AI Coding Models

AI Coding Models — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Token maxxing

Token Maxxing or Token Maxing is a metric used in an attempt to track productivity in the workplace especially for those using Artificial Intelligence (AI) based services. AI services charge for each token which represent units of effort expended by an AI service to solve a problem. Some believe that token consumption equates to productivity and thus can be used as a metric to monitor an employee's work. Supporters believe that higher token usage indicates higher productivity and higher utilization of powerful AI services. This also suggests that those not consuming enough tokens may be less productive and underutilizing powerful AI services. This belief might lead to an environment that incentivizes higher token usage to predict increased productivity. Critics of token maxxing as a metric claim that prudent workers will maximize any metric that management wants increased to gain a workplace advantage. For example: Engineers in the tech industries pressed to consume as many tokens as possible might run several AI agents in tandem, enter longer input prompts, or automate their tasks to maximize their token consumption. To management, this higher token usage may indicate potential productivity, but in reality may cause additional token costs, worker burnout, or actually create more bloated code of lower quality. Another claim is AI service companies potentially benefit from such an emphasis on token consumption and actively encourage the trend. Some developers have publicly advocated the practice. Developer Sigrid Jin, who said he used 50 billion tokens in a single year, has argued that maximizing token consumption is the best way to understand the value of AI, advising others to spend as much on AI usage as they pay in rent to obtain a return on investment. == See Also == Goodhart's law Perverse incentive Jevons Paradox
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International Journal of Pattern Recognition and Artificial Intelligence

The International Journal of Pattern Recognition and Artificial Intelligence was founded in 1987 and is published by World Scientific. The journal covers developments in artificial intelligence, and its sub-field, pattern recognition. This includes articles on image and language processing, robotics and neural networks. == Abstracting and indexing == The journal is abstracted and indexed in: SciSearch ISI Alerting Services CompuMath Citation Index Current Contents/Engineering, Computing & Technology Inspec io-port.net Compendex Computer Abstracts
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Aurora (supercomputer)

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

The Ratio Club was a small British informal dining club from 1949 to 1958 of young psychiatrists, psychologists, physiologists, mathematicians and engineers who met to discuss issues in cybernetics. == History == The idea of the club arose from a symposium on animal behaviour held in July 1949 by the Society of Experimental Biology in Cambridge. The club was founded by the neurologist John Bates, with other notable members such as W. Ross Ashby. The name Ratio was suggested by Albert Uttley, it being the Latin root meaning "computation or the faculty of mind which calculates, plans and reasons". He pointed out that it is also the root of rationarium, meaning a statistical account, and ratiocinatius, meaning argumentative. The use was probably inspired by an earlier suggestion by Donald Mackay of the 'MR club', from Machina ratiocinatrix, a term used by Norbert Wiener in the introduction to his then recently published book Cybernetics, or Control and Communication in the Animal and the Machine. Wiener used the term in reference to calculus ratiocinator, a calculating machine constructed by Leibniz. The initial membership was W. Ross Ashby, Horace Barlow, John Bates, George Dawson, Thomas Gold, W. E. Hick, Victor Little, Donald MacKay, Turner McLardy, P. A. Merton, John Pringle, Harold Shipton, Donald Sholl, Eliot Slater, Albert Uttley, W. Grey Walter and John Hugh Westcott. Alan Turing joined after the first meeting with I. J. Good, Philip Woodward and William Rushton added soon after. Giles Brindley attended several meetings as a guest. Warren McCulloch made presentations to the club twice, the first time at its inaugural meeting (a talk which the members found disappointing), and became a correspondent with and supporter of a number of its members. Others who attended at least one Ratio Club event as guests included Walter Pitts, Claude Shannon, J.Z. Young, C.H. Waddington, Peter Elias, J. C. R. Licklider, Oliver Selfridge, Benoît Mandelbrot, Colin Cherry and Anthony Oettinger. One one occasion I.J. Good brought along the then director of the USA's National Security Agency (presumably either Ralph Canine or John Samford given the dates). Several members admired the work of psychologist and philosopher Kenneth Craik and considered him an important influence; according to Husbands and Holland "there is no doubt Craik would have been a leading member of the club" had he not died young in 1945. The club has been considered the most influential cybernetics group in the UK, and many of its members went on to become prominent scientists.
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Information retrieval

Information retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an information need. The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images, or sounds. Cross-modal retrieval implies retrieval across modalities. Automated information retrieval systems are used to reduce what has been called information overload. An IR system is a software system that provides access to books, journals, and other documents, as well as storing and managing those documents. Web search engines are the most visible IR applications. == Overview == An information retrieval process begins when a user enters a query into the system. Queries are formal statements of information needs, for example search strings in web search engines. In information retrieval, a query does not uniquely identify a single object in the collection. Instead, several objects may match the query, perhaps with different degrees of relevance. An object is an entity that is represented by information in a content collection or database. User queries are matched against the database information. However, as opposed to classical SQL queries of a database, in information retrieval the results returned may or may not match the query, so results are typically ranked. This ranking of results is a key difference of information retrieval searching compared to database searching. Depending on the application the data objects may be, for example, text documents, images, audio, mind maps or videos. Often the documents themselves are not kept or stored directly in the IR system, but are instead represented in the system by document surrogates or metadata. Most IR systems compute a numeric score on how well each object in the database matches the query, and rank the objects according to this value. The top ranking objects are then shown to the user. The process may then be iterated if the user wishes to refine the query. == History == there is ... a machine called the Univac ... whereby letters and figures are coded as a pattern of magnetic spots on a long steel tape. By this means the text of a document, preceded by its subject code symbol, can be recorded ... the machine ... automatically selects and types out those references which have been coded in any desired way at a rate of 120 words a minute The idea of using computers to search for relevant pieces of information was popularized in the article As We May Think by Vannevar Bush in 1945. It would appear that Bush was inspired by patents for a 'statistical machine' – filed by Emanuel Goldberg in the 1920s and 1930s – that searched for documents stored on film. The first description of a computer searching for information was described by Holmstrom in 1948, detailing an early mention of the Univac computer. Automated information retrieval systems were introduced in the 1950s: one even featured in the 1957 romantic comedy Desk Set. In the 1960s, the first large information retrieval research group was formed by Gerard Salton at Cornell. By the 1970s several different retrieval techniques had been shown to perform well on small text corpora such as the Cranfield collection (several thousand documents). Large-scale retrieval systems, such as the Lockheed Dialog system, came into use early in the 1970s. In 1992, the US Department of Defense along with the National Institute of Standards and Technology (NIST), cosponsored the Text Retrieval Conference (TREC) as part of the TIPSTER text program. The aim of this was to look into the information retrieval community by supplying the infrastructure that was needed for evaluation of text retrieval methodologies on a very large text collection. This catalyzed research on methods that scale to huge corpora. The introduction of web search engines has boosted the need for very large scale retrieval systems even further. By the late 1990s, the rise of the World Wide Web fundamentally transformed information retrieval. While early search engines such as AltaVista (1995) and Yahoo! (1994) offered keyword-based retrieval, they were limited in scale and ranking refinement. The breakthrough came in 1998 with the founding of Google, which introduced the PageRank algorithm, using the web's hyperlink structure to assess page importance and improve relevance ranking. During the 2000s, web search systems evolved rapidly with the integration of machine learning techniques. These systems began to incorporate user behavior data (e.g., click-through logs), query reformulation, and content-based signals to improve search accuracy and personalization. In 2009, Microsoft launched Bing, introducing features that would later incorporate semantic web technologies through the development of its Satori knowledge base. Academic analysis have highlighted Bing's semantic capabilities, including structured data use and entity recognition, as part of a broader industry shift toward improving search relevance and understanding user intent through natural language processing. A major leap occurred in 2018, when Google deployed BERT (Bidirectional Encoder Representations from Transformers) to better understand the contextual meaning of queries and documents. This marked one of the first times deep neural language models were used at scale in real-world retrieval systems. BERT's bidirectional training enabled a more refined comprehension of word relationships in context, improving the handling of natural language queries. Because of its success, transformer-based models gained traction in academic research and commercial search applications. Simultaneously, the research community began exploring neural ranking models that outperformed traditional lexical-based methods. Long-standing benchmarks such as the Text REtrieval Conference (TREC), initiated in 1992, and more recent evaluation frameworks Microsoft MARCO(MAchine Reading COmprehension) (2019) became central to training and evaluating retrieval systems across multiple tasks and domains. MS MARCO has also been adopted in the TREC Deep Learning Tracks, where it serves as a core dataset for evaluating advances in neural ranking models within a standardized benchmarking environment. As deep learning became integral to information retrieval systems, researchers began to categorize neural approaches into three broad classes: sparse, dense, and hybrid models. Sparse models, including traditional term-based methods and learned variants like SPLADE, rely on interpretable representations and inverted indexes to enable efficient exact term matching with added semantic signals. Dense models, such as dual-encoder architectures like ColBERT, use continuous vector embeddings to support semantic similarity beyond keyword overlap. Hybrid models aim to combine the advantages of both, balancing the lexical (token) precision of sparse methods with the semantic depth of dense models. This way of categorizing models balances scalability, relevance, and efficiency in retrieval systems. As IR systems increasingly rely on deep learning, concerns around bias, fairness, and explainability have also come to the picture. Research is now focused not just on relevance and efficiency, but on transparency, accountability, and user trust in retrieval algorithms. == Applications == Areas where information retrieval techniques are employed include (the entries are in alphabetical order within each category): === General applications === Digital libraries Information filtering Recommender systems Media search Blog search Image retrieval 3D retrieval Music retrieval News search Speech retrieval Video retrieval Search engines Site search Desktop search Enterprise search Federated search Mobile search Social search Web search === Domain-specific applications === Expert search finding Genomic information retrieval Geographic information retrieval Information retrieval for chemical structures Information retrieval in software engineering Legal information retrieval Vertical search === Other retrieval methods === Methods/Techniques in which information retrieval techniques are employed include: Cross-modal retrieval Adversarial information retrieval Automatic summarization Multi-document summarization Compound term processing Cross-lingual retrieval Document classification Spam filtering Question answering == Model types == In order to effectively retrieve relevant documents by IR strategies, the documents are typically transformed into a suitable representation. Each retrieval strategy incorporates a specific model for its document representation purposes. The picture on the right illustrates the relationship of som
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ICAD (software)

ICAD (Corporate history: ICAD, Inc., Concentra (name change at IPO in 1995), KTI (name change in 1998), Dassault Systèmes (purchase in 2001) () is a knowledge-based engineering (KBE) system that enables users to encode design knowledge using a semantic representation that can be evaluated for Parasolid output. ICAD has an open architecture that can utilize all the power and flexibility of the underlying language. KBE, as implemented via ICAD, received a lot of attention due to the remarkable results that appeared to take little effort. ICAD allowed one example of end-user computing that in a sense is unparalleled. Most ICAD developers were degreed engineers. Systems developed by ICAD users were non-trivial and consisted of highly complicated code. In the sense of end-user computing, ICAD was the first to allow the power of a domain tool to be in the hands of the user, at the same time being open to allow extensions as identified and defined by the domain expert or subject-matter expert (SME). A COE article looked at the resulting explosion of expectations (see AI winter), which were not sustainable. However, such a bubble burst does not diminish the existence of ability that would exist were expectations and use reasonable or properly managed. == History == The original implementation of ICAD was on a Lisp machine (Symbolics). Some of the principals involved with the development were Larry Rosenfeld, Avrum Belzer, Patrick M. O'Keefe, Philip Greenspun, and David F. Place. The time frame was 1984–85. ICAD started on special-purpose Symbolics Lisp hardware and was then ported to Unix when Common Lisp became portable to general-purpose workstations. The original domain for ICAD was mechanical design with many application successes. However, ICAD has found use in other domains, such as electrical design, shape modeling, etc. An example project could be wind tunnel design or the development of a support tool for aircraft multidisciplinary design. Further examples can be found in the presentations at the annual IIUG (International ICAD Users Group) that have been published in the KTI Vault (1999 through 2002). Boeing and Airbus used ICAD extensively to develop various components in the 1990s and early 21st century. As of 2003, ICAD was featured strongly in several areas as evidenced by the Vision & Strategy Product Vision and Strategy presentation. After 2003, ICAD use diminished. At the end of 2001, the KTI Company faced financial difficulties and laid off most of its best staff. They were eventually bought out by Dassault who effectively scuppered the ICAD product. See IIUG at COE, 2003 (first meeting due to Dassault by KTI) The ICAD system was very expensive, relatively, and was in the price range of high-end systems. Market dynamics couldn't support this as there may not have been sufficient differentiating factors between ICAD and the lower-end systems (or the promises from Dassault). KTI was absorbed by Dassault Systèmes and ICAD is no longer considered the go-forward tool for knowledge-based engineering (KBE) applications by that company. Dassault Systèmes is promoting a suite of tools oriented around version 5 of their popular CATIA CAD application, with Knowledgeware the replacement for ICAD. As of 2005, things were still a bit unclear. ICAD 8.3 was delivered. The recent COE Aerospace Conference had a discussion about the futures of KBE. One issue involves the stacking of 'meta' issues within a computer model. How this is resolved, whether by more icons or the availability of an external language, remains to be seen. The Genworks GDL product (including kernel technology from the Gendl Project) is the nearest functional equivalent to ICAD currently available. == Particulars == ICAD provided a declarative language (IDL) using New Flavors (never converted to Common Lisp Object System (CLOS)) that supported a mechanism for relating parts (defpart) via a hierarchical set of relationships. Technically, the ICAD Defpart was a Lisp macro; the ICAD defpart list was a set of generic classes that can be instantiated with specific properties depending upon what was represented. This defpart list was extendible via composited parts that represented domain entities. Along with the part-subpart relations, ICAD supported generic relations via the object modeling abilities of Lisp. Example applications of ICAD range from a small collection of defparts that represents a part or component to a larger collection that represents an assembly. In terms of power, an ICAD system, when fully specified, can generate thousands of instances of parts on a major assembly design. One example of an application driving thousands of instances of parts is that of an aircraft wing – where fastener type and placement may number in the thousands, each instance requiring evaluation of several factors driving the design parameters. == Futures (KBE, etc.) == One role for ICAD may be serving as the defining prototype for KBE which would require that we know more about what occurred the past 15 years (much information is tied up behind corporate firewalls and under proprietary walls). With the rise of functional programming languages (an example is Haskell) in the markets, perhaps some of the power attributable to Lisp may be replicated.
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POSC Caesar

POSC Caesar Association (PCA) is an international, open and not-for-profit, member organization that promotes the development of open specifications to be used as standards for enabling the interoperability of data, software and related matters. PCA is the initiator of ISO 15926 "Integration of life-cycle data for process plants including oil and gas production facilities" and is committed to its maintenance and enhancement. Nils Sandsmark has been the General Manager of POSC Caesar Association since 1999 and Thore Langeland, Norwegian Oil Industry Association (Norwegian: Oljeindustriens Landsforening, OLF), is the chairman of the board. == History == === Caesar Offshore === The first predecessor of POSC Caesar Association, the Caesar Offshore program, started in 1993. The original focus was on standardizing technical data definitions for capital intensive projects at the handover from the EPC contractor to the owner/operators of onshore and offshore oil and gas production facilities. The program was sponsored by The Research Council of Norway, two EPC contractors (Aker Maritime and Kværner), three owners/operators (Norsk Hydro, Saga Petroleum and Statoil) and DNV as service provider and project owner. === POSC Caesar project === During the period 1994–96, Caesar Offshore Program was defined as a project of Petrotechnical Open Software Corporation (POSC) (now Energistics), and changed its name to the POSC Caesar Project. In 1995 the project was joined by BP, Brown and Root and Elf Aquitaine and in 1997 by Intergraph, IBM, Oracle, Lloyd's, Shell, ABB and UMOE Technologies. During that time, POSC Caesar also became a member of European Process Industries STEP Technical Liaison Executive (EPISTLE) where it collaborates with PISTEP (UK), and USPI-NL (The Netherlands) on the development of ISO 10303, also known as "Standard for the Exchange of Product model data (STEP)". === POSC Caesar Association === In 1997, POSC Caesar Association was founded as an independent, global, non-profit, member organization. POSC Caesar Association serves an international membership and collaborates with other international organizations. It has its main office in Norway. Albeit the name of POSC Caesar Association still hints to its past as a project within the Petrotechnical Open Software Corporation (POSC) (now Energistics), from 1997 onwards, the organization has been independent. Energistics and POSC Caesar Association do collaborate, and are formally member in each other's organization. == Membership == POSC Caesar Association has with its current 36 members from around the world and has established an international footprint (with a strong membership in Norway) that includes a variety of backgrounds, from academia and solution providers to engineering contractors and owners/operators. The members are (subdivided by organization type): Associations: Energistics (USA) and The Norwegian Oil Industry Association (OLF, Norway); Universities and Research Institutes: International Research Institute of Stavanger (IRIS, Norway), Norwegian University of Science and Technology (NTNU, Norway), Korea Advanced Institute of Science and Technology (KAIST, Korea), SINTEF (Norway), University of Bergen (Norway), University of Oslo (Norway), University of Stavanger (Norway), University of Tromsø (Norway) and Western Norway Research Institute (Norway); Oil and Gas Companies: BP (UK), Petronas (Malaysia) and Statoil (Norway); Engineering contractors and consultants: Akvaplan-niva (Norway), Aker Solutions (Norway), Asset Life Cycle Information Management (ALCIM, Malaysia), CAESAR systems (USA), Bechtel (USA), Det Norske Veritas (DNV, Norway), Information Logic (USA) and iXIT Engineering Technology (Germany), Phusion IM Ltd (UK); Solution providers: Aveva (UK), Bentley Systems (USA), Jotne EPM Technology (Norway), Epsis (Norway), Eurostep (Sweden), International Business Machines Corporation (IBM, USA), Siemens - Comos Industry Solutions (before Innotec) (Germany), Intergraph (USA), Invenia (Norway), Keel Solution (Denmark), Noumenon (UK), NRX (Canada), Octaga (Norway) and Tektonisk (Norway). In general, the organization holds three membership meetings a year; one in January / February in North-America (typically USA), one in April / May in Europe (typically Norway) and one in October in Asia (typically Malaysia). == Activities and services == === Initiator and custodian of ISO 15926 === In consultation with the other EPISTLE members and the International Organization for Standardization (ISO), it was decided in 2003 (some say already in 1997) that for modeling-technical reasons it was better to discontinue the development of ISO 10303 and to initiate the development of ISO 15926 "Integration of life-cycle data for process plants including oil and gas production facilities." Over the years, the scope of the standard has increased from the initial capital-intensive projects in the upstream oil and gas industry, to include also relevant terminology for downstream oil and gas industry applications and to deal with real-time data related to the actual oil and gas production. ISO 15926 has also over the years evolved from a dictionary (a list of terms with definitions), over a taxonomy (added hierarchy) to an ontology (a formal representation of a set of concepts within a domain and the relationships between those concepts). ISO 15926 is therefore sometimes nicknamed the "Oil and Gas Ontology", for some considered to be an essential prerequisite together with Semantic Web technologies to get to better interoperability, an optimal use of all available data across boundaries and an increase in efficiency. This is what some call the next generation of Integrated Operations. === Reference data services === Placeholders: Flow scheme of WIP - RDS - ISO and role of SIGs RDS Standards in database pilot (ISO) === Special interest groups === Placeholders: Overview of SIGs Drilling and Completion Reservoir and Production Operations and Maintenance == Projects == There are a number of projects (co-)organized by POSC Caesar Association working on the extension of the ISO 15926 standard in different application areas. === Capital intensive projects application domain === The following projects are running at the moment (August 2009): The ADI Project of FIATECH, to build the tools (which will then be made available in the public domain) The IDS Project of POSC Caesar Association, to define product models required for data sheets A joint collaboration project between FIATECH POSC Caesar Association is the ADI-IDS project is the ISO 15926 WIP === Upstream oil and gas industry application domain === The following projects are currently running (August 2009): The Integrated Operations in the High North (IOHN) project is working on extending ISO 15926 to handle real-time data transmission and (pre-)processing to enable the next generation of Integrated Operations. The Environment Web project to include environmental reporting terms and definitions as used in EPIM's EnvironmentWeb in ISO 15926. Finalised projects include: The Integrated Information Platform (IIP) project working on establishing a real-time information pipeline based on open standards. It worked among others on: Daily Drilling Report (DDR) to including all terms and definitions in ISO 15926. This standard became mandatory on February 1, 2008 for reporting on the Norwegian Continental Shelf by the Norwegian Petroleum Directorate (NPD) and Safety Authority Norway (PSA). NPD says that the quality of the reports has improved considerably since. Daily Production Report (DPR) to including all terms and definitions in ISO 15926. This standard was tested successfully on the Valhall (BP-operated) and Åsgard (StatoilHydro-operated) fields offshore Norway. The terminology and XML schemata developed have also been included in Energistics’ PRODML standard. == Conferences and events == === Semantic Days === === Sogndal academic network meeting === == Collaborations == POSC Caesar is collaborating with a number of standardization bodies, including: Mimosa: collaboration on open information standards for Operations and Maintenance mainly for the downstream oil and gas industry; FIATECH: collaboration on open information standards for life cycle data of capital projects; Energistics: collaboration on information standards for the upstream oil and gas industry, including WITSML and PRODML; OASIS: collaboration on e-business standards; ISO TC184/SC4: the host of the ISO 15926 standard.
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Omar Al Olama

Omar Sultan Al Olama (Arabic: عمر سلطان العلماء; born 16 February 1990) is Minister of State for Artificial Intelligence, Digital Economy, and Remote Work Applications in the United Arab Emirates. He was appointed in October 2017 by Vice President and Prime Minister of the UAE and Ruler of Dubai, Sheikh Mohammed bin Rashid Al Maktoum. The UAE was the first country to appoint a minister for artificial intelligence. == Early life and education == Al Olama was born on 16 February 1990 in Dubai. He has a bachelor's degree in Business and Administration and Management from the American University in Dubai, and a Diploma in Excellence and Project Management from the American University in Sharjah. == Career == Between February 2012 and May 2014, Al Olama was member of the corporate planning at the UAE's Prime Minister's Office. From November 2015 to November 2016, he was Deputy Head of Minister's Office at the UAE's Prime Minister's Office. Between December 2015 and October 2017, he was Secretary General of the World Organization of Racing Drones. In November 2017, he was appointed member of the Board of Trustees of Dubai Future Foundation and Deputy Managing Director of the Foundation. In July 2016, Al Olama was appointed the managing director, and later in 2021 appointed Vice-Chair of the World Government Summit. In 2021, Al Olama was appointed as the Chairman of the Dubai Chamber of Digital Economy, a sub-section of Dubai Chamber of Commerce and Industry. During the cabinet reshuffle in 2023, Al Olama was appointed as the Director General of the Prime Minister's Office, concurrently maintaining his role as the Minister of State for Artificial Intelligence, Digital Economy and Remote Work Applications. == Memberships == In November 2017, Al Olama was appointed as a member of the Future of Digital Economy and Society Council, part of the World Economic Forum (WEF). Later in 2023, the World Economic Forum selected Al Olama to join the steering committee of the AI Governance Alliance, a group comprising 10 global leaders in the digital and technological fields. In 2019, Al Olama was appointed as Chair of the Advisory Board of the Mohamed bin Zayed University of Artificial Intelligence. In 2022, Al Olama was appointed by the UAE Cabinet as Vice-Chair of the Higher Committee for Government Digital Transformation, and also appointed by the Government of Dubai as Vice-Chair of the Higher Committee for Future Technology. In 2022, Al Olama was appointed Chairman of the oversight committee of the Dubai Future District Fund. Since 2023, Al Olama has been on the High-Level Advisory Body on Artificial Intelligence. In 2023, Al Olama, recognized as the world's first minister for artificial intelligence, was included in Time Magazine's inaugural list of the 100 most influential people in AI.
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Vulnerabilities Equities Process

The Vulnerabilities Equities Process (VEP) is a process used by the U.S. federal government to determine on a case-by-case basis how it should treat zero-day computer security vulnerabilities: whether to disclose them to the public to help improve general computer security, or to keep them secret for offensive use against the government's adversaries. The VEP was first developed during the period 2008–2009, but only became public in 2016, when the government released a redacted version of the VEP in response to a FOIA request by the Electronic Frontier Foundation. Following public pressure for greater transparency in the wake of the Shadow Brokers affair, the U.S. government made a more public disclosure of the VEP process in November 2017. == Participants == According to the VEP plan published in 2017, the Equities Review Board (ERB) is the primary forum for interagency deliberation and determinations concerning the VEP. The ERB meets monthly, but may also be convened sooner if an immediate need arises. The ERB consists of representatives from the following agencies: Office of Management and Budget Office of the Director of National Intelligence (including the Intelligence Community-Security Coordination Center) United States Department of the Treasury United States Department of State United States Department of Justice (including the Federal Bureau of Investigation and the National Cyber Investigative Joint Task Force) Department of Homeland Security (including the National Cybersecurity and Communications Integration Center and the United States Secret Service) United States Department of Energy United States Department of Defense (to include the National Security Agency, including Information Assurance and Signals Intelligence elements), United States Cyber Command, and DoD Cyber Crime Center) United States Department of Commerce Central Intelligence Agency The National Security Agency serves as the executive secretariat for the VEP. == Process == According to the November 2017 version of the VEP, the process is as follows: === Submission and notification === When an agency finds a vulnerability, it will notify the VEP secretariat as soon as is possible. The notification will include a description of the vulnerability and the vulnerable products or systems, together with the agency's recommendation to either disseminate or restrict the vulnerability information. The secretariat will then notify all participants of the submission within one business day, requesting them to respond if they have an relevant interest. === Equity and discussions === An agency expressing an interest must indicate whether it concurs with the original recommendation to disseminate or restrict within five business days. If it does not, it will hold discussions with the submitting agency and the VEP secretariat within seven business days to attempt to reach consensus. If no consensus is reached, the participants will suggest options for the Equities Review Board. === Determination to disseminate or restrict === Decisions whether to disclose or restrict a vulnerability should be made quickly, in full consultation with all concerned agencies, and in the overall best interest of the competing interests of the missions of the U.S. government. As far as possible, determinations should be based on rational, objective methodologies, taking into account factors such as prevalence, reliance, and severity. If the review board members cannot reach consensus, they will vote on a preliminary determination. If an agency with an equity disputes that decision, they may, by providing notice to the VEP secretariat, elect to contest the preliminary determination. If no agency contests a preliminary determination, it will be treated as a final decision. === Handling and follow-on actions === If vulnerability information is released, this will be done as quickly as possible, preferably within seven business days. Disclosure of vulnerabilities will be conducted according to guidelines agreed on by all members. The submitting agency is presumed to be most knowledgeable about the vulnerability and, as such, will be responsible for disseminating vulnerability information to the vendor. The submitting agency may elect to delegate dissemination responsibility to another agency on its behalf. The releasing agency will promptly provide a copy of the disclosed information to the VEP secretariat for record keeping. Additionally, the releasing agency is expected to follow up so the ERB can determine whether the vendor's action meets government requirements. If the vendor chooses not to address a vulnerability, or is not acting with urgency consistent with the risk of the vulnerability, the releasing agency will notify the secretariat, and the government may take other mitigation steps. == Criticism == The VEP process has been criticized for a number of deficiencies, including restriction by non-disclosure agreements, lack of risk ratings, special treatment for the NSA, and less than whole-hearted commitment to disclosure as the default option. == UK equivalent == British intelligence agencies—GCHQ in particular—follow a similar approach, also known as the Equities Process, to determine whether to disclose or retain security vulnerabilities. The Investigatory Powers Act 2016 was amended in 2022 to bring oversight of the operation of the process within the remit of the Investigatory Powers Commissioner. Details of the process were made public in 2018.
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Predictive Model Markup Language

The Predictive Model Markup Language (PMML) is an XML-based predictive model interchange format conceived by Robert Lee Grossman, then the director of the National Center for Data Mining at the University of Illinois at Chicago. PMML provides a way for analytic applications to describe and exchange predictive models produced by data mining and machine learning algorithms. It supports common models such as logistic regression and other feedforward neural networks. Version 0.9 was published in 1998. Subsequent versions have been developed by the Data Mining Group. Since PMML is an XML-based standard, the specification comes in the form of an XML schema. PMML itself is a mature standard with over 30 organizations having announced products supporting PMML. == PMML components == A PMML file can be described by the following components: Header: contains general information about the PMML document, such as copyright information for the model, its description, and information about the application used to generate the model such as name and version. It also contains an attribute for a timestamp which can be used to specify the date of model creation. Data Dictionary: contains definitions for all the possible fields used by the model. It is here that a field is defined as continuous, categorical, or ordinal (attribute optype). Depending on this definition, the appropriate value ranges are then defined as well as the data type (such as, string or double). Data Transformations: transformations allow for the mapping of user data into a more desirable form to be used by the mining model. PMML defines several kinds of simple data transformations. Normalization: map values to numbers, the input can be continuous or discrete. Discretization: map continuous values to discrete values. Value mapping: map discrete values to discrete values. Functions (custom and built-in): derive a value by applying a function to one or more parameters. Aggregation: used to summarize or collect groups of values. Model: contains the definition of the data mining model. E.g., A multi-layered feedforward neural network is represented in PMML by a "NeuralNetwork" element which contains attributes such as: Model Name (attribute modelName) Function Name (attribute functionName) Algorithm Name (attribute algorithmName) Activation Function (attribute activationFunction) Number of Layers (attribute numberOfLayers) This information is then followed by three kinds of neural layers which specify the architecture of the neural network model being represented in the PMML document. These attributes are NeuralInputs, NeuralLayer, and NeuralOutputs. Besides neural networks, PMML allows for the representation of many other types of models including support vector machines, association rules, Naive Bayes classifier, clustering models, text models, decision trees, and different regression models. Mining Schema: a list of all fields used in the model. This can be a subset of the fields as defined in the data dictionary. It contains specific information about each field, such as: Name (attribute name): must refer to a field in the data dictionary Usage type (attribute usageType): defines the way a field is to be used in the model. Typical values are: active, predicted, and supplementary. Predicted fields are those whose values are predicted by the model. Outlier Treatment (attribute outliers): defines the outlier treatment to be use. In PMML, outliers can be treated as missing values, as extreme values (based on the definition of high and low values for a particular field), or as is. Missing Value Replacement Policy (attribute missingValueReplacement): if this attribute is specified then a missing value is automatically replaced by the given values. Missing Value Treatment (attribute missingValueTreatment): indicates how the missing value replacement was derived (e.g. as value, mean or median). Targets: allows for post-processing of the predicted value in the format of scaling if the output of the model is continuous. Targets can also be used for classification tasks. In this case, the attribute priorProbability specifies a default probability for the corresponding target category. It is used if the prediction logic itself did not produce a result. This can happen, e.g., if an input value is missing and there is no other method for treating missing values. Output: this element can be used to name all the desired output fields expected from the model. These are features of the predicted field and so are typically the predicted value itself, the probability, cluster affinity (for clustering models), standard error, etc. The latest release of PMML, PMML 4.1, extended Output to allow for generic post-processing of model outputs. In PMML 4.1, all the built-in and custom functions that were originally available only for pre-processing became available for post-processing too. == PMML 4.0, 4.1, 4.2 and 4.3 == PMML 4.0 was released on June 16, 2009. Examples of new features included: Improved Pre-Processing Capabilities: Additions to built-in functions include a range of Boolean operations and an If-Then-Else function. Time Series Models: New exponential Smoothing models; also place holders for ARIMA, Seasonal Trend Decomposition, and Spectral density estimation, which are to be supported in the near future. Model Explanation: Saving of evaluation and model performance measures to the PMML file itself. Multiple Models: Capabilities for model composition, ensembles, and segmentation (e.g., combining of regression and decision trees). Extensions of Existing Elements: Addition of multi-class classification for Support Vector Machines, improved representation for Association Rules, and the addition of Cox Regression Models. PMML 4.1 was released on December 31, 2011. New features included: New model elements for representing Scorecards, k-Nearest Neighbors (KNN) and Baseline Models. Simplification of multiple models. In PMML 4.1, the same element is used to represent model segmentation, ensemble, and chaining. Overall definition of field scope and field names. A new attribute that identifies for each model element if the model is ready or not for production deployment. Enhanced post-processing capabilities (via the Output element). PMML 4.2 was released on February 28, 2014. New features include: Transformations: New elements for implementing text mining New built-in functions for implementing regular expressions: matches, concat, and replace Simplified outputs for post-processing Enhancements to Scorecard and Naive Bayes model elements PMML 4.3 was released on August 23, 2016. New features include: New Model Types: Gaussian Process Bayesian Network New built-in functions Usage clarifications Documentation improvements Version 4.4 was released in November 2019. == Release history == == Data Mining Group == The Data Mining Group is a consortium managed by the Center for Computational Science Research, Inc., a nonprofit founded in 2008. The Data Mining Group also developed a standard called Portable Format for Analytics, or PFA, which is complementary to PMML.
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Pinakes

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

John Schulman (born 1987 or 1988) is an American artificial intelligence researcher and co-founder of OpenAI. In August 2024, he announced he would be joining Anthropic. In February 2025, he announced he was leaving to join Thinking Machines Lab, where he is chief scientist. == Early life and education == Schulman had an interest in science and math from a young age. He enjoyed science fiction, especially the work of Isaac Asimov. When he was in seventh grade, he became deeply interested in the television program BattleBots, which featured combat between remote-controlled robots. In what he said was his first self-directed study, he read extensively in subject areas that would help him design a superior robot, but the robot he and his friends worked on was never built. He attended Great Neck South High School. He was a member of the US Physics olympiad Team in 2005. In 2010, he graduated from Caltech with a degree in physics. He has a PhD in electrical engineering and computer sciences from the University of California, Berkeley, where he was advised by Pieter Abbeel. == Career == In December 2015, shortly before finishing his PhD, Schulman co-founded OpenAI with Sam Altman, Elon Musk, Ilya Sutskever, Greg Brockman, Trevor Blackwell, Vicki Cheung, Andrej Karpathy, Durk Kingma, Pamela Vagata, and Wojciech Zaremba, with Sam Altman and Elon Musk as the co-chairs. There, he led the reinforcement learning team that created ChatGPT. He has been referred to as the "architect" of ChatGPT. In August 2024, Schulman announced he would be joining Anthropic. He stated his move was to allow him to deepen his focus on AI alignment and return to more hands-on technical work. In February 2025, he announced he was leaving to join Thinking Machines Lab, where he is chief scientist. == Awards and honors == In 2025, Schulman received the Mark Bingham Award for Excellence in Achievement by Young Alumni from his alma mater, UC Berkeley.
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AppBlock

AppBlock is a software tool for managing screen time that limits access to selected mobile applications and websites. Developed by the Czech studio MobileSoft, it is distributed for Android and iOS devices as well as through browser extensions for Google Chrome, Microsoft Edge and Brave, and as desktop solutions. The application is used primarily to restrict time spent on social media and similar distracting services while working and studying. By 2025, the application reported 700,000 monthly active users, with the domestic Czech market accounting for less than one percent of its total user base and revenue. == History == === Origins === AppBlock was created by the Czech software studio MobileSoft, based in Hradec Králové. The studio was founded in 2012 by Miroslav Novosvětský, who remains the sole owner. The idea for the application arose from the use of browser-based website blockers on desktop computers. AppBlock was conceived as a way to reduce the time spent on mobile devices. === Early releases === In its early phase, AppBlock was available only for phones running on Android. Early versions allowed users to limit access to selected applications and websites during specified periods. From the outset, the application was distributed internationally rather than only within the Czech market, and early coverage reported a multi-million number of downloads worldwide. === Expansion of functionality === Over time, AppBlock has expanded beyond basic application blocking to include additional functions related to limiting procrastination and managing attention. The development of AppBlock accelerated during the COVID-19 pandemic. Following a reduction in external client orders, the studio reallocated resources from contract development to the application. Increased digital content consumption during lockdowns contributed to a rise in the application's usage and revenue. As the application developed, it became the company's product with the largest user base. Novosvětský described an increase in downloads over a twelve-month period, which he linked in part to the company's activities abroad, including participation in events focused on mobile marketing in the United States. These activities were an important factor in the further development of AppBlock. === Internationalization and market expansion === Within roughly the first eight years of the company's existence, MobileSoft became active both in the domestic Czech market and in the United States, supported among other things by participation in the CzechAccelerator program, which is intended to help Czech firms enter foreign markets. In mid-August 2021 the developers launched a version for iOS, which soon began to attract paying users. The expansion to iOS was accompanied by plans for cooperation with the Procrastination.com platform, intended to complement the blocking functions with educational content related to digital media use, sleep and work habits. By 2025, AppBlock was localised into 15 languages, with the largest share of users in the United States, the United Kingdom, Germany, and France, with recent growth in Brazil, and usage extending across several continents. AppBlock has reached more than 10 million installations. In the same period its creators announced plans to refine existing functions and to expand support beyond mobile phones to desktop use, including through support for additional web browsers. == Features == === Supported platforms === AppBlock is distributed as a mobile application for Android and iOS users through Google Play and the Apple App Store. Browser extensions for desktop systems are available for Google Chrome, Microsoft Edge and Brave. === Functionality === AppBlock's core function is to restrict access to selected applications and websites. The mobile application shows a list of installed apps and lets the user select which ones to block. It also includes tools to block specific websites and, on iOS, to block certain phrases entered in the Safari browser. AppBlock can mute notifications from selected applications, so alerts from those apps do not appear while blocking is active. In addition to choosing which apps or content to block, the software also offers an allowlist mode, where only selected applications remain accessible and all others are blocked. Blocking rules are organized into configurable schedules, called profiles. Users can create profiles that define time periods when selected apps and websites are unavailable. Newer versions also allow profiles to be activated automatically based on the time of day, days of the week, the device's location, or connection to specific Wi-Fi networks. The iOS version lets users set limits on how often or how long certain apps can be used before they are blocked, and it can track and restrict screen time for individual apps. In addition to these recurring rules, AppBlock includes a Quick Block feature that temporarily blocks selected apps and websites with a single action, without requiring a separate long-term schedule. Strict Mode is an optional setting that limits the ability to change blocking once it is active. For a specified period, it prevents editing AppBlock's rules and can be configured to stop the app from being uninstalled during that time. While Strict Mode is enabled, users cannot modify or disable the restrictions they have set. Deactivation requires specific verification steps, such as connecting the device to a charger or obtaining approval from a designated contact person. The mobile application also includes statistical and reporting features. In addition to blocking, AppBlock lets users view statistics and data about their use of applications and websites, including screen-time summaries and focus sessions that silence notifications and enforce blocking during defined work or study periods. Browser extensions for desktop environments apply AppBlock's website-blocking functions on Windows and macOS systems through supported web browsers. == Business model == AppBlock uses a freemium revenue model. The basic version of the application is available free of charge and allows blocking of up to three applications at the same time. The premium version removes this limit and adds further configuration options. In 2020, the application shifted from a one-time payment structure to a subscription model. By 2021, AppBlock had more than seven thousand paying users and annual revenue of about four million Czech crowns. By 2025, annual revenue reached approximately 4 million US dollars (80 million CZK) before taxes and platform fees, with roughly 20 percent of active users subscribing to the paid version. == Usage == AppBlock limits access to selected applications and websites in order to reduce smartphone overuse and digital distraction. It is used to block social media, games and other services considered addictive, with the aim of reducing frequent checking of mobile devices and creating time intervals in which these services are unavailable. Reported use cases of AppBlock cover work, students, parents, ADHD, mental health, well-being and business. The application is used both by individual users and within workplace initiatives in which employees install it to reduce digital distractions during working hours.
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No Fakes Act

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

Reification in knowledge representation is the process of turning a predicate or statement into an addressable object. Reification allows the representation of assertions so that they can be referred to or qualified by other assertions, i.e., meta-knowledge. The message "John is six feet tall" is an assertion involving truth that commits the speaker to its factuality, whereas the reified statement "Mary reports that John is six feet tall" defers such commitment to Mary. In this way, the statements can be incompatible without creating contradictions in reasoning. For example, the statements "John is six feet tall" and "John is five feet tall" are mutually exclusive (and thus incompatible), but the statements "Mary reports that John is six feet tall" and "Paul reports that John is five feet tall" are not incompatible, as they are both governed by a conclusive rationale that either Mary or Paul is (or both are), in fact, incorrect. In linguistics, reporting, telling, and saying are recognized as verbal processes that project a wording (or locution). If a person says that "Paul told x" and "Mary told y", this person stated only that the telling took place. In this case, the person who made these two statements did not represent a person inconsistently. In addition, if two people are talking to each other, let's say Paul and Mary, and Paul tells Mary "John is five feet tall" and Mary rejects Paul's statement by saying "No, he is actually six feet tall", the socially constructed model of John does not become inconsistent. The reason for that is that statements are to be understood as an attempt to convince the addressee of something (Austin's How to do things with words), alternatively as a request to add some attribute to the model of Paul. The response to a statement can be an acknowledgement, in which case the model is changed, or it can be a statement rejection, in which case the model does not get changed. Finally, the example above for which John is said to be "five feet tall" or "six feet tall" is only incompatible because John can only be a single number of feet tall. If the attribute were a possession as in "he has a dog" or "he also has a cat", a model inconsistency would not happen. In other words, the issue of model inconsistency has to do with our model of the domain element (John) and not with the ascription of different range elements (measurements such as "five feet tall" or "six feet tall").
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