AI Coder Youtube

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Termcap

Termcap (terminal capability) is a legacy software library and database used on Unix-like computers that enables programs to use display computer terminals in a terminal-independent manner, which greatly simplifies the process of writing portable text mode applications. It was superseded by the terminfo database used by ncurses, tput, and other programs. A termcap database can describe the capabilities of hundreds of different display terminals. This allows programs to have character-based display output, independent of the type of terminal. On-screen text editors such as vi and Emacs are examples of programs that may use termcap. Other programs are listed in the Termcap category. Access to the termcap database was usually provided by separate libraries, e.g. GNU Termcap. Examples of what the database describes: how many columns wide the display is what string to send to move the cursor to an arbitrary position (including how to encode the row and column numbers) how to scroll the screen up one or several lines how much padding is needed for such a scrolling operation. == History == Bill Joy wrote the first termcap library in 1978 for the Berkeley Unix operating system; it has since been ported to most Unix and Unix-like environments, even OS-9. Joy's design was reportedly influenced by the design of the terminal data store in the earlier Incompatible Timesharing System. == Data model == Termcap databases consist of one or more descriptions of terminals. === Indices === Each description must contain the canonical name of the terminal. It may also contain one or more aliases for the name of the terminal. The canonical name or aliases are the keys by which the library searches the termcap database. === Data values === The description contains one or more capabilities, which have conventional names. The capabilities are typed: boolean, numeric and string. The termcap library has no predetermined type for each capability name. It determines the types of each capability by the syntax: string capabilities have an "=" between the capability name and its value, numeric capabilities have a "#" between the capability name and its value, and boolean capabilities have no associated value (they are always true if specified). Applications which use termcap do expect specific types for the commonly used capabilities, and obtain the values of capabilities from the termcap database using library calls that return successfully only when the database contents matches the assumed type. === Hierarchy === Termcap descriptions can be constructed by including the contents of one description in another, suppressing capabilities from the included description or overriding or adding capabilities. No matter what storage model is used, the termcap library constructs the terminal description from the requested description, including, suppressing or overriding at the time of the request. == Storage model == Termcap data is stored as text, making it simple to modify. The text can be retrieved by the termcap library from files or environment variables. === Environment variables === The TERM environment variable contains the terminal type name. The TERMCAP environment variable may contain a termcap database. It is most often used to store a single termcap description, set by a terminal emulator to provide the terminal's characteristics to the shell and dependent programs. The TERMPATH environment variable is supported by newer termcap implementations and defines a search path for termcap files. === Flat file === The original (and most common) implementation of the termcap library retrieves data from a flat text file. Searching a large termcap file, e.g., 500 kB, can be slow. To aid performance, a utility such as reorder is used to put the most frequently used entries near the beginning of the file. === Hashed database === 4.4BSD based implementations of termcap store the terminal description in a hashed database (e.g., something like Berkeley DB version 1.85). These store two types of records: aliases which point to the canonical entry, and the canonical entry itself. The text of the termcap entry is stored literally. == Limitations and extensions == The original termcap implementation was designed to use little memory: the first name is two characters, to fit in 16 bits capability names are two characters descriptions are limited to 1023 characters. only one termcap entry with its definitions can be included, and must be at the end. Newer implementations of the termcap interface generally do not require the two-character name at the beginning of the entry. Capability names are still two characters in all implementations. The tgetent function used to read the terminal description uses a buffer whose size must be large enough for the data, and is assumed to be 1024 characters. Newer implementations of the termcap interface may relax this constraint by allowing a null pointer in place of the fixed buffer, or by hiding the data which would not fit, e.g., via the ZZ capability in NetBSD termcap. The terminfo library interface also emulates the termcap interface, and does not actually use the fixed-size buffer. The terminfo library's emulation of termcap allows multiple other entries to be included without restricting the position. A few other newer implementations of the termcap library may also provide this ability, though it is not well documented. == Obsolete features == A special capability, the "hz" capability, was defined specifically to support the Hazeltine 1500 terminal, which had the unfortunate characteristic of using the ASCII tilde character ('~') as a control sequence introducer. In order to support that terminal, not only did code that used the database have to know about using the tilde to introduce certain control sequences, but it also had to know to substitute another printable character for any tildes in the displayed text, since a tilde in the text would be interpreted by the terminal as the start of a control sequence, resulting in missing text and screen garbling. Additionally, attribute markers (such as start and end of underlining) themselves took up space on the screen. Comments in the database source code often referred to this as "Hazeltine braindamage". Since the Hazeltine 1500 was a widely used terminal in the late 1970s, it was important for applications to be able to deal with its limitations.
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AI Pair Programmers Reviews: What Actually Works in 2026

Curious about the best AI pair programmer? An AI pair programmer is software that uses machine learning to help you get more done — it combines speed, accuracy, and an interface that just works. Hands-on testing shows real-world results vary, so a short free trial is the smartest way to decide. Whether you are a beginner or a pro, the right AI pair programmer 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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Sepp Hochreiter

Josef "Sepp" Hochreiter (born 14 February 1967) is a German computer scientist. Since 2018 he has led the Institute for Machine Learning at the Johannes Kepler University of Linz after having led the Institute of Bioinformatics from 2006 to 2018. In 2017 he became the head of the Linz Institute of Technology (LIT) AI Lab. Hochreiter is also a founding director of the Institute of Advanced Research in Artificial Intelligence (IARAI). Previously, he was at Technische Universität Berlin, at University of Colorado Boulder, and at the Technical University of Munich. He is a chair of the Critical Assessment of Massive Data Analysis (CAMDA) conference. Hochreiter has made contributions in the fields of machine learning, deep learning and bioinformatics, most notably the development of the long short-term memory (LSTM) neural network architecture, but also in meta-learning, reinforcement learning and biclustering with application to bioinformatics data. == Scientific career == === Long short-term memory (LSTM) === Hochreiter developed the long short-term memory (LSTM) neural network architecture in his diploma thesis in 1991 leading to the main publication in 1997. LSTM overcomes the problem of numerical instability in training recurrent neural networks (RNNs) that prevents them from learning from long sequences (vanishing or exploding gradient). In 2007, Hochreiter and others successfully applied LSTM with an optimized architecture to very fast protein homology detection without requiring a sequence alignment. LSTM networks have also been used in Google Voice for transcription and search, and in the Google Allo chat app for generating response suggestion with low latency. === Other machine learning contributions === Beyond LSTM, Hochreiter has developed "Flat Minimum Search" to increase the generalization of neural networks and introduced rectified factor networks (RFNs) for sparse coding which have been applied in bioinformatics and genetics. Hochreiter introduced modern Hopfield networks with continuous states and applied them to the task of immune repertoire classification. Hochreiter worked with Jürgen Schmidhuber in the field of reinforcement learning on actor-critic systems that learn by "backpropagation through a model". Hochreiter has been involved in the development of factor analysis methods with application to bioinformatics, including FABIA for biclustering, HapFABIA for detecting short segments of identity by descent and FARMS for preprocessing and summarizing high-density oligonucleotide DNA microarrays to analyze RNA gene expression. In 2006, Hochreiter and others proposed an extension of the support vector machine (SVM), the "Potential Support Vector Machine" (PSVM), which can be applied to non-square kernel matrices and can be used with kernels that are not positive definite. Hochreiter and his collaborators have applied PSVM to feature selection, including gene selection for microarray data. == Awards == Hochreiter was awarded the IEEE CIS Neural Networks Pioneer Prize in 2021 for his work on LSTM.
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Noam Slonim

Noam Slonim (Hebrew: נעם סלונים; born in Jerusalem) is an Israeli computer scientist, specializing in Natural Language Processing and the application of Large language models. He is a Research Scientist at Google Research Israel (since September 2025) and formerly an IBM Distinguished Engineer. He founded and served as Principal Investigator of Project Debater and led Language Model Utilization at IBM Research. Beyond his scientific achievements, Slonim had a writing and media career. He was a writer for Season 4 of The Cameric Five TV comedy show, published a weekly column in Haaretz on brain science, and co-created and wrote the Israeli sitcom Puzzle. He was also the head writer for Seasons 2 and 3 of the sitcom Ha-movilim and featured in the 2020 documentary The Debater. In October 2025, his debut novel, Questionable Memories, was published by Kinneret Publishing Group. == Education and research interests == Slonim graduated from the Hebrew University of Jerusalem in 1996 with a B.S. degree in Computer Science, Physics, and Mathematics. In 2002 he completed Ph.D. summa cum laude at the Interdisciplinary Center for Neural Computation at the Hebrew University, under the supervision of Professor Naftali Tishby. His thesis focused on the theory and applications of the Information Bottleneck method. From 2003 till 2006 he did post-doctoral studies at the Lewis-Sigler Institute for Integrative Genomics at Princeton University, working with Professor Bill Bialek and Professor Saeed Tavazoie. He joined IBM Research in 2007. Slonim holds over 30 patents (granted or pending) and has co-authored more than 100 scientific publications. In 2025, he joined Google Research Israel as a research scientist. == Research activities == From 1998 to 2003 he worked on the theory and applications of the Information Bottleneck method, suggesting various cluster analysis algorithms inspired by this method, and demonstrating the practical value of these algorithms on various domains. From 2003 to 2006 he worked on developing Machine Learning algorithms that rely on Information Theory concepts, and applied these algorithms to the analysis of various types of Genomics data. In 2011 he proposed to develop the first Artificial Intelligence system that can meaningfully participate in a full live debate with an expert human debater. This work gave rise to Project Debater, that debated expert human debaters in several live events during 2018 and 2019. In 2020, Slonim delivered the opening keynote at the EMNLP conference, describing the IBM Research work on developing Project Debater. From 2022 to 2025, he led IBM Research efforts applying large language models to practical use cases; in 2025 he moved to Google Research Israel as a Research Scientist. == Writing and video career == In 1996 Slonim was a writer for Season 4 of The Cameric Five TV comedy show. In 1997–1998 he published a weekly column in Haaretz newspaper, focused on brain science research. In 1997–1999 he co-created and co-wrote the Israeli sitcom, Puzzle. In 2008–2010 he was the head writer of Season 2 and Season 3 of the Israeli Sitcom, Ha-movilim. In 2020 he was featured in the documentary The Debater, an official selection of the 2020 Copenhagen International Documentary Film Festival. In 2025, his debut novel, Questionable Memories, was published by Kinneret Publishing Group.
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Attensity

Attensity was an American company that provided social analytics and engagement applications for social customer relationship management (social CRM). Attensity's text analytics software applications extracted facts, relationships and sentiment from unstructured data. == History == Attensity was founded in 2000. An early investor in Attensity was In-Q-Tel, which funds technology to support the missions of the US Government and the broader DOD. InTTENSITY, an independent company that has combined Inxight with Attensity Software (the only joint development project that combines two InQTel funded software packages), was the exclusive distributor and outlet for Attensity in the Federal Market. In 2009, Attensity Corp., then based in Palo Alto, merged with Germany's Empolis and Living-e AG to form Attensity Group. In 2010, Attensity Group acquired Biz360, a provider of social media monitoring and market intelligence solutions. In early 2012, Attensity Group divested itself of the Empolis business unit via a management buyout; that unit currently conducts business under its pre-merger name. Attensity Group was a closely held private company. Its majority shareholder was Aeris Capital, a private Swiss investment office advising a high-net-worth individual and his charitable foundation. Foundation Capital, Granite Ventures, and Scale Venture Partners were among Biz360's investors and thus became shareholders in Attensity Group. In February 2016, Attensity's IP assets were acquired by InContact, and Attensity closed.
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How to Choose an AI Chatbot

Looking for the best AI chatbot? An AI chatbot 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 chatbot 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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Jian Ma (computational biologist)

Jian Ma (Chinese: 马坚) is an American computer scientist and computational biologist. He is the Ray and Stephanie Lane Professor of Computational Biology in the School of Computer Science at Carnegie Mellon University. He is a faculty member in the Ray and Stephanie Lane Computational Biology Department. His lab develops AI/ML methods to study the structure and function of the human genome and cellular organization and their implications for health and disease. During his Ph.D. and postdoc training, he developed algorithms to reconstruct the ancestral mammalian genome and evolutionary history. His research group has recently pioneered a series of new machine learning solutions for 3D genome organization, single-cell epigenomics, spatial omics, and complex molecular interactions. His lab also explores large language models to uncover gene regulatory mechanisms and the intricate connections among cellular components, with the aim of driving discovery and guiding experimentation. He received an NSF CAREER award in 2011. In 2020, he was awarded a Guggenheim Fellowship in Computer Science. He received the Allen Newell Award for Research Excellence (2025). He is an elected Fellow of the American Association for the Advancement of Science, the American Institute for Medical and Biological Engineering, the International Society for Computational Biology, and the Association for Computing Machinery. He leads an NIH 4D Nucleome Center to develop machine learning algorithms to better understand the cell nucleus. He served as the Program Chair for RECOMB 2024. He is also a member of the Scientific Advisory Board of the Chan Zuckerberg Biohub Chicago (CZ Biohub Chicago) and the RECOMB Steering Committee. In 2024, he launched the Center for AI-Driven Biomedical Research (AI4BIO) at CMU, which will be a catalyst for innovations at the intersection of AI and biomedicine across the School of Computer Science and campus. == Selected Recent Publications == Chen V#, Yang M#, Cui W, Kim JS, Talwalkar A, and Ma J. Applying interpretable machine learning in computational biology - pitfalls, recommendations and opportunities for new developments. Nature Methods, 21(8):1454-1461, 2024. Xiong K#, Zhang R#, and Ma J. scGHOST: Identifying single-cell 3D genome subcompartments. Nature Methods, 21(5):814-822, 2024. Zhou T, Zhang R, Jia D, Doty RT, Munday AD, Gao D, Xin L, Abkowitz JL, Duan Z, and Ma J. GAGE-seq concurrently profiles multiscale 3D genome organization and gene expression in single cells. Nature Genetics, 56(8):1701-1711, 2024. Zhang Y, Boninsegna L, Yang M, Misteli T, Alber F, and Ma J. Computational methods for analysing multiscale 3D genome organization. Nature Reviews Genetics, 5(2):123-141, 2024. Chidester B#, Zhou T#, Alam S, and Ma J. SPICEMIX enables integrative single-cell spatial modeling of cell identity. Nature Genetics, 55(1):78-88, 2023. [Cover Article] Zhang R#, Zhou T#, and Ma J. Ultrafast and interpretable single-cell 3D genome analysis with Fast-Higashi. Cell Systems, 13(10):P798-807.E6, 2022. [Cover Article] Zhu X#, Zhang Y#, Wang Y, Tian D, Belmont AS, Swedlow JR, and Ma J. Nucleome Browser: An integrative and multimodal data navigation platform for 4D Nucleome. Nature Methods, 19(8):911-913, 2022. Zhang R, Zhou T, and Ma J. Multiscale and integrative single-cell Hi-C analysis with Higashi. Nature Biotechnology, 40:254–261, 2022.
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Noémie Elhadad

Noémie Elhadad is an American data scientist who is an associate professor of biomedical informatics at the Columbia University Vagelos College of Physicians and Surgeons. As of 2022, she serves as the chair of the Department of Biomedical Informatics. Her research considers machine learning in bioinformatics, natural language processing and medicine. == Early life and education == Elhadad studied computer software engineering at École nationale supérieure d'électronique, informatique, télécommunications, mathématique et mécanique de Bordeaux (ENSEIRB). She completed her doctoral research at Columbia University. She was based in the Department of Computer Science, where she developed patient-focused text summaries of clinical literature. == Research and career == Elhadad joined the faculty at the City College of New York. In 2007 she joined the Department of Biomedical Informatics at Columbia University. She was made Chair of the Health Analytics Center at the Columbia Data Science Institute in 2013. Her research considers how clinical data, electronic health records and patient-generated data can enhance access to information for researchers, patients and physicians. She developed an artificial intelligence tool that supported patients in the NewYork-Presbyterian Hospital. Elhadad is interested in using data to advance women's health. She led the Citizen Endo Project that looks to comprehensively describe how patients experience endometriosis. It was built using principles of citizen science, using patient testimonials from focus groups in New York City and data aggregation. She created the app, Phendo, which asks patients about their experience of the disease. The name Phendo is a portmanteau of phenotyping endometriosis. Elhadad was announced as chair of the Department of Biomedical Informatics in December 2022. == Selected publications == Caruana, Rich; Lou, Yin; Gehrke, Johannes; Koch, Paul; Sturm, Marc; Elhadad, Noemie (August 10, 2015). "Intelligible Models for HealthCare". Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM. pp. 1721–1730. doi:10.1145/2783258.2788613. ISBN 9781450336642. S2CID 14190268. Chaitanya Shivade; Preethi Raghavan; Eric Fosler-Lussier; Peter J Embi; Noemie Elhadad; Stephen B Johnson; Albert M Lai (November 7, 2013). "A review of approaches to identifying patient phenotype cohorts using electronic health records". Journal of the American Medical Informatics Association. 21 (2): 221–230. doi:10.1136/AMIAJNL-2013-001935. ISSN 1067-5027. PMC 3932460. PMID 24201027. Wikidata Q37598951. Shivade, Chaitanya; Raghavan, Preethi; Fosler-Lussier, Eric; Embi, Peter J; Elhadad, Noemie; Johnson, Stephen B; Lai, Albert M (March 2014). "A review of approaches to identifying patient phenotype cohorts using electronic health records". Journal of the American Medical Informatics Association. 21 (2): 221–230. doi:10.1136/amiajnl-2013-001935. ISSN 1067-5027. PMC 3932460. PMID 24201027. == Personal life == Elhadad suffers from endometriosis.
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Behavior informatics

Behavior informatics (BI) is the informatics of behaviors so as to obtain behavior intelligence and behavior insights. BI is a research method combining science and technology, specifically in the area of engineering. The purpose of BI includes analysis of current behaviors as well as the inference of future possible behaviors. This occurs through pattern recognition. Different from applied behavior analysis from the psychological perspective, BI builds computational theories, systems and tools to qualitatively and quantitatively model, represent, analyze, and manage behaviors of individuals, groups and/or organizations. BI is built on classic study of behavioral science, including behavior modeling, applied behavior analysis, behavior analysis, behavioral economics, and organizational behavior. Typical BI tasks consist of individual and group behavior formation, representation, computational modeling, analysis, learning, simulation, and understanding of behavior impact, utility, non-occurring behaviors, etc. for behavior intervention and management. The Behavior Informatics approach to data utilizes cognitive as well as behavioral data. By combining the data, BI has the potential to effectively illustrate the big picture when it comes to behavioral decisions and patterns. One of the goals of BI is also to be able to study human behavior while eliminating issues like self-report bias. This creates more reliable and valid information for research studies. == Behavior == From an Informatics perspective, a behavior consists of three key elements: actors (behavioral subjects and objects), operations (actions, activities) and interactions (relationships), and their properties. A behavior can be represented as a behavior vector, all behaviors of an actor or an actor group can be represented as behavior sequences and multi-dimensional behavior matrix. The following table explains some of the elements of behavior. Behavior Informatics takes into account behavior when analyzing business patterns and intelligence. The inclusion of behavior in these analyses provides prominent information on social and driving factors of patterns. == Applications == Behavior Informatics is being used in a variety of settings, including but not limited to health care management, telecommunications, marketing, and security. Behavior Informatics provides a manner in which to analyze and organize the many aspects that go into a person's health care needs and decisions. When it comes to business models, behavior informatics may be utilized for a similar role. Organizations implement behavior informatics to enhance business structure and regime, where it helps moderate ideal business decisions and situations.
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Deterministic acyclic finite state automaton

In computer science, a deterministic acyclic finite state automaton (DAFSA), is a data structure that represents a set of strings, and allows for a query operation that tests whether a given string belongs to the set in time proportional to its length. Algorithms exist to construct and maintain such automata, while keeping them minimal. DAFSA is the rediscovery of a data structure called Directed Acyclic Word Graph (DAWG), although the same name had already been given to a different data structure which is related to suffix automaton. A DAFSA is a special case of a finite state recognizer that takes the form of a directed acyclic graph with a single source vertex (a vertex with no incoming edges), in which each edge of the graph is labeled by a letter or symbol, and in which each vertex has at most one outgoing edge for each possible letter or symbol. The strings represented by the DAFSA are formed by the symbols on paths in the graph from the source vertex to any sink vertex (a vertex with no outgoing edges). In fact, a deterministic finite state automaton is acyclic if and only if it recognizes a finite set of strings. == History == Blumer et al first defined terminology Directed Acyclic Word Graph (DAWG) in 1983. Appel and Jacobsen used the same naming for a different data structure in 1988. Independent of earlier work, Daciuk et al rediscovered the latter data structure in 2000 but called it DAFSA. == Comparison to tries == By allowing the same vertices to be reached by multiple paths, a DAFSA may use significantly fewer vertices than the strongly related trie data structure. Consider, for example, the four English words "tap", "taps", "top", and "tops". A trie for those four words would have 12 vertices, one for each of the strings formed as a prefix of one of these words, or for one of the words followed by the end-of-string marker. However, a DAFSA can represent these same four words using only six vertices vi for 0 ≤ i ≤ 5, and the following edges: an edge from v0 to v1 labeled "t", two edges from v1 to v2 labeled "a" and "o", an edge from v2 to v3 labeled "p", an edge v3 to v4 labeled "s", and edges from v3 and v4 to v5 labeled with the end-of-string marker. There is a tradeoff between memory and functionality, because a standard DAFSA can tell you if a word exists within it, but it cannot point you to auxiliary information about that word, whereas a trie can. The primary difference between DAFSA and trie is the elimination of suffix and infix redundancy in storing strings. The trie eliminates prefix redundancy since all common prefixes are shared between strings, such as between doctors and doctorate the doctor prefix is shared. In a DAFSA common suffixes are also shared, for words that have the same set of possible suffixes as each other. For dictionary sets of common English words, this translates into major memory usage reduction. Because the terminal nodes of a DAFSA can be reached by multiple paths, a DAFSA cannot directly store auxiliary information relating to each path, e.g. a word's frequency in the English language. However, if for each node we store the number of unique paths through that point in the structure, we can use it to retrieve the index of a word, or a word given its index. The auxiliary information can then be stored in an array.
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Vlado Keselj

Vlado Keselj (Vlado Kešelj) is a Serbian-Canadian computer scientist known for his research in natural language processing and authorship attribution. He is a professor at Dalhousie University. == Education == As a high school student in Yugoslavia, Keselj competed in the 1987 International Mathematical Olympiad, earning a bronze medal. He earned his Ph.D. in 2002 at the University of Waterloo, with the dissertation Modular Stochastic HPSGs for Question Answering supervised by Nick Cercone. == Awards == Vlado Keselj is a recipient of the 2019 CAIAC Distinguished Service Award, awarded by the Canadian Artificial Intelligence Association (CAIAC). == Selected publications == Kešelj, V., Peng, F., Cercone, N., & Thomas, C. (2003, August). N-gram-based author profiles for authorship attribution. In Proceedings of the Conference of the Pacific Association for Computational Linguistics, PACLING 2003 (Vol. 3, pp. 255–264).
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Is an AI Marketing Tool Worth It in 2026?

Trying to pick the best AI marketing tool? An AI marketing tool 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 marketing tool 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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Czekanowski distance

The Czekanowski distance (sometimes shortened as CZD) is a per-pixel quality metric that estimates quality or similarity by measuring differences between pixels. Because it compares vectors with strictly non-negative elements, it is often used to compare colored images, as color values cannot be negative. This different approach has a better correlation with subjective quality assessment than PSNR. == Definition == Androutsos et al. give the Czekanowski coefficient as follows: d z ( i , j ) = 1 − 2 ∑ k = 1 p min ( x i k , x j k ) ∑ k = 1 p ( x i k + x j k ) {\displaystyle d_{z}(i,j)=1-{\frac {2\sum _{k=1}^{p}{\text{min}}(x_{ik},\ x_{jk})}{\sum _{k=1}^{p}(x_{ik}+x_{jk})}}} Where a pixel x i {\displaystyle x_{i}} is being compared to a pixel x j {\displaystyle x_{j}} on the k-th band of color – usually one for each of red, green and blue. For a pixel matrix of size M × N {\displaystyle M\times N} , the Czekanowski coefficient can be used in an arithmetic mean spanning all pixels to calculate the Czekanowski distance as follows: 1 M N ∑ i = 0 M − 1 ∑ j = 0 N − 1 ( 1 − 2 ∑ k = 1 3 min ( A k ( i , j ) , B k ( i , j ) ) ∑ k = 1 3 ( A k ( i , j ) + B k ( i , j ) ) ) {\displaystyle {\frac {1}{MN}}\sum _{i=0}^{M-1}\sum _{j=0}^{N-1}{\begin{pmatrix}1-{\frac {2\sum _{k=1}^{3}{\text{min}}(A_{k}(i,j),\ B_{k}(i,j))}{\sum _{k=1}^{3}(A_{k}(i,j)+B_{k}(i,j))}}\end{pmatrix}}} Where A k ( i , j ) {\displaystyle A_{k}(i,j)} is the (i, j)-th pixel of the k-th band of a color image and, similarly, B k ( i , j ) {\displaystyle B_{k}(i,j)} is the pixel that it is being compared to. == Uses == In the context of image forensics – for example, detecting if an image has been manipulated –, Rocha et al. report the Czekanowski distance is a popular choice for Color Filter Array (CFA) identification.
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Is an AI Virtual Assistant Worth It in 2026?

Shopping for the best AI virtual assistant? An AI virtual assistant 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 virtual assistant 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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How to Choose an AI Video Generator

Looking for the best AI video generator? An AI video 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 video 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.
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