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Information extraction

Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources. Typically, this involves processing human language texts by means of natural language processing (NLP). Recent activities in multimedia document processing like automatic annotation and content extraction out of images/audio/video/documents could be seen as information extraction. Recent advances in NLP techniques have allowed for significantly improved performance compared to previous years. An example is the extraction from newswire reports of corporate mergers, such as denoted by the formal relation: MergerBetween ⁡ ( c o m p a n y 1 , c o m p a n y 2 , d a t e ) {\displaystyle \operatorname {MergerBetween} (\mathrm {company} _{1},\mathrm {company} _{2},\mathrm {date} )} , from an online news sentence such as: "Yesterday, New York based Foo Inc. announced their acquisition of Bar Corp." A broad goal of IE is to allow computation to be done on the previously unstructured data. A more specific goal is to allow automated reasoning about the logical form of the input data. Structured data is semantically well-defined data from a chosen target domain, interpreted with respect to category and context. Information extraction is the part of a greater puzzle which deals with the problem of devising automatic methods for text management, beyond its transmission, storage and display. The discipline of information retrieval (IR) has developed automatic methods, typically of a statistical flavor, for indexing large document collections and classifying documents. Another complementary approach is that of natural language processing (NLP) which has solved the problem of modelling human language processing with considerable success when taking into account the magnitude of the task. In terms of both difficulty and emphasis, IE deals with tasks in between both IR and NLP. In terms of input, IE assumes the existence of a set of documents in which each document follows a template, i.e. describes one or more entities or events in a manner that is similar to those in other documents but differing in the details. An example, consider a group of newswire articles on Latin American terrorism with each article presumed to be based upon one or more terroristic acts. We also define for any given IE task a template, which is a(or a set of) case frame(s) to hold the information contained in a single document. For the terrorism example, a template would have slots corresponding to the perpetrator, victim, and weapon of the terroristic act, and the date on which the event happened. An IE system for this problem is required to "understand" an attack article only enough to find data corresponding to the slots in this template. == History == Information extraction dates back to the late 1970s in the early days of NLP. An early commercial system from the mid-1980s was JASPER built for Reuters by the Carnegie Group Inc with the aim of providing real-time financial news to financial traders. Beginning in 1987, IE was spurred by a series of Message Understanding Conferences. MUC is a competition-based conference that focused on the following domains: MUC-1 (1987), MUC-3 (1989): Naval operations messages. MUC-3 (1991), MUC-4 (1992): Terrorism in Latin American countries. MUC-5 (1993): Joint ventures and microelectronics domain. MUC-6 (1995): News articles on management changes. MUC-7 (1998): Satellite launch reports. Considerable support came from the U.S. Defense Advanced Research Projects Agency (DARPA), who wished to automate mundane tasks performed by government analysts, such as scanning newspapers for possible links to terrorism. == Present significance == The present significance of IE pertains to the growing amount of information available in unstructured form. Tim Berners-Lee, inventor of the World Wide Web, refers to the existing Internet as the web of documents and advocates that more of the content be made available as a web of data. Until this transpires, the web largely consists of unstructured documents lacking semantic metadata. Knowledge contained within these documents can be made more accessible for machine processing by means of transformation into relational form, or by marking-up with XML tags. An intelligent agent monitoring a news data feed requires IE to transform unstructured data into something that can be reasoned with. A typical application of IE is to scan a set of documents written in a natural language and populate a database with the information extracted. == Tasks and subtasks == Applying information extraction to text is linked to the problem of text simplification in order to create a structured view of the information present in free text. The overall goal being to create a more easily machine-readable text to process the sentences. Typical IE tasks and subtasks include: Template filling: Extracting a fixed set of fields from a document, e.g. extract perpetrators, victims, time, etc. from a newspaper article about a terrorist attack. Event extraction: Given an input document, output zero or more event templates. For instance, a newspaper article might describe multiple terrorist attacks. Knowledge Base Population: Fill a database of facts given a set of documents. Typically the database is in the form of triplets, (entity 1, relation, entity 2), e.g. (Barack Obama, Spouse, Michelle Obama) Named entity recognition: recognition of known entity names (for people and organizations), place names, temporal expressions, and certain types of numerical expressions, by employing existing knowledge of the domain or information extracted from other sentences. Typically the recognition task involves assigning a unique identifier to the extracted entity. A simpler task is named entity detection, which aims at detecting entities without having any existing knowledge about the entity instances. For example, in processing the sentence "M. Smith likes fishing", named entity detection would denote detecting that the phrase "M. Smith" does refer to a person, but without necessarily having (or using) any knowledge about a certain M. Smith who is (or, "might be") the specific person whom that sentence is talking about. Coreference resolution: detection of coreference and anaphoric links between text entities. In IE tasks, this is typically restricted to finding links between previously extracted named entities. For example, "International Business Machines" and "IBM" refer to the same real-world entity. If we take the two sentences "M. Smith likes fishing. But he doesn't like biking", it would be beneficial to detect that "he" is referring to the previously detected person "M. Smith". Relationship extraction: identification of relations between entities, such as: PERSON works for ORGANIZATION (extracted from the sentence "Bill works for IBM.") PERSON located in LOCATION (extracted from the sentence "Bill is in France.") Semi-structured information extraction which may refer to any IE that tries to restore some kind of information structure that has been lost through publication, such as: Table extraction: finding and extracting tables from documents. Table information extraction : extracting information in structured manner from the tables. This task is more complex than table extraction, as table extraction is only the first step, while understanding the roles of the cells, rows, columns, linking the information inside the table and understanding the information presented in the table are additional tasks necessary for table information extraction. Comments extraction : extracting comments from the actual content of articles in order to restore the link between authors of each of the sentences Language and vocabulary analysis Terminology extraction: finding the relevant terms for a given corpus Audio extraction Template-based music extraction: finding relevant characteristic in an audio signal taken from a given repertoire; for instance time indexes of occurrences of percussive sounds can be extracted in order to represent the essential rhythmic component of a music piece. Note that this list is not exhaustive and that the exact meaning of IE activities is not commonly accepted and that many approaches combine multiple sub-tasks of IE in order to achieve a wider goal. Machine learning, statistical analysis and/or natural language processing are often used in IE. IE on non-text documents is becoming an increasingly interesting topic in research, and information extracted from multimedia documents can now be expressed in a high level structure as it is done on text. This naturally leads to the fusion of extracted information from multiple kinds of documents and sources. == World Wide Web applications == IE has been the focus of the MUC conferences. The proliferation of the Web, however, intensified the need for developing IE systems that help people
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David Horn (Israeli physicist)

David Horn (Hebrew: דוד הורן; born 10 September 1937) is a Professor (Emeritus) of Physics in the School of Physics and Astronomy at Tel Aviv University (TAU), Israel. He has served as Vice-Rector of TAU, Chairman of the School of Physics and Astronomy and as Dean of the Faculty of Exact Sciences in TAU. He is a fellow of the American Physical Society, nominated for "contributions to theoretical particle physics, including the seminal work on finite energy sum rules, research of the phenomenology of hadronic processes, and investigation of Hamiltonian lattice theories". == Early life and education == David Horn was born and educated in Haifa. He graduated from the Reali School in 1955. He began his academic studies in Physics at the Technion in Haifa in 1957, and received his B.Sc. (Summa Cum Laude) in 1961, and M.Sc. in 1962. He continued his Ph.D. studies at the Hebrew University of Jerusalem until 1965. His thesis on "Some Aspects of the Structure of Weak Interactions" was supervised by Prof. Yuval Ne'eman. == Career == Horn joined the newly founded Tel Aviv University as an assistant in 1962. He became a lecturer in 1965, a senior lecturer in 1967 and an associate professor in 1968. He was promoted to full professor of Physics in 1972. In 1974 he became the incumbent of the Edouard and Francoise Jaupart Chair of Theoretical Physics of Particles and Fields, a position he held until 2007. Horn has supervised 43 graduate students at TAU and authored over 240 scientific publications. He retired as a professor emeritus in 2005, and continues to be an active researcher. Horn spent a significant part of his career holding visiting academic positions at other universities and research institutes, including: Postdoctoral Fellow at Argonne National Lab, ILL, Research Fellow and three times Visiting Associate at California Institute of Technology, Pasadena, CA, Visitor at CERN in Geneva, Visiting Professor at Cornell University, NY, Member of the Institute for Advanced Study, Princeton, NJ, Visiting Professor at SLAC in Stanford University, CA, and Visiting Professor at Kyoto University, Japan. Beginning from 1980, Horn held official positions at Tel Aviv University, starting with tenure as Vice-Rector (1980-1983), a position he left for research at SLAC. After returning he was nominated Chairman of the Department of High Energy Physics (1984-1986), followed by tenures as Chairman of the School of Physics and Astronomy (1986-9), Dean of the Raymond and Beverly Sackler Faculty of Exact Sciences (1990-1995), and first Director of the Adams Super Center for Brain Studies (1993-2000). Horn has also held national and international professional positions. He was Chairman of the Israel Commission for High Energy Physics (1983-2003), and, in this capacity, served as an Israeli observer of the council of CERN (1991-2003). He served as member of the Israel Council for Higher Education (1987-1991), member of the Executive Committee of the European Physical Society (1989-1992) and member of the European Strategy Forum on Research Infrastructures (2005-2017). He chaired the Israeli Committee of Research Infrastructures (2012-2016), issuing roadmaps for scientific RI in 2013 and 2016. == Research == Horn's research work focused on theory and phenomenology of High Energy Physics until 1990. He then shifted his interests to Neural Computation and Machine Learning and, since 2005, he has also published in Bioinformatics. Together with Richard Dolen and Christoph Schmid he discovered the Finite Energy Sum Rules in 1967. It was a realization of the bootstrap approach to hadronic structure, and became known as the Dolen-Horn-Schmid Duality. Together with Richard Silver he investigated a model of coherent production of pions at high energy hadron collisions in 1971, and together with Jeffrey Mandula he undertook the investigation of mesons with constituent gluons in 1978. Moving to lattice gauge theories in 1979, he discovered, together with Shimon Yankielowic and Marvin Weinstein, a non-confining phase in Z(N) theories for large N. In 1981 he demonstrated the existence of finite matrix models with link gauge fields, nowadays known as quantum link models. In 1984 Horn and Weinstein developed the t-expansion methodology. Horn's contributions to neural modeling include a novel mechanism for memory maintenance via neuronal regulation in 1998, developed with Nir Levy and Eytan Ruppin and unsupervised learning of natural languages in 2005, a joint work with Zach Solan, Eytan Ruppin and Shimon Edelman, introducing novel algorithms for motif and grammar extraction from text. Horn has contributed to algorithms of clustering, an important topic in Machine Learning, by developing Support Vector Clustering (SVC) in 2001, together with Asa Ben Hur, Hava Siegelmann and Vladimir Vapnik. This was followed shortly thereafter by a joint work with Assaf Gottlieb on Quantum Clustering (QC). His contributions to Bioinformatics include motif descriptions of function and structure of proteins, as well as motif studies of genomic structures. Together with Erez Persi he studied compositional order of proteomes, and repeat instability of genomes, as evolution markers of organisms and of cancer (a joint work with Persi and others). == Honors == Horn is a Fellow of the American Physical Society (1985) and a Fellow of the Israel Physical Society (2018). == Publications == === Selected articles === R. Dolen, D. Horn and C. Schmid; Prediction of Regge-parameters of rho poles from low-energy pi-N scattering data Phys. Rev. Lett. 19 (1967) 402–407. Finite-Energy Sum Rules and Their Application to pi-N Charge Exchange Phys. Rev. 166 (1968) 1768–1781. D. Horn and R. Silver: Coherent production of pions, Annals Phys. 66 (1971) 509-541 T. Banks, D. Horn and H. Neuberger: Bosonization of the SU(N) Thirring Models, Nucl. Phys. B108, 119 (1976). D. Horn and J. Mandula: Model of Mesons with Constituent Gluons, Phys. Rev. D17, 898 (1978). D. Horn, M. Weinstein and S. Yankielowicz: Hamiltonian Approach to Z(N) Lattice Gauge Theories, Phys. Rev. D19, 3715 (1979). D. Horn: Finite Matrix Models with Continuous Local Gauge Invariance, Phys. Lett. 100B, 149-151 (1981). T. Banks, Y. Dothan and D. Horn: Geometric Fermions, Phys. Lett. 117B, 413 (1982). D. Horn and M. Weinstein: The t expansion: A nonperturbative analytic tool for Hamiltonian systems. Phys. Rev. D 30, 1256-1270 (1984). Ury Naftaly, Nathan Intrator and David Horn: Optimal Ensemble Averaging of Neural Networks. Network, Computation in Neural Systems, 8, 283-296 (1997). David Horn, Nir Levy, Eytan Ruppin: Memory Maintenance via Neuronal Regulation, Neural Computation, 10, 1-18 (1998). Asa Ben-Hur, David Horn, Hava Siegelmann and Vladimir Vapnik: Support Vector Clustering. Journal of Machine Learning Research 2, 125-137 (2001). David Horn and Assaf Gottlieb: Algorithm for data clustering in pattern recognition problems based on quantum mechanics, Phys. Rev. Lett. 88 (2002) 18702 Zach Solan, David Horn, Eytan Ruppin and Shimon Edelman: Unsupervised learning of natural languages, Proc. Natl. Acad. Sc. 102 (2005) 11629–11634. Vered Kunik, Yasmine Meroz, Zach Solan, Ben Sandbank, Uri Weingart, Eytan Ruppin and David Horn: Functional representation of enzymes by specific peptides. PLOS Computational Biology 2007, 3(8):e167. Benny Chor, David Horn, Yaron Levy, Nick Goldman and Tim Massingham: Genomic DNA k-mer spectra: models and modalities. Genome Biology 2009, 10(10):R108 Erez Persi and David Horn. Systematic Analysis of Compositional Order of Proteins Reveals New Characteristics of Biological Functions and a Universal Correlate of Macroevolution. PLoS Comput Biol 9 (2013): e1003346. David Horn. Taxa counting using Specific Peptides of Aminoacyl tRNA Synthetases Encyclopedia of Metagenomics, Springer, 2013. Sagi Shporer, Benny Chor, Saharon Rosset, David Horn. Inversion symmetry of DNA k-mer counts: validity and deviations. BMC Genomics 2016, 17:696 Erez Persi, Davide Prandi, Yuri I. Wolf, Yair Pozniak, Christopher Barbieri, Paola Gasperini, Himisha Beltran, Bishoy M. Faltas, Mark A. Rubin, Tamar Geiger, Eugene V. Koonin, Francesca Demichelis, David Horn. Proteomic and Genomic Signatures of Repeat Instability in Cancer and Adjacent Normal Tissues. PNAS 116, 34, 2019 - 08790 === Book === David Horn and Fredrick Zachariasen: Hadron Physics at Very High Energies. Benjamin 1973. === Patents === Method and Apparatus for Quantum Clustering. USA Patent No. 7,653,646 B2. Method for discovering relationships in data by dynamic quantum clustering USA Patent No 8874412 and USA Patent No. 9,646,074. == Personal life == Horn was married to Nira Fuss since 1963 until her death in 2019. He is a father of three, Yuval, Tamar, and Oded, and grandfather of nine. He lives in Tel Aviv, Israel.
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Halbert White

Halbert Lynn White Jr. (November 19, 1950 – March 31, 2012) was the Chancellor's Associates Distinguished Professor of Economics at the University of California, San Diego, and a Fellow of the Econometric Society and the American Academy of Arts and Sciences. == Education and career == White, a native of Kansas City, Missouri, graduated salutatorian from Southwest High School in 1968. He went on to study at Princeton University, receiving his B.A. in economics in 1972. He earned his Ph.D. in economics at the Massachusetts Institute of Technology in 1976, under the supervision of Jerry A. Hausman and Robert Solow. White spent his first years as an assistant professor in the University of Rochester before moving to University of California, San Diego (UCSD) in 1979. He remained at UCSD until his untimely death from cancer. == Research == White was well known in the field of econometrics for his 1980 paper on robust standard errors (which is among the most-cited paper in economics since 1970), and for the heteroscedasticity-consistent estimator and the test for heteroskedasticity that are named after him. A 1982 paper by White contributed strongly to the development of quasi-maximum likelihood estimation. He also contributed to numerous other areas such as neural networks and medicine. In 1999, White co-founded an economic consulting firm, Bates White, which is based in Washington, D.C.
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How to Choose an AI Website Builder

Shopping for the best AI website builder? An AI website builder 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 website builder 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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User-defined function

A user-defined function (UDF) is a function provided by the user of a program or environment, in a context where the usual assumption is that functions are built into the program or environment. UDFs are usually written for the requirement of its creator. == BASIC language == In some old implementations of the BASIC programming language, user-defined functions are defined using the "DEF FN" syntax. More modern dialects of BASIC are influenced by the structured programming paradigm, where most or all of the code is written as user-defined functions or procedures, and the concept becomes practically redundant. == COBOL language == In the COBOL programming language, a user-defined function is an entity that is defined by the user by specifying a FUNCTION-ID paragraph. A user-defined function must return a value by specifying the RETURNING phrase of the procedure division header and they are invoked using the function-identifier syntax. See the ISO/IEC 1989:2014 Programming Language COBOL standard for details. As of May 2022, the IBM Enterprise COBOL for z/OS 6.4 (IBM COBOL) compiler contains support for user-defined functions. == Databases == In relational database management systems, a user-defined function provides a mechanism for extending the functionality of the database server by adding a function, that can be evaluated in standard query language (usually SQL) statements. The SQL standard distinguishes between scalar and table functions. A scalar function returns only a single value (or NULL), whereas a table function returns a (relational) table comprising zero or more rows, each row with one or more columns. User-defined functions in SQL are declared using the CREATE FUNCTION statement. For example, a user-defined function that converts Celsius to Fahrenheit (a temperature scale used in USA) might be declared like this: Once created, a user-defined function may be used in expressions in SQL statements. For example, it can be invoked where most other intrinsic functions are allowed. This also includes SELECT statements, where the function can be used against data stored in tables in the database. Conceptually, the function is evaluated once per row in such usage. For example, assume a table named Elements, with a row for each known chemical element. The table has a column named BoilingPoint for the boiling point of that element, in Celsius. The query would retrieve the name and the boiling point from each row. It invokes the CtoF user-defined function as declared above in order to convert the value in the column to a value in Fahrenheit. Each user-defined function carries certain properties or characteristics. The SQL standard defines the following properties: Language - defines the programming language in which the user-defined function is implemented; examples include SQL, C, C# and Java. Parameter style - defines the conventions that are used to pass the function parameters and results between the implementation of the function and the database system (only applicable if language is not SQL). Specific name - a name for the function that is unique within the database. Note that the function name does not have to be unique, considering overloaded functions. Some SQL implementations require that function names are unique within a database, and overloaded functions are not allowed. Determinism - specifies whether the function is deterministic or not. The determinism characteristic has an influence on the query optimizer when compiling a SQL statement. SQL-data access - tells the database management system whether the function contains no SQL statements (NO SQL), contains SQL statements but does not access any tables or views (CONTAINS SQL), reads data from tables or views (READS SQL DATA), or actually modifies data in the database (MODIFIES SQL DATA). User-defined functions should not be confused with stored procedures. Stored procedures allow the user to group a set of SQL commands. A procedure can accept parameters and execute its SQL statements depending on those parameters. A procedure is not an expression and, thus, cannot be used like user-defined functions. Some database management systems allow the creation of user defined functions in languages other than SQL. Microsoft SQL Server, for example, allows the user to use .NET languages including C# for this purpose. DB2 and Oracle support user-defined functions written in C or Java programming languages. === SQL Server 2000 === There are three types of UDF in Microsoft SQL Server 2000: scalar functions, inline table-valued functions, and multistatement table-valued functions. Scalar functions return a single data value (not a table) with RETURNS clause. Scalar functions can use all scalar data types, with exception of timestamp and user-defined data types. Inline table-valued functions return the result set of a single SELECT statement. Multistatement table-valued functions return a table, which was built with many TRANSACT-SQL statements. User-defined functions can be invoked from a query like built‑in functions such as OBJECT_ID, LEN, DATEDIFF, or can be executed through an EXECUTE statement like stored procedures. Performance Notes: User-defined functions are subroutines made of one or more Transact-SQL statements that can be used to encapsulate code for reuse. It takes zero or more arguments and evaluates a return value. Has both control-flow and DML statements in its body similar to stored procedures. Does not allow changes to any Global Session State, like modifications to database or external resource, such as a file or network. Does not support output parameter. DEFAULT keyword must be specified to pass the default value of parameter. Errors in UDF cause UDF to abort which, in turn, aborts the statement that invoked the UDF. === Apache Hive === Apache Hive defines, in addition to the regular user-defined functions (UDF), also user-defined aggregate functions (UDAF) and table-generating functions (UDTF). Hive enables developers to create their own custom functions with Java. === Apache Doris === Apache Doris, an open-source real-time analytical database, allows external users to contribute their own UDFs written in C++ to it.
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Bixby (software)

Bixby ( ) is a virtual assistant developed by Samsung Electronics that runs on various Samsung-branded appliances, primarily mobile devices but also some refrigerators televisions and PCs. It uses voice commands and a natural-language user interface to answer questions and perform tasks, while adapting to the users' preferences and behavior. Samsung first launched Bixby in 2017. Along with Bixby voice assistant, its other main component currently is Bixby Vision, a contextual and visual search augmented reality camera app. Formerly, the Bixby suite consisted of a number of other tools, but these have since been renamed, such as Bixby Routines (now Modes and Routines). == History == On 20 March 2017, Samsung announced the voice-powered digital assistant named "Bixby" as a replacement of the S Voice assistant. It was introduced alongside the Galaxy S8 and S8+ and the Galaxy Tab A (2017) during the Galaxy Unpacked 2017 event. Although released for these devices, it could also be sideloaded on older Galaxy devices running Android Nougat. Before the phone's release, the Bixby Button was reprogrammable and could be set to open other applications or assistants, such as Google Assistant. However, near the phone's release, this ability was removed with a firmware update. Remapping remained possible through third-party apps. Bixby was launched in Korean on 1 May 2017 (KST). Bixby Voice was intended to be made available in the US later that spring. However, Samsung postponed the release, as Bixby had issues understanding English. The English version was finally rolled out in July 2017, followed by a Chinese language version later that year. In October 2017, Samsung announced the release of Bixby 2.0 during its annual developer conference in San Francisco. The new version was rolled out across the company's line of connected products, including smartphones, TVs, and refrigerators. Third parties were allowed to develop applications for Bixby using the Samsung Developer Kit. In August 2018, Samsung announced the Bixby-integrated Galaxy Home smart speaker. In 2019, UX developers at Samsung stated that they intended to use AR Emoji avatars as a personified Bixby assistant. At SDC19, Samsung displayed the Galaxy Home Mini speaker, which also supported Bixby. Bixby 3.0 was released with One UI 3 at the start of 2021. With version 3.0, Home and Reminders features were separated from Bixby. In June 2021, screenshots surfaced for what some thought as a replacement for Bixby. The three-dimensional virtual assistant, Sam, was popular on social media, though it was not intended as a replacement for Bixby. Bixby launched for Microsoft Windows in October 2021, with distribution through the Microsoft Store. This version of Bixby was optimized for Samsung's Galaxy Book computers. Samsung launched an AI Bixby custom voice creator in 2023, allowing users to record their own voice commands. Most recently, in July 2024, Samsung confirmed that it plans to launch an upgraded version of Bixby later that year. This new Bixby would be powered by Samsung's proprietary large language model (LLM) technology, promising a significant boost to Bixby's capabilities with the help of generative AI. In January 2025, with the announcement of Galaxy S25 and the One UI 7 update, Bixby was no longer the default voice assistant, having been replaced by Google Gemini. Despite this, Bixby still continued to be developed and expanded by Samsung and was revamped at the same time with new AI capabilities. Samsung brought the "smarter" Bixby to Samsung televisions, allowing users to speak to their TV sets and control their homes with it. A visual refresh was planned for One UI 8.5. == Functionality == Bixby is a voice assistant developed by Samsung that provides device control, information retrieval, and task automation using voice input and artificial intelligence. It can answer contextual queries, adjust system settings, perform searches, and manage reminders or schedules. The service also personalizes responses by recognizing individual user voices. Bixby itself was also formerly called Bixby Voice to differentiate from other Bixby tools in the suite. === Bixby Vision === Bixby Vision is a visual recognition feature that analyzes images captured through the device camera and provides context-specific information or actions. It combines on-device processing with cloud-based AI resources to identify objects, detect text, and interpret scenes within supported applications. It comes pre-installed on Samsung Galaxy phones. It is considered to be the imaging component of Bixby. ==== Translate ==== Detects foreign text in the camera view and provides real-time translation by overlaying translated text on the preview. ==== Text ==== Uses optical character recognition(OCR) to extract printed or handwritten text for copying, searching, or sharing. ==== Discover ==== Identifies consumer products, fashion items, or furniture and retrieves visually similar items or related online information. ==== Wine ==== Recognizes wine labels and provides information such as variety, region of origin, average price, and reviews. ==== Scene Describer ==== Generates written and spoken descriptions of captured scenes, supporting accessibility for users with visual impairments. ==== Object Identifier ==== Identifies plants, animals, food items, or landmarks and displays corresponding names or classification details. ==== Text Reader ==== Converts detected text into spoken audio using text-to-speech functionality. ==== Color Detector ==== Identifies and names colors within the frame, displaying or reading the recognized color aloud. === Former Bixby tools === Bixby Home was a vertically scrolling home screen displaying cards of information such as weather, fitness activity, and smart home controls. It was renamed Samsung Daily with the release of One UI 2.1 in 2020, then replaced by Samsung Free in One UI 3.0. Samsung Free was eventually discontinued in some markets. Its successor, Samsung News, now functions as a news aggregation service with optional home-screen integration similar to Bixby Home. Bixby Routines was an automation feature that allowed users to create custom rules based on triggers such as time, location, or device conditions. Beginning with One UI 5.0, it was renamed Modes and Routines. Bixby Text Call, introduced in One UI 5.0 (2022) in select regions, enabled users to handle incoming calls via speech-to-text conversion and vice versa. It is now named simply Text Call and can be found in the Phone app settings. Bixby Touch allowed users to trigger context-aware actions by touching on-screen content. It analyzed images, text, and other visual elements displayed on the device and provided related options such as translation, image search, product lookup, or other content-based information. Several of its capabilities overlapped with, or were later superseded by, features offered through Bixby Vision. Other legacy components including Bixby Touch, Bixby Global Action, Bixby Dictation, and Bixby Wakeup, formed part of the early Bixby suite and have since been phased out, though exact discontinuation details vary by region. == Regions and languages == As of April 2018, Bixby is available in over 195 countries, but only in Korean, English (American), and Chinese (Mandarin). The limitation is that the models not intended for the Japanese market, like S10e, are not allowed to login to Bixby services from Japan; therefore Bixby becomes blocked. The choice of languages has since expanded: Samsung has deployed Bixby's voice command function in French, and on 20 February 2019 Samsung announced the addition of further languages: English (British), German, Italian and Spanish (Spain). On 22 February 2020, Samsung announced the addition of Portuguese (Brazil), for Galaxy S10 & Note10, in Beta, and later for other models. == Compatible devices == === Flagship series === Galaxy S series: All models since Galaxy S7 Galaxy Tab S: All models since Galaxy Tab S4 Galaxy Note: All models since Galaxy Note FE and Galaxy Note 8 Galaxy Z series: All models === Other series === Galaxy A Galaxy A6/A6+ (Bixby Home, Reminder and Vision) Galaxy A7 (2017) (available to users in South Korea only; Bixby Home and Reminder only) Galaxy A7 (2018) (Bixby Home, Reminder and Vision only) Galaxy A8 (2018) (including A8 Star; Bixby Home, Reminder and Vision only; S Voice used instead) Galaxy A8s (Bixby Home, Reminder and Vision only) Galaxy A9 (2018)/A9s/A9 Star Pro (including A9 Star and A9 Star Lite; Bixby Home, Reminder and Vision only; S Voice used instead) Galaxy A9 Pro (2019) (Bixby Home, Reminder and Vision only) Galaxy A20 (Bixby Home and Service) Galaxy A21s Galaxy A30s (Bixby Home, Vision, Reminder and Routines) Galaxy A40 (Bixby Home and Reminder) Galaxy A41 (Bixby Home, Vision, Routines and Reminder) Galaxy A50 (Bixby Home, Voice, Vision, Reminder and Routines) Galaxy A50s (Bixby Home, Voice, Vision, Reminder and Routines) G
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Is an AI Clip Maker Worth It in 2026?

Trying to pick the best AI clip maker? An AI clip maker 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 clip maker 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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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.
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Empowerment (artificial intelligence)

Empowerment in the field of artificial intelligence formalises and quantifies (via information theory) the potential an agent perceives that it has to influence its environment. An agent which follows an empowerment maximising policy, acts to maximise future options (typically up to some limited horizon). Empowerment can be used as a (pseudo) utility function that depends only on information gathered from the local environment to guide action, rather than seeking an externally imposed goal, thus is a form of intrinsic motivation. The empowerment formalism depends on a probabilistic model commonly used in artificial intelligence. An autonomous agent operates in the world by taking in sensory information and acting to change its state, or that of the environment, in a cycle of perceiving and acting known as the perception-action loop. Agent state and actions are modelled by random variables ( S : s ∈ S , A : a ∈ A {\displaystyle S:s\in {\mathcal {S}},A:a\in {\mathcal {A}}} ) and time ( t {\displaystyle t} ). The choice of action depends on the current state, and the future state depends on the choice of action, thus the perception-action loop unrolled in time forms a causal bayesian network. == Definition == Empowerment ( E {\displaystyle {\mathfrak {E}}} ) is defined as the channel capacity ( C {\displaystyle C} ) of the actuation channel of the agent, and is formalised as the maximal possible information flow between the actions of the agent and the effect of those actions some time later. Empowerment can be thought of as the future potential of the agent to affect its environment, as measured by its sensors. E := C ( A t ⟶ S t + 1 ) ≡ max p ( a t ) I ( A t ; S t + 1 ) {\displaystyle {\mathfrak {E}}:=C(A_{t}\longrightarrow S_{t+1})\equiv \max _{p(a_{t})}I(A_{t};S_{t+1})} In a discrete time model, Empowerment can be computed for a given number of cycles into the future, which is referred to in the literature as 'n-step' empowerment. E ( A t n ⟶ S t + n ) = max p ( a t , . . . , a t + n − 1 ) I ( A t , . . . , A t + n − 1 ; S t + n ) {\displaystyle {\mathfrak {E}}(A_{t}^{n}\longrightarrow S_{t+n})=\max _{p(a_{t},...,a_{t+n-1})}I(A_{t},...,A_{t+n-1};S_{t+n})} The unit of empowerment depends on the logarithm base. Base 2 is commonly used in which case the unit is bits. === Contextual Empowerment === In general the choice of action (action distribution) that maximises empowerment varies from state to state. Knowing the empowerment of an agent in a specific state is useful, for example to construct an empowerment maximising policy. State-specific empowerment can be found using the more general formalism for 'contextual empowerment'. C {\displaystyle C} is a random variable describing the context (e.g. state). E ( A t n ⟶ S t + n ∣ C ) = ∑ c ∈ C p ( c ) E ( A t n ⟶ S t + n ∣ C = c ) {\displaystyle {\mathfrak {E}}(A_{t}^{n}\longrightarrow S_{t+n}{\mid }C)=\sum _{c{\in }C}p(c){\mathfrak {E}}(A_{t}^{n}\longrightarrow S_{t+n}{\mid }C=c)} == Application == Empowerment maximisation can be used as a pseudo-utility function to enable agents to exhibit intelligent behaviour without requiring the definition of external goals, for example balancing a pole in a cart-pole balancing scenario where no indication of the task is provided to the agent. Empowerment has been applied in studies of collective behaviour and in continuous domains. As is the case with Bayesian methods in general, computation of empowerment becomes computationally expensive as the number of actions and time horizon extends, but approaches to improve efficiency have led to usage in real-time control. Empowerment has been used for intrinsically motivated reinforcement learning agents playing video games, and in the control of underwater vehicles.
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Statistical machine translation

Statistical machine translation (SMT) is a machine translation approach where translations are generated on the basis of statistical models whose parameters are derived from the analysis of bilingual text corpora. The statistical approach contrasts with the rule-based approaches to machine translation as well as with example-based machine translation, that superseded the previous rule-based approach that required explicit description of each and every linguistic rule, which was costly, and which often did not generalize to other languages. The first ideas of statistical machine translation were introduced by Warren Weaver in 1949, including the ideas of applying Claude Shannon's information theory. Statistical machine translation was re-introduced in the late 1980s and early 1990s by researchers at IBM's Thomas J. Watson Research Center. Before the introduction of neural machine translation, it was by far the most widely studied machine translation method. == Basis == The idea behind statistical machine translation comes from information theory. A document is translated according to the probability distribution p ( e | f ) {\displaystyle p(e|f)} that a string e {\displaystyle e} in the target language (for example, English) is the translation of a string f {\displaystyle f} in the source language (for example, French). The problem of modeling the probability distribution p ( e | f ) {\displaystyle p(e|f)} has been approached in a number of ways. One approach which lends itself well to computer implementation is to apply Bayes' theorem, that is p ( e | f ) ∝ p ( f | e ) p ( e ) {\displaystyle p(e|f)\propto p(f|e)p(e)} , where the translation model p ( f | e ) {\displaystyle p(f|e)} is the probability that the source string is the translation of the target string, and the language model p ( e ) {\displaystyle p(e)} is the probability of seeing that target language string. This decomposition is attractive as it splits the problem into two subproblems. Finding the best translation e ~ {\displaystyle {\tilde {e}}} is done by picking up the one that gives the highest probability: e ~ = a r g max e ∈ e ∗ p ( e | f ) = a r g max e ∈ e ∗ p ( f | e ) p ( e ) {\displaystyle {\tilde {e}}=arg\max _{e\in e^{}}p(e|f)=arg\max _{e\in e^{}}p(f|e)p(e)} . For a rigorous implementation of this one would have to perform an exhaustive search by going through all strings e ∗ {\displaystyle e^{}} in the native language. Performing the search efficiently is the work of a machine translation decoder that uses the foreign string, heuristics and other methods to limit the search space and at the same time keeping acceptable quality. This trade-off between quality and time usage can also be found in speech recognition. As the translation systems are not able to store all native strings and their translations, a document is typically translated sentence by sentence. Language models are typically approximated by smoothed n-gram models, and similar approaches have been applied to translation models, but this introduces additional complexity due to different sentence lengths and word orders in the languages. Statistical translation models were initially word based (Models 1-5 from IBM Hidden Markov model from Stephan Vogel and Model 6 from Franz-Joseph Och), but significant advances were made with the introduction of phrase based models. Later work incorporated syntax or quasi-syntactic structures. == Benefits == The most frequently cited benefits of statistical machine translation (SMT) over rule-based approach are: More efficient use of human and data resources There are many parallel corpora in machine-readable format and even more monolingual data. Generally, SMT systems are not tailored to any specific pair of languages. More fluent translations owing to use of a language model == Shortcomings == Corpus creation can be costly. Specific errors are hard to predict and fix. Results may have superficial fluency that masks translation problems. Statistical machine translation usually works less well for language pairs with significantly different word order. The benefits obtained for translation between Western European languages are not representative of results for other language pairs, owing to smaller training corpora and greater grammatical differences. == Word-based translation == In word-based translation, the fundamental unit of translation is a word in some natural language. Typically, the number of words in translated sentences are different, because of compound words, morphology and idioms. The ratio of the lengths of sequences of translated words is called fertility, which tells how many foreign words each native word produces. Necessarily it is assumed by information theory that each covers the same concept. In practice this is not really true. For example, the English word corner can be translated in Spanish by either rincón or esquina, depending on whether it is to mean its internal or external angle. Simple word-based translation cannot translate between languages with different fertility. Word-based translation systems can relatively simply be made to cope with high fertility, such that they could map a single word to multiple words, but not the other way about. For example, if we were translating from English to French, each word in English could produce any number of French words— sometimes none at all. But there is no way to group two English words producing a single French word. An example of a word-based translation system is the freely available GIZA++ package (GPLed), which includes the training program for IBM models and HMM model and Model 6. The word-based translation is not widely used today; phrase-based systems are more common. Most phrase-based systems are still using GIZA++ to align the corpus. The alignments are used to extract phrases or deduce syntax rules. And matching words in bi-text is still a problem actively discussed in the community. Because of the predominance of GIZA++, there are now several distributed implementations of it online. == Phrase-based translation == In phrase-based translation, the aim is to reduce the restrictions of word-based translation by translating whole sequences of words, where the lengths may differ. The sequences of words are called blocks or phrases. These are typically not linguistic phrases, but phrasemes that were found using statistical methods from corpora. It has been shown that restricting the phrases to linguistic phrases (syntactically motivated groups of words, see syntactic categories) decreased the quality of translation. The chosen phrases are further mapped one-to-one based on a phrase translation table, and may be reordered. This table could be learnt based on word-alignment, or directly from a parallel corpus. The second model is trained using the expectation maximization algorithm, similarly to the word-based IBM model. == Syntax-based translation == Syntax-based translation is based on the idea of translating syntactic units, rather than single words or strings of words (as in phrase-based MT), i.e. (partial) parse trees of sentences/utterances. Until the 1990s, with advent of strong stochastic parsers, the statistical counterpart of the old idea of syntax-based translation did not take off. Examples of this approach include DOP-based MT and later synchronous context-free grammars. == Hierarchical phrase-based translation == Hierarchical phrase-based translation combines the phrase-based and syntax-based approaches to translation. It uses synchronous context-free grammar rules, but the grammars can be constructed by an extension of methods for phrase-based translation without reference to linguistically motivated syntactic constituents. This idea was first introduced in Chiang's Hiero system (2005). == Language models == A language model is an essential component of any statistical machine translation system, which aids in making the translation as fluent as possible. It is a function that takes a translated sentence and returns the probability of it being said by a native speaker. A good language model will for example assign a higher probability to the sentence "the house is small" than to "small the is house". Other than word order, language models may also help with word choice: if a foreign word has multiple possible translations, these functions may give better probabilities for certain translations in specific contexts in the target language. == Systems implementing statistical machine translation == Google Translate (started transition to neural machine translation in 2016) Microsoft Translator (started transition to neural machine translation in 2016) Yandex.Translate (switched to hybrid approach incorporating neural machine translation in 2017) == Challenges with statistical machine translation == Problems with statistical machine translation include: === Sentence alignment === Single sentences in one language can be found translated into several sentences in the o
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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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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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The Triple Revolution

"The Triple Revolution" was an open memorandum sent to U.S. President Lyndon B. Johnson and other government figures on March 22, 1964. It concerned three megatrends of the time: increasing use of automation, the nuclear arms race, and advancements in human rights. Drafted under the auspices of the Center for the Study of Democratic Institutions, it was signed by an array of noted social activists, professors, and technologists who identified themselves as the Ad Hoc Committee on the Triple Revolution. The chief initiator of the proposal was W. H. "Ping" Ferry, at that time a vice-president of CSDI, basing it in large part on the ideas of the futurist Robert Theobald. == Overview == The statement identified three revolutions underway in the world: the cybernation revolution of increasing automation; the weaponry revolution of mutually assured destruction; and the human rights revolution. It discussed primarily the cybernation revolution. The committee claimed that machines would usher in "a system of almost unlimited productive capacity" while continually reducing the number of manual laborers needed, and increasing the skill needed to work, thereby producing increasing levels of unemployment. It proposed that the government should ease this transformation through large-scale public works, low-cost housing, public transit, electrical power development, income redistribution, union representation for the unemployed, and government restraint on technology deployment. == Legacy == Martin Luther King Jr.'s final Sunday sermon, delivered six days before his April 1968 assassination, explicitly references the thesis of "The Triple Revolution": There can be no gainsaying of the fact that a great revolution is taking place in the world today. In a sense it is a triple revolution: that is, a technological revolution, with the impact of automation and cybernation; then there is a revolution in weaponry, with the emergence of atomic and nuclear weapons of warfare; then there is a human rights revolution, with the freedom explosion that is taking place all over the world. Yes, we do live in a period where changes are taking place. And there is still the voice crying through the vista of time saying, "Behold, I make all things new; former things are passed away." In Harlan Ellison's 1967 anthology Dangerous Visions, Philip José Farmer's story "Riders of the Purple Wage" uses the Triple Revolution document as the premise of a future society, in which the "purple wage" of the title is a guaranteed income dole on which most of the population lives. At the 1968 World Science Fiction Convention in San Francisco, Farmer delivered a lengthy Guest of Honor speech in which he called for the founding of a grassroots activist organization called REAP which would work for implementation of the Ad Hoc Committee's recommendations. Looking back on the proposal in his 2008 book, Daniel Bell wrote: "the cybernetic revolution quickly proved to be illusory. There were no spectacular jumps in productivity. ... Cybernation had proved to be one more instance of the penchant for overdramatizing a momentary innovation and blowing it up far out of proportion to its actuality. ... The image of a completely automated production economy—with an endless capacity to turn out goods—was simply a social-science fiction of the early 1960s. Paradoxically, the vision of Utopia was suddenly replaced by the spectre of Doomsday. In place of the early-sixties theme of endless plenty, the picture by the end of the decade was one of a fragile planet of limited resources whose finite stocks were being rapidly depleted, and whose wastes from soaring industrial production were polluting the air and waters." In his 2015 book Rise of the Robots, Martin Ford claims The Triple Revolution's predictions of steady decline in future employment were not wrong, but rather premature. He cites "Seven Deadly Trends" that began in the 1970s-1980s and by the mid-2010s appeared set to continue: Stagnation in real wages Decline in labor's share of national income in many countries (breakdown of Bowley's law), while corporate profits increased Declining labor force participation Diminishing job creation, lengthening jobless recoveries, and soaring long-term unemployment Rising inequality Declining incomes, and underemployment for recent college graduates Polarization and part-time jobs (middle-class jobs are disappearing, to be replaced by a small number of high-paying jobs and large number of low-paying jobs) According to Ford, the 1960s were part of what in retrospect seems like a golden age for labor in the United States, when productivity and wages rose together in near lockstep, and unemployment was low. But after about 1980, wages began stagnating while productivity continued to rise. Labor's share of the economic output began to decline. Ford describes the role that automation and information technology play in these trends, and how new technologies including narrow AI threaten to destroy jobs faster than displaced workers can be retrained for new jobs, before automation takes the new jobs as well. This includes many job categories, such as in transportation, that were never threatened by automation before. According to a 2013 study, about 47% of US jobs are susceptible to automation. == Signatories ==
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Baum–Welch algorithm

In electrical engineering, statistical computing and bioinformatics, the Baum–Welch algorithm is a special case of the expectation–maximization algorithm used to find the unknown parameters of a hidden Markov model (HMM). It makes use of the forward-backward algorithm to compute the statistics for the expectation step. The Baum–Welch algorithm, the primary method for inference in hidden Markov models, is numerically unstable due to its recursive calculation of joint probabilities. As the number of variables grows, these joint probabilities become increasingly small, leading to the forward recursions rapidly approaching values below machine precision. == History == The Baum–Welch algorithm was named after its inventors Leonard E. Baum and Lloyd R. Welch. The algorithm and the Hidden Markov models were first described in a series of articles by Baum and his peers at the IDA Center for Communications Research, Princeton in the late 1960s and early 1970s. One of the first major applications of HMMs was to the field of speech processing. In the 1980s, HMMs were emerging as a useful tool in the analysis of biological systems and information, and in particular genetic information. They have since become an important tool in the probabilistic modeling of genomic sequences. == Description == A hidden Markov model describes the joint probability of a collection of "hidden" and observed discrete random variables. It relies on the assumption that the i-th hidden variable given the (i − 1)-th hidden variable is independent of previous hidden variables, and the current observation variables depend only on the current hidden state. The Baum–Welch algorithm uses the well known EM algorithm to find the maximum likelihood estimate of the parameters of a hidden Markov model given a set of observed feature vectors. Let X t {\displaystyle X_{t}} be a discrete hidden random variable with N {\displaystyle N} possible values (i.e. We assume there are N {\displaystyle N} states in total). We assume the P ( X t ∣ X t − 1 ) {\displaystyle P(X_{t}\mid X_{t-1})} is independent of time t {\displaystyle t} , which leads to the definition of the time-independent stochastic transition matrix A = { a i j } = P ( X t = j ∣ X t − 1 = i ) . {\displaystyle A=\{a_{ij}\}=P(X_{t}=j\mid X_{t-1}=i).} The initial state distribution (i.e. when t = 1 {\displaystyle t=1} ) is given by π i = P ( X 1 = i ) . {\displaystyle \pi _{i}=P(X_{1}=i).} The observation variables Y t {\displaystyle Y_{t}} can take one of K {\displaystyle K} possible values. We also assume the observation given the "hidden" state is time independent. The probability of a certain observation y i {\displaystyle y_{i}} at time t {\displaystyle t} for state X t = j {\displaystyle X_{t}=j} is given by b j ( y i ) = P ( Y t = y i ∣ X t = j ) . {\displaystyle b_{j}(y_{i})=P(Y_{t}=y_{i}\mid X_{t}=j).} Taking into account all the possible values of Y t {\displaystyle Y_{t}} and X t {\displaystyle X_{t}} , we obtain the N × K {\displaystyle N\times K} matrix B = { b j ( y i ) } {\displaystyle B=\{b_{j}(y_{i})\}} where b j {\displaystyle b_{j}} belongs to all the possible states and y i {\displaystyle y_{i}} belongs to all the observations. An observation sequence is given by Y = ( Y 1 = y 1 , Y 2 = y 2 , … , Y T = y T ) {\displaystyle Y=(Y_{1}=y_{1},Y_{2}=y_{2},\ldots ,Y_{T}=y_{T})} . Thus we can describe a hidden Markov chain by θ = ( A , B , π ) {\displaystyle \theta =(A,B,\pi )} . The Baum–Welch algorithm finds a local maximum for θ ∗ = a r g m a x θ ⁡ P ( Y ∣ θ ) {\displaystyle \theta ^{}=\operatorname {arg\,max} _{\theta }P(Y\mid \theta )} (i.e. the HMM parameters θ {\displaystyle \theta } that maximize the probability of the observation). === Algorithm === Set θ = ( A , B , π ) {\displaystyle \theta =(A,B,\pi )} with random initial conditions. They can also be set using prior information about the parameters if it is available; this can speed up the algorithm and also steer it toward the desired local maximum. ==== Forward procedure ==== Let α i ( t ) = P ( Y 1 = y 1 , … , Y t = y t , X t = i ∣ θ ) {\displaystyle \alpha _{i}(t)=P(Y_{1}=y_{1},\ldots ,Y_{t}=y_{t},X_{t}=i\mid \theta )} , the probability of seeing the observations y 1 , y 2 , … , y t {\displaystyle y_{1},y_{2},\ldots ,y_{t}} and being in state i {\displaystyle i} at time t {\displaystyle t} . This is found recursively: α i ( 1 ) = π i b i ( y 1 ) , {\displaystyle \alpha _{i}(1)=\pi _{i}b_{i}(y_{1}),} α i ( t + 1 ) = b i ( y t + 1 ) ∑ j = 1 N α j ( t ) a j i . {\displaystyle \alpha _{i}(t+1)=b_{i}(y_{t+1})\sum _{j=1}^{N}\alpha _{j}(t)a_{ji}.} Since this series converges exponentially to zero, the algorithm will numerically underflow for longer sequences. However, this can be avoided in a slightly modified algorithm by scaling α {\displaystyle \alpha } in the forward and β {\displaystyle \beta } in the backward procedure below. ==== Backward procedure ==== Let β i ( t ) = P ( Y t + 1 = y t + 1 , … , Y T = y T ∣ X t = i , θ ) {\displaystyle \beta _{i}(t)=P(Y_{t+1}=y_{t+1},\ldots ,Y_{T}=y_{T}\mid X_{t}=i,\theta )} that is the probability of the ending partial sequence y t + 1 , … , y T {\displaystyle y_{t+1},\ldots ,y_{T}} given starting state i {\displaystyle i} at time t {\displaystyle t} . We calculate β i ( t ) {\displaystyle \beta _{i}(t)} as, β i ( T ) = 1 , {\displaystyle \beta _{i}(T)=1,} β i ( t ) = ∑ j = 1 N β j ( t + 1 ) a i j b j ( y t + 1 ) . {\displaystyle \beta _{i}(t)=\sum _{j=1}^{N}\beta _{j}(t+1)a_{ij}b_{j}(y_{t+1}).} ==== Update ==== We can now calculate the temporary variables, according to Bayes' theorem: γ i ( t ) = P ( X t = i ∣ Y , θ ) = P ( X t = i , Y ∣ θ ) P ( Y ∣ θ ) = α i ( t ) β i ( t ) ∑ j = 1 N α j ( t ) β j ( t ) , {\displaystyle \gamma _{i}(t)=P(X_{t}=i\mid Y,\theta )={\frac {P(X_{t}=i,Y\mid \theta )}{P(Y\mid \theta )}}={\frac {\alpha _{i}(t)\beta _{i}(t)}{\sum _{j=1}^{N}\alpha _{j}(t)\beta _{j}(t)}},} which is the probability of being in state i {\displaystyle i} at time t {\displaystyle t} given the observed sequence Y {\displaystyle Y} and the parameters θ {\displaystyle \theta } ξ i j ( t ) = P ( X t = i , X t + 1 = j ∣ Y , θ ) = P ( X t = i , X t + 1 = j , Y ∣ θ ) P ( Y ∣ θ ) = α i ( t ) a i j β j ( t + 1 ) b j ( y t + 1 ) ∑ k = 1 N ∑ w = 1 N α k ( t ) a k w β w ( t + 1 ) b w ( y t + 1 ) , {\displaystyle \xi _{ij}(t)=P(X_{t}=i,X_{t+1}=j\mid Y,\theta )={\frac {P(X_{t}=i,X_{t+1}=j,Y\mid \theta )}{P(Y\mid \theta )}}={\frac {\alpha _{i}(t)a_{ij}\beta _{j}(t+1)b_{j}(y_{t+1})}{\sum _{k=1}^{N}\sum _{w=1}^{N}\alpha _{k}(t)a_{kw}\beta _{w}(t+1)b_{w}(y_{t+1})}},} which is the probability of being in state i {\displaystyle i} and j {\displaystyle j} at times t {\displaystyle t} and t + 1 {\displaystyle t+1} respectively given the observed sequence Y {\displaystyle Y} and parameters θ {\displaystyle \theta } . The denominators of γ i ( t ) {\displaystyle \gamma _{i}(t)} and ξ i j ( t ) {\displaystyle \xi _{ij}(t)} are the same ; they represent the probability of making the observation Y {\displaystyle Y} given the parameters θ {\displaystyle \theta } . The parameters of the hidden Markov model θ {\displaystyle \theta } can now be updated: π i ∗ = γ i ( 1 ) , {\displaystyle \pi _{i}^{}=\gamma _{i}(1),} which is the expected frequency spent in state i {\displaystyle i} at time 1 {\displaystyle 1} . a i j ∗ = ∑ t = 1 T − 1 ξ i j ( t ) ∑ t = 1 T − 1 γ i ( t ) , {\displaystyle a_{ij}^{}={\frac {\sum _{t=1}^{T-1}\xi _{ij}(t)}{\sum _{t=1}^{T-1}\gamma _{i}(t)}},} which is the expected number of transitions from state i to state j compared to the expected total number of transitions starting in state i, including from state i to itself. The number of transitions starting in state i is equivalent to the number of times state i is observed in the sequence from t = 1 to t = T − 1. b i ∗ ( v k ) = ∑ t = 1 T 1 y t = v k γ i ( t ) ∑ t = 1 T γ i ( t ) , {\displaystyle b_{i}^{}(v_{k})={\frac {\sum _{t=1}^{T}1_{y_{t}=v_{k}}\gamma _{i}(t)}{\sum _{t=1}^{T}\gamma _{i}(t)}},} where 1 y t = v k = { 1 if y t = v k , 0 otherwise {\displaystyle 1_{y_{t}=v_{k}}={\begin{cases}1&{\text{if }}y_{t}=v_{k},\\0&{\text{otherwise}}\end{cases}}} is an indicator function, and b i ∗ ( v k ) {\displaystyle b_{i}^{}(v_{k})} is the expected number of times the output observations have been equal to v k {\displaystyle v_{k}} while in state i {\displaystyle i} over the expected total number of times in state i {\displaystyle i} . These steps are now repeated iteratively until a desired level of convergence. Note: It is possible to over-fit a particular data set. That is, P ( Y ∣ θ final ) > P ( Y ∣ θ true ) {\displaystyle P(Y\mid \theta _{\text{final}})>P(Y\mid \theta _{\text{true}})} . The algorithm also does not guarantee a global maximum. ==== Multiple sequences ==== The algorithm described thus far assumes a single observed sequence Y = y 1 , … , y T {\displaystyle Y=y_{1},\ldots ,y_{T}} . However, in many situations, there are several sequences observed: Y 1 ,
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Yorick Wilks

Yorick Alexander Wilks FBCS (27 October 1939 – 14 April 2023) was a British computer scientist. He was an emeritus professor of artificial intelligence at the University of Sheffield, visiting professor of artificial intelligence at Gresham College (a post created especially for him), senior research fellow at the Oxford Internet Institute, senior scientist at the Florida Institute for Human and Machine Cognition, and a member of the Epiphany Philosophers. In February 2023, Wilks joined WiredVibe as Director of AI and a Board Member, with the goal of commercializing his previous research and ideas. He remained in this role until his death, which occurred shortly before WiredVibe was acquired by AKY X, a company that continues to build on his legacy and contributions. == Biography == Wilks was born in Gerrards Cross, Buckinghamshire in England. He was educated at Torquay Boys' Grammar School, followed by Pembroke College, Cambridge, where he read Philosophy, joined the Epiphany Philosophers and obtained his Doctor of Philosophy degree (1968) under Professor R. B. Braithwaite for the thesis 'Argument and Proof'; he was an early pioneer in meaning-based approaches to the understanding of natural language content by computers. His main early contribution in the 1970s was called "Preference Semantics" (Wilks, 1973; Wilks and Fass, 1992), an algorithmic method for assigning the "most coherent" interpretation to a sentence in terms of having the maximum number of internal preferences of its parts (normally verbs or adjectives) satisfied. That early work was hand-coded with semantic entries (of the order of some hundreds) as was normal at the time, but since then has led to the empirical determinations of preferences (chiefly of English verbs) in the 1980s and 1990s. A key component of the notion of preference in semantics was that the interpretation of an utterance is not a well- or ill-formed notion, as was argued in Chomskyan approaches, such as those of Jerry Fodor and Jerrold Katz. It was rather that a semantic interpretation was the best available, even though some preferences might not be satisfied. So, in "The machine answered the question with a low whine" the agent of "answer" does not satisfy that verb's preference for a human answerer—which would cause it to be deemed ill-formed by Fodor and Katz—but is accepted as sub-optimal or metaphorical, and, now, conventional. The function of the algorithm is not to determine well-formedness at all but to make the optimal selection of word-senses to participate in the overall interpretation. Thus, in "The Pole answered..." the system will always select the human sense of the agent and not the inanimate one if it gives a more coherent interpretation overall. Preference Semantics is thus some of the earliest computational work—with programs run at Systems Development Corporation in Santa Monica in 1967 in LISP on an IBM360—in the now established field of word sense disambiguation. This approach was used in the first operational machine translation system based principally on meaning structures and built by Wilks at Stanford Artificial Intelligence Laboratory in the early 1970s (Wilks, 1973) at the same time and place as Roger Schank was applying his "Conceptual Dependency" approach to machine translation. The LISP code of Wilks' system was in The Computer Museum, Boston. Wilks was elected a fellow of the American and European Associations for Artificial Intelligence, of the British Computer Society, a member of the UK Computing Research Committee, and a permanent member of ICCL, the International Committee on Computational Linguistics. He was professor of artificial intelligence at the University of Sheffield and a senior research fellow at the Oxford Internet Institute. In 1991 he received a Defense Advanced Projects Agency grant on interlingual pragmatics-based machine translation and in 1994 he received a grant by the Engineering and Physical Sciences Research Council to investigate in the field of large-scale information extraction (LaSIE); in the following years he would obtain more grants to carry on exploring the field of information extraction (AVENTINUS, ECRAN, PASTA...). In the 1990s Wilks also became interested in modelling human-computer dialogue and the team led by David Levy and him as chief researcher won the Loebner Prize in 1997. He was the founding director of the EU funded Companions Project on creating long-term computer companions for people. At his Festschrift in 2007 at the British Computer Society in London a volume of his own papers was presented along with a volume of essays in his honour. He was awarded the Antonio Zampolli prize in honour of his lifetime work at the LREC 2008 conference on 28 May 2008, and the Lifetime Achievement Award at the ACL 2008 conference on 18 June 2008. In 2009, he was awarded the British Computer Society's Lovelace Medal, its annual award for research achievement, and was awarded the Fellowship of the Association for Computing Machinery. In 1998, Wilks became head of the Department of Computer Science of the University of Sheffield, where he had started working in the year 1993 as professor of artificial intelligence, a post he still held. In 1993 he became the founding director of the Institute of Language, Speech and Hearing (ILASH). Wilks also set up the Natural Language Processing Group of the University of Sheffield. In 1994 he (along with Rob Gaizauskas and Hamish Cunningham) designed GATE, an advanced NLP architecture that has been widely distributed. National Life Stories conducted an oral history interview (C1672/24) with Yorick Wilks in 2016 for its Science and Religion collection held by the British Library. Wilks died on 14 April 2023, at the age of 83. == Awards == Wilks received many awards: (2009) Elected Fellow of the Association for Computing Machinery (2009) Lovelace Medal by the British Computer Society (2008) Zampolli Prize (ELRA, awarded at LREC in Marrakech, Morocco) (2008) Lifetime Achievement Award (Association for Computational Linguistics, in Columbus) (2006) Visiting Professor, University of Oxford (2004) Elected to UK Computing Research Committee (2004) Elected Fellow, British Computer Society (2003) Visiting Fellow, Oxford Internet Institute (1998) Elected Fellow of European Association for Artificial Intelligence (1997) Elected Fellow, EPSRC College of Computing (1991) Visiting Fellow, Trinity Hall, Cambridge (1991) Elected Fellow of the American Association for Artificial Intelligence (1983) Royal Society Travel Fellowship (1983) Commonwealth of Australia Visiting Professor (1981) Visiting Sloan Fellow, University of California, Berkeley (1980) Invited Participant in the Nobel Symposium on Language, Stockholm (1979) NATO Senior Scientist Fellowship (1979) Visiting Sloan Fellow, Yale University (1975) SRC Senior Visiting Fellowship, University of Edinburgh == Membership == Wilks was an active member of the following associations: Association for Computational Linguistics Society for the Study of AI and Simulation of Behaviour Association for Computing Machinery Cognitive Science Society British Society for the Philosophy of Science American Association for Artificial Intelligence Aristotelian Society == Selected works == === Books === Wilks, Y. (2019) Artificial Intelligence: Modern Magic or Dangerous Future?.Icon Books. New illustrated edition, 2023, MIT Press. Wilks, Y. (2015) Machine Translation: its scope and limits. Springer Wilks, Y (ed.) (2010) Close Engagements with Artificial Companions: Key Social, Psychological and Design issues. John Benjamins; Amsterdam Wilks, Y., Brewster, C. (2009) Natural Language Processing as a Foundation of the Semantic Web. Now Press: London. Wilks, Y. (2007) Words and Intelligence I, Selected papers by Yorick Wilks. In K. Ahmad, C. Brewster & M. Stevenson (eds.), Springer: Dordrecht. Wilks, Y. (ed. and with introduction and commentaries). (2006) Language, cohesion and form: selected papers of Margaret Masterman. Cambridge: Cambridge University Press. Wilks, Y., Nirenburg, S., Somers, H. (eds.) (2003) Readings in Machine Translation. Cambridge, MA: MIT Press. Wilks, Y.(ed.). (1999) Machine Conversations. Kluwer: New York. Wilks, Y., Slator, B., Guthrie, L. (1996) Electric Words: dictionaries, computers and meanings. Cambridge, MA: MIT Press. Ballim, A., Wilks, Y. (1991) Artificial Believers. Norwood, NJ: Erlbaum. Wilks, Y.(ed.). (1990) Theoretical Issues in Natural Language Processing. Norwood, NJ: Erlbaum. Wilks, Y., Partridge, D. (eds. plus three YW chapters and an introduction). (1990) The Foundations of Artificial Intelligence: a sourcebook. Cambridge: Cambridge University Press. Wilks, Y., Sparck-Jones, K.(eds.). (1984) Automatic Natural Language Processing, paperback edition. New York: Wiley. Originally published by Ellis Horwood. Wilks, Y., Charniak, E. (eds and principal authors). (1976) Computational Semantics—an Introduction to Artificial Intelligence and
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