, discussing the possibilities for an ecosystemic, sustainable reinvention of corporations. July 28, 2007: Hundreds of people at LiberaFesta (Free Party) in Rome listened to Angel_F in a speech discussing new politics and hacker ethics. 2007: The Glocal & Outsiders conference held in Prague at the Academy of Sciences was the first academic presentation of the Angel_F project, together with the Biodoll. September 2007: Angel_F was not allowed to post its contribution to the DFIR (Dialogue Forum for Internet Rights) held in Rome in preparation for Rio de Janeiro's Internet Governance Forum (IGF) edition. The case quickly turned into a collaboration among the involved parties and Angel_F was invited to the global event in Brazil where it was the only digital being present. Angel_F contributed a videomessage, in the digital freedoms workshop, which suggested some ideas for action to the United Nations and to all the parties involved in the IGF organization. October 2007: Angel_F was presented live at the FE/MALE 2 event, as an example of an atypical family during a public debate on new sexualities and social change. October 2007: Angel_F made a series of public performances Florence's Festival della Creatività (Festival of Creativity), an institutional event held periodically to showcase Italy's and other countries' best technological projects. During the festival Derrick de Kerckhove publicly recognized the little AI as his digital son. December 2007: Several international associations, and scientific researchers had been involved with Angel_F, eventually producing the system and process used to set up the Talker Mind digital school for the AI with Angel_F's professors. March 2008: The Tecnológico de Monterrey university in Mexico City organized the Computer Art Congress 2 international event, featuring Angel_F's project among with the ones by scientific researchers worldwide. July 2008: The project was presented in Austria at the Planetary Collegium's Consciousness Reframed 9 conference, together with the 'NeoRealismo Virtuale'. October 2008: Angel_F was used at a public event on a European scale called Freedom not Fear discussing privacy and civil liberties. July 2009: Angel_F has been seen with its digital father Derrick de Kerckhove to protest against Italy's harsh politics on freedom of speech. The project concluded in 2009 with the publication of a book entitled 'Angel F. Diario di una intelligenza artificiale' (Angel_F, the diaries of an Artificial Intelligence).

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  • Vivification

    Vivification

    Vivification is an operation on a description logic knowledge base to improve performance of a semantic reasoner. Vivification replaces a disjunction of concepts C 1 ⊔ C 2 … ⊔ C n {\displaystyle C_{1}\sqcup C_{2}\ldots \sqcup C_{n}} by the least common subsumer of the concepts C 1 , C 2 , … C n {\displaystyle C_{1},C_{2},\ldots C_{n}} . The goal of this operation is to improve the performance of the reasoner by replacing a complex set of concepts with a single concept which subsumes the original concepts. For example, consider the example given in (Cohen 92): Suppose we have the concept PIANIST(Jill) ∨ ORGANIST(Jill) {\displaystyle {\textrm {PIANIST(Jill)}}\vee {\textrm {ORGANIST(Jill)}}} . This concept can be vivified into a simpler concept KEYBOARD-PLAYER(Jill) {\displaystyle {\textrm {KEYBOARD-PLAYER(Jill)}}} . This summarization leads to an approximation that may not be exactly equivalent to the original. == An approximation == Knowledge base vivification is not necessarily exact. If the reasoner is operating under the open world assumption we may get surprising results. In the previous example, if we replace the disjunction with the vivified concept, we will arrive at a surprising results. First, we find that the reasoner will no longer classify Jill as either a pianist or an organist. Even though ORGANIST {\displaystyle {\textrm {ORGANIST}}} and PIANIST {\displaystyle {\textrm {PIANIST}}} are the only two sub-classes, under the OWA we can no longer classify Jill as playing one or the other. The reason is that there may be another keyboard instrument (e.g. a harpsichord) that Jill plays but which does not have a specific subclass.

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  • Brave Leo

    Brave Leo

    Brave Leo is a large language model-based chatbot developed by Brave Software and included with the Brave browser. == History == In November 2023, the company said versions for iOS and Android would be available "in the coming months". == Features == Since January 2024, Leo has used the open-source Mixtral 8x7B from Mistral AI as its default large language model, in addition to LLaMA 2 from Meta Platforms and Claude from Anthropic, both of which have been used previously. Leo can suggest follow-up questions, and summarize webpages, PDFs, and videos. Leo has a $15 (US) per month premium version that enables more requests and uses larger LLMs. == Privacy == The answers given by Leo are not saved. Brave uses the slogan Love Privacy to emphasize its focus on user privacy and data protection. The phrase has been featured in Brave's official marketing campaigns and has been cited in media coverage of the browser's privacy-first approach. == Controversies == In 2023, PC World reported that Leo evades questions about US elections.

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  • General Data Protection Regulation

    General Data Protection Regulation

    The General Data Protection Regulation (Regulation (EU) 2016/679), abbreviated GDPR, is a European Union regulation on information privacy in the European Union (EU) and the European Economic Area (EEA). The GDPR is an important component of EU privacy law and human rights law, in particular Article 8(1) of the Charter of Fundamental Rights of the European Union. It also governs the transfer of personal data outside the EU and EEA. The GDPR's goals are to enhance individuals' control and rights over their personal information and to simplify the regulations for international business. It supersedes the Data Protection Directive 95/46/EC and, among other things, simplifies the terminology. The European Parliament and Council of the European Union adopted the GDPR on 14 April 2016, to become effective on 25 May 2018. As an EU regulation (instead of a directive), the GDPR has direct legal effect and does not require transposition into national law. However, it also provides flexibility for individual member states to modify (derogate from) some of its provisions. As an example of the Brussels effect, the regulation became a model for many other laws around the world, including in Brazil, Japan, Singapore, South Africa, South Korea, Sri Lanka, and Thailand. After leaving the European Union, the United Kingdom enacted its "UK GDPR", identical to the GDPR. The California Consumer Privacy Act (CCPA), adopted on 28 June 2018, has many similarities with the GDPR. == Contents == The GDPR 2016 has eleven chapters, concerning general provisions, principles, rights of the data subject, duties of data controllers or processors, transfers of personal data to third-party countries, supervisory authorities, cooperation among member states, remedies, liability or penalties for breach of rights, provisions related to specific processing situations, and miscellaneous final provisions. The GDPR also contains 173 recitals purposed to clarify scope and rationale for the regulatory provisions, as well as its legislative intents – Recital 4, for instance, begins by saying that the processing of personal data should be "designed to serve mankind". === General provisions === The regulation applies if the data controller, or processor, or the data subject (person) is based in the EU. The regulation also applies to organisations based outside the EU if they collect or process personal data of individuals located inside the EU. The regulation does not apply to the processing of data by private persons provided that the purpose has no connection to a professional or commercial activity." (Recital 18). According to the European Commission, "Personal data is information that relates to an identified or identifiable individual. If you cannot directly identify an individual from that information, then you need to consider whether the individual is still identifiable. You should take into account the information you are processing together with all the means reasonably likely to be used by either you or any other person to identify that individual." The precise definitions of terms such as "personal data", "processing", "data subject", "controller", and "processor" are stated in Article 4. The regulation does not purport to apply to the processing of personal data for national security activities or law enforcement of the EU; however, industry groups concerned about facing a potential conflict of laws have questioned whether Article 48 could be invoked to seek to prevent a data controller subject to a third country's laws from complying with a legal order from that country's law enforcement, judicial, or national security authorities to disclose to such authorities the personal data of an EU person, regardless of whether the data resides in or out of the EU. Article 48 states that any judgement of a court or tribunal and any decision of an administrative authority of a third country requiring a controller or processor to transfer or disclose personal data may not be recognised or enforceable in any manner unless based on an international agreement, like a mutual legal assistance treaty in force between the requesting third (non-EU) country and the EU or a member state. The data protection reform package also includes a separate Data Protection Directive for the police and criminal justice sector that provides rules on personal data exchanges at State level, Union level, and international levels. A single set of rules applies to all EU member states. Each member state establishes an independent supervisory authority (SA) to hear and investigate complaints, sanction administrative offences, etc. SAs in each member state co-operate with other SAs, providing mutual assistance and organising joint operations. If a business has multiple establishments in the EU, it must have a single SA as its "lead authority", based on the location of its "main establishment" where the main processing activities take place. The lead authority thus acts as a "one-stop shop" to supervise all the processing activities of that business throughout the EU. A European Data Protection Board (EDPB) co-ordinates the SAs. EDPB thus replaces the Article 29 Data Protection Working Party. There are exceptions for data processed in an employment context or in national security that still might be subject to individual country regulations. === Principles and lawful purposes === Article 5 sets out six principles relating to the lawfulness of processing personal data. The first of these specifies that data must be processed lawfully, fairly and in a transparent manner. Article 6 develops this principle by specifying that personal data may not be processed unless there is at least one legal basis for doing so. The other principles refer to "purpose limitation", "data minimisation", "accuracy", "storage limitation", and "integrity and confidentiality". Article 6 states that the lawful purposes are: (a) If the data subject has given consent to the processing of his or her personal data; (b) To fulfill contractual obligations with a data subject, or for tasks at the request of a data subject who is in the process of entering into a contract; (c) To comply with a data controller's legal obligations; (d) To protect the vital interests of a data subject or another individual; (e) To perform a task in the public interest or in official authority; (f) For the legitimate interests of a data controller or a third party, unless these interests are overridden by interests of the data subject or her or his rights according to the Charter of Fundamental Rights (especially in the case of children). If informed consent is used as the lawful basis for processing, consent must have been explicit for data collected and each purpose data is used for. Consent must be a specific, freely given, plainly worded, and unambiguous affirmation given by the data subject; an online form which has consent options structured as an opt-out selected by default is a violation of the GDPR, as the consent is not unambiguously affirmed by the user. In addition, multiple types of processing may not be "bundled" together into a single affirmation prompt, as this is not specific to each use of data, and the individual permissions are not freely given. (Recital 32). Data subjects must be allowed to withdraw this consent at any time, and the process of doing so must not be harder than it was to opt in. A data controller may not refuse service to users who decline consent to processing that is not strictly necessary in order to use the service. Consent for children, defined in the regulation as being less than 16 years old (although with the option for member states to individually make it as low as 13 years old), must be given by the child's parent or custodian, and verifiable. If consent to processing was already provided under the Data Protection Directive, a data controller does not have to re-obtain consent if the processing is documented and obtained in compliance with the GDPR's requirements (Recital 171). === Rights of the data subject === ==== Transparency and modalities ==== Article 12 requires the data controller to provide information to the "data subject in a concise, transparent, intelligible and easily accessible form, using clear and plain language, in particular for any information addressed specifically to a child." ==== Information and access ==== The right of access (Article 15) is a data subject right. It gives people the right to access their personal data and information about how this personal data is being processed. A data controller must provide, upon request, an overview of the categories of data that are being processed as well as a copy of the actual data; furthermore, the data controller has to inform the data subject on details about the processing, such as the purposes of the processing, with whom the data is shared, and how it acquired the data. A data subject must be able to transfer personal data from one electro

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  • Leela Zero

    Leela Zero

    Leela Zero is a free and open-source computer Go program released on 25 October 2017. It is developed by Belgian programmer Gian-Carlo Pascutto, the author of chess engine Sjeng and Go engine Leela. Leela Zero's algorithm is based on DeepMind's 2017 paper about AlphaGo Zero. Unlike the original Leela, which has a lot of human knowledge and heuristics programmed into it, the program code in Leela Zero only knows the basic rules and nothing more. The knowledge that makes Leela Zero a strong player is contained in a neural network, which is trained based on the results of previous games that the program played. Leela Zero is trained by a distributed effort, which is coordinated at the Leela Zero website. Members of the community provide computing resources by running the client, which generates self-play games and submits them to the server. The self-play games are used to train newer networks. Generally, over 500 clients have connected to the server to contribute resources. The community has provided high quality code contributions as well. == Version history == Leela Zero finished third at the BerryGenomics Cup World AI Go Tournament in Fuzhou, Fujian, China on 28 April 2018. The New Yorker at the end of 2018 characterized Leela and Leela Zero as "the world’s most successful open-source Go engines". In early 2018, another team branched Leela Chess Zero from the same code base, also to verify the methods in the AlphaZero paper as applied to the game of chess. AlphaZero's use of Google TPUs was replaced by a crowd-sourcing infrastructure and the ability to use graphics card GPUs via the OpenCL library. Even so, it is expected to take a year of crowd-sourced training to make up for the dozen hours that AlphaZero was allowed to train for its chess match in the paper. The distributed training server was shut down on 2021-02-15, marking the end of Leela Zero project. The page now directs visitors to KataGo and SAI. The model sizes increased steadily over time. The first released model has hash name d645af97, size 1x8 (1 layer, 8 channels), and released at 2017-11-10 13:04. The last released model has hash name 0e9ea880, size 40x256, and was released at 2021-02-15 09:04. == Technology == Leela Zero is an (almost) exact replication of AlphaGo Zero in both training process and architecture. The training process is Monte-Carlo Tree Search with self-play, exactly the same as AlphaGo Zero. The architecture is the same as AlphaGo Zero (with one difference). Consider the last released model, 0e9ea880. It has 47 million parameters, and the following architecture: The stem of the network takes as input a 18x19x19 tensor representation of the Go board. 8 channels are the positions of the current player's stones from the last eight time steps. (1 if there is a stone, 0 otherwise. If the time step go before the beginning of the game, then 0 in all positions.) 8 channels are the positions of the other player's stones from the last eight time steps. 1 channel is all 1 if black is to move, and 0 otherwise. 1 channel is all 1 if white is to move, and 0 otherwise. (This channel is not present in the original AlphaGo Zero) The body is a ResNet with 40 residual blocks and 256 channels. There are two heads, a policy head and a value head. Policy head outputs a logit array of size 19 × 19 + 1 {\displaystyle 19\times 19+1} , representing the logit of making a move in one of the points, plus the logit of passing. Value head outputs a number in the range ( − 1 , + 1 ) {\displaystyle (-1,+1)} , representing the expected score for the current player. -1 represents current player losing, and +1 winning.

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