Quantum machine learning (QML) is the study of quantum algorithms for machine learning. It often refers to quantum algorithms for machine learning tasks which analyze classical data, sometimes called quantum-enhanced machine learning. QML algorithms use qubits and quantum operations to try to improve the space and time complexity of classical machine learning algorithms. Hybrid QML methods involve both classical and quantum processing, where computationally difficult subroutines are outsourced to a quantum device. These routines can be more complex in nature and executed faster on a quantum computer. Furthermore, quantum algorithms can be used to analyze quantum states instead of classical data. The term "quantum machine learning" is sometimes used to refer classical machine learning methods applied to data generated from quantum experiments (i.e. machine learning of quantum systems), such as learning the phase transitions of a quantum system or creating new quantum experiments. QML also extends to a branch of research that explores methodological and structural similarities between certain physical systems and learning systems, in particular neural networks. For example, some mathematical and numerical techniques from quantum physics are applicable to classical deep learning and vice versa. Furthermore, researchers investigate more abstract notions of learning theory with respect to quantum information, sometimes referred to as "quantum learning theory". == Machine learning with quantum computers == Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving and often expediting classical machine learning techniques. Such algorithms typically require one to encode the given classical data set into a quantum computer to make it accessible for quantum information processing. Subsequently, quantum information processing routines are applied and the result of the quantum computation is read out by measuring the quantum system. For example, the outcome of the measurement of a qubit reveals the result of a binary classification task. While many proposals of QML algorithms are still purely theoretical and require a full-scale universal quantum computer to be tested, others have been implemented on small-scale or special purpose quantum devices. === Quantum associative memories and quantum pattern recognition === Early work on quantum associative memories has been done by Dan Ventura and Tony Martinez and by Carlo A. Trugenberger in the late 1990s and early 2000s. Associative (or content-addressable) memories are able to recognize stored content on the basis of a similarity measure, while random access memories are accessed by the address of stored information and not its content. As such they must be able to retrieve both incomplete and corrupted patterns, the essential machine learning task of pattern recognition. Typical classical associative memories store p patterns in the O ( n 2 ) {\displaystyle O(n^{2})} interactions (synapses) of a real, symmetric energy matrix over a network of n artificial neurons. The encoding is such that the desired patterns are local minima of the energy functional and retrieval is done by minimizing the total energy, starting from an initial configuration. Unfortunately, classical associative memories are severely limited by the phenomenon of cross-talk. When too many patterns are stored, spurious memories appear which quickly proliferate, so that the energy landscape becomes disordered and no retrieval is anymore possible. The number of storable patterns is typically limited by a linear function of the number of neurons, p ≤ O ( n ) {\displaystyle p\leq O(n)} . Quantum associative memories (in their simplest realization) store patterns in a unitary matrix U acting on the Hilbert space of n qubits. Retrieval is realized by the unitary evolution of a fixed initial state to a quantum superposition of the desired patterns with probability distribution peaked on the most similar pattern to an input. By its very quantum nature, the retrieval process is thus probabilistic. Because quantum associative memories are free from cross-talk, however, spurious memories are never generated. Correspondingly, they have a superior capacity than classical ones. The number of parameters in the unitary matrix U is O ( p n ) {\displaystyle O(pn)} . One can thus have efficient, spurious-memory-free quantum associative memories for any polynomial number of patterns. If the matrix U is encoded as a unique operator (as opposed as to a sequence of gates as in the circuit model), e.g. by an optical interferometer, the retrieval becomes efficient even for an exponential number of patterns. === Linear algebra simulation with quantum amplitudes === A number of quantum algorithms for machine learning are based on the idea of amplitude encoding, that is, to associate the amplitudes of a quantum state with the inputs and outputs of computations. Since a state of n {\displaystyle n} qubits is described by 2 n {\displaystyle 2^{n}} complex amplitudes, this information encoding can allow for an exponentially compact representation. Intuitively, this corresponds to associating a discrete probability distribution over binary random variables with a classical vector. The goal of algorithms based on amplitude encoding is to formulate quantum algorithms whose resources grow polynomially in the number of qubits n {\displaystyle n} , which amounts to a logarithmic time complexity in the number of amplitudes and thereby the dimension of the input. Many QML algorithms in this category are based on variations of the quantum algorithm for linear systems of equations (colloquially called HHL, after the paper's authors) which, under specific conditions, performs a matrix inversion using an amount of physical resources growing only logarithmically in the dimensions of the matrix. One of these conditions is that a Hamiltonian which entry-wise corresponds to the matrix can be simulated efficiently, which is known to be possible if the matrix is sparse or low rank. For reference, any known classical algorithm for matrix inversion requires a number of operations that grows more than quadratically in the dimension of the matrix (e.g. O ( n 2.373 ) {\displaystyle O{\mathord {\left(n^{2.373}\right)}}} ), but they are not restricted to sparse matrices. Quantum matrix inversion can be applied to machine learning methods in which the training reduces to solving a linear system of equations, for example in least-squares linear regression, the least-squares version of support vector machines, and Gaussian processes. A crucial bottleneck of methods that simulate linear algebra computations with the amplitudes of quantum states is state preparation, which often requires one to initialise a quantum system in a state whose amplitudes reflect the features of the entire dataset. Although efficient methods for state preparation are known for specific cases, this step easily hides the complexity of the task. === Variational quantum algorithms (VQAs) === In a variational quantum algorithm, a classical computer optimizes the parameters used to prepare a quantum state, while a quantum computer is used to do the actual state preparation and measurement. VQAs are considered promising candidates for noisy intermediate-scale quantum computers. Variational quantum circuits (or parameterized quantum circuits) are a popular class of VQAs where the parameters are those used in a fixed quantum circuit. Researchers have studied VQCs to solve optimization problems and find the ground state energy of complex quantum systems, which were difficult to solve using a classical computer. === Quantum binary classifier === Pattern reorganization is one of the important tasks of machine learning, binary classification is one of the tools or algorithms to find patterns. Binary classification is used in supervised learning and in unsupervised learning. In QML, classical bits are converted to qubits and they are mapped to Hilbert space; complex value data are used in a quantum binary classifier to use the advantage of Hilbert space. By exploiting the quantum mechanic properties such as superposition, entanglement, interference the quantum binary classifier produces the accurate result in short period of time. === Quantum machine learning algorithms based on Grover search === Another approach to improving classical machine learning with quantum information processing uses amplitude amplification methods based on Grover's search algorithm, which has been shown to solve unstructured search problems with a quadratic speedup compared to classical algorithms. These quantum routines can be employed for learning algorithms that translate into an unstructured search task, as can be done, for instance, in the case of the k-medians and the k-nearest neighbors algorithms. Other applications include quadratic speedups in the training of perceptrons. An e
Server.com
Server.com is a domain name that was owned by software as a service (SaaS) company Server Corporation. They offered a suite of services from 1996 until 2007. It was the first SaaS site to offer a variety of services and the first to use the term WebApp to describe its services. It was selected as an Incredibly Useful Site by Yahoo! Internet Life magazine. net magazine listed Server.com among the 100 most influential websites of all time. Server.com launched in 1996 offering the first online personal information manager. In 1997, they rolled out the first threaded message board service; the first web based mailing list manager; one of the first online calendar services; and one of the first online form builders. In 2000, Server.com partnered with NBCi and became server.snap.com until 2001. In 2001, Server.com was serving 100 million monthly pageviews. Media Life declared it one of the 20 biggest ad domains on the Web. In 2002, Server.com developed one of the first web-based RSS aggregators. In 2007, all services were moved to YourWebApps.com. The domain name Server.com was sold in 2009 for $770,000.
KL-ONE
KL-ONE (pronounced "kay ell won") is a knowledge representation system in the tradition of semantic networks and frames; that is, it is a frame language. The system is an attempt to overcome semantic indistinctness in semantic network representations and to explicitly represent conceptual information as a structured inheritance network. == Overview == There is a whole family of KL-ONE-like systems. One of the innovations that KL-ONE initiated was the use of a deductive classifier, an automated reasoning engine that can validate a frame ontology and deduce new information about the ontology based on the initial information provided by a domain expert. Frames in KL-ONE are called concepts. These form hierarchies using subsume-relations; in the KL-ONE terminology a super class is said to subsume its subclasses. Multiple inheritance is allowed. Actually a concept is said to be well-formed only if it inherits from more than one other concept. All concepts, except the top concept (usually THING), must have at least one super class. In KL-ONE descriptions are separated into two basic classes of concepts: primitive and defined. Primitives are domain concepts that are not fully defined. This means that given all the properties of a concept, this is not sufficient to classify it. They may also be viewed as incomplete definitions. Using the same view, defined concepts are complete definitions. Given the properties of a concept, these are necessary and sufficient conditions to classify the concept. The slot-concept is called roles and the values of the roles are role-fillers. There are several different types of roles to be used in different situations. The most common and important role type is the generic RoleSet that captures the fact that the role may be filled with more than one filler.
TasteDive
TasteDive (formerly named TasteKid) is an entertainment recommendation engine for films, TV shows, music, video games, books, people, places, and brands. It also has elements of a social media site; it allows users to connect with "tastebuds", people with like minded interests. == History == TasteDive was founded in 2008 as TasteKid by brothers Andrei Oghina and Felix Oghina. In 2019, it was acquired by Qloo headquartered in NYC. "Qloo has built for developers and enterprises what TasteDive has built for individuals". == Description == When a user types in the title of a film or TV show, the site's algorithm provides a list of similar content. It provides recommendations for TV shows to watch based on films liked by the user, and vice versa. It also provides recommendations for music, video games, and books, and includes film and TV trailers and music videos. An account is free and is not required to receive recommendations, but recommendations are more accurate for those with an account. The more a user explores the site, the more the site learns about the user's preferences and the better the results become. The site also has a social media aspect where one can see activity and gain recommendations from other users, how many others in the community like or dislike any recommendation, and how popular their tastes are within the TasteDive community. The main competitors of TasteDive are Taste App, Trakt.tv and Tastoid.
Mind map
A mind map is a diagram used to visually organize information into a hierarchy, showing relationships among pieces of the whole. It is often based on a single concept, drawn as an image in the center of a blank page, to which associated representations of ideas such as images, words and parts of words are added. Major ideas are connected directly to the central concept, and other ideas branch out from those major ideas. Mind maps can also be drawn by hand, either as "notes" during a lecture, meeting or planning session, for example, or as higher quality pictures when more time is available. Mind maps are considered to be a type of spider diagram. == Origin == Although the term "mind map" was first popularized by British popular psychology author and television personality Tony Buzan, the use of diagrams that visually "map" information using branching and radial maps traces back centuries. These pictorial methods record knowledge and model systems, and have a long history in learning, brainstorming, memory, visual thinking, and problem solving by educators, engineers, psychologists, and others. Some of the earliest examples of such graphical records were developed by Porphyry of Tyros, a noted thinker of the 3rd century, as he graphically visualized the concept categories of Aristotle. Philosopher Ramon Llull (1235–1315) also used such techniques. Buzan's specific approach, and the introduction of the term "mind map", started with a 1974 BBC TV series he hosted, called Use Your Head. In this show, and companion book series, Buzan promoted his conception of radial tree, diagramming key words in a colorful, radiant, tree-like structure. == Differences from other visualizations == Concept maps: Mind maps differ from concept maps in that mind maps are based on a radial hierarchy (tree structure) denoting relationships with a central concept, whereas concept maps can be more free-form, based on connections between concepts in more diverse patterns. Also, concept maps typically have text labels on the links between nodes. However, either can be part of a larger personal knowledge base system. Modeling graphs or graphical modeling languages: There is no rigorous right or wrong with mind maps, which rely on the arbitrariness of mnemonic associations to aid people's information organization and memory. In contrast, a modeling graph such as a UML diagram structures elements using a precise standardized iconography to aid the design of systems. == Research == === Effectiveness === Cunningham (2005) conducted a user study in which 80% of the students thought "mindmapping helped them understand concepts and ideas in science". Other studies also report some subjective positive effects of the use of mind maps. Positive opinions on their effectiveness, however, were much more prominent among students of art and design than in students of computer and information technology, with 62.5% vs 34% (respectively) agreeing that they were able to understand concepts better with mind mapping software. Farrand, Hussain, and Hennessy (2002) found that spider diagrams (similar to concept maps) had limited, but significant, impact on memory recall in undergraduate students (a 10% increase over baseline for a 600-word text only) as compared to preferred study methods (a 6% increase over baseline). This improvement was only robust after a week for those in the diagram group and there was a significant decrease in motivation compared to the subjects' preferred methods of note taking. A meta study about concept mapping concluded that concept mapping is more effective than "reading text passages, attending lectures, and participating in class discussions". The same study also concluded that concept mapping is slightly more effective "than other constructive activities such as writing summaries and outlines". However, results were inconsistent, with the authors noting "significant heterogeneity was found in most subsets". In addition, they concluded that low-ability students may benefit more from mind mapping than high-ability students. === Features === Joeran Beel and Stefan Langer conducted a comprehensive analysis of the content of mind maps. They analysed 19,379 mind maps from 11,179 users of the mind mapping applications SciPlore MindMapping (now Docear) and MindMeister. Results include that average users create only a few mind maps (mean=2.7), average mind maps are rather small (31 nodes) with each node containing about three words (median). However, there were exceptions. One user created more than 200 mind maps, the largest mind map consisted of more than 50,000 nodes and the largest node contained ~7,500 words. The study also showed that between different mind mapping applications (Docear vs MindMeister) significant differences exist related to how users create mind maps. === Automatic creation === There have been some attempts to create mind maps automatically. Brucks & Schommer created mind maps automatically from full-text streams. Rothenberger et al. extracted the main story of a text and presented it as mind map. There is also a patent application about automatically creating sub-topics in mind maps. == Tools == Mind-mapping software can be used to organize large amounts of information, combining spatial organization, dynamic hierarchical structuring and node folding.Software packages can extend the concept of mind-mapping by allowing individuals to map more than thoughts and ideas with information on their computers and the Internet, like spreadsheets, documents, Internet sites, images and videos. It has been suggested that mind-mapping can improve learning/study efficiency up to 15% over conventional note-taking. == Gallery == The following dozen examples of mind maps show the range of styles that a mind map may take, from hand-drawn to computer-generated and from mostly text to highly illustrated. Despite their stylistic differences, all of the examples share a tree structure that hierarchically connects sub-topics to a main topic.
Minimum resolvable contrast
Minimum resolvable contrast (MRC) is a subjective measure of a visible spectrum sensor’s or camera's sensitivity and ability to resolve data. A snapshot image of a series of three bar targets of selected spatial frequencies and various contrast coatings captured by the unit under test (UUT) is used to determine the MRC of the UUT, i.e., the visible spectrum camera or sensor. A trained observer selects the smallest target resolvable at each contrast level. Typically, specialized computer software collects the inputted data of the observer and provides a graph of contrast vs. spatial frequency at a given luminance level. A first order polynomial is fitted to the data and an MRC curve of spatial frequency versus contrast is generated.
Predictions of the end of Wikipedia
Various observers have predicted the end of Wikipedia since it rose to prominence, with potential pitfalls from lack of quality-control, artificial intelligence or inconsistencies among contributors. Alternative online encyclopedias have been proposed as replacements for Wikipedia, including WolframAlpha, as well as the both now-defunct Knol (from Google) and Owl (from AOL). A 2013 review raised alarms regarding Wikipedia's shortcomings on hoaxes, on vandalism, an imbalance of material, and inadequate quality control of articles. Earlier critiques lamented the vulgar content and absence of sufficient references in articles. Others suggest that the unwarranted deletion of useful articles from Wikipedia may portend its end, which itself inspired the creation of the now inactive Deletionpedia. Contrary to such predictions, Wikipedia has constantly grown in both size and influence. Recent developments with artificial intelligence in Wikimedia projects have prompted new predictions that AI applications, which consume free and open content, will replace Wikipedia. == Personnel == Wikipedia is crowdsourced by a few million volunteer editors. Of the millions of registered editors, only tens of thousands contribute the majority of its contents, and a few thousand do quality control and maintenance work. As the encyclopedia expanded in the 2010s, the number of active editors did not grow proportionately. Various sources predicted that Wikipedia will eventually have too few editors to be functional and collapse from lack of participation. English Wikipedia has 818 volunteer administrators who perform various functions, including functions similar to those carried out by a forum moderator. Critics have described their actions as harsh, bureaucratic, biased, unfair, or capricious and predicted that the resulting outrage would lead to the site's closure. Various 2012 articles reported that a decline in English Wikipedia's recruitment of new administrators could end Wikipedia. === Decline in editors (2014–2015) === A 2014 trend analysis published in The Economist stated that "The number of editors for the English-language version has fallen by a third in seven years." The attrition rate for active editors in English Wikipedia was described by The Economist as substantially higher than in other (non-English) Wikipedias. It reported that in other languages, the number of "active editors" (those with at least five edits per month) has been relatively constant since 2008: some 42,000 editors, with narrow seasonal variances of about 2,000 editors up or down. In the English Wikipedia, the number of active editors peaked in 2007 at about 50,000 editors, and fell to 30,000 editors in 2014. Given that the trend analysis published in The Economist presented the number of active editors for non-English Wikipedias as remaining relatively constant, sustaining their numbers at approximately 42,000 active editors, the contrast pointed to the effectiveness of Wikipedia in those languages to retain their active editors on a renewable and sustained basis. Though different language versions of Wikipedia have different policies, no comment identified a particular policy difference as potentially making a difference in the rate of editor attrition for English Wikipedia. Editor count showed a slight uptick a year later, and no clear trend after that. In a 2013 article, Tom Simonite of MIT Technology Review said that for several years running, the number of Wikipedia editors had been falling, and cited the bureaucratic structure and rules as a factor. Simonite alleged that some Wikipedians use the labyrinthine rules and guidelines to dominate others and have a vested interest in keeping the status quo. A January 2016 article in Time by Chris Wilson said Wikipedia might lose many editors because a collaboration of occasional editors and smart software will take the lead. Andrew Lih and Andrew Brown both maintain editing Wikipedia with smartphones is difficult and discourages new potential contributors. Lih alleges there is serious disagreement among existing contributors on how to resolve this. In 2015, Lih feared for Wikipedia's long-term future while Brown feared problems with Wikipedia would remain and rival encyclopedias would not replace it. == Viewers and fundraisers == As of 2015, with more viewing by smartphones, there had been a marked decline in persons who viewed Wikipedia from their computers, and according to The Washington Post "[people are] far less likely to donate". At the time, the Wikimedia Foundation reported reserves equivalent to one year's budgeted expenditures. On the other hand, the number of paid staff had ballooned, so those expenses increased. In 2021, Andreas Kolbe, a former co-editor-in-chief of The Signpost, wrote that the Wikimedia Foundation was reaching its 10-year goal of a US$100 million endowment, five years earlier than planned, which may surprise donors and users around the world who regularly see Wikipedia fundraising banners. He also said accounting methods disguise the size of operating surpluses, top managers earn $300,000 – 400,000 a year, and over 40 people work exclusively on fundraising. == Artificial intelligence == Wikipedia faces a decline in human visitors, raising concerns about its long-term sustainability and community participation. The Wikimedia Foundation (WMF), when reporting this decline, attributed this in part to the lack of clicks from users of large language models and search engines that are using content from Wikipedia. Data published in August 2025 showed that after the launch of ChatGPT and the rise of other AI-powered search summaries, some types of articles on Wikipedia — especially those that closely resemble the kind of content ChatGPT produces — experienced a noticeable drop in readership. Overall human pageviews reportedly fell by about 8% between 2024 and 2025, suggesting that AI-overviews and chatbots are increasingly being used in place of direct visits to Wikipedia. According to industry web analytics data, ChatGPT's estimated monthly web traffic surpassed that of Wikipedia since May 2025, as visits to ChatGPT continued to grow while Wikipedia’s total site traffic declined. == Timeline of predictions == On the eve of the 20th anniversary of Wikipedia, associate professor of the Department of Communication Studies at Northeastern University Joseph Reagle conducted a retrospective study of numerous "predictions of the ends of Wikipedia" over two decades, divided into chronological waves: "Early growth (2001–2002)", "Nascent identity (2001–2005)", "Production model (2005–2010)", "Contributor attrition (2009–2017)" and the current period "(2020–)". Each wave brought its distinctive fatal predictions, which never came true; as a result, Reagle concluded Wikipedia was not in danger. Concern grew in 2023 that the ubiquity and proliferation of artificial intelligence (AI) may adversely affect Wikipedia. Rapid improvements and widespread application of AI may render Wikipedia obsolete or reduce its importance. A 2023 study found that AI, when applied to Wikipedia, works most efficiently for error-correction, while Wikipedia still needs to be written by humans.