A distributed transaction operates within a distributed environment, typically involving multiple nodes across a network depending on the location of the data. A key aspect of distributed transactions is atomicity, which ensures that the transaction is completed in its entirety or not executed at all. It's essential to note that distributed transactions are not limited to databases. The Open Group, a vendor consortium, proposed the X/Open Distributed Transaction Processing Model (X/Open XA), which became a de facto standard for the behavior of transaction model components. Databases are common transactional resources and, often, transactions span a couple of such databases. In this case, a distributed transaction can be seen as a database transaction that must be synchronized (or provide ACID properties) among multiple participating databases which are distributed among different physical locations. The isolation property (the I of ACID) poses a special challenge for multi database transactions, since the (global) serializability property could be violated, even if each database provides it (see also global serializability). In practice most commercial database systems use strong strict two-phase locking (SS2PL) for concurrency control, which ensures global serializability, if all the participating databases employ it. A common algorithm for ensuring correct completion of a distributed transaction is the two-phase commit (2PC). This algorithm is usually applied for updates able to commit in a short period of time, ranging from couple of milliseconds to couple of minutes. There are also long-lived distributed transactions, for example a transaction to book a trip, which consists of booking a flight, a rental car and a hotel. Since booking the flight might take up to a day to get a confirmation, two-phase commit is not applicable here, it will lock the resources for this long. In this case more sophisticated techniques that involve multiple undo levels are used. The way you can undo the hotel booking by calling a desk and cancelling the reservation, a system can be designed to undo certain operations (unless they are irreversibly finished). In practice, long-lived distributed transactions are implemented in systems based on web services. Usually these transactions utilize principles of compensating transactions, Optimism and Isolation Without Locking. The X/Open standard does not cover long-lived distributed transactions. Several technologies, including Jakarta Enterprise Beans and Microsoft Transaction Server fully support distributed transaction standards. == Synchronization == In event-driven architectures, distributed transactions can be synchronized through using request–response paradigm and it can be implemented in two ways: Creating two separate queues: one for requests and the other for replies. The event producer must wait until it receives the response. Creating one dedicated ephemeral queue for each request.
Apptek
Applications Technology (AppTek) is a U.S. company headquartered in McLean, Virginia that specializes in artificial intelligence and machine learning for human language technologies. The company provides both managed and professional services for natural language processing (NLP) technologies including automatic speech recognition (ASR), neural machine translation (MT), natural-language understanding (NLU) and neural speech synthesis. AppTek's Head of Science, Prof. Dr. -Ing Hermann Ney, was awarded the IEEE James L. Flanagan Speech and Audio Processing Award in 2019 and the ISCA Medal for Scientific Achievement in 2021 for his work in natural language processing. == History == AppTek was acquired in 1998 by Lernout & Hauspie (at the time a NASDAQ publicly traded company), AppTek organized a management buy-out and went private again in 2001. In 2014, the company sold its hybrid machine translation technology to eBay and has since rebuilt the platform to modern neural-based approaches for machine translation. In 2020, SOSi acquired non-controlling interest in AppTek and became an exclusive reseller of AppTek products for U.S. federal, state, and local government entities.
Alliance for Secure AI
The Alliance for Secure AI is a U.S.-based nonprofit organization which educates the public about the risks of advanced artificial intelligence (AI). Politico has described the Alliance as a "bipartisan nonprofit trying to push a middle-ground approach to AI guardrails." == History == In June 2025, the Alliance was launched as a 501(c)(3) nonprofit watchdog in Washington, D.C. That same month, the organization rolled out a six-figure advertising campaign featuring bipartisan warnings about advanced AI. The ad campaign presented different messages for different political audiences. The Alliance opposed the idea of a moratorium on state AI laws as part of the July 2025 budget bill, in addition to President Donald Trump's December 2025 executive order on the issue. The group has also criticized AI companies like Meta and OpenAI for what it says are failures to prevent harms to children. In addition, the Alliance has criticized OpenAI for subpoenaing nonprofit organizations in the AI safety space. In March 2026, the Alliance launched JobLoss.ai, a website that tracks the jobs that have been eliminated with AI cited as a contributing factor. As of April 2026, JobLoss.ai has tracked more than 127,000 lost jobs. == Leadership == Brendan Steinhauser, a longtime political and communications strategist, is the founder and CEO of the Alliance. He was an early Tea Party movement organizer, and ran campaigns for multiple members of Congress, including Sen. John Cornyn, Rep. Dan Crenshaw, and Rep. Michael McCaul. Peyton Hornberger is the group's communications director. In July 2025, Hornberger criticized Palantir for its use of AI in a USA Today op-ed column.
Six Little Dragons
Six Little Dragons (Chinese: 杭州六小龙), or Six Little Dragons of Hangzhou, are an informal grouping of the tech startups Game Science, DeepSeek, Unitree Robotics, DEEP Robotics, BrainCo and Manycore Tech. All six were established in Hangzhou, They are active in artificial intelligence, robotics, gaming, and brain-computer interface technology. Hangzhou is referred to as the China’s “e-commerce capital” (电商之都). The nickname "Six Little Dragons" originated from the Chinese internet. == Background == === Chinese government investments (2002 — 2010s) === From 2002 to 2007, under Xi Jinping's leadership as party secretary of Zhejiang, provincial spending on technology research grew over four times to 28 billion RMB. The province launched "Digital Zhejiang" (数字浙江) to advance modernization and the "Eight Eight Strategy" (八八战略), focusing on eight advantages and actions to boost industrial development, including specialized industries. In 2010, Hangzhou's government started "Project Eagle" (雏鹰计划) to aid science and technology startups. The project works with incubators and accelerators to find promising tech companies and offers public funding and other help, especially for startups by graduates and returning students. Unitree received support in the initial phase, along with government subsidies from Binjiang District. === AI-startups and further investments (2025 — present) === In January 2025, the Chinese government created the "Hangzhou AI Industry Chain High-Quality Development Action Plan" which focuses on computing power, LLM technologies, and AI applications. The plan was made to certify over 2,000 new high-tech enterprises, initiate over 300 major tech projects, and invest more than 300 billion RMB (US$40 billion) annually. The Chinese government also renewed "Project Eagle" and to allocate 15% of industrial policy funds for future industries. Hangzhou aimed to become a center for tech startups, highlighting the "six little dragons of Hangzhou," a nickname popularized in early 2025. This group includes DeepSeek, Game Science, Unitree Robotics, Manycore Tech, BrainCo, and DEEP Robotics, companies in gaming, robotics, and software development. Earlier in 2025, DeepSeek, one of the six dragons, launched an AI system at a much lower cost than those from Silicon Valley. Since then, DeepSeek and Alibaba have produced top-performing open source AI models. Game Science launched the successful video game Black Myth: Wukong in 2024, while Unitree gained attention for their dancing robots in the 2025 annual spring gala broadcast by Chinese state media. The group was acknowledged by Chinese authorities in Hangzhou in a New Years message for local businesses in January 2025. Hangzhou’s universities were given credit for the development of Chinese technological industry. Zhejiang University alumni founded three of the "Six Little Dragons". By September 2024, the university produced 102 executives in Chinese AI start-ups, ranking third among China's top institutions. On February 20, 2025, Alibaba's Eddie Wu stated that the company would focus on artificial generative intelligence and plans significant investment in AI. The company also sought to boost foreign investment to China's "Six Little Dragons" following Alibaba's founder Jack Ma attended General Secretary of the Chinese Communist Party Xi Jinping's business symposium with corporate leaders and entrepreneurs that same month. == Challenges == China's net foreign direct investment (FDI) fell by US$168 billion in 2024, marking the largest capital flight since 1990. Foreign investment peaked at US$344 billion in 2021 but has since declined according to the State Administration of Foreign Exchange. In 2024, foreign investors put in only US$4.5 billion while Chinese firms invested US$173 billion abroad. According to interviews conducted by The New York Times, some start-up company founders believe that Chinese government's support for Hangzhou's technological sector has deterred foreign investors. Tensions with the United States led many international companies to adopt a China Plus One strategy, while Chinese firms build factories overseas to avoid potential Trump tariffs. China also faced US restrictions on its access of advanced chips, forcing Chinese tech companies to stockpile Nvidia chips while Chinese producers like Huawei and Semiconductor Manufacturing International Corporation (SMIC) were competing to produce their own.
Mira Murati
Ermira "Mira" Murati (born 16 December 1988) is an Albanian-American business executive. She launched an AI startup called Thinking Machines Lab in February 2025. Previously she was the chief technology officer of OpenAI, and a senior product manager at Tesla. == Early life and education == Murati was born on 16 December 1988 in Vlorë, Albania. She is fluent in Italian. At age 16, she won a United World Colleges (UWC) scholarship to study at Pearson College on Vancouver Island in Canada, from which she graduated in 2007 with an International Baccalaureate. After Pearson, she went to the United States to pursue further studies through a dual-degree program, earning a Bachelor of Arts from Colby College in 2011, and a Bachelor of Engineering degree from Dartmouth College's Thayer School of Engineering in 2012. == Career == === Early career === Murati interned in 2011 as a summer analyst at Goldman Sachs in Tokyo, Japan. She then briefly worked for Zodiac Aerospace as an intern before joining the electric car company Tesla in 2013 as a product manager on the Model X. From 2016 to 2018, she worked for the augmented reality start-up Leap Motion (now Ultraleap). === OpenAI === In 2018, she joined OpenAI as the VP of Applied AI and partnerships. She became chief technology officer (CTO) in May 2022. She led OpenAI's work on ChatGPT, Dall-E, Codex and Sora, while overseeing its research, product and safety teams. She oversaw technical advancements and direction of OpenAI's various projects, including the development of advanced AI models and tools. Murati worked on several of OpenAI's notable products, such as the Generative Pretrained Transformer (GPT) series of language models. Commenting about the potential loss of creative jobs to AI, Murati said that "maybe [the jobs] shouldn’t have been there in the first place". In October 2023, Murati was ranked 57th on Fortune's list of "The 100 Most Powerful Women in Business of 2023". In November 2023, Murati became interim chief executive officer of OpenAI following the removal of Sam Altman from the job. She had collaborated with Ilya Sutskever, whose 52-page memo outlining concerns about Altman relied heavily on screenshots and information she provided, which contributed to the board's decision to oust him. Murati was replaced by Emmett Shear three days later, who left when Altman was reinstated five days later. Following these events, Murati returned to her role as CTO. In June 2024, Dartmouth College awarded Murati an honorary Doctor of Science for having "democratized technology and advanced a better, safer world for us all". In September 2024, Murati announced that she was stepping down as CTO to allow her the opportunity to "do my own exploration". This move came amid a wider executive exodus as OpenAI chief research officer Bob McGrew and a vice president of research, Barret Zoph, also announced their departures soon after. === Thinking Machines Lab === In February 2025, Murati launched Thinking Machines Lab, a new public benefit corporation aiming "to make AI systems more widely understood, customizable, and generally capable". She was reported to have hired "a team of about 30 leading researchers and engineers from competitors including Meta, Mistral, and OpenAI." People involved with the startup include OpenAI cofounder John Schulman, and advisors Alec Radford and Bob McGrew. The following month, Bloomberg reported that the company had reached an estimated valuation of $9 billion, with an "average founder stake value" of $1.4 billion. In April 2025, Thinking Machines Lab reportedly aimed for a $2 billion seed round (requiring a minimum investment of $50 million). The round was led by Andreessen Horowitz and included participation from the government of Albania, valuing the company at $12 billion. Thinking Machines Lab follows a governance structure wherein Mira Murati holds a deciding vote on board matters, weighted to provide her with a majority decision-making capability. In October 2025, Thinking Machines Lab announced its first product, Tinker, a tool used to create custom frontier AI models. == Publications == Murati, Ermira (Spring 2022). "Language & Coding Creativity". Daedalus. 151 (2). Cambridge, MA: American Academy of Arts and Sciences (AAAS): 156–167. doi:10.1162/daed_a_01907. Retrieved 25 September 2024.
Dominant resource fairness
Dominant resource fairness (DRF) is a rule for fair division. It is particularly useful for dividing computing resources in among users in cloud computing environments, where each user may require a different combination of resources. DRF was presented by Ali Ghodsi, Matei Zaharia, Benjamin Hindman, Andy Konwinski, Scott Shenker and Ion Stoica in 2011. == Motivation == In an environment with a single resource, a widely used criterion is max-min fairness, which aims to maximize the minimum amount of resource given to a user. But in cloud computing, it is required to share different types of resource, such as: memory, CPU, bandwidth and disk-space. Previous fair schedulers, such as in Apache Hadoop, reduced the multi-resource setting to a single-resource setting by defining nodes with a fixed amount of each resource (e.g. 4 CPU, 32 MB memory, etc.), and dividing slots which are fractions of nodes. But this method is inefficient, since not all users need the same ratio of resources. For example, some users need more CPU whereas other users need more memory. As a result, most tasks either under-utilize or over-utilize their resources. DRF solves the problem by maximizing the minimum amount of the dominant resource given to a user (then the second-minimum etc., in a leximin order). The dominant resource may be different for different users. For example, if user A runs CPU-heavy tasks and user B runs memory-heavy tasks, DRF will try to equalize the CPU share given to user A and the memory share given to user B. == Definition == There are m resources. The total capacities of the resources are r1,...,rm. There are n users. Each users runs individual tasks. Each task has a demand-vector (d1,..,dm), representing the amount it needs of each resource. It is implicitly assumed that the utility of a user equals the number of tasks he can perform. For example, if user A runs tasks with demand-vector [1 CPU, 4 GB RAM], and receives 3 CPU and 8 GB RAM, then his utility is 2, since he can perform only 2 tasks. More generally, the utility of a user receiving x1,...,xm resources is minj(xj/dj), that is, the users have Leontief utilities. The demand-vectors are normalized to fractions of the capacities. For example, if the system has 9 CPUs and 18 GB RAM, then the above demand-vector is normalized to [1/9 CPU, 2/9 GB]. For each user, the resource with the highest demand-fraction is called the dominant resource. In the above example, the dominant resource is memory, as 2/9 is the largest fraction. If user B runs a task with demand-vector [3 CPU, 1 GB], which is normalized to [1/3 CPU, 1/18 GB], then his dominant resource is CPU. DRF aims to find the maximum x such that all agents can receive at least x of their dominant resource. In the above example, this maximum x is 2/3: User A gets 3 tasks, which require 3/9 CPU and 2/3 GB. User B gets 2 tasks, which require 2/3 CPU and 1/9 GB. The maximum x can be found by solving a linear program; see Lexicographic max-min optimization. Alternatively, the DRF can be computed sequentially. The algorithm tracks the amount of dominant resource used by each user. At each round, it finds a user with the smallest allocated dominant resource so far, and allocates the next task of this user. Note that this procedure allows the same user to run tasks with different demand vectors. == Properties == DRF has several advantages over other policies for resource allocation. Proportionality: each user receives at least as much resources as they could get in a system in which all resources are partitioned equally among users (the authors call this condition "sharing incentive"). Strategyproofness: a user cannot get a larger allocation by lying about his needs. Strategyproofness is important, as evidence from cloud operators show that users try to manipulate the servers in order to get better allocations. Envy-freeness: no user would prefer the allocation of another user. Pareto efficiency: no other allocation is better for some users and not worse for anyone. Population monotonicity: when a user leaves the system, the allocations of remaining users do not decrease. When there is a single resource that is a bottleneck resource (highly demanded by all users), DRF reduces to max-min fairness. However, DRF violates resource monotonicity: when resources are added to the system, some allocations might decrease. == Extensions == Weighted DRF is an extension of DRF to settings in which different users have different weights (representing their different entitlements). Parkes, Procaccia and Shah formally extend weighted DRF to a setting in which some users do not need all resources (that is, they may have demand 0 to some resource). They prove that the extended version still satisfies proportionality, Pareto-efficiency, envy-freeness, strategyproofness, and even Group strategyproofness. On the other hand, they show that DRF may yield poor utilitarian social welfare, that is, the sum of utilities may be only 1/m of the optimum. However, they prove that any mechanism satisfying one of proportionality, envy-freeness or strategyproofness may suffers from the same low utilitarian welfare. They also extend DRF to the setting in which the users' demands are indivisible (as in fair item allocation). For the indivisible setting, they relax envy-freeness to EF1. They show that strategyproofness is incompatible with PO+EF1 or with PO+proportionality. However, a mechanism called SequentialMinMax satisfies efficiency, proportionality and EF1. Wang, Li and Liang present DRFH - an extension of DRF to a system with several heterogeneous servers. == Implementation == DRF was first implemented in Apache Mesos - a cluster resource manager, and it led to better throughput and fairness than previously used fair-sharing schemes.
Reification (computer science)
In computer science, reification is the process by which an abstract idea about a program is turned into an explicit data model or other object created in a programming language. A computable/addressable object—a resource—is created in a system as a proxy for a non computable/addressable object. By means of reification, something that was previously implicit, unexpressed, and possibly inexpressible is explicitly formulated and made available to conceptual (logical or computational) manipulation. Informally, reification is often referred to as "making something a first-class citizen" within the scope of a particular system. Some aspect of a system can be reified at language design time, which is related to reflection in programming languages. It can be applied as a stepwise refinement at system design time. Reification is one of the most frequently used techniques of conceptual analysis and knowledge representation. == Reflective programming languages == In the context of programming languages, reification is the process by which a user program or any aspect of a programming language that was implicit in the translated program and the run-time system, are expressed in the language itself. This process makes it available to the program, which can inspect all these aspects as ordinary data. In reflective languages, reification data is causally connected to the related reified aspect such that a modification to one of them affects the other. Therefore, the reification data is always a faithful representation of the related reified aspect . Reification data is often said to be made a first class object. Reification, at least partially, has been experienced in many languages to date: in early Lisp dialects and in current Prolog dialects, programs have been treated as data, although the causal connection has often been left to the responsibility of the programmer. In Smalltalk-80, the compiler from the source text to bytecode has been part of the run-time system since the very first implementations of the language. The C programming language reifies the low-level detail of memory addresses.Many programming language designs encapsulate the details of memory allocation in the compiler and the run-time system. In the design of the C programming language, the memory address is reified and is available for direct manipulation by other language constructs. For example, the following code may be used when implementing a memory-mapped device driver. The buffer pointer is a proxy for the memory address 0xB8000000. Functional programming languages based on lambda-calculus reify the concept of a procedure abstraction and procedure application in the form of the Lambda expression. The Scheme programming language reifies continuations (approximately, the call stack). In C#, reification is used to make parametric polymorphism implemented in the form of generics as a first-class feature of the language. In the Java programming language, there exist "reifiable types" that are "completely available at run time" (i.e. their information is not erased during compilation). REBOL reifies code as data and vice versa. Many languages, such as Lisp, JavaScript, and Curl, provide an eval or evaluate procedure that effectively reifies the language interpreter. Smalltalk and Actor languages permit the reification of blocks and messages, which are equivalent of lambda expressions in Lisp, and thisContext in Smalltalk, which is a reification of the current executing block. Homoiconic languages reify the syntax of the language as data that is understood by the language itself. This allows the user to write programs whose inputs and outputs are code (see macros, eval). Common representations of code include S-expressions (e.g. Clojure, Lisp), and abstract syntax trees (e.g. Rust). == Data reification vs. data refinement == Data reification (stepwise refinement) involves finding a more concrete representation of the abstract data types used in a formal specification. Data reification is the terminology of the Vienna Development Method (VDM) that most other people would call data refinement. An example is taking a step towards an implementation by replacing a data representation without a counterpart in the intended implementation language, such as sets, by one that does have a counterpart (such as maps with fixed domains that can be implemented by arrays), or at least one that is closer to having a counterpart, such as sequences. The VDM community prefers the word "reification" over "refinement", as the process has more to do with concretising an idea than with refining it. For similar usages, see Reification (linguistics). == In conceptual modeling == Reification is widely used in conceptual modeling. Reifying a relationship means viewing it as an entity. The purpose of reifying a relationship is to make it explicit, when additional information needs to be added to it. Consider the relationship type IsMemberOf(member:Person, Committee). An instance of IsMemberOf is a relationship that represents the fact that a person is a member of a committee. The figure below shows an example population of IsMemberOf relationship in tabular form. Person P1 is a member of committees C1 and C2. Person P2 is a member of committee C1 only. The same fact, however, could also be viewed as an entity. Viewing a relationship as an entity, one can say that the entity reifies the relationship. This is called reification of a relationship. Like any other entity, it must be an instance of an entity type. In the present example, the entity type has been named Membership. For each instance of IsMemberOf, there is one and only one instance of Membership, and vice versa. Now, it becomes possible to add more information to the original relationship. As an example, we can express the fact that "person p1 was nominated to be the member of committee c1 by person p2". Reified relationship Membership can be used as the source of a new relationship IsNominatedBy(Membership, Person). For related usages see Reification (knowledge representation). == In Unified Modeling Language (UML) == UML provides an association class construct for defining reified relationship types. The association class is a single model element that is both a kind of association and a kind of class. The association and the entity type that reifies are both the same model element. Note that attributes cannot be reified. == On Semantic Web == === RDF and OWL === In Semantic Web languages, such as Resource Description Framework (RDF) and Web Ontology Language (OWL), a statement is a binary relation. It is used to link two individuals or an individual and a value. Applications sometimes need to describe other RDF statements, for instance, to record information like when statements were made, or who made them, which is sometimes called "provenance" information. As an example, we may want to represent properties of a relation, such as our certainty about it, severity or strength of a relation, relevance of a relation, and so on. The example from the conceptual modeling section describes a particular person with URIref person:p1, who is a member of the committee:c1. The RDF triple from that description is Consider to store two further facts: (i) to record who nominated this particular person to this committee (a statement about the membership itself), and (ii) to record who added the fact to the database (a statement about the statement). The first case is a case of classical reification like above in UML: reify the membership and store its attributes and roles etc.: Additionally, RDF provides a built-in vocabulary intended for describing RDF statements. A description of a statement using this vocabulary is called a reification of the statement. The RDF reification vocabulary consists of the type rdf:Statement, and the properties rdf:subject, rdf:predicate, and rdf:object. Using the reification vocabulary, a reification of the statement about the person's membership would be given by assigning the statement a URIref such as committee:membership12345 so that describing statements can be written as follows: These statements say that the resource identified by the URIref committee:membership12345Stat is an RDF statement, that the subject of the statement refers to the resource identified by person:p1, the predicate of the statement refers to the resource identified by committee:isMemberOf, and the object of the statement refers to the resource committee:c1. Assuming that the original statement is actually identified by committee:membership12345, it should be clear by comparing the original statement with the reification that the reification actually does describe it. The conventional use of the RDF reification vocabulary always involves describing a statement using four statements in this pattern. Therefore, they are sometimes referred to as the "reification quad". Using reification according to this convention, we could record the fact that pe