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Pray.com

Pray.com is a Christian social networking service and mobile application designed to facilitate religious communities. Launched in 2016, it was founded by Steve Gatena, Michael Lynn, Ryan Beck and Matthew Potter. The platform offers features for social networking, daily prayers, sermons, biblical content, and podcasts. The COVID-19 pandemic significantly increased Pray.com's user base, with downloads surging by 955%. During this period, the platform collaborated with churches to support virtual ministry services as in-person gatherings were restricted. The Federal Election Commission issued an opinion in 2021 that allows the platform to feature members of the United States Congress. Pray.com serves as a specialized social media platform for religious groups. Congregations can establish their own groups where members and leaders can participate in discussions, livestream services, and manage donations. Additionally, users can join "prayer communities" to post and respond to prayer requests. For those who subscribe to premium services, the platform provides access to biblically-inspired meditations and bedtime stories, and Bible stories for children. Pray.com also produces Radio drama-style productions with notable actors such as Kristen Bell and Blair Underwood narrating biblical stories. == History == === Funding and development === Pray.com has secured significant funding to support its development and growth. In 2017, the platform raised $2 million in seed funding from Science Inc., Greylock Partners, and Spark Capital. This was followed by a Series A funding round in March 2018, in which the company secured an additional $14 million from TPG Growth, Science Inc., and Greylock Partners. Founder Steve Gatena has highlighted difficulties in securing funding, noting some venture capitalists' negative attitudes towards faith-based technology. === Clinical studies === There have been clinical studies on Pray.com. In one study, the app was found to be acceptable and easy to use among racial and ethnic minority groups, with participants reporting improved mental health and well-being. Greater app use was associated with better outcomes, though low and variable usage suggests the need for further research to fully understand its impact. Another study examined Pray.com's impact on mental health by assigning 192 participants to use the app freely, use its meditative prayer function, or not use it at all. Over two months, participants reported overall improvements in mental health and well-being. Although no significant differences were found between groups, greater app usage correlated with better mental health outcomes. This suggests that religiously based mobile apps may help improve mental health and well-being. Another study of pray.com had similar findings. === National Day of Prayer === Pray first hosted a National Day of Prayer event in 2020 when it streamed to nearly one million viewers on Facebook. In 2021, Pray hosted a virtual event for the National Day of Prayer in the United States. The event featured remarks from public figures including United States President Joe Biden and former Vice President Mike Pence. President Biden spoke of his faith and prayed for an end to the COVID-19 pandemic. Biden remarked: "It means the world to me to know that there are people across the country who include Jill and me in their prayers. And I hope you know that you and your families are in our prayers as well. Today I am praying for the end of this great COVID crisis." The event featured musical performances from Gary Valenciano, Brooke Ligertwood from the Christian band Hillsong Worship, Lecrae, Heather Headley and Michael Neale. Other notable speakers included Ronnie Floyd, Ed Young, Mark Driscoll, and Samuel Rodriguez. Pray.com partnered with Sirius XM, DirecTV and Facebook to stream the event across multiple platforms. Pray.com was featured as a pop-up channel on Sirius XM, channel 154, to host the prayer event and celebrate people of all faith. === Partnerships and sponsorships === In 2024, Pray.com partnered with Sting Ray Robb as the primary sponsor for his No. 41 Chevrolet in the 2024 NTT IndyCar Series. The partnership, highlighting Robb's Christian faith, aims to engage younger audiences with faith-based content. The car, featuring Pray.com's branding, was set to debut at the Firestone Grand Prix of St. Petersburg. A partnership with Palantir Technologies for use of its AI systems was also announced in 2024. === Censorship in China === The app was removed from Apple's App Store in China as part of the country's broader efforts to restrict access to religious content. The app was targeted due to China's stringent regulations on religious material, particularly content distributed through digital platforms. The removal aligns with China's ongoing campaign to control online religious expression and maintain state-approved religious activities.
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Jean Véronis

Jean Véronis (3 June 1955 – 8 September 2013) was a French linguist, computer scientist and blogger, and a research professor at Aix-Marseille University. His research interests included natural language processing, text mining and standardisation. He was a founder of the field that is now called digital humanities. In 2006, his blog was listed among the 15 most influential by Le Monde.
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Katia Sycara

Ekaterini Panagiotou Sycara (Greek: Κάτια Συκαρά) is a Greek computer scientist. She is an Edward Fredkin Research Professor of Robotics in the Robotics Institute, School of Computer Science at Carnegie Mellon University internationally known for her research in artificial intelligence, particularly in the fields of negotiation, autonomous agents and multi-agent systems. She directs the Advanced Agent-Robotics Technology Lab at Robotics Institute, Carnegie Mellon University. She also serves as academic advisor for PhD students at both Robotics Institute and Tepper School of Business. == Education and early life == Born in Greece, she went to the United States to pursue advanced education through various scholarships, including a Fulbright (1965-1969). She received a B.S. in applied mathematics from Brown University, M.S. in electrical engineering from the University of Wisconsin–Milwaukee, and PhD in computer science from Georgia Institute of Technology. == Research and career == Sycara is a pioneer in the field of semantic web, case-based reasoning, autonomous agents and multi-agent systems. She has authored or co-authored more than 700 technical papers dealing with multi-agent systems, software agents, web services, semantic web, human–computer interaction, human-robot interaction, negotiation, case-based reasoning and the application of these techniques to crisis action planning, scheduling, manufacturing, healthcare management, financial planning and e-commerce.[1] She has led multimillion-dollar research effort funded by DARPA, NASA, AFOSR, ONR, AFRL, NSF and industry. Through an ONR MURI program and though the COABS DARPA program, Prof. Sycara's group has developed the RETSINA multiagent infrastructure, a toolkit that enables the development of heterogeneous software agents that can dynamically coordinate in open information environments (e.g. the Internet). RETSINA has been used in multiple applications including supporting human joint mission teams for crisis response; creating autonomous agents for situation awareness and information fusion; financial portfolio management, negotiations and coalition formation for e-commerce, and coordinating robots for Urban Search and Rescue. Sycara is one of the contributors to the development of OWL-S, the Darpa-sponsored language for Semantic Web services, as well as matchmaking and brokering software for agent discovery, service integration and semantic interoperation. === Academic service === Sycara is the founding Editor-in-Chief of the journal Autonomous Agents and Multi-Agent Systems; Editor-in-Chief, of the Springer Series on Agents; and Area Editor of AI and Management Science, the journal "Group Decision and Negotiation." She is a member of the Editorial Board, the Kluwer book series on "Multiagent Systems, Artificial Societies and Simulated Organizations"; member of the editorial board, the journals "Agent Oriented Software Engineering", "Web Intelligence and Agent Technologies", "Journal of Infonomics", "Fundamenda Informaticae", and "Concurrent Engineering: Research and Applications"; and member of the editorial board of the "ETAI journal on the Semantic Web" (1998–2001). She was on the Editorial Board of "IEEE Intelligent Systems and their Applications" (1992–1996), and "AI in Engineering" (1990–1996). She is a member of the Scientific Advisory Board of France Telecom, 2003-2009; member of the Scientific Advisory Board of the Institute of Informatics and Telecommunications of the Greek National Research Center Demokritos, 2004-2012; member of the AAAI Executive Council (1996–99); member of the OASIS Technical committee on the development of UDDI (Universal Description and Discovery for Interoperability) software which is an industry standard; and an invited expert for W3C (the World Wide Web Consortium) Working Group on Web Services Architecture. She was a founding member of the Board of Directors of the International Foundation of Multiagent Systems (IFMAS), and founding member of the Semantic Web Science Association. Sycara served as the program chair of the Second International Semantic Web Conference (ISWC 2003); general chair, of the Second International Conference on Autonomous Agents (Agents 98); chair of the Steering Committee of the Agents Conference (1999–2001); scholarship chair of AAAI (1993–1999); and the US co-chair for the US-Europe Semantic Web Services Initiative. === Awards and honors === Sycara is a Fellow of Institute of Electrical and Electronics Engineers (IEEE), and a Fellow of American Association for Artificial Intelligence (AAAI). Sycara is the recipient of the 2002 ACM/SIGART Agents Research Award. She is also the recipient of the 2015 Group Decision and Negotiation (GDN) Award of the Institute for Operations Research and the Management Sciences (INFORMS) GDN Section for her outstanding contributions to the field of group decision and negotiation. According to the citation of the award: Katia Sycara is widely acknowledged as one of the leading researchers in the field of autonomous software agents and in particular on problems related to joint decision making and negotiations of such agents. Her work is characterized by a unique combination of methods from Artificial Intelligence and research on human negotiations, and thus has contributed to significant advances in both fields. Sycara's robot teams have won multiple international awards. In the 2005 Robocup Urban Search and Rescue (US Open) held in Atlanta, her team won the First-in-Class Award for Autonomy, and the First-in-Class Award for Mobility. Two years later, again in Atlanta, she led another team that became a world champions in the 2007 International Robocup Search and Rescue Simulation League Competition. In 2008, her robotic team placed third in the Worldwide Robocup Championship Competition in the Urban Search and Rescue Virtual robots League held in Beijing, China. In 2005, she received the Outstanding Alumnus Award from the University of Wisconsin–Milwaukee. She was awarded an Honorary Doctorate from the University of the Aegean in 2004.
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Semiautomaton

In mathematics and theoretical computer science, a semiautomaton is a deterministic finite automaton having inputs but no output. It consists of a set Q of states, a set Σ called the input alphabet, and a function T: Q × Σ → Q called the transition function. Associated with any semiautomaton is a monoid called the characteristic monoid, input monoid, transition monoid or transition system of the semiautomaton, which acts on the set of states Q. This may be viewed either as an action of the free monoid of strings in the input alphabet Σ, or as the induced transformation semigroup of Q. In older books like Clifford and Preston (1967) semigroup actions are called "operands". In category theory, semiautomata essentially are functors. == Transformation semigroups and monoid acts == A transformation semigroup or transformation monoid is a pair ( M , Q ) {\displaystyle (M,Q)} consisting of a set Q (often called the "set of states") and a semigroup or monoid M of functions, or "transformations", mapping Q to itself. They are functions in the sense that every element m of M is a map m : Q → Q {\displaystyle m\colon Q\to Q} . If s and t are two functions of the transformation semigroup, their semigroup product is defined as their function composition ( s t ) ( q ) = ( s ∘ t ) ( q ) = s ( t ( q ) ) {\displaystyle (st)(q)=(s\circ t)(q)=s(t(q))} . Some authors regard "semigroup" and "monoid" as synonyms. Here a semigroup need not have an identity element; a monoid is a semigroup with an identity element (also called "unit"). Since the notion of functions acting on a set always includes the notion of an identity function, which when applied to the set does nothing, a transformation semigroup can be made into a monoid by adding the identity function. === M-acts === Let M be a monoid and Q be a non-empty set. If there exists a multiplicative operation μ : Q × M → Q {\displaystyle \mu \colon Q\times M\to Q} ( q , m ) ↦ q m = μ ( q , m ) {\displaystyle (q,m)\mapsto qm=\mu (q,m)} which satisfies the properties q 1 = q {\displaystyle q1=q} for 1 the unit of the monoid, and q ( s t ) = ( q s ) t {\displaystyle q(st)=(qs)t} for all q ∈ Q {\displaystyle q\in Q} and s , t ∈ M {\displaystyle s,t\in M} , then the triple ( Q , M , μ ) {\displaystyle (Q,M,\mu )} is called a right M-act or simply a right act. In long-hand, μ {\displaystyle \mu } is the right multiplication of elements of Q by elements of M. The right act is often written as Q M {\displaystyle Q_{M}} . A left act is defined similarly, with μ : M × Q → Q {\displaystyle \mu \colon M\times Q\to Q} ( m , q ) ↦ m q = μ ( m , q ) {\displaystyle (m,q)\mapsto mq=\mu (m,q)} and is often denoted as M Q {\displaystyle \,_{M}Q} . An M-act is closely related to a transformation monoid. However the elements of M need not be functions per se, they are just elements of some monoid. Therefore, one must demand that the action of μ {\displaystyle \mu } be consistent with multiplication in the monoid (i.e. μ ( q , s t ) = μ ( μ ( q , s ) , t ) {\displaystyle \mu (q,st)=\mu (\mu (q,s),t)} ), as, in general, this might not hold for some arbitrary μ {\displaystyle \mu } , in the way that it does for function composition. Once one makes this demand, it is completely safe to drop all parenthesis, as the monoid product and the action of the monoid on the set are completely associative. In particular, this allows elements of the monoid to be represented as strings of letters, in the computer-science sense of the word "string". This abstraction then allows one to talk about string operations in general, and eventually leads to the concept of formal languages as being composed of strings of letters. Another difference between an M-act and a transformation monoid is that for an M-act Q, two distinct elements of the monoid may determine the same transformation of Q. If we demand that this does not happen, then an M-act is essentially the same as a transformation monoid. === M-homomorphism === For two M-acts Q M {\displaystyle Q_{M}} and B M {\displaystyle B_{M}} sharing the same monoid M {\displaystyle M} , an M-homomorphism f : Q M → B M {\displaystyle f\colon Q_{M}\to B_{M}} is a map f : Q → B {\displaystyle f\colon Q\to B} such that f ( q m ) = f ( q ) m {\displaystyle f(qm)=f(q)m} for all q ∈ Q M {\displaystyle q\in Q_{M}} and m ∈ M {\displaystyle m\in M} . The set of all M-homomorphisms is commonly written as H o m ( Q M , B M ) {\displaystyle \mathrm {Hom} (Q_{M},B_{M})} or H o m M ( Q , B ) {\displaystyle \mathrm {Hom} _{M}(Q,B)} . The M-acts and M-homomorphisms together form a category called M-Act. == Semiautomata == A semiautomaton is a triple ( Q , Σ , T ) {\displaystyle (Q,\Sigma ,T)} where Σ {\displaystyle \Sigma } is a non-empty set, called the input alphabet, Q is a non-empty set, called the set of states, and T is the transition function T : Q × Σ → Q . {\displaystyle T\colon Q\times \Sigma \to Q.} When the set of states Q is a finite set—it need not be—, a semiautomaton may be thought of as a deterministic finite automaton ( Q , Σ , T , q 0 , A ) {\displaystyle (Q,\Sigma ,T,q_{0},A)} , but without the initial state q 0 {\displaystyle q_{0}} or set of accept states A. Alternately, it is a finite-state machine that has no output, and only an input. Any semiautomaton induces an act of a monoid in the following way. Let Σ ∗ {\displaystyle \Sigma ^{}} be the free monoid generated by the alphabet Σ {\displaystyle \Sigma } (so that the superscript is understood to be the Kleene star); it is the set of all finite-length strings composed of the letters in Σ {\displaystyle \Sigma } . For every word w in Σ ∗ {\displaystyle \Sigma ^{}} , let T w : Q → Q {\displaystyle T_{w}\colon Q\to Q} be the function, defined recursively, as follows, for all q in Q: If w = ε {\displaystyle w=\varepsilon } , then T ε ( q ) = q {\displaystyle T_{\varepsilon }(q)=q} , so that the empty word ε {\displaystyle \varepsilon } does not change the state. If w = σ {\displaystyle w=\sigma } is a letter in Σ {\displaystyle \Sigma } , then T σ ( q ) = T ( q , σ ) {\displaystyle T_{\sigma }(q)=T(q,\sigma )} . If w = σ v {\displaystyle w=\sigma v} for σ ∈ Σ {\displaystyle \sigma \in \Sigma } and v ∈ Σ ∗ {\displaystyle v\in \Sigma ^{}} , then T w ( q ) = T v ( T σ ( q ) ) {\displaystyle T_{w}(q)=T_{v}(T_{\sigma }(q))} . Let M ( Q , Σ , T ) {\displaystyle M(Q,\Sigma ,T)} be the set M ( Q , Σ , T ) = { T w | w ∈ Σ ∗ } . {\displaystyle M(Q,\Sigma ,T)=\{T_{w}\vert w\in \Sigma ^{}\}.} The set M ( Q , Σ , T ) {\displaystyle M(Q,\Sigma ,T)} is closed under function composition; that is, for all v , w ∈ Σ ∗ {\displaystyle v,w\in \Sigma ^{}} , one has T w ∘ T v = T v w {\displaystyle T_{w}\circ T_{v}=T_{vw}} . It also contains T ε {\displaystyle T_{\varepsilon }} , which is the identity function on Q. Since function composition is associative, the set M ( Q , Σ , T ) {\displaystyle M(Q,\Sigma ,T)} is a monoid: it is called the input monoid, characteristic monoid, characteristic semigroup or transition monoid of the semiautomaton ( Q , Σ , T ) {\displaystyle (Q,\Sigma ,T)} . == Properties == If the set of states Q is finite, then the transition functions are commonly represented as state transition tables. The structure of all possible transitions driven by strings in the free monoid has a graphical depiction as a de Bruijn graph. The set of states Q need not be finite, or even countable. As an example, semiautomata underpin the concept of quantum finite automata. There, the set of states Q are given by the complex projective space C P n {\displaystyle \mathbb {C} P^{n}} , and individual states are referred to as n-state qubits. State transitions are given by unitary n×n matrices. The input alphabet Σ {\displaystyle \Sigma } remains finite, and other typical concerns of automata theory remain in play. Thus, the quantum semiautomaton may be simply defined as the triple ( C P n , Σ , { U σ 1 , U σ 2 , … , U σ p } ) {\displaystyle (\mathbb {C} P^{n},\Sigma ,\{U_{\sigma _{1}},U_{\sigma _{2}},\dotsc ,U_{\sigma _{p}}\})} when the alphabet Σ {\displaystyle \Sigma } has p letters, so that there is one unitary matrix U σ {\displaystyle U_{\sigma }} for each letter σ ∈ Σ {\displaystyle \sigma \in \Sigma } . Stated in this way, the quantum semiautomaton has many geometrical generalizations. Thus, for example, one may take a Riemannian symmetric space in place of C P n {\displaystyle \mathbb {C} P^{n}} , and selections from its group of isometries as transition functions. The syntactic monoid of a regular language is isomorphic to the transition monoid of the minimal automaton accepting the language. == Literature == A. H. Clifford and G. B. Preston, The Algebraic Theory of Semigroups. American Mathematical Society, volume 2 (1967), ISBN 978-0-8218-0272-4. F. Gecseg and I. Peak, Algebraic Theory of Automata (1972), Akademiai Kiado, Budapest. W. M. L. Holcombe, Algebraic Automata Theory (1982), Cambridge University Press J. M. Howie, Automata and Languages, (1991), Cla
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Computational heuristic intelligence

Computational heuristic intelligence (CHI) refers to specialized programming techniques in computational intelligence (also called artificial intelligence, or AI). These techniques have the express goal of avoiding complexity issues, also called NP-hard problems, by using human-like techniques. They are best summarized as the use of exemplar-based methods (heuristics), rather than rule-based methods (algorithms). Hence the term is distinct from the more conventional computational algorithmic intelligence, or symbolic AI. An example of a CHI technique is the encoding specificity principle of Tulving and Thompson. In general, CHI principles are problem solving techniques used by people, rather than programmed into machines. It is by drawing attention to this key distinction that the use of this term is justified in a field already replete with confusing neologisms. Note that the legal systems of all modern human societies employ both heuristics (generalisations of cases) from individual trial records as well as legislated statutes (rules) as regulatory guides. Another recent approach to the avoidance of complexity issues is to employ feedback control rather than feedforward modeling as a problem-solving paradigm. This approach has been called computational cybernetics, because (a) the term 'computational' is associated with conventional computer programming techniques which represent a strategic, compiled, or feedforward model of the problem, and (b) the term 'cybernetic' is associated with conventional system operation techniques which represent a tactical, interpreted, or feedback model of the problem. Of course, real programs and real problems both contain both feedforward and feedback components. A real example which illustrates this point is that of human cognition, which clearly involves both perceptual (bottom-up, feedback, sensor-oriented) and conceptual (top-down, feedforward, motor-oriented) information flows and hierarchies. The AI engineer must choose between mathematical and cybernetic problem solution and machine design paradigms. This is not a coding (program language) issue, but relates to understanding the relationship between the declarative and procedural programming paradigms. The vast majority of STEM professionals never get the opportunity to design or implement pure cybernetic solutions. When pushed, most responders will dismiss the importance of any difference by saying that all code can be reduced to a mathematical model anyway. Unfortunately, not only is this belief false, it fails most spectacularly in many AI scenarios. Mathematical models are not time agnostic, but by their very nature are pre-computed, i.e. feedforward. Dyer [2012] and Feldman [2004] have independently investigated the simplest of all somatic governance paradigms, namely control of a simple jointed limb by a single flexor muscle. They found that it is impossible to determine forces from limb positions- therefore, the problem cannot have a pre-computed (feedforward) mathematical solution. Instead, a top-down command bias signal changes the threshold feedback level in the sensorimotor loop, e.g. the loop formed by the afferent and efferent nerves, thus changing the so-called ‘equilibrium point’ of the flexor muscle/ elbow joint system. An overview of the arrangement reveals that global postures and limb position are commanded in feedforward terms, using global displacements (common coding), with the forces needed being computed locally by feedback loops. This method of sensorimotor unit governance, which is based upon what Anatol Feldman calls the ‘equilibrium Point’ theory, is formally equivalent to a servomechanism such as a car's ‘cruise control’.
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Heikki Mannila

Heikki Olavi Mannila (born 4 January 1960 in Espoo) is a Finnish computer scientist, the president of the Academy of Finland. Mannila earned his Ph.D. in 1985 from the University of Helsinki under the supervision of Esko Ukkonen and for many years he was a professor at the University of Helsinki himself. From 2004 to 2008 he was Academy Professor at the Academy of Finland. He became Vice President for Academic Affairs at Aalto University in 2009, and was appointed by the Finnish government as president of the Academy of Finland for a term lasting from 2012 to 2017. The appointment was renewed for the period 2017–2022. Mannila is known for his research in data mining, and has published highly cited papers on association rule learning and sequence mining. With David Hand and Padhraic Smyth, he is the co-author of the book Principles of Data Mining (MIT Press, 2001). Heikki Mannila is son to the professor Elina Haavio-Mannila.
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Ellen Voorhees

Ellen Marie Voorhees (born March 13, 1958) is an American computer scientist known for her work in document retrieval, information retrieval, and natural language processing. She works in the retrieval group at the National Institute of Standards and Technology (NIST). == Education and career == Voorhees was born in Bensalem Township, Pennsylvania, and was the 1976 valedictorian at Bensalem High School. She completed her undergraduate studies at Pennsylvania State University, graduating in 1979 with a bachelor's degree in computer science. She attended Cornell University, where she received her master's degree and then went on to complete her Ph.D. in 1985. Her dissertation, The Effectiveness and Efficiency of Agglomerative Hierarchic Clustering in Document Retrieval, was supervised by Gerard Salton. Prior to joining NIST, she was a senior member of the technical staff at Siemens Corporate Research in Princeton, New Jersey. == Recognition == Voorhees was elected as an ACM Fellow in 2018 for "contributions in evaluation of information retrieval, question answering, and other language technologies". In 2023, Voorhees was awarded an honorary Doctor of Science degree from the University of Glasgow in recognition of her body of work in the evaluation of information retrieval, question answering, and other language technologies. In 2024, Voorhees received the Gerard Salton Award, a lifetime achievement award given by ACM's Special Interest Group on Information Retrieval (SIGIR).
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Cortana (virtual assistant)

Cortana is a discontinued virtual assistant developed by Microsoft that used the Bing search engine to perform tasks such as setting reminders and answering questions for users. Cortana was available in English, Portuguese, French, German, Italian, Spanish, Chinese, and Japanese language editions, depending on the software platform and region in which it was used. In 2019, Microsoft began reducing the prevalence of Cortana and converting it from an assistant into different software integrations. It was split from the Windows 10 search bar in April 2019. In January 2020, the Cortana mobile app was removed from certain markets, and on March 31, 2021, the Cortana mobile app was shut down globally. On June 2, 2023, Microsoft announced that support for the Cortana standalone app on Microsoft Windows would end in late 2023 and would be replaced by Microsoft Copilot, an AI chatbot. Support for Cortana in the Microsoft Outlook and Microsoft 365 mobile apps was discontinued in fall of 2023. == History == === Beginnings (2009–2014) === The development of Cortana started in 2009 in the Microsoft Speech products team with general manager Zig Serafin and Chief Scientist Larry Heck. Heck and Serafin established the vision, mission, and long-range plan for Microsoft's digital personal assistant and they built a team with the expertise to create the initial prototypes for Cortana. Some of the key researchers in these early efforts included Microsoft Research researchers Dilek Hakkani-Tür, Gokhan Tur, Andreas Stolcke, and Malcolm Slaney, research software developer Madhu Chinthakunta, and user experience designer Lisa Stifelman. To develop the Cortana digital assistant, the team interviewed human personal assistants. The interviews inspired a number of unique features in Cortana, including the assistant's "notebook" feature. Originally, Cortana was meant to be only a codename, but a petition on Windows Phone's UserVoice site proved to be popular and made the codename official. Cortana was demonstrated for the first time at the Microsoft Build developer conference in San Francisco in April 2014. It was launched as a key ingredient of Microsoft's planned "makeover" of future operating systems for Windows Phone and Windows. It was named after Cortana, a synthetic intelligence character in Microsoft's Halo video game franchise originating in Bungie folklore, with Jen Taylor, the character's voice actress, returning to voice the personal assistant's US-specific version. === Expansion (2015–2018) === In January 2015, Microsoft announced the availability of Cortana for Windows 10 desktops and mobile devices as part of merging Windows Phone into the operating system at large. On May 26, 2015, Microsoft announced that Cortana would also be available on other mobile platforms. An Android release was set for July 2015, but the Android APK file containing Cortana was leaked ahead of its release. It was officially released, along with an iOS version, in December 2015. During E3 2015, Microsoft announced that Cortana would come to the Xbox One as part of a universally designed Windows 10 update for the console. Microsoft integrated Cortana into numerous products such as Microsoft Edge. Microsoft's Cortana assistant was deeply integrated into the browser. Cortana was able to find opening hours when on restaurant sites, show retail coupons for websites, or show weather information in the address bar. At the Worldwide Partners Conference 2015 Microsoft demonstrated Cortana integration with products such as GigJam. Conversely, Microsoft announced in late April 2016 that it would block anything other than Bing and Edge from being used to complete Cortana searches, again raising questions of anti-competitive practices by the company. Microsoft's "Windows in the car" concept included Cortana. The concept makes it possible for drivers to make restaurant reservations and see places before they go there. At Microsoft Build 2016, Microsoft announced plans to integrate Cortana into Skype (Microsoft's video-conferencing and instant messaging service) as a bot to allow users to order food, book trips, transcribe video messages and make calendar appointments through Cortana in addition to other bots. As of 2016, Cortana was able to underline certain words and phrases in Skype conversations that relate to contacts and corporations. A writer from Engadget has criticised the Cortana integration in Skype for responding only to very specific keywords, feeling as if she was "chatting with a search engine" due to the impersonal way the bots replied to certain words such as "Hello" causing the Bing Music bot to bring up Adele's song of that name. Microsoft also announced at Microsoft Build 2016 that Cortana would be able to cloud-synchronise notifications between Windows 10 Mobile's and Windows 10's Action Center, as well as notifications from Android devices. In December 2016, Microsoft announced the preview of Calendar.help, a service that enabled people to delegate the scheduling of meetings to Cortana. Users interact with Cortana by including her in email conversations. Cortana would then check people's availability in Outlook Calendar or Google Calendar, and work with others Cc'd on the email to schedule the meeting. The service relied on automation and human-based computation. In May 2017, Microsoft announced INVOKE, a voice-activated speaker featuring Cortana, in collaboration with Harman Kardon. The premium speaker has a cylindrical design and offers 360-degree sound, the ability to make and receive calls with Skype, and all of the other features currently available with Cortana. In 2017, Microsoft partnered with Amazon to integrate Echo and Cortana with each other, allowing users of each smart assistant to summon the other via a command. This feature preview was released in August 2018. Windows 10 users were able to just say "Hey Cortana, open Alexa" and Echo users were able to say "Alexa, open Cortana" to summon the other assistant. === Decreasing focus and discontinuation (2019–2024) === In January 2019, Microsoft CEO Satya Nadella stated that he no longer saw Cortana as a direct competitor against Alexa and Siri. Shortly thereafter, Microsoft began reducing the prevalence of Cortana and converting it from an assistant into different software integrations. It was split from the Windows 10 search bar in April 2019. In January 2020, the Cortana mobile app was removed from certain markets, and then, on July 24, 2020, Cortana was removed from the Xbox dashboard as part of a redesign. On January 31, 2021, Microsoft removed the Cortana mobile application in many markets, including the UK, Australia, Germany, Mexico, China, Spain, Canada, and India. On March 31, 2021, Microsoft shut down the Cortana apps globally for iOS and Android and removed the apps entirely from their corresponding app stores. To access previously recorded content, users had to use Cortana on Windows 10 or other specialized Microsoft applications. Microsoft also reduced emphasis on Cortana in Windows with the 2021 release of Windows 11. Cortana was not used during the device setup process or pinned to the taskbar by default. On June 2, 2023, Microsoft announced the Cortana standalone app on Windows 10 and Windows 11 which would shut down later in the year. In its support article, Microsoft listed several alternatives, most of which have since been rebranded as Microsoft Copilot. They also added that the change would not impact Cortana in Office 365 and Teams environments. On August 11, 2023, Microsoft updated the Cortana standalone app in Windows, informing that it was deprecated and can no longer be used. Microsoft's support article announcing the deprecation of Cortana was updated to reflect this change. Along with the deprecation of the standalone app, it was announced that Cortana support in Teams mobile, Microsoft Teams displays, and Teams rooms would end in late 2023. The support article states that Cortana in the “Play my emails” feature of the Microsoft Outlook mobile app would continue to be available. Later in June 2024, the support article was updated, stating that Cortana in the voice search and the "Play my emails" feature is now removed from the Microsoft Outlook mobile app, officially marking the discontinuation of Cortana across all Microsoft products. On May 22, 2024, Microsoft announced the Windows 11 24H2 update, which removed Cortana, Tips, and WordPad from systems. == Functionality == Cortana was able to set reminders, recognize natural voice without the requirement for keyboard input, and answer questions using information from the Bing search engine. Searches using Windows 10 are made only with the Microsoft Bing search engine, and all links will open with Microsoft Edge, except when a screen reader such as Narrator was being used, where the links will open in Internet Explorer. Windows Phone 8.1's universal Bing SmartSearch features were incorporated into Cortana, which replaced the
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Unspent transaction output

In cryptocurrencies, an unspent transaction output (UTXO, often capitalized as UTxO) is a distinctive element in a subset of digital currency models. A UTXO represents a certain amount of cryptocurrency that has been authorized by a sender and is available to be spent by a recipient. The utilization of UTXOs in transaction processes is a key feature of many cryptocurrencies, but it primarily characterizes those implementing the UTXO model. UTXOs employ public key cryptography to ascertain and transfer ownership. More specifically, the recipient's public key is formatted into the UTXO, thereby limiting the capability to spend the UTXO to the account that can demonstrate ownership of the corresponding private key. A valid digital signature associated with the public key must be included for the UTXO to be spent. In the UTXO model, each unit of currency is treated as a discrete object. The history of a UTXO is documented only within the blocks where it is transferred. To ascertain the total balance of an account, one must scan each block to find the latest UTXOs linked to that account. While all nodes within a blockchain network must consent to the block history, the blocks relevant to an account's balance are unique to that account. UTXOs constitute a chain of ownership depicted as a series of digital signatures dating back to the coin's inception, regardless of whether the coin was minted via mining, staking, or another procedure determined by the cryptocurrency protocol. The UTXO model was invented for Bitcoin. Cardano uses an extended version of the UTXO model known as EUTXO. == Origins == The conceptual framework of the UTXO model can be traced back to Hal Finney's Reusable Proofs of Work proposal, which itself was based on Adam Back's 1997 Hashcash proposal. Bitcoin, released in 2009, was the first widespread implementation of the UTXO model in practice. == UTXO model vs. account Model == Cryptocurrencies that utilize the UTXO model function differently compared to those using the account model. In the UTXO model, individual units of cryptocurrency, termed as unspent transaction outputs (UTXOs), are transferred between users, analogous to the exchange of physical cash. This model impacts how transactions and ownership are recorded and verified within the blockchain network. The account model preserves a record of each account and its corresponding balance for every block added to the network. This setup enables quicker balance verification without the need to scan historical blocks, but it increases the raw size of each block (though data compression techniques can be utilized to alleviate this). However, both models necessitate the inspection of past blocks to fully authenticate the origin of coins. In the UTXO model, each object is immutable - units of coins cannot be 'edited' in the same way an account balance is modified when a transaction occurs. Rather, the balance is computed from the transaction history dating back to when the coins were first minted. This simplicity enhances security, as a UTXO either exists in its anticipated form or it does not. In contrast, the account model requires meticulous verification of the account's status during transactions, which can lead to oversights if not conducted correctly. In valid blockchain transactions, only unspent outputs (UTXOs) are permissible for funding subsequent transactions. This requirement is critical to prevent double-spending and fraud. Accordingly, inputs in a transaction are removed from the UTXO set, while outputs create new UTXOs that are added to the set. The holders of private keys, such as those with cryptocurrency wallets, can utilize these UTXOs for future transactions.
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The Best Free AI Website Builder for Beginners

In search of the best AI website builder? An AI website builder 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 website builder 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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How to Choose an AI Image Generator

Shopping for the best AI image generator? An AI image generator 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 image generator 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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Is an AI Sales Assistant Worth It in 2026?

Shopping for the best AI sales assistant? An AI sales assistant 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 sales 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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Data preprocessing

Data preprocessing can refer to manipulation, filtration or augmentation of data before it is analyzed, and is often an important step in the data mining process. Data collection methods are often loosely controlled, resulting in out-of-range values, impossible data combinations, and missing values, amongst other issues. Preprocessing is the process by which unstructured data is transformed into intelligible representations suitable for machine-learning models. This phase of model deals with noise in order to arrive at better and improved results from the original data set which was noisy. This dataset also has some level of missing value present in it. The preprocessing pipeline used can often have large effects on the conclusions drawn from the downstream analysis. Thus, representation and quality of data is necessary before running any analysis. If there is a high proportion of irrelevant and redundant information present or noisy and unreliable data, then knowledge discovery during the training phase may be more difficult. Data preparation and filtering steps can take a considerable amount of processing time. Examples of methods used in data preprocessing include cleaning, instance selection, normalization, one-hot encoding, data transformation, feature extraction and feature selection. == Applications == === Data mining === Data preprocessing allows for the removal of unwanted data with the use of data cleaning, this allows the user to have a dataset to contain more valuable information after the preprocessing stage for data manipulation later in the data mining process. Editing such dataset to either correct data corruption or human error is a crucial step to get accurate quantifiers like true positives, true negatives, false positives and false negatives found in a confusion matrix that are commonly used for a medical diagnosis. Users are able to join data files together and use preprocessing to filter any unnecessary noise from the data which can allow for higher accuracy. Users use Python programming scripts accompanied by the pandas library which gives them the ability to import data from a comma-separated values as a data-frame. The data-frame is then used to manipulate data that can be challenging otherwise to do in Excel. Pandas (software) which is a powerful tool that allows for data analysis and manipulation; which makes data visualizations, statistical operations and much more, a lot easier. Many also use the R programming language to do such tasks as well. The reason why a user transforms existing files into a new one is because of many reasons. Aspects of data preprocessing may include imputing missing values, aggregating numerical quantities and transforming continuous data into categories (data binning). More advanced techniques like principal component analysis and feature selection are working with statistical formulas and are applied to complex datasets which are recorded by GPS trackers and motion capture devices. === Semantic data preprocessing === Semantic data mining is a subset of data mining that specifically seeks to incorporate domain knowledge, such as formal semantics, into the data mining process. Domain knowledge is the knowledge of the environment the data was processed in. Domain knowledge can have a positive influence on many aspects of data mining, such as filtering out redundant or inconsistent data during the preprocessing phase. Domain knowledge also works as constraint. It does this by using working as set of prior knowledge to reduce the space required for searching and acting as a guide to the data. Simply put, semantic preprocessing seeks to filter data using the original environment of said data more correctly and efficiently. There are increasingly complex problems which are asking to be solved by more elaborate techniques to better analyze existing information. Instead of creating a simple script for aggregating different numerical values into a single value, it make sense to focus on semantic based data preprocessing. The idea is to build a dedicated ontology, which explains on a higher level what the problem is about. In regards to semantic data mining and semantic pre-processing, ontologies are a way to conceptualize and formally define semantic knowledge and data. The Protégé (software) is the standard tool for constructing an ontology. In general, the use of ontologies bridges the gaps between data, applications, algorithms, and results that occur from semantic mismatches. As a result, semantic data mining combined with ontology has many applications where semantic ambiguity can impact the usefulness and efficiency of data systems. Applications include the medical field, language processing, banking, and even tutoring, among many more. There are various strengths to using a semantic data mining and ontological based approach. As previously mentioned, these tools can help during the per-processing phase by filtering out non-desirable data from the data set. Additionally, well-structured formal semantics integrated into well designed ontologies can return powerful data that can be easily read and processed by machines. A specifically useful example of this exists in the medical use of semantic data processing. As an example, a patient is having a medical emergency and is being rushed to hospital. The emergency responders are trying to figure out the best medicine to administer to help the patient. Under normal data processing, scouring all the patient’s medical data to ensure they are getting the best treatment could take too long and risk the patients’ health or even life. However, using semantically processed ontologies, the first responders could save the patient’s life. Tools like a semantic reasoner can use ontology to infer the what best medicine to administer to the patient is based on their medical history, such as if they have a certain cancer or other conditions, simply by examining the natural language used in the patient's medical records. This would allow the first responders to quickly and efficiently search for medicine without having worry about the patient’s medical history themselves, as the semantic reasoner would already have analyzed this data and found solutions. In general, this illustrates the incredible strength of using semantic data mining and ontologies. They allow for quicker and more efficient data extraction on the user side, as the user has fewer variables to account for, since the semantically pre-processed data and ontology built for the data have already accounted for many of these variables. However, there are some drawbacks to this approach. Namely, it requires a high amount of computational power and complexity, even with relatively small data sets. This could result in higher costs and increased difficulties in building and maintaining semantic data processing systems. This can be mitigated somewhat if the data set is already well organized and formatted, but even then, the complexity is still higher when compared to standard data processing. Below is a simple a diagram combining some of the processes, in particular semantic data mining and their use in ontology. The diagram depicts a data set being broken up into two parts: the characteristics of its domain, or domain knowledge, and then the actual acquired data. The domain characteristics are then processed to become user understood domain knowledge that can be applied to the data. Meanwhile, the data set is processed and stored so that the domain knowledge can applied to it, so that the process may continue. This application forms the ontology. From there, the ontology can be used to analyze data and process results. Fuzzy preprocessing is another, more advanced technique for solving complex problems. Fuzzy preprocessing and fuzzy data mining make use of fuzzy sets. These data sets are composed of two elements: a set and a membership function for the set which comprises 0 and 1. Fuzzy preprocessing uses this fuzzy data set to ground numerical values with linguistic information. Raw data is then transformed into natural language. Ultimately, fuzzy data mining's goal is to help deal with inexact information, such as an incomplete database. Currently fuzzy preprocessing, as well as other fuzzy based data mining techniques see frequent use with neural networks and artificial intelligence.
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Alex James (professor)

Alex James is an Indian scientist who is a professor of AI hardware at School of Electronic Systems and Automation, and Dean at Digital University Kerala (IIITM-K). He is the professor in charge of Maker Village, Kochi, Chief Investigator of the centre for Intelligent IoT Sensors, and India Innovation Centre for Graphene. James features in top 1% scientists list published by Elsevier BV in the world in the field of Electrical and Electronics Engineering. He appeared in the list for the third consecutive time. He specializes in the scientific field of Memristive Systems, AI hardware, Neuromorphic VLSI (very-large-scale integration) system, Intelligent Imaging and Machine learning, and Analogue electronics. == Education and career == James earned his Ph.D. degree from the Queensland Micro and Nanotechnology Centre, Griffith University, Brisbane, Australia. Since 2009, he has been working as a faculty member at different universities in Australia and India. He was a Member of IET Vision and Imaging Network, and is a Member of BCS’ Fellows Technical Advisory Group (F-TAG). He is the founding chair for IEEE Kerala Section Circuits and Systems Society, and is a fellow of British Computer Society (FBCS), and Institution of Engineering and Technology. He was an Editorial Board Member of Information Fusion (2010–2014), Elsevier, and associate editor for HCIS (2015–2020), Springer; and Guest Associate Editor for IEEE Transactions on Emerging Topics in Computational Intelligence (2017). Currently he is serving as an Associate Editor of IEEE Access, Frontiers in Neuroscience, and IEEE Transactions on Circuits and Systems I: Regular Papers journal. == Scientific research == IIITM-K has achieved a breakthrough in developing Analogue Integrated circuit for implementing Generative Adversarial Networks (GAN) in a joint research project with Analogue Circuits and Image Sensors Lab, Siegen university and Fraunhofer, Germany, and Centre for Excellence in Artificial general intelligence and Neuromorphic Systems (neuroAGI). According to A. P. James, professor at the School of Electronics at IIITM-K, this complicated and meticulous AI circuits research can accelerate and operate GAN applications in low power devices. It also can be used to analyze and interpret 2019-nCoV data for a possible solution to the pandemic. An AI Semantic search engine has been created by a research team led by A.P. James to help researchers gain deeper insights into Scientific Investigation, particularly since the COVID-19 issue has necessitated the collection of a significant amount of complex scientific data. The search engine is called "www.vilokana.in, which is Sanskrit for "finding out. == Awards and honors == James is a member of IEEE CASS Technical committee on Nonlinear Circuits and Systems, IEEE CASS Technical committee on Cellular Nanoscale networks and Memristor Array Computing, IEEE Consumer Technology Society Technical Committee on Quantum in Consumer Technology (QCT), Technical Committee on Machine learning, Deep learning and AI in CE (MDA) and Member of BCS’ Fellows Technical Advisory Group (F-TAG). James was awarded best associate editor of IEEE Transactions on Circuits and Systems I: Regular Papers TCAS-I, by the IEEE Circuits and Systems Society (IEEE CASS) for the year 2020–21. He has been an associate editor for the journal since 2017. He is also an editorial board member of PeerJ CS and a Senior Member of IEEE, Life Member of ACM, Senior Fellow of HEA.
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Is an AI Logo Maker Worth It in 2026?

Looking for the best AI logo maker? An AI logo maker is software that uses machine learning to help you get more done — it can save you hours every week by automating repetitive work. Most options offer a generous free tier, with paid plans unlocking higher limits, faster processing, and team features. Whether you are a beginner or a pro, the right AI logo maker slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.
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