AI for Business

Explore the best AI for Business — independent reviews, comparisons, pricing and step-by-step how-to guides, curated by Aizhi.

BevQ

BevQ is a queue management mobile application developed by Faircode Technologies of Kochi, Kerala. It is provided by the Kerala State Beverages Corporation under Government of Kerala. == History == This app was released together by the Government of Kerala and the Kerala State Beverages Corporation in order to implement social distancing in the liquor stores Kerala in the case of the COVID-19 pandemic in Kerala and to reduce the congestion of people. The BevQ App was released by Faircode Technologies on 27 May 2020 on the Google Play Store. In January 2021, the app was withdrawn as bars had opened. In June 2021, there was a commitment from the Kerala CM that the App will be relaunched again. It has been reported that over 132,000 new users downloaded the app in the 48 hours after the announcement. == Achievements == The BEVQ app, which works only in the state of Kerala, beat all other Indian food and drink apps in 2020 to see the highest growth in year-on-year sessions, according to the State of Mobile 2021 report by App Annie. The app even beat the likes of Domino’s, which is used all across India. Around 300 government Liquor shops and 900 private liquor shops were enlisted in the platform. More than 200 million unique users registered in the platform. About 250,000 tokens were given out a day.
Read more →
How to Choose an AI Essay Writer

Shopping for the best AI essay writer? An AI essay writer 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 essay writer slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
Read more →
AI Video Generators: Free vs Paid (2026)

In search of the best AI video generator? An AI video generator 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 video generator slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
Read more →
AI Video Generators: Free vs Paid (2026)

In search of the best AI video generator? An AI video generator 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 video generator slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
Read more →
Engineering Historical Memory

Engineering Historical Memory (EHM) is an online database in the digital humanities, serving as an open-access research tool for primary historical materials focused on 11th to 15th century Afro-Eurasia. It adopts computational methods to make historical documents machine-understandable. EHM parses traditional artifacts such as historical maps, travel accounts, chronicles and codices into computer-readable formats, and links them to secondary multi-media references, a process referred to as the "automatic narrative generation". This approach generates cultural narratives and facilitates interaction with the historical artifacts, making them accessible to audiences from various backgrounds. == History == EHM was first theorised in 2007 by researcher Andrea Nanetti when he was a visiting scholar at Princeton University, and the preliminary test results were published between 2008 and 2011. In 2013, the EHM research team was set up in Singapore following Nanetti's professorship at Nanyang Technological University (NTU). Two years later, after receiving several Microsoft research grants, EHM went live on Microsoft Azure. In 2018, the College of Humanities, Arts and Social Sciences (CoHASS) at NTU Singapore formed the Digital Humanities Research Cluster, as part of which, EHM has been an ongoing interdisciplinary research project led by Nanetti. Partnering with international educational and cultural institutions such as Ca' Foscari University of Venice, University of Florence, Taylor & Francis Group, Delft University of Technology (TUDelft), and SenticNet, EHM has been supported by over 130 scholars and engineers. == Applications == Primary historical materials on EHM are curated into several categories, including maps, travel accounts, chronicles, codices, sites, archival documents, and paintings, such as the Morosini Codex (listed under Chronicles) and Pope Gregory X's Privilege for the Holy Monastery of St Catherine of Sinai (listed under Archival Documents). EHM has been adopted by cultural organisations as an exhibition and research tool in the digital humanities field. An example is the publication of a digital interactive edition of Fra Mauro's Map of the World on EHM, a collaboration project between NTU Singapore and the Biblioteca Nazionale Marciana of Venice. The digitisation process of the map on EHM involved transcribing and geo-referencing the textual content in the 15th-century map, followed by creating semantic annotations to connect the map's content with related secondary data sources. The e-map was subsequently adopted and launched online by Museo Galileo in March 2022 and incorporated into the virtual exhibition "Venezia and Suzhou: Water Cities along the Silk Roads" (online, September-December 2022). In 2024, the Fra Mauro's Map of the World application on EHM was awarded the Digital Humanities and Multimedia Studies Prize (DHMS) by the Medieval Academy of America. Image-Based Video Search Engine is another experimental project under the EHM scope led by the research teams at Delft University of Technology (TUDelft) and NTU Singapore. This ongoing project aims to improve the efficiency of retrieving targeted objects from audio-visuals. == Awards == In 2021, EHM won the GLAMi Awards (MuseWeb Conference - Galleries, Libraries, Archives, and Museums Innovation awards) in the "Resources for Scholars and Researchers" category. In the same year, EHM was a Falling Walls finalist for Science Breakthrough of the Year in the category Social Sciences and Humanities after nominated by the School of Advanced Study at the University of London. In April 2022, the Italian National Commission for UNESCO has selected and sent the EHM project to the organisers of the "Jikji Memory of the World" Award for final evaluation. In January 2024, the Medieval Academy of America announced its 2024 Digital Humanities and Multimedia Studies Prize (DHMS) goes to the Fra Mauro's Map of the World application on EHM.
Read more →
Top 10 AI Clip Makers Compared (2026)

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

Android Auto is a mobile app developed by Google to mirror features of a smartphone (or other Android device) on a car's dashboard information and entertainment head unit. Once an Android device is paired with the car's head unit, the system can mirror some apps on the vehicle's display. Supported apps include GPS mapping and navigation, music playback, SMS, telephone, and Web search. The system supports both touchscreen and button-controlled head units. Hands-free operation through voice commands is available and recommended to reduce driver distraction. Android Auto is part of the Open Automotive Alliance, a joint effort of 28 automobile manufacturers, with Nvidia as tech supplier, available in 36 countries. == History == Android Auto was revealed at Google I/O 2014. The app was released to the public on March 19, 2015. In November 2016, Google implemented an app that would run the Android Auto UI on the mobile device. In July 2019, Android Auto received its first major UI rework, which among other changes, brought an app drawer to Android Auto for the first time. Google also announced that the app's ability to be used on a phone would be discontinued in favor of Google Assistant's drive mode. In December 2020, Google announced the expansion of Android Auto to 36 additional countries in Europe, Indonesia, and more. In April 2021, Android Auto launched in Belgium, Denmark, Netherlands, Norway, Portugal, and Sweden. Google announced in May 2022 a user interface redesign for Android Auto, codenamed CoolWalk, which aims to simplify the app's usage, and make it more adaptable to screens of different orientations and aspect ratios. The redesign incorporates a new split-screen layout, where Google Maps can be displayed alongside a music player. CoolWalk was originally slated to launch in Q3 2022. In June 2022, Android Auto no longer ran directly on a mobile device; the app permitting this was decommissioned, in favor of a Driving Mode built into the Google Assistant app for a similar purpose. In November 2022, the CoolWalk user interface was released in Android Auto's beta program. == Functionality == Android Auto is software that can be utilized from an Android mobile device, acting as a vehicle's dashboard head unit. Once the user's Android device is connected to the vehicle, the head unit will serve as an external display for the Android device, presenting supported software in a car-specific user interface provided by the Android Auto app. In Android Auto's first iterations, the device was required to be connected via USB to the car. For some time, starting in November 2016, Google added the option to run Android Auto as a regular app on an Android device, allowing users to choose whether to use Android Auto on a personal phone or tablet, rather than on a compatible automotive head unit. This app was decommissioned in June 2022 in favor of a Driving Mode built into the Google Assistant app. At CES 2018, Google confirmed that the Google Assistant would be coming to Android Auto later in the year. An Android Auto SDK has been released, allowing third parties to modify their apps to work with Android Auto; initially, only APIs for music and messaging apps were available. == Head unit support == In May 2015, Hyundai became the first manufacturer to offer Android Auto support, making it first available in the 2015 Hyundai Sonata. Automobile manufacturers that will offer Android Auto support in their cars include Abarth, Acura, Alfa Romeo, Aston Martin, Audi, Bentley, Buick, BMW, BYD, Cadillac, Chevrolet, Chrysler, Citroën, Dodge, Ferrari, Fiat, Ford, GMC, Genesis, Holden, Honda, Hyundai, Infiniti, Jaguar Land Rover, Jeep, Kia, Lamborghini, Lexus, Lincoln, Mahindra and Mahindra, Maserati, Maybach, Mazda, Mercedes-Benz, Mitsubishi, Nissan, Opel, Peugeot, Porsche, RAM, Renault, SEAT, Škoda, SsangYong, Subaru, Suzuki, Tata Motors Cars, Toyota, Volkswagen and Volvo. Additionally, aftermarket car-audio systems supporting Android Auto add the technology into host vehicles, including Pioneer, Kenwood, Panasonic, and Sony. == Criticism == In May 2019, Italy filed an antitrust complaint targeting Android Auto, citing a Google policy of allowing third-parties to only offer media and messaging apps on the platform, preventing Enel from offering an app for locating vehicle charging stations. Google announced a new SDK, to be released to select partners in August 2020 and made generally available by the end of the year. == Availability == As of December 2025, Android Auto is available in 46 countries:
Read more →
Pushpak Bhattacharyya

Pushpak Bhattacharyya (3 July 1962 – 5 October 2025) was an Indian computer scientist and professor in the Department of Computer Science and Engineering at the IIT Bombay. He served as the Director of the IIT Patna from 2015 to 2021. He was a past President of the Association for Computational Linguistics (2016–17), and held the Vijay and Sita Vashee Chair Professorship at IIT Bombay. Bhattacharyya led the Natural Language Processing (NLP) research group at the Centre for Indian Language Technology (CFILT) at IIT Bombay until his death. At the inauguration of the Nilekani Centre at AI4Bharat, IIT Madras, Nandan Nilekani, Co-founder and Non-Executive Chairman of Infosys, referred to Bhattacharyya as the "Godfather of Indian NLP". == Early life and education == Bhattacharyya was born in Shillong in 1962. He completed his schooling at Jail Road Boys' High School, Shillong. He obtained a B.Tech. in Computer Science from the IIT Kharagpur, followed by an M.Tech. from the IIT Kanpur, and a Ph.D. in Computer Science from IIT Bombay in 1994. == Research == Bhattacharyya’s research areas includes Natural language processing, Artificial intelligence, Machine learning, Psycholinguistics, Eye tracking, and Information retrieval. He made contributions to the development of multilingual lexical databases such as IndoWordNet and other projects related to machine translation and computational linguistics. He authored and co-authored multiple academic works, including Investigations in Computational Sarcasm (with Aditya Joshi), Cognitively Inspired Natural Language Processing: An Investigation Based on Eye Tracking (with Abhijit Mishra), and Machine Translation and Transliteration of Low Resource Related Languages (with Anoop Kunchukuttan). Over his career, Bhattacharyya published more than 350 research papers in journals and conference proceedings and supervised over 300 undergraduate, master’s, and doctoral students. His projects often addressed computational challenges for Indian languages, such as developing wordnets, building translation systems for low-resource languages, and studying cognitive aspects of language processing. He also led government- and industry-funded research initiatives supported by organizations including IBM, Microsoft, Yahoo, and the United Nations. == Death == Bhattacharyya died on 5 October 2025, at the age of 63. == Awards == Patwardhan Award, IIT Bombay, for Technology Development VNMM Award, IIT Roorkee, for Technology Development Fellow, Indian National Academy of Engineering Eminent Engineer Award, Institution of Engineers (India)
Read more →
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
Read more →
Probabilistic context-free grammar

In theoretical linguistics and computational linguistics, probabilistic context free grammars (PCFGs) extend context-free grammars, similar to how hidden Markov models extend regular grammars. Each production is assigned a probability. The probability of a derivation (parse) is the product of the probabilities of the productions used in that derivation. These probabilities can be viewed as parameters of the model, and for large problems it is convenient to learn these parameters via machine learning. A probabilistic grammar's validity is constrained by context of its training dataset. PCFGs originated from grammar theory, and have application in areas as diverse as natural language processing to the study the structure of RNA molecules and design of programming languages. Designing efficient PCFGs has to weigh factors of scalability and generality. Issues such as grammar ambiguity must be resolved. The grammar design affects results accuracy. Grammar parsing algorithms have various time and memory requirements. == Definitions == Derivation: The process of recursive generation of strings from a grammar. Parsing: Finding a valid derivation using an automaton. Parse Tree: The alignment of the grammar to a sequence. An example of a parser for PCFG grammars is the pushdown automaton. The algorithm parses grammar nonterminals from left to right in a stack-like manner. This brute-force approach is not very efficient. In RNA secondary structure prediction variants of the Cocke–Younger–Kasami (CYK) algorithm provide more efficient alternatives to grammar parsing than pushdown automata. Another example of a PCFG parser is the Stanford Statistical Parser which has been trained using Treebank. == Formal definition == Similar to a CFG, a probabilistic context-free grammar G can be defined by a quintuple: G = ( M , T , R , S , P ) {\displaystyle G=(M,T,R,S,P)} where M is the set of non-terminal symbols T is the set of terminal symbols R is the set of production rules S is the start symbol P is the set of probabilities on production rules == Relation with hidden Markov models == PCFGs models extend context-free grammars the same way as hidden Markov models extend regular grammars. The Inside-Outside algorithm is an analogue of the Forward-Backward algorithm. It computes the total probability of all derivations that are consistent with a given sequence, based on some PCFG. This is equivalent to the probability of the PCFG generating the sequence, and is intuitively a measure of how consistent the sequence is with the given grammar. The Inside-Outside algorithm is used in model parametrization to estimate prior frequencies observed from training sequences in the case of RNAs. Dynamic programming variants of the CYK algorithm find the Viterbi parse of a RNA sequence for a PCFG model. This parse is the most likely derivation of the sequence by the given PCFG. == Grammar construction == Context-free grammars are represented as a set of rules inspired from attempts to model natural languages. The rules are absolute and have a typical syntax representation known as Backus–Naur form. The production rules consist of terminal { a , b } {\displaystyle \left\{a,b\right\}} and non-terminal S symbols and a blank ϵ {\displaystyle \epsilon } may also be used as an end point. In the production rules of CFG and PCFG the left side has only one nonterminal whereas the right side can be any string of terminal or nonterminals. In PCFG nulls are excluded. An example of a grammar: S → a S , S → b S , S → ϵ {\displaystyle S\to aS,S\to bS,S\to \epsilon } This grammar can be shortened using the '|' ('or') character into: S → a S | b S | ϵ {\displaystyle S\to aS|bS|\epsilon } Terminals in a grammar are words and through the grammar rules a non-terminal symbol is transformed into a string of either terminals and/or non-terminals. The above grammar is read as "beginning from a non-terminal S the emission can generate either a or b or ϵ {\displaystyle \epsilon } ". Its derivation is: S ⇒ a S ⇒ a b S ⇒ a b b S ⇒ a b b {\displaystyle S\Rightarrow aS\Rightarrow abS\Rightarrow abbS\Rightarrow abb} Ambiguous grammar may result in ambiguous parsing if applied on homographs since the same word sequence can have more than one interpretation. Pun sentences such as the newspaper headline "Iraqi Head Seeks Arms" are an example of ambiguous parses. One strategy of dealing with ambiguous parses (originating with grammarians as early as Pāṇini) is to add yet more rules, or prioritize them so that one rule takes precedence over others. This, however, has the drawback of proliferating the rules, often to the point where they become difficult to manage. Another difficulty is overgeneration, where unlicensed structures are also generated. Probabilistic grammars circumvent these problems by ranking various productions on frequency weights, resulting in a "most likely" (winner-take-all) interpretation. As usage patterns are altered in diachronic shifts, these probabilistic rules can be re-learned, thus updating the grammar. Assigning probability to production rules makes a PCFG. These probabilities are informed by observing distributions on a training set of similar composition to the language to be modeled. On most samples of broad language, probabilistic grammars where probabilities are estimated from data typically outperform hand-crafted grammars. CFGs when contrasted with PCFGs are not applicable to RNA structure prediction because while they incorporate sequence-structure relationship they lack the scoring metrics that reveal a sequence structural potential == Weighted context-free grammar == A weighted context-free grammar (WCFG) is a more general category of context-free grammar, where each production has a numeric weight associated with it. The weight of a specific parse tree in a WCFG is the product (or sum ) of all rule weights in the tree. Each rule weight is included as often as the rule is used in the tree. A special case of WCFGs are PCFGs, where the weights are (logarithms of ) probabilities. An extended version of the CYK algorithm can be used to find the "lightest" (least-weight) derivation of a string given some WCFG. When the tree weight is the product of the rule weights, WCFGs and PCFGs can express the same set of probability distributions. == Applications == === RNA structure prediction === Since the 1990s, PCFG has been applied to model RNA structures. Energy minimization and PCFG provide ways of predicting RNA secondary structure with comparable performance. However structure prediction by PCFGs is scored probabilistically rather than by minimum free energy calculation. PCFG model parameters are directly derived from frequencies of different features observed in databases of RNA structures rather than by experimental determination as is the case with energy minimization methods. The types of various structure that can be modeled by a PCFG include long range interactions, pairwise structure and other nested structures. However, pseudoknots can not be modeled. PCFGs extend CFG by assigning probabilities to each production rule. A maximum probability parse tree from the grammar implies a maximum probability structure. Since RNAs preserve their structures over their primary sequence, RNA structure prediction can be guided by combining evolutionary information from comparative sequence analysis with biophysical knowledge about a structure plausibility based on such probabilities. Also search results for structural homologs using PCFG rules are scored according to PCFG derivations probabilities. Therefore, building grammar to model the behavior of base-pairs and single-stranded regions starts with exploring features of structural multiple sequence alignment of related RNAs. S → a S a | b S b | a a | b b {\displaystyle S\to aSa|bSb|aa|bb} The above grammar generates a string in an outside-in fashion, that is the basepair on the furthest extremes of the terminal is derived first. So a string such as a a b a a b a a {\displaystyle aabaabaa} is derived by first generating the distal a's on both sides before moving inwards: S ⇒ a S a ⇒ a a S a a ⇒ a a b S b a a ⇒ a a b a a b a a {\displaystyle S\Rightarrow aSa\Rightarrow aaSaa\Rightarrow aabSbaa\Rightarrow aabaabaa} A PCFG model extendibility allows constraining structure prediction by incorporating expectations about different features of an RNA . Such expectation may reflect for example the propensity for assuming a certain structure by an RNA. However incorporation of too much information may increase PCFG space and memory complexity and it is desirable that a PCFG-based model be as simple as possible. Every possible string x a grammar generates is assigned a probability weight P ( x | θ ) {\displaystyle P(x|\theta )} given the PCFG model θ {\displaystyle \theta } . It follows that the sum of all probabilities to all possible grammar productions is ∑ x P ( x | θ ) = 1 {\displaystyle \sum _{\text{x}}P(x|\theta )=1} . The scores
Read more →
SNNS

SNNS (Stuttgart Neural Network Simulator) is a neural network simulator originally developed at the University of Stuttgart. While it was originally built for X11 under Unix, there are Windows ports. Its successor JavaNNS never reached the same popularity. == Features == SNNS is written around a simulation kernel to which user written activation functions, learning procedures and output functions can be added. It has support for arbitrary network topologies and the standard release contains support for a number of standard neural network architectures and training algorithms. == Status == There is currently no ongoing active development of SNNS. In July 2008 the license was changed to the GNU LGPL.
Read more →
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.
Read more →
Apache Kudu

Apache Kudu is a free and open source column-oriented data store of the Apache Hadoop ecosystem. It is compatible with most of the data processing frameworks in the Hadoop environment. It provides completeness to Hadoop's storage layer to enable fast analytics on fast data. The open source project to build Apache Kudu began as internal project at Cloudera. The first version Apache Kudu 1.0 was released 19 September 2016. == Comparison with other storage engines == Kudu was designed and optimized for OLAP workloads. Like HBase, it is a real-time store that supports key-indexed record lookup and mutation. Kudu differs from HBase since Kudu's datamodel is a more traditional relational model, while HBase is schemaless. Kudu's "on-disk representation is truly columnar and follows an entirely different storage design than HBase/Bigtable".
Read more →
Nabil Ali

Nabil Ali Mohammed Abd AL Azeez (Arabic:نبيل علي) (3 January 1938 – 27 January 2016) was an Egyptian scientist, writer, and intellectual who worked in the field of natural language processing and computational linguistics. Ali is considered a pioneer of Arabic language computing, making significant innovations in early computational linguistics. == Education and career == Ali earned a bachelor's degree in Aeronautical Engineering in 1960, and a master's degree in 1967. In 1971, he earned a PhD in Aeronautics. From 1961 to 1972 Ali worked as an engineering officer in the Egyptian Air Force, specializing in maintenance and training. In 1972, he shifted focus to computing, and from 1972 to 1977 he worked as a computer manager at Egyptair. While in this position, Ali introduced the first automated reservation system for airlines in the Arab world. He later held various computing positions in Egypt, Kuwait, Europe, Canada and the US. Ali started working for Sakhr Software, an Arabic language technology company, in 1983. From 1985 to 1999, he was vice president of Sakhr's council for Research and Development. As a director of the Multilingual Advanced Systems Foundation and project manager at the Egyptian National Company for Scientific and Technical Information, Ali did extensive research on information culture and artificial intelligence relating to the Arabic language. Over the course of his career, Ali developed more than 20 educational programs relating to computational linguistics. He developed the first Arabic lexical database and the first knowledge base for Arabic poetry, as well as many other pieces of Arabic language software. == Awards == 1994: General Book Authority Award for Best Book (in the field of future studies). 2003: General Book Authority Award for Best Culture Book (in the field of "Challenges of the Information Age"). 2007: General Book Authority "Innovation in Information Technology" Award. 2012: King Faisal International Award, with Professor Ali Helmy Mousa, in the field of computer processing of the Arabic Language. == Works == Arabic Language and Computer (Research study), Dar Localization, 1988. Al Arab and the Information Age, Knowledge World Series No. 184, April 1994. Arab Culture and the Information Age: A Vision for the Future of Arab Culture Discourse, World of Knowledge Series, No. 265 January 2001. The Digital Gap: an Arab Vision for a Knowledge Society (in partnership with Dr. Nadia Hegazy), World of Knowledge Series, No. 318 August 2005. The Arab Mind and the Knowledge Society: Manifestations of the Crisis and Suggestions for Solutions, Part 1, The World of Knowledge Series, No. 369, November 2009. The Arab Mind and the Knowledge Society: Manifestations of the Crisis and Suggestions for Solutions, Part 2, The World of Knowledge Series, No. 370, December 2009. == Tribute == On 3 January 2020, Google Doodle celebrated Nabil Ali Mohamed's 82nd Birthday.
Read more →
Lillian Lee (computer scientist)

Lillian Lee is a computer scientist whose research involves natural language processing, sentiment analysis, and computational social science. She is a professor of computer science and information science at Cornell University, and co-editor-in-chief of the journal Transactions of the Association for Computational Linguistics. == Education == Lee graduated from Cornell University in 1993 with an undergraduate degree in math and science. She completed her Ph.D. at Harvard University in 1997. Her dissertation, Similarity-Based Approaches to Natural Language Processing, was supervised by Stuart M. Shieber. == Career == Lee has been a member of the Cornell faculty since 1997. == Recognition == Lee has been a fellow of the Association for the Advancement of Artificial Intelligence since 2013, and of the Association for Computational Linguistics since 2017. Lee was elected as an ACM Fellow in 2018 for "contributions to natural language processing, sentiment analysis, and computational social science".
Read more →