AI Coding Scaffold

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Two-phase locking

In databases and transaction processing, two-phase locking (2PL) is a pessimistic concurrency control method that guarantees conflict-serializability. It is also the name of the resulting set of database transaction schedules (histories). The protocol uses locks, applied by a transaction to data, which may block (interpreted as signals to stop) other transactions from accessing the same data during the transaction's life. By the 2PL protocol, locks are applied and removed in two phases: Expanding phase: locks are acquired and no locks are released. Shrinking phase: locks are released and no locks are acquired. Two types of locks are used by the basic protocol: Shared and Exclusive locks. Refinements of the basic protocol may use more lock types. Using locks that block processes, 2PL, S2PL, and SS2PL may be subject to deadlocks that result from the mutual blocking of two or more transactions. == Read and write locks == Locks are used to guarantee serializability. A transaction is holding a lock on an object if that transaction has acquired a lock on that object which has not yet been released. For 2PL, the only used data-access locks are read-locks (shared locks) and write-locks (exclusive locks). Below are the rules for read-locks and write-locks: A transaction is allowed to read an object if and only if it is holding a read-lock or write-lock on that object. A transaction is allowed to write an object if and only if it is holding a write-lock on that object. A schedule (i.e., a set of transactions) is allowed to hold multiple locks on the same object simultaneously if and only if none of those locks are write-locks. If a disallowed lock attempts on being held simultaneously, it will be blocked. == Variants == Note that all conflict serializable schedules are also view serializable (but not vice-versa). === Two-phase locking === According to the two-phase locking protocol, each transaction handles its locks in two distinct, consecutive phases during the transaction's execution: Expanding phase (aka Growing phase): locks are acquired and no locks are released (the number of locks can only increase). Shrinking phase (aka Contracting phase): locks are released and no locks are acquired. The two phase locking rules can be summarized as: each transaction must never acquire a lock after it has released a lock. The serializability property is guaranteed for a schedule with transactions that obey this rule. Typically, without explicit knowledge in a transaction on end of phase 1, the rule is safely determined only when a transaction has completed processing and requested commit. In this case, all the locks can be released at once (phase 2). === Conservative two-phase locking === Conservative two-phase locking (C2PL) differs from 2PL in that transactions obtain all the locks they need before the actual execution begins. This is to ensure that a transaction that already holds some locks will not block waiting for other locks. C2PL prevents deadlocks. In cases of heavy lock contention, C2PL reduces the time locks are held on average, relative to 2PL and Strict 2PL, because transactions that hold locks are never blocked. In light lock contention, C2PL holds more locks than is necessary, because it is difficult to predict which locks will be needed in the future, thus leading to higher overhead. A C2PL transaction will not obtain any locks if it cannot obtain all the locks it needs in its initial request. Furthermore, each transaction needs to declare its read and write set (the data items that will be read/written), which is not always possible. Because of these limitations, C2PL is not used very frequently. === Strict two-phase locking === To comply with the strict two-phase locking (S2PL) protocol, a transaction needs to comply with 2PL, and release its write (exclusive) locks only after the transaction has ended (i.e., either committed or aborted). On the other hand, read (shared) locks are released regularly during the shrinking phase. Unlike 2PL, S2PL provides strictness (a special case of cascade-less recoverability). This protocol is not appropriate in B-trees because it causes Bottleneck (while B-trees always starts searching from the parent root). === Strong strict two-phase locking === or Rigorousness, or Rigorous scheduling, or Rigorous two-phase locking To comply with strong strict two-phase locking (SS2PL), a transaction's read and write locks are released only after that transaction has ended (i.e., either committed or aborted). A transaction obeying SS2PL has only a phase 1 and lacks a phase 2 until the transaction has completed. Every SS2PL schedule is also an S2PL schedule, but not vice versa.
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Kinect

Kinect is a discontinued line of motion sensing input devices produced by Microsoft and first released in 2010. The devices generally contain RGB cameras, and infrared projectors and detectors that map depth through either structured light or time of flight calculations, which can in turn be used to perform real-time gesture recognition and body skeletal detection, among other capabilities. They also contain microphones that can be used for speech recognition and voice control. Kinect was originally developed as a motion controller peripheral for Xbox video game consoles, distinguished from competitors (such as Nintendo's Wii Remote and Sony's PlayStation Move) by not requiring physical controllers. The first-generation Kinect was based on technology from Israeli company PrimeSense, and unveiled at E3 2009 as a peripheral for Xbox 360 codenamed "Project Natal". It was first released on November 4, 2010, and would go on to sell eight million units in its first 60 days of availability. The majority of the games developed for Kinect were casual, family-oriented titles, which helped to attract new audiences to Xbox 360, but did not result in wide adoption by the console's existing, overall userbase. As part of the 2013 unveiling of Xbox 360's successor, Xbox One, Microsoft unveiled a second-generation version of Kinect with improved tracking capabilities. Microsoft also announced that Kinect would be a required component of the console, and that it would not function unless the peripheral is connected. The requirement proved controversial among users and critics due to privacy concerns, prompting Microsoft to backtrack on the decision. However, Microsoft still bundled the new Kinect with Xbox One consoles upon their launch in November 2013. A market for Kinect-based games still did not emerge after the Xbox One's launch; Microsoft would later offer Xbox One hardware bundles without Kinect included, and later revisions of the console removed the dedicated ports used to connect it (requiring a powered USB adapter instead). Microsoft ended production of Kinect for Xbox One in October 2017. Kinect has also been used as part of non-game applications in academic and commercial environments, as it was cheaper and more robust than other depth-sensing technologies at the time. While Microsoft initially objected to such applications, it later released software development kits (SDKs) for the development of Microsoft Windows applications that use Kinect. In 2020, Microsoft released Azure Kinect as a continuation of the technology integrated with the Microsoft Azure cloud computing platform. Part of the Kinect technology was also used within Microsoft's HoloLens project. Microsoft discontinued the Azure Kinect developer kits in October 2023. == History == === Development === The origins of the Kinect started around 2005, at a point where technology vendors were starting to develop depth-sensing cameras. Microsoft had been interested in a 3D camera for the Xbox line earlier but because the technology had not been refined, had placed it in the "Boneyard", a collection of possible technology they could not immediately work on. In 2005, Israeli company PrimeSense was founded by mathematicians and engineers to develop the "next big thing" for video games, incorporating cameras that were capable of mapping a human body in front of them and sensing hand motions. They showed off their system at the 2006 Game Developers Conference, where Microsoft's Alex Kipman, the general manager of hardware incubation, saw the potential in PrimeSense's technology for the Xbox system. Microsoft began discussions with PrimeSense about what would need to be done to make their product more consumer-friendly: not only improvements in the capabilities of depth-sensing cameras, but a reduction in size and cost, and a means to manufacture the units at scale was required. PrimeSense spent the next few years working at these improvements. Nintendo released the Wii in November 2006. The Wii's central feature was the Wii Remote, a handheld device that was detected by the Wii through a motion sensor bar mounted onto a television screen to enable motion controlled games. Microsoft felt pressure from the Wii, and began looking into depth-sensing in more detail with PrimeSense's hardware, but could not get to the level of motion tracking they desired. While they could determine hand gestures, and sense the general shape of a body, they could not do skeletal tracking. A separate path within Microsoft looked to create an equivalent of the Wii Remote, considering that this type of unit may become standardized similar to how two-thumbstick controllers became a standard feature. However, it was still ultimately Microsoft's goal to remove any device between the player and the Xbox. Kudo Tsunoda and Darren Bennett joined Microsoft in 2008, and began working with Kipman on a new approach to depth-sensing aided by machine learning to improve skeletal tracking. They internally demonstrated this and established where they believed the technology could be in a few years, which led to the strong interest to fund further development of the technology; this has also occurred at a time that Microsoft executives wanted to abandon the Wii-like motion tracking approach, and favored the depth-sensing solution to present a product that went beyond the Wii's capabilities. The project was greenlit by late 2008 with work started in 2009. The project was codenamed "Project Natal" after the Brazilian city Natal, Kipman's birthplace. Additionally, Kipman recognized the Latin origins of the word "natal" to mean "to be born", reflecting the new types of audiences they hoped to draw with the technology. Much of the initial work was related to ethnographic research to see how video game players' home environments were laid out, lit, and how those with Wiis used the system to plan how Kinect units would be used. The Microsoft team discovered from this research that the up-and-down angle of the depth-sensing camera would either need to be adjusted manually, or would require an expensive motor to move automatically. Upper management at Microsoft opted to include the motor despite the increased cost to avoid breaking game immersion. Kinect project work also involved packaging the system for mass production and optimizing its performance. Hardware development took around 22 months. During hardware development, Microsoft engaged with software developers to use Kinect. Microsoft wanted to make games that would be playable by families since Kinect could sense multiple bodies in front of it. One of the first internal titles developed for the device was the pack-in game Kinect Adventures developed by Good Science Studio that was part of Microsoft Studios. One of the game modes of Kinect Adventures was "Reflex Ridge", based on the Japanese Brain Wall game where players attempt to contort their bodies in a short time to match cutouts of a wall moving at them. This type of game was a key example of the type of interactivity they wanted with Kinect, and its development helped feed into the hardware improvements. Another development was Project Milo, a prototype game developed by Lionhead Studios led by Peter Molyneux where the player could interact with a virtual avatar through motion controls and voice recognition. Lionhead had developed the project based on original capabilities of the Kinect, but according to Molyneux, Microsoft had found that a consumer-grade version of the Kinect would cost thousands of dollars, so they scaled back the device and refocused the role of games for the Kinect to be more casual games as seen on the Wii. As a result, Project Milo no longer fit Microsoft's portfolio and was cancelled. Nearing the planned release, there was a problem of widespread testing of Kinect in various room types and different bodies accounting for age, gender, and race among other factors, while keeping the details of the unit confidential. Microsoft engaged in a company-wide program offering employees to take home Kinect units to test them. Microsoft also brought other non-gaming divisions, including its Microsoft Research, Microsoft Windows, and Bing teams to help complete the system. Microsoft established its own large-scale manufacturing facility to bulk product Kinect units and test them. === Introduction === Kinect was first announced to the public as "Project Natal" on June 1, 2009, during Microsoft's press conference at E3 2009; film director Steven Spielberg joined Microsoft's Don Mattrick to introduce the technology and its potential. Three demos were presented during the conference—Microsoft's Ricochet and Paint Party, and Lionhead Studios' Milo & Kate created by Peter Molyneux—while a Project Natal-enabled version of Criterion Games' Burnout Paradise was shown during the E3 exhibition. By E3 2009, the skeletal mapping technology was capable of simultaneously tracking four people, with a feature extraction of 4
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Greg Brockman

Gregory Brockman (born November 29, 1987) is an American entrepreneur and software engineer. He is co-founder and president of OpenAI. He began his career at Stripe in 2010, upon leaving MIT, and became CTO in 2013. He left Stripe in 2015 to co-found OpenAI, where he also served as CTO. == Early life == Brockman was born in Thompson, North Dakota, and attended Red River High School, where he excelled in mathematics, chemistry, and computer science. He won a silver medal in the 2006 International Chemistry Olympiad and became the first finalist from North Dakota to participate in the Intel science talent search since 1973. In 2003, 2005, and 2007, he attended Canada/USA Mathcamp, a summer program for mathematically talented high-school students. In 2008, Brockman enrolled at Harvard University but left a year later, briefly enrolling at the Massachusetts Institute of Technology. == Career == In 2010, he dropped out of MIT to join Stripe, a company founded by Patrick Collison, his MIT classmate, and John Collison. In 2013, he became Stripe's first CTO, while the company grew from 5 to 205 employees. Brockman left Stripe in May 2015. === OpenAI === Brockman met with Sam Altman and Elon Musk, and led the recruiting of the OpenAI founding team. Many of its members, including Ilya Sutskever, were top AI research talent that left high paying jobs for the opportunity at OpenAI. He co-founded OpenAI in December 2015 alongside Altman, Sutskever and others. The company initially operated from Brockman's living room. He led various projects at OpenAI, including OpenAI Gym and OpenAI Five, a Dota 2 bot. On February 14, 2019, OpenAI announced that they had developed a new large language model called GPT-2, but kept it private due to their concern for its potential misuse. They released the model to a limited group of beta testers in May 2019. On March 14, 2023, in a live video demo, Brockman unveiled GPT-4, the fourth iteration in the GPT series. On November 17, 2023, alongside the firing of Sam Altman, Brockman was told he had been removed from the board. Sutskever supplied the board with a document of alleged bullying by Brockman. Mira Murati said Brockman's relationship with Altman made it impossible for her to do her job, and Altman had already "fielded many requests from OpenAI employees to rein in Brockman". Brockman was to report to Murati, but on November 17, he announced that he had quit the company. On November 20, 2023, Microsoft CEO Satya Nadella announced that Brockman and Altman would join Microsoft to lead a new advanced AI research team. The following day, after a deal was reached to reinstate Altman as CEO, Brockman returned to OpenAI. Brockman took a sabbatical from August to November 2024. === Elon Musk lawsuit === Jury selection for OpenAI cofounder Elon Musk's lawsuit against OpenAI and its current executives, including Brockman, began on April 27, 2026. On April 28, 2026, trial testimony was by now underway, with Elon Musk beginning his testimony against Altman and OpenAI. On April 30, 2026 Musk would enter his third day of testimony. == Personal life == In November 2019 after a year of dating, Brockman married Anna at OpenAI's offices on a workday. Ilya Sutskever officiated. == Political activities == Brockman and his wife were the biggest donors to Donald Trump's Super PAC, MAGA Inc., in 2025 with each of them donating US$12.5 million. Brockman and his wife also donated $50 million to Leading the Future, a super PAC dedicated to AI deregulation that he helped found with Andreessen Horowitz co-founders Marc Andreessen and Ben Horowitz. OpenAI publicly expressed openness to increased regulatory oversight and has a policy against donating to such Super PACs.
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KataGo

KataGo is a free and open-source computer Go program, capable of defeating top-level human players. First released on 27 February 2019, it is developed by David Wu, who also developed the Arimaa playing program bot_Sharp which defeated three top human players to win the Arimaa AI Challenge in 2015. KataGo's first release was trained by David Wu using resources provided by his employer Jane Street Capital, but it is now trained by a distributed effort. Members of the computer Go community provide computing resources by running the client, which generates self-play games and rating games, and submits them to a server. The self-play games are used to train newer networks and the rating games to evaluate the networks' relative strengths. KataGo supports the Go Text Protocol, with various extensions, thus making it compatible with popular GUIs such as Lizzie. As an alternative, it also implements a custom "analysis engine" protocol, which is used by the KaTrain GUI, among others. KataGo is widely used by strong human go players, including the South Korean national team, for training purposes. KataGo is also used as the default analysis engine in the online Go website AI Sensei, as well as OGS (the Online Go Server). == Technology == Based on techniques used by DeepMind's AlphaGo Zero, KataGo implements Monte Carlo tree search with a convolutional neural network providing position evaluation and policy guidance. Compared to AlphaGo, KataGo introduces many refinements that enable it to learn faster and play more strongly. Notable features of KataGo that are absent in many other Go-playing programs include score estimation; support for small boards, rectangular boards, and large boards; arbitrary values of komi and handicaps; and the ability to use various Go rulesets and adjust its play and evaluation for the small differences between them. === Network === The network used in KataGo are ResNets with pre-activation. While AlphaGo Zero has only game board history as input features (as it was designed as a general architecture for board games, subsequently becoming AlphaZero), the input to the network contains additional features designed by hand specifically for playing Go. These features include liberties, komi parity, pass-alive, and ladders. The trunk is essentially the same as in AlphaGo Zero, but with global pooling layers added to allow the network to be conditioned on global context such as ko fights. This is similar to the Squeeze-and-Excitation Network. The network has two heads: a policy head and a value head. The policy and value heads are mostly the same as in AlphaGo Zero, but both heads have auxiliary subheads to provide auxiliary loss signal for faster training: Policy head: predicts policy for the current player's move this turn, and the opponent player's move in the next turn. A policy Each is 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: predicts game outcome, expected score difference, expected board ownership, etc. The network is described in detail in Appendix A of the report. The code base switched from using TensorFlow to PyTorch in version 1.12. === Training === Let its trunk have b {\displaystyle b} residual blocks and c {\displaystyle c} channels. During its first training run, multiple networks were trained with increasing ( b , c ) {\displaystyle (b,c)} . It took 19 days using a maximum of 28 Nvidia V100 GPUs at 4.2 million games. After the first training run, training became a distributed project run by volunteers, with increasing network sizes. As of August 2024, it has reached b28c512 (28 blocks, 512 channels). == Adversarial attacks == In 2022, KataGo was used as the target for adversarial attack research, designed to demonstrate the "surprising failure modes" of AI systems. The researchers were able to trick KataGo into ending the game prematurely. Adversarial training improves defense against adversarial attacks, though not perfectly.
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Conduit (company)

Conduit Ltd. is an international software company. From its founding in 2005 to 2013, its most well-known product was the Conduit toolbar, which was widely-described as malware. In 2013, it spun off its toolbar business; today, its main product is a mobile development platform that allows users to create native and web mobile applications for smartphones. == Products == From 2005 to 2013, the company's most well-known product was the Conduit toolbar, which is flagged by most antivirus software as potentially unwanted and adware. Conduit's toolbar software is often downloaded by malware packages from other publishers. The company spun off the toolbar division that manages the Conduit toolbar in 2013. Today, the company's main product is a mobile development platform that allows users to create native and web mobile applications for smartphones. App creation for its App Gallery is free, but it charges a monthly subscription fee to place apps on the App Store or Google Play. == History == Conduit was founded in 2005 by Shilo, Dror Erez, and Gaby Bilcyzk. Between years 2005 and 2013, it ran a successful but controversial toolbar platform business. Conduit was part of the so-called Download Valley companies monetizing free software and downloads by bundling adware. The toolbars were criticized by some as being very difficult to uninstall. The toolbar software was referred to as a "potentially unwanted program" by some in the computer industry because it could be used to change browser settings. The company had more than 400 employees in 2013. In September same year, Conduit spun off its entire website toolbar business division, which combined with Perion Network. After the deal, Conduit shareholders owned 81% of Perion's existing shares and both Perion and Conduit remained independent companies. The substantial size of the Conduit user base allowed Perion to immediately surpass AOL in U.S. searches. In 2015, Conduit announced it would purchase Keeprz, a mobile customer loyalty platform, for $45 million.
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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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TRAIGA

TRAIGA, or the Texas Responsible Artificial Intelligence Governance Act, is a state law regulating the development and deployment of artificial intelligence (AI) systems in Texas. Sponsored by Representative Giovanni Capriglione, the Act establishes a framework governing certain uses of AI, outlines prohibited uses, and creates obligations on state government entities, among other provisions. TRAIGA was signed into law in 2025 and took effect on January 1, 2026. The law applies to AI developers and deployers that conduct business in Texas or whose systems are used by Texas residents. It prohibits the intentional development or deployment of AI systems to incite harm, violate constitutional rights, engage in unlawful discrimination, and produce child sexual abuse material or unlawful deepfakes. TRAIGA also establishes the Texas Artificial Intelligence Council and creates a regulatory sandbox program. The Texas Attorney General is charged with enforcement. It has received attention as one of the first comprehensive AI-related laws enacted by a U.S. state. Legal analysts have compared it to the European Union (EU) Artificial Intelligence Act and the Colorado AI Act, noting its intent-based discrimination standard and narrower scope relative to those frameworks. == Background == In June 2023, Texas Governor Greg Abbott signed House Bill 2060, which created an Artificial Intelligence Advisory Council within the Texas Department of Information Resources. The Council was tasked with monitoring the use of AI systems across state government. Its membership included representatives from law enforcement, academia, and the legal profession. After submitting a report to state policymakers, the Council was disbanded in December 2024. Separately, the Texas House Select Committee on Artificial Intelligence and Emerging Technologies was created in 2023 to examine the political and social implications of artificial intelligence. Among its recommendations was the creation of a regulatory sandbox to allow for controlled testing of AI systems. This recommendation informed the regulatory sandbox provision included in TRAIGA. == History == In December 2024, Representative Capriglione introduced House Bill 1709, the Texas Responsible Artificial Intelligence Governance Act. The bill sought to create a statewide framework for artificial intelligence, including transparency requirements for companies deploying AI systems, restrictions on certain uses of AI, and the creation of a regulatory sandbox. Modeled in part on the EU Artificial Intelligence Act and the Colorado AI Act, House Bill 1709 focused on "high-risk" AI systems and included provisions addressing private sector liability. House Bill 1709 did not advance during the legislative session. Industry stakeholders raised concerns that several provisions were overly burdensome. The bill informed the development of a revised proposal, House Bill 149, also titled the Texas Responsible Artificial Intelligence Governance Act. The revised version removed requirements for private companies to notify consumers when they interact with AI systems and to conduct impact assessments, among other provisions. In April 2025, an amended version of House Bill 149 passed the Texas House of Representatives and was referred to the Senate Committee on Business and Commerce. The bill later received approval from both chambers, where the House voted on amendments adopted by the Senate. On May 31, 2025, the state legislature passed House Bill 149, one of several AI-related bills considered during the legislative session. Governor Abbott signed TRAIGA into law on June 22, 2025. During the legislative process, a proposed federal moratorium on state-level AI regulation initially raised questions about the enforceability of state AI laws, including TRAIGA. At the time of signing, Governor Abbott stated that Texas would ensure compliance with applicable federal requirements. In July 2025, the United States Senate voted to remove the proposed moratorium from federal legislation. The Act took effect on January 1, 2026. == Provisions == === Definitions and scope === TRAIGA applies to AI developers and deployers that advertise or conduct business in Texas, develop products used by Texas residents, or develop or deploy AI systems within the state. The Act also applies to Texas state and local government entities. The Act defines a developer as a person who develops an AI system and a deployer as one who deploys an AI system in Texas. Consumers are defined as Texas residents. The Act defines an artificial intelligence system as a machine-based system that "infers from the inputs the system receives how to generate outputs, including content, decisions, predictions, or recommendations, that can influence physical or virtual environments." === Government use === The Act requires government agencies to provide consumers with plain language notices before interacting with AI systems. It also prohibits government agencies from using artificial intelligence systems to assign social scores to consumers. It also restricts the use of AI systems to identify individuals using biometric data without the individual’s consent. === Prohibitions === The Act prohibits the development or deployment of artificial intelligence systems intended to cause harm, self-harm, or criminal activity. It also prohibits the development or deployment of AI systems designed to violate constitutional rights or unlawfully discriminate based on protected classes. In addition, the Act prohibits the development or deployment of AI systems that are intended to produce or distribute child sexual abuse material or unlawful deepfakes. === Enforcement === Enforcement authority under the Act rests with the Texas Attorney General. The Act does not create a private right of action. The Act requires the Texas Attorney General to create an online complaint system where consumers may submit allegations of potential violations. The Attorney General can investigate complaints received through this system and may request information relevant to the operation of an AI system, including information about training data. Before initiating an enforcement action, the Attorney General must provide a written notice to the alleged violator, who is then provided with a 60-day period to cure the alleged violation. === Penalties === If a violation is not cured, the Act authorizes civil penalties. Penalties range from $10,000 to $12,000 per curable violation and from $80,000 to $200,000 per non-curable violation. The Act also authorizes additional penalties of $2,000 to $40,000 for each day the violation continues. If the Attorney General determines that a person certified or licensed by a state agency has violated the Act and recommends enforcement, the relevant agency may impose additional administrative sanctions, including license suspension or further monetary penalties. === Safe harbor === The Act provides an affirmative defense for AI developers and deployers who identify potential violations through internal testing or auditing or who demonstrate compliance with National Institute of Standards and Technology (NIST)'s Artificial Intelligence Risk Management Framework or a comparable risk management framework. The Act also affords protection to developers and deployers when a third party uses their AI systems in a way that violates the Act. === Texas Artificial Intelligence Council === The Act creates the Texas Artificial Intelligence Council to assist the state legislatures in evaluating artificial intelligence policy and oversight. The Council is charged with developing recommendations for state agencies regarding the use of AI systems and with overseeing the regulatory sandbox. TRAIGA gives the Council the ability to organize AI-related training for state entities and issue reports concerning artificial intelligence. The Council does not have binding rulemaking authority. The Council consists of seven members appointed by the governor, the lieutenant governor, and the speaker of the Texas House of Representatives. === Regulatory sandbox === The Act directs the Texas Department of Information Resources to create a regulatory sandbox program that allows participants to test AI systems under state supervision in a modified regulatory setting. To join the program, companies must submit applications that describe their AI systems and intended use. Approved participants may operate within the sandbox for up to 36 months. During that period, the Attorney General is restricted from initiating enforcement actions for certain categories of violations. == Reception == === Support === During legislative testimony, the Texas Public Policy Foundation stated that TRAIGA would benefit Texas businesses by reducing legal ambiguity and creating clearer compliance standards. Representatives of business groups also expressed support, stating that the Act would not impose overly burdensome regulations. The consum
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Knowledge value chain

A knowledge value chain is a sequence of intellectual tasks by which knowledge workers build their employer's unique competitive advantage and/or social and environmental benefit. As an example, the components of a research and development project form a knowledge value chain. Productivity improvements in a knowledge value chain may come from knowledge integration in its original sense of data systems consolidation. Improvements also flow from the knowledge integration that occurs when knowledge management techniques are applied to the continuous improvement of a business process or processes. The term first started coming into common use around 1999, appearing in management-related talks and papers. It was registered as a trademark in 2004 by TW Powell Co., a Manhattan company. Knowledge value chain processes Knowledge acquisition Knowledge storage Knowledge dissemination Knowledge application
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Workplace impact of artificial intelligence

The impact of artificial intelligence on workers includes both applications to improve worker safety and health, and potential hazards that must be controlled. One potential application is using AI to eliminate hazards by removing humans from hazardous situations that involve risk of stress, overwork, or musculoskeletal injuries. Predictive analytics may also be used to identify conditions that may lead to hazards such as fatigue, repetitive strain injuries, or toxic substance exposure, leading to earlier interventions. Another is to streamline workplace safety and health workflows through automating repetitive tasks, enhancing safety training programs through virtual reality, or detecting and reporting near misses. When used in the workplace, AI also presents the possibility of new hazards. These may arise from machine learning techniques leading to unpredictable behavior and inscrutability in their decision-making, or from cybersecurity and information privacy issues. Many hazards of AI are psychosocial due to its potential to cause changes in work organization. These include increased monitoring leading to micromanagement, algorithms unintentionally or intentionally mimicking undesirable human biases, and assigning blame for machine errors to the human operator instead. AI may also lead to physical hazards in the form of human–robot collisions, and ergonomic risks of control interfaces and human–machine interactions. Hazard controls include cybersecurity and information privacy measures, communication and transparency with workers about data usage, and limitations on collaborative robots. From a workplace safety and health perspective, only "weak" or "narrow" AI that is tailored to a specific task is relevant, as there are many examples that are currently in use or expected to come into use in the near future. Certain digital technologies are predicted to result in job losses. Starting in the 2020s, the adoption of modern robotics has led to net employment growth. However, many businesses anticipate that automation, or employing robots would result in job losses in the future. This is especially true for companies in Central and Eastern Europe. Other digital technologies, such as platforms or big data, are projected to have a more neutral impact on employment. A large number of tech workers have been laid off starting in 2023; many such job cuts have been attributed to artificial intelligence. == Health and safety applications == In order for any potential AI health and safety application to be adopted, it requires acceptance by both managers and workers. For example, worker acceptance may be diminished by concerns about information privacy, or from a lack of trust and acceptance of the new technology, which may arise from inadequate transparency or training. Alternatively, managers may emphasize increases in economic productivity rather than gains in worker safety and health when implementing AI-based systems. === Eliminating hazardous tasks === AI may increase the scope of work tasks where a worker can be removed from a situation that carries risk. In a sense, while traditional automation can replace the functions of a worker's body with a robot, AI effectively replaces the functions of their brain with a computer. Hazards that can be avoided include stress, overwork, musculoskeletal injuries, and boredom. This can expand the range of affected job sectors into white-collar and service sector jobs such as in medicine, finance, and information technology. === Analytics to reduce risk === Machine learning is used for people analytics to make predictions about worker behavior to assist management decision-making, such as hiring and performance assessment. These could also be used to improve worker health. The analytics may be based on inputs such as online activities, monitoring of communications, location tracking, and voice analysis and body language analysis of filmed interviews. For example, sentiment analysis may be used to spot fatigue to prevent overwork. Decision support systems have a similar ability to be used to, for example, prevent industrial disasters or make disaster response more efficient. For manual material handling workers, predictive analytics and artificial intelligence may be used to reduce musculoskeletal injury. Traditional guidelines are based on statistical averages and are geared towards anthropometrically typical humans. The analysis of large amounts of data from wearable sensors may allow real-time, personalized calculation of ergonomic risk and fatigue management, as well as better analysis of the risk associated with specific job roles. Wearable sensors may also enable earlier intervention against exposure to toxic substances than is possible with area or breathing zone testing on a periodic basis. Furthermore, the large data sets generated could improve workplace health surveillance, risk assessment, and research. === Streamlining safety and health workflows === AI has also been used to attempt to make the workplace safety and health workflow more efficient. One example is coding of workers' compensation claims, which are submitted in a prose narrative form and must manually be assigned standardized codes. AI is being investigated to perform this task faster, more cheaply, and with fewer errors. == Hazards == There are several broad aspects of AI that may give rise to specific hazards. The risks depend on implementation rather than the mere presence of AI. Systems using sub-symbolic AI such as machine learning may behave unpredictably and are more prone to inscrutability in their decision-making. This is especially true if a situation is encountered that was not part of the AI's training dataset, and is exacerbated in environments that are less structured. Undesired behavior may also arise from flaws in the system's perception (arising either from within the software or from sensor degradation), knowledge representation and reasoning, or from software bugs. They may arise from improper training, such as a user applying the same algorithm to two problems that do not have the same requirements. Machine learning applied during the design phase may have different implications than that applied at runtime. Systems using symbolic AI are less prone to unpredictable behavior. The use of AI also increases cybersecurity risks relative to platforms that do not use AI, and information privacy concerns about collected data may pose a hazard to workers. === Psychosocial === Psychosocial hazards are those that arise from the way work is designed, organized, and managed, or its economic and social contexts, rather than arising from a physical substance or object. They cause not only psychiatric and psychological outcomes such as occupational burnout, anxiety disorders, and depression, but they can also cause physical injury or illness such as cardiovascular disease or musculoskeletal injury. Many hazards of AI are psychosocial in nature due to its potential to cause changes in work organization, in terms of increasing complexity and interaction between different organizational factors. However, psychosocial risks are often overlooked by designers of advanced manufacturing systems. Einola and Khoreva explore how different organizational groups perceive and interact with AI technologies. Their research shows that successful AI integration depends on human ownership and contextual understanding. They caution against blind technological optimism and stress the importance of tailoring AI use to specific workplace ecosystems. This perspective reinforces the need for inclusive design and transparent implementation strategies. ==== Changes in work practices ==== Over-reliance on AI tools may lead to deskilling of some professions. When AI becomes a substitute for traditional peer collaboration and mentorship, there is a risk of diminishing opportunities for interpersonal skill development and team-based learning. Increased monitoring may lead to micromanagement and thus to stress and anxiety. A perception of surveillance may also lead to stress. Controls for these include consultation with worker groups, extensive testing, and attention to introduced bias. Wearable sensors, activity trackers, and augmented reality may also lead to stress from micromanagement, both for assembly line workers and gig workers. Gig workers also lack the legal protections and rights of formal workers. Newell & Marabelli argue that AI alters power dynamics and employee autonomy, requiring a more nuanced understanding of its social and organizational implications. There is also the risk of people being forced to work at a robot's pace, or to monitor robot performance at nonstandard hours. A 2025 preprint paper based on users' interactions with the AI chatbot Microsoft Copilot identified forty jobs that the author's claimed had high overlaps with the capabilities of AI. Some media outlets used this paper to report on jobs becoming obsolete. Cri
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Portable Format for Analytics

The Portable Format for Analytics (PFA) is a JSON-based predictive model interchange format conceived and developed by Jim Pivarski. PFA provides a way for analytic applications to describe and exchange predictive models produced by analytics and machine learning algorithms. It supports common models such as logistic regression and decision trees. Version 0.8 was published in 2015. Subsequent versions have been developed by the Data Mining Group. As a predictive model interchange format developed by the Data Mining Group, PFA is complementary to the DMG's XML-based standard called the Predictive Model Markup Language or PMML. == 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. == Examples == reverse array: # reverse input array of doubles input: {"type": "array", "items": "double"} output: {"type": "array", "items": "double"} action: - let: { x : input} - let: { z : input} - let: { l : {a.len: [x]}} - let: { i : l} - while : { ">=" : [i,0]} do: - set : {z : {attr: z, path : [i] , to: {attr : x ,path : [ {"-":[{"-" : [l ,i]},1]}] } } } - set : {i : {-:[i,1]}} - z Bubblesort input: {"type": "array", "items": "double"} output: {"type": "array", "items": "double"} action: - let: { A : input} - let: { N : {a.len: [A]}} - let: { n : {-:[N,1]}} - let: { i : 0} - let: { s : 0.0} - while : { ">=" : [n,0]} do : - set : { i : 0 } - while : { "<=" : [i,{-:[n,1]}]} do : - if: {">": [ {attr: A, path : [i]} , {attr: A, path:[{+:[i,1]}]} ]} then : - set : {s : {attr: A, path: [i]}} - set : {A : {attr: A, path: [i], to: {attr: A, path:[{+:[i,1]}]} } } - set : {A : {attr: A, path: [{+:[i,1]}], to: s }} - set : {i : {+:[i,1]}} - set : {n : {-:[n,1]}} - A == Implementations == Hadrian (Java/Scala/JVM) - Hadrian is a complete implementation of PFA in Scala, which can be accessed through any JVM language, principally Java. It focuses on model deployment, so it is flexible (can run in restricted environments) and fast. Titus (Python 2.x) - Titus is a complete, independent implementation of PFA in pure Python. It focuses on model development, so it includes model producers and PFA manipulation tools in addition to runtime execution. Currently, it works for Python 2. Titus 2 (Python 3.x) - Titus 2 is a fork of Titus which supports PFA implementation for Python 3. Aurelius (R) - Aurelius is a toolkit for generating PFA in the R programming language. It focuses on porting models to PFA from their R equivalents. To validate or execute scoring engines, Aurelius sends them to Titus through rPython (so both must be installed). Antinous (Model development in Jython) - Antinous is a model-producer plugin for Hadrian that allows Jython code to be executed anywhere a PFA scoring engine would go. It also has a library of model producing algorithms.
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ML.NET

ML.NET is a free software machine learning library for the C# and F# programming languages. It also supports Python models when used together with NimbusML. The preview release of ML.NET included transforms for feature engineering like n-gram creation, and learners to handle binary classification, multi-class classification, and regression tasks. Additional ML tasks like anomaly detection and recommendation systems have since been added, and other approaches like deep learning will be included in future versions. == Machine learning == ML.NET brings model-based Machine Learning analytic and prediction capabilities to existing .NET developers. The framework is built upon .NET Core and .NET Standard inheriting the ability to run cross-platform on Linux, Windows and macOS. Although the ML.NET framework is new, its origins began in 2002 as a Microsoft Research project named TMSN (text mining search and navigation) for use internally within Microsoft products. It was later renamed to TLC (the learning code) around 2011. ML.NET was derived from the TLC library and has largely surpassed its parent says Dr. James McCaffrey, Microsoft Research. Developers can train a Machine Learning Model or reuse an existing Model by a 3rd party and run it on any environment offline. This means developers do not need to have a background in Data Science to use the framework. Support for the open-source Open Neural Network Exchange (ONNX) Deep Learning model format was introduced from build 0.3 in ML.NET. The release included other notable enhancements such as Factorization Machines, LightGBM, Ensembles, LightLDA transform and OVA. The ML.NET integration of TensorFlow is enabled from the 0.5 release. Support for x86 & x64 applications was added to build 0.7 including enhanced recommendation capabilities with Matrix Factorization. A full roadmap of planned features have been made available on the official GitHub repo. The first stable 1.0 release of the framework was announced at Build (developer conference) 2019. It included the addition of a Model Builder tool and AutoML (Automated Machine Learning) capabilities. Build 1.3.1 introduced a preview of Deep Neural Network training using C# bindings for Tensorflow and a Database loader which enables model training on databases. The 1.4.0 preview added ML.NET scoring on ARM processors and Deep Neural Network training with GPU's for Windows and Linux. === Performance === Microsoft's paper on machine learning with ML.NET demonstrated it is capable of training sentiment analysis models using large datasets while achieving high accuracy. Its results showed 95% accuracy on Amazon's 9GB review dataset. === Model builder === The ML.NET CLI is a Command-line interface which uses ML.NET AutoML to perform model training and pick the best algorithm for the data. The ML.NET Model Builder preview is an extension for Visual Studio that uses ML.NET CLI and ML.NET AutoML to output the best ML.NET Model using a GUI. === Model explainability === AI fairness and explainability has been an area of debate for AI Ethicists in recent years. A major issue for Machine Learning applications is the black box effect where end users and the developers of an application are unsure of how an algorithm came to a decision or whether the dataset contains bias. Build 0.8 included model explainability API's that had been used internally in Microsoft. It added the capability to understand the feature importance of models with the addition of 'Overall Feature Importance' and 'Generalized Additive Models'. When there are several variables that contribute to the overall score, it is possible to see a breakdown of each variable and which features had the most impact on the final score. The official documentation demonstrates that the scoring metrics can be output for debugging purposes. During training & debugging of a model, developers can preview and inspect live filtered data. This is possible using the Visual Studio DataView tools. === Infer.NET === Microsoft Research announced the popular Infer.NET model-based machine learning framework used for research in academic institutions since 2008 has been released open source and is now part of the ML.NET framework. The Infer.NET framework utilises probabilistic programming to describe probabilistic models which has the added advantage of interpretability. The Infer.NET namespace has since been changed to Microsoft.ML.Probabilistic consistent with ML.NET namespaces. === NimbusML Python support === Microsoft acknowledged that the Python programming language is popular with Data Scientists, so it has introduced NimbusML the experimental Python bindings for ML.NET. This enables users to train and use machine learning models in Python. It was made open source similar to Infer.NET. === Machine learning in the browser === ML.NET allows users to export trained models to the Open Neural Network Exchange (ONNX) format. This establishes an opportunity to use models in different environments that don't use ML.NET. It would be possible to run these models in the client side of a browser using ONNX.js, a JavaScript client-side framework for deep learning models created in the Onnx format. === AI School Machine Learning Course === Along with the rollout of the ML.NET preview, Microsoft rolled out free AI tutorials and courses to help developers understand techniques needed to work with the framework.
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Ilya Sutskever

Ilya Sutskever (Hebrew: איליה סוצקבר; born 1986) is a computer scientist who specializes in machine learning. He has made several major contributions to the field of deep learning, including sequence-to-sequence learning, reasoning models, GPT models, and contributions to CLIP, DALL-E, and AlphaGo. With Alex Krizhevsky and Geoffrey Hinton, he co-created AlexNet, a convolutional neural network. One of the most highly cited computer scientists in history, he has won the NeurIPS Test of Time Award for his lasting impact on AI research three times in a row (2022–2024) and received the National Academy of Sciences Award for the Industrial Application of Science in 2026. Sutskever co-founded and was chief scientist at OpenAI, where he oversaw the research breakthroughs that led to large language models and to the launch of ChatGPT. He also led the research that led to reasoning models such as o1. In 2023, he was one of the members of OpenAI's board that ousted Sam Altman as its CEO; Altman was reinstated a week later, and Sutskever stepped down from the board. In June 2024, Sutskever co-founded the company Safe Superintelligence Inc., alongside Daniel Gross and Daniel Levy. Within a year, the company was valued at more than $30 billion. == Early life and education == Sutskever was born in 1986 into a Jewish family in Nizhny Novgorod, Russia (then Gorky, Russian SFSR, Soviet Union). At the age of 5, he immigrated to Israel with his family and grew up in Jerusalem. Sutskever proved to be a good student in school, and in eighth grade started taking classes at the Open University of Israel. At 16, he moved with his family to Canada, where he attended high school for a month before being admitted to the University of Toronto in Ontario as a third-year undergraduate student. At the University of Toronto, Sutskever received a bachelor's degree in mathematics in 2005, a master's degree in computer science in 2007, and a PhD in computer science in 2013. His doctoral advisor was Geoffrey Hinton. In 2012, Sutskever built AlexNet in collaboration with Geoffrey Hinton and Alex Krizhevsky. == Career and research == In 2012, Sutskever spent about two months as a postdoc with Andrew Ng at Stanford University. He then returned to the University of Toronto and joined Hinton's new research company DNNResearch, a spinoff of Hinton's research group. In 2013, Google acquired DNNResearch and hired Sutskever as a research scientist at Google Brain. At Google Brain, Sutskever worked with Oriol Vinyals and Quoc Viet Le to create the sequence-to-sequence learning algorithm, and worked on TensorFlow. He is also one of the AlphaGo paper's many co-authors. At the end of 2015, Sutskever left Google to become cofounder and chief scientist of the newly founded organization OpenAI. In 2022, Sutskever tweeted, "it may be that today's large neural networks are slightly conscious", which triggered debates about AI consciousness. He is considered to have played a key role in the development of ChatGPT, and later in leading the research that led to reasoning models. He is credited with establishing OpenAI’s scaling ethos. In 2023, he announced that he would co-lead OpenAI's new "Superalignment" project, which was trying to solve the alignment of superintelligences within four years. He wrote that even if superintelligence seems far off, it could happen this decade. Sutskever was formerly one of the six board members of the nonprofit entity that controlled OpenAI. In November 2023, the board fired Sam Altman, saying that "he was not consistently candid in his communications with the board". He authored a 52-page memo that relied heavily on information from Mira Murati, accusing Altman of lying, manipulating executives, and fostering internal division. Sutskever submitted the memo to the board after months of tension and dissatisfaction with Altman's leadership style, and ultimately joined the board in voting for Altman's termination. In an all-hands company meeting shortly after the board meeting, Sutskever said that firing Altman was "the board doing its duty", but the next week, he expressed regret at having participated in Altman's ouster. Altman's firing and OpenAI's co-founder Greg Brockman's resignation led three senior researchers to resign from OpenAI. After that, Sutskever stepped down from the OpenAI board and was absent from OpenAI's office. Some sources suggested he was leading the team remotely, while others said he no longer had access to the team's work. In May 2024, Sutskever announced his departure from OpenAI to focus on a new project that was "very personally meaningful" to him. His decision followed a turbulent period at OpenAI marked by leadership crises and internal debates about the direction of AI development and alignment protocols. Jan Leike, the other leader of the superalignment project, announced his departure hours later, citing an erosion of safety and trust in OpenAI's leadership. In June 2024, Sutskever announced Safe Superintelligence Inc., a new company he founded with Daniel Gross and Daniel Levy with offices in Palo Alto and Tel Aviv. In contrast to OpenAI, which releases revenue-generating products, Sutskever said the new company's "first product will be the safe superintelligence, and it will not do anything else up until then". In September 2024, the company announced that it had raised $1 billion from venture capital firms including Andreessen Horowitz, Sequoia Capital, DST Global, and SV Angel. In March 2025, Safe Superintelligence Inc. raised $2 billion more and reportedly reached a $32 billion valuation, notably due to Sutskever's reputation. In June 2025, SSI rejected an offer from Meta Platforms to buy the company. Sutskever became CEO of SSI shortly thereafter, after co-founder and CEO Gross left for Meta. In an October 2024 interview after winning the Nobel Prize in Physics, Geoffrey Hinton expressed support for Sutskever's decision to fire Altman, emphasizing concerns about AI safety. During the Musk v. Altman trial in 2026, Sutskever confirmed he had a $7 billion stake in OpenAI. === Awards and honors === In 2015, Sutskever was named in MIT Technology Review's 35 Innovators Under 35. In 2018, he was the keynote speaker at Nvidia Ntech 2018 and AI Frontiers Conference 2018. In 2022, he was elected a Fellow of the Royal Society (FRS). In 2023 and 2024, included in Time's list of the 100 most influential people in AI In 2022, 2023, and 2024, he won Neural Information Processing Systems’ Test of Time award, which recognizes papers that significantly shaped the AI field over at least ten years. In 2025, he received an honorary doctorate from his alma mater, the University of Toronto In 2026, he received the National Academy of Sciences Award for the Industrial Application of Science, presented for the first time in artificial intelligence.
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Adobe Presenter

Adobe Presenter is eLearning software released by Adobe Systems available on the Microsoft Windows platform as a Microsoft PowerPoint plug-in, and on both Windows and OS X as the screencasting and video editing tool Adobe Presenter Video Express. It is mainly targeted towards learning professionals and trainers. In addition to recording one's computer desktop and speech, it also provides the option to add quizzes and track performance by integrating with learning management systems. Adobe Presenter was designed to replace the discontinued Adobe Ovation software, which had similar functions. == Predecessor == Adobe Ovation was originally released by Serious Magic. It converted PowerPoint slides into visual presentations with additional effects. Ovation included themes called PowerLooks that could add motion and polish the presentations. They were available in a variety of color variations complete with animated backgrounds and dynamic text effects. Ovation could make text with jagged edges more readable. TimeKeeper could be used to set the period of the presentation, and the PointPrompter scrolled down the notes. Ovation's development has been discontinued, nor does it support PowerPoint 2007. == Features == The main purpose of Adobe Presenter is to capture on-screen presentations and convert them into more interactive and engaging videos. Support is given to convert Microsoft PowerPoint 2010 and 2013 presentations into videos. It also allows for content authoring on PowerPoint and ActionScript 3, and offers integration with Adobe Captivate. Slide branching enables users to control slide navigation and titles and create complex slide branching to guide viewers through the content of the presentation. Video editing tools are also provided, and offer the ability to upload to video-sharing platforms such as YouTube, Vimeo and other sites. Multimedia features such as annotations, eLearning templates, actors, audio narration and drag-and-drop elements enrich users' presentations. Quizzes and surveys is another highlighted feature, which include generating question pools, importing questions from existing quizzes and in-course collaboration which allows presenters to receive feedback by allowing them to comment on specific content within a course or ask questions for more clarity. Presenters could opt to receive feedback from viewers through video analytics and create Experience API, SCORM and AICC-compliant content. Options to publish to Adobe Connect are provided. Other unique features include universal standards support, file size control, navigational restrictions among others.
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Historical Thesaurus of English

The Historical Thesaurus of English (HTE) is the largest thesaurus in the world. It is called a historical thesaurus as it arranges the whole vocabulary of English, from the earliest written records in Old English to the present, according to the first documented occurrence of a word in the entire history of the English language. The HTE was conceived and begun in 1965 by the English Language & Linguistics department of the University of Glasgow, who have ever since continued to compile the thesaurus. From the 1980s onwards the project was moved from paper-based records to a computer database. Today, the HTE is available to the public online, but a print version, the Historical Thesaurus of the Oxford English Dictionary (HTOED), was published in 2009. == Main project: The Historical Thesaurus of English (HTE) == The Historical Thesaurus of English (HTE) is a complete database of all the words in the Oxford English Dictionary and other dictionaries (including Old English), arranged by semantic field and date. In this way, the HTE arranges the whole vocabulary of English, from the earliest written records in Old English to the present, alongside dates of use. It is the first historical thesaurus to be compiled for any of the world's languages and contains 800,000 meanings for 600,000 words, within 230,000 categories. As the HTE website states, "in addition to providing hitherto unavailable information for linguistic and textual scholars, the Historical Thesaurus online is a rich resource for students of social and cultural history, showing how concepts developed through the words that refer to them." === Structure === The work is divided into three main sections: the External World, the Mind, and Society. These are broken down into successively narrower domains. The text eventually discriminates more than 236,000 categories. The second order categories are: === History === The ambitious project was announced at a 1965 meeting of the Philological Society by its originator, Michael Samuels. Work on the HTE started in the same year. In 2017, the University of Glasgow was awarded the Queen's Anniversary Prize for Higher Education for the HTE. A second edition of the online HTE is currently in progress and is expected to be launched in late 2020. Work is released on the freely-available HTE website when available. == Print edition: Historical Thesaurus of the Oxford English Dictionary (HTOED) == On 22 October 2009, after 44 years of work, version 1.0 of the HTE was published by Oxford University Press in a two-volume slipcased set as the Historical Thesaurus of the Oxford English Dictionary (HTOED). The two hardcover volumes together total nearly 4,500 pages.
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John M. Jumper

John Michael Jumper (born 1 January 1985) is an American chemist and computer scientist. Jumper and Demis Hassabis were awarded the 2024 Nobel Prize in Chemistry for protein structure prediction. As of 2025 Jumper serves as director at Google DeepMind. Jumper and his colleagues created AlphaFold, an artificial intelligence (AI) model to predict protein structures from their amino acid sequence with high accuracy. The AlphaFold team had released 214 million protein structures as of January 2024. The scientific journal Nature included Jumper as one of the ten "people who mattered" in science in their annual listing of Nature's 10 in 2021. == Education == Jumper graduated from Pulaski Academy in 2003. He received a Bachelor of Science with majors in physics and mathematics from Vanderbilt University in 2007, a Master of Philosophy in theoretical condensed matter physics from the University of Cambridge where he was a student of St Edmund's College, Cambridge in 2010 on a Marshall Scholarship, a Master of Science in theoretical chemistry from the University of Chicago in 2012, and a Doctor of Philosophy in theoretical chemistry from the University of Chicago in 2017. His doctoral advisors at the University of Chicago were Tobin R. Sosnick and Karl Freed. == Career and research == Jumper's research investigates algorithms for protein structure prediction. === AlphaFold === AlphaFold is a deep learning algorithm developed by Jumper and his team at DeepMind, a research lab acquired by Google's parent company Alphabet Inc. It is an artificial intelligence program which performs predictions of protein structure. === Awards and honors === In November 2020, AlphaFold was named the winner of the 14th Critical Assessment of Structure Prediction (CASP) competition. This international competition benchmarks algorithms to determine which one can best predict the 3D structure of proteins. AlphaFold won the competition, outperforming other algorithms scoring above 90 for around two-thirds of the proteins in CASP's global distance test (GDT), a test that measures the degree to which a computational program predicted structure is similar to the lab experiment determined structure, with 100 being a complete match, within the distance cutoff used for calculating GDT. In 2021, Jumper was awarded the BBVA Foundation Frontiers of Knowledge Award in the category "Biology and Biomedicine". In 2022 Jumper received the Wiley Prize in Biomedical Sciences and for 2023 the Breakthrough Prize in Life Sciences for developing AlphaFold, which accurately predicts the structure of a protein. In 2023 he was awarded the Canada Gairdner International Award and the Albert Lasker Award for Basic Medical Research. In 2024, Jumper and Demis Hassabis shared half of the Nobel Prize in Chemistry for their protein folding predictions, the other half went to David Baker for computational protein design. In 2025, Jumper received the Golden Plate Award of the American Academy of Achievement and the Marshall Medal of the Marshall Aid Commemoration Commission. He was elected a Fellow of the Royal Society (FRS) that same year. In 2026, he was elected a member of the National Academy of Engineering.
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