AI Assistant In Adobe Acrobat

AI Assistant In Adobe Acrobat — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Motor theory of speech perception

The motor theory of speech perception is the hypothesis that people perceive spoken words by identifying the vocal tract gestures with which they are pronounced rather than by identifying the sound patterns that speech generates. It originally claimed that speech perception is done through a specialized module that is innate and human-specific. Though the idea of a module has been qualified in more recent versions of the theory, the idea remains that the role of the speech motor system is not only to produce speech articulations but also to detect them. The hypothesis has gained more interest outside the field of speech perception than inside. This has increased particularly since the discovery of mirror neurons that link the production and perception of motor movements, including those made by the vocal tract. The theory was initially proposed in the Haskins Laboratories in the 1950s by Alvin Liberman and Franklin S. Cooper, and developed further by Donald Shankweiler, Michael Studdert-Kennedy, Ignatius Mattingly, Carol Fowler and Douglas Whalen. == Origins and development == The hypothesis has its origins in research using pattern playback to create reading machines for the blind that would substitute sounds for orthographic letters. This led to a close examination of how spoken sounds correspond to the acoustic spectrogram of them as a sequence of auditory sounds. This found that successive consonants and vowels overlap in time with one another (a phenomenon known as coarticulation). This suggested that speech is not heard like an acoustic "alphabet" or "cipher," but as a "code" of overlapping speech gestures. === Associationist approach === Initially, the theory was associationist: infants mimic the speech they hear and that this leads to behavioristic associations between articulation and its sensory consequences. Later, this overt mimicry would be short-circuited and become speech perception. This aspect of the theory was dropped, however, with the discovery that prelinguistic infants could already detect most of the phonetic contrasts used to separate different speech sounds. === Cognitivist approach === The behavioristic approach was replaced by a cognitivist one in which there was a speech module. The module detected speech in terms of hidden distal objects rather than at the proximal or immediate level of their input. The evidence for this was the research finding that speech processing was special such as duplex perception. === Changing distal objects === Initially, speech perception was assumed to link to speech objects that were both the invariant movements of speech articulators the invariant motor commands sent to muscles to move the vocal tract articulators This was later revised to include the phonetic gestures rather than motor commands, and then the gestures intended by the speaker at a prevocal, linguistic level, rather than actual movements. === Modern revision === The "speech is special" claim has been dropped, as it was found that speech perception could occur for nonspeech sounds (for example, slamming doors for duplex perception). === Mirror neurons === The discovery of mirror neurons has led to renewed interest in the motor theory of speech perception, and the theory still has its advocates, although there are also critics. == Support == === Nonauditory gesture information === If speech is identified in terms of how it is physically made, then nonauditory information should be incorporated into speech percepts even if it is still subjectively heard as "sounds". This is, in fact, the case. The McGurk effect shows that seeing the production of a spoken syllable that differs from an auditory cue synchronized with it affects the perception of the auditory one. In other words, if someone hears "ba" but sees a video of someone pronouncing "ga", what they hear is different—some people believe they hear "da". People find it easier to hear speech in noise if they can see the speaker. People can hear syllables better when their production can be felt haptically. === Categorical perception === Using a speech synthesizer, speech sounds can be varied in place of articulation along a continuum from /bɑ/ to /dɑ/ to /ɡɑ/, or in voice onset time on a continuum from /dɑ/ to /tɑ/ (for example). When listeners are asked to discriminate between two different sounds, they perceive sounds as belonging to discrete categories, even though the sounds vary continuously. In other words, 10 sounds (with the sound on one extreme being /dɑ/ and the sound on the other extreme being /tɑ/, and the ones in the middle varying on a scale) may all be acoustically different from one another, but the listener will hear all of them as either /dɑ/ or /tɑ/. Likewise, the English consonant /d/ may vary in its acoustic details across different phonetic contexts (the /d/ in /du/ does not technically sound the same as the one in /di/, for example), but all /d/'s as perceived by a listener fall within one category (voiced alveolar plosive) and that is because "linguistic representations are abstract, canonical, phonetic segments or the gestures that underlie these segments." This suggests that humans identify speech using categorical perception, and thus that a specialized module, such as that proposed by the motor theory of speech perception, may be on the right track. === Speech imitation === If people can hear the gestures in speech, then the imitation of speech should be very fast, as in when words are repeated that are heard in headphones as in speech shadowing. People can repeat heard syllables more quickly than they would be able to produce them normally. === Speech production === Hearing speech activates vocal tract muscles, and the motor cortex and premotor cortex. The integration of auditory and visual input in speech perception also involves such areas. Disrupting the premotor cortex disrupts the perception of speech units such as plosives. The activation of the motor areas occurs in terms of the phonemic features which link with the vocal track articulators that create speech gestures. The perception of a speech sound is aided by pre-emptively stimulating the motor representation of the articulators responsible for its pronunciation . Auditory and motor cortical coupling is restricted to a specific range of neuronal firing frequency. === Perception-action meshing === Evidence exists that perception and production are generally coupled in the motor system. This is supported by the existence of mirror neurons that are activated both by seeing (or hearing) an action and when that action is carried out. Another source of evidence is that for common coding theory between the representations used for perception and action. == Criticisms == The motor theory of speech perception is not widely held in the field of speech perception, though it is more popular in other fields, such as theoretical linguistics. As three of its advocates have noted, "it has few proponents within the field of speech perception, and many authors cite it primarily to offer critical commentary".p. 361 Several critiques of it exist. === Multiple sources === Speech perception is affected by nonproduction sources of information, such as context. Individual words are hard to understand in isolation but easy when heard in sentence context. It therefore seems that speech perception uses multiple sources that are integrated together in an optimal way. === Production === The motor theory of speech perception would predict that speech motor abilities in infants predict their speech perception abilities, but in actuality it is the other way around. It would also predict that defects in speech production would impair speech perception, but they do not. However, this only affects the first and already superseded behaviorist version of the theory, where infants were supposed to learn all production-perception patterns by imitation early in childhood. This is no longer the mainstream view of motor-speech theorists. === Speech module === Several sources of evidence for a specialized speech module have failed to be supported. Duplex perception can be observed with door slams. The McGurk effect can also be achieved with nonlinguistic stimuli, such as showing someone a video of a basketball bouncing but playing the sound of a ping-pong ball bouncing. As for categorical perception, listeners can be sensitive to acoustic differences within single phonetic categories. As a result, this part of the theory has been dropped by some researchers. === Sublexical tasks === The evidence provided for the motor theory of speech perception is limited to tasks such as syllable discrimination that use speech units not full spoken words or spoken sentences. As a result, "speech perception is sometimes interpreted as referring to the perception of speech at the sublexical level. However, the ultimate goal of these studies is presumably to understand the neural processes supporting the ability to process spee
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Revoscalepy

revoscalepy is a machine learning package in Python created by Microsoft. It is available as part of Machine Learning Services in Microsoft SQL Server 2017 and Machine Learning Server 9.2.0 and later. The package contains functions for creating linear model, logistic regression, random forest, decision tree and boosted decision tree, in addition to some summary functions for inspecting data. Other machine learning algorithms such as neural network are provided in microsoftml, a separate package that is the Python version of MicrosoftML. revoscalepy also contains functions designed to run machine learning algorithms in different compute contexts, including SQL Server, Apache Spark, and Hadoop. In June 2021, Microsoft announced to open source the revoscalepy and RevoScaleR packages, making them freely available under the MIT License.
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BRFplus

BRFplus (Business Rule Framework plus) is a business rule management system (BRMS) offered by SAP AG. BRFplus is part of the SAP NetWeaver ABAP stack. Therefore, all SAP applications that are based on SAP NetWeaver can access BRFplus within the boundaries of an SAP system. However, it is also possible to generate web services so that BRFplus rules can also be offered as a service in a SOA landscape, regardless of the software platform used by the service consumers. BRFplus development started as a supporting tool that was part of SAP Business ByDesign, an ERP solution targeted at small and medium size companies. By that time, the tool was called "Formula and Derivation Tool" (FDT). Later on, it was decided to maintain BRFplus on those codelines that serve as the basis for SAP Business Suite. With that, business rules that have been created for Business ByDesign can easily be taken over in a full-size SAP system where they are ready for use without any changes. == Overview == BRFplus offers a unified modeling and runtime environment for business rules that addresses both technical users (programmers, system administrators) as well as business users who take care of operational business processes (like procurement, bidding, tax form validation, etc.). The different requirements and usage scenarios of the different target groups can be covered with the help of the SAP authorization system and a user interface that can be individually customized. Being integrated into SAP NetWeaver, BRFplus-based applications can look at, and model, business rules from a strictly business-oriented perspective, rather than starting with the underlying technical artifacts. This is because the integration allows for direct access to the business objects available in the SAP dictionary (like customer, supplier, material, bill, etc.). In addition to the predefined expression types (decision table, decision tree, formula, database access, loops, etc.) and actions (sending e-mails, triggering a workflow, etc.), BRFplus can be extended by custom expression types. Also, direct calls of function modules as well as ABAP OO class methods are supported so that the entire range of the ABAP programming language is available for solving business tasks. BRFplus comes with an optional versioning mechanism. Versioning can be switched on and off for individual objects as well as for entire applications. Versioned business rules are needed in certain use cases for legal reasons, but they also allow for simulating the system behavior as it would have been at a particular point in time. Once the rule objects are in a consistent state and active, the system automatically generates ABAP OO classes that encapsulate the functional scope of the underlying rule object. This is done on an on-demand base and speeds up processing. The execution of functions as well as of single expressions can be simulated. The processing log of the simulation is useful for checking the implementation and for investigating problems. BRFplus applications can be exported and imported as an XML file. This is an easy way of creating a data backup. XML files can also be used for deploying rule applications throughout the company. == Main object types == === Application === The application object serves as a container for all the BRFplus objects that have been assembled to solve a particular business task. It is possible to define certain default settings on application level that are inherited by all objects that are created in the scope of that application. === Function === A function is used to connect a business application with the rule processing framework of BRFplus. The calling business application passes input values to the function which are then processed by the expressions and rulesets that are associated with the called function. The calculated result is then returned to the calling business application. === Expression types and action types === Boolean BRMS Connector Case Database Lookup Decision Table Decision Tree Formula Function Call Loop Procedure Call Random Number Search Tree Step Sequence Value Range1 XSL Transformation === Ruleset === A ruleset is a container for an arbitrary number of rule objects which in turn carry out the necessary calculations with the help of assigned expressions and actions. Instead of assigning an expression to a function, it is also possible to assign any number of rulesets to a function. When the function is called, all assigned rulesets are subsequently processed. === Data objects === BRFplus supports elementary data objects (text, number, boolean, time point, amount, quantity) as well as structures and tables. Structures can be nested. For all types of data objects it is possible to reference data objects that reside in the data dictionary of the backend system. With that, a BRFplus data object does not only inherit the type definition of the referenced object but can also access associated data like domain value lists or object documentation. === Other objects === With catalogs, it is possible to define business-specific subsets of the rule objects that reside in the system. This is helpful for hiding the complexity of a rule system, thus improving usability. Object filters are used by system administrators to ensure that for selected users, only a predefined subset of object types is visible. This is useful to enforce access rights as well as modeling policies. == Other BRM solutions offered by SAP == BRFplus is positioned as the successor product of an older business rule solution known as BRF (Business Rule Framework). For a longer transition phase, both solutions exist in parallel. However, an increasing number of SAP applications that used to be based on BRF are migrating to BRFplus. While BRFplus supports business rules for applications based on the SAP NetWeaver ABAP stack, SAP is offering another product named SAP NetWeaver Business Rules Management (BRM). BRM supports business rule modeling for the SAP NetWeaver Java stack. Both products do not compete. They are available in parallel and can be used in a collaborative approach to deal with use cases where both technology stacks are used in parallel. BRFplus comes with a special expression type that helps bridging the gap between the two different technologies. == Availability == BRFplus has been delivered to the public with SAP NetWeaver 7.0 Enhancement Package 1 for the first time. Being part of SAP NetWeaver, the usage of BRFplus is covered by the "SAP NetWeaver Foundation for Third Party Applications" license, with no additional costs. == Literature == Carsten Ziegler, Thomas Albrecht: BRFplus – Business Rule Management for ABAP Applications. Galileo Press 2011. ISBN 978-1-59229-293-6
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Semantic knowledge management

In computer science, semantic knowledge management is a set of practices that seeks to classify content so that the knowledge it contains may be immediately accessed and transformed for delivery to the desired audience, in the required format. This classification of content is semantic in its nature – identifying content by its type or meaning within the content itself and via external, descriptive metadata – and is achieved by employing XML technologies. The specific outcomes of these practices are: Maintain content for multiple audiences together in a single document Transform content into various delivery formats without re-authoring Search for content more effectively Involve more subject-matter experts in the creation of content without reducing quality Reduce production costs for delivery formats Reduce the manual administration of getting the right knowledge to the right people Reduce the cost and time to localize content == Notable semantic knowledge management systems == Learn eXact Thinking Cap LCMS Thinking Cap LMS Xyleme LCMS iMapping
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Machine-learned interatomic potential

Machine-learned interatomic potentials (MLIPs), or simply machine learning potentials (MLPs), are interatomic potentials constructed using machine learning. Beginning in the 1990s, researchers have employed such programs to construct interatomic potentials by mapping atomic structures to their potential energies. These potentials are referred to as MLIPs or MLPs. Such machine learning potentials promised to fill the gap between density functional theory, a highly accurate but computationally intensive modelling method, and empirically derived or intuitively-approximated potentials, which were far lighter computationally but substantially less accurate. Improvements in artificial intelligence technology heightened the accuracy of MLPs while lowering their computational cost, increasing the role of machine learning in fitting potentials. Machine learning potentials began by using neural networks to tackle low-dimensional systems. While promising, these models could not systematically account for interatomic energy interactions; they could be applied to small molecules in a vacuum, or molecules interacting with frozen surfaces, but not much else – and even in these applications, the models often relied on force fields or potentials derived empirically or with simulations. These models thus remained confined to academia. Modern neural networks construct highly accurate and computationally light potentials, as theoretical understanding of materials science was increasingly built into their architectures and preprocessing. Almost all are local, accounting for all interactions between an atom and its neighbor up to some cutoff radius. There exist some nonlocal models, but these have been experimental for almost a decade. For most systems, reasonable cutoff radii enable highly accurate results. Almost all neural networks intake atomic coordinates and output potential energies. For some, these atomic coordinates are converted into atom-centered symmetry functions. From this data, a separate atomic neural network is trained for each element; each atomic network is evaluated whenever that element occurs in the given structure, and then the results are pooled together at the end. This process – in particular, the atom-centered symmetry functions which convey translational, rotational, and permutational invariances – has greatly improved machine learning potentials by significantly constraining the neural network search space. Other models use a similar process but emphasize bonds over atoms, using pair symmetry functions and training one network per atom pair. Other models to learn their own descriptors rather than using predetermined symmetry-dictating functions. These models, called message-passing neural networks (MPNNs), are graph neural networks. Treating molecules as three-dimensional graphs (where atoms are nodes and bonds are edges), the model takes feature vectors describing the atoms as input, and iteratively updates these vectors as information about neighboring atoms is processed through message functions and convolutions. These feature vectors are then used to predict the final potentials. The flexibility of this method often results in stronger, more generalizable models. In 2017, the first-ever MPNN model (a deep tensor neural network) was used to calculate the properties of small organic molecules. == Gaussian Approximation Potential (GAP) == One popular class of machine-learned interatomic potential is the Gaussian Approximation Potential (GAP), which combines compact descriptors of local atomic environments with Gaussian process regression to machine learn the potential energy surface of a given system. To date, the GAP framework has been used to successfully develop a number of MLIPs for various systems, including for elemental systems such as carbon, silicon, phosphorus, and tungsten, as well as for multicomponent systems such as Ge2Sb2Te5 and austenitic stainless steel, Fe7Cr2Ni. == Equivariant graph neural networks == A significant limitation of early MPNNs was that they were not inherently equivariant to rotations and reflections of atomic structures — meaning predictions could change depending on how a molecule was oriented in space. Beginning around 2021, a new class of models addressed this by incorporating equivariance directly into the message-passing layers using spherical harmonics and irreducible representations. Notable examples include NequIP (2021), MACE (2022), and GemNet-OC (2022). These equivariant architectures proved substantially more data-efficient and accurate than their predecessors, and became the dominant paradigm for high-accuracy MLIPs. == Universal MLIPs and large-scale datasets == Early MLIPs were system-specific, trained on a few thousand structures of a single material. A major shift occurred with the creation of large, chemically diverse datasets enabling models that generalize across many elements, bonding environments, and application domains — so-called universal MLIPs. A key driver was the Open Catalyst Project (OC20, OC22), a collaboration between Meta AI (FAIR) and Carnegie Mellon University launched in 2020. OC20 comprises approximately 1.3 million DFT relaxations across 82 elements, designed to accelerate the discovery of catalysts for renewable energy applications. It was among the first datasets large enough to train GNNs that generalize across diverse chemical systems, and established a widely-used benchmark for the field. A subsequent dataset, Open Direct Air Capture (OpenDAC 2023 and OpenDAC 2025), applied the same approach to carbon capture, providing a large computational database of metal-organic frameworks and sorbent candidates evaluated for CO₂ capture, generated using nearly 400 million CPU hours of quantum chemistry calculations in collaboration with Georgia Tech. These datasets revealed a new challenge: the GNN architectures most effective for atomic simulations were memory-intensive, as they model higher-order interactions between triplets or quadruplets of atoms, making it difficult to scale model size. Graph Parallelism, introduced by Sriram et al. (ICLR 2022), addressed this by distributing a single input graph across multiple GPUs — a distinct strategy from data parallelism (which distributes training examples) or model parallelism (which distributes layers). This enabled training GNNs with hundreds of millions to billions of parameters for the first time. Building on these foundations, Meta FAIR released the Universal Model for Atoms (UMA) in 2025, trained on approximately 500 million unique 3D atomic structures spanning molecules, materials, and catalysts — the largest training run to date for an MLIP. UMA introduced a Mixture of Linear Experts (MoLE) architecture, enabling one model to learn from datasets generated by different DFT codes and settings without significant inference overhead. It matches or surpasses specialized models across catalysis, materials, and molecular benchmarks without task-specific fine-tuning, and has been described as marking a "pre/post-UMA" divide in the field. == Applications == Catalyst discovery: MLIPs have significantly accelerated the computational screening of heterogeneous catalysts by replacing expensive DFT relaxations with fast neural network surrogates. The Open Catalyst Project explicitly targets this application, aiming to identify new catalysts for green hydrogen production and other renewable energy reactions. Carbon capture: The OpenDAC project applies universal MLIPs to screening sorbent materials for direct air capture of CO₂, a key technology for climate change mitigation. AI-accelerated screening allows evaluation of orders of magnitude more candidate materials than traditional DFT workflows. Drug discovery and molecular design: MLIPs are increasingly used in pharmaceutical research to model molecular conformations and binding energies. The Open Molecules 2025 (OMol25) dataset, released by Meta FAIR in 2025, provides high-accuracy calculations for a large set of molecular systems to support this use case. Materials discovery: Universal MLIPs enable high-throughput screening of novel inorganic materials, including battery electrolytes, semiconductors, and superconductors, by rapidly estimating stability and properties across large chemical spaces.
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National Library of Medicine classification

The National Library of Medicine (NLM) classification system is a library indexing system covering the fields of medicine and preclinical basic sciences. Operated and maintained by the U.S. National Library of Medicine, the NLM classification is patterned after the Library of Congress (LC) Classification system: alphabetical letters denote broad subject categories which are subdivided by numbers. For example, QW 279 would indicate a book on an aspect of microbiology or immunology. The one- or two-letter alphabetical codes in the NLM classification use a limited range of letters: only QS–QZ and W–WZ. This allows the NLM system to co-exist with the larger LC coding scheme as neither of these ranges are used in the LC system. There are, however, three pre-existing codes in the LC system which overlap with the NLM: Human Anatomy (QM), Microbiology (QR), and Medicine (R). To avoid further confusion, these three codes are not used in the NLM. The headings for the individual schedules (letters or letter pairs) are given in brief form (e.g., QW - Microbiology and Immunology; WG - Cardiovascular System) and together they provide an outline of the subjects covered by the NLM classification. Headings are interpreted broadly and include the physiological system, the specialties connected with them, the regions of the body chiefly concerned and subordinate related fields. The NLM system is hierarchical, and within each schedule, division by organ usually has priority. Each main schedule, as well as some sub-sections, begins with a group of form numbers ranging generally from 1–49 which classify materials by publication type, e.g., dictionaries, atlases, laboratory manuals, etc. The main schedules QS-QZ, W-WY, and WZ (excluding the range WZ 220–270) classify works published after 1913; the 19th century schedule is used for works published 1801–1913; and WZ 220-270 is used to provide century groupings for works published before 1801. == Classification categories == === Preclinical Sciences === QS Human Anatomy QT Physiology QU Biochemistry QV Pharmacology QW Microbiology & Immunology QX Parasitology QY Clinical Pathology QZ Pathology === Medicine and Related Subjects === W Health Professions WA Public Health WB Practice of Medicine WC Communicable Diseases WD Disorders of Systemic, Metabolic, or Environmental Origin, etc. WE Musculoskeletal System WF Respiratory System WG Cardiovascular System WH Hemic and Lymphatic Systems WI Digestive System WJ Urogenital System WK Endocrine System WL Nervous System WM Psychiatry WN Radiology. Diagnostic Imaging WO Surgery WP Gynecology WQ Obstetrics WR Dermatology WS Pediatrics WT Geriatrics. Chronic Disease WU Dentistry. Oral Surgery WV Otolaryngology WW Ophthalmology WX Hospitals & Other Health Facilities WY Nursing WZ History of Medicine 19th Century Schedule
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OntoUML

OntoUML is a language for ontology-driven conceptual modeling. OntoUML is built as a UML extension based on the Unified Foundational Ontology. The foundations of UFO and OntoUML can be traced back to Giancarlo Guizzardi's Ph.D. thesis "Ontological foundations for structural conceptual models". In his work, he proposed a novel foundational ontology for conceptual modeling (UFO) and employed it to evaluate and re-design a fragment of the UML 2.0 metamodel for the purposes of conceptual modeling and domain ontology engineering. == Supporting tools == In 2006, Guizzardi co-founded the Ontology & Conceptual Modeling Research Group (NEMO) located at the Federal University of Espírito Santo (UFES) in Vitória city, state of Espírito Santo, Brazil. Since then, NEMO has been responsible for most of the developments in OntoUML. Several papers about ontologies and OntoUML have been authored by members of the NEMO group.
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Is-a

In knowledge representation, ontology components and ontology engineering, including for object-oriented programming and design, is-a (also written as is_a or is a) is a subsumptive relationship between abstractions (e.g., types, classes), wherein one class A is a subclass of another class B (and so B is a superclass of A). In other words, type A is a subtype of type B when A's specification implies B's specification. That is, any object (or class) that satisfies A's specification also satisfies B's specification, because B's specification is weaker. For example, a cat 'is a[n]' animal, but not vice versa. All cats are animals, but not all animals are cats. Behaviour that is relevant to all animals is defined on an animal class, whereas behaviour that is relevant only for cats is defined in a cat class. By defining the cat class as 'extending' the animal class, all cats 'inherit' the behaviour defined for animals, without the need to explicitly code that behaviour for cats. == Related concepts == The is-a relationship is to be contrasted with the has-a (has_a or has a) relationship between types (classes); confusing the relations has-a and is-a is a common error when designing a model (e.g., a computer program) of the real-world relationship between an object and its subordinate. The is-a relationship may also be contrasted with the instance-of relationship between objects (instances) and types (classes): see Type–token distinction. To summarize the relations, there are: hyperonym–hyponym (supertype/superclass–subtype/subclass) relations between types (classes) defining a taxonomic hierarchy, where for a subsumption relation: a hyponym (subtype, subclass) has a type-of (is-a) relationship with its hyperonym (supertype, superclass); holonym–meronym (whole/entity/container–part/constituent/member) relations between types (classes) defining a possessive hierarchy, where for an aggregation (i.e. without ownership) relation: a holonym (whole) has a has-a relationship with its meronym (part), for a composition (i.e. with ownership) relation: a meronym (constituent) has a part-of relationship with its holonym (entity), for a containment relation: a meronym (member) has a member-of relationship with its holonym (container); concept–object (type–token) relations between types (classes) and objects (instances), where a token (object) has an instance-of relationship with its type (class).
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Retained mode

Retained mode in computer graphics is a major pattern of API design in graphics libraries, in which the graphics library, instead of the client, retains the scene (complete object model of the rendering primitives) to be rendered and the client calls into the graphics library do not directly cause actual rendering, but make use of extensive indirection to resources, managed – thus retained – by the graphics library. It does not preclude the use of double-buffering. Immediate mode is an alternative approach. Historically, retained mode has been the dominant style in GUI libraries; however, both can coexist in the same library and are not necessarily exclusionary in practice. == Overview == In retained mode the client calls do not directly cause actual rendering, but instead update an abstract internal model (typically a list of objects) which is maintained within the library's data space. This allows the library to optimize when actual rendering takes place along with the processing of related objects. Some techniques to optimize rendering include: managing double buffering treatment of hidden surfaces by backface culling/occlusion culling (Z-buffering) only transferring data that has changed from one frame to the next from the application to the library Example of coexistence with immediate mode in the same library is OpenGL. OpenGL has immediate mode functions that can use previously defined server side objects (textures, vertex buffers and index buffers, shaders, etc.) without resending unchanged data. Examples of retained mode rendering systems include Windows Presentation Foundation, SceneKit on macOS, and PHIGS.
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Tag (metadata)

In information systems, a tag is a keyword or term assigned to a piece of information (such as an Internet bookmark, multimedia, database record, or computer file). This kind of metadata helps describe an item and allows it to be found again by browsing or searching. Tags are generally chosen informally and personally by the item's creator or by its viewer, depending on the system, although they may also be chosen from a controlled vocabulary. Tagging was popularized by websites associated with Web 2.0 and is an important feature of many Web 2.0 services. It is now also part of other database systems, desktop applications, and operating systems. == Overview == People use tags to aid classification, mark ownership, note boundaries, and indicate online identity. Tags may take the form of words, images, or other identifying marks. An analogous example of tags in the physical world is museum object tagging. People were using textual keywords to classify information and objects long before computers. Computer based search algorithms made the use of such keywords a rapid way of exploring records. Tagging gained popularity due to the growth of social bookmarking, image sharing, and social networking websites. These sites allow users to create and manage labels (or "tags") that categorize content using simple keywords. Websites that include tags often display collections of tags as tag clouds, as do some desktop applications. On websites that aggregate the tags of all users, an individual user's tags can be useful both to them and to the larger community of the website's users. Tagging systems have sometimes been classified into two kinds: top-down and bottom-up. Top-down taxonomies are created by an authorized group of designers (sometimes in the form of a controlled vocabulary), whereas bottom-up taxonomies (called folksonomies) are created by all users. This definition of "top down" and "bottom up" should not be confused with the distinction between a single hierarchical tree structure (in which there is one correct way to classify each item) versus multiple non-hierarchical sets (in which there are multiple ways to classify an item); the structure of both top-down and bottom-up taxonomies may be either hierarchical, non-hierarchical, or a combination of both. Some researchers and applications have experimented with combining hierarchical and non-hierarchical tagging to aid in information retrieval. Others are combining top-down and bottom-up tagging, including in some large library catalogs (OPACs) such as WorldCat. When tags or other taxonomies have further properties (or semantics) such as relationships and attributes, they constitute an ontology. In folder system a file cannot exist in two or more folders so tag system has been thought more convenient. But transitioning to tag system requires awareness of difference between properties of two systems. In folder system the information of classification is put outside of the file and we can change folder at once. In tag system the information of classification is put inside the file so changing its tag means changing the file and it needs to be saved again and takes time. Metadata tags as described in this article should not be confused with the use of the word "tag" in some software to refer to an automatically generated cross-reference; examples of the latter are tags tables in Emacs and smart tags in Microsoft Office. == History == The use of keywords as part of an identification and classification system long predates computers. Paper data storage devices, notably edge-notched cards, that permitted classification and sorting by multiple criteria were already in use prior to the twentieth century, and faceted classification has been used by libraries since the 1930s. In the late 1970s and early 1980s, Emacs, the text editor for Unix systems, offered a companion software program called Tags that could automatically build a table of cross-references called a tags table that Emacs could use to jump between a function call and that function's definition. This use of the word "tag" did not refer to metadata tags, but was an early use of the word "tag" in software to refer to a word index. Online databases and early websites deployed keyword tags as a way for publishers to help users find content. In the early days of the World Wide Web, the keywords meta element was used by web designers to tell web search engines what the web page was about, but these keywords were only visible in a web page's source code and were not modifiable by users. In 1997, the collaborative portal "A Description of the Equator and Some ØtherLands" produced by documenta X, Germany, used the folksonomic term Tag for its co-authors and guest authors on its Upload page. In "The Equator" the term Tag for user-input was described as an abstract literal or keyword to aid the user. However, users defined singular Tags, and did not share Tags at that point. In 2003, the social bookmarking website Delicious provided a way for its users to add "tags" to their bookmarks (as a way to help find them later); Delicious also provided browseable aggregated views of the bookmarks of all users featuring a particular tag. Within a couple of years, the photo sharing website Flickr allowed its users to add their own text tags to each of their pictures, constructing flexible and easy metadata that made the pictures highly searchable. The success of Flickr and the influence of Delicious popularized the concept, and other social software websites—such as YouTube, Technorati, and Last.fm—also implemented tagging. In 2005, the Atom web syndication standard provided a "category" element for inserting subject categories into web feeds, and in 2007 Tim Bray proposed a "tag" URN. == Examples == === Within a blog === Many systems (and other web content management systems) allow authors to add free-form tags to a post, along with (or instead of) placing the post into a predetermined category. For example, a post may display that it has been tagged with baseball and tickets. Each of those tags is usually a web link leading to an index page listing all of the posts associated with that tag. The blog may have a sidebar listing all the tags in use on that blog, with each tag leading to an index page. To reclassify a post, an author edits its list of tags. All connections between posts are automatically tracked and updated by the blog software; there is no need to relocate the page within a complex hierarchy of categories. === Within application software === Some desktop applications and web applications feature their own tagging systems, such as email tagging in Gmail and Mozilla Thunderbird, bookmark tagging in Firefox, audio tagging in iTunes or Winamp, and photo tagging in various applications. Some of these applications display collections of tags as tag clouds. === Assigned to computer files === There are various systems for applying tags to the files in a computer's file system. In Apple's Mac System 7, released in 1991, users could assign one of seven editable colored labels (with editable names such as "Essential", "Hot", and "In Progress") to each file and folder. In later iterations of the Mac operating system ever since OS X 10.9 was released in 2013, users could assign multiple arbitrary tags as extended file attributes to any file or folder, and before that time the open-source OpenMeta standard provided similar tagging functionality for Mac OS X. Several semantic file systems that implement tags are available for the Linux kernel, including Tagsistant. Microsoft Windows allows users to set tags only on Microsoft Office documents and some kinds of picture files. Cross-platform file tagging standards include Extensible Metadata Platform (XMP), an ISO standard for embedding metadata into popular image, video and document file formats, such as JPEG and PDF, without breaking their readability by applications that do not support XMP. XMP largely supersedes the earlier IPTC Information Interchange Model. Exif is a standard that specifies the image and audio file formats used by digital cameras, including some metadata tags. TagSpaces is an open-source cross-platform application for tagging files; it inserts tags into the filename. === For an event === An official tag is a keyword adopted by events and conferences for participants to use in their web publications, such as blog entries, photos of the event, and presentation slides. Search engines can then index them to make relevant materials related to the event searchable in a uniform way. In this case, the tag is part of a controlled vocabulary. === In research === A researcher may work with a large collection of items (e.g. press quotes, a bibliography, images) in digital form. If he/she wishes to associate each with a small number of themes (e.g. to chapters of a book, or to sub-themes of the overall subject), then a group of tags for these themes can be attached to each of the items in
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Richard S. Sutton

Richard Stuart Sutton (born 1957 or 1958) is a Canadian computer scientist. He is a professor of computing science at the University of Alberta, fellow & Chief Scientific Advisor at the Alberta Machine Intelligence Institute, and a research scientist at Keen Technologies. Sutton is considered one of the founders of modern computational reinforcement learning. In particular, he contributed to temporal difference learning and policy gradient methods. He received the 2024 Turing Award with Andrew Barto. == Education and early life == Richard Sutton was born in either 1957 or 1958 in Toledo, Ohio, and grew up in Oak Brook, Illinois, a suburb of Chicago, United States. Sutton received his Bachelor of Arts (BA) degree in psychology from Stanford University in 1978 before taking a Master of Science (1980) and PhD (1984) in computer science from the University of Massachusetts Amherst supervised by Andrew Barto. His doctoral dissertation introduced actor-critic architectures and temporal credit assignment. He was influenced by Harry Klopf's work in the 1970s, which proposed that supervised learning is insufficient for AI or explaining intelligent behavior, and trial-and-error learning, driven by "hedonic aspects of behavior", is necessary. This focused his interest to reinforcement learning. == Career and research == Sutton held a postdoctoral research position at the University of Massachusetts Amherst in 1984. He worked at GTE Laboratories in Waltham, Massachusetts as principal member of technical staff from 1985 to 1994, then returned to the University of Massachusetts Amherst as a senior research scientist. He joined AT&T Labs Shannon Laboratory in Florham Park, New Jersey as principal technical staff member from 1998 to 2002. He has been a professor of computing science at the University of Alberta since 2003, where he helped establish the Reinforcement Learning and Artificial Intelligence Laboratory. In 2017 he became a distinguished research scientist with Google DeepMind and helped launch DeepMind Alberta in Edmonton, a research office operated in close collaboration with the University of Alberta. 1984: Postdoctoral researcher, University of Massachusetts Amherst (Amherst, Massachusetts) 1985–1994: Principal member of technical staff, Computer and Intelligent Systems Laboratory, GTE Laboratories (Waltham, Massachusetts) 1995–1998: Senior research scientist, University of Massachusetts Amherst (Amherst, Massachusetts) 1998–2002: Principal technical staff member, Artificial Intelligence Department, AT&T Labs Shannon Laboratory (Florham Park, New Jersey) 2003–present: Professor of computing science, University of Alberta (Edmonton, Alberta) 2017–2023: Distinguished research scientist, DeepMind Alberta, Google DeepMind (Edmonton, Alberta) 2024–Present: Research scientist, Keen Technologies === Reinforcement learning === Sutton joined Andrew Barto in the early 1980s at UMass, trying to explore the behavior of neurons in the human brain as the basis for human intelligence, a concept that had been advanced by computer scientist A. Harry Klopf. Sutton and Barto used mathematics toward furthering the concept and using it as the basis for artificial intelligence. This concept became known as reinforcement learning and went on to becoming a key part of artificial intelligence techniques. Barto and Sutton used Markov decision processes (MDP) as the mathematical foundation to explain how agents (algorithmic entities) made decisions when in a stochastic or random environment, receiving rewards at the end of every action. Traditional MDP theory assumed the agents knew all information about the MDPs in their attempt toward maximizing their cumulative rewards. Barto and Sutton's reinforcement learning techniques allowed for both the environment and the rewards to be unknown, and thus allowed for these category of algorithms to be applied to a wide array of problems. Sutton returned to Canada in the 2000s and continued working on the topic which continued to develop in academic circles until one of its first major real world applications saw Google's AlphaGo program built on this concept defeating the then prevailing human champion. Barto and Sutton have widely been credited and accepted as pioneers of modern reinforcement learning, with the technique itself being foundational to the AI boom. In a 2019 essay, Sutton proposed the "bitter lesson", which criticized the field of AI research for failing to learn that "building in how we think we think does not work in the long run", arguing that "70 years of AI research [had shown] that general methods that leverage computation are ultimately the most effective, and by a large margin", beating efforts building on human knowledge about specific fields like computer vision, speech recognition, chess or Go. Sutton argues that large language models aren’t capable of learning on-the-job, and so new model architectures are required to enable continual learning. Sutton further argues that a special training phase will be unnecessary — the agent will learn on-the-fly, rendering large language models obsolete. In 2023, Sutton and John Carmack announced a partnership for the development of artificial general intelligence (AGI). === Awards and honors === Sutton has been a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) since 2001; his nomination read: "For significant contributions to many topics in machine learning, including reinforcement learning, temporal difference techniques, and neural networks." In 2003, he received the President's Award from the International Neural Network Society and in 2013, the Outstanding Achievement in Research award from the University of Massachusetts Amherst. He received the 2024 Turing Award from the Association for Computing Machinery together with Andrew Barto; the citation of the award read: "For developing the conceptual and algorithmic foundations of reinforcement learning." In 2016, Sutton was elected Fellow of the Royal Society of Canada. In 2021, he was elected Fellow of the Royal Society (FRS) of London. === Research === Sutton introduced temporal-difference methods for prediction and control, establishing convergence properties and practical algorithms. He proposed integrated learning and planning through the Dyna architecture. He co-developed the options framework for temporal abstraction in reinforcement learning. He co-authored the first modern policy gradient formulation with function approximation. Sutton's essay The Bitter Lesson argued that general methods that scale with computation dominate domain-specific approaches in the long run. His former doctoral students include David Silver and Doina Precup. === Selected publications === His publications include: == Personal life == Sutton became a Canadian citizen in 2015, and his renunciation of US citizenship was reported in 2017.
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Knowledge space

In mathematical psychology and education theory, a knowledge space is a combinatorial structure used to formulate mathematical models describing the progression of a human learner. Knowledge spaces were introduced in 1985 by Jean-Paul Doignon and Jean-Claude Falmagne, and remain in extensive use in the education theory. Modern applications include two computerized tutoring systems, ALEKS and the defunct RATH. Formally, a knowledge space assumes that a domain of knowledge is a collection of concepts or skills, each of which must be eventually mastered. Not all concepts are interchangeable; some require other concepts as prerequisites. Conversely, competency at one skill may ease the acquisition of another through similarity. A knowledge space marks out which collections of skills are feasible: they can be learned without mastering any other skills. Under reasonable assumptions, the collection of feasible competencies forms the mathematical structure known as an antimatroid. Researchers and educators usually explore the structure of a discipline's knowledge space as a latent class model. == Motivation == Knowledge Space Theory attempts to address shortcomings of standardized testing when used in educational psychometry. Common tests, such as the SAT and ACT, compress a student's knowledge into a very small range of ordinal ranks, in the process effacing the conceptual dependencies between questions. Consequently, the tests cannot distinguish between true understanding and guesses, nor can they identify a student's particular weaknesses, only the general proportion of skills mastered. The goal of knowledge space theory is to provide a language by which exams can communicate What the student can do and What the student is ready to learn. == Model structure == Knowledge Space Theory-based models presume that an educational subject S can be modeled as a finite set Q of concepts, skills, or topics. Each feasible state of knowledge about S is then a subset of Q; the set of all such feasible states is K. The precise term for the information (Q, K) depends on the extent to which K satisfies certain axioms: A knowledge structure assumes that K contains the empty set (a student may know nothing about S) and Q itself (a student may have fully mastered S). A knowledge space is a knowledge structure that is closed under set union: if, for each topic, there is an expert in a class on that topic, then it is possible, with enough time and effort, for each student in the class to become an expert on all those topics simultaneously. A quasi-ordinal knowledge space is a knowledge space that is also closed under set intersection: if student a knows topics A and B; and student c knows topics B and C; then it is possible for another student b to know only topic B. A well-graded knowledge space or learning space is a knowledge space satisfying the following axiom: If S∈K, then there exists x∈S such that S\{x}∈K In educational terms, any feasible body of knowledge can be learned one concept at a time. === Prerequisite partial order === The more contentful axioms associated with quasi-ordinal and well-graded knowledge spaces each imply that the knowledge space forms a well-understood (and heavily studied) mathematical structure: A quasi-ordinal knowledge space can be associated with a distributive lattice under set union and set intersection. The name "quasi-ordinal" arises from Birkhoff's representation theorem, which explains that distributive lattices uniquely correspond to partial orders. A well-graded knowledge space is an antimatroid, a type of mathematical structure that describes certain problems solvable with a greedy algorithm. In either case, the mathematical structure implies that set inclusion defines partial order on K, interpretable as an educational prerequirement: if a(⪯)b in this partial order, then a must be learned before b. === Inner and outer fringe === The prerequisite partial order does not uniquely identify a curriculum; some concepts may lead to a variety of other possible topics. But the covering relation associated with the prerequisite partial does control curricular structure: if students know a before a lesson and b immediately after, then b must cover a in the partial order. In such a circumstance, the new topics covered between a and b constitute the outer fringe of a ("what the student was ready to learn") and the inner fringe of b ("what the student just learned"). == Construction of knowledge spaces == In practice, there exist several methods to construct knowledge spaces. The most frequently used method is querying experts. There exist several querying algorithms that allow one or several experts to construct a knowledge space by answering a sequence of simple questions. Another method is to construct the knowledge space by explorative data analysis (for example by item tree analysis) from data. A third method is to derive the knowledge space from an analysis of the problem solving processes in the corresponding domain.
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Dave's Redistricting

Dave's Redistricting App (DRA) is an online web app originally created by Dave Bradlee that allows anyone to simulate redistricting a U.S. state's congressional and legislative districts. == Purpose == According to Bradlee, the software was designed to "put power in people's hands," and so that they "can see how the process works, so it's a little less mysterious than it was 10 years ago." Bradlee has noticed that many citizens are taking this process seriously and using his app to create legitimate redistricting maps that could be put in place. Some websites have called Bradlee the pioneer and cause of the rise of do-it-yourself redistricting. States such as Montana in 2021 allowed the general population to use it to submit redistricting proposals following the 2020 United States Census. Dave's Redistricting has frequently been mentioned as a resource that can be used to combat gerrymandering, given that the public has free access to it. Political science firms such as FiveThirtyEight have used the website to draw examples of gerrymandered districts, including on their famous Atlas of Redistricting. Dave Bradlee built the first generation of DRA. DRA 2020 is built by a small team of volunteers—Dave Bradlee, Terry Crowley, Alec Ramsay, and David Rinn—all with a shared passion for technology & democracy and all Microsoft veterans. Their mission is to empower civic organizations and citizen activists to advocate for fair congressional and legislative districts and increased transparency in the redistricting process. == Functions == Users can redraw the congressional and state legislative districts for all 50 states, the District of Columbia, and Puerto Rico using a variety of census and election datasets including Cook PVI. Maps can be optimized for different criteria. DRA 2020 added several major features to the first generation app: Sharing & collaborative editing of maps, like Google Docs Multiple statewide elections for all 50 states including the ability to import your own data Comprehensive analytics for evaluating and comparing maps Custom overlays, and Block-level editing DRA remains free to use. == Versions == 2.2: This uses Bing Maps, an outdated software that projects the districts of a single state onto a map of the United States. 2.5: After Bing Maps announced that it would no longer be updating for the foreseen future, the U.S. Map feature was removed. DRA 2020: At the end of 2018, a beta version of 2020 was released. This version that did not require Microsoft Silverlight and could be used in any web browser. DRA 2020 has been under continuous development since and is built using React (JavaScript library), Mapbox, OpenStreetMap, TypeScript, Node.js, Amazon Web Services, as well as many open source components, tools, and icons.
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Colossus (supercomputer)

Colossus is a supercomputer developed by xAI. Construction began in 2024 in Memphis, Tennessee; the system became operational in July 2024. It is currently the world's largest AI supercomputer. Colossus's primary purpose is to train the company's chatbot, Grok. In addition, Colossus provides computing support to the social-media platform X and to other projects of Elon Musk, such as SpaceX. In 2025, it expanded to neighboring Southaven, Mississippi across the Tennessee–Mississippi border. As of May 6, 2026, Anthropic has agreed to rent all compute capacity at the Colossus 1 data center. == Background == Colossus was launched in September 2024 at a former Electrolux site in South Memphis to train the AI language model Grok. Within 19 days of the project's conception, xAI was ready to begin construction. The site was chosen because the abandoned Electrolux building could be repurposed to expedite construction and its proximity to a nearby wastewater treatment facility provided a water source. As of February 2025, xAI plans to build an $80 million facility to process additional wastewater for use at the supercomputer. === xAI === Musk incorporated xAI in March 2023 with the stated purpose of understanding the "nature of the universe". The team includes former members of OpenAI, DeepMind, Microsoft, and Tesla. Musk was one of the founding members of the company OpenAI, investing up to US$45 million in 2015. He left OpenAI in 2018, reportedly to avoid conflicts of interest with Tesla. It has also been reported that he had made a bid for leadership at OpenAI and left when his proposal was rejected. The exact reasons for his departure from the company are unclear. Both Dell Technologies and Supermicro partnered with xAI to build the supercomputer. It was originally powered by 100,000 Nvidia graphics processing units (GPUs) and was constructed in 122 days. 3 months after the first 100,000 GPUs were deployed, xAI announced that they had increased the system to 200,000 GPUs and that they intended to continue increasing the computer's processing power to 1 million GPUs. As of April 2025, xAI claimed Colossus was the largest AI training platform in the world. == Choice of location == xAI selected Memphis, in southwestern Tennessee, as the site for Colossus in part because an existing industrial facility allowed the project to proceed more quickly than constructing a new data center. Elon Musk was initially told that building a data center would take 18–24 months. The company instead searched for a vacant facility and selected the former Electrolux factory in Memphis. Electrolux opened the facility in 2012 and operated it for about eight years before closing it in 2020 after relocating operations to Springfield, Tennessee. The building covered 785,000 sq ft (72,900 m2) and had been purchased by Phoenix Investors in December 2023 for $35 million . Because the structure was already in place, work on the supercomputer could begin immediately rather than waiting for a new facility to be constructed. According to Forbes, xAI considered seven or eight other sites before selecting Memphis, and Musk finalized the decision to build in Memphis in about a week. The decision was finalized in March 2024, after which construction began. xAI publicly announced in June 2024 that Colossus would be built in Memphis. The building itself was not the only reason xAI selected Memphis. According to the Greater Memphis Chamber, the company chose the city because of its "reliable power grid, ability to create a water recycling facility, proximity to the Mississippi River and ample land". The city was also able to provide the large amounts of electricity and water needed to operate the supercomputer. At full capacity, the system was expected to require 150 megawatts of electricity and millions of gallons of water per day. The project also relied on partnerships with local and regional organizations including Memphis Light, Gas and Water (MLGW), Tennessee Valley Authority (TVA), the City of Memphis, and Shelby County. The city also provided financial incentives for the project. == Environmental impact == AI data centers consume large amounts of energy. At the site of Colossus in South Memphis, the grid connection was only 8 MW, so xAI applied to temporarily set up more than a dozen gas turbines (Voltagrid’s 2.5 MW units and Solar Turbines’ 16 MW SMT-130s) which would steadily burn methane gas from a 16-inch natural gas main. Aerial imagery in April 2025 showed 35 gas turbines had been set up at a combined 422 MW. These turbines have been estimated to generate about "72 megawatts, which is approximately 3% of the (TVA) power grid". The higher number of gas turbines and the subsequent emissions requires xAI to have a major source permit. In Memphis, xAI was able to avoid some environmental rules in the construction of Colossus, such as operating without permits for the on-site methane gas turbines because they are "portable". The Shelby County Health Department told NPR that "it only regulates gas-burning generators if they're in the same location for more than 364 days". However, in a January 2026 ruling, the EPA revised its New Source Performance Standard and announced that large methane gas turbines require permits even for temporary operations. In November 2024, the grid connection was upgraded to 150 MW, and some turbines were removed. Along with high electricity needs, the expected water demand is over five million gallons of water per day. While xAI has stated they plan to work with MLGW on a wastewater treatment facility and the installation of 50 megawatts of large battery storage facilities, there are currently no concrete plans in place aside from a one-page factsheet shared by MLGW. == Community response == The plan to build Colossus in Memphis was unknown to residents, City Council members, and environmental agencies. Many did not find out about the project until the day before, or the day of, as they watched the announcement on the local news. Keshaun Pearson, president of Memphis Community Against Pollution, stated that there is a historical lack of transparency and communication surrounding environmental issues in Memphis. Some community members in Memphis have expressed concern about the potential for additional air and water pollution caused by the supercomputer. In a letter to the Shelby County Health Department, the Southern Environmental Law Center stated the emissions from the turbines make the facility "...likely the largest industrial emitter of NOx in Memphis..." This is due to data supplied by the manufacturer showing that "...xAI emits between 1,200 and 2,000 tons of smog-forming nitrogen oxides (NOx)..." At a public Shelby County Commissioner's hearing on April 9, 2025, residents living near the site of Colossus voiced complaints about air quality, noting that they have chronic respiratory issues related to living in a polluted section of Memphis. One woman said she smells "everything but the right thing and the right thing is the clean air." Other residents voiced frustration that Brent Mayo, the senior xAI official responsible for building out xAI's infrastructure, did not attend the meeting to discuss community concerns. Keshaun Pearson also stated that "We're getting more and more days a year where it is unhealthy for us to go outside." People living near the site of Colossus have said they were not offered the opportunity for a public review of the plans, nor were they provided with information on how their community could potentially benefit. The community is also concerned about the strain on the power grid. Memphis's peak demand is around 3 GW. In November 2024, TVA approved xAI's request for access to more than 100 megawatts of power to Colossus which is supplied by MLGW. In December 2022, MLGW imposed (then rescinded) rolling blackouts during several days of extreme cold, straining the power grid. In a letter to the TVA, the SELC "urged the agency to 'prioritize Memphis families' access to reliable power over the 'secondary purpose' of serving xAI". == Current progress == In early December 2024, Ted Townsend detailed how the power of Colossus doubled in its processing capability. When it first went online in September 2024, it was using "100,000 Nvidia H100 processing chips". This initial launch demonstrated Colossus to be the largest supercomputer globally. The maximum power consumption increased from 150 to 250 MW. As of June 2025, the supercomputer consists of 150,000 H100 GPUs, 50,000 H200 GPUs, and 30,000 GB200 GPUs. Another 110,000 GB200 GPUs are to be brought online at a second data center, also in the Memphis area. The expansion of this supercomputer has already been discussed and will be the second phase of the project. xAI also plans to increase Colossus to 1 million GPUs. Because the supercomputer currently utilizes gas turbines for power, alongside 168 Tesla Megapack battery storage units. xAI is also looking to add more
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Conflict resolution strategy

Conflict resolution strategies are used in production systems in artificial intelligence, such as in rule-based expert systems, to help in choosing which production rule to fire. The need for such a strategy arises when the conditions of two or more rules are satisfied by the currently known facts. == Categories == Conflict resolution strategies fall into several main categories. They each have advantages which form their rationales. Specificity - If all of the conditions of two or more rules are satisfied, choose the rule according to how specific its conditions are. It is possible to favor either the more general or the more specific case. The most specific may be identified roughly as the one having the greatest number of preconditions. This usefully catches exceptions and other special cases before firing the more general (default) rules. Recency - When two or more rules could be chosen, favor the one that matches the most recently added facts, as these are most likely to describe the current situation. Not previously used - If a rule's conditions are satisfied, but previously the same rule has been satisfied by the same facts, ignore the rule. This helps to prevent the system from entering infinite loops. Order - Pick the first applicable rule in order of presentation. This is the strategy that Prolog interpreters use by default, but any strategy may be implemented by building suitable rules in a Prolog system. Arbitrary choice - Pick a rule at random. This has the merit of being simple to compute.
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