AI Chatbot That Sends Pictures

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Social software engineering

Social software engineering (SSE) is a branch of software engineering that is concerned with the social aspects of software development and the developed software. SSE focuses on the socialness of both software engineering and developed software. On the one hand, the consideration of social factors in software engineering activities, processes and CASE tools is deemed to be useful to improve the quality of both development process and produced software. Examples include the role of situational awareness and multi-cultural factors in collaborative software development. On the other hand, the dynamicity of the social contexts in which software could operate (e.g., in a cloud environment) calls for engineering social adaptability as a runtime iterative activity. Examples include approaches which enable software to gather users' quality feedback and use it to adapt autonomously or semi-autonomously. SSE studies and builds socially-oriented tools to support collaboration and knowledge sharing in software engineering. SSE also investigates the adaptability of software to the dynamic social contexts in which it could operate and the involvement of clients and end-users in shaping software adaptation decisions at runtime. Social context includes norms, culture, roles and responsibilities, stakeholder's goals and interdependencies, end-users perception of the quality and appropriateness of each software behaviour, etc. The participants of the 1st International Workshop on Social Software Engineering and Applications (SoSEA 2008) proposed the following characterization: Community-centered: Software is produced and consumed by and/or for a community rather than focusing on individuals Collaboration/collectiveness: Exploiting the collaborative and collective capacity of human beings Companionship/relationship: Making explicit the various associations among people Human/social activities: Software is designed consciously to support human activities and to address social problems Social inclusion: Software should enable social inclusion enforcing links and trust in communities Thus, SSE can be defined as "the application of processes, methods, and tools to enable community-driven creation, management, deployment, and use of software in online environments". One of the main observations in the field of SSE is that the concepts, principles, and technologies made for social software applications are applicable to software development itself as software engineering is inherently a social activity. SSE is not limited to specific activities of software development. Accordingly, tools have been proposed supporting different parts of SSE, for instance, social system design or social requirements engineering. Consequently vertical market software, such as software development tools, engineering tools, marketing tools or software that helps users in a decision-making process can profit from social components. Such vertical social software differentiates strongly in its user-base from traditional social software such as Yammer.
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How to Choose an AI Virtual Assistant

In search of the best AI virtual assistant? An AI virtual assistant 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 virtual assistant slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
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Collocation extraction

Collocation extraction is the task of using a computer to extract collocations automatically from a corpus. The traditional method of performing collocation extraction is to find a formula based on the statistical quantities of those words to calculate a score associated to every word pairs. Proposed formulas are mutual information, t-test, z test, chi-squared test and likelihood ratio. Within the area of corpus linguistics, collocation is defined as a sequence of words or terms which co-occur more often than would be expected by chance. 'Crystal clear', 'middle management', 'nuclear family', and 'cosmetic surgery' are examples of collocated pairs of words. Some words are often found together because they make up a compound noun, for example 'riding boots' or 'motor cyclist' or ‘collocation extraction’ its very self.
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Is an AI Text-to-video Tool Worth It in 2026?

In search of the best AI text-to-video tool? An AI text-to-video tool is software that uses machine learning to help you get more done — it 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 text-to-video tool slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
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Josh (app)

Josh (stylized as JOSH) was a video-sharing social networking service but it has since evolved into a live call and chat application owned by VerSe Innovation – an Indian technology company based in Bangalore, India. Josh was an Indian short video app that was launched in immediately after the Indian Government banned TikTok and other Chinese apps in June 2020. The founders of the platform have promoted the app as the “Instagram for Bharat” referring to their focus on the Indian audience that speaks its own regional and state languages. Josh was among the top 10 most downloaded apps social and entertainment apps in India of 2021 and had 150 million monthly active users as per April 2022. The word 'Josh' translates to fervour or passion. The app was launched under the aegis of the Atmanirbhar Bharat campaign and to compete with the duopoly of Google and Facebook in India. Josh's parent company VerSe Innovations Pvt. Ltd. owns another startup Dailyhunt, which a content and news aggregator application. Both Dailyhunt and Josh are a part of the VerSe's focus on the "next billion" regional language users of India. Founders Virendra Gupta and Umang Bedi conceptualised Josh as a short-video platform that made content creation accessible to vernacular language users, essentially the non-English speaking audience in India. == Features == Josh is currently available in 12 Indian languages and allows users to upload, share, remix bite-sized videos of up to 120 seconds. There are various categories across the video section including viral, trending, glamour, dance, devotion, yoga and cooking among others. Similar to Instagram and TikTok, it has a video feed which is curated for individuals on the basis of their app behaviour. The app hosts many daily, weekly and monthly social media challenges. == Funding == In December 2020, within 3 months of its launch, Josh's parent app VerSe Innovation raised more than $100 million from investors including Alphabet Inc's Google and Microsoft. In February 2021, VerSe Innovation raised $100 million in Series H funding from Qatar Investment Authority, the sovereign wealth fund of the State of Qatar, and Glade Brook Capital Partners. In August 2021, VerSe raised over $450 million in its Series I financing round with a valuation of $1 billion. Investors included Canada Pension Plan Investment Board (CPPIB), Siguler Guff, Baillie Gifford, Carlyle Asia Partners Growth II affiliates, and others. The startup announced its plan to expand overseas and broaden its ecommerce play for both Dailyhunt and Josh. In April 2022, VerSe announced that it has raised $805 million in funding from investors at a valuation of nearly $5 billion. ByteDance Offloads Stake In Josh Parent VerSe, Exits At 56% Discount == Partnerships == In February 2021, Saregama and Josh signed a music licensing deal, wherein Josh expanded its musical library with 1.3 lakh songs from Saregama in 25 different languages. To improve their user experience, Josh partnered with computer vision company D-ID in August 2021. The company helped Josh introduce photo-to-video features, live portrait technology, animate their photos etc. In order to solidify their efforts in enhancing Josh, VerSe acquired Indian social networking platform GolBol in October 2021. The move came as an effort by the startup to strengthen their discovery initiatives on the platform and classify content at scale and understand the core behaviour of Indian regional audiences. Josh has also announced its plans to include live commerce as a potential revenue stream through its partnership with multiple large e-commerce players. == Notable campaigns == Say No To Dowry – In association with Josh, the Kerala Police partook in the #SayNo2Dowry online social media campaign that was started to highlight and stop the social evil in the state. Salute India – Josh entered the Guinness World Records by creating the largest online video album of people saluting (29,529). It organised an online campaign #SaluteIndia on the app during the 75th Independence Day of India during 10–15 August 2021.
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OCR Systems

OCR Systems, Inc., was an American computer hardware manufacturer and software publisher dedicated to optical character recognition technologies. The company's first product, the System 1000 in 1970, was used by numerous large corporations for bill processing and mail sorting. Following a series of pitfalls in the 1970s and early 1980s, founder Theodor Herzl Levine put the company in the hands of Gregory Boleslavsky and Vadim Brikman, the company's vice presidents and recent immigrants from the Soviet Ukraine, who were able to turn OCR System's fortunes around and expand its employee base. The company released the software-based OCR application ReadRight for DOS, later ported to Windows, in the late 1980s. Adobe Inc. bought the company in 1992. == History == OCR Systems was co-founded by Theodor Herzl Levine (c. 1923 – May 30, 2005). Levine served in the U.S. Army Signal Corps during World War II in the Solomon Islands, where he helped develop a sonar to find ejected pilots in the ocean. After the war, Levine spent 22 years at the University of Pennsylvania, earning his bachelor's degree in 1951, his master's degree in electrical engineering in 1957, and his doctorate in 1968. Alongside his studies, Levine taught statistics and calculus at Temple University, Rutgers University, La Salle University and Penn State Abington. Sometime in the 1960s, Levine was hired at Philco. He and two of his co-workers decided to form their own company dedicated to optical character recognition, founding OCR Systems in 1969 in Bensalem, Pennsylvania. OCR Systems's first product, the System 1000, was announced in 1970. OCR Systems entered a partnership with 3M to resell the System 1000 throughout the United States in March 1973. This was 3M's entry into the data entry field, managed by the company's Microfilm Products Division and accompanying 3M's suite of data retrieval systems. It soon found use among Texas Instruments, AT&T, Ricoh, Panasonic and Canon for bill processing and mail sorting. Later in the mid-1970s an unspecified Fortune 500 company reneged on a contract to distribute the System 1000; later still a Canadian company distributing the System 1000 in Canada went defunct. Both incidents led OCR Systems to go nearly bankrupt, although it eventually recovered. By the early 1980s, however, the company was almost insolvent. In 1983 Levine had only $8,000 in his savings and became bedridden with an illness. He left the company in the hands of Gregory Boleslavsky and Vadim Brikman, two Soviet Ukraine expats whom Levine had hired earlier in the 1980s. Boleslavsky was hired as a wire wrapper for the System 1000 and as a programmer and beta tester for ReadRight—a software package developed by Levine implementing patents from Nonlinear Technology, another OCR-centric company from Greenbelt, Maryland. Boleslavsky in turn recommended Brikman to Levine. The two soon became vice presidents of the company while Levine was bedridden; in Boleslavsky's case, he worked 14-hour work days for over half a year in pursuit of the title. The two presented OCR Systems' products to the National Computer Conference in Chicago, where they were massively popular. The company soon gained such clients as Allegheny Energy in Pennsylvania and the postal service of Belgium and received an influx of employees—mostly expats from Russia but also Poland and South Korea, as well as American-born workers. To accommodate the company's employee base, which had grown to over 30 in 1988, Levine moved OCR System's headquarters from Bensalem to the Masons Mill Business Park in Bryn Athyn. Chinon Industries of Japan signed an agreement with OCR Systems in 1987 to distribute OCR's ReadRight 1.0 software with Chinon's scanners, starting with their N-205 overhead scanner. In 1988, OCR opened their agreement to distribute ReadRight to other scanner manufacturers, including Canon, Hewlett-Packard, Skyworld, Taxan, Diamond Flower and Abaton. That year, the company posted a revenue of $3 million. OCR Systems extended their agreement with Chinon in 1989 and introduced version 2.0 of ReadRight. OCR Systems faced stiff competition in the software OCR market in the turn of the 1990s. The Toronto-based software firm Delrina signed a letter of intent to purchase the company in November 1991, expecting the deal to close in December and have OCR software available by Christmas. OCR was to receive $3 million worth of Delrina shares in a stock swap, but the deal collapsed in January 1992. Delrine later marketed its own Extended Character Recognition, or XCR, software package to compete with ReadRight. In July 1992, OCR Systems was purchased by Adobe Inc. for an undisclosed sum. == Products == === System 1000 === The System 1000 was based on the 16-bit Varian Data 620/i minicomputer with 4 KB of core memory. The system used the 620/i for controlling the paper feed, interpreting the format of the documents, the optical character recognition process itself, error detection, sequencing and output. The System was initially programmed to recognize 1428 OCR (used by Selectrics); IBM 407 print; and the full character sets of OCR-A, OCR-B and Farrington 7B; as well as optical marks and handwritten numbers. OCR Systems promised added compatibility with more fonts available down the line—per request—in 1970. The number of fonts supported was limited by the amount of core memory, which was expandable in 4 KB increments up to 32 KB. The System 1000 later supported generalized typewriter and photocopier fonts. The rest of the System 1000 comprised the document transport, one or more scanner elements, a CRT display and a Teletype Model 33 or 35. Pages are fed via friction with a rubber belt. Up to three lines could be scanned per document, while the rest of the scanned document could be laid out in any manner granted there was enough space around the fields to be read. The reader initially supported pages as small as 3.25 in by 3.5 in dimension (later supporting 2.6 in by 3.5 in utility cash stubs) all the way to the standard ANSI letter size (8.5 in by 11 in; later 8.5 in by 12 in as used in stock certificates). The initial System 1000 had a maximum throughput of 420 documents per minute per transport (later 500 documents per minute), contingent on document size and content. A feature unique to the System 1000 over other optical character recognition systems of the time was its ability to alert the operator when a field was unreadable or otherwise invalid. This feature, called Document Referral, placed the document in front of the operator and displayed a blank field on the screen of the included CRT monitor for manual re-entry via keyboard. Once input, data could be output to 7- or 9-track tape, paper tape, punched cards and other mass storage media or to System/360 mainframes for further processing. The complete System 1000 could be purchased for US$69,000. Options for renting were $1,800 per month on a three-year lease or $1,600 per month for five years. Computerworld wrote that it was less than half the cost of its competitors while more capable and user-friendly. Competing systems included the Recognition Equipment Retina, the Scan-Optics IC/20 and the Scan-Data 250/350. === ReadRight === ReadRight processes individual letters topographically: it breaks down the scanned letter into parts—strokes, curves, angles, ascenders and descenders—and follows a tree structure of letters broken down into these parts to determine the corresponding character code. ReadRight was entirely software-based, requiring no expansion card to work. Version 2.01, the last version released for DOS, runs in real mode in under 640 KB of RAM. OCR Systems released the Windows-only version 3.0 in 1991 while offering version 2.01 alongside it. The company unveiled a sister product, ReadRight Personal, dedicated to handheld scanners and for Windows only in October 1991. This version adds real-time scanning—each word is updated to the screen while lines are being scanned. ReadRight proper was later made a Windows-only product with version 3.1 in 1992. The inclusion of ReadRight 2.0 with Canon's IX-12F flatbed scanner led PC Magazine to award it an Editor's Choice rating in 1989. Despite this, reviewer Robert Kendall found qualification with ReadRight's ability to parse proportional typefaces such as Helvetica and Times New Roman. Mitt Jones of the same publication found version 2.01 to have improved its ability to read such typefaces and praised its ease of use and low resource intensiveness. Jones disliked the inability to handle uneven page paragraph column widths and graphics, noting that the manual recommended the user block out graphics with a Post-it Note. Version 3.1 for Windows received mixed reviews. Mike Heck of InfoWorld wrote that its "low cost and rich collection of features are hard to ignore" but rated its speed and accuracy average. Barry Simon of PC Magazine called it economical but inaccurate, unable to correct errors it did
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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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Mark Heimann

Mark A. Heimann is an American chess grandmaster and machine learning researcher. == Chess career == Heimann began playing chess at the age of 5 after his father bought him and his twin brother Alexander a chess set. He then won several national grade-level championships as well as the Pennsylvania and Ohio state championships in middle school and high school. In October 2007, he was ranked as the national #2 under-14 player, only behind future grandmaster Marc Tyler Arnold. In the February 2008 national rankings, he moved up to being the top-ranked under-14 player. In December 2012, he played for Washington University St. Louis' "A" team in the Pan-American Intercollegiate Chess Championships, where he was the second-most successful player, recording 4 wins, 1 draw, and 1 loss. The university's team also won the Division II championship title. In three tournaments between September and December 2022, Heimann earned three international master title norms, earning the international master title at the age of 29. In November 2024, he scored a GM norm at the U.S. Masters Chess Championship. He finished the event in joint-6th place. The following week, at the Saint Louis Masters tournament, he earned his final grandmaster norm and crossed 2500 in live rating, achieving the Grandmaster title. It was formally awarded to him in April 2025. == Research career == He obtained a bachelor's degree from Washington University in St. Louis in the School of Arts and Sciences and got his PhD from the University of Michigan. He is a machine learning researcher at Lawrence Livermore National Laboratory. == Personal life == Outside of chess and research, he also plays several instruments and is a competitive powerlifter.
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Language identification

In natural language processing, language identification or language guessing is the problem of determining which natural language a given content is in. Computational approaches to this problem view it as a special case of text categorization, solved with various statistical methods. == Overview == === Logical approach === A common non-statistical intuitive approach (though highly uncertain) is to look for common letter combinations, or distinctive diacritics or punctuation. === Statistical approach === There are several statistical approaches to language identification. An older statistical method by Grefenstette was based on the frequency of short n-grams, which are often function morphemes. For example, "ing" is more common in English than in French, while the sequence "que" is more common in French. Given a new page found on the Web, one counts the number of occurrences of each such short sequence and picks the language whose frequency table it matches the most. One technique is to compare the compressibility of the text to the compressibility of texts in a set of known languages. This approach is known as mutual information based distance measure. The same technique can also be used to empirically construct family trees of languages which closely correspond to the trees constructed using historical methods. Mutual information based distance measure is essentially equivalent to more conventional model-based methods and is not generally considered to be either novel or better than simpler techniques. Another technique, as described by Cavnar and Trenkle (1994) and Dunning (1994) is to create a language n-gram model from a "training text" for each of the languages. These models can be based on characters (Cavnar and Trenkle) or encoded bytes (Dunning); in the latter, language identification and character encoding detection are integrated. Then, for any piece of text needing to be identified, a similar model is made, and that model is compared to each stored language model. The most likely language is the one with the model that is most similar to the model from the text needing to be identified. This approach can be problematic when the input text is in a language for which there is no model. In that case, the method may return another, "most similar" language as its result. Also problematic for any approach are pieces of input text that are composed of several languages, as is common on the Web. As of 2025, a commonly used baseline method is via the fastText library, which has comparable classification accuracy as deep learning techniques, but much faster. == Identifying similar languages == One of the great bottlenecks of language identification systems is to distinguish between closely related languages. Similar languages like Bulgarian and Macedonian or Indonesian and Malay present significant lexical and structural overlap, making it challenging for systems to discriminate between them. In 2014 the DSL shared task has been organized providing a dataset (Tan et al., 2014) containing 13 different languages (and language varieties) in six language groups: Group A (Bosnian, Croatian, Serbian), Group B (Indonesian, Malaysian), Group C (Czech, Slovak), Group D (Brazilian Portuguese, European Portuguese), Group E (Peninsular Spanish, Argentine Spanish), Group F (American English, British English). The best system reached performance of over 95% results (Goutte et al., 2014). Results of the DSL shared task are described in Zampieri et al. 2014. == Software == Apache OpenNLP includes char n-gram based statistical detector and comes with a model that can distinguish 103 languages Apache Tika contains a language detector for 18 languages
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Bidyut Baran Chaudhuri

Bidyut Baran Chaudhuri (B. B. Chauduri) is a senior computer scientist and an emeritus professor of Techno India University in West Bengal, India. He is also adjuncted to Indian Statistical Institute, where he was a professor for about three decades. He was the founding Head of Computer Vision and Pattern Recognition Unit (which was established in 1994) of ISI. Moreover, he was a J.C. Bose Fellow and Indian National Academy of Engineering Distinguished Professor at ISI. He was the vice-president of the Society for Natural Language Technology Research (SNLTR). His primary research contributes to the fields of computer vision, image processing and pattern recognition. He is a pioneer of "Indian language script OCR". == Education == Chaudhuri received his BSc (Hons.), BTech and MTech degrees from University of Calcutta, India in 1969, 1972 and 1974, respectively and PhD Degree from Indian Institute of Technology Kanpur in 1980. He did his post-doc work during 1981-1982 from Queen's University, U.K, through Leverhulme Overseas Fellowship. He also worked as a visiting faculty at Tech University, Hannover during 1986-87 as well as at GSF Institute of Radiation Protection (now Leibnitz Institute), Munich in 1990 and 1992. == Awards and recognition == Chaudhuri has been elected as a Life Fellow of IEEE "for contributions to pattern recognition, especially Indian language script OCR, document processing and natural language processing". He has become a Fellow of International Association for Pattern Recognition (IAPR) "for contributions to character recognition and speech synthesis in Indian language". He is also Fellow of The World Academy of Sciences (TWAS), Indian National Science Academy (INSA), Indian National Academy of Engineering (INAE), National Academy of Sciences (NASI), and Institute of Electronics and Telecommunication Engineering (IETE). In 2011, Chaudhuri received the Om Prakash Bhasin Award for his contribution in the field of electronics and information technology. Chaudhuri's interview on some of his works has been reported in Indian newspaper as well. He is within world's top 2% scientists and top-10 Indian AI scientists according to a study conducted by Stanford University. He has also been featured as top-10 machine learning researcher from India.
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Suffix automaton

In computer science, a suffix automaton is an efficient data structure for representing the substring index of a given string which allows the storage, processing, and retrieval of compressed information about all its substrings. The suffix automaton of a string S {\displaystyle S} is the smallest directed acyclic graph with a dedicated initial vertex and a set of "final" vertices, such that paths from the initial vertex to final vertices represent the suffixes of the string. In terms of automata theory, a suffix automaton is the minimal partial deterministic finite automaton that recognizes the set of suffixes of a given string S = s 1 s 2 … s n {\displaystyle S=s_{1}s_{2}\dots s_{n}} . The state graph of a suffix automaton is called a directed acyclic word graph (DAWG), a term that is also sometimes used for any deterministic acyclic finite state automaton. Suffix automata were introduced in 1983 by a group of scientists from the University of Denver and the University of Colorado Boulder. They suggested a linear time online algorithm for its construction and showed that the suffix automaton of a string S {\displaystyle S} having length at least two characters has at most 2 | S | − 1 {\textstyle 2|S|-1} states and at most 3 | S | − 4 {\textstyle 3|S|-4} transitions. Further works have shown a close connection between suffix automata and suffix trees, and have outlined several generalizations of suffix automata, such as compacted suffix automaton obtained by compression of nodes with a single outgoing arc. Suffix automata provide efficient solutions to problems such as substring search and computation of the largest common substring of two and more strings. == History == The concept of suffix automaton was introduced in 1983 by a group of scientists from University of Denver and University of Colorado Boulder consisting of Anselm Blumer, Janet Blumer, Andrzej Ehrenfeucht, David Haussler and Ross McConnell, although similar concepts had earlier been studied alongside suffix trees in the works of Peter Weiner, Vaughan Pratt and Anatol Slissenko. In their initial work, Blumer et al. showed a suffix automaton built for the string S {\displaystyle S} of length greater than 1 {\displaystyle 1} has at most 2 | S | − 1 {\displaystyle 2|S|-1} states and at most 3 | S | − 4 {\displaystyle 3|S|-4} transitions, and suggested a linear algorithm for automaton construction. In 1983, Mu-Tian Chen and Joel Seiferas independently showed that Weiner's 1973 suffix-tree construction algorithm while building a suffix tree of the string S {\displaystyle S} constructs a suffix automaton of the reversed string S R {\textstyle S^{R}} as an auxiliary structure. In 1987, Blumer et al. applied the compressing technique used in suffix trees to a suffix automaton and invented the compacted suffix automaton, which is also called the compacted directed acyclic word graph (CDAWG). In 1997, Maxime Crochemore and Renaud Vérin developed a linear algorithm for direct CDAWG construction. In 2001, Shunsuke Inenaga et al. developed an algorithm for construction of CDAWG for a set of words given by a trie. == Definitions == Usually when speaking about suffix automata and related concepts, some notions from formal language theory and automata theory are used, in particular: "Alphabet" is a finite set Σ {\displaystyle \Sigma } that is used to construct words. Its elements are called "characters"; "Word" is a finite sequence of characters ω = ω 1 ω 2 … ω n {\displaystyle \omega =\omega _{1}\omega _{2}\dots \omega _{n}} . "Length" of the word ω {\displaystyle \omega } is denoted as | ω | = n {\displaystyle |\omega |=n} ; "Formal language" is a set of words over given alphabet; "Language of all words" is denoted as Σ ∗ {\displaystyle \Sigma ^{}} (where the "" character stands for Kleene star), "empty word" (the word of zero length) is denoted by the character ε {\displaystyle \varepsilon } ; "Concatenation of words" α = α 1 α 2 … α n {\displaystyle \alpha =\alpha _{1}\alpha _{2}\dots \alpha _{n}} and β = β 1 β 2 … β m {\displaystyle \beta =\beta _{1}\beta _{2}\dots \beta _{m}} is denoted as α ⋅ β {\displaystyle \alpha \cdot \beta } or α β {\displaystyle \alpha \beta } and corresponds to the word obtained by writing β {\displaystyle \beta } to the right of α {\displaystyle \alpha } , that is, α β = α 1 α 2 … α n β 1 β 2 … β m {\displaystyle \alpha \beta =\alpha _{1}\alpha _{2}\dots \alpha _{n}\beta _{1}\beta _{2}\dots \beta _{m}} ; "Concatenation of languages" A {\displaystyle A} and B {\displaystyle B} is denoted as A ⋅ B {\displaystyle A\cdot B} or A B {\displaystyle AB} and corresponds to the set of pairwise concatenations A B = { α β : α ∈ A , β ∈ B } {\displaystyle AB=\{\alpha \beta :\alpha \in A,\beta \in B\}} ; If the word ω ∈ Σ ∗ {\displaystyle \omega \in \Sigma ^{}} may be represented as ω = α γ β {\displaystyle \omega =\alpha \gamma \beta } , where α , β , γ ∈ Σ ∗ {\displaystyle \alpha ,\beta ,\gamma \in \Sigma ^{}} , then words α {\displaystyle \alpha } , β {\displaystyle \beta } and γ {\displaystyle \gamma } are called "prefix", "suffix" and "subword" (substring) of the word ω {\displaystyle \omega } correspondingly; If T = T 1 … T n {\displaystyle T=T_{1}\dots T_{n}} and T l T l + 1 … T r = S {\displaystyle T_{l}T_{l+1}\dots T_{r}=S} (with 1 ≤ l ≤ r ≤ n {\displaystyle 1\leq l\leq r\leq n} ) then S {\displaystyle S} is said to "occur" in T {\displaystyle T} as a subword. Here l {\displaystyle l} and r {\displaystyle r} are called left and right positions of occurrence of S {\displaystyle S} in T {\displaystyle T} correspondingly. == Automaton structure == Formally, deterministic finite automaton is determined by 5-tuple A = ( Σ , Q , q 0 , F , δ ) {\displaystyle {\mathcal {A}}=(\Sigma ,Q,q_{0},F,\delta )} , where: Σ {\displaystyle \Sigma } is an "alphabet" that is used to construct words, Q {\displaystyle Q} is a set of automaton "states", q 0 ∈ Q {\displaystyle q_{0}\in Q} is an "initial" state of automaton, F ⊂ Q {\displaystyle F\subset Q} is a set of "final" states of automaton, δ : Q × Σ ↦ Q {\displaystyle \delta :Q\times \Sigma \mapsto Q} is a partial "transition" function of automaton, such that δ ( q , σ ) {\displaystyle \delta (q,\sigma )} for q ∈ Q {\displaystyle q\in Q} and σ ∈ Σ {\displaystyle \sigma \in \Sigma } is either undefined or defines a transition from q {\displaystyle q} over character σ {\displaystyle \sigma } . Most commonly, deterministic finite automaton is represented as a directed graph ("diagram") such that: Set of graph vertices corresponds to the state of states Q {\displaystyle Q} , Graph has a specific marked vertex corresponding to initial state q 0 {\displaystyle q_{0}} , Graph has several marked vertices corresponding to the set of final states F {\displaystyle F} , Set of graph arcs corresponds to the set of transitions δ {\displaystyle \delta } , Specifically, every transition δ ( q 1 , σ ) = q 2 {\textstyle \delta (q_{1},\sigma )=q_{2}} is represented by an arc from q 1 {\displaystyle q_{1}} to q 2 {\displaystyle q_{2}} marked with the character σ {\displaystyle \sigma } . This transition also may be denoted as q 1 σ ⟶ q 2 {\textstyle q_{1}{\begin{smallmatrix}{\sigma }\\[-5pt]{\longrightarrow }\end{smallmatrix}}q_{2}} . In terms of its diagram, the automaton recognizes the word ω = ω 1 ω 2 … ω m {\displaystyle \omega =\omega _{1}\omega _{2}\dots \omega _{m}} only if there is a path from the initial vertex q 0 {\displaystyle q_{0}} to some final vertex q ∈ F {\displaystyle q\in F} such that concatenation of characters on this path forms ω {\displaystyle \omega } . The set of words recognized by an automaton forms a language that is set to be recognized by the automaton. In these terms, the language recognized by a suffix automaton of S {\displaystyle S} is the language of its (possibly empty) suffixes. === Automaton states === "Right context" of the word ω {\displaystyle \omega } with respect to language L {\displaystyle L} is a set [ ω ] R = { α : ω α ∈ L } {\displaystyle [\omega ]_{R}=\{\alpha :\omega \alpha \in L\}} that is a set of words α {\displaystyle \alpha } such that their concatenation with ω {\displaystyle \omega } forms a word from L {\displaystyle L} . Right contexts induce a natural equivalence relation [ α ] R = [ β ] R {\displaystyle [\alpha ]_{R}=[\beta ]_{R}} on the set of all words. If language L {\displaystyle L} is recognized by some deterministic finite automaton, there exists unique up to isomorphism automaton that recognizes the same language and has the minimum possible number of states. Such an automaton is called a minimal automaton for the given language L {\displaystyle L} . Myhill–Nerode theorem allows it to define it explicitly in terms of right contexts: In these terms, a "suffix automaton" is the minimal deterministic finite automaton recognizing the language of suffixes of the word S = s 1 s 2 … s n {\displaystyle S=s_{1}s_{2}\dots s_{n}} . The right context of the word ω {\displaystyle \omeg
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Lorien Pratt

Lorien Pratt is an American computer scientist known for contributions to transfer learning and for her work in promoting and developing the concept of decision intelligence. She is chief scientist and founder of Quantellia. Since 1988, she has conducted research on the use of machine learning as an academic, professor, industry analyst, and practicing data scientist. Pratt received her AB degree in computer science from Dartmouth College and her master's and doctorate degrees in computer science from Rutgers University. == Learning to Learn == She is best known for her book "Learning to Learn," co-edited with Sebastian Thrun, which provided an overview on how to use machine learning to better understand bias and generalization of discrete subjects. This approach, still largely theoretical when the book was published in 1998, is also called metalearning and is now a foundational underpinning of machine learning algorithms such as GPT-3 and DALL-E. == Research == === Transfer learning === Pratt's research includes early work in transfer learning where she developed the discriminability-based transfer (DBT) algorithm in 1993 during her tenure as a professor of computer science at Colorado School of Mines. This paper is considered one of the earliest academic works referring to the use of transfer in machine learning and has been cited over 400 times as foundational research for deep neural networks. === Decision intelligence === Since then, Pratt's research has continued to explore the relationships between machine learning and human cognition with the concept of decision intelligence, an emerging field of machine learning guided analytics designed to support human decision. Pratt introduced this concept in 2008, and this term has since been used by a number of vendors providing machine learning-guided analytics including Diwo, Peak AI, Sisu, and Tellius as the technologies used to support machine learning at scale have become easier to deploy, manage, and embed into software platforms. Pratt's work is cited as a core starting point for defining modern aspects of decision intelligence. Pratt's work at Quantellia since 2020 has focused on the use of decision intelligence to improve COVID-19-based outcomes.
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Intelligent decision support system

An intelligent decision support system (IDSS) is a decision support system that makes extensive use of artificial intelligence (AI) techniques. Use of AI techniques in management information systems has a long history – indeed terms such as "Knowledge-based systems" (KBS) and "intelligent systems" have been used since the early 1980s to describe components of management systems, but the term "Intelligent decision support system" is thought to originate with Clyde Holsapple and Andrew Whinston in the late 1970s. Examples of specialized intelligent decision support systems include Flexible manufacturing systems (FMS), intelligent marketing decision support systems and medical diagnosis systems. Ideally, an intelligent decision support system should behave like a human consultant: supporting decision makers by gathering and analysing evidence, identifying and diagnosing problems, proposing possible courses of action and evaluating such proposed actions. The aim of the AI techniques embedded in an intelligent decision support system is to enable these tasks to be performed by a computer, while emulating human capabilities as closely as possible. Many IDSS implementations are based on expert systems, a well established type of KBS that encode knowledge and emulate the cognitive behaviours of human experts using predicate logic rules, and have been shown to perform better than the original human experts in some circumstances. Expert systems emerged as practical applications in the 1980s based on research in artificial intelligence performed during the late 1960s and early 1970s. They typically combine knowledge of a particular application domain with an inference capability to enable the system to propose decisions or diagnoses. Accuracy and consistency can be comparable to (or even exceed) that of human experts when the decision parameters are well known (e.g. if a common disease is being diagnosed), but performance can be poor when novel or uncertain circumstances arise. Research in AI focused on enabling systems to respond to novelty and uncertainty in more flexible ways is starting to be used in IDSS. For example, intelligent agents that perform complex cognitive tasks without any need for human intervention have been used in a range of decision support applications. Capabilities of these intelligent agents include knowledge sharing, machine learning, data mining, and automated inference. A range of AI techniques such as case based reasoning, rough sets and fuzzy logic have also been used to enable decision support systems to perform better in uncertain conditions. A 2009 research about a multi-artificial system intelligence system named IILS is proposed to automate problem-solving processes within the logistics industry. The system involves integrating intelligence modules based on case-based reasoning, multi-agent systems, fuzzy logic, and artificial neural networks aiming to offer advanced logistics solutions and support in making well-informed, high-quality decisions to address a wide range of customer needs and challenges.
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Jun'ichi Tsujii

Jun'ichi Tsujii (辻井潤一, Tsujii Jun'ichi; born 7 February 1949) is a Japanese computer scientist specializing in natural language processing and text mining, particularly in the field of biology and bioinformatics. == Education == Tsujii received his Bachelor of Engineering, Master of Engineering and PhD degrees in electrical engineering from Kyoto University in 1971, 1973, and 1978 respectively. He was Assistant Professor and Associate Professor at Kyoto University, before accepting a position as Professor of Computational Linguistics at the University of Manchester Institute of Science and Technology (UMIST) in 1988. He was President of the Association for Computational Linguistics (ACL) in 2006, and has been a permanent member of the International Committee on Computational Linguistics (ICCL) since 1992, and the chair of the committee since 2014. == Research == Since May 2015, Tsujii has been the director of the Artificial Intelligence Research Center at the National Institute of Advanced Industrial Science and Technology, Japan. Tsujii was previously a Principal Researcher at Microsoft Research Asia (MSRA). Before joining MSRA, he was a professor at the University of Tokyo, where he belonged to both the School of Inter-faculty Initiative on Informatics and the Graduate School of Information Science and Technology. Tsujii is also a Visiting Professor and Scientific Advisor at the National Centre for Text Mining (NaCTeM) at the University of Manchester in the United Kingdom. == Awards == On 14 May 2010, Tsujii was awarded the Medals of Honor with Purple Ribbon, one of Japan's highest awards, presented to influential contributors in the fields of art, academics or sports. In September 2014, Tsujii was awarded the FUNAI Achievement Award at the Forum on Information Technology (FIT), which took place at the University of Tsukuba. The award is presented to distinguished individuals engaged in research or related business activities in the field of Information Technology who have produced excellent achievements in the field, are still active in leading positions and have strong impact on young students and researchers. In December 2014, Tsujii was named as an ACL Fellow, in recognition of his significant contributions to MT, parsing by unification-based grammar and text mining for biology. In March 2016, Tsujii was awarded Okawa Prize for his contribution to the field of Natural Language Processing, Machine Translation and Text Mining, together with Professor Jaime Carbonnel of CMU. In August 2021, Tsujii received ACL Lifetime Achievement Award, which is considered the most prestigious award in the field of Computational Linguistics and Natural Language Processing. In May 2022, Tsujii received the Order of the Sacred Treasure, Gold Rays and Neck Ribbon, from the Japanese government. In October 2024, Tsujii was designated a Person of Cultural Merit. == Selected publications == Oiwa, Hidekazu; Tsujii, Jun'ichi (2014). Common Space Embedding of Primal-Dual Relation Semantic Spaces. COLING 2014. Dublin. pp. 1579–1590. Taura, K.; Matsuzaki, T.; Miwa, M.; Kamoshida, Y.; Yokoyama, D.; Dun, N.; Shibata, T.; Jun, C. S.; Tsujii, J. (2013). "Design and implementation of GXP make – A workflow system based on make". Future Generation Computer Systems. 29 (2): 662–672. doi:10.1016/j.future.2011.05.026. S2CID 31627886. Sun, X.; Zhang, Y.; Matsuzaki, T.; Tsuruoka, Y.; Tsujii, J. (2013). "Probabilistic Chinese word segmentation with non-local information and stochastic training". Information Processing & Management. 49 (3): 626–636. doi:10.1016/j.ipm.2012.12.003. Mu, T.; Goulermas, J. Y.; Tsujii, J.; Ananiadou, S. (2012). "Proximity-Based Frameworks for Generating Embeddings from Multi-Output Data". IEEE Transactions on Pattern Analysis and Machine Intelligence. 34 (11): 2216–2232. Bibcode:2012ITPAM..34.2216M. doi:10.1109/TPAMI.2012.20. PMID 23289130. S2CID 711467. Miwa, M.; Sætre, R.; Kim, J. D.; Tsujii, J. (2010). "Event Extraction with Complex Event Classification Using Rich Features". Journal of Bioinformatics and Computational Biology. 08 (1): 131–146. doi:10.1142/S0219720010004586. PMID 20183879. Kim, J. D.; Ohta, T.; Tsujii, J. (2008). "Corpus annotation for mining biomedical events from literature". BMC Bioinformatics. 9 10. doi:10.1186/1471-2105-9-10. PMC 2267702. PMID 18182099. Miyao, Y.; Tsujii, J. (2008). "Feature Forest Models for Probabilistic HPSG Parsing". Computational Linguistics. 34: 35–80. doi:10.1162/coli.2008.34.1.35. S2CID 885002. Sagae, Kenji; Tsujii, Jun'ichi (2007). Dependency Parsing and Domain Adaptation with LR Models and Parser Ensembles. EMNLP-CoNLL. pp. 1044–1050. Ananiadou, S; Pyysalo, S; Tsujii, J; Kell, D. B. (2010). "Event extraction for systems biology by text mining the literature". Trends in Biotechnology. 28 (7): 381–90. doi:10.1016/j.tibtech.2010.04.005. PMID 20570001. Tsuruoka, Y.; Tateishi, Y.; Kim, J. D.; Ohta, T.; McNaught, J.; Ananiadou, S.; Tsujii, J. (2005). "Developing a Robust Part-of-Speech Tagger for Biomedical Text". Advances in Informatics. Lecture Notes in Computer Science. Vol. 3746. p. 382. doi:10.1007/11573036_36. ISBN 978-3-540-29673-7. S2CID 206592413. Tsuruoka, Y.; Tsujii, J. (2005). Bidirectional inference with the easiest-first strategy for tagging sequence data. Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05. pp. 467–474. doi:10.3115/1220575.1220634. Tsujii, J.; Ananiadou, S. (2005). "Thesaurus or Logical Ontology, Which One Do We Need for Text Mining?". Language Resources and Evaluation. 39: 77–90. doi:10.1007/s10579-005-2697-0. S2CID 3204827. Kazama, J. I.; Tsujii, J. I. (2005). "Maximum Entropy Models with Inequality Constraints: A Case Study on Text Categorization". Machine Learning. 60 (1–3): 159–194. doi:10.1007/s10994-005-0911-3. hdl:10119/3305. Matsuzaki, T.; Miyao, Y.; Tsujii, J. I. (2005). Probabilistic CFG with latent annotations. 43rd Annual Meeting on Association for Computational Linguistics - ACL '05. p. 75. doi:10.3115/1219840.1219850. Kim, J. -D.; Ohta, T.; Tateisi, Y.; Tsujii, J. (2003). "GENIA corpus--a semantically annotated corpus for bio-textmining". Bioinformatics. 19: i180–i182. doi:10.1093/bioinformatics/btg1023. PMID 12855455. Hirschman, L.; Park, J. C.; Tsujii, J.; Wong, L.; Wu, C. H. (2002). "Accomplishments and challenges in literature data mining for biology". Bioinformatics. 18 (12): 1553–1561. doi:10.1093/bioinformatics/18.12.1553. PMID 12490438. Torisawa, K.; Tsujii, J. I. (1996). Computing phrasal-signs in HPSG prior to parsing. 16th conference on Computational linguistics -. Vol. 2. p. 949. doi:10.3115/993268.993332.
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Ronald J. Williams

Ronald James Williams (1945 – February 16, 2024) was an American mathematician and computer scientist who spent the majority of his career at Northeastern University. He is considered one of the pioneers of neural networks. In 1986, he co-authored the seminal paper in Nature on the backpropagation algorithm along with David Rumelhart and Geoffrey Hinton, which triggered a boom in neural network research. == Education and career == Williams was born in Southern California. He studied at California Institute of Technology as a undergraduate student and received a B.S. in mathematics there in 1966. He received his M.A. and Ph.D. in mathematics, both at University of California, San Diego (UCSD) in 1972 and 1975, respectively. His Ph.D. thesis was supervised by Donald Werner Anderson. He worked for a defense contractor for some time after graduation. From 1983 to 1986, Williams was a member of the Parallel Distributed Processing research group headed by David Rumelhart at the Institute for Cognitive Science at UCSD. In 1986, Williams accepted a professorship in computer science at Northeastern University in Boston, where he remained afterwards. In addition to the backpropagation paper, Williams made fundamental contributions to the fields of recurrent neural networks, where he, along with David Zipser, invented the teacher forcing algorithm and made important contributions to backpropagation through time. In reinforcement learning, Williams introduced the REINFORCE algorithm in 1992, which became the first policy gradient method. Besides his works on neural networks, Williams, together with Wenxu Tong and Mary Jo Ondrechen, developed Partial Order Optimum Likelihood (POOL), a machine learning method used in the prediction of active amino acids in protein structures. POOL is a maximum likelihood method with a monotonicity constraint and is a general predictor of properties that depend monotonically on the input features.
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