AI Data Center Financing Surge

AI Data Center Financing Surge — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Example-based machine translation

    Example-based machine translation

    Example-based machine translation (EBMT) is a method of machine translation often characterized by its use of a bilingual corpus with parallel texts as its main knowledge base at run-time. It is essentially a translation by analogy and can be viewed as an implementation of a case-based reasoning approach to machine learning. == Translation by analogy == At the foundation of example-based machine translation is the idea of translation by analogy. When applied to the process of human translation, the idea that translation takes place by analogy is a rejection of the idea that people translate sentences by doing deep linguistic analysis. Instead, it is founded on the belief that people translate by first decomposing a sentence into certain phrases, then by translating these phrases, and finally by properly composing these fragments into one long sentence. Phrasal translations are translated by analogy to previous translations. The principle of translation by analogy is encoded to example-based machine translation through the example translations that are used to train such a system. Other approaches to machine translation, including statistical machine translation, also use bilingual corpora to learn the process of translation. == History == Example-based machine translation was first suggested by Makoto Nagao in 1984. He pointed out that it is especially adapted to translation between two totally different languages, such as English and Japanese. In this case, one sentence can be translated into several well-structured sentences in another language, therefore, it is no use to do the deep linguistic analysis characteristic of rule-based machine translation. == Example == Example-based machine translation systems are trained from bilingual parallel corpora containing sentence pairs like the example shown in the table above. Sentence pairs contain sentences in one language with their translations into another. The particular example shows an example of a minimal pair, meaning that the sentences vary by just one element. These sentences make it simple to learn translations of portions of a sentence. For example, an example-based machine translation system would learn three units of translation from the above example: How much is that X ? corresponds to Ano X wa ikura desu ka. red umbrella corresponds to akai kasa small camera corresponds to chiisai kamera Composing these units can be used to produce novel translations in the future. For example, if we have been trained using some text containing the sentences: President Kennedy was shot dead during the parade. and The convict escaped on July 15th., then we could translate the sentence The convict was shot dead during the parade. by substituting the appropriate parts of the sentences. == Phrasal verbs == Example-based machine translation is best suited for sub-language phenomena like phrasal verbs. Phrasal verbs have highly context-dependent meanings. They are common in English, where they comprise a verb followed by an adverb and/or a preposition, which are called the particle to the verb. Phrasal verbs produce specialized context-specific meanings that may not be derived from the meaning of the constituents. There is almost always an ambiguity during word-to-word translation from source to the target language. As an example, consider the phrasal verb "put on" and its Hindustani translation. It may be used in any of the following ways: Ram put on the lights. (Switched on) (Hindustani translation: Jalana) Ram put on a cap. (Wear) (Hindustani translation: Pahenna)

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  • Maike Osborne

    Maike Osborne

    Maike Osborne (born Michael Osborne, 1982) is an Australian academic and scientist who serves as a professor of machine learning at University of Oxford in the Machine Learning Research Group in the Department of Engineering Science. In 2016 she co-founded Mind Foundry, an artificial intelligence company, along with fellow professor Stephen Roberts. == Education == She has a BEng in Mechanical Engineering and a BSc in both Pure Mathematics and Physics from the University of Western Australia. She has a PhD in Machine Learning from the University of Oxford. == Career == Osborne has contributed to over 100 publications, and her work has received over 24,000 citations with an h-index of 46 according to Google Scholar. and has acted as principal or co-investigator for £10.6M of research funding. Her career has focused in particular on Bayesian approaches to AI and machine learning, named after the famous British statistician Thomas Bayes. Osborne's work has contributed to Probabilistic numerics, with Osborne co-authoring the first textbook on the subject. In 2013, Osborne co-authored a paper alongside Swedish-German economist Carl Benedikt Frey called "The Future of Employment: How Susceptible are Jobs to Computerisation?". The paper has received over 13,000 citations and extensive media coverage. In 2023 Osborne gave oral evidence to the UK House of Commons Science and Technology Committee on the subject of the "Governance of Artificial Intelligence". Her testimony received significant coverage around her warnings of the threat of "rogue AI". == Honors == She is also an Official Fellow of Exeter College, and St Peter's College, Oxford, a Fellow of the ELLIS society, and a Faculty Member of the Oxford-Man Institute of Quantitative Finance. She joined the Oxford Martin School as Lead Researcher on the Oxford Martin Programme on Technology and Employment in 2015. She is a Director of the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems.

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  • Top 10 AI Humanizers Compared (2026)

    Top 10 AI Humanizers Compared (2026)

    Looking for the best AI humanizer? An AI humanizer is software that uses machine learning to help you get more done — it can save you hours every week by automating repetitive work. Most options offer a generous free tier, with paid plans unlocking higher limits, faster processing, and team features. Whether you are a beginner or a pro, the right AI humanizer slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.

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  • Nicolò Cesa-Bianchi

    Nicolò Cesa-Bianchi

    Nicolò Cesa-Bianchi (Italian pronunciation: [nikoˈlɔ tˈtʃɛːza ˈbjaŋki]) is an Italian computer scientist and Professor of Computer Science at the Department of Computer Science of the University of Milan. He is a researcher in the field of machine learning, and co-author of the books "Prediction, Learning, and Games" with Gabor Lugosi and "Regret analysis of stochastic and nonstochastic multi-armed bandit problems" with Sébastien Bubeck == Education and career == Cesa-Bianchi graduated in Computer Science from the University of Milan in 1988 where he received a PhD in Computer Science in 1993 supervised by Alberto Bertoni. During his PhD, he visited UC Santa Cruz where he worked with Manfred Warmuth and David Haussler. He did his postdoctoral studies at Graz University of Technology under the supervision of Wolfgang Maass. == Research == His research contributions focus on the following areas: design and analysis of machine learning algorithms, especially in online machine learning algorithms for multi-armed bandit problems, with applications to recommender systems and online auctions graph analytics, with applications to social networks and bioinformatics == Awards and honors == Cesa-Bianchi received a Google Research Award in 2010, a Xerox University Affairs Committee Award in 2011, a Criteo Faculty Award in 2017, a Google Faculty Award in 2018, and a IBM Academic Award in 2021. Since 2023 he is corresponding member of the Accademia dei Lincei.

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  • Embodied agent

    Embodied agent

    In artificial intelligence, an embodied agent, also sometimes referred to as an interface agent, is an intelligent agent that interacts with the environment through a physical body within that environment. Agents that are represented graphically with a body, for example a human or a cartoon animal, are also called embodied agents, although they have only virtual, not physical, embodiment. A branch of artificial intelligence focuses on empowering such agents to interact autonomously with human beings and the environment. Mobile robots are one example of physically embodied agents; Ananova and Microsoft Agent are examples of graphically embodied agents. Embodied conversational agents are embodied agents (usually with a graphical front-end as opposed to a robotic body) that are capable of engaging in conversation with one another and with humans employing the same verbal and nonverbal means that humans do (such as gesture, facial expression, and so forth). == Embodied conversational agents == Embodied conversational agents are a form of intelligent user interface. Graphically embodied agents aim to unite gesture, facial expression and speech to enable face-to-face communication with users, providing a powerful means of human-computer interaction. == Advantages == Face-to-face communication allows communication protocols that give a much richer communication channel than other means of communicating. It enables pragmatic communication acts such as conversational turn-taking, facial expression of emotions, information structure and emphasis, visualization and iconic gestures, and orientation in a three-dimensional environment. This communication takes place through both verbal and non-verbal channels such as gaze, gesture, spoken intonation and body posture. Research has found that users prefer a non-verbal visual indication of an embodied system's internal state to a verbal indication, demonstrating the value of additional non-verbal communication channels. As well as this, the face-to-face communication involved in interacting with an embodied agent can be conducted alongside another task without distracting the human participants, instead improving the enjoyment of such an interaction. Furthermore, the use of an embodied presentation agent results in improved recall of the presented information. Embodied agents also provide a social dimension to the interaction. Humans willingly ascribe social awareness to computers, and thus interaction with embodied agents follows social conventions, similar to human to human interactions. This social interaction both raises the believably and perceived trustworthiness of agents, and increases the user's engagement with the system. Rickenberg and Reeves found that the presence of an embodied agent on a website increased the level of user trust in that website, but also increased users' anxiety and affected their performance, as if they were being watched by a real human. Another effect of the social aspect of agents is that presentations given by an embodied agent are perceived as being more entertaining and less difficult than similar presentations given without an agent. Research shows that perceived enjoyment, followed by perceived usefulness and ease of use, is the major factor influencing user adoption of embodied agents. A study in January 2004 by Byron Reeves at Stanford demonstrated how digital characters could "enhance online experiences" through explaining how virtual characters essentially add a sense of familiarity to the user experience and make it more approachable. This increase in likability in turn helps make the products better, which benefits both the end users and those creating the product. === Applications === The rich style of communication that characterizes human conversation makes conversational interaction with embodied conversational agents ideal for many non-traditional interaction tasks. A familiar application of graphically embodied agents is computer games; embodied agents are ideal for this setting because the richer communication style makes interacting with the agent enjoyable. Embodied conversational agents have also been used in virtual training environments, portable personal navigation guides, interactive fiction and storytelling systems, interactive online characters and automated presenters and commentators. Major virtual assistants like Siri, Amazon Alexa and Google Assistant do not come with any visual embodied representation, which is believed to limit the sense of human presence by users. The U.S. Department of Defense utilizes a software agent called SGT STAR on U.S. Army-run Web sites and Web applications for site navigation, recruitment and propaganda purposes. Sgt. Star is run by the Army Marketing and Research Group, a division operated directly from The Pentagon. Sgt. Star is based upon the ActiveSentry technology developed by Next IT, a Washington-based information technology services company. Other such bots in the Sgt. Star "family" are utilized by the Federal Bureau of Investigation and the Central Intelligence Agency for intelligence gathering purposes.

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  • SYSTRAN

    SYSTRAN

    SYSTRAN, founded by Dr. Peter Toma in 1968, is one of the oldest machine translation companies. SYSTRAN has done extensive work for the United States Department of Defense and the European Commission. SYSTRAN provided the technology for Yahoo! Babel Fish until May 30, 2012, among others. It was used by Google's language tools until 2007. SYSTRAN is used by the Dashboard Translation widget in macOS. Commercial versions of SYSTRAN can run on Microsoft Windows (including Windows Mobile), Linux, and Solaris. Historically, SYSTRAN systems used rule-based machine translation (RbMT) technology. With the release of SYSTRAN Server 7 in 2010, SYSTRAN implemented a hybrid rule-based/statistical machine translation (SMT) technology which was the first of its kind in the marketplace. As of 2008, the company had 59 employees of whom 26 are computational experts and 15 computational linguists. The number of employees decreased from 70 in 2006 to 59 in 2008. In January 2024, ChapsVision acquired Systran. == History == With its origin in the Georgetown machine translation effort, SYSTRAN was one of the few machine translation systems to survive the major decrease of funding after the ALPAC Report of the mid-1960s. The company was established in La Jolla in California to work on translation of Russian to English text for the United States Air Force during the Cold War. Large numbers of Russian scientific and technical documents were translated using SYSTRAN under the auspices of the USAF Foreign Technology Division (later the National Air and Space Intelligence Center) at Wright-Patterson Air Force Base, Ohio. The quality of the translations, although only approximate, was usually adequate for understanding content. The company headquarters is in Paris, while its U.S. headquarters is in San Diego, CA. During the dot-com boom, the international language industry started a new era, and SYSTRAN entered into agreements with a number of translation integrators, the most successful of these being WorldLingo. In 2016, the Harvard NLP group and SYSTRAN founded OpenNMT, an open source ecosystem for neural machine translation and neural sequence learning. This has enabled machine translation software with learning capabilities, dramatically increasing MT translation quality. The project has since been used in several research and industry applications, and its open source ecosystem is currently maintained by SYSTRAN and Ubiqus. == Business situation == Most of SYSTRAN's revenue comes from a few customers. 57.1% comes from the 10 main customers and the three largest customers account for 10.9%, 8.9%, and 8.9% of its revenues, respectively. Revenues had been declining in the early 2000s: 10.2 million euros in 2004, 10.1 million euros in 2005, 9.3 million euros in 2006, 8.8 million euros in 2007, and 7.6 million euros in 2008, before seeing a rebound in 2009 with 8.6 million euros. == Languages == The following is a list of the languages in which SYSTRAN translate from and to English: Russian into English in 1968 and English into Russian in 1973 for the Apollo–Soyuz project.

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  • Seppo Linnainmaa

    Seppo Linnainmaa

    Seppo Ilmari Linnainmaa (born 28 September 1945) is a Finnish mathematician and computer scientist known for creating the modern version of backpropagation. == Biography == He was born in Pori. He received his MSc in 1970 and introduced a reverse mode of automatic differentiation in his MSc thesis. In 1974 he obtained the first doctorate ever awarded in computer science at the University of Helsinki. In 1976, he became Assistant Professor. From 1984 to 1985 he was Visiting Professor at the University of Maryland, USA. From 1986 to 1989 he was Chairman of the Finnish Artificial Intelligence Society. From 1989 to 2007, he was Research Professor at the VTT Technical Research Centre of Finland. He retired in 2007. == Backpropagation == Explicit, efficient error backpropagation in arbitrary, discrete, possibly sparsely connected, neural networks-like networks was first described in Linnainmaa's 1970 master's thesis, albeit without reference to NNs, when he introduced the reverse mode of automatic differentiation (AD), in order to efficiently compute the derivative of a differentiable composite function that can be represented as a graph, by recursively applying the chain rule to the building blocks of the function. Linnainmaa published it first, following Gerardi Ostrowski who had used it in the context of certain process models in chemical engineering some five years earlier, but didn't publish.

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  • Self-verifying finite automaton

    Self-verifying finite automaton

    In automata theory, a self-verifying finite automaton (SVFA) is a special kind of a nondeterministic finite automaton (NFA) with a symmetric kind of nondeterminism introduced by Hromkovič and Schnitger. Generally, in self-verifying nondeterminism, each computation path is concluded with any of the three possible answers: yes, no, and I do not know. For each input string, no two paths may give contradictory answers, namely both answers yes and no on the same input are not possible. At least one path must give answer yes or no, and if it is yes then the string is considered accepted. SVFA accept the same class of languages as deterministic finite automata (DFA) and NFA but have different state complexity. == Formal definition == An SVFA is represented formally by a 6-tuple, A=(Q, Σ, Δ, q0, Fa, Fr) such that (Q, Σ, Δ, q0, Fa) is an NFA, and Fa, Fr are disjoint subsets of Q. For each word w = a1a2 … an, a computation is a sequence of states r0,r1, …, rn, in Q with the following conditions: r0 = q0 ri+1 ∈ Δ(ri, ai+1), for i = 0, …, n−1. If rn ∈ Fa then the computation is accepting, and if rn ∈ Fr then the computation is rejecting. There is a requirement that for each w there is at least one accepting computation or at least one rejecting computation but not both. == Results == Each DFA is a SVFA, but not vice versa. Jirásková and Pighizzini proved that for every SVFA of n states, there exists an equivalent DFA of g ( n ) = Θ ( 3 n / 3 ) {\displaystyle g(n)=\Theta (3^{n/3})} states. Furthermore, for each positive integer n, there exists an n-state SVFA such that the minimal equivalent DFA has exactly g ( n ) {\displaystyle g(n)} states. Other results on the state complexity of SVFA were obtained by Jirásková and her colleagues.

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  • Glossary of machine vision

    Glossary of machine vision

    The following are common definitions related to the machine vision field. General related fields Machine vision Computer vision Image processing Signal processing == 0-9 == 1394. FireWire is Apple Inc.'s brand name for the IEEE 1394 interface. It is also known as i.Link (Sony's name) or IEEE 1394 (although the 1394 standard also defines a backplane interface). It is a personal computer (and digital audio/digital video) serial bus interface standard, offering high-speed communications and isochronous real-time data services. 1D. One-dimensional. 2D computer graphics. The computer-based generation of digital images—mostly from two-dimensional models (such as 2D geometric models, text, and digital images) and by techniques specific to them. 3D computer graphics. 3D computer graphics are different from 2D computer graphics in that a three-dimensional representation of geometric data is stored in the computer for the purposes of performing calculations and rendering 2D images. Such images may be for later display or for real-time viewing. Despite these differences, 3D computer graphics rely on many of the same algorithms as 2D computer vector graphics in the wire frame model and 2D computer raster graphics in the final rendered display. In computer graphics software, the distinction between 2D and 3D is occasionally blurred; 2D applications may use 3D techniques to achieve effects such as lighting, and primarily 3D may use 2D rendering techniques. 3D scanner. This is a device that analyzes a real-world object or environment to collect data on its shape and possibly color. The collected data can then be used to construct digital, three dimensional models useful for a wide variety of applications. == A == Aberration. Optically, defocus refers to a translation along the optical axis away from the plane or surface of best focus. In general, defocus reduces the sharpness and contrast of the image. What should be sharp, high-contrast edges in a scene become gradual transitions. Algebraic distance or algebraic error. The algebraic distance from a point xi to a curve or surface defined by f ( x , β ) = 0 {\displaystyle f(x,\beta )=0} is the value of f ( x i , β ) {\displaystyle f(x_{i},\beta )} , i.e. the residual in the least squares problem with data point (xi, 0) and model function f. This term is mainly used in computer vision.[1][2] Aperture. In context of photography or machine vision, aperture refers to the diameter of the aperture stop of a photographic lens. The aperture stop can be adjusted to control the amount of light reaching the film or image sensor. aspect ratio (image). The aspect ratio of an image is its displayed width divided by its height (usually expressed as "x:y"). Angular resolution. Describes the resolving power of any image forming device such as an optical or radio telescope, a microscope, a camera, or an eye. Automated optical inspection. == B == Barcode. A barcode (also bar code) is a machine-readable representation of information in a visual format on a surface. Blob discovery. Inspecting an image for discrete blobs of connected pixels (e.g. a black hole in a grey object) as image landmarks. These blobs frequently represent optical targets for machining, robotic capture, or manufacturing failure. Bitmap. A raster graphics image, digital image, or bitmap, is a data file or structure representing a generally rectangular grid of pixels, or points of color, on a computer monitor, paper, or other display device. == C == Camera. A camera is a device used to take pictures, either singly or in sequence. A camera that takes pictures singly is sometimes called a photo camera to distinguish it from a video camera. Camera Link. Camera Link is a serial communication protocol designed for computer vision applications based on the National Semiconductor interface Channel-link. It was designed for the purpose of standardizing scientific and industrial video products including cameras, cables and frame grabbers. The standard is maintained and administered by the Automated Imaging Association, or AIA, the global machine vision industry's trade group. Charge-coupled device. A charge-coupled device (CCD) is a sensor for recording images, consisting of an integrated circuit containing an array of linked, or coupled, capacitors. CCD sensors and cameras tend to be more sensitive, less noisy, and more expensive than CMOS sensors and cameras. CIE 1931 Color Space. In the study of the perception of color, one of the first mathematically defined color spaces was the CIE XYZ color space (also known as CIE 1931 color space), created by the International Commission on Illumination (CIE) in 1931. CMOS. CMOS ("see-moss")stands for complementary metal-oxide semiconductor, is a major class of integrated circuits. CMOS imaging sensors for machine vision are cheaper than CCD sensors but more noisy. CoaXPress. CoaXPress (CXP) is an asymmetric high speed serial communication standard over coaxial cable. CoaXPress combines high speed image data, low speed camera control and power over a single coaxial cable. The standard is maintained by JIIA, the Japan Industrial Imaging Association. Color. The perception of the frequency (or wavelength) of light, and can be compared to how pitch (or a musical note) is the perception of the frequency or wavelength of sound. Color blindness. Also known as color vision deficiency, in humans is the inability to perceive differences between some or all colors that other people can distinguish Color temperature. "White light" is commonly described by its color temperature. A traditional incandescent light source's color temperature is determined by comparing its hue with a theoretical, heated black-body radiator. The lamp's color temperature is the temperature in kelvins at which the heated black-body radiator matches the hue of the lamp. Color vision. CV is the capacity of an organism or machine to distinguish objects based on the wavelengths (or frequencies) of the light they reflect or emit. computer vision. The study and application of methods which allow computers to "understand" image content. Contrast. In visual perception, contrast is the difference in visual properties that makes an object (or its representation in an image) distinguishable from other objects and the background. C-Mount. Standardized adapter for optical lenses on CCD - cameras. C-Mount lenses have a back focal distance 17.5 mm vs. 12.5 mm for "CS-mount" lenses. A C-Mount lens can be used on a CS-Mount camera through the use of a 5 mm extension adapter. C-mount is a 1" diameter, 32 threads per inch mounting thread (1"-32UN-2A.) CS-Mount. Same as C-Mount but the focal point is 5 mm shorter. A CS-Mount lens will not work on a C-Mount camera. CS-mount is a 1" diameter, 32 threads per inch mounting thread. == D == Data matrix. A two dimensional Barcode. Depth of field. In optics, particularly photography and machine vision, the depth of field (DOF) is the distance in front of and behind the subject which appears to be in focus. Depth perception. DP is the visual ability to perceive the world in three dimensions. It is a trait common to many higher animals. Depth perception allows the beholder to accurately gauge the distance to an object. Diaphragm. In optics, a diaphragm is a thin opaque structure with an opening (aperture) at its centre. The role of the diaphragm is to stop the passage of light, except for the light passing through the aperture. == E == Edge detection. ED marks the points in a digital image at which the luminous intensity changes sharply. It also marks the points of luminous intensity changes of an object or spatial-taxon silhouette. Electromagnetic interference. Radio Frequency Interference (RFI) is electromagnetic radiation which is emitted by electrical circuits carrying rapidly changing signals, as a by-product of their normal operation, and which causes unwanted signals (interference or noise) to be induced in other circuits. == F == FireWire. FireWire (also known as i. Link or IEEE 1394) is a personal computer (and digital audio/video) serial bus interface standard, offering high-speed communications. It is often used as an interface for industrial cameras. Fixed-pattern noise. Flat-field correction. Frame grabber. An electronic device that captures individual, digital still frames from an analog video signal or a digital video stream. Fringe Projection Technique. 3D data acquisition technique employing projector displaying fringe pattern on a surface of measured piece, and one or more cameras recording image(s). Field of view. The field of view (FOV) is the part which can be seen by the machine vision system at one moment. The field of view depends from the lens of the system and from the working distance between object and camera. Focus. An image, or image point or region, is said to be in focus if light from object points is converged about as well as possible in the image; conversely, it is out of focus if light is not w

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  • How to Choose an AI Resume Builder

    How to Choose an AI Resume Builder

    Trying to pick the best AI resume builder? An AI resume builder is software that uses machine learning to help you get more done — it scales effortlessly from a single task to thousands. The best picks balance beginner-friendly simplicity with the depth power users need, and they ship updates often. Whether you are a beginner or a pro, the right AI resume builder slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.

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  • Is an AI Photo Editor Worth It in 2026?

    Is an AI Photo Editor Worth It in 2026?

    Shopping for the best AI photo editor? An AI photo editor is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI photo editor slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.

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  • Topic model

    Topic model

    In natural language processing, a topic model is a type of probabilistic, neural, or algebraic model for discovering the abstract topics that occur in a collection of documents. Topic modeling is a frequently used text mining tool for discovering hidden semantic features and structures in a text. The topics produced by topic models are generated through a variety of mathematical frameworks, including probabilistic generative models, matrix factorization methods based on word co-occurrence, and clustering algorithms applied to semantic embeddings. Topic models are commonly used to organize and discover latent features in large collections of unstructured text and other forms of big data. Beyond text mining, topic models have also been used to uncover latent structures in fields such as genetic information, bioinformatics, computer vision, and social networks. == History == An early topic model was described by Papadimitriou, Raghavan, Tamaki and Vempala in 1998. Another one, called probabilistic latent semantic analysis (PLSA), was created by Thomas Hofmann in 1999. Latent Dirichlet allocation (LDA), perhaps the most common topic model currently in use, is a generalization of PLSA. Developed by David Blei, Andrew Ng, and Michael I. Jordan in 2002, LDA introduces sparse Dirichlet prior distributions over document-topic and topic-word distributions, encoding the intuition that documents cover a small number of topics and that topics often use a small number of words. Other topic models are generally extensions on LDA, such as Pachinko allocation, which improves on LDA by modeling correlations between topics in addition to the word correlations which constitute topics. Hierarchical latent tree analysis (HLTA) is an alternative to LDA, which models word co-occurrence using a tree of latent variables and the states of the latent variables, which correspond to soft clusters of documents, are interpreted as topics. == Topic models for context information == Approaches for temporal information include Block and Newman's determination of the temporal dynamics of topics in the Pennsylvania Gazette during 1728–1800. Griffiths & Steyvers used topic modeling on abstracts from the journal PNAS to identify topics that rose or fell in popularity from 1991 to 2001 whereas Lamba & Madhusushan used topic modeling on full-text research articles retrieved from DJLIT journal from 1981 to 2018. In the field of library and information science, Lamba & Madhusudhan applied topic modeling on different Indian resources like journal articles and electronic theses and resources (ETDs). Nelson has been analyzing change in topics over time in the Richmond Times-Dispatch to understand social and political changes and continuities in Richmond during the American Civil War. Yang, Torget and Mihalcea applied topic modeling methods to newspapers from 1829 to 2008. Mimno used topic modelling with 24 journals on classical philology and archaeology spanning 150 years to look at how topics in the journals change over time and how the journals become more different or similar over time. Yin et al. introduced a topic model for geographically distributed documents, where document positions are explained by latent regions which are detected during inference. Chang and Blei included network information between linked documents in the relational topic model, to model the links between websites. The author-topic model by Rosen-Zvi et al. models the topics associated with authors of documents to improve the topic detection for documents with authorship information. HLTA was applied to a collection of recent research papers published at major AI and Machine Learning venues. The resulting model is called The AI Tree. The resulting topics are used to index the papers at aipano.cse.ust.hk to help researchers track research trends and identify papers to read, and help conference organizers and journal editors identify reviewers for submissions. To improve the qualitative aspects and coherency of generated topics, some researchers have explored the efficacy of "coherence scores", or otherwise how computer-extracted clusters (i.e. topics) align with a human benchmark. Coherence scores are metrics for optimising the number of topics to extract from a document corpus. == Algorithms == In practice, researchers attempt to fit appropriate model parameters to the data corpus using one of several heuristics for maximum likelihood fit. A survey by D. Blei describes this suite of algorithms. Several groups of researchers starting with Papadimitriou et al. have attempted to design algorithms with provable guarantees. Assuming that the data were actually generated by the model in question, they try to design algorithms that probably find the model that was used to create the data. Techniques used here include singular value decomposition (SVD) and the method of moments. In 2012 an algorithm based upon non-negative matrix factorization (NMF) was introduced that also generalizes to topic models with correlations among topics. Since 2017, neural networks has been leveraged in topic modeling in order to improve the speed of inference, and leading to further advancements like vONTSS, which allows humans to incorporate domain knowledge via weakly supervised learning. In 2018, a new approach to topic models was proposed based on the stochastic block model. Topic modeling has leveraged LLMs through contextual embedding and fine tuning. == Applications of topic models == === To quantitative biomedicine === Topic models are being used also in other contexts. For examples uses of topic models in biology and bioinformatics research emerged. Recently topic models has been used to extract information from dataset of cancers' genomic samples. In this case topics are biological latent variables to be inferred. === To analysis of music and creativity === Topic models can be used for analysis of continuous signals like music. For instance, they were used to quantify how musical styles change in time, and identify the influence of specific artists on later music creation.

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  • Cloud management

    Cloud management

    Cloud management refers to the administration and oversight of cloud computing products and services. Public clouds are managed by cloud service providers, which operate the underlying infrastructure such as servers, storage, networking, and data center facilities. Users may also opt to manage their public cloud services with a third-party cloud management tool. Users of public cloud services can generally select from three basic cloud provisioning categories: User self-provisioning: Customers purchase cloud services directly from the provider, typically through a web form or console interface. The customer pays on a per-transaction basis. Advanced provisioning: Customers contract in advance a predetermined amount of resources, which are prepared in advance of service. The customer pays a flat fee or a monthly fee. Dynamic provisioning: The provider allocates resources when the customer needs them, then decommissions them when they are no longer needed. The customer is charged on a pay-per-use basis. Managing a private cloud requires software tools to help create a virtualized pool of compute resources, provide a self-service portal for end users and handle security, resource allocation, tracking and billing. Management tools for private clouds tend to be service driven, as opposed to resource driven, because cloud environments are typically highly virtualized and organized in terms of portable workloads. In hybrid cloud environments, compute, network and storage resources must be managed across multiple domains, so a good management strategy should start by defining what needs to be managed, and where and how to do it. Policies to help govern these domains should include configuration and installation of images, access control, and budgeting and reporting. Access control often includes the use of Single sign-on (SSO), in which a user logs in once and gains access to all systems without being prompted to log in again at each of them. == Characteristics of Cloud Management == Cloud management combines software and technologies in a design for managing cloud environments. Software developers have responded to the management challenges of cloud computing with a variety of cloud management platforms and tools. These tools include native tools offered by public cloud providers as well as third-party tools designed to provide consistent functionality across multiple cloud providers. Administrators must balance the competing requirements of efficient consistency across different cloud platforms with access to different native functionality within individual cloud platforms. The growing acceptance of public cloud and increased multicloud usage is driving the need for consistent cross-platform management. Rapid adoption of cloud services is introducing a new set of management challenges for those technical professionals responsible for managing IT systems and services. Cloud-management platforms and tools should have the ability to provide minimum functionality in the following categories. Functionality can be both natively provided or orchestrated via third-party integration. Provisioning and orchestration: create, modify, and delete resources as well as orchestrate workflows and management of workloads Automation: Enable cloud consumption and deployment of app services via infrastructure-as-code and other DevOps concepts Security and compliance: manage role-based access of cloud services and enforce security configurations Service request: collect and fulfill requests from users to access and deploy cloud resources. Monitoring and logging: collect performance and availability metrics as well as automate incident management and log aggregation Inventory and classification: discover and maintain pre-existing brownfield cloud resources plus monitor and manage changes Cost management and optimization: track and rightsize cloud spend and align capacity and performance to actual demand Migration, backup, and DR: enable data protection, disaster recovery, and data mobility via snapshots and/or data replication Organizations may group these criteria into key use cases including Cloud Brokerage, DevOps Automation, Governance, and Day-2 Life Cycle Operations. Enterprises with large-scale cloud implementations may require more robust cloud management tools which include specific characteristics, such as the ability to manage multiple platforms from a single point of reference, or intelligent analytics to automate processes like application lifecycle management. High-end cloud management tools should also have the ability to handle system failures automatically with capabilities such as self-monitoring, an explicit notification mechanism, and include failover and self-healing capabilities. == Multi-Cloud and Hybrid Cloud Management Challenges == Legacy management infrastructures, which are based on the concept of dedicated system relationships and architecture constructs, are not well suited to cloud environments where instances are continually launched and decommissioned. Instead, the dynamic nature of cloud computing requires monitoring and management tools that are adaptable, extensible and customizable. Cloud computing presents a number of management challenges. Companies using public clouds do not have ownership of the equipment hosting the cloud environment, and because the environment is not contained within their own networks, public cloud customers do not have full visibility or control. Users of public cloud services must also integrate with an architecture defined by the cloud provider, using its specific parameters for working with cloud components. Integration includes tying into the cloud APIs for configuring IP addresses, subnets, firewalls and data service functions for storage. Because control of these functions is based on the cloud provider’s infrastructure and services, public cloud users must integrate with the cloud infrastructure management. Capacity management is a challenge for both public and private cloud environments because end users have the ability to deploy applications using self-service portals. Applications of all sizes may appear in the environment, consume an unpredictable amount of resources, then disappear at any time. A possible solution is profiling the applications impact on computational resources. As result, the performance models allow the prediction of how resource utilization changes according to application patterns. Thus, resources can be dynamically scaled to meet the expected demand. This is critical to cloud providers that need to provision resources quickly to meet a growing demand by their applications. Charge-back—or, pricing resource use on a granular basis—is a challenge for both public and private cloud environments. Charge-back is a challenge for public cloud service providers because they must price their services competitively while still creating profit. Users of public cloud services may find charge-back challenging because it is difficult for IT groups to assess actual resource costs on a granular basis due to overlapping resources within an organization that may be paid for by an individual business unit, such as electrical power. For private cloud operators, charge-back is fairly straightforward, but the challenge lies in guessing how to allocate resources as closely as possible to actual resource usage to achieve the greatest operational efficiency. Exceeding budgets can be a risk. Hybrid cloud environments, which combine public and private cloud services, sometimes with traditional infrastructure elements, present their own set of management challenges. These include security concerns if sensitive data lands on public cloud servers, budget concerns around overuse of storage or bandwidth and proliferation of mismanaged images. Managing the information flow in a hybrid cloud environment is also a significant challenge. On-premises clouds must share information with applications hosted off-premises by public cloud providers, and this information may change constantly. Hybrid cloud environments also typically include a complex mix of policies, permissions and limits that must be managed consistently across both public and private clouds. == Cloud Management Platforms (CMP) == CMPs provide a means for a cloud service customer to manage the deployment and operation of applications and associated datasets across multiple cloud service infrastructures, including both on-premises cloud infrastructure and public cloud service provider infrastructure. In other words, CMPs provide management capabilities for hybrid cloud and multi-cloud environments. A cloud management platform (CMP) provides broad cloud management functionality atop both public cloud provider platforms and private cloud platforms. CMPs manage cloud services and resources that are distributed across multiple cloud platforms. The value of CMPs stands in delivering the maximum level of consistency between platforms without comp

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  • Gato (DeepMind)

    Gato (DeepMind)

    Gato is a deep neural network for a range of complex tasks that exhibits multimodality. It can perform tasks such as engaging in a dialogue, playing video games, controlling a robot arm to stack blocks, and more. == Overview == Gato was created by researchers at London-based AI firm DeepMind. It is a transformer, like GPT-3. According to MIT Technology Review, the system "learns multiple different tasks at the same time, which means it can switch between them without having to forget one skill before learning another" whereas "[t]he AI systems of today are called “narrow,” meaning they can only do a specific, restricted set of tasks such as generate text", and according to The Independent, it is a "'generalist agent' that can carry out a huge range of complex tasks, from stacking blocks to writing poetry". It uses supervised learning with 1.2B parameters. The technology has been described as "general purpose" artificial intelligence and a "step toward" artificial general intelligence.

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  • Edward Stabler

    Edward Stabler

    Edward Stabler is a Professor of Linguistics at the University of California, Los Angeles. His primary areas of research are (1) Natural Language Processing (NLP), (2) Parsing and formal language theory, and (3) Philosophy of Logic and Language. He was a member of the faculty at UCLA from 1984 to 2016. His work involves the production of software for minimalist grammars (MGs) and related systems. == Early life and education == Stabler received his Ph.D. from the Department of Linguistics and Philosophy at MIT in 1981. == Recent publications == Edward Stabler (2011) Computational perspectives on minimalism. Revised version in C. Boeckx, ed, Oxford Handbook of Linguistic Minimalism, pp. 617–642. Edward Stabler (2010) A defense of this perspective against the Evans&Levinson critique appears here, with revised version in Lingua 120(12): 2680-2685. Edward Stabler (2010) After GB. Revised version in J. van Benthem & A. ter Meulen, eds, Handbook of Logic and Language, pp. 395–414. Edward Stabler (2010) Recursion in grammar and performance. Presented at the 2009 UMass recursion conference. Edward Stabler (2009) Computational models of language universals. Revised version appears in M. H. Christiansen, C. Collins, and S. Edelman, eds., Language Universals, Oxford: Oxford University Press, pages 200-223. Edward Stabler (2008) Tupled pregroup grammars. Revised version appears in P. Casadio and J. Lambek, eds., Computational Algebraic Approaches to Natural Language, Milan: Polimetrica, pages 23–52. Edward Stabler (2006) Sidewards without copying. Proceedings of the 11th Conference on Formal Grammar, edited by P. Monachesi, G. Penn, G. Satta, and S. Wintner. Stanford: CSLI Publications, 2006, pages 133-146.

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