Color vision (CV), a feature of visual perception, is an ability to perceive differences between light composed of different frequencies independently of light intensity. Color perception is a part of the larger visual system and is mediated by a complex process between neurons that begins with differential stimulation of different types of photoreceptors by light entering the eye. Those photoreceptors then emit outputs that are propagated through many layers of neurons ultimately leading to higher cognitive functions in the brain. Color vision is found in many animals and is mediated by similar underlying mechanisms with common types of biological molecules and a complex history of the evolution of color vision within different animal taxa. In primates, color vision may have evolved under selective pressure for a variety of visual tasks including the foraging for nutritious young leaves, ripe fruit, and flowers, as well as detecting predator camouflage and emotional states in other primates. == Wavelength == Isaac Newton discovered that white light after being split into its component colors when passed through a dispersive prism could be recombined to make white light by passing them through a different prism. The visible light spectrum ranges from about 380 to 740 nanometers. Spectral colors (colors that are produced by a narrow band of wavelengths) such as red, orange, yellow, green, cyan, blue, and violet can be found in this range. These spectral colors do not refer to a single wavelength, but rather to a set of wavelengths: red, 625–740 nm; orange, 590–625 nm; yellow, 565–590 nm; green, 500–565 nm; cyan, 485–500 nm; blue, 450–485 nm; violet, 380–450 nm. Wavelengths longer or shorter than this range are called infrared or ultraviolet, respectively. Humans cannot generally see these wavelengths, but other animals may. === Hue detection === Sufficient differences in wavelength cause a difference in the perceived hue; the just-noticeable difference in wavelength varies from about 1 nm in the blue-green and yellow wavelengths to 10 nm and more in the longer red and shorter blue wavelengths. Although the human eye can distinguish up to a few hundred hues, when those pure spectral colors are mixed together or diluted with white light, the number of distinguishable chromaticities can be much higher. In very low light levels, vision is scotopic: light is detected by rod cells of the retina. Rods are maximally sensitive to wavelengths near 500 nm and play little, if any, role in color vision. In brighter light, such as daylight, vision is photopic: light is detected by cone cells which are responsible for color vision. Cones are sensitive to a range of wavelengths, but are most sensitive to wavelengths near 555 nm. Between these regions, mesopic vision comes into play and both rods and cones provide signals to the retinal ganglion cells. The shift in color perception from dim light to daylight gives rise to differences known as the Purkinje effect. The perception of "white" is formed by the entire spectrum of visible light, or by mixing colors of just a few wavelengths in animals with few types of color receptors. In humans, white light can be perceived by combining wavelengths such as red, green, and blue, or just a pair of complementary colors such as blue and yellow. === Non-spectral colors === There are a variety of colors in addition to spectral colors and their hues. These include grayscale colors, shades of colors obtained by mixing grayscale colors with spectral colors, violet-red colors, impossible colors, and metallic colors. Grayscale colors include white, gray, and black. Rods contain rhodopsin, which reacts to light intensity, providing grayscale coloring. Shades include colors such as pink or brown. Pink is obtained from mixing red and white. Brown may be obtained from mixing orange with gray or black. Navy is obtained from mixing blue and black. Violet-red colors include hues and shades of magenta. The light spectrum is a line on which violet is one end and the other is red, and yet we see hues of purple that connect those two colors. Impossible colors are a combination of cone responses that cannot be naturally produced. For example, medium cones cannot be activated completely on their own; if they were, we would see a 'hyper-green' color. == Dimensionality == Color vision is categorized foremost according to the dimensionality of the color gamut, which is defined by the number of primaries required to represent the color vision. This is generally equal to the number of photopsins expressed: a correlation that holds for vertebrates but not invertebrates. The common vertebrate ancestor possessed four photopsins (expressed in cones) plus rhodopsin (expressed in rods), so was tetrachromatic. However, many vertebrate lineages have lost one or many photopsin genes, leading to lower-dimension color vision. The dimensions of color vision range from 1-dimensional and up: == Physiology of color perception == Perception of color begins with specialized retinal cells known as cone cells. Cone cells contain different forms of opsin – a pigment protein – that have different spectral sensitivities. Humans contain three types, resulting in trichromatic color vision. Each individual cone contains pigments composed of opsin apoprotein covalently linked to a light-absorbing prosthetic group: either 11-cis-hydroretinal or, more rarely, 11-cis-dehydroretinal. The cones are conventionally labeled according to the ordering of the wavelengths of the peaks of their spectral sensitivities: short (S), medium (M), and long (L) cone types. These three types do not correspond well to particular colors as we know them. Rather, the perception of color is achieved by a complex process that starts with the differential output of these cells in the retina and which is finalized in the visual cortex and associative areas of the brain. For example, while the L cones have been referred to simply as red receptors, microspectrophotometry has shown that their peak sensitivity is in the greenish-yellow region of the spectrum. Similarly, the S cones and M cones do not directly correspond to blue and green, although they are often described as such. The RGB color model, therefore, is a convenient means for representing color but is not directly based on the types of cones in the human eye. The peak response of human cone cells varies, even among individuals with typical color vision; in some non-human species this polymorphic variation is even greater, and it may well be adaptive. === Theories === Two complementary theories of color vision are the trichromatic theory and the opponent process theory. The trichromatic theory, or Young–Helmholtz theory, proposed in the 19th century by Thomas Young and Hermann von Helmholtz, posits three types of cones preferentially sensitive to blue, green, and red, respectively. Others have suggested that the trichromatic theory is not specifically a theory of color vision but a theory of receptors for all vision, including color but not specific or limited to it. Equally, it has been suggested that the relationship between the phenomenal opponency described by Ewald Hering and the physiological opponent processes are not straightforward (see below), making of physiological opponency a mechanism that is relevant to the whole of vision, and not just to color vision alone. Hering proposed the opponent process theory in 1872. It states that the visual system interprets color in an antagonistic way: red vs. green, blue vs. yellow, black vs. white. Both theories are generally accepted as valid, describing different stages in visual physiology, visualized in the adjacent diagram. Green–magenta and blue–yellow are scales with mutually exclusive boundaries. In the same way that there cannot exist a "slightly negative" positive number, a single eye cannot perceive a bluish-yellow or a reddish-green. Although these two theories are both currently widely accepted theories, past and more recent work has led to criticism of the opponent process theory, stemming from a number of what are presented as discrepancies in the standard opponent process theory. For example, the phenomenon of an after-image of complementary color can be induced by fatiguing the cells responsible for color perception, by staring at a vibrant color for a length of time, and then looking at a white surface. This phenomenon of complementary colors shows that cyan, rather than green, is the complement of red, and that magenta, rather than red, is the complement of green. It therefore also shows that the reddish-green color supposed to be impossible by opponent process theory is actually the color yellow. Although this phenomenon is more readily explained by the trichromatic theory, explanations for the discrepancy may include alterations to the opponent process theory, such as redefining the opponent colors as red vs. cyan, to reflect this effect. Despite such criticis
Language Server Protocol
The Language Server Protocol (LSP) is an open, JSON-RPC-based protocol for use between source-code editors or integrated development environments (IDEs) and servers that provide "language intelligence tools": programming language-specific features like code completion, syntax highlighting and marking of warnings and errors, as well as refactoring routines. The goal of the protocol is to allow programming language support to be implemented and distributed independently of any given editor or IDE. In the early 2020s, LSP quickly became a "norm" for language intelligence tools providers. == History == LSP was originally developed for Microsoft Visual Studio Code and is now an open standard. On June 27, 2016, Microsoft announced a collaboration with Red Hat and Codenvy to standardize the protocol's specification. Its specification is hosted and developed on GitHub. == Background == Modern IDEs provide programmers with sophisticated features like code completion, refactoring, navigating to a symbol's definition, syntax highlighting, and error and warning markers. For example, in a text-based programming language, a programmer might want to rename a method read. The programmer could either manually edit the respective source code files and change the appropriate occurrences of the old method name into the new name, or instead use an IDE's refactoring capabilities to make all the necessary changes automatically. To be able to support this style of refactoring, an IDE needs a sophisticated understanding of the programming language that the program's source is written in. A programming tool without such an understanding—for example, one that performs a naive search-and-replace instead—could introduce errors. When renaming a read method, for example, the tool should not replace the partial match in a variable that might be called readyState, nor should it replace the portion of a code comment containing the word "already". Neither should renaming a local variable read, for example, end up altering identically-named variables in other scopes. Conventional compilers or interpreters for a specific programming language are typically unable to provide these language services, because they are written with the goal of either transforming the source code into object code or immediately executing the code. Additionally, language services must be able to handle source code that is not well-formed, e.g. because the programmer is in the middle of editing and has not yet finished typing a statement, procedure, or other construct. Additionally, small changes to a source code file which are done during typing usually change the semantics of the program. In order to provide instant feedback to the user, the editing tool must be able to very quickly evaluate the syntactical and semantical consequences of a specific modification. Compilers and interpreters therefore provide a poor candidate for producing the information needed for an editing tool to consume. Prior to the design and implementation of the Language Server Protocol for the development of Visual Studio Code, most language services were generally tied to a given IDE or other editor. In the absence of the Language Server Protocol, language services are typically implemented by using a tool-specific extension API. Providing the same language service to another editing tool requires effort to adapt the existing code so that the service may target the second editor's extension interfaces. The Language Server Protocol allows for decoupling language services from the editor so that the services may be contained within a general-purpose language server. Any editor can inherit sophisticated support for many different languages by making use of existing language servers. Similarly, a programmer involved with the development of a new programming language can make services for that language available to existing editing tools. Making use of language servers via the Language Server Protocol thus also reduces the burden on vendors of editing tools, because vendors do not need to develop language services of their own for the languages the vendor intends to support, as long as the language servers have already been implemented. The Language Server Protocol also enables the distribution and development of servers contributed by an interested third party, such as end users, without additional involvement by either the vendor of the compiler for the programming language in use or the vendor of the editor to which the language support is being added. LSP is not restricted to programming languages. It can be used for any kind of text-based language, like specifications or domain-specific languages (DSL). == Technical overview == When a user edits one or more source code files using a language server protocol-enabled tool, the tool acts as a client that consumes the language services provided by a language server. The tool may be a text editor or IDE and the language services could be refactoring, code completion, etc. The client informs the server about what the user is doing, e.g., opening a file or inserting a character at a specific text position. The client can also request the server to perform a language service, e.g. to format a specified range in the text document. The server answers a client's request with an appropriate response. For example, the formatting request is answered either by a response that transfers the formatted text to the client or by an error response containing details about the error. The Language Server Protocol defines the messages to be exchanged between client and language server. They are JSON-RPC preceded by headers similar to HTTP. Messages may originate from the server or client. The protocol does not make any provisions about how requests, responses and notifications are transferred between client and server. For example, client and server could be components within the same process exchanging JSON strings via method calls. They could also be different processes on the same or on different machines communicating via network sockets. == Registry == There are lists of LSP-compatible implementations, maintained by the community-driven Langserver.org or Microsoft.
Paul Christiano
Paul Christiano is an American researcher in the field of artificial intelligence (AI), with a specific focus on AI alignment, which is the subfield of AI safety research that aims to steer AI systems toward human interests. He serves as the Head of Safety for the Center for AI Standards and Innovation inside NIST. He formerly led the language model alignment team at OpenAI and became founder and head of the non-profit Alignment Research Center (ARC), which works on theoretical AI alignment and evaluations of machine learning models. In 2023, Christiano was named as one of the TIME 100 Most Influential People in AI (TIME100 AI). In September 2023, Christiano was appointed to the UK government's Frontier AI Taskforce advisory board. Before working at the Center for AI Standards and Innovation, he was an initial trustee on Anthropic's Long-Term Benefit Trust. == Education == Christiano attended the Harker School in San Jose, California. He competed on the U.S. team and won a silver medal at the 49th International Mathematics Olympiad (IMO) in 2008. In 2012, Christiano graduated from the Massachusetts Institute of Technology (MIT) with a degree in mathematics. At MIT, he researched data structures, quantum cryptography, and combinatorial optimization. He then went on to complete a PhD at the University of California, Berkeley. While at Berkeley, Christiano collaborated with researcher Katja Grace on AI Impacts, co-developing a preliminary methodology for comparing supercomputers to brains, using traversed edges per second (TEPS). He also experimented with putting Carl Shulman's donor lottery theory into practice, raising nearly $50,000 in a pool to be donated to a single charity. == Career == At OpenAI, Christiano co-authored the paper "Deep Reinforcement Learning from Human Preferences" (2017) and other works developing reinforcement learning from human feedback (RLHF). He is considered one of the principal architects of RLHF, which in 2017 was "considered a notable step forward in AI safety research", according to The New York Times. Other works such as "AI safety via debate" (2018) focus on the problem of scalable oversight – supervising AIs in domains where humans would have difficulty judging output quality. Christiano left OpenAI in 2021 to work on more conceptual and theoretical issues in AI alignment and subsequently founded the Alignment Research Center to focus on this area. One subject of study is the problem of eliciting latent knowledge from advanced machine learning models. ARC also develops techniques to identify and test whether an AI model is potentially dangerous. In April 2023, Christiano told The Economist that ARC was considering developing an industry standard for AI safety. As of April 2024, Christiano was listed as the head of AI safety for the US AI Safety Institute at NIST. One month earlier in March 2024, staff members and scientists at the institute threatened to resign upon being informed of Christiano's pending appointment to the role, stating that his ties to the effective altruism movement may jeopardize the AI Safety Institute's objectivity and integrity. === Views on AI risks === He is known for his views on the potential risks of advanced AI. In 2017, Wired magazine stated that Christiano and his colleagues at OpenAI weren't worried about the destruction of the human race by "evil robots", explaining that "[t]hey’re more concerned that, as AI progresses beyond human comprehension, the technology’s behavior may diverge from our intended goals." However, in a widely quoted interview with Business Insider in 2023, Christiano said that there is a “10–20% chance of AI takeover, [with] many [or] most humans dead.” He also conjectured a “50/50 chance of doom shortly after you have AI systems that are human level.” == Personal life == Christiano is married to Ajeya Cotra, a member of METR's technical staff.
AI Subtitle Generators: Free vs Paid (2026)
Looking for the best AI subtitle generator? An AI subtitle generator 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 subtitle generator 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.
Jiaya Jia
Jiaya Jia (Chinese: 贾佳亚) is a Chair Professor of the Department of Computer Science and Engineering at The Hong Kong University of Science and Technology (HKUST). He is an IEEE Fellow, the associate editor-in-chief of one of IEEE’s flagship and premier journals- Transactions on Pattern Analysis and Machine Intelligence (TPAMI), as well as on the editorial board of International Journal of Computer Vision (IJCV). == Early life and education == Jiaya Jia joined CUHK in 2004 as an assistant professor, and was promoted to full professor in 2015. He obtained his PhD degree in computer science jointly from Hong Kong University of Science and Technology and Microsoft Research in 2004. From March 2003 to August 2004, he was a visiting scholar at Microsoft. He conducted collaborative research at Adobe Research in 2007. == Career == Jiaya Jia is a distinguished scientist in the fields of computer vision and artificial intelligence. His research team at HKUST, DV Lab, is one of the largest vision AI research teams in the world and has been making significant contribution to advanced development of computer vision algorithms and technologies with focuses on image/video understanding, detection and segmentation, multi-modal AI, computational imaging, practical optimization, and advanced learning for visual content since 2000. Jiaya Jia has published 200+ top papers and was cited 80,000+ times on Google Scholar with H-Index 110+. 40+ PhDs and fellows from this group are now active in academia and industry, and have become prominent AI tech leaders as professors, directors in major research labs, and founders of several successful startups. Jiaya Jia assumes the position of associate editor-in-chief of IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) since 2021. He is also on the editorial board of International Journal of Computer Vision (IJCV). Jiaya Jia has served as the area chair of ICCV, CVPR, AAAI, ECCV, and several other premium international AI conferences for years. He was on program committees of major conferences in graphics and computational imaging, including ICCP, SIGGRAPH, and SIGGRAPH Asia. == Research == The research areas of Jiaya Jia are computer vision, large X models, and deep learning. Jiaya Jia has made outstanding contributions to computer vision technology, algorithms and engineering, and is among the world's leading experts in the field. His research partners include numerous renowned multinational technology companies, such as Microsoft, Qualcomm, Adobe, Intel, NVIDIA, Amazon, and Lenovo. Jia has cultivated a number of outstanding talents with Master's and PhDs who continue to engage in scientific research and development in computer vision. Many technologies in image analysis and processing developed by Jiaya Jia are still leading in the field worldwide. Wherein, his achievements in image deblurring, filtering, image sparse processing, multi-band image signal fusion and enhancement, large range motion estimation, texture and structure-based layering, etc. have been published in the industry's most influential conferences and publications, and implemented in the real-world applications. These achievements have demonstrated outstanding performance in established systems, and most of which are open source so as to enable wider applications across industries such as aviation, medical imaging, safety management, robotic design, meteorological analysis and many more. == Selected publications == In his over 20 years of research experience, Jiaya Jia has published 200+ top papers that have been cited more than 80,000 times. According to HKUST Website in August 2024, Jiaya Jia has accumulatively published over 200 scientific papers in books, journals and conferences, such as IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), International Journal of Computer Vision (IJCV) "Computer Vision and Pattern Recognition (CVPR)", and "International Conference on Computer Vision (ICCV)". Representative papers include: Jiaya Jia: Mathematical Models and Practical Solvers for Uniform Motion Deblurring (in Motion Deblurring: Algorithms and Systems), Cambridge University Press, ISBN 9781107044364, 2014; Jiaya Jia: “Matte Extraction” Book: Computer Vision - A Reference Guide, Springer, ISBN 9780387307718 Editor-in-chief: Ikeuchi, Katsushi; Jiaya Jia, Chi-Keung Tang：Image Stitching Using Structure Deformation，IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Vol. 30, No. 4, 2008; Jiaya Jia, Jian Sun, Chi-Keung Tang, Heung-Yeung Shum：Drag-and-Drop Pasting，ACM Transactions on Graphics (also in SIGGRAPH 2006), Vol. 25, No. 3, 2006. Xiaojuan Qi, Zheng zhe Liu, Renjie Liao, Philip HS Torr, Raquel Urtasun, Jiaya Jia：GeoNet++: Iterative Geometric Neural Network with Edge-Aware Refinement for Joint Depth and Surface Normal Estimation，IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). Accepted. == Selected honors and awards == ACM Fellow. 1st Place of WAD Drivable Area Segmentation Challenge 2018; 1st Place of LSUN'17 Instance and Semantic Segmentation Challenges; 1st Place of COCO Instance Segmentation Challenge 2017; 2nd Place in COCO Detection Challenge 2017; 1st Place of ImageNet Scene Parsing Challenge 2016 with the paper PSPNet presented in CVPR 2017.
NetOwl
NetOwl is a suite of multilingual text and identity analytics products that analyze big data in the form of text data – reports, web, social media, etc. – as well as structured entity data about people, organizations, places, and things. NetOwl utilizes artificial intelligence (AI)-based approaches, including natural language processing (NLP), machine learning (ML), and computational linguistics, to extract entities, relationships, and events; to perform sentiment analysis; to assign latitude/longitude to geographical references in text; to translate names written in foreign languages; and to perform name matching and identity resolution. NetOwl's uses include semantic search and discovery, geospatial analysis, intelligence analysis, content enrichment, compliance monitoring, cyber threat monitoring, risk management, and bioinformatics. == History == The first NetOwl product was NetOwl Extractor, which was initially released in 1996. Since then, Extractor has added many new capabilities, including relationship and event extraction, categorization, name translation, geotagging, and sentiment analysis, as well as entity extraction in other languages. Other products were added later to the NetOwl suite, namely TextMiner, NameMatcher, and EntityMatcher. NetOwl has participated in several 3rd party-sponsored text and entity analytics software benchmarking events. NetOwl Extractor was the top-scoring named entity extraction system at the DARPA-sponsored Message Understanding Conference MUC-6 and the top-scoring link and event extraction system in MUC-7. It was also the top-scoring system at several of the NIST-sponsored Automatic Content Extraction (ACE) evaluation tasks. NetOwl NameMatcher was the top-scoring system at the MITRE Challenge for Multicultural Person Name Matching. == Products == The NetOwl suite includes, among others, the following text and entity analytics products: === Text Analytics === NetOwl Extractor performs entity extraction from unstructured texts using natural language processing (NLP), machine learning (ML), and computational linguistics. Extractor also performs semantic relationship and event extraction as well as geotagging of text. It is used for a variety of data sources including both traditional sources (e.g., news, reports, web pages, email) and social media (e.g., Twitter, Facebook, chats, blogs). It runs on a variety of Big Data analytics platforms, including Apache Hadoop and LexisNexis’s High-Performance Computer Cluster (HPCC) technology. It has been integrated with a number of 3rd party analytical tools such as Esri ArcGIS and Google Earth/Maps. === Identity Analytics === NetOwl NameMatcher and EntityMatcher perform name matching and identity resolution for large multicultural and multilingual entity databases using machine learning (ML) and computational linguistics approaches. They are used for applications such as anti–money laundering (AML), watch lists, regulatory compliance, fraud detection, etc.
The Best Free AI Text-to-video Tool for Beginners
Shopping for the best AI text-to-video tool? An AI text-to-video tool 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 text-to-video tool slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.