In computing, a connection string is a string that specifies information about a data source and the means of connecting to it. It is passed in code to an underlying driver or provider in order to initiate the connection. Whilst commonly used for a database connection, the data source could also be a spreadsheet or text file. The connection string may include attributes such as the name of the driver, server and database, as well as security information such as user name and password. == Examples == This example shows a PostgreSQL connection string for connecting to wikipedia.com with SSL and a connection timeout of 180 seconds: DRIVER={PostgreSQL Unicode};SERVER=www.wikipedia.com;SSL=true;SSLMode=require;DATABASE=wiki;UID=wikiuser;Connect Timeout=180;PWD=ashiknoor Users of Oracle databases can specify connection strings: on the command line (as in: sqlplus scott/tiger@connection_string ) via environment variables ($TWO_TASK in Unix-like environments; %TWO_TASK% in Microsoft Windows environments) in local configuration files (such as the default $ORACLE_HOME/network/admin.tnsnames.ora) in LDAP-capable directory services
Olio (app)
Olio is a mobile app for sharing by giving away, getting, borrowing or lending things in your community for free, aiming to reduce household and food waste. It does this by connecting neighbours with spare food or household items to others nearby who wish to pick up those items. The food must be edible; it can be raw or cooked, sealed or open. Non-food items often listed on Olio include books, clothes and furniture. Those donating surplus food can be individuals or companies such as food retailers, restaurants, corporate canteens, food photographers etc., and donations can take place on an ad-hoc or recurrent basis. For example, some supermarket chains in the UK, including Tesco, the Midcounties Co-operative, Morrisons, Sainsbury's and Iceland have piloted Olio as an 'online food bank' to donate food and to reduce their waste. In March 2022, Olio partnered with Pandamart in Singapore. First launched in early 2015 by Tessa Clarke and Saasha Celestial-One, by October 2017 the company had raised $2.2 million in funding. Olio subsequently performed a series A funding round of $6 million in 2018 and a Series B of $43 million. Notable investors include Accel, Octopus Ventures and VNV Global. The Olio app had around 7 million registered users as of May 2023.
AI Analytics Tools: Free vs Paid (2026)
In search of the best AI analytics tool? An AI analytics 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 analytics tool slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
Eugene Charniak
Eugene Charniak (June 2, 1946 – June 13, 2023) was a professor of computer Science and cognitive Science at Brown University. He held an A.B. in Physics from the University of Chicago and a Ph.D. from M.I.T. in Computer Science. His research was in the area of language understanding or technologies which relate to it, such as knowledge representation, reasoning under uncertainty, and learning. Since the early 1990s he was interested in statistical techniques for language understanding. His research in this area included work in the subareas of part-of-speech tagging, probabilistic context-free grammar induction, and, more recently, syntactic disambiguation through word statistics, efficient syntactic parsing, and lexical resource acquisition through statistical means. He was a Fellow of the American Association of Artificial Intelligence and was previously a Councilor of the organization. He was also honored with the 2011 Association for Computational Linguistics Lifetime Achievement Award and awarded the 2011 Calvin & Rose G Hoffman Prize. In 2011, he was named a fellow of the Association for Computational Linguistics. In 2015, he won the Association for the Advancement of Artificial Intelligence (AAAI) Classic Paper Award for a paper (“Statistical Parsing with a Context-Free Grammar and Word Statistics”) that he presented at the Fourteenth National Conference on Artificial Intelligence in 1997. == Books == He published six books: Computational Semantics, (with Yorick Wilks), Amsterdam: North-Holland (1976) Artificial Intelligence Programming (now in a second edition) (with Chris Riesbeck, Drew McDermott, and James Meehan), Hillsdale NJ: Lawrence Erlbaum Associates (1980, 1987) Introduction to Artificial Intelligence (with Drew McDermott), Reading MA: Addison-Wesley (1985) Statistical Language Learning, Cambridge: MIT Press (1993) Introduction to Deep Learning, Cambridge: MIT Press (2019) AI & I: An Intellectual History of Artificial Intelligence, Cambridge: MIT Press (2024)
IBM alignment models
The IBM alignment models are a sequence of increasingly complex models used in statistical machine translation to train a translation model and an alignment model, starting with lexical translation probabilities and moving to reordering and word duplication. They underpinned the majority of statistical machine translation systems for almost twenty years starting in the early 1990s, until neural machine translation began to dominate. These models offer principled probabilistic formulation and (mostly) tractable inference. The IBM alignment models were published in parts in 1988 and 1990, and the entire series is published in 1993. Every author of the 1993 paper subsequently went to the hedge fund Renaissance Technologies. The original work on statistical machine translation at IBM proposed five models, and a model 6 was proposed later. The sequence of the six models can be summarized as: Model 1: lexical translation Model 2: additional absolute alignment model Model 3: extra fertility model Model 4: added relative alignment model Model 5: fixed deficiency problem. Model 6: Model 4 combined with a HMM alignment model in a log linear way == Mathematical setup == The IBM alignment models translation as a conditional probability model. For each source-language ("foreign") sentence f {\displaystyle f} , we generate both a target-language ("English") sentence e {\displaystyle e} and an alignment a {\displaystyle a} . The problem then is to find a good statistical model for p ( e , a | f ) {\displaystyle p(e,a|f)} , the probability that we would generate English language sentence e {\displaystyle e} and an alignment a {\displaystyle a} given a foreign sentence f {\displaystyle f} . The meaning of an alignment grows increasingly complicated as the model version number grew. See Model 1 for the most simple and understandable version. == Model 1 == === Word alignment === Given any foreign-English sentence pair ( e , f ) {\displaystyle (e,f)} , an alignment for the sentence pair is a function of type { 1 , . , . . . , l e } → { 0 , 1 , . , . . . , l f } {\displaystyle \{1,.,...,l_{e}\}\to \{0,1,.,...,l_{f}\}} . That is, we assume that the English word at location i {\displaystyle i} is "explained" by the foreign word at location a ( i ) {\displaystyle a(i)} . For example, consider the following pair of sentences It will surely rain tomorrow -- 明日 は きっと 雨 だWe can align some English words to corresponding Japanese words, but not everyone:it -> ? will -> ? surely -> きっと rain -> 雨 tomorrow -> 明日This in general happens due to the different grammar and conventions of speech in different languages. English sentences require a subject, and when there is no subject available, it uses a dummy pronoun it. Japanese verbs do not have different forms for future and present tense, and the future tense is implied by the noun 明日 (tomorrow). Conversely, the topic-marker は and the grammar word だ (roughly "to be") do not correspond to any word in the English sentence. So, we can write the alignment as 1-> 0; 2 -> 0; 3 -> 3; 4 -> 4; 5 -> 1where 0 means that there is no corresponding alignment. Thus, we see that the alignment function is in general a function of type { 1 , . , . . . , l e } → { 0 , 1 , . , . . . , l f } {\displaystyle \{1,.,...,l_{e}\}\to \{0,1,.,...,l_{f}\}} . Future models will allow one English world to be aligned with multiple foreign words. === Statistical model === Given the above definition of alignment, we can define the statistical model used by Model 1: Start with a "dictionary". Its entries are of form t ( e i | f j ) {\displaystyle t(e_{i}|f_{j})} , which can be interpreted as saying "the foreign word f j {\displaystyle f_{j}} is translated to the English word e i {\displaystyle e_{i}} with probability t ( e i | f j ) {\displaystyle t(e_{i}|f_{j})} ". After being given a foreign sentence f {\displaystyle f} with length l f {\displaystyle l_{f}} , we first generate an English sentence length l e {\displaystyle l_{e}} uniformly in a range U n i f o r m [ 1 , 2 , . . . , N ] {\displaystyle Uniform[1,2,...,N]} . In particular, it does not depend on f {\displaystyle f} or l f {\displaystyle l_{f}} . Then, we generate an alignment uniformly in the set of all possible alignment functions { 1 , . , . . . , l e } → { 0 , 1 , . , . . . , l f } {\displaystyle \{1,.,...,l_{e}\}\to \{0,1,.,...,l_{f}\}} . Finally, for each English word e 1 , e 2 , . . . e l e {\displaystyle e_{1},e_{2},...e_{l_{e}}} , generate each one independently of every other English word. For the word e i {\displaystyle e_{i}} , generate it according to t ( e i | f a ( i ) ) {\displaystyle t(e_{i}|f_{a(i)})} . Together, we have the probability p ( e , a | f ) = 1 / N ( 1 + l f ) l e ∏ i = 1 l e t ( e i | f a ( i ) ) {\displaystyle p(e,a|f)={\frac {1/N}{(1+l_{f})^{l_{e}}}}\prod _{i=1}^{l_{e}}t(e_{i}|f_{a(i)})} IBM Model 1 uses very simplistic assumptions on the statistical model, in order to allow the following algorithm to have closed-form solution. === Learning from a corpus === If a dictionary is not provided at the start, but we have a corpus of English-foreign language pairs { ( e ( k ) , f ( k ) ) } k {\displaystyle \{(e^{(k)},f^{(k)})\}_{k}} (without alignment information), then the model can be cast into the following form: fixed parameters: the foreign sentences { f ( k ) } k {\displaystyle \{f^{(k)}\}_{k}} . learnable parameters: the entries of the dictionary t ( e i | f j ) {\displaystyle t(e_{i}|f_{j})} . observable variables: the English sentences { e ( k ) } k {\displaystyle \{e^{(k)}\}_{k}} . latent variables: the alignments { a ( k ) } k {\displaystyle \{a^{(k)}\}_{k}} In this form, this is exactly the kind of problem solved by expectation–maximization algorithm. Due to the simplistic assumptions, the algorithm has a closed-form, efficiently computable solution, which is the solution to the following equations: { max t ′ ∑ k ∑ i ∑ a ( k ) t ( a ( k ) | e ( k ) , f ( k ) ) ln t ( e i ( k ) | f a ( k ) ( i ) ( k ) ) ∑ x t ′ ( e x | f y ) = 1 ∀ y {\displaystyle {\begin{cases}\max _{t'}\sum _{k}\sum _{i}\sum _{a^{(k)}}t(a^{(k)}|e^{(k)},f^{(k)})\ln t(e_{i}^{(k)}|f_{a^{(k)}(i)}^{(k)})\\\sum _{x}t'(e_{x}|f_{y})=1\quad \forall y\end{cases}}} This can be solved by Lagrangian multipliers, then simplified. For a detailed derivation of the algorithm, see chapter 4 and. In short, the EM algorithm goes as follows:INPUT. a corpus of English-foreign sentence pairs { ( e ( k ) , f ( k ) ) } k {\displaystyle \{(e^{(k)},f^{(k)})\}_{k}} INITIALIZE. matrix of translations probabilities t ( e x | f y ) {\displaystyle t(e_{x}|f_{y})} .This could either be uniform or random. It is only required that every entry is positive, and for each y {\displaystyle y} , the probability sums to one: ∑ x t ( e x | f y ) = 1 {\displaystyle \sum _{x}t(e_{x}|f_{y})=1} . LOOP. until t ( e x | f y ) {\displaystyle t(e_{x}|f_{y})} converges: t ( e x | f y ) ← t ( e x | f y ) λ y ∑ k , i , j δ ( e x , e i ( k ) ) δ ( f y , f j ( k ) ) ∑ j ′ t ( e i ( k ) | f j ′ ( k ) ) {\displaystyle t(e_{x}|f_{y})\leftarrow {\frac {t(e_{x}|f_{y})}{\lambda _{y}}}\sum _{k,i,j}{\frac {\delta (e_{x},e_{i}^{(k)})\delta (f_{y},f_{j}^{(k)})}{\sum _{j'}t(e_{i}^{(k)}|f_{j'}^{(k)})}}} where each λ y {\displaystyle \lambda _{y}} is a normalization constant that makes sure each ∑ x t ( e x | f y ) = 1 {\displaystyle \sum _{x}t(e_{x}|f_{y})=1} .RETURN. t ( e x | f y ) {\displaystyle t(e_{x}|f_{y})} .In the above formula, δ {\displaystyle \delta } is the Dirac delta function -- it equals 1 if the two entries are equal, and 0 otherwise. The index notation is as follows: k {\displaystyle k} ranges over English-foreign sentence pairs in corpus; i {\displaystyle i} ranges over words in English sentences; j {\displaystyle j} ranges over words in foreign language sentences; x {\displaystyle x} ranges over the entire vocabulary of English words in the corpus; y {\displaystyle y} ranges over the entire vocabulary of foreign words in the corpus. === Limitations === There are several limitations to the IBM model 1. No fluency: Given any sentence pair ( e , f ) {\displaystyle (e,f)} , any permutation of the English sentence is equally likely: p ( e | f ) = p ( e ′ | f ) {\displaystyle p(e|f)=p(e'|f)} for any permutation of the English sentence e {\displaystyle e} into e ′ {\displaystyle e'} . No length preference: The probability of each length of translation is equal: ∑ e has length l p ( e | f ) = 1 N {\displaystyle \sum _{e{\text{ has length }}l}p(e|f)={\frac {1}{N}}} for any l ∈ { 1 , 2 , . . . , N } {\displaystyle l\in \{1,2,...,N\}} . Does not explicitly model fertility: some foreign words tend to produce a fixed number of English words. For example, for German-to-English translation, ja is usually omitted, and zum is usually translated to one of to the, for the, to a, for a. == Model 2 == Model 2 allows alignment to be conditional on sentence lengths. That is, we have a probability distribution p a ( j | i , l e , l f ) {\displaystyle
Cumulus (software)
Cumulus is a digital asset management software designed for client/server system which is developed by Canto Software. The product makes use of metadata for indexing, organizing, and searching. == History == Cumulus was first released as a Macintosh application in 1992, and was named by Apple Computer as the "Most Innovative Product of 1992". Cumulus introduced search capabilities beyond those available in the Macintosh at the time, particularly relating to thumbnails. Cumulus 1.0 was a single-user product with no network capabilities. Among the main features of Cumulus 1.0, the search function automatically generated previews and contained support for the included AppleTalk – Peer-to-Peer – network. Cumulus 2.5 was available in five different languages and received the 1993 MacUser magazine Eddy award for "Best Publishing & Graphics Utility". In 1995, Canto introduced the scanner software "Cirrus" to focus on the development of Cumulus. Cumulus 3, released in 1996, introduced a server version for the first time and contained the possibility to spread files over the Internet via the "Web Publisher". Since Apple offered Cumulus 3 with its "Workgroup Server" as a bundle, Cumulus became one of the leading digital asset management systems. Cumulus 4 was the first version that was network-ready, and was available for Macintosh, Windows and UNIX operating systems allowing for cross-platform file sharing. Released in 1998, the support of Solaris was discounted later. Cumulus 5 modified the software core to use an open architecture providing an API to external systems and databases. The open architecture of Cumulus 5 also enabled a more functional bridge between Cumulus and the Internet. Cumulus 6 introduced Embedded Java Plugin (EJP) which allowed system integrators to build custom Java plug-ins in order to extend the functionality of the Cumulus client. Cumulus 6.5 marked the end of the Cumulus Single User Edition product, which was licensed to MediaDex for further development and distribution. Cumulus 7 was introduced summer of 2006. Cumulus 8 was released in June 2009, with new indexing capabilities taking advantage of multicore/multiprocessor systems, and ability to manage a wider variety of file formats. Cumulus 8.5 was released in May 2011. Support was added for multilingual metadata, sometimes referred to as "World Metadata." Cumulus Sites was updated to support metadata editing and file uploads. Cumulus 8.6 was released in July 2012, and contains an updated user interface for the administration of Cumulus Sites and additional features for web-based administration of Cumulus. Other additions include features for collaboration links, multi-language support and automated version control. Cumulus 9 was released in September 2013 and introduced a new Web Client User Interface and the Cumulus Video Cloud. The Cumulus Web Client UI was redesigned to provide users with a modern, easy-to-use interface to support and guide the user while addressing modern business needs. The Cumulus Video Cloud extends the Cumulus video handling capabilities to add conversion and global streaming. Cumulus 9 also saw the addition of upload collection links which allow external collaborators to drag and drop files directly into Cumulus without needing a Cumulus account. Cumulus 9.1 was released in May 2014 and introduced the Adobe Drive Adapter for Cumulus which allows users to browse and search digital assets in Cumulus directly from Adobe work environments such as Photoshop, InDesign, Illustrator, Premier and other Adobe applications. Cumulus 10 (Cumulus X) was released July 2015 and introduced two mobile-friendly products: the Cumulus app and Portals. The Cumulus app on iOS was designed to allow users to collaborate either on an iPhone or iPad. Portals is the read-only version of the Cumulus Web Client where users can work with assets that admins allow. Cumulus 10.1 was introduced in January 2016 and included the InDesign Client integration where users can work with Adobe InDesign while accessing their assets from Cumulus. Cumulus 10.2 was introduced in September 2016 and brought the Media Delivery Cloud using Amazon Web Services (AWS). It allows users to manage their media rendition in a single source and distribute media files globally across different channels and devices. Cumulus 10.2.3 was released in February 2017 and came with a "crop and customize photos" feature for Portals and the Web Client. == Product overview == The cataloging of the file via upload into the archive is where Cumulus transfers maximum information about the file from the metadata. For image or photo files, this is typically Exif and IPTC data. The metadata is mainly used to search the archive. The use of embargo data supports license management for copyrighted material. The managed files can be cataloged and their usage can be set. The indexing is based on a predefined taxonomy, which is governed by the internal rules of the organization or by industry standards. You can specify whether files can only be used for specific purposes or only by certain groups of people. The production management system includes version management for files. Via the publication function, the files can be distributed directly via links or e-mails. It's also possible to access from the outside via the Cumulus Portals web interface, which allows a read access to released content from the catalog. There are different variants, starting with the "Workgroup archive server" up to the "Enterprise Business Server" for large companies. Both server and client are extensible through a Java-based plug-in architecture. Since version 7.0, there is a web application based on Ajax with a separate user interface. For access to the Cumulus catalog on mobile, there has been an application for Apple devices based on iOS since 2010. == Miscellaneous == In 2015, Cumulus developer Canto established the first Canto digital asset management (DAM) event. The event is held annually in Berlin. The Henry Stewart team has been hosting DAM conferences since 2006.
Margaret Mitchell (scientist)
Margaret Mitchell is a computer scientist who works on algorithmic bias and fairness in machine learning. She is most well known for her work on automatically removing undesired biases concerning demographic groups from machine learning models, as well as more transparent reporting of their intended use. == Education == Mitchell obtained a bachelor's degree in linguistics from Reed College, Portland, Oregon, in 2005. After having worked as a research assistant at the OGI School of Science and Engineering for two years, she subsequently obtained a Master's in Computational Linguistics from the University of Washington in 2009. She enrolled in a PhD program at the University of Aberdeen, where she wrote a doctoral thesis on the topic of Generating Reference to Visible Objects, graduating in 2013. == Career and research == Mitchell is best known for her work on fairness in machine learning and methods for mitigating algorithmic bias. This includes her work on introducing the concept of 'Model Cards' for more transparent model reporting, and methods for debiasing machine learning models using adversarial learning. Margaret Mitchell created the framework for recognizing and avoiding biases by testing with a variable for the group of interest, predictor and an adversary. In 2012, Mitchell joined the Human Language Technology Center of Excellence at Johns Hopkins University as a postdoctoral researcher, before taking up a position at Microsoft Research in 2013. At Microsoft, Mitchell was the research lead of the Seeing AI project, an app that offers support for the visually impaired by narrating texts and images. In November 2016, she became a senior research scientist at Google Research and Machine intelligence. While at Google, she founded and co-led the Ethical Artificial Intelligence team together with Timnit Gebru. In May 2018, she represented Google in the Partnership on AI. In February 2018, she gave a TED talk on "How we can build AI to help humans, not hurt us". In January 2021, after Timnit Gebru's termination from Google, Mitchell reportedly used a script to search through her corporate account and download emails that allegedly documented discriminatory incidents involving Gebru. An automated system locked Mitchell's account in response. In response to media attention Google claimed that she "exfiltrated thousands of files and shared them with multiple external accounts". After a five-week investigation, Mitchell was fired. Prior to her dismissal, Mitchell had been a vocal advocate for diversity at Google, and had voiced concerns about research censorship at the company. In late 2021, she joined AI start-up Hugging Face. Mitchell is a co-founder of Widening NLP, a special interest group within the Association for Computational Linguistics (ACL) seeking to increase the proportion of women and minorities working in natural language processing; and Computational Linguistics and Clinical Psychology, an annual workshop within the ACL that brings together clinicians and computational linguists to advance the state of the art in clinical psychology.