AI Assistant Intellij

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

Winner-take-all in action selection

Winner-take-all is a computer science concept that has been widely applied in behavior-based robotics as a method of action selection for intelligent agents. Winner-take-all systems work by connecting modules (task-designated areas) in such a way that when one action is performed it stops all other actions from being performed, so only one action is occurring at a time. The name comes from the idea that the "winner" action takes all of the motor system's power. == History == In the 1980s and 1990s, many roboticists and cognitive scientists were attempting to find speedier and more efficient alternatives to the traditional world modeling method of action selection. In 1982, Jerome A. Feldman and D.H. Ballard published the "Connectionist Models and Their Properties", referencing and explaining winner-take-all as a method of action selection. Feldman's architecture functioned on the simple rule that in a network of interconnected action modules, each module will set its own output to zero if it reads a higher input than its own in any other module. In 1986, Rodney Brooks introduced behavior-based artificial intelligence. Winner-take-all architectures for action selection soon became a common feature of behavior-based robots, because selection occurred at the level of the action modules (bottom-up) rather than at a separate cognitive level (top-down), producing a tight coupling of stimulus and reaction. == Types of winner-take-all architectures == === Hierarchy === In the hierarchical architecture, actions or behaviors are programmed in a high-to-low priority list, with inhibitory connections between all the action modules. The agent performs low-priority behaviors until a higher-priority behavior is stimulated, at which point the higher behavior inhibits all other behaviors and takes over the motor system completely. Prioritized behaviors are usually key to the immediate survival of the agent, while behaviors of lower priority are less time-sensitive. For example, "run away from predator" would be ranked above "sleep." While this architecture allows for clear programming of goals, many roboticists have moved away from the hierarchy because of its inflexibility. === Heterarchy and fully distributed === In the heterarchy and fully distributed architecture, each behavior has a set of pre-conditions to be met before it can be performed, and a set of post-conditions that will be true after the action has been performed. These pre- and post-conditions determine the order in which behaviors must be performed and are used to causally connect action modules. This enables each module to receive input from other modules as well as from the sensors, so modules can recruit each other. For example, if the agent's goal were to reduce thirst, the behavior "drink" would require the pre-condition of having water available, so the module would activate the module in charge of "find water". The activations organize the behaviors into a sequence, even though only one action is performed at a time. The distribution of larger behaviors across modules makes this system flexible and robust to noise. Some critics of this model hold that any existing set of division rules for the predecessor and conflictor connections between modules produce sub-par action selection. In addition, the feedback loop used in the model can in some circumstances lead to improper action selection. === Arbiter and centrally coordinated === In the arbiter and centrally coordinated architecture, the action modules are not connected to each other but to a central arbiter. When behaviors are triggered, they begin "voting" by sending signals to the arbiter, and the behavior with the highest number of votes is selected. In these systems, bias is created through the "voting weight", or how often a module is allowed to vote. Some arbiter systems take a different spin on this type of winner-take-all by using a "compromise" feature in the arbiter. Each module is able to vote for or against each smaller action in a set of actions, and the arbiter selects the action with the most votes, meaning that it benefits the most behavior modules. This can be seen as violating the general rule against creating representations of the world in behavior-based AI, established by Brooks. By performing command fusion, the system is creating a larger composite pool of knowledge than is obtained from the sensors alone, forming a composite inner representation of the environment. Defenders of these systems argue that forbidding world-modeling puts unnecessary constraints on behavior-based robotics, and that agents benefits from forming representations and can still remain reactive.
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Maia and Marco

Maia and Marco are artificial intelligence used by GMA Network. Unveiled in 2023, they are used to fulfill the role of sports newscasters. == Background == Maia and Marco are artificial intelligence (AI) which take the form of three-dimensional human avatars. Maia makes use of a female avatar while Marco uses a male likeness. They have aesthetic features that are typical to Filipino showbusiness personalities. Among the technologies used in making and operating the AI include image generation, text-to-speech AI voice synthesis/generation, and deep learning face animation. They are also demonstrated to be bilingual, being able to speak in English and Tagalog (Filipino). == Use == The AI pair was unveiled by GMA Network on September 24, 2023, for their coverage of Season 99 of the National Collegiate Athletic Association (NCAA). Fulfilling the role of sports newscasters, Maia and Marco would join GMA's courtside human reporters. The AI pair are scheduled to appear four times a month on GMA's digital media platforms. They will not appear in traditional television broadcast. == Reception == The launch of the Maia and Marco was met with strong reactions. Various journalists and other personalities across the Philippine media industry expressed concern that their employment be at risk with the introduction of AI. The quality of the AI ability to emulate human behavior was characterized by critics as "soulless". GMA responding to concerns has stated that the AI would complement rather than replace its live human journalists including sportscasters. The National Union of Journalists of the Philippines urged dialogue among its peers in the newsroom on policy on how to use AI, which the group acknowledge as "inevitable".
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CatBoost

CatBoost is an open-source software library developed by Yandex. It provides a gradient boosting framework which, among other features, attempts to solve for categorical features using a permutation-driven alternative to the classical algorithm. It works on Linux, Windows, macOS, and is available in Python, R, and models built using CatBoost can be used for predictions in C++, Java, C#, Rust, Core ML, ONNX, and PMML. The source code is licensed under Apache License and available on GitHub. InfoWorld magazine awarded the library "The best machine learning tools" in 2017. along with TensorFlow, Pytorch, XGBoost and 8 other libraries. Kaggle listed CatBoost as one of the most frequently used machine learning (ML) frameworks in the world. It was listed as the top-8 most frequently used ML framework in the 2020 survey and as the top-7 most frequently used ML framework in the 2021 survey. As of April 2022, CatBoost is installed about 100000 times per day from PyPI repository == Features == CatBoost has gained popularity compared to other gradient boosting algorithms primarily due to the following features Native handling for categorical features Fast GPU training Visualizations and tools for model and feature analysis Using oblivious trees or symmetric trees for faster execution Ordered boosting to overcome overfitting == History == In 2009 Andrey Gulin developed MatrixNet, a proprietary gradient boosting library that was used in Yandex to rank search results. Since 2009 MatrixNet has been used in different projects at Yandex, including recommendation systems and weather prediction. In 2014–2015 Andrey Gulin worked with a team of researchers to start a new project called Tensornet which was aimed at solving the problem of "how to work with categorical data". Their work resulted in several proprietary Gradient Boosting libraries with different approaches to handling categorical data. In 2016 the Machine Learning Infrastructure team led by Anna Dorogush started working on Gradient Boosting in Yandex, including Matrixnet and Tensornet. They implemented and open-sourced the next version of Gradient Boosting library called CatBoost, which has support for categorical and text data, GPU training, model analysis, and visualization tools. CatBoost was open-sourced in July 2017 and is under active development in Yandex and the open-source community. == Application == JetBrains uses CatBoost for code completion Cloudflare uses CatBoost for bot detection Careem uses CatBoost to predict future destinations of the rides
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Social History and Industrial Classification

Social History and Industrial Classification (SHIC) is a classification system used by many British museums for social history and industrial collections. It was first published in 1983. == Purpose == SHIC classifies materials (books, objects, recordings etc.) by their interaction with the people who used them. For example, a carpenter's hammer is classified with other tools of the carpenter, and not with a blacksmith's hammer. In contrast other classification systems, for example the Dewey Decimal Classification, might class all hammers together and close to the classification for other percussive tools. The specialist subject network, Social History Curator's Group (SHCG), obtained funding in 2012 to develop an on-line version, now on their website http://www.shcg.org.uk/ == Scheme == Materials are classified under four major category numbers: Community life Domestic and family life Personal life Working life Further classification within a category is by the use of further numbers after the decimal point. It is permissible to assign more than one classification in cases where the object had more than one use.
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Data-centric AI

Data-centric AI is an approach within artificial intelligence that emphasizes on improving the quality, consistency and representativeness of the data used to train machine learning models, rather than focusing primarily on optimizing model architectures or algorithms. This idea has gained traction as researchers and practitioners have come to believe that many performance limitations of machine learning systems stem from issues such as noisy labels, biased datasets, and lack of coverage in the data. Data-centric AI involves disciplined approach to data cleaning, augmentation, labeling, and governance that improves model performance and reliability in applications such as computer vision, natural language processing, and further.
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Thomas Bolander

Thomas Bolander is a Danish professor at DTU Compute, Technical University of Denmark, where he studies logic and artificial intelligence. Most of his studies focus on the social aspect of artificial intelligence, and how we can make future AI able to navigate in social interactions. Thomas Bolander also sits in different commissions, expert panels and boards, among these he is a member of the Siri Commission, the TeckDK Commission, a member of the editorial board of the journal Studia Logica and co-organizer of Science and Cocktails. Bolander is known for his dissemination of science. In 2019 he was awarded the H. C. Ørsted Medal. Which he was the first to achieve after a break of three years.
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WYSIWYM (interaction technique)

What you see is what you meant (WYSIWYM) is a text editing interaction technique that emerged from two projects at University of Brighton. It allows users to create abstract knowledge representations such as those required by the Semantic Web using a natural language interface. Natural language understanding (NLU) technology is not employed. Instead, natural language generation (NLG) is used in a highly interactive manner. The text editor accepts repeated refinement of a selected span of text as it becomes progressively less vacuous of authored semantics. Using a mouse, a text property held in the evolving text can be further refined by a set of options derived by NLG from a built-in ontology. An invisible representation of the semantic knowledge is created which can be used for multilingual document generation, formal knowledge formation, or any other task that requires formally specified information. The two projects at Brighton worked in the field of Conceptual Authoring to lay a foundation for further research and development of a Semantic Web Authoring Tool (SWAT). This tool has been further explored as a means for developing a knowledge base by those without prior experience with Controlled Natural Language tools.
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Ramification problem

In philosophy and artificial intelligence (especially, knowledge based systems), the ramification problem is concerned with the indirect consequences of an action. It might also be posed as how to represent what happens implicitly due to an action or how to control the secondary and tertiary effects of an action. It is strongly connected to, and is opposite the qualification side of, the frame problem. Limit theory helps in operational usage. For instance, in KBE derivation of a populated design (geometrical objects, etc., similar concerns apply in shape theory), equivalence assumptions allow convergence where potentially large, and perhaps even computationally indeterminate, solution sets are handled deftly. Yet, in a chain of computation, downstream events may very well find some types of results from earlier resolutions of ramification as problematic for their own algorithms.
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AI content watermarking

AI content watermarking is the process of embedding imperceptible yet detectable signals into content generated by artificial intelligence systems, such as text, images, audio, or video. The technique allows the content to be traced and identified as machine-generated without compromising its quality for the end user. AI watermarking has emerged as a key approach to address growing concerns about misinformation, deepfakes, copyright infringement, and the traceability of synthetic content in the context of the rapid development of generative artificial intelligence. Unlike traditional visible watermarks used in photography, AI content watermarks are typically invisible to humans and can only be detected and deciphered algorithmically. The concept is distinct from the watermarking of AI models themselves (to prevent model theft) and from the watermarking of training data (to combat unauthorized data use). Modern AI watermarking schemes are typically formalized as a pair of algorithms, an embedding (or generation) algorithm and a detection algorithm, sharing a secret key, whose performance is evaluated along three competing axes: quality (the watermark must not noticeably degrade outputs), detectability (the watermark must be statistically distinguishable from unwatermarked content), and robustness (the watermark must persist under adversarial or incidental modifications). == Background == Digital watermarking has been used for decades to protect physical and digital media, from paper currency to photographs. Classical schemes typically embedded a fixed bit-string into a fixed cover signal, with robustness criteria defined against a small fixed set of distortions such as JPEG compression or additive Gaussian noise. The rapid advancement of generative AI in the early 2020s, however, created a new and qualitatively different demand: rather than protecting a single artifact, watermarks for AI content must be embedded automatically across an open-ended distribution of generated outputs while remaining robust to a much wider class of adversarial transformations, including paraphrasing, image regeneration via diffusion models, and re-recording. Large image generation models such as DALL-E, Stable Diffusion, and Midjourney, along with large language models like ChatGPT, made it possible to produce highly realistic synthetic text, images, audio, and video at scale, raising significant ethical and security concerns. In July 2023, the Biden administration secured voluntary commitments from leading AI companies, including OpenAI, Alphabet, Meta, and Amazon, to develop watermarking and other provenance technologies to help users identify AI-generated content. == Formal definitions and design goals == Most modern AI watermarking schemes can be formalized as a pair of algorithms ( W m , D e t e c t ) {\displaystyle ({\mathsf {Wm}},{\mathsf {Detect}})} parameterized by a secret key k {\displaystyle k} . The embedding algorithm W m {\displaystyle {\mathsf {Wm}}} takes a generative model M {\displaystyle M} (and optionally a prompt) and returns a watermarked output x {\displaystyle x} ; the detection algorithm D e t e c t ( x , k ) {\displaystyle {\mathsf {Detect}}(x,k)} outputs a real-valued score (typically a p-value or log-likelihood ratio) used to decide whether x {\displaystyle x} was produced by the watermarked generator. The literature evaluates such schemes along several largely conflicting criteria: Criteria for evaluation include imperceptibility or quality preservation, measured for text via perplexity and human preference judgments, and for images and audio via metrics such as PSNR, SSIM, LPIPS, or PESQ. Detectability is typically expressed as the true positive rate at a fixed false positive rate (e.g. 1% or 10^-6), or as the number of tokens or pixels needed to reach a given confidence level. Robustness refers to the requirement that the watermark should survive expected modifications like JPEG or MP3 compression, cropping, noise, paraphrasing, or machine translation. Distortion-freeness is a stronger property requiring that the marginal distribution of any single watermarked output be statistically identical to the unwatermarked model's distribution. Schemes due to Aaronson, Christ et al., and Kuditipudi et al. are distortion-free in this sense, while the original Kirchenbauer et al. scheme is not. Forgery resistance or unforgeability means an adversary without the secret key should be unable to produce content that passes detection. == Techniques == AI watermarking techniques vary significantly depending on the type of content being watermarked. At its core, the process involves two main stages: embedding (or encoding) the watermark, and detection. There are two primary methods for embedding: watermarking during content generation, which requires access to the AI model itself but is generally more robust, and post-generation watermarking, which can be applied to content from any source, including closed-source models. Watermarks can be broadly classified as visible, including overt marks such as logos or text overlays, or imperceptible, which are detectable only by algorithms. They can also be classified by durability: robust watermarks are designed to withstand common transformations such as compression, cropping, and re-encoding, while fragile watermarks are easily destroyed by any alteration, making them useful for tamper detection. A further axis distinguishes zero-bit watermarks, which only signal "this content was generated by model M," from multi-bit watermarks, which embed an arbitrary payload (such as a user identifier) that can be recovered at detection time. === Text === Text watermarking is considered one of the most challenging modalities because natural language offers relatively limited redundancy compared to images or audio. Modern approaches for large language models alter the autoregressive sampling process so that some statistical signature is left in the choice of tokens, while leaving the surface form of the text unchanged. The literature distinguishes three main families of generation-time text watermarks. Logit-biasing schemes (e.g. KGW) add a fixed bias δ {\displaystyle \delta } to a pseudorandomly selected subset of vocabulary logits before softmax sampling. Reweighting or sampling-based schemes (e.g. SynthID-Text) compose multiple pseudorandom tournaments over the model's full distribution. Distortion-free schemes based on the Gumbel-max trick or inverse transform sampling (Aaronson 2022; Kuditipudi et al. 2023; Christ et al. 2024) preserve the marginal output distribution of the model. ==== KGW: token-probability shifting ==== The pioneering "green list / red list" scheme of Kirchenbauer et al. (KGW), introduced at ICML 2023, is the foundation for most subsequent text watermarks. At each decoding step t {\displaystyle t} , a pseudorandom function (PRF) keyed by a secret k {\displaystyle k} is applied to a context window of h {\displaystyle h} previous tokens to deterministically partition the vocabulary V {\displaystyle V} of size N {\displaystyle N} into a "green list" G ⊂ V {\displaystyle G\subset V} of size γ N {\displaystyle \gamma N} and its complement, the "red list" R = V ∖ G {\displaystyle R=V\setminus G} , where γ ∈ ( 0 , 1 ) {\displaystyle \gamma \in (0,1)} (typically γ = 1 / 2 {\displaystyle \gamma =1/2} ) is the green fraction. A logits processor then increments every green-list logit by a fixed bias δ > 0 {\displaystyle \delta >0} before softmax: ℓ v ′ = ℓ v + δ ⋅ 1 [ v ∈ G ] {\displaystyle \ell '_{v}=\ell _{v}+\delta \cdot \mathbf {1} [v\in G]} so that, after sampling, green tokens are over-represented but generation is not constrained to green tokens alone; high-entropy positions tolerate the bias gracefully, while low-entropy positions (where one token dominates the logits) override the watermark and preserve correctness on factual content. Detection requires only the secret key and the candidate text, not the language model itself. The detector recomputes the partition g ( ⋅ ) {\displaystyle g(\cdot )} for each token, counts the number of green hits | G | hits {\displaystyle |G|_{\text{hits}}} in a sequence of length T {\displaystyle T} , and computes a one-proportion z-test statistic: z = | G | hits − γ T T γ ( 1 − γ ) {\displaystyle z={\frac {|G|_{\text{hits}}-\gamma T}{\sqrt {T\gamma (1-\gamma )}}}} Under the null hypothesis that the text was written by an unwatermarked source (human or another model), the green-hit count is approximately binomially distributed with mean γ T {\displaystyle \gamma T} ; a large positive z {\displaystyle z} rejects the null hypothesis. The original paper reports that fewer than 25 watermarked tokens are sufficient to detect a watermark with a false positive rate below 10^-5 on the OPT-1.3B model. A follow-up study by the same group documented robustness under temperature sampling, top-p (nucleus) sampling, and human paraphrasing, and proposed sliding-window
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Lobsang Monlam

Geshe Lobsang Monlam (Tibetan: དགེ་བཤེས་བློ་བཟང་སྨོན་ལམ, Wylie: dge bshes blo bzang smon lam), born in 1976 in Ngawa eastern Tibet, is a Tibetan Buddhist scholar and programmer who uses digital technologies to preserve the Tibetan language and culture. He is best known for developing Tibetan typefaces and for the multi-volume Great Monlam Tibetan Dictionary. In 2025, he received the Snow Lion Award for Human Rights from the International Campaign for Tibet. He is also working on developing a "Dalai Lama AI," a specialized language model. == Biography == Lobsang Monlam was born in 1976 in Ngawa, eastern Tibet, anciently Tibetan Amdo, where he became a monk at the age of 12.. At the age of 17, in 1993, Lobsang Monlam fled Tibet by crossing the Himalayas to reach southern India and discovered computer science in a monastery. In 1993, he was ordained monk in the Sera Mey College in Bylakuppe, Karnataka, India, where he obtained a Geshe title in 2013.. By the early 2000s, Lobsang Monlam had already learned to paint thangkas and to compose plans and drawings. He used this knowledge to design a new assembly hall for Sera Mey, which the monks needed. Thanks to his work, Lobsang Monlam received donations from patrons of the monastery, which he was able to use to buy his first computer. He bought his first laptop in 2002 and largely taught himself how to use the hardware and software with the help of manuals. As a Buddhist scholar, he combines meditation practice with his digital work. In 2012, he founded and directs the Monlam Tibetan Information Technology Research Center in Dharamsala, which specializes in Tibetan language and software projects. Since then, he is its director, researching Tibetan language-related software. In 2019, advised by the 14th Dalai Lama, he founded Monlam IT and Research (OPC) Private Limited. Since the 2000s, Monlam has been developing Tibetan typefaces; the first Monlam Tibetan font was created in 2005. Under his direction, the Monlam Great Tibetan Dictionary was created, comprising 223 printed volumes and over 300,000 entries; approximately 150 people worked on this project for over nine years. On May 27, 2022, the Dalai Lama inaugurated the Monlam Tibetan Dictionary, produced by the Monlam Tibetan Information Technology Research Center, at Namgyal Monastery in McLeod Ganj. According to Penpa Tsering, this is the world's largest dictionary, created with guidance from the Dalai Lama, based on proposals from Lobsang Monlam and his team under the direction of Samdhong Rinpoche, and other lamas from all schools of Tibetan Buddhism and Yungdrung Bön. On December 5, 2024, Lobsang Monlam testified at a hearing of the US Congressional-Executive Commission on China in Washington, chaired by Christopher Smith, on the difficulties of preserving the Tibetan language and culture in Tibet and the Tibetan diaspora, and on the interest of the Monlam Tibetan Informatics Research Center in developing technologies for the preservation of the Tibetan language. On December 12, 2024, the work was presented to the Library of Congress in Washington, D.C., and launched at an event. The free Monlam Great Tibetan Dictionary app is available in several languages; the German version was created in collaboration with the Tibet Institute Rikon and has been downloaded millions of times. In total, Monlam has created over 37 apps related to the Tibetan language and translation; In 2023, its center launched the Monlam artificial intelligence platform, equipped with modules for machine translation, optical character recognition, speech transcription and speech synthesis.. For their efforts, he and Sophie Richardson received the Snow Lion Award in 2025, which was presented by Richard Gere and came with a prize of €3,000. In 2019, he started a PhD at Bangalore University on Library Science. He obtained his doctorate on November 30, 2023. Currently, he spearheads Monlam AI. Lobsang Monlam is developing "Dalai Lama AI" to digitally preserve the teachings of the 14th Dalai Lama, now 90 years old, for future generations. Lobsang Monlam states, "If we succeed in preserving the Dalai Lama, we also preserve the movement."
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UMBEL

UMBEL (Upper Mapping and Binding Exchange Layer) is a logically organized knowledge graph of 34,000 concepts and entity types that can be used in information science for relating information from disparate sources to one another. It was retired at the end of 2019. UMBEL was first released in July 2008. Version 1.00 was released in February 2011. Its current release is version 1.50. The grounding of this information occurs by common reference to the permanent URIs for the UMBEL concepts; the connections within the UMBEL upper ontology enable concepts from sources at different levels of abstraction or specificity to be logically related. Since UMBEL is an open-source extract of the OpenCyc knowledge base, it can also take advantage of the reasoning capabilities within Cyc. UMBEL has two means to promote the semantic interoperability of information:. It is: An ontology of about 35,000 reference concepts, designed to provide common mapping points for relating different ontologies or schema to one another, and A vocabulary for aiding that ontology mapping, including expressions of likelihood relationships distinct from exact identity or equivalence. This vocabulary is also designed for interoperable domain ontologies. UMBEL is written in the Semantic Web languages of SKOS and OWL 2. It is a class structure used in Linked Data, along with OpenCyc, YAGO, and the DBpedia ontology. Besides data integration, UMBEL has been used to aid concept search, concept definitions, query ranking, ontology integration, and ontology consistency checking. It has also been used to build large ontologies and for online question answering systems. Including OpenCyc, UMBEL has about 65,000 formal mappings to DBpedia, PROTON, GeoNames, and schema.org, and provides linkages to more than 2 million Wikipedia pages (English version). All of its reference concepts and mappings are organized under a hierarchy of 31 different "super types", which are mostly disjoint from one another. Each of these "super types" has its own typology of entity classes to provide flexible tie-ins for external content. 90% of UMBEL is contained in these entity classes.
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Kinect

Kinect is a discontinued line of motion sensing input devices produced by Microsoft and first released in 2010. The devices generally contain RGB cameras, and infrared projectors and detectors that map depth through either structured light or time of flight calculations, which can in turn be used to perform real-time gesture recognition and body skeletal detection, among other capabilities. They also contain microphones that can be used for speech recognition and voice control. Kinect was originally developed as a motion controller peripheral for Xbox video game consoles, distinguished from competitors (such as Nintendo's Wii Remote and Sony's PlayStation Move) by not requiring physical controllers. The first-generation Kinect was based on technology from Israeli company PrimeSense, and unveiled at E3 2009 as a peripheral for Xbox 360 codenamed "Project Natal". It was first released on November 4, 2010, and would go on to sell eight million units in its first 60 days of availability. The majority of the games developed for Kinect were casual, family-oriented titles, which helped to attract new audiences to Xbox 360, but did not result in wide adoption by the console's existing, overall userbase. As part of the 2013 unveiling of Xbox 360's successor, Xbox One, Microsoft unveiled a second-generation version of Kinect with improved tracking capabilities. Microsoft also announced that Kinect would be a required component of the console, and that it would not function unless the peripheral is connected. The requirement proved controversial among users and critics due to privacy concerns, prompting Microsoft to backtrack on the decision. However, Microsoft still bundled the new Kinect with Xbox One consoles upon their launch in November 2013. A market for Kinect-based games still did not emerge after the Xbox One's launch; Microsoft would later offer Xbox One hardware bundles without Kinect included, and later revisions of the console removed the dedicated ports used to connect it (requiring a powered USB adapter instead). Microsoft ended production of Kinect for Xbox One in October 2017. Kinect has also been used as part of non-game applications in academic and commercial environments, as it was cheaper and more robust than other depth-sensing technologies at the time. While Microsoft initially objected to such applications, it later released software development kits (SDKs) for the development of Microsoft Windows applications that use Kinect. In 2020, Microsoft released Azure Kinect as a continuation of the technology integrated with the Microsoft Azure cloud computing platform. Part of the Kinect technology was also used within Microsoft's HoloLens project. Microsoft discontinued the Azure Kinect developer kits in October 2023. == History == === Development === The origins of the Kinect started around 2005, at a point where technology vendors were starting to develop depth-sensing cameras. Microsoft had been interested in a 3D camera for the Xbox line earlier but because the technology had not been refined, had placed it in the "Boneyard", a collection of possible technology they could not immediately work on. In 2005, Israeli company PrimeSense was founded by mathematicians and engineers to develop the "next big thing" for video games, incorporating cameras that were capable of mapping a human body in front of them and sensing hand motions. They showed off their system at the 2006 Game Developers Conference, where Microsoft's Alex Kipman, the general manager of hardware incubation, saw the potential in PrimeSense's technology for the Xbox system. Microsoft began discussions with PrimeSense about what would need to be done to make their product more consumer-friendly: not only improvements in the capabilities of depth-sensing cameras, but a reduction in size and cost, and a means to manufacture the units at scale was required. PrimeSense spent the next few years working at these improvements. Nintendo released the Wii in November 2006. The Wii's central feature was the Wii Remote, a handheld device that was detected by the Wii through a motion sensor bar mounted onto a television screen to enable motion controlled games. Microsoft felt pressure from the Wii, and began looking into depth-sensing in more detail with PrimeSense's hardware, but could not get to the level of motion tracking they desired. While they could determine hand gestures, and sense the general shape of a body, they could not do skeletal tracking. A separate path within Microsoft looked to create an equivalent of the Wii Remote, considering that this type of unit may become standardized similar to how two-thumbstick controllers became a standard feature. However, it was still ultimately Microsoft's goal to remove any device between the player and the Xbox. Kudo Tsunoda and Darren Bennett joined Microsoft in 2008, and began working with Kipman on a new approach to depth-sensing aided by machine learning to improve skeletal tracking. They internally demonstrated this and established where they believed the technology could be in a few years, which led to the strong interest to fund further development of the technology; this has also occurred at a time that Microsoft executives wanted to abandon the Wii-like motion tracking approach, and favored the depth-sensing solution to present a product that went beyond the Wii's capabilities. The project was greenlit by late 2008 with work started in 2009. The project was codenamed "Project Natal" after the Brazilian city Natal, Kipman's birthplace. Additionally, Kipman recognized the Latin origins of the word "natal" to mean "to be born", reflecting the new types of audiences they hoped to draw with the technology. Much of the initial work was related to ethnographic research to see how video game players' home environments were laid out, lit, and how those with Wiis used the system to plan how Kinect units would be used. The Microsoft team discovered from this research that the up-and-down angle of the depth-sensing camera would either need to be adjusted manually, or would require an expensive motor to move automatically. Upper management at Microsoft opted to include the motor despite the increased cost to avoid breaking game immersion. Kinect project work also involved packaging the system for mass production and optimizing its performance. Hardware development took around 22 months. During hardware development, Microsoft engaged with software developers to use Kinect. Microsoft wanted to make games that would be playable by families since Kinect could sense multiple bodies in front of it. One of the first internal titles developed for the device was the pack-in game Kinect Adventures developed by Good Science Studio that was part of Microsoft Studios. One of the game modes of Kinect Adventures was "Reflex Ridge", based on the Japanese Brain Wall game where players attempt to contort their bodies in a short time to match cutouts of a wall moving at them. This type of game was a key example of the type of interactivity they wanted with Kinect, and its development helped feed into the hardware improvements. Another development was Project Milo, a prototype game developed by Lionhead Studios led by Peter Molyneux where the player could interact with a virtual avatar through motion controls and voice recognition. Lionhead had developed the project based on original capabilities of the Kinect, but according to Molyneux, Microsoft had found that a consumer-grade version of the Kinect would cost thousands of dollars, so they scaled back the device and refocused the role of games for the Kinect to be more casual games as seen on the Wii. As a result, Project Milo no longer fit Microsoft's portfolio and was cancelled. Nearing the planned release, there was a problem of widespread testing of Kinect in various room types and different bodies accounting for age, gender, and race among other factors, while keeping the details of the unit confidential. Microsoft engaged in a company-wide program offering employees to take home Kinect units to test them. Microsoft also brought other non-gaming divisions, including its Microsoft Research, Microsoft Windows, and Bing teams to help complete the system. Microsoft established its own large-scale manufacturing facility to bulk product Kinect units and test them. === Introduction === Kinect was first announced to the public as "Project Natal" on June 1, 2009, during Microsoft's press conference at E3 2009; film director Steven Spielberg joined Microsoft's Don Mattrick to introduce the technology and its potential. Three demos were presented during the conference—Microsoft's Ricochet and Paint Party, and Lionhead Studios' Milo & Kate created by Peter Molyneux—while a Project Natal-enabled version of Criterion Games' Burnout Paradise was shown during the E3 exhibition. By E3 2009, the skeletal mapping technology was capable of simultaneously tracking four people, with a feature extraction of 4
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Vero (app)

Vero (stylized as VERO) is a social media platform and mobile app company. Vero markets itself as a social network free from advertisements, data mining and algorithms. == History == The app was founded by French-Lebanese billionaire Ayman Hariri who is the son of former Lebanese prime minister Rafic Hariri. The name is taken from the Italian word for true. The app launched officially in 2015 as an alternative to Facebook and their popular photo-blogging app Instagram. Within weeks of its release the app surged in popularity although users expressed mixed reports with some feeling confused about how the app worked. Cosplayers were early to adopt the app as their photo-sharing platform of choice, favouring the app's pinch and zoom magnification feature over Instagram's zoom feature. Other creative communities soon followed, and the app became popular with niche groups of makeup artists, tattoo artists, and skateboarders. In March 2018, Vero's popularity surged, partly helped by an exodus from Facebook and Instagram following the Cambridge Analytica data scandal. In the wake of the scandal, Vero devised an advertising campaign aimed at defected Facebook and Instagram users, hoping the app's policies and privacy settings would assuage concerns over sharing personal information on the internet. Within the space of one week, the app went from being a small service, akin to Ello or Peach, to being the most downloaded app in eighteen countries. In December 2020, Vero released its most significant update to date, Vero 2.0 which introduced new features including voice and video calls, game and app posts and bookmarks, and refinements to the UI. In October 2021, Vero introduced their Desktop app (beta) with multiple post options and a re-sizable multi-column feed. == Concept and funding == Vero's content feed resembles Instagram's although users can share a wider variety of content and the app has a chronological content feed whereas Facebook and Instagram's feeds are algorithm based. Vero's business plan is also distinct from similar social media apps. Whereas its competitors such as Facebook or Instagram make money from in-app advertising revenue and the sale of user data, Vero's business plan was to invite the first one million users to use the app for free then charge any subsequent users a subscription fee. The app was entirely funded by its founder and generated additional revenues by charging affiliate fees when someone buys a product they find on Vero. == Awards == Vero was recognized at the 2021 Webbys, being named as an Honoree in the Best Visual Design - Aesthetic Category. == Controversies == === Privacy === Vero has faced some criticism over the wording of their manifesto, in particular, the statement "Vero only collects the data we believe is necessary to provide users with a great experience and to ensure the security of their accounts." Because this policy does not explicitly state that the app will not sell data on to third parties some users fear that the need to monetise the app through data might prove too tempting. Users have also complained about not being able to delete their accounts. While this was never the case, the option was hidden deep in the app's settings. === Russian involvement === Although Vero remains transparent about the app's Russian development team, they have been caught up in concerns about Russian interference on social media platforms. The app's founder Ayman Hariri was quick to dismiss the remarks as xenophobic and defend the nationality of his employees, stating in an interview with Time Magazine; "At the end of the day, where people are from is really not how anybody should judge anyone". === Criticism of the app's founder === Until 2013, Vero's founder Ayman Harari was deputy CEO and chairman of Saudi Oger, the Saudi Arabian construction company which collapsed in 2017, mired by controversies over the welfare and treatment of their employees. However, Hariri is quick to point out that he divested from the firm in 2014 and the worker's rights violations occurred after he had left the company.
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4E cognition

4E cognition refers to a group of theories in (the philosophy of) cognitive science that challenge traditional views of the mind as something that happens only inside the brain. The four Es stand for: embodied, meaning that a brain is found in and, more importantly, vitally interconnected with a larger physical/biological body; embedded, which refers to the limitations placed on the body by the external environment and laws of nature; extended, which argues that the mind is supplemented and even enhanced by the exterior world (e.g., writing, a calculator, etc.); and enactive, which is the argument that without dynamic processes, actions that require reactions, the mind would be ineffectual. It could be argued that the four Es are compounding extensions of cognition or the mind, being part of a body that is, in turn, part of an environment which limits it but also allows for certain extensions, all of which require dynamic actions and reactions. == History == Ideas of embodied cognition, or rather the idea that our physical bodies play a crucial role in our decision making, can be traced back as far as Plato's dialogues and Aristotelian thought. It was, however, in the twentieth century that this debate began to resemble the current discussion, fueled by disagreements between cognitivists and behaviourists. Tensions within cognitivism, as well as the increasing popularity of neurobiology, led, on the one side, to a predominant focus on internal, cognitive processes while neglecting environmental factors, which in turn caused a push-back fuelling our modern understanding of embodied cognition. The term 4E cognition is hard to trace back to its first use, however, some sources attribute it to Shaun Gallagher and the conference on 4E cognition he organised in 2007, while others indicate the term to be first used in 2006 at an 'Embodied mind workshop' at Cardiff University that Gallagher attended. Embodiment or embodied cognition arguably presents the bridge between cognitivism and 4E cognition as the embodiment of cognitive function provides the necessary conditions for embeddedness, enactedness, and extendedness to connect to cognition. 4E cognition was and is heavily influenced by phenomenology. The ideas are still rather fragmented in nature due to their four main components, which can not be neatly divided, causing conceptual questions of internal boundary concepts. As a young field, it is held back both by its fragmented nature and a relative lack of critical evaluations. It is important to acknowledge that 4E cognition, though young, is a broad field containing and combining several different theoretical perspectives that conflict with one another to varying degrees. The somewhat convoluted and competing nature of the theories that can be grouped as 4E cognition, as well as the field's relative youth, make it difficult to put together an exhaustive history beyond the history of its four main theoretical pillars: embodiment, embeddedness, extendedness, and enactedness. == Importance and core tenets of 4E == If there are separate theories of cognition (e.g., embodied, extended, etc.), why group them under this umbrella, causing important epistemological and especially ontological dilemmas? Notably, other theories of 'non-traditional' cognition are not included under the 4E umbrella. The four E's in 4E cognition importantly all reject, or at a minimum draw into question, some of the core tenets of traditional cognitivism. Importantly, 4E cognition is seen as deindividualizing cognition to some extent, allowing for a broader examination of the interplay of personal, social, political, and ethical aspects that shape human cognition. This can be compared to advancements in the field of epigenetics, which have allowed for a broader examination of environmental (both natural and social) factors and their influence on what had previously only been subject to genetic theorizing. In a similar vein, 4E cognition might also help ground cognition in evolutionary theory by extending cognition to a biological account subject to development over time by means of evolution. Overall, the importance of the extension that is 4E cognition aims to reexamine ideas of a self-centered view of cognition, advocating for a more holistic approach. Ideally, this would allow us to reconsider ideas of justice and individual rights and responsibilities that take into account a more nuanced understanding of the relations between people and their context, balancing self-agency with factors beyond it. === Conceptual differences from cognitive psychology === According to the traditional teachings of cognitive psychology, cognition is a type of information processing based on representational mental structures. This idea, as the name suggests, was heavily influenced by computer science. In this light, the brain is a kind of central processing unit that organises and directs all else. The classical cognitivist view draws a strong boundary between 'the internal' and 'the external', where cognition is solely a subject of 'the internal' realm. The four E's, however, break down this boundary. Cognition can not reside solely within the confines of our heads if it is also embodied, embedded, enacted, and extended. In a way, 4E cognition is interested in the extracranial processes affecting cognition. == From embodied cognition to 4E cognition == === The strong and the weak view === ==== Embodied cognition ==== Broadly speaking, there is a strong and a weak perspective of embodied cognition in 4E cognition. The weak understanding refers to mental processes being causally dependent on extracranial processes. This essentially means that there is a cause and effect or action-reaction relationship between the mind and the body and its environment, etc. The strong perspective views extracranial processes as a (partial) constitutive aspect of cognition. An example here could be using a calculator to solve math problems. The calculator is not part of your brain or mind, but it supports your cognitive processes. === Extracranial processes: bodily or extrabodily === In addition to the weak and the strong reading of 4E cognition, there is also the distinction between bodily and extrabodily extracranial processes. Bodily extracranial processes refer to processes within the body, e.g., sensory perception. Extrabodily extracranial processes refer to processes outside of the body, like the aforementioned calculator example. === Four claims of embodied cognition === ==== Embedded and extended cognition ==== When combining the weak/strong reading of embodied cognition and bodily/extrabodily extracranial process, four claims about embodied cognition emerge: strongly embodied and bodily processes strongly embodied and extrabodily processes weakly embodied and bodily processes weakly embodied and extrabodily processes The first and third claims signify a strong and a weak reading of embodied cognition in the more classical sense. The second claim fits almost perfectly with embedded cognition. Claim two is most compatible with extended cognition. ==== Enacted cognition ==== Finally, enacted cognition refers to cognition being connected to active interaction between a conscious agent and their environment. Here, too, there can be a weak and a strong reading. == Criticisms == Given the divided nature of the field, much criticism surrounding the lack of unity within the field has emerged. In particular, the claims of embodied cognition centering around the body appear to conflict with the tenets of extended cognition, which also appear to conflict with the body/environment distinction that is central to enactivism. Some theoreticians argue that the umbrella of 4E theories is still lacking a common language that might bridge the gaps between the theories that constitute it. There is also the concern that the grouping of such variable theories results in an important loss of nuance and complexity, which is a part of human cognition. Another concern raised is the "dogma of harmony". The criticism contained there regards the notion that within 4E theorizing, there is generally an optimistic and harmonic expectation of the extension between humans and their technologies, ignoring the possibility of those extensions detracting from cognition in some way rather than adding to it. Recent attempts to incorporate embodied cognitive neuroscience have been argued to hold the potential to resolve internal issues within 4E cognition. Overall, a concern often voiced regarding 4E cognition is that its proponents are at best only vaguely interested in cognition. More broadly, this concern reflects the arguably too distracted nature of this emerging field.
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Rnn (software)

rnn is an open-source machine learning framework that implements recurrent neural network architectures, such as LSTM and GRU, natively in the R programming language, that has been downloaded over 100,000 times (from the RStudio servers alone). The rnn package is distributed through the Comprehensive R Archive Network under the open-source GPL v3 license. == Workflow == The below example from the rnn documentation show how to train a recurrent neural network to solve the problem of bit-by-bit binary addition. == sigmoid == The sigmoid functions and derivatives used in the package were originally included in the package, from version 0.8.0 onwards, these were released in a separate R package sigmoid, with the intention to enable more general use. The sigmoid package is a dependency of the rnn package and therefore automatically installed with it. == Reception == With the release of version 0.3.0 in April 2016 the use in production and research environments became more widespread. The package was reviewed several months later on the R blog The Beginner Programmer as "R provides a simple and very user friendly package named rnn for working with recurrent neural networks.", which further increased usage. The book Neural Networks in R by Balaji Venkateswaran and Giuseppe Ciaburro uses rnn to demonstrate recurrent neural networks to R users. It is also used in the r-exercises.com course "Neural network exercises". The RStudio CRAN mirror download logs show that the package is downloaded on average about 2,000 per month from those servers , with a total of over 100,000 downloads since the first release, according to RDocumentation.org, this puts the package in the 15th percentile of most popular R packages .
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