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Charge-coupled device

A charge-coupled device (CCD) is an integrated circuit containing an array of linked, or coupled, capacitors. Under the control of an external circuit, each capacitor can transfer its electric charge to a neighboring capacitor. CCD sensors are a major technology used in digital imaging. In a CCD image sensor, pixels are represented by p-doped metal–oxide–semiconductor (MOS) capacitors. These MOS capacitors, the basic building blocks of a CCD, are biased above the threshold for inversion when image acquisition begins, allowing the conversion of incoming photons into electron charges at the semiconductor-oxide interface; the CCD is then used to read out these charges. Although CCDs are not the only technology to allow for light detection, CCD image sensors are widely used in professional, medical, and scientific applications where high-quality image data are required. In applications with less exacting quality demands, such as consumer and professional digital cameras, active pixel sensors, also known as CMOS sensors (complementary MOS sensors), are generally used. However, the large quality advantage CCDs enjoyed early on has narrowed over time and since the late 2010s CMOS sensors are the dominant technology, having largely if not completely replaced CCD image sensors. == History == The basis for the CCD is the metal–oxide–semiconductor (MOS) structure, with MOS capacitors being the basic building blocks of a CCD, and a depleted MOS structure used as the photodetector in early CCD devices. In the late 1960s, Willard Boyle and George E. Smith at Bell Labs were researching MOS technology while working on semiconductor bubble memory. They realized that an electric charge was the analog of the magnetic bubble and that it could be stored on a tiny MOS capacitor. As it was fairly straightforward to fabricate a series of MOS capacitors in a row, they connected a suitable voltage to them so that the charge could be stepped along from one to the next. This led to the invention of the charge-coupled device by Boyle and Smith in 1969. They conceived of the design of what they termed, in their notebook, "Charge 'Bubble' Devices". The initial paper describing the concept in April 1970 listed possible uses as memory, a delay line, and an imaging device. The device could also be used as a shift register. The essence of the design was the ability to transfer charge along the surface of a semiconductor from one storage capacitor to the next. The first experimental device demonstrating the principle was a row of closely spaced metal squares on an oxidized silicon surface electrically accessed by wire bonds. It was demonstrated by Gil Amelio, Michael Francis Tompsett and George Smith in April 1970. This was the first experimental application of the CCD in image sensor technology, and used a depleted MOS structure as the photodetector. The first patent (U.S. patent 4,085,456) on the application of CCDs to imaging was assigned to Tompsett, who filed the application in 1971. The first working CCD made with integrated circuit technology was a simple 8-bit shift register, reported by Tompsett, Amelio and Smith in August 1970. This device had input and output circuits and was used to demonstrate its use as a shift register and as a crude eight pixel linear imaging device. Development of the device progressed at a rapid rate. By 1971, Bell researchers led by Michael Tompsett were able to capture images with simple linear devices. Several companies, including Fairchild Semiconductor, RCA and Texas Instruments, picked up on the invention and began development programs. Fairchild's effort, led by ex-Bell researcher Gil Amelio, was the first with commercial devices, and by 1974 had a linear 500-element device and a 2D 100 × 100 pixel device. Peter L. P. Dillon, a scientist at Kodak Research Labs, invented the first color CCD image sensor by overlaying a color filter array on this Fairchild 100 x 100 pixel Interline CCD starting in 1974. Steven Sasson, an electrical engineer working for the Kodak Apparatus Division, invented a digital still camera using this same Fairchild 100 × 100 CCD in 1975. The interline transfer (ILT) CCD device was proposed by L. Walsh and R. Dyck at Fairchild in 1973 to reduce smear and eliminate a mechanical shutter. To further reduce smear from bright light sources, the frame-interline-transfer (FIT) CCD architecture was developed by K. Horii, T. Kuroda and T. Kunii at Matsushita (now Panasonic) in 1981. The first KH-11 KENNEN reconnaissance satellite equipped with charge-coupled device array (800 × 800 pixels) technology for imaging was launched in December 1976. Under the leadership of Kazuo Iwama, Sony started a large development effort on CCDs involving a significant investment. Eventually, Sony managed to mass-produce CCDs for their camcorders. Before this happened, Iwama died in August 1982. Subsequently, a CCD chip was placed on his tombstone to acknowledge his contribution. The first mass-produced consumer CCD video camera, the CCD-G5, was released by Sony in 1983, based on a prototype developed by Yoshiaki Hagiwara in 1981. Early CCD sensors suffered from shutter lag. This was largely resolved with the invention of the pinned photodiode (PPD). It was invented by Nobukazu Teranishi, Hiromitsu Shiraki and Yasuo Ishihara at NEC in 1980. They recognized that lag can be eliminated if the signal carriers could be transferred from the photodiode to the CCD. This led to their invention of the pinned photodiode, a photodetector structure with low lag, low noise, high quantum efficiency and low dark current. It was first publicly reported by Teranishi and Ishihara with A. Kohono, E. Oda and K. Arai in 1982, with the addition of an anti-blooming structure. The new photodetector structure invented at NEC was given the name "pinned photodiode" (PPD) by B.C. Burkey at Kodak in 1984. In 1987, the PPD began to be incorporated into most CCD devices, becoming a fixture in consumer electronic video cameras and then digital still cameras. Since then, the PPD has been used in nearly all CCD sensors and then CMOS sensors. In January 2006, Boyle and Smith were awarded the National Academy of Engineering Charles Stark Draper Prize, and in 2009 they were awarded the Nobel Prize for Physics for their invention of the CCD concept. Michael Tompsett was awarded the 2010 National Medal of Technology and Innovation, for pioneering work and electronic technologies including the design and development of the first CCD imagers. He was also awarded the 2012 IEEE Edison Medal for "pioneering contributions to imaging devices including CCD Imagers, cameras and thermal imagers". == Basics of operation == In a CCD for capturing images, there is a photoactive region (an epitaxial layer of silicon), and a transmission region made out of a shift register (the CCD, properly speaking). An image is projected through a lens onto the capacitor array (the photoactive region), causing each capacitor to accumulate an electric charge proportional to the light intensity at that location. A one-dimensional array, used in line-scan cameras, captures a single slice of the image, whereas a two-dimensional array, used in video and still cameras, captures a two-dimensional picture corresponding to the scene projected onto the focal plane of the sensor. Once the array has been exposed to the image, a control circuit causes each capacitor to transfer its contents to its neighbor (operating as a shift register). The last capacitor in the array dumps its charge into a charge amplifier, which converts the charge into a voltage. By repeating this process, the controlling circuit converts the entire contents of the array in the semiconductor to a sequence of voltages. In a digital device, these voltages are then sampled, digitized, and usually stored in memory; in an analog device (such as an analog video camera), they are processed into a continuous analog signal (e.g. by feeding the output of the charge amplifier into a low-pass filter), which is then processed and fed out to other circuits for transmission, recording, or other processing. == Detailed physics of operation == === Charge generation === Before the MOS capacitors are exposed to light, they are biased into the depletion region; in n-channel CCDs, the silicon under the bias gate is slightly p-doped or intrinsic. The gate is then biased at a positive potential, above the threshold for strong inversion, which will eventually result in the creation of an n channel below the gate as in a MOSFET. However, it takes time to reach this thermal equilibrium: up to hours in high-end scientific cameras cooled at low temperature. Initially after biasing, the holes are pushed far into the substrate, and no mobile electrons are at or near the surface; the CCD thus operates in a non-equilibrium state called deep depletion. Then, when electron–hole pairs are generated in the depletion region, they are separated by the electric field, the elec
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Andrei Knyazev (mathematician)

Andrew Knyazev is an American mathematician. He graduated from the Faculty of Computational Mathematics and Cybernetics of Moscow State University under the supervision of Evgenii Georgievich D'yakonov (Russian: Евгений Георгиевич Дьяконов) in 1981 and obtained his PhD in Numerical Mathematics at the Russian Academy of Sciences under the supervision of Vyacheslav Ivanovich Lebedev (Russian: Вячеслав Иванович Лебедев) in 1985. He worked at the Kurchatov Institute between 1981–1983, and then to 1992 at the Marchuk Institute of Numerical Mathematics (Russian: ru:Институт вычислительной математики имени Г. И. Марчука РАН) of the Russian Academy of Sciences, headed by Gury Marchuk (Russian: Гурий Иванович Марчук). From 1993–1994, Knyazev held a visiting position at the Courant Institute of Mathematical Sciences of New York University, collaborating with Olof B. Widlund. From 1994 until retirement in 2014, he was a Professor of Mathematics at the University of Colorado Denver, supported by the National Science Foundation and United States Department of Energy grants. He was a recipient of the 2008 Excellence in Research Award, the 2000 college Teaching Excellence Award, and a finalist of the CU President's Faculty Excellence Award for Advancing Teaching and Learning through Technology in 1999. He was awarded the title of Professor Emeritus at the University of Colorado Denver and named the SIAM Fellow Class of 2016 and AMS Fellow Class of 2019. From 2012–2018, Knyazev worked at the Mitsubishi Electric Research Laboratories on algorithms for image and video processing, data sciences, optimal control, and material sciences, resulting in dozens of publications and 13 patent applications. Since 2018, he contributed to numerical techniques in quantum computing at Zapata Computing, real-time embedded anomaly detection in automotive data, and algorithms for silicon photonics-based hardware. Knyazev is mostly known for his work in numerical solution of large sparse eigenvalue problems, particularly preconditioning and the iterative method LOBPCG. Knyazev's implementation of LOBPCG is available in many open source software packages, e.g., BLOPEX, SciPy, and ABINIT. Knyazev collaborated with John Osborn on the theory of the Ritz method in the finite element method context and with Nikolai Sergeevich Bakhvalov (Russian: Николай Серге́евич Бахвалов) (Erdős number 3 via Leonid Kantorovich) on numerical solution of elliptic partial differential equations with large jumps in the main coefficients. Jointly with his Ph.D. students, Knyazev pioneered using majorization for bounds in the Rayleigh–Ritz method (see and references there) and contributed to the theory of angles between flats.
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Glushkov's construction algorithm

In computer science theory – particularly formal language theory – Glushkov's construction algorithm, invented by Victor Mikhailovich Glushkov, transforms a given regular expression into an equivalent nondeterministic finite automaton (NFA). Thus, it forms a bridge between regular expressions and nondeterministic finite automata: two abstract representations of the same class of formal languages. A regular expression may be used to conveniently describe an advanced search pattern in a "find and replace"–like operation of a text processing utility. Glushkov's algorithm can be used to transform it into an NFA, which furthermore is small by nature, as the number of its states equals the number of symbols of the regular expression, plus one. Subsequently, the NFA can be made deterministic by the powerset construction and then be minimized to get an optimal automaton corresponding to the given regular expression. The latter format is best suited for execution on a computer. From another, more theoretical point of view, Glushkov's algorithm is a part of the proof that NFA and regular expressions both accept exactly the same languages; that is, the regular languages. The converse of Glushkov's algorithm is Kleene's algorithm, which transforms a finite automaton into a regular expression. The automaton obtained by Glushkov's construction is the same as the one obtained by Thompson's construction algorithm, once its ε-transitions are removed. Glushkov's construction algorithm is also called The algorithm of Berry-Sethi, named after Gérard Berry and Ravi Sethi who worked on this construction. == Construction == Given a regular expression e, the Glushkov Construction Algorithm creates a non-deterministic automaton that accepts the language L ( e ) {\displaystyle L(e)} accepted by e. The construction uses four steps: === Step 1 === Linearisation of the expression. Each letter of the alphabet appearing in the expression e is renamed, so that each letter occurs at most once in the new expression e ′ {\displaystyle e'} . Glushkov's construction essentially relies on the fact that e ′ {\displaystyle e'} represents a local language L ( e ′ ) {\displaystyle L(e')} . Let A be the old alphabet and let B be the new one. === Step 2a === Computation of the sets P ( e ′ ) {\displaystyle P(e')} , D ( e ′ ) {\displaystyle D(e')} , and F ( e ′ ) {\displaystyle F(e')} . The first, P ( e ′ ) {\displaystyle P(e')} , is the set of letters which occurs as first letter of a word of L ( e ′ ) {\displaystyle L(e')} . The second, D ( e ′ ) {\displaystyle D(e')} , is the set of letters that can end a word of L ( e ′ ) {\displaystyle L(e')} . The last one, F ( e ′ ) {\displaystyle F(e')} , is the set of letter pairs that can occur in words of L ( e ′ ) {\displaystyle L(e')} , i.e. it is the set of factors of length two of the words of L ( e ′ ) {\displaystyle L(e')} . Those sets are mathematically defined by P ( e ′ ) = { x ∈ B ∣ x B ∗ ∩ L ( e ′ ) ≠ ∅ } {\displaystyle P(e')=\{x\in B\mid xB^{}\cap L(e')\neq \emptyset \}} , D ( e ′ ) = { y ∈ B ∣ B ∗ y ∩ L ( e ′ ) ≠ ∅ } {\displaystyle D(e')=\{y\in B\mid B^{}y\cap L(e')\neq \emptyset \}} , F ( e ′ ) = { u ∈ B 2 ∣ B ∗ u B ∗ ∩ L ( e ′ ) ≠ ∅ } {\displaystyle F(e')=\{u\in B^{2}\mid B^{}uB^{}\cap L(e')\neq \emptyset \}} . They are computed by induction over the structure of the expression, as explained below. === Step 2b === Computation of the set Λ ( e ′ ) {\displaystyle \Lambda (e')} which contains the empty word ε {\displaystyle \varepsilon } if this word belongs to L ( e ′ ) {\displaystyle L(e')} , and is the empty set otherwise. Formally, this is Λ ( e ′ ) = { ε } ∩ L ( e ′ ) {\displaystyle \Lambda (e')=\{\varepsilon \}\cap L(e')} . === Step 3 === Computation of automaton recognizing the local language, as defined by P ( e ′ ) {\displaystyle P(e')} , D ( e ′ ) {\displaystyle D(e')} , F ( e ′ ) {\displaystyle F(e')} , and Λ ( e ′ ) {\displaystyle \Lambda (e')} . By definition, the local language defined by the sets P, D, and F is the set of words which begin with a letter of P, end by a letter of D, and whose factors of length 2 belong to F, optionally also including the empty word; that is, it is the language: L ′ = ( P B ∗ ∩ B ∗ D ) ∖ B ∗ ( B 2 ∖ F ) B ∗ ∪ Λ ( e ′ ) {\displaystyle L'=(PB^{}\cap B^{}D)\setminus B^{}(B^{2}\setminus F)B^{}\cup \Lambda (e')} . Strictly speaking, it is the computation of the automaton for the local language denoted by this linearised expression that is Glushkov's construction. === Step 4 === Remove the linearisation, replacing each indexed letter B by the original letter of A. == Example == Consider the regular expression e = ( a ( a b ) ∗ ) ∗ + ( b a ) ∗ {\displaystyle e=(a(ab)^{})^{}+(ba)^{}} . == Computation of the set of letters == The computation of the sets P, D, F, and Λ is done inductively over the regular expression e ′ {\displaystyle e'} . One must give the values for ∅, ε (the symbols for the empty language and the singleton language containing the empty word), the letters, and the results of the operations + , ⋅ , ∗ {\displaystyle +,\cdot ,^{}} . The most costly operations are the cartesian products of sets for the computation of F. == Properties == The obtained automaton is non-deterministic, and it has as many states as the number of letters of the regular expression, plus one. It has been proven that every Thompson's automaton can be transformed into Glushkov's automaton via a ε-transitions elimination method. == Applications and deterministic expressions == The computation of the automaton by the expression occurs often; it has been systematically used in search functions, in particular by the Unix grep command. Similarly, XML's specification also uses such constructions; for more efficiency, regular expressions of a certain kind, called deterministic expressions, have been studied.
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Trie

In computer science, a trie (, ), also known as a digital tree or prefix tree, is a specialized search tree data structure used to store and retrieve strings from a dictionary or set. Unlike a binary search tree, nodes in a trie do not store their associated key. Instead, each node's position within the trie determines its associated key, with the connections between nodes defined by individual characters rather than the entire key. Tries are particularly effective for tasks such as autocomplete, spell checking, and IP routing, offering advantages over hash tables due to their prefix-based organization and lack of hash collisions. Every child node shares a common prefix with its parent node, and the root node represents the empty string. While basic trie implementations can be memory-intensive, various optimization techniques such as compression and bitwise representations have been developed to improve their efficiency. A notable optimization is the radix tree, which provides more efficient prefix-based storage. While tries store character strings, they can be adapted to work with any ordered sequence of elements, such as permutations of digits or shapes. A notable variant is the bitwise trie, which uses individual bits from fixed-length binary data (such as integers or memory addresses) as keys. == History, etymology, and pronunciation == The idea of a trie for representing a set of strings was first abstractly described by Axel Thue in 1912. Tries were first described in a computer context by René de la Briandais in 1959. The idea was independently described in 1960 by Edward Fredkin, who coined the term trie, pronouncing it (as "tree"), after the middle syllable of retrieval. However, other authors pronounce it (as "try"), in an attempt to distinguish it verbally from "tree". == Overview == Tries are a form of string-indexed look-up data structure, which is used to store a dictionary list of words that can be searched on in a manner that allows for efficient generation of completion lists. A prefix trie is an ordered tree data structure used in the representation of a set of strings over a finite alphabet set, which allows efficient storage of words with common prefixes. Tries can be efficacious on string-searching algorithms such as predictive text, approximate string matching, and spell checking in comparison to binary search trees. A trie can be seen as a tree-shaped deterministic finite automaton. == Operations == Tries support various operations: insertion, deletion, and lookup of a string key. Tries are composed of nodes that contain links, which either point to other suffix child nodes or null. As for every tree, each node except the root is pointed to by only one other node, called its parent. Each node contains as many links as the number of characters in the applicable alphabet (although tries tend to have a substantial number of null links). In some cases, the alphabet used is simply that of the character encoding—resulting in, for example, a size of 128 in the case of ASCII. The null links within the children of a node emphasize the following characteristics: Characters and string keys are implicitly stored in the trie, and include a character sentinel value indicating string termination. Each node contains one possible link to a prefix of strong keys of the set. A basic structure type of nodes in the trie is as follows: Node {\displaystyle {\text{Node}}} may contain an optional Value {\displaystyle {\text{Value}}} , which is associated with the key that corresponds to the node. === Searching === Searching for a value in a trie is guided by the characters in the search string key, as each node in the trie contains a corresponding link to each possible character in the given string. Thus, following the string within the trie yields the associated value for the given string key. A null link during the search indicates the inexistence of the key. The following pseudocode implements the search procedure for a given string key in a rooted trie x. In the above pseudocode, x and key correspond to the pointer of the trie's root node and the string key, respectively. The search operation takes O ( m ) {\displaystyle O(m)} time, where m {\displaystyle m} is the size of the string parameter key. In a balanced binary search tree, on the other hand, it takes O ( m log ⁡ n ) {\displaystyle O(m\log n)} time, in the worst case, since key needs to be compared with O ( log ⁡ n ) {\displaystyle O(\log n)} other keys and each comparison takes O ( m ) {\displaystyle O(m)} time, in the worst case. The trie occupies less space, in comparison with a binary search tree, in the case of a large number of short strings, since nodes share common initial string subsequences and store the keys implicitly. === Insertion === Insertion into a trie is guided by using the character sets as indexes to the children array until the last character of the string key is reached. Each node in the trie corresponds to one call of the radix sorting routine, as the trie structure reflects the execution pattern of the top-down radix sort. If null links are encountered before reaching the last character of the string key, new nodes are created. The input value is assigned to the value of the last node traversed, which is the node that corresponds to the key. === Deletion === Deletion of a key–value pair from a trie involves finding the node corresponding to the key, setting its value to null, and recursively removing nodes that have no children. The procedure begins by examining key; an empty string indicates arrival at the node corresponding to the (original) key, in which case its value is set to null. If the node, then, has null value and no children, it is removed from the trie by returning null; otherwise, the node is kept by returning the node itself. == Replacing other data structures == === Replacement for hash tables === A trie can be used to replace a hash table, over which it has the following advantages: Searching for a node with an associated key of size m {\displaystyle m} has the complexity of O ( m ) {\displaystyle O(m)} , whereas an imperfect hash function may have numerous colliding keys, and the worst-case lookup speed of such a table would be O ( N ) {\displaystyle O(N)} , where N {\displaystyle N} denotes the total number of nodes within the table. Tries do not need a hash function for the operation, unlike a hash table; there are also no collisions of different keys in a trie. Within a trie, keys can be efficiently sorted lexicographically. However, tries are less efficient than a hash table when the data is directly accessed on a secondary storage device such as a hard disk drive that has higher random access time than the main memory. == Implementation strategies == Tries can be represented in several ways, corresponding to different trade-offs between memory use and speed of the operations. Using a vector of pointers for representing a trie consumes enormous space; however, memory space can be reduced at the expense of running time if a singly linked list is used for each node vector, as most entries of the vector contains nil {\displaystyle {\text{nil}}} . Techniques such as alphabet reduction may reduce the large space requirements by reinterpreting the original string as a longer string over a smaller alphabet. For example, a string of n bytes can alternatively be regarded as a string of 2n four-bit units. This can reduce memory usage by a factor of eight; but lookups need to visit twice as many nodes in the worst case. Another technique includes storing a vector of 256 ASCII pointers as a bitmap of 256 bits representing ASCII alphabet, which reduces the size of individual nodes dramatically. === Bitwise tries === Bitwise tries are used to address the enormous space requirement for the trie nodes in a naive simple pointer vector implementations. Each character in the string key set is represented via individual bits, which are used to traverse the trie over a string key. The implementations for these types of trie use vectorized CPU instructions to find the first set bit in a fixed-length key input (e.g. GCC's __builtin_clz() intrinsic function). Accordingly, the set bit is used to index the first item, or child node, in the 32- or 64-entry based bitwise tree. Search then proceeds by testing each subsequent bit in the key. This procedure is also cache-local and highly parallelizable due to register independency, and thus performant on out-of-order execution CPUs. === Compressed tries === Radix tree, also known as a compressed trie, is a space-optimized variant of a trie in which any node with only one child gets merged with its parent; elimination of branches of the nodes with a single child results in better metrics in both space and time. This works best when the trie remains static and set of keys stored are very sparse within their representation space. One more approach for static tries is to "pack" the trie by storing disjoint
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Line Drawing System-1

LDS-1 (Line Drawing System-1) was a calligraphic (vector, rather than raster) display processor and display device created by Evans & Sutherland in 1969. This model was known as the first graphics device with a graphics processing unit. == Features == It was controlled by a variety of host computers. Straight lines were smoothly rendered in real-time animation. General principles of operation were similar to the systems used today: 4x4 transformation matrices, 1x4 vertices. Possible uses included flight simulation (in the product brochure there are screenshots of landing on a carrier), scientific imaging and GIS systems. == History == The first LDS-1 was shipped to the customer (BBN) in August 1969. Only a few of these systems were ever built. One was used by the Los Angeles Times as their first typesetting/layout computer. One went to NASA Ames Research Center for Human Factors Research. Another was bought by the Port Authority of New York to develop a tugboat pilot trainer for navigation in the harbor. The MIT Dynamic Modeling had one, and there was a program for viewing an ongoing game of Maze War.
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Aslı Çelikyılmaz

Aslı Çelikyılmaz is an engineer specializing in natural language processing, and particularly in natural language generation for software agents with advanced reasoning and real-world modeling capabilities. Educated in Turkey and Canada, she works in the US as senior research lead at Fundamentals AI Research, Meta. She also holds an affiliate faculty position in computer science at the University of Washington, and is co-editor-in-chief of the journal Transactions of the Association for Computational Linguistics. == Education and career == Çelikyılmaz is a 1997 graduate of Istanbul Technical University, where she studied industrial engineering. After a 2002 master's degree in computer and information science from Seneca Polytechnic in Toronto, and a second master's degree in information science from the University of Toronto in 2005, she completed a Ph.D. in information science at the University of Toronto in 2008. She worked as a postdoctoral researcher in California, at the University of California, Berkeley, from 2008 to 2010. In 2010 she joined Microsoft in Sunnyvale, California, where she became a senior scientist and later a senior principal researcher in Redmond, Washington. She added her affiliation with the University of Washington in 2018, and moved to Meta in Seattle in 2021. == Recognition == Çelikyılmaz was named to the 2026 class of IEEE Fellows, "for contributions to conversational systems and language generation".
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Is an AI Customer-support Bot Worth It in 2026?

In search of the best AI customer-support bot? An AI customer-support bot is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI customer-support bot slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
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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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Aporia (company)

Aporia is a machine learning observability platform based in Tel Aviv, Israel. The company has a US office located in San Jose, California. Aporia has developed software for monitoring and controlling undetected defects and failures used by other companies to detect and report anomalies, and warn in the early stages of faults. == History == Aporia was founded in 2019 by Liran Hason and Alon Gubkin. In April 2021, the company raised a $5 million seed round for its monitoring platform for ML models. In February 2022, the company closed a Series A round of $25 million for its ML observability platform. Aporia was named by Forbes as the Next Billion-Dollar Company in June 2022. In November, the company partnered with ClearML, an MLOPs platform, to improve ML pipeline optimization. In January 2023, Aporia launched Direct Data Connectors, a novel technology allowing organizations to monitor their ML models in minutes (previously the process of integrating ML monitoring into a customer’s cloud environment took weeks or more.) DDC (Direct Data Connectors) enables users to connect Aporia to their preferred data source and monitor all of their data at once, without data sampling or data duplication (which is a huge security risk for major organizations. In April 2023, Aporia announced the company partnered with Amazon Web Services (AWS) to provide more reliable ML observability to AWS consumers by deploying Aporia's architecture to their AWS environment, this will allow customers to monitor their models in production regardless of platform.
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Top 10 AI Blog Writers Compared (2026)

Comparing the best AI blog writer? An AI blog writer is software that uses machine learning to help you get more done — it lowers the barrier so anyone can produce professional output. Privacy matters too: check whether your data trains the model and whether a no-log or enterprise tier is available. Whether you are a beginner or a pro, the right AI blog writer slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
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JOONE

JOONE (Java Object Oriented Neural Engine) is a component based neural network framework built in Java. == Features == Joone consists of a component-based architecture based on linkable components that can be extended to build new learning algorithms and neural networks architectures. Components are plug-in code modules that are linked to produce an information flow. New components can be added and reused. Beyond simulation, Joone also has to some extent multi-platform deployment capabilities. Joone has a GUI Editor to graphically create and test any neural network, and a distributed training environment that allows for neural networks to be trained on multiple remote machines. == Comparison == As of 2010, Joone, Encog and Neuroph are the major free component based neural network development environment available for the Java platform. Unlike the two other (commercial) systems that are in existence, Synapse and NeuroSolutions, it is written in Java and has direct cross-platform support. A limited number of components exist and the graphical development environment is rudimentary so it has significantly fewer features than its commercial counterparts. Joone can be considered to be more of a neural network framework than a full integrated development environment. Unlike its commercial counterparts, it has a strong focus on code-based development of neural networks rather than visual construction. While in theory Joone can be used to construct a wider array of adaptive systems (including those with non-adaptive elements), its focus is on backpropagation based neural networks.
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The Best Free AI Content Generator for Beginners

Looking for the best AI content generator? An AI content 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 content 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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VueScan

VueScan is a computer program for image scanning, especially of photographs, including negatives. It supports optical character recognition (OCR) of text documents. The software can be downloaded and used free of charge, but adds a watermark on scans until a license is purchased. == Purpose == VueScan is intended to work with a large number of image scanners, excluding specialised professional scanners such as drum scanners, on many computer operating systems (OS), even if drivers for the scanner are not available for the OS. These scanners are supplied with device drivers and software to operate them, included in their price. A 2014 review considered that the reasons to purchase VueScan are to allow older scanners not supported by drivers for newer operating systems to be used in more up-to-date systems and for better scanning and processing of photographs (prints; also slides and negatives when supported by scanners) than is afforded by manufacturers' software. The review did not report any advantages to VueScan's processing of documents over other software. The reviewer considered VueScan comparable to SilverFast, a similar program, with support for some specific scanners better in one or the other. Vuescan supports more scanners, with a single purchase giving access to the full range of both film and flatbed scanners, and costs less. The VueScan program can be used with its own drivers or with drivers supplied by the scanner manufacturer, if supported by the operating system. VueScan drivers can also be used without the VueScan program by application software that supports scanning directly, such as Adobe Photoshop, again enabling the use of scanners without current manufacturers' drivers. In 2019 when Apple released macOS Catalina, they removed support for running 32-bit programs, including 32-bit drivers for scanning equipment. In response, Hamrick released VueScan 9.7, effectively saving thousands of scanners from being rendered obsolete. == Overview == VueScan enables the user to modify and fine-tune the scanning parameters. The program uses its own independent method to interface with scanner hardware, and can support many older scanners under computer operating systems for which drivers are not available, allowing old scanners to be used with newer platforms that do not otherwise support them. VueScan supports an increasing number of scanners and digital cameras; 2,400 on Windows, 2,100 on Mac OS X and 1,900 on Linux in 2018. VueScan is supplied as one downloadable file for each operating system, which supports the full range of scanners. Without the purchase of a license, the program runs in fully functional demonstration mode, identical to Professional mode, except that watermarks are superimposed on saved and printed images. Purchase of a license removes the watermark. A standard license allows updates for one year; a professional license allows unlimited updates and provides some additional features. VueScan supports optical character recognition (OCR), with English included, and 32 additional language packages available on its website. In September 2011, VueScan co-developer Ed Hamrick said that he was selling US$3 million per year of VueScan licenses.
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Nando de Freitas

Nando de Freitas is a researcher in the field of machine learning, and in particular in the subfields of neural networks, Bayesian inference and Bayesian optimization, and deep learning. == Biography == De Freitas was born in Zimbabwe. He did his undergraduate studies (1991–94) and MSc (1994–96) at the University of the Witwatersrand, and his PhD at Trinity College, Cambridge (1996-2000). From 2001, he was a professor at the University of British Columbia, before joining the Department of Computer Science at the University of Oxford from 2013 to 2017. In 2014, he joined Google's DeepMind when the company acquired Oxford spinoff Dark Blue Labs. He was in charge of the team that worked on creating tools for generating audio and images at DeepMind. In September 2024, de Freitas joined Microsoft AI as VP of AI. == Awards and recognition == De Freitas has been recognised for his contributions to machine learning through the following awards: Best Paper Award at the International Conference on Machine Learning (2016) Best Paper Award at the International Conference on Learning Representations (2016) Google Faculty Research Award (2014) Distinguished Paper Award at the International Joint Conference on Artificial Intelligence (2013) Charles A. McDowell Award for Excellence in Research (2012) Mathematics of Information Technology and Complex Systems Young Researcher Award (2010)
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P4-metric

The P4 metric (also known as FS or Symmetric F ) enables performance evaluation of a binary classifier. The P4 metric is calculated from precision, recall, specificity, and NPV (negative predictive value). The definition of the P4 metric is similar to that of the F1 metric, however the P4 metric definition addresses criticisms leveled against the definition of the F1 metric. The definition of the P4 metric may, therefore, be understood as an extension of the F1 metric. Like the other known metrics, the P4 metric is a function of: TP (true positives), TN (true negatives), FP (false positives), FN (false negatives). == Justification == The key concept of the P4 metric is to leverage the four key conditional probabilities: P ( + ∣ C + ) {\displaystyle P(+\mid C{+})} — the probability that the sample is positive, provided the classifier result was positive. P ( C + ∣ + ) {\displaystyle P(C{+}\mid +)} — the probability that the classifier result will be positive, provided the sample is positive. P ( C − ∣ − ) {\displaystyle P(C{-}\mid -)} — the probability that the classifier result will be negative, provided the sample is negative. P ( − ∣ C − ) {\displaystyle P(-\mid C{-})} — the probability the sample is negative, provided the classifier result was negative. The main assumption behind this metric is that all the probabilities mentioned above are close to 1 for a properly designed binary classifier. Indeed, P 4 = 1 {\displaystyle \mathrm {P} _{4}=1} if, and only if, all of the probabilities above are equal to 1. Another important feature is that P 4 {\displaystyle \mathrm {P} _{4}} tends to zero any of the above probabilities tend to zero. == Definition == P4 is defined as a harmonic mean of four key conditional probabilities: P 4 = 4 1 P ( + ∣ C + ) + 1 P ( C + ∣ + ) + 1 P ( C − ∣ − ) + 1 P ( − ∣ C − ) = 4 1 p r e c i s i o n + 1 r e c a l l + 1 s p e c i f i c i t y + 1 N P V . {\displaystyle \mathrm {P} _{4}={\frac {4}{{\frac {1}{P(+\mid C{+})}}+{\frac {1}{P(C{+}\mid +)}}+{\frac {1}{P(C{-}\mid -)}}+{\frac {1}{P(-\mid C{-})}}}}={\frac {4}{{\frac {1}{\mathit {precision}}}+{\frac {1}{\mathit {recall}}}+{\frac {1}{\mathit {specificity}}}+{\frac {1}{\mathit {NPV}}}}}.} In terms of TP,TN,FP,FN it can be calculated as follows: P 4 = 4 ⋅ T P ⋅ T N 4 ⋅ T P ⋅ T N + ( T P + T N ) ⋅ ( F P + F N ) . {\displaystyle \mathrm {P} _{4}={\frac {4\cdot \mathrm {TP} \cdot \mathrm {TN} }{4\cdot \mathrm {TP} \cdot \mathrm {TN} +(\mathrm {TP} +\mathrm {TN} )\cdot (\mathrm {FP} +\mathrm {FN} )}}.} == Evaluation of the binary classifier performance == Evaluating the performance of binary classifiers is a multidisciplinary concept. It spans from the evaluation of medical tests, psychiatric tests to machine learning classifiers from a variety of fields. Thus, many of the metrics in use exist under several names, some defined independently. == Properties of P4 metric == Symmetry — contrasting to the F1 metric, P4 is symmetrical. It means - it does not change its value when dataset labeling is changed - positives named negatives and negatives named positives. Range: P 4 ∈ [ 0 , 1 ] {\displaystyle \mathrm {P} _{4}\in [0,1]} . Achieving P 4 ≈ 1 {\displaystyle \mathrm {P} _{4}\approx 1} requires all the key four conditional probabilities being close to 1. For P 4 ≈ 0 {\displaystyle \mathrm {P} _{4}\approx 0} it is sufficient that one of the key four conditional probabilities is close to 0. == Examples, comparing with the other metrics == Dependency table for selected metrics ("true" means depends, "false" - does not depend): Metrics that do not depend on a given probability are prone to misrepresentation when the probability approaches 0. === Example 1: Rare disease detection test === Let us consider a medical test used to detect a rare disease. Suppose a population size of 100000 and 0.05% of the population is infected. Further suppose the following test performance: 95% of all positive individuals are classified correctly (TPR=0.95) and 95% of all negative individuals are classified correctly (TNR=0.95). In such a case, due to high population imbalance and in spite of having high test accuracy (0.95), the probability that an individual who has been classified as positive is in fact positive is very low: P ( + ∣ C + ) = 0.0095. {\displaystyle P(+\mid C{+})=0.0095.} We can observe how this low probability is reflected in some of the metrics: P 4 = 0.0370 {\displaystyle \mathrm {P} _{4}=0.0370} , F 1 = 0.0188 {\displaystyle \mathrm {F} _{1}=0.0188} , J = 0.9100 {\displaystyle \mathrm {J} =\mathbf {0.9100} } (Informedness / Youden index), M K = 0.0095 {\displaystyle \mathrm {MK} =0.0095} (Markedness). === Example 2: Image recognition — cats vs dogs === Consider the problem of training a neural network based image classifier with only two types of images: those containing dogs (labeled as 0) and those containing cats (labeled as 1). Thus, the goal is to distinguish between the cats and dogs. Suppose that the classifier overpredicts in favour of cats ("positive" samples): 99.99% of cats are classified correctly and only 1% of dogs are classified correctly. Further, suppose that the image dataset consists of 100000 images, 90% of which are pictures of cats and 10% are pictures of dogs. In this situation, the probability that the picture containing dog will be classified correctly is pretty low: P ( C − | − ) = 0.01. {\displaystyle P(C-|-)=0.01.} Not all metrics are notice this low probability: P 4 = 0.0388 {\displaystyle \mathrm {P} _{4}=0.0388} , F 1 = 0.9478 {\displaystyle \mathrm {F} _{1}=\mathbf {0.9478} } , J = 0.0099 {\displaystyle \mathrm {J} =0.0099} (Informedness / Youden index), M K = 0.8183 {\displaystyle \mathrm {MK} =\mathbf {0.8183} } (Markedness).
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