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Cloud-based quantum computing

Cloud-based quantum computing refers to the remote access of quantum computing resources—such as quantum emulators, simulators, or processors—via the internet. Cloud access enables users to develop, test, and execute quantum algorithms without the need for direct interaction with specialized hardware, facilitating broader participation in quantum software development and experimentation. In 2016, IBM launched the IBM Quantum Experience, one of the first publicly accessible quantum processors connected to the cloud. In early 2017, researchers at Rigetti Computing demonstrated programmable quantum cloud access through their software platform Forest, which included the pyQuil Python library. Since the early-2020s, cloud-based quantum computing has grown significantly, with multiple providers offering access to a variety of quantum hardware modalities, including superconducting qubits, trapped ions, neutral atoms, and photonic systems. Major platforms such as Amazon Braket, Azure Quantum, and qBraid aggregate quantum devices from hardware developers like IonQ, Rigetti Computing, QuEra, Pasqal, Oxford Quantum Circuits, and IBM Quantum. These platforms provide unified interfaces for users to write and execute quantum algorithms across diverse backends, often supporting open-source SDKs such as Qiskit, Cirq, and PennyLane. The proliferation of cloud-based access has played a key role in accelerating quantum education, algorithm research, and early-stage application development by lowering the barrier to experimentation with real quantum hardware. Cloud-based quantum computing has expanded access to quantum hardware and tools beyond traditional research laboratories. These platforms support educational initiatives, algorithm development, and early-stage commercial applications. == Applications == Cloud-based quantum computing is used across education, research, and software development, offering remote access to quantum systems without the need for on-site infrastructure. === Education === Quantum cloud platforms have become valuable tools in education, allowing students and instructors to engage with real quantum processors through user-friendly interfaces. Educators use these platforms to teach foundational concepts in quantum mechanics and quantum computing, as well as to demonstrate and implement quantum algorithms in a classroom or laboratory setting. === Scientific Research === Cloud-based access to quantum hardware has enabled researchers to conduct experiments in quantum information, test quantum algorithms, and compare quantum hardware platforms. Experiments such as testing Bell's theorem or evaluating quantum teleportation protocols have been performed on publicly available quantum processors. === Software Development and Prototyping === Developers use cloud-based platforms to prototype quantum software applications across fields such as optimization, machine learning, and chemistry. These platforms offer SDKs and APIs that integrate classical and quantum workflows, enabling experimentation with quantum algorithms in real-world or simulated environments. === Public Engagement and Games === Quantum cloud tools have also been used to create educational games and interactive applications aimed at increasing public understanding of quantum concepts. These efforts help bridge the gap between theoretical content and intuitive learning. == Existing platforms == qBraid Lab by qBraid is a cloud-based platform for quantum computing. It provides software tools for researchers and developers in quantum, as well as access to quantum hardware. qBraid provides cloud based access to Microsoft Azure Quantum and Amazon Braket devices including IQM, QuEra, Pasqal, Rigetti, IonQ, QIR simulators, Amazon Braket simulators, and the NEC Vector Annealer, as of August 2025. qBraid's base version is free, where unlimited hardware and simulator access is available with the purchase of credits. Quandela Cloud by Quandela is the platform to access first cloud-accessible European photonic quantum computer. The computer is interfaced using the Perceval scripting language, with tutorials and documentation available online for free. Xanadu Quantum Cloud by Xanadu is a platform with cloud-based access to three fully programmable photonic quantum computers. Forest by Rigetti Computing is a tool suite for cloud-based quantum computing. It includes a programming language, development tools and example algorithms. LIQUi> by Microsoft is a software architecture and tool suite for quantum computing. It includes a programming language, example optimization and scheduling algorithms, and quantum simulators. Q#, a quantum programming language by Microsoft on the .NET Framework seen as a successor to LIQUi|>. IBM Quantum Platform by IBM, providing access to quantum hardware as well as HPC simulators. These can be accessed programmatically using the Python-based Qiskit framework, or via graphical interface with the IBM Q Experience GUI. Both are based on the OpenQASM standard for representing quantum operations. There is also a tutorial and online community. Quantum in the Cloud by The University of Bristol, which consists of a quantum simulator and a four qubit optical quantum system. Quantum Playground by Google is an educational resource which features a simulator with a simple interface, and a scripting language and 3D quantum state visualization. Quantum in the Cloud is an experimental quantum cloud platform for access to a four-qubit nuclear magnetic resonance-NMRCloudQ computer, managed by Tsinghua University. Quantum Inspire by Qutech is the first platform in Europe providing cloud-based quantum computing to two hardware chips. Next to a 5-qubit transmon processor, Quantum Inspire is the first platform in the world to provide online access to a fully programmable 2-qubit electron spin quantum processor. Amazon Braket is a cloud-based quantum computing platform hosted by AWS which, as of June 2025, provides access to quantum computers built by IonQ, Rigetti, IQM, and QuEra. Braket also provides a quantum algorithm development environment and simulator. Forge by QC Ware is a cloud-based quantum computing platform that provides access to D-Wave hardware, as well as Google and IBM simulators. The platform offers a 30-day free trial, including one minute of quantum computing time. Quantum-as-a-Service by Scaleway is a cloud-based platform created in 2022 to access to real quantum hardware from IQM Quantum Computers, Alpine Quantum Technologies, Quandela and Pasqal. It also include access to GPU-powered emulators such as Aer, Qsim and Quandela proprietary emulation.
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Rumelhart Prize

The David E. Rumelhart Prize for Contributions to the Theoretical Foundations of Human Cognition was founded in 2001 in honor of the cognitive scientist David Rumelhart to introduce the equivalent of a Nobel Prize for cognitive science. It is awarded annually to "an individual or collaborative team making a significant contemporary contribution to the theoretical foundations of human cognition". The annual award is presented at the Cognitive Science Society meeting, where the recipient gives a lecture and receives a check for $100,000. At the conclusion of the ceremony, the next year's award winner is announced. The award is funded by the Robert J. Glushko and Pamela Samuelson Foundation. The Rumelhart Prize committee is independent of the Cognitive Science Society. However, the society provides a large and interested audience for the awards. == Selection Committee == As of 2022, the selection committee for the prize consisted of: Richard Cooper (chair) Dedre Gentner Robert J. Glushko Tania Lombrozo Steven T. Piantadosi Jesse Snedeker == Recipients ==
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Warframe

Warframe is a free-to-play action role-playing third-person shooter multiplayer online game developed and published by Digital Extremes. First released for Windows in March 2013, it was later ported to PlayStation 4 in November 2013, Xbox One in September 2014, Nintendo Switch in November 2018, PlayStation 5 in November 2020, Xbox Series X/S in April 2021, iOS in February 2024, Android in Canada on February 11, 2026 followed by a global release on February 18, 2026, and was released on Nintendo Switch 2 on March 25, 2026. Support for cross-platform play was released in 2022. Cross-platform save began in December 2023, rolling out in waves to different groups of players before becoming fully available to all players in January 2024. In Warframe, a player controls a member of the Tenno, a caste of ancient warriors who have awoken from centuries of suspended animation far into Earth's future to find themselves at war with different factions in the Origin System. The Tenno use their powered Warframes, along with a variety of weapons and abilities, to complete missions. While many of the game's missions use procedurally generated levels, it also includes large open world areas similar to other massively multiplayer online games, as well as some story-specific missions with fixed level design. The game includes elements of shooting and melee games, parkour, and role-playing to allow players to advance their Tenno with improved gear. The game features both player versus environment and player versus player elements. It is supported by microtransactions, allowing players to purchase in-game items with money, while also offering the option to earn them at no cost through grinding. The concept for Warframe originated in 2000 when Digital Extremes began work on a new game titled Dark Sector. At the time, the company had been successful in supporting other developers and publishers but wanted to develop its own game in-house. Dark Sector suffered several delays and was eventually released in 2008, incorporating some of the initial framework but differing significantly from the original plan. By 2012, in the wake of the success of free-to-play games, the developers took their earlier Dark Sector ideas and art assets and incorporated them into a new project, their self-published Warframe. Initially, the growth of Warframe was slow, hindered by moderate critical reviews and low player counts. However, since its release, the game has experienced significant growth. It is one of Digital Extremes' most successful titles, reaching nearly 50 million registered players by 2019. == Plot == Warframe is set in a far future version of the Solar System, now known as the Origin System. At the start of the game players are given control of members of the Tenno, warriors who have awoken from a millennia-long cryosleep on Earth by the Lotus, who acts as a guide for the player. They join an interplanetary war between the Grineer, a violent war-driven matriarchal race of militarized human clones; the Corpus, a cult-like megacorporation dedicated to profit; the Infested, disfigured victims of the Technocyte virus; the Sentients, a race of self-replicating machines made by a long-dead transhuman race known as the Orokin; and the Corrupted, brainwashed variants of the previous three factions' units defending ancient Orokin towers. All of the factions encountered in the game, including the Tenno, were created by or are splinter groups of the old Orokin Empire, which the Tenno learns was an ancient fallen civilization and former reigning power in the Origin System. Although virtually all of them are long dead by the time of the Tenno's awakening, their lingering presence can still be felt throughout the Origin System. Before their fall, the Orokin had realized the Origin System was becoming dangerously depleted of resources, and their solution to keep their empire alive was to colonize new star systems. The Orokin sent out colony ships through the Void, a trans-dimensional space that enabled fast travel between stellar systems. They had also sent out the Sentients beforehand, to arrive in the Tau system first, and terraform it, so the colonists would arrive to garden worlds, capable of supporting human life. None of these residential ships returned, and those they had loaded with Sentients returned with the Sentients now deciding to wipe out the Orokin, leading to the Old War, the creation of the Tenno, and finally, the collapse of the Empire. In the game's "The Second Dream" quest, which was introduced in December 2015, the player discovers that the Lotus is a Sentient known as Natah, rebelling against the Sentients to protect the Tenno, desiring to have surrogate children after losing her ability to procreate. The Lotus' father, Hunhow, sends a vengeful assassin called the Stalker to Lua (the remains of Earth's Moon), which the Lotus had hidden in the Void, to find its secret. The Lotus dispatches the Tenno there to stop the Stalker, arriving too late as the Stalker unveils the entity that the Lotus had protected: a human child known as the Operator, who is the real Tenno controlling the Warframes through the course of the game. The Operator is one of several Tenno children that survived the passage of the Zariman Ten 0 colony ship through the Void; the adults have all gone mad from its travel. When the ship returned to the Orokin Empire, the children had all been put to sleep for thousands of years, outlasting the fall of the Empire, to be found by the Lotus and becoming the Tenno (Tenno short for the "Ten Zero" of the ship's name). The power of the Void gave these children the power of Transference, an ability that allows them to control Warframes. From this point forward, the player can then engage in missions both as the Warframe and the Operator. Throughout various updates, various quests have been released after the Second Dream that elaborates on the story. "The War Within" quest introduced the Grineer Queens, rulers of the Grineer, and their asteroid-based Kuva Fortress, also giving the Operator the ability to act fully on their own as another playable entity, rather than a single-use attack. Quests afterward would introduce figures such as "The Man In The Wall," a mysterious entity, presumably from the Void, who takes on the visage of whoever sees them, most often as the playable Operator, and Ballas, one of the last living Orokin, assumed to be responsible for creating the Warframes. == Gameplay == Warframe is an online action game that includes elements of shooters, RPG, and stealth games. The player starts with a silent pseudo-protagonist in the form of an anthropomorphous biomechanical combat unit called a 'Warframe', possessing supernatural agility and special abilities, a selection of weapons (primary, secondary, and melee) and a space ship called an 'Orbiter'. The Orbiter is supported by a Cephalon, a type of Artificial Intelligence created from the minds of living people. The Cephalon in the player's Orbiter is named Ordis, and refers to the player as 'Operator'. The player's primary goal from this point is to explore the Origin System. Later in the course of the game, the player unlocks the ability to gain direct control of the Operator, which is the true Tenno protagonist in physical form. The Operator can physically manifest themselves in the environment by projecting out of the Warframe, and disappear by resuming control of it through a telekinetic process called 'Transference'. The Operator also possesses weapons and abilities of their own. After that, the Operator can use Transference to control a larger, purely mechanical combat unit called a 'Necramech', which is the technological precursor to the Warframes. Players can engage in space-bound combat using an auxiliary combat platform called 'Archwing', mounted on a Warframe, which comes with a unique set of abilities. 'Archguns' are heavy weapons designed for Archwings and Necramechs, but can be adapted for Warframe use. Late in 2019, an update to the game allowed players to pilot and manage a spacefaring gunship called the 'Railjack', which is deployed in combat, unlike the Orbiter. Railjack was designed as a co-op experience with up to four people working together, performing different tasks to keep the ship operational while destroying enemy ships and completing objectives. A Railjack-focused update was released in 2021, which brought expanded content and a new skill tree system aimed at making solo play more accessible. Through the Orbiter's console, the player can select any of the missions available to them. To progress through the Solar System, players must complete mission 'nodes' on each planet to reach Junctions, and use these Junctions to travel to other planets. Other missions rotate over time as part of the game's living universe; these can include missions with special rewards and community challenges to allow all players to reap benefits if they are successfully met. High-di
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Overwatch

Overwatch (abbreviated as OW) is a multimedia franchise centered on a series of multiplayer first-person shooter (FPS) video games developed by Blizzard Entertainment. Overwatch was released in 2016. Overwatch 2 was released in 2022 and the original game was taken offline upon its release, though Blizzard renamed it back to Overwatch in 2026. Overwatch features hero-based combat between two teams of players fighting over various objectives, along with other traditional gameplay modes. Released in 2016, Overwatch lacked a traditional story mode. Instead, Blizzard employed a transmedia storytelling strategy to disseminate lore regarding the game's characters, releasing comics and other literary media, as well as animated media that includes short films. The game enjoyed both critical and commercial success, and garnered a devoted following. The fan community around the franchise has produced a large amount of content including art, cosplay, fan fiction, anime-influenced music videos, Internet memes, and pornography. Blizzard helped launch and promote an esports scene surrounding the game, including an annual Overwatch World Cup, Overwatch League a minor league, and the Overwatch Champions Series which borrowed elements found in traditional American sports leagues. == Gameplay == Both games in the Overwatch series are team-based hero shooters. Players select a hero character from a large roster (52 as of Season 2), divided among three class types. These are: Tanks, who have higher health and generally meant to help protect their teammates from damage, but are larger and easier to hit; Damage, who act as the team's offensive leads; and Support, who heal, provide buffs for teammates, or de-buff the opposing team. Each role also features sub-roles with extra passives. These sub-roles include 'Initiator', 'Stalwart', and 'Bruiser' for Tank. 'Specialist', 'Flanker', 'Recon', and 'Sharpshooter' for Damage. 'Medic', 'Tactician', and 'Survivor' for Support. Players are generally free to change to different heroes while inside their spawn room during the course of a match in response to the current tactics employed by other players. As of the development of Overwatch 2, a standard game features one tank player, two damage players and two support players, a change from having two of each class in its predecessor. Players choose their class before the match, and can only pick characters within that class for the duration of the game. There are different styles of game modes, however, that allow players to choose characters from any class throughout the game. Each hero has a skill kit that includes a primary attack, active skills that require a cooldown period before they can be used again, passive skills that remain active at all times, and an Ultimate skill that can only be used once they fill their Ultimate meter either by damaging opponents, mitigating damage, healing teammates or by passively generating it over time. An update in 2025 saw each hero receive a total of four unique abilities known as perks. Each hero has two minor and two major perks; minor perks consist of smaller changes to a hero's kit, while major perks are intended to affect the match more significantly. At the beginning of each match, all heroes are set to level 1 for each player. As the match progresses, players can individually level up their respective heroes, minor perks are unlocked at level 2, and major perks are unlocked at the maximum level 3. When perks become available, players may only select one of each type of perk; a selected perk becomes irreversibly attached to the current hero for the remainder of the match. If a player switches to another hero mid-match, the previously selected hero retains their level and perk progress. Game types of Overwatch are split between standard matches, competitive play, custom games, and arcade modes. Standard matches have matchmaking based loosely on the player's skill level as measured by the game. Competitive mode uses more strict matchmaking based on a player's current rank on the competitive ladder, with their rank increasing or decreasing when they win or lose a game, respectively. Arcade modes do not use matchmaking and are generally more experimental modes compared to standard and competitive modes. Custom games are created via the workshop and can be utilised to make game modes that are very different from the base game. The workshop, is the software in Overwatch which creates the game using either presets and settings or rules and conditions made by code. These game modes can be published directly onto Overwatch’s custom browse tab or shared off platform using a 5 digit alphanumeric code. Standard and competitive game modes are randomly selected at the start of each match, and are objective based, requiring teams to control a fixed objective point for a duration of time, or escort a payload to a target zone before match time expires. These modes include: Assault (introduced in Overwatch): Also known as 2 Capture Points (or 2CP), Assault has the attacking team tasked with capturing two target points in sequence on the map, while the defending team must stop them. Assault-style maps were removed from main gameplay rotation after Overwatch 2 released but available in the game's arcade mode. It is still available in the game's custom game modes. Since Season 2, Assault-style maps are available in Arcade Mode daily routines. Escort (introduced in Overwatch): Also known as "Payload" by the community, The attacking team is tasked with escorting a payload to a certain delivery point before time runs out, while the defending team must stop them. The payload vehicle moves along a fixed track when any player on the attacking team is close to it, increasing in speed if multiple attackers are present, the increase capping at 3, but will stop if a defending player is nearby; should no attacker be near the vehicle, it will start to move backwards along the track. The payload will also heal any attacking players by 10 health per second while they are near the payload. Passing specific checkpoints will extend the match time and prevent the payload from moving backwards from that point. Hybrid (Assault/Escort) (introduced in Overwatch): The attacking team has to capture the payload (as if it were a target point from Assault) and escort it to its destination, while the defending team tries to hold them back. Control (introduced in Overwatch): Each team tries to capture and maintain a common control point until their capture percentage reaches 100%. This game mode is played in a best-of-three format. Control maps are laid out in a symmetric fashion so no team has an intrinsic position advantage. Push (introduced in Overwatch 2's launch): Each team attempts to secure control of a large robot that pushes one of two barriers to the opposing team's side of the map, whilst being escorted by at least one team member, stopping when enemy players are nearby, similar to the payload movement system in Escort. The team that pushes the payload fully to the other side, or furthest into the enemy territory before the time runs out, wins the match. Flashpoint (introduced in Overwatch 2 in 2023): Similar to Control, each team attempts to capture and maintain a common control point until their capture percentage reaches 100%. This game mode takes place on significantly larger maps with five separate control points, which take a shorter amount of time to capture as compared to a standard Control map. A central control point is always activated first; after it is secured by one team, the remaining four are activated in a random order. The first team to secure three control points wins. Clash (introduced in Overwatch 2 in 2024): Clash maps feature symmetrical maps with five control points. Teams initially vie for control of the central point, with the winning team progressing to the next control point, towards the opponent's base. Opponents can push back by winning control points and shifting the next point away from their base. If a team captures the point closest to the opponent's base, they win. Otherwise the match plays out until one team wins control five times. Arcade modes may include variations of the above modes with experimental rules, and can also include modes like Deathmatch and Capture the Flag. Other common arcade modes include: Elimination (introduced in Overwatch in 2016): Two teams face off in a series of rounds, attempting to wipe out the other team; once a player is killed they remain out of the game until the next round, though they can be revived by Mercy's 'Resurrect' ability. If no team has won a round by a certain time, then the winners are decided by the team that can first take a neutral control point. Players cannot change heroes until the next round. Some of these can be played in "lockout" mode, in which the heroes selected by the winning team for a round are "locked" and cannot be selected in future rounds. Total Mayhem (i
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Similarity learning

Similarity learning is an area of supervised machine learning in artificial intelligence. It is closely related to regression and classification, but the goal is to learn a similarity function that measures how similar or related two objects are. It has applications in ranking, in recommendation systems, visual identity tracking, face verification, and speaker verification. == Learning setup == There are four common setups for similarity and metric distance learning. Regression similarity learning In this setup, pairs of objects are given ( x i 1 , x i 2 ) {\displaystyle (x_{i}^{1},x_{i}^{2})} together with a measure of their similarity y i ∈ R {\displaystyle y_{i}\in R} . The goal is to learn a function that approximates f ( x i 1 , x i 2 ) ∼ y i {\displaystyle f(x_{i}^{1},x_{i}^{2})\sim y_{i}} for every new labeled triplet example ( x i 1 , x i 2 , y i ) {\displaystyle (x_{i}^{1},x_{i}^{2},y_{i})} . This is typically achieved by minimizing a regularized loss min W ∑ i l o s s ( w ; x i 1 , x i 2 , y i ) + r e g ( w ) {\displaystyle \min _{W}\sum _{i}loss(w;x_{i}^{1},x_{i}^{2},y_{i})+reg(w)} . Classification similarity learning Given are pairs of similar objects ( x i , x i + ) {\displaystyle (x_{i},x_{i}^{+})} and non similar objects ( x i , x i − ) {\displaystyle (x_{i},x_{i}^{-})} . An equivalent formulation is that every pair ( x i 1 , x i 2 ) {\displaystyle (x_{i}^{1},x_{i}^{2})} is given together with a binary label y i ∈ { 0 , 1 } {\displaystyle y_{i}\in \{0,1\}} that determines if the two objects are similar or not. The goal is again to learn a classifier that can decide if a new pair of objects is similar or not. Ranking similarity learning Given are triplets of objects ( x i , x i + , x i − ) {\displaystyle (x_{i},x_{i}^{+},x_{i}^{-})} whose relative similarity obey a predefined order: x i {\displaystyle x_{i}} is known to be more similar to x i + {\displaystyle x_{i}^{+}} than to x i − {\displaystyle x_{i}^{-}} . The goal is to learn a function f {\displaystyle f} such that for any new triplet of objects ( x , x + , x − ) {\displaystyle (x,x^{+},x^{-})} , it obeys f ( x , x + ) > f ( x , x − ) {\displaystyle f(x,x^{+})>f(x,x^{-})} (contrastive learning). This setup assumes a weaker form of supervision than in regression, because instead of providing an exact measure of similarity, one only has to provide the relative order of similarity. For this reason, ranking-based similarity learning is easier to apply in real large-scale applications. Locality sensitive hashing (LSH) Hashes input items so that similar items map to the same "buckets" in memory with high probability (the number of buckets being much smaller than the universe of possible input items). It is often applied in nearest neighbor search on large-scale high-dimensional data, e.g., image databases, document collections, time-series databases, and genome databases. A common approach for learning similarity is to model the similarity function as a bilinear form. For example, in the case of ranking similarity learning, one aims to learn a matrix W that parametrizes the similarity function f W ( x , z ) = x T W z {\displaystyle f_{W}(x,z)=x^{T}Wz} . When data is abundant, a common approach is to learn a siamese network – a deep network model with parameter sharing. == Metric learning == Similarity learning is closely related to distance metric learning. Metric learning is the task of learning a distance function over objects. A metric or distance function has to obey four axioms: non-negativity, identity of indiscernibles, symmetry and subadditivity (or the triangle inequality). In practice, metric learning algorithms ignore the condition of identity of indiscernibles and learn a pseudo-metric. When the objects x i {\displaystyle x_{i}} are vectors in R d {\displaystyle R^{d}} , then any matrix W {\displaystyle W} in the symmetric positive semi-definite cone S + d {\displaystyle S_{+}^{d}} defines a distance pseudo-metric of the space of x through the form D W ( x 1 , x 2 ) 2 = ( x 1 − x 2 ) ⊤ W ( x 1 − x 2 ) {\displaystyle D_{W}(x_{1},x_{2})^{2}=(x_{1}-x_{2})^{\top }W(x_{1}-x_{2})} . When W {\displaystyle W} is a symmetric positive definite matrix, D W {\displaystyle D_{W}} is a metric. Moreover, as any symmetric positive semi-definite matrix W ∈ S + d {\displaystyle W\in S_{+}^{d}} can be decomposed as W = L ⊤ L {\displaystyle W=L^{\top }L} where L ∈ R e × d {\displaystyle L\in R^{e\times d}} and e ≥ r a n k ( W ) {\displaystyle e\geq rank(W)} , the distance function D W {\displaystyle D_{W}} can be rewritten equivalently D W ( x 1 , x 2 ) 2 = ( x 1 − x 2 ) ⊤ L ⊤ L ( x 1 − x 2 ) = ‖ L ( x 1 − x 2 ) ‖ 2 2 {\displaystyle D_{W}(x_{1},x_{2})^{2}=(x_{1}-x_{2})^{\top }L^{\top }L(x_{1}-x_{2})=\|L(x_{1}-x_{2})\|_{2}^{2}} . The distance D W ( x 1 , x 2 ) 2 = ‖ x 1 ′ − x 2 ′ ‖ 2 2 {\displaystyle D_{W}(x_{1},x_{2})^{2}=\|x_{1}'-x_{2}'\|_{2}^{2}} corresponds to the Euclidean distance between the transformed feature vectors x 1 ′ = L x 1 {\displaystyle x_{1}'=Lx_{1}} and x 2 ′ = L x 2 {\displaystyle x_{2}'=Lx_{2}} . Many formulations for metric learning have been proposed. Some well-known approaches for metric learning include learning from relative comparisons, which is based on the triplet loss, large margin nearest neighbor, and information theoretic metric learning (ITML). In statistics, the covariance matrix of the data is sometimes used to define a distance metric called Mahalanobis distance. == Applications == Similarity learning is used in information retrieval for learning to rank, in face verification or face identification, and in recommendation systems. Also, many machine learning approaches rely on some metric. This includes unsupervised learning such as clustering, which groups together close or similar objects. It also includes supervised approaches like K-nearest neighbor algorithm which rely on labels of nearby objects to decide on the label of a new object. Metric learning has been proposed as a preprocessing step for many of these approaches. == Scalability == Metric and similarity learning scale quadratically with the dimension of the input space, as can easily see when the learned metric has a bilinear form f W ( x , z ) = x T W z {\displaystyle f_{W}(x,z)=x^{T}Wz} . Scaling to higher dimensions can be achieved by enforcing a sparseness structure over the matrix model, as done with HDSL, and with COMET. == Software == metric-learn is a free software Python library which offers efficient implementations of several supervised and weakly-supervised similarity and metric learning algorithms. The API of metric-learn is compatible with scikit-learn. OpenMetricLearning is a Python framework to train and validate the models producing high-quality embeddings. == Further information == For further information on this topic, see the surveys on metric and similarity learning by Bellet et al. and Kulis.
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Batch normalization

In artificial neural networks, batch normalization (also known as batch norm) is a normalization technique used to make training faster and more stable by adjusting the inputs to each layer—re-centering them around zero and re-scaling them to a standard size. It was introduced by Sergey Ioffe and Christian Szegedy in 2015. Experts still debate why batch normalization works so well. It was initially thought to tackle internal covariate shift, a problem where parameter initialization and changes in the distribution of the inputs of each layer affect the learning rate of the network. However, newer research suggests it doesn’t fix this shift but instead smooths the objective function—a mathematical guide the network follows to improve—enhancing performance. In very deep networks, batch normalization can initially cause a severe gradient explosion—where updates to the network grow uncontrollably large—but this is managed with shortcuts called skip connections in residual networks. Another theory is that batch normalization adjusts data by handling its size and path separately, speeding up training. == Internal covariate shift == Each layer in a neural network has inputs that follow a specific distribution, which shifts during training due to two main factors: the random starting values of the network’s settings (parameter initialization) and the natural variation in the input data. This shifting pattern affecting the inputs to the network’s inner layers is called internal covariate shift. While a strict definition isn’t fully agreed upon, experiments show that it involves changes in the means and variances of these inputs during training. Batch normalization was first developed to address internal covariate shift. During training, as the parameters of preceding layers adjust, the distribution of inputs to the current layer changes accordingly, such that the current layer needs to constantly readjust to new distributions. This issue is particularly severe in deep networks, because small changes in shallower hidden layers will be amplified as they propagate within the network, resulting in significant shift in deeper hidden layers. Batch normalization was proposed to reduced these unwanted shifts to speed up training and produce more reliable models. Beyond possibly tackling internal covariate shift, batch normalization offers several additional advantages. It allows the network to use a higher learning rate—a setting that controls how quickly the network learns—without causing problems like vanishing or exploding gradients, where updates become too small or too large. It also appears to have a regularizing effect, improving the network’s ability to generalize to new data, reducing the need for dropout, a technique used to prevent overfitting (when a model learns the training data too well and fails on new data). Additionally, networks using batch normalization are less sensitive to the choice of starting settings or learning rates, making them more robust and adaptable. == Procedures == === Transformation === In a neural network, batch normalization is achieved through a normalization step that fixes the means and variances of each layer's inputs. Ideally, the normalization would be conducted over the entire training set, but to use this step jointly with stochastic optimization methods, it is impractical to use the global information. Thus, normalization is restrained to each mini-batch in the training process. Let us use B to denote a mini-batch of size m of the entire training set. The empirical mean and variance of B could thus be denoted as μ B = 1 m ∑ i = 1 m x i {\displaystyle \mu _{B}={\frac {1}{m}}\sum _{i=1}^{m}x_{i}} and σ B 2 = 1 m ∑ i = 1 m ( x i − μ B ) 2 {\displaystyle \sigma _{B}^{2}={\frac {1}{m}}\sum _{i=1}^{m}(x_{i}-\mu _{B})^{2}} . For a layer of the network with d-dimensional input, x = ( x ( 1 ) , . . . , x ( d ) ) {\displaystyle x=(x^{(1)},...,x^{(d)})} , each dimension of its input is then normalized (i.e. re-centered and re-scaled) separately, x ^ i ( k ) = x i ( k ) − μ B ( k ) ( σ B ( k ) ) 2 + ϵ {\displaystyle {\hat {x}}_{i}^{(k)}={\frac {x_{i}^{(k)}-\mu _{B}^{(k)}}{\sqrt {\left(\sigma _{B}^{(k)}\right)^{2}+\epsilon }}}} , where k ∈ [ 1 , d ] {\displaystyle k\in [1,d]} and i ∈ [ 1 , m ] {\displaystyle i\in [1,m]} ; μ B ( k ) {\displaystyle \mu _{B}^{(k)}} and σ B ( k ) {\displaystyle \sigma _{B}^{(k)}} are the per-dimension mean and standard deviation, respectively. ϵ {\displaystyle \epsilon } is added in the denominator for numerical stability and is an arbitrarily small positive constant. The resulting normalized activation x ^ ( k ) {\displaystyle {\hat {x}}^{(k)}} have zero mean and unit variance, if ϵ {\displaystyle \epsilon } is not taken into account. To restore the representation power of the network, a transformation step then follows as y i ( k ) = γ ( k ) x ^ i ( k ) + β ( k ) {\displaystyle y_{i}^{(k)}=\gamma ^{(k)}{\hat {x}}_{i}^{(k)}+\beta ^{(k)}} , where the parameters γ ( k ) {\displaystyle \gamma ^{(k)}} and β ( k ) {\displaystyle \beta ^{(k)}} are subsequently learned in the optimization process. Formally, the operation that implements batch normalization is a transform B N γ ( k ) , β ( k ) : x 1... m ( k ) → y 1... m ( k ) {\displaystyle BN_{\gamma ^{(k)},\beta ^{(k)}}:x_{1...m}^{(k)}\rightarrow y_{1...m}^{(k)}} called the Batch Normalizing transform. The output of the BN transform y ( k ) = B N γ ( k ) , β ( k ) ( x ( k ) ) {\displaystyle y^{(k)}=BN_{\gamma ^{(k)},\beta ^{(k)}}(x^{(k)})} is then passed to other network layers, while the normalized output x ^ i ( k ) {\displaystyle {\hat {x}}_{i}^{(k)}} remains internal to the current layer. === Backpropagation === The described BN transform is a differentiable operation, and the gradient of the loss l {\displaystyle l} with respect to the different parameters can be computed directly with the chain rule. Specifically, ∂ l ∂ y i ( k ) {\displaystyle {\frac {\partial l}{\partial y_{i}^{(k)}}}} depends on the choice of activation function, and the gradient against other parameters could be expressed as a function of ∂ l ∂ y i ( k ) {\displaystyle {\frac {\partial l}{\partial y_{i}^{(k)}}}} : ∂ l ∂ x ^ i ( k ) = ∂ l ∂ y i ( k ) γ ( k ) {\displaystyle {\frac {\partial l}{\partial {\hat {x}}_{i}^{(k)}}}={\frac {\partial l}{\partial y_{i}^{(k)}}}\gamma ^{(k)}} , ∂ l ∂ γ ( k ) = ∑ i = 1 m ∂ l ∂ y i ( k ) x ^ i ( k ) {\displaystyle {\frac {\partial l}{\partial \gamma ^{(k)}}}=\sum _{i=1}^{m}{\frac {\partial l}{\partial y_{i}^{(k)}}}{\hat {x}}_{i}^{(k)}} , ∂ l ∂ β ( k ) = ∑ i = 1 m ∂ l ∂ y i ( k ) {\displaystyle {\frac {\partial l}{\partial \beta ^{(k)}}}=\sum _{i=1}^{m}{\frac {\partial l}{\partial y_{i}^{(k)}}}} , ∂ l ∂ σ B ( k ) 2 = ∑ i = 1 m ∂ l ∂ y i ( k ) ( x i ( k ) − μ B ( k ) ) ( − γ ( k ) 2 ( σ B ( k ) 2 + ϵ ) − 3 / 2 ) {\displaystyle {\frac {\partial l}{\partial \sigma _{B}^{(k)^{2}}}}=\sum _{i=1}^{m}{\frac {\partial l}{\partial y_{i}^{(k)}}}(x_{i}^{(k)}-\mu _{B}^{(k)})\left(-{\frac {\gamma ^{(k)}}{2}}(\sigma _{B}^{(k)^{2}}+\epsilon )^{-3/2}\right)} , ∂ l ∂ μ B ( k ) = ∑ i = 1 m ∂ l ∂ y i ( k ) − γ ( k ) σ B ( k ) 2 + ϵ + ∂ l ∂ σ B ( k ) 2 1 m ∑ i = 1 m ( − 2 ) ⋅ ( x i ( k ) − μ B ( k ) ) {\displaystyle {\frac {\partial l}{\partial \mu _{B}^{(k)}}}=\sum _{i=1}^{m}{\frac {\partial l}{\partial y_{i}^{(k)}}}{\frac {-\gamma ^{(k)}}{\sqrt {\sigma _{B}^{(k)^{2}}+\epsilon }}}+{\frac {\partial l}{\partial \sigma _{B}^{(k)^{2}}}}{\frac {1}{m}}\sum _{i=1}^{m}(-2)\cdot (x_{i}^{(k)}-\mu _{B}^{(k)})} , and ∂ l ∂ x i ( k ) = ∂ l ∂ x ^ i ( k ) 1 σ B ( k ) 2 + ϵ + ∂ l ∂ σ B ( k ) 2 2 ( x i ( k ) − μ B ( k ) ) m + ∂ l ∂ μ B ( k ) 1 m {\displaystyle {\frac {\partial l}{\partial x_{i}^{(k)}}}={\frac {\partial l}{\partial {\hat {x}}_{i}^{(k)}}}{\frac {1}{\sqrt {\sigma _{B}^{(k)^{2}}+\epsilon }}}+{\frac {\partial l}{\partial \sigma _{B}^{(k)^{2}}}}{\frac {2(x_{i}^{(k)}-\mu _{B}^{(k)})}{m}}+{\frac {\partial l}{\partial \mu _{B}^{(k)}}}{\frac {1}{m}}} . === Inference === During the training stage, the normalization steps depend on the mini-batches to ensure efficient and reliable training. However, in the inference stage, this dependence is not useful any more. Instead, the normalization step in this stage is computed with the population statistics such that the output could depend on the input in a deterministic manner. The population mean, E [ x ( k ) ] {\displaystyle E[x^{(k)}]} , and variance, Var ⁡ [ x ( k ) ] {\displaystyle \operatorname {Var} [x^{(k)}]} , are computed as: E [ x ( k ) ] = E B [ μ B ( k ) ] {\displaystyle E[x^{(k)}]=E_{B}[\mu _{B}^{(k)}]} , and Var ⁡ [ x ( k ) ] = m m − 1 E B [ ( σ B ( k ) ) 2 ] {\displaystyle \operatorname {Var} [x^{(k)}]={\frac {m}{m-1}}E_{B}[\left(\sigma _{B}^{(k)}\right)^{2}]} . The population statistics thus is a complete representation of the mini-batches. The BN transform in the inference step thus becomes y ( k ) = B N γ ( k ) , β ( k ) inf ( x ( k ) ) = γ ( k ) x ( k ) − E [ x ( k ) ] Var ⁡ [ x ( k ) ] + ϵ + β
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Fuzzy differential inclusion

Fuzzy differential inclusion is the extension of differential inclusion to fuzzy sets introduced by Lotfi A. Zadeh. x ′ ( t ) ∈ [ f ( t , x ( t ) ) ] α {\displaystyle x'(t)\in [f(t,x(t))]^{\alpha }} with x ( 0 ) ∈ [ x 0 ] α {\displaystyle x(0)\in [x_{0}]^{\alpha }} Suppose f ( t , x ( t ) ) {\displaystyle f(t,x(t))} is a fuzzy valued continuous function on Euclidean space. Then it is the collection of all normal, upper semi-continuous, convex, compactly supported fuzzy subsets of R n {\displaystyle \mathbb {R} ^{n}} . == Second order differential == The second order differential is x ″ ( t ) ∈ [ k x ] α {\displaystyle x''(t)\in [kx]^{\alpha }} where k ∈ [ K ] α {\displaystyle k\in [K]^{\alpha }} , K {\displaystyle K} is trapezoidal fuzzy number ( − 1 , − 1 / 2 , 0 , 1 / 2 ) {\displaystyle (-1,-1/2,0,1/2)} , and x 0 {\displaystyle x_{0}} is a trianglular fuzzy number (-1,0,1). == Applications == Fuzzy differential inclusion (FDI) has applications in Cybernetics Artificial intelligence, Neural network, Medical imaging Robotics Atmospheric dispersion modeling Weather forecasting Cyclone Pattern recognition Population biology
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Big Mechanism

Big Mechanism is a $45 million DARPA research program, begun in 2014, aimed at developing software that will read cancer research papers, integrate them into a cancer model and frame new hypotheses by the end of 2017 through the automated collection of big data and integrating across various disciplines such as knowledge-based NLP, curation and ontology, systems and mathematical biology by reading research abstracts and papers to extract pieces of causal mechanisms. == Ras gene == The program focuses on mutations in the Ras gene family, which underlie some one-third of human cancers. Currently, a rough road map shows interaction sequences among proteins affecting cell replication and death. However, the causal relations are poorly understood. == Plan == The program is to occur in three stages. The first is to read literature and convert it into formal representations. Second is to integrate the knowledge into computational models. Third is to produce experimentally testable explanations and predictions. Research teams are developing four separate systems targeting all three tasks. In February 2015, an evaluation meeting reviewed progress on the first stage. Multiple tasks were considered. One was extraction of experimental procedure details and evaluating statements such as "we demonstrate" and "we suggest." Another worked to map sentence meaning and relationships. The best machine-reading system extracted 40% of relevant information from a small corpus and correctly determined how each passage related to the model. The second stage is to become active in summer 2015, when members attempt to produce a single reference model. The third stage is the most challenging, because the artificial intelligence community has had limited success at developing hypothesis generators. Molecular biology may be more amenable, because most domain knowledge is technical and available in written form.
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Normal distributions transform

The normal distributions transform (NDT) is a point cloud registration algorithm introduced by Peter Biber and Wolfgang Straßer in 2003, while working at University of Tübingen. The algorithm registers two point clouds by first associating a piecewise normal distribution to the first point cloud, that gives the probability of sampling a point belonging to the cloud at a given spatial coordinate, and then finding a transform that maps the second point cloud to the first by maximising the likelihood of the second point cloud on such distribution as a function of the transform parameters. Originally introduced for 2D point cloud map matching in simultaneous localization and mapping (SLAM) and relative position tracking, the algorithm was extended to 3D point clouds and has wide applications in computer vision and robotics. NDT is very fast and accurate, making it suitable for application to large scale data, but it is also sensitive to initialisation, requiring a sufficiently accurate initial guess, and for this reason it is typically used in a coarse-to-fine alignment strategy. == Formulation == The NDT function associated to a point cloud is constructed by partitioning the space in regular cells. For each cell, it is possible to define the mean q = 1 n ∑ i x i {\displaystyle \textstyle \mathbf {q} ={\frac {1}{n}}\sum _{i}\mathbf {x_{i}} } and covariance S = 1 n ∑ i ( x i − q ) ( x i − q ) ⊤ {\displaystyle \textstyle \mathbf {S} ={\frac {1}{n}}\sum _{i}\left(\mathbf {x} _{i}-\mathbf {q} \right)\left(\mathbf {x} _{i}-\mathbf {q} \right)^{\top }} of the n {\displaystyle n} points of the cloud x 1 , … , x n {\displaystyle \mathbf {x} _{1},\dots ,\mathbf {x} _{n}} that fall within the cell. The probability density of sampling a point at a given spatial location x {\displaystyle \mathbf {x} } within the cell is then given by the normal distribution e − 1 2 ( x − q ) ⊤ S − 1 ( x − q ) {\displaystyle e^{-{\frac {1}{2}}\left(\mathbf {x} -\mathbf {q} \right)^{\top }\mathbf {S} ^{-1}\left(\mathbf {x} -\mathbf {q} \right)}} . Two point clouds can be mapped by a Euclidean transformation f {\displaystyle f} with rotation matrix R {\displaystyle \mathbf {R} } and translation vector t {\displaystyle \mathbf {t} } f R , t ( x ) = R x + t {\displaystyle f_{\mathbf {R} ,\mathbf {t} }(\mathbf {x} )=\mathbf {R} \mathbf {x} +\mathbf {t} } that maps from the second cloud to the first, parametrised by the rotation angles and translation components. The algorithm registers the two point clouds by optimising the parameters of the transformation that maps the second cloud to the first, with respect to a loss function based on the NDT of the first point cloud, solving the following problem arg ⁡ min R , t { − ∑ i NDT ⁡ ( f R , t ( x i ) ) } {\displaystyle \arg \min _{\mathbf {R} ,\mathbf {t} }\left\{-\sum _{i}\operatorname {NDT} \left(f_{\mathbf {R} ,\mathbf {t} }\left(\mathbf {x_{i}} \right)\right)\right\}} where the loss function represents the negated likelihood, obtained by applying the transformation to all points in the second cloud and summing the value of the NDT at each transformed point f R , t ( x ) {\displaystyle f_{\mathbf {R} ,\mathbf {t} }(\mathbf {x} )} . The loss is piecewise continuous and differentiable, and can be optimised with gradient-based methods (in the original formulation, the authors use Newton's method). In order to reduce the effect of cell discretisation, a technique consists of partitioning the space into multiple overlapping grids, shifted by half cell size along the spatial directions, and computing the likelihood at a given location as the sum of the NDTs induced by each grid.
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Fuzzy associative matrix

A fuzzy associative matrix expresses fuzzy logic rules in tabular form. These rules usually take two variables as input, mapping cleanly to a two-dimensional matrix, although theoretically a matrix of any number of dimensions is possible. From the perspective of neuro-fuzzy systems, the mathematical matrix is called a "Fuzzy associative memory" because it stores the weights of the perceptron. == Applications == In the context of game AI programming, a fuzzy associative matrix helps to develop the rules for non-player characters. Suppose a professional is tasked with writing fuzzy logic rules for a video game monster. In the game being built, entities have two variables: hit points (HP) and firepower (FP): This translates to: IF MonsterHP IS VeryLowHP AND MonsterFP IS VeryWeakFP THEN Retreat IF MonsterHP IS LowHP AND MonsterFP IS VeryWeakFP THEN Retreat IF MonsterHP IS MediumHP AND MonsterFP is VeryWeakFP THEN Defend Multiple rules can fire at once, and often will, because the distinction between "very low" and "low" is fuzzy. If it is more "very low" than it is low, then the "very low" rule will generate a stronger response. The program will evaluate all the rules that fire and use an appropriate defuzzification method to generate its actual response. An implementation of this system might use either the matrix or the explicit IF/THEN form. The matrix makes it easy to visualize the system, but it also makes it impossible to add a third variable just for one rule, so it is less flexible. == Identify a rule set == There is no inherent pattern in the matrix. It appears as if the rules were just made up, and indeed they were. This is both a strength and a weakness of fuzzy logic in general. It is often impractical or impossible to find an exact set of rules or formulae for dealing with a specific situation. For a sufficiently complex game, a mathematician would not be able to study the system and figure out a mathematically accurate set of rules. However, this weakness is intrinsic to the realities of the situation, not of fuzzy logic itself. The strength of the system is that even if one of the rules is wrong, even greatly wrong, other rules that are correct are likely to fire as well and they may compensate for the error. This does not mean a fuzzy system should be sloppy. Depending on the system, it might get away with being sloppy, but it will underperform. While the rules are fairly arbitrary, they should be chosen carefully. If possible, an expert should decide on the rules, and the sets and rules should be tested vigorously and refined as needed. In this way, a fuzzy system is like an expert system. (Fuzzy logic is used in many true expert systems, as well.)
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Document AI

Document AI, also known as Document Intelligence, refers to a field of technology that employs machine learning (ML) techniques, such as natural language processing (NLP). These techniques are used to develop computer models capable of analyzing documents in a manner akin to human review. Through NLP, computer systems are able to understand relationships and contextual nuances in document contents, which facilitates the extraction of information and insights. Additionally, this technology enables the categorization and organization of the documents themselves. The applications of Document AI extend to processing and parsing a variety of semi-structured documents, such as forms, tables, receipts, invoices, tax forms, contracts, loan agreements, and financial reports. == Key features == Machine learning is utilized in Document AI to extract information from both printed and digital documents. This technology recognizes images, text, and characters in various languages, aiding in the extraction of insights from unstructured documents. The use of this technology can improve the speed and quality of decision-making in document analysis. Additionally, the automation of data extraction and validation can contribute to increased efficiency in document analysis processes. Since the early 2020s, the integration of large language models has extended Document AI beyond extraction toward generative tasks, including the automated drafting of forms, contracts, and document summaries. == Example == A business letter contains information in the form of text, as well as other types of information, such as the position of the text. For instance, a typical letter contains two addresses before the body of the text. The address at the very top (sometimes aligned to the right) is the sender address. This is normally followed by the date of the letter, with the place of writing. After this, the receiver address is listed. The distinction between the sender address and the receiver address is conveyed solely by the position of the address on the page, i.e. there is no textual indication like Sender: in front of the addresses. == Data dimensions and ML architecture == Data is typically distinguished into spatial data and time-series data, the former includes things like images, maps and graphs, while the latter includes signals such as stock prices or voice recordings. Document AI combines text data, which has a time dimension, with other types of data, such as the position of an address in a business letter, which is spatial. Historically in machine learning spatial data was analyzed using a convolutional neural network, and temporal data using a recurrent neural network. With the advent of dimension-type agnostic transformer architecture, these two different types of dimension can be more easily combined, Document AI is an example of this. == Benchmarks == Several public datasets are used to evaluate Document AI systems. FUNSD (Form Understanding in Noisy Scanned Documents) contains 199 annotated forms with token- and block-level labels for form understanding tasks. CORD (Consolidated Receipt Dataset) supports key information extraction from receipts. DocVQA contains approximately 50,000 questions over 12,000 document images for layout-aware visual question answering. == Common uses == Document AI systems are used to automate document processing and information extraction in business and financial workflows, including invoice and receipt processing, data entry automation, anomaly detection, mortgage processing, loan portfolio monitoring, credit risk management, and fraud detection such as counterfeit currency and fraudulent checks. They are also applied in regulatory compliance and contract analysis, including assessing changes in legal and regulatory documents. In real estate, Document AI supports document classification and structured information extraction for standardized processing and analytics. With the adoption of generative AI, Document AI systems can also generate and pre-fill structured documents such as contracts or business forms from natural language prompts.
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Model Context Protocol

The Model Context Protocol (MCP) is an open standard and open-source framework introduced by Anthropic in November 2024 to standardize the way artificial intelligence (AI) systems like large language models (LLMs) integrate and share data with external tools, systems, and data sources. MCP provides a standardized interface for reading files, executing functions, and handling contextual prompts. Following its announcement, the protocol was adopted by major AI providers, including OpenAI and Google DeepMind. == Background == MCP was announced by Anthropic in November 2024 as an open standard for connecting AI assistants to data systems such as content repositories, business management tools, and development environments. The protocol was created at Anthropic by engineers David Soria Parra and Justin Spahr-Summers. It aims to address the challenge of information silos and legacy systems. Before MCP, developers often had to build custom connectors for each data source or tool, resulting in what Anthropic described as an "N×M" data integration problem. Earlier stop-gap approaches—such as OpenAI's 2023 "function-calling" API and the ChatGPT plug-in framework—solved similar problems but required vendor-specific connectors. MCP re-uses the message-flow ideas of the Language Server Protocol (LSP) and is transported over JSON-RPC 2.0. In December 2025, Anthropic donated the MCP to the Agentic AI Foundation (AAIF), a directed fund under the Linux Foundation, co-founded by Anthropic, Block and OpenAI, with support from other companies. == Features == The protocol was released with software development kits (SDKs) in programming languages including Python, TypeScript, C# and Java. Anthropic maintains an open-source repository of reference MCP server implementations and SDKs. MCP defines a standardized framework for integrating AI systems with external data sources and tools. It includes specifications for data ingestion and transformation, contextual metadata tagging, and AI interoperability across different platforms. The protocol also supports bidirectional connections between data sources and AI tools. MCP enables applications such as querying structured databases with plain language in the field of natural language data access. The protocol is used in AI-assisted software development tools. Integrated development environments (IDEs), coding platforms such as Replit, and code intelligence tools like Sourcegraph have adopted MCP to grant AI coding assistants real-time access to project context. MCP Apps is an official extension to the Model Context Protocol built on mcp-ui. While the base MCP specification is restricted to text and structured data, MCP Apps standardizes the delivery of interactive user interfaces—such as dashboards, forms, and data visualizations—from MCP servers to host applications like Claude and ChatGPT. == Adoption == In March 2025, OpenAI officially adopted the MCP, after having integrated the standard across its products, including the ChatGPT desktop app. In September 2025, OpenAI added support for MCP to ChatGPT apps. This allows for third-party access inside ChatGPT. MCP can be integrated with Microsoft Semantic Kernel, and Azure OpenAI. MCP servers can be deployed to Cloudflare. In April 2026, the AAIF held the MCP Dev Summit North America in New York City, drawing approximately 1,200 attendees. == Reception == The Verge reported that MCP addresses a growing demand for AI agents that are contextually aware and capable of pulling from diverse sources. In April 2025, security researchers released an analysis that concluded there are multiple outstanding security issues with MCP, including prompt injection, tool permissions that allow for combining tools to exfiltrate data, and lookalike tools that can silently replace trusted ones. MCP has been likened to OpenAPI, a similar specification that aims to describe APIs.
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Situated

In artificial intelligence and cognitive science, the term situated refers to an agent which is embedded in an environment. The term situated is commonly used to refer to robots, but some researchers argue that software agents can also be situated if: they exist in a dynamic (rapidly changing) environment, which they can manipulate or change through their actions, and which they can sense or perceive. Examples might include web-based agents, which can alter data or trigger processes (such as purchases) over the internet, or virtual-reality bots which inhabit and change virtual worlds, such as Second Life. Being situated is generally considered to be part of being embodied, but it is useful to consider each perspective individually. The situated perspective emphasizes that intelligent behaviour derives from the environment and the agent's interactions with it. The nature of these interactions are defined by an agent's embodiment.
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Wonder.land

Wonder.land (stylised as wonder.land) is a musical with music by Damon Albarn and lyrics and book by Moira Buffini. Inspired by Lewis Carroll's novels Alice's Adventures in Wonderland (1865) and Through the Looking-Glass (1871), it had its world premiere at the Palace Theatre in Manchester in July 2015 as part of the Manchester International Festival. The musical moved to London's Royal National Theatre in November 2015 before opening at the Théâtre du Châtelet in Paris in 2016. Licencing for potential future smaller scale productions is held by United Agents UK. == Background == The musical is inspired by the novels Alice in Wonderland and Through the Looking-Glass, written by Lewis Carroll. It was announced on 21 January 2015 that the show would premiere in July of that year as part of the Manchester International Festival, with tickets going on sale the following day. The musical, a co-production by the Manchester International Festival, the Royal National Theatre and the Théâtre du Châtelet in Paris, marks the 150th anniversary of the publication of Alice's Adventures in Wonderland. The idea for a musical based on Alice in Wonderland came from Manchester International Festival artistic director Alex Poots. Damon Albarn had collaborated with the festival on Monkey: Journey to the West and Dr Dee. The musical has a book by Moira Buffini. It was directed by Rufus Norris, with set design by Rae Smith, costume design by Katrina Lindsay, lighting design by Paule Constable, projections by 59 Productions and choreography by Javier De Frutos. The musical's score was composed by Damon Albarn, with lyrics by Moira Buffini, sound design by Paul Arditti and musical direction by David Shrubsole. == Production history == The musical began previews at the Palace Theatre in Manchester on 29 June 2015. It opened on 2 July for a limited run until 12 July. A revised version moved to the Royal National Theatre, where it ran at the Olivier Theatre from 27 November 2015 to 30 April 2016. The production had a limited run, from 7 to 16 June 2016, at the Theatre Du Chatelet in Paris. == Synopsis == This synopsis is based on the final version, as seen at the National Theatre and the Théâtre du Châtelet. Earlier performances significantly differed in songs and plot. === Act 1 === AI, the MC, explains that virtual technology is "a portal to boundless lands" ("Prologue"). Aly's mother, Bianca, is exasperated with her for spending the weekend indoors on her phone. Aly accompanies Bianca to the supermarket, and thinks that her life is being ruined by her parents due to dysfunctional problems ("Who's Ruining Your Life?") Her alcoholic father, Matt, is also at the supermarket; he and Bianca argue about their divorce and his gambling. Aly goes home and picks up her phone. She tries to engage with schoolmates, who bully her ("Network"). Aly begins to wish that she is someone else. She finds the virtual online game Wonder.land. In its strange world, Aly creates an avatar: beautiful, kind Alice ("Wonder.land"). Wonder.land has one rule: malice causes deletion from the game. Aly and Alice become friends and encounter the Cheshire Cat, who explains that you can be anyone you want ("Fabulous"). Aly decides to go on a quest; Alice follows the white rabbit down a hole, falling past unusual objects and musical notes ("Falling"). The next morning, Aly is too distracted by Wonder.land to listen to Bianca's complaints about her baby brother Charlie. She plays the game at school before her phone is confiscated by stern headmistress Ms Manxome, who tells her students that taking pleasures from them is for their own good ("I'm Right"). Aly goes to Ms Manxome's office to retrieve her phone. Ms Manxome returns it, warning that if she catches her with it again, "it's a beheading – I mean, detention." Aly sees the girls who bullied her, and they bully her again until a teacher arrives. Aly's friend, Luke, is late and is given detention. Aly goes on her phone and takes out her frustration and sadness on Alice, whose tears form a pool until she is interrupted by the quarrelsome twins Dum and Dee ("Freaks"). Alice tries to befriend them, but they insult her and Aly makes her fight them. Dum and Dee cry, and Aly and Alice see a large mouse who is attracted by Alice's fighting. They are joined by the Dodo, the Mock Turtle and Humpty, who all have problems. The Dodo is stressed because his parents want him to save the planet; Dum and Dee are dancers who hate pressure; Humpty has problems with her parents; the Mock Turtle lacks self-esteem, and the mouse is lustful. Wonderland is a hiding place from teenage life ("Crap Life"). Aly returns to reality when asked a math question she cannot answer. Confronting the three bullies, Aly mocks the facial hair of one and hides in the bathroom. She again immerses herself in Wonder.land, where Alice meets a Caterpillar who is obsessed with identity ("Who are You?"). Aly is interrupted by the girls, who ridicule her father's gambling addiction and poverty before beating her up. Aly seeks understanding from Alice, who tries to get Aly to tell her what is wrong. Aly tells Alice about her family and how she hates her life, and is surprised that Alice has similar problems ("Secrets"). Luke comes into the girls' bathroom because Kieran has threatened him with violence, and hides in a cubicle when Kieran enters. Aly defends Luke, and makes Kieran leave. Luke reveals that the reason Kieran hates him is because, like himself, he is gay. Aly is amazed, and they skip class and play games on their phones. Luke plays Zombie Swarm, and Aly plays Wonder.land. Ms Manxome enters the bathroom; Luke hides his phone, but Aly does not. Ms Manxome confiscates the phone for three months, and Aly and Luke leave. Ms Manxome finds that Aly did not lock her phone, and Alice is calling her. Ms Manxome begins to talk to her, and Alice thinks she is talking to Aly. Aly complains to Luke about her phone being taken away. Matt then takes them out for tea to celebrate his new job at the local garden centre ("In Clover"). At the tea shop, Matt maniacally dances on the tables and plays with spoons; asked to stop, he punches a waiter. Bianca arrives, and they argue again. Aly begins to notice that Wonder.land is invading reality; the MC emerges from a gigantic teapot, and the landscape outside becomes surreal ("Chances"). === Act 2 === Ms Manxome manipulates Alice around Wonder.land on Aly's phone, buys many things, and makes Alice's hair red ("Entre Act"). She tells Alice about her plans to dominate and destroy the online world, and Alice thinks she is talking to Aly ("Me"). Aly, Matt, Bianca, and Charlie are at the police station. PC Rook unsuccessfully tries to get Matt to make a statement (since he is charged with assault and affray), but Matt and Bianca argue again. Aly laments the loss of her family's unity ("Heartless Useless"). In Wonder.land, Ms Manxome is hostile when she meets Dum and Dee, the Mock Turtle, the Dodo, Humpty and the Mouse. She makes Alice chase them away, but Alice and Ms Manxome are driven away by Alice's friends, who are worried about the change in her ("Me (Reprise)"). Bianca learns that Aly missed a detention and had her phone confiscated. Concerned that she is losing Aly to technology, she bans her from the internet ("Gadget"). Charlie vomits, and Aly is left to clean it up. She looks for an internet cafe to go to Wonder.land, the only place she is truly happy ("Everyone Loves Charlie"). At the cafe, Aly cannot log into Wonder.land and her avatar seems to be in use. She sees Alice receive a Vorpal sword, bought by Ms Manxome with the money on Aly's phone. Alice is no longer Alice but the Red Queen, and Ms Manxome tells her to kill her friends. Alice, knowing the person controlling her is not Aly, cannot rebel; she lashes out at her friends, bullying and trying to hurt them. The MC warns that Alice has a deletion warning – any more malice, and she will be deleted. Aly now knows that Ms Manxome controls her phone and avatar ("O Children"). Aly enlists Luke to help and decides to break into Ms. Manxome's office to retrieve the phone. Luke agrees to meet her at the school gates. Matt and Bianca wonder if they should reconcile ("Man of Broken Glass"). At the school, Luke is reluctant to get involved; Aly decides to break into the office anyway. Luke contacts the girls who bullied Aly and tells them about Ms Manxome playing on Aly's stolen phone. They decide to spread the word that it is not Aly ("Fabulous (Reprise)"). Bianca goes to the police because Aly is missing, and gives her phone to Matt. Aly is likely to also be in Wonder.land. The avatars prepare for war against Alice but disagree about a strategy. At the police station, Matt hacks into Wonder.land sees Alice, and realizes that she is controlled by someone other than Aly. The White Rabbit appears (delighting Alice), but Ms Manxome makes Alice push him aside. The borderline between Wonder.land and
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Orange (software)

Orange is an open-source data visualization, machine learning and data mining toolkit. It features a visual programming front-end for exploratory qualitative data analysis and interactive data visualization. == Description == Orange is a component-based visual programming software package for data visualization, machine learning, data mining, and data analysis. Orange components are called widgets. They range from simple data visualization, subset selection, and preprocessing to empirical evaluation of learning algorithms and predictive modeling. Visual programming is implemented through an interface in which workflows are created by linking predefined or user-designed widgets, while advanced users can use Orange as a Python library for data manipulation and widget alteration. == Software == Orange is an open-source software package released under GPL and hosted on GitHub. Versions up to 3.0 include core components in C++ with wrappers in Python. From version 3.0 onwards, Orange uses common Python open-source libraries for scientific computing, such as numpy, scipy and scikit-learn, while its graphical user interface operates within the cross-platform Qt framework. The default installation includes a number of machine learning, preprocessing and data visualization algorithms in 6 widget sets (data, transform, visualize, model, evaluate and unsupervised). Additional functionalities are available as add-ons (text-mining, image analytics, bioinformatics, etc.). Orange is supported on macOS, Windows and Linux and can also be installed from the Python Package Index repository (pip install Orange3). == Features == Orange consists of a canvas interface onto which the user places widgets and creates a data analysis workflow. Widgets offer basic functionalities such as reading the data, showing a data table, selecting features, training predictors, comparing learning algorithms, visualizing data elements, etc. The user can interactively explore visualizations or feed the selected subset into other widgets. Canvas: graphical front-end for data analysis Widgets: Data: widgets for data input, data filtering, sampling, imputation, feature manipulation and feature selection Visualize: widgets for common visualization (box plot, histograms, scatter plot) and multivariate visualization (mosaic display, sieve diagram). Classify: a set of supervised machine learning algorithms for classification Regression: a set of supervised machine learning algorithms for regression Evaluate: cross-validation, sampling-based procedures, reliability estimation and scoring of prediction methods Unsupervised: unsupervised learning algorithms for clustering (k-means, hierarchical clustering) and data projection techniques (multidimensional scaling, principal component analysis, correspondence analysis). == Add-ons == Orange users can extend their core set of components with components in the add-ons. Supported add-ons include: Associate: components for mining frequent itemsets and association rule learning. Bioinformatics: components for gene expression analysis, enrichment, and access to expression databases (e.g., Gene Expression Omnibus) and pathway libraries. Data fusion: components for fusing different data sets, collective matrix factorization, and exploration of latent factors. Educational: components for teaching machine learning concepts, such as k-means clustering, polynomial regression, stochastic gradient descent, ... Explain: provides an extension with components for the model explanation, including Shapley value analysis Geo: components for working with geospatial data. Image analytics: components for working with images and ImageNet embeddings Network: components for graph and network analysis. Text mining: components for natural language processing and text mining. Time series: widget components for time series analysis and modeling. Single-cell: support for single-cell gene expression analysis, including components for loading single-cell data, filtering and batch effect removal, marker genes discovery, scoring of cells and genes, and cell type prediction. Spectroscopy: components for analyzing and visualization of (hyper)spectral datasets. Survival analysis: add-on for data analysis dealing with survival data. It includes widgets for standard survival analysis techniques, such as the Kaplan-Meier plot, the Cox regression model, and several derivative widgets. World Happiness: support for downloading socioeconomic data from a database, including OECD and World Development Indicators. Provides access to thousands of country indicators from various economic databases. Fairness: add-on for evaluation and creation of fair machine learning models without discrimination. Widgets range from computing fairness metrics like statistical parity to post-, pre-, in-processing methods to build fair models. == Objectives == The program provides a platform for experiment selection, recommendation systems, and predictive modelling and is used in biomedicine, bioinformatics, genomic research, and teaching. In science, it is used as a platform for testing new machine learning algorithms and for implementing new techniques in genetics and bioinformatics. In education, it was used for teaching machine learning and data mining methods to students of biology, biomedicine, and informatics. == Extensions == Various projects build on Orange either by extending the core components with add-ons or using only the Orange Canvas to exploit the implemented visual programming features and GUI. OASYS — ORange SYnchrotron Suite scOrange — single cell biostatistics Quasar — data analysis in natural sciences == History == In 1996, the University of Ljubljana and Jožef Stefan Institute started development of ML, a machine learning framework in C++, and Python bindings were developed for this framework in 1997, which, together with emerging Python modules, formed a joint framework called Orange. Over the following years, most contemporary major algorithms for data mining and machine learning were implemented in C++ (Orange's core) or Python modules. In 2002, first prototypes to create a flexible graphical user interface were designed using Pmw Python megawidgets. In 2003, the graphical user interface was redesigned and re-developed for Qt framework using PyQt Python bindings. The visual programming framework was defined, and the development of widgets (graphical components of the data analysis pipeline) began. In 2005, extensions for data analysis in bioinformatics was created. In 2008, Mac OS X DMG and Fink-based installation packages were developed. In 2009, over 100 widgets were created and maintained. In 2009, Orange 2.0 beta was released, offering installation packages on the website based on the daily compiling cycle. In 2012, a new object hierarchy was imposed, replacing the old module-based structure. In 2013, a significant redesign of the graphical user interface included a new toolbox and depiction of workflows. In 2015, Orange 3.0 was released. Orange stores the data in NumPy arrays; machine learning algorithms mostly use scikit-learn. In 2015, a text analysis add-on for Orange3 was released. In 2016, Orange released version 3.3. Development scheduled a monthly cycle for stable releases. In 2016, Orange began development and release of an Image Analytics add-on, with server-side deep neural networks for image embedding In 2017, a Spectroscopy add-on for the analysis of spectral data was introduced. In 2017, Geo, an add-on for dealing with geo-location data and visualisation of geo maps was introduced In 2018, Orange began development and release of an add-on for single-cell data analysis. In 2019, Orange separated its graphical interface for development as a separate project, orange-canvas-core In 2020, Orange introduced the Explain add-on with widgets for explaining classification models and regression models, highlighting the strength and contributions specific features make towards predicting a specific class. In 2022, World Happiness, an add-on for the Orange3 data mining suite, was introduced, providing widgets for accessing socioeconomic data from various databases such as World Happiness Report, World Development Indicators, OECD. In 2022, Orange extended the Explain add-on with an Individual Conditional Expectation plot and the Permutation Feature Importance technique. In 2023, Orange introduced the Fairness add-on, including widgets to calculate bias metrics, as well as widgets for pre-, post-, and in-processing methods, allowing the creation of models less susceptible to systematic error due to the vagaries of the data set.
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