AI Avatar Meaning

AI Avatar Meaning — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Intelligent automation

Intelligent automation (IA), or intelligent process automation, is a software term that refers to a combination of artificial intelligence (AI) and robotic process automation (RPA). Companies use intelligent automation to cut costs and streamline tasks by using artificial-intelligence-powered robotic software to mitigate repetitive tasks. As it accumulates data, the system learns in an effort to improve its efficiency. Intelligent automation applications consist of, but are not limited to, pattern analysis, data assembly, and classification. The term is similar to hyperautomation, a concept identified by research group Gartner as being one of the top technology trends of 2020. == Technology == Intelligent automation applies the assembly line concept of breaking tasks into repetitive steps to improve business processes. Rather than having humans perform each step, intelligent automation can replace steps with an intelligent software robot, improving efficiency. Intelligent automation integrates robotic process automation (RPA) with artificial intelligence techniques (such as machine learning, natural-language processing, and computer vision) enabling systems to interpret data, make decisions, and adapt to changing inputs. Modern platforms use a layered architecture combining workflow orchestration, low-code tools, integration middleware, and AI services to coordinate bots and data pipelines across organisational systems. == Applications == Intelligent automation is used to process unstructured content. Common real-world applications include self-driving cars, self-checkouts at grocery stores, smart home assistants, and appliances. Businesses can apply data and machine learning to build predictive analytics that react to consumer behavior changes, or to implement RPA to improve manufacturing floor operations. For example, the technology has also been used to automate the workflow behind distributing COVID-19 vaccines. Data provided by hospital systems’ electronic health records can be processed to identify and educate patients, and schedule vaccinations. Intelligent automation can provide real-time insights on profitability and efficiency. However, in an April 2022 survey by Alchemmy, despite three quarters of businesses acknowledging the importance of Artificial Intelligence to their future development, just a quarter of business leaders (25%) considered Intelligent Automation a “game changer” in understanding current performance. 42% of CTOs see “shortage of talent” as the main obstacle to implementing Intelligent Automation in their business, while 36% of CEOs see ‘upskilling and professional development of existing workforce’ as the most significant adoption barrier. IA is becoming increasingly accessible for firms of all sizes. With this in mind, it is expected to continue to grow rapidly in all industries. This technology has the potential to change the workforce. As it advances, it will be able to perform increasingly complex and difficult tasks. In addition, this may expose certain workforce issues as well as change how tasks are allocated. Tools such as Semrush's AI Visibility Toolkit and Enterprise AIO reflect these developments by analysing how entities are referenced and represented within responses produced by large-language-model-based systems. == Benefits == Streamline processes: Repetitive manual tasks can put a strain on the workforce. However, with AI agents, these tasks can be automated to allow teams to focus on more important matters that require human cognition. Intelligent automation can also be used to mitigate tasks with human error which in turn increases proficiency. This allows the opportunity for firms to scale production without the traditional negative consequences such as reduced quality or increased risk. Customer service improvement: Customer service can be significantly improved, providing the firm with a competitive advantage. IA utilizing chat features allows for instant curated responses to customers. In addition, it can give updates to customers, make appointments, manage calls, and personalize campaigns. Flexibility: Due to the wide range of applications, IA is useful across a variety of fields, technologies, projects and industries. In addition, IA can be integrated with current automated systems in place. This allows for optimized systems unique to each firm to best fit their individual needs. == Capabilities == Cognitive automation: Employs AI techniques to assist humans in decision-making and task completion Natural language processing: Allows computers to automate knowledge work Business process management: Enhances the consistency and agility of corporate operations Process mining: Applies data mining methods to discover, analyze, and improve business processes Intelligent document processing: Utilizes OCR and other advanced technologies to extract data from documents and convert it into structured, usable data Computer vision: Allows computers to extract information from digital images, videos, and other visual inputs Integration automation: Establishes a unified platform with automated workflows that integrate data, applications, and devices.
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Is an AI Analytics Tool Worth It in 2026?

Curious about the best AI analytics tool? An AI analytics tool is software that uses machine learning to help you get more done — it combines speed, accuracy, and an interface that just works. Hands-on testing shows real-world results vary, so a short free trial is the smartest way to decide. Whether you are a beginner or a pro, the right AI analytics tool slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.
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Ginger Software

Ginger Software is an American and Israeli start-up specialized in natural language processing and AI. The main products are tools aiming to improve written communications, develop English speaking skills and boost productivity. The company was founded in 2008 by Yael Karov and Avner Zangvil. Ginger Software uses the context of complete sentences to suggest corrections. In December 2011, Ginger Software was one of nine projects approved by the Board of Governors of the Israel-U.S. Binational Industrial Research and Development Foundation for a funding of $8.1 million. The company also raised $3 million from private Israeli and US investors in 2009. In May, 2014 Intel acquired one of Ginger's business units and the rights to use the company's patented technology. == Founders == Before founding Ginger Software, Yael Karov had worked with Rosetta Genomics as its Chief Technology Officer and Vice President of Research and Development from 2003 to 2006, and with ClickSoftware Technologies as a Director of Research and Development from 1990 to 1994. Karov also founded Agentics, a company specializing in free-text classification of e-commerce product information based on natural language processing, in 1996. Avner Zangvil is the co-founder of Ginger Software. Zangvil co-founded Menta Software in 1996 with his brother Arnon Zangvil to develop a product that transforms any Windows-based application into a Web-enabled application usable from any remote computer running a Web browser. Menta was acquired by GraphOn Corporation in 2001. == Technology == Ginger Software uses patented software algorithms in the field of natural language processing. The company claims that the algorithm allows it to correct the written sentences with relatively high accuracy (eliminating up to 95 percent of writing errors), compared to standard spell checkers. Its unique algorithm allows the software to understand the context of the sentence rather than correcting based solely on a word. According to its founder, Karov, the software operates on the logic of sentence context in addition to the memory of a database of words. The company is at the heart of a growing revolution in the world of assistive technology. Ginger claims that the benefits of the software have been leveraged by native English and non-native speakers alike, and have also found value in niche markets like dyslexia management. They further claim that ESL users derive great benefit from the use of the software, as it lets them write error-free English text. Its use also extends to native English speaking business professionals and students who use it as a 'safety net' for their email edits, as well as international students writing in English. More recently, the company has focused on implementing its technology in mobile devices as an integral component of its mobile keyboard products. == Products == Ginger Software products include Ginger Page, a cross-platform writing enhancement app, and Ginger Keyboard which is available for Android devices. Ginger Writer can be used as an online service or installed on your PC or Mac. It supports MS-Word, MS-Outlook, MS-PowerPoint, Microsoft Edge, Chrome, and functions as a writing enhancement app for Android and iOS mobile devices. Its main feature is English grammar and spelling checker that runs seamlessly with the different user interfaces. It also has an advanced paraphrasing tool, contextual synonyms and definitions, translation and a text-to-speech function that enables users to hear sentences before and after correction. Ginger Keyboard for Android replaces the stock keyboard and functions as a productivity boosting keyboard app. Featuring a full set of advanced keyboard features like Stream (swipe-like) typing, adaptive word prediction, a wide variety of customizable themes and emoji, Ginger Keyboard is the only 3rd party keyboard to offer proofreading and other writing tools via one tap access to Ginger Page. == Target segment == Ginger Software started off targeting people with dyslexia. The algorithm underlying the software studies a vast pool of proper sentences in English and builds a model of proper language. The software does not analyze the text at the level of the word, but of the whole sentence. Dyslexics can have trouble choosing the right word – hence the attention to the sentence as a whole. From 2010, Ginger Software included a new target segment in its marketing outreach – users of English as a second language (ESL). Its contextual-based writing correction tool could benefit those who are not proficient in the English language. == Business model == The main business model for consumers is freemium. The free version offers contextual-based grammar and spelling checker with some limitations. Its premium features include unlimited access to Grammar Checker, the grammar and spelling checker, and Sentence Rephraser the rephrasing tool. Ginger Keyboard is free to download and use, although it does offer in-app purchases like themes and theme packs. It also disables your original spell checker. Ginger also provides a powerful Rest API which can correct full documents in one call.
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The Best Free AI Video Editor for Beginners

Comparing the best AI video editor? An AI video editor 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 video editor 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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Night Sky (app)

Night Sky (app) is an application developed and published by indie studio iCandi Apps Ltd. from the UK. Night Sky is a stargazing reference app, where the user can explore a virtual representation of the night sky to identify stars, planets, constellations and satellites. The app is developed specifically for iOS, tvOS and watchOS devices. Night Sky was first released on November 1, 2011 for iOS, and has had multiple updates since launch. Night Sky was mentioned in the September 2016 Apple Keynote during the Apple Watch Series 2 announcement. In October 2016, Night Sky was featured as the Free App of The Week on the Apple App Store. == Reception == Night Sky was featured in Apple's 'Best of 2012' and has also been pre-installed onto iPads in Apple retail stores worldwide.
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Powerset construction

In the theory of computation and automata theory, the powerset construction or subset construction is a standard method for converting a nondeterministic finite automaton (NFA) into a deterministic finite automaton (DFA) that recognizes the same formal language. It is important in theory because it establishes that NFAs, despite their additional flexibility, are unable to recognize any language that cannot be recognized by some DFA. It is also important in practice for converting easier-to-construct NFAs into more efficiently executable DFAs. However, if the NFA has n states, the resulting DFA may have up to 2n states, an exponentially larger number, which sometimes makes the construction impractical for large NFAs. The construction, sometimes called the Rabin–Scott powerset construction (or subset construction) to distinguish it from similar constructions for other types of automata, was first published by Michael O. Rabin and Dana Scott in 1959. == Intuition == To simulate the operation of a DFA on a given input string, one needs to keep track of a single state at any time: the state that the automaton will reach after seeing a prefix of the input. In contrast, to simulate an NFA, one needs to keep track of a set of states: all of the states that the automaton could reach after seeing the same prefix of the input, according to the nondeterministic choices made by the automaton. If, after a certain prefix of the input, a set S of states can be reached, then after the next input symbol x the set of reachable states is a deterministic function of S and x. Therefore, the sets of reachable NFA states play the same role in the NFA simulation as single DFA states play in the DFA simulation, and in fact the sets of NFA states appearing in this simulation may be re-interpreted as being states of a DFA. == Construction == The powerset construction applies most directly to an NFA that does not allow state transformations without consuming input symbols (aka: "ε-moves"). Such an automaton may be defined as a 5-tuple (Q, Σ, T, q0, F), in which Q is the set of states, Σ is the set of input symbols, T is the transition function (mapping a state and an input symbol to a set of states), q0 is the initial state, and F is the set of accepting states. The corresponding DFA has states corresponding to subsets of Q. The initial state of the DFA is {q0}, the (one-element) set of initial states. The transition function of the DFA maps a state S (representing a subset of Q) and an input symbol x to the set T(S,x) = ∪{T(q,x) | q ∈ S}, the set of all states that can be reached by an x-transition from a state in S. A state S of the DFA is an accepting state if and only if at least one member of S is an accepting state of the NFA. In the simplest version of the powerset construction, the set of all states of the DFA is the powerset of Q, the set of all possible subsets of Q. However, many states of the resulting DFA may be useless as they may be unreachable from the initial state. An alternative version of the construction creates only the states that are actually reachable. === NFA with ε-moves === For an NFA with ε-moves (also called an ε-NFA), the construction must be modified to deal with these by computing the ε-closure of states: the set of all states reachable from some given state using only ε-moves. Van Noord recognizes three possible ways of incorporating this closure computation in the powerset construction: Compute the ε-closure of the entire automaton as a preprocessing step, producing an equivalent NFA without ε-moves, then apply the regular powerset construction. This version, also discussed by Hopcroft and Ullman, is straightforward to implement, but impractical for automata with large numbers of ε-moves, as commonly arise in natural language processing application. During the powerset computation, compute the ε-closure { q ′ | q → ε ∗ q ′ } {\displaystyle \{q'~|~q\to _{\varepsilon }^{}q'\}} of each state q that is considered by the algorithm (and cache the result). During the powerset computation, compute the ε-closure { q ′ | ∃ q ∈ Q ′ , q → ε ∗ q ′ } {\displaystyle \{q'~|~\exists q\in Q',q\to _{\varepsilon }^{}q'\}} of each subset of states Q' that is considered by the algorithm, and add its elements to Q'. === Multiple initial states === If NFAs are defined to allow for multiple initial states, the initial state of the corresponding DFA is the set of all initial states of the NFA, or (if the NFA also has ε-moves) the set of all states reachable from initial states by ε-moves. == Example == The NFA below has four states; state 1 is initial, and states 3 and 4 are accepting. Its alphabet consists of the two symbols 0 and 1, and it has ε-moves. The initial state of the DFA constructed from this NFA is the set of all NFA states that are reachable from state 1 by ε-moves; that is, it is the set {1,2,3}. A transition from {1,2,3} by input symbol 0 must follow either the arrow from state 1 to state 2, or the arrow from state 3 to state 4. Additionally, neither state 2 nor state 4 have outgoing ε-moves. Therefore, T({1,2,3},0) = {2,4}, and by the same reasoning the full DFA constructed from the NFA is as shown below. As can be seen in this example, there are five states reachable from the start state of the DFA; the remaining 11 sets in the powerset of the set of NFA states are not reachable. == Complexity == Because the DFA states consist of sets of NFA states, an n-state NFA may be converted to a DFA with at most 2n states. For every n, there exist n-state NFAs such that every subset of states is reachable from the initial subset, so that the converted DFA has exactly 2n states, giving Θ(2n) worst-case time complexity. A simple example requiring nearly this many states is the language of strings over the alphabet {0,1} in which there are at least n characters, the nth from last of which is 1. It can be represented by an (n + 1)-state NFA, but it requires 2n DFA states, one for each n-character suffix of the input; cf. picture for n=4. == Applications == Brzozowski's algorithm for DFA minimization uses the powerset construction, twice. It converts the input DFA into an NFA for the reverse language, by reversing all its arrows and exchanging the roles of initial and accepting states, converts the NFA back into a DFA using the powerset construction, and then repeats its process. Its worst-case complexity is exponential, unlike some other known DFA minimization algorithms, but in many examples it performs more quickly than its worst-case complexity would suggest. Safra's construction, which converts a non-deterministic Büchi automaton with n states into a deterministic Muller automaton or into a deterministic Rabin automaton with 2O(n log n) states, uses the powerset construction as part of its machinery.
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Is an AI Humanizer Worth It in 2026?

Shopping for the best AI humanizer? An AI humanizer 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 humanizer 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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Paola Velardi

Paola Velardi (born in Rome, April 26, 1955) is a full professor of computer science at Sapienza University in Rome, Italy. Her research encompasses Artificial Intelligence and specifically, natural language processing, machine learning business intelligence and semantic web. Velardi is one of the hundred female scientists included in the database "100esperte.it" (translated from Italian with "100 female experts"). This online, open database champions the recognition of top-rated female scientists in Science, Technology, Engineering and Mathematics (STEM) areas. Among her prestigious appointments and honors, her inclusion stands out —alongside 45 other international female scientists from the past, present, and future— in the Women in Science pavilion of UNESCO’s Virtual Science Museum. == Research == Paola Velardi's research activity has focused, since the early 1980s, on Artificial Intelligence, with a particular emphasis on natural language processing (NLP), Machine learning, and data mining. Her scientific contributions have evolved over time, following the sector's primary paradigms: Semantic Web and Ontologies: She is known for her pioneering work on semantic disambiguation and automated ontology learning, collaborating on the development of systems such as OntoLearn. Social Computing and Predictive Analysis: She has conducted research on extracting information from social media for epidemiological monitoring (syndromic surveillance) and for the identification of opinion leaders. In the educational field, she has developed machine learning models to predict the risk of student dropout. AI for Health and Elder Monitoring: She has coordinated projects to support frailty in the elderly, developing systems based on ambient intelligence and wearables to detect clinical and behavioral anomalies. She has also contributed to models for analyzing behavioral changes through dynamic clustering. Generative AI and Finance: More recently, her research has expanded into the use of generative AI and deep learning for finance, including benchmark studies on price trend prediction based on Limit Order Books (LOB) and the development of diffusion models for realistic market simulation (the TRADES project). According to Google Scholar bibliometrics updated until December 2025, Velardi's scientific publications have been cited more than 8100 times. Her h-index was 42. She has published more than 200 papers in international journals and conference proceedings. Some of her publications have been published in top rated journals such as Artificial Intelligence, Computational Linguistics, Knowledge-Based Systems, IEEE Transactions on Data and Knowledge Engineering , IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Computers, IEEE Transactions on Software Engineering , Data Mining and Knowledge Discovery, and Journal of Web Semantics. == Education and previous employments == Velardi graduated in electronic engineering from Sapienza University in 1978. From 1978 to 1983, she worked for the Ugo Bordoni Foundation, a research institution focusing on ICT and working under the supervision of the Italian Ministry of Economic Development. In 1983, she was a visiting scholar at Stanford University. During this period she became passionate about Artificial Intelligence, which will remain her area of research throughout her career. From 1984 to 1986, she came back to her natal city and worked as a researcher for IBM. From 1986 to 1996 she was an associate professor in the engineering faculty of Polytechnic University of the Marches (Ancona, Italy). Starting in November 1996, she taught in and did research for the Department of Computer Science at the Sapienza University. Velardi was the head of Bachelor and Master Programs in Computer Science at Sapienza University from 2010 to 2013 and from 2015 to 2016. == Current employment == Since November 2001, Velardi has been a full professor in the department of computer science ("Dipartimento di Informatica" in Italian) at Sapienza University in Rome, Italy. Since 2013, she has been the coordinator of the Distance Learning Degree in Computer Science at Sapienza University. As of today, Velardi is a Senior Associate at the Institute of Cognitive Sciences and Technologies (ISTC) of the CNR. == Recognition == Velardi is one of the hundred female scientists included in the database "100esperte.it" (translated from Italian with "100 female experts"). This database lists top Italian female STEM scientists. Six out of one hundred scientists in the 100esperte's database are computer scientists like Velardi. Velardi is in the list of the top Italian scientists. A top scientist appearing in the Top-Italian-Scientists database is a scientist whose h-index is greater than 30. In March 2017, she was given an IBM Faculty Award for her research on social recommender systems. In December 2018, Velardi was included in the list of the 50 most influential Italian women in science and technology by Inspiring Fifty, a non-profit that aims to increase diversity in STEM by making female role models in tech more visible. In September 2019 she was the local co-organizer and Program Chair of the 6th ACM Celebration of Women in Computing. In November 2019 Velardi received the Standout Woman Award International at the seat of the Italian Parliament in Montecitorio. == Causes == Velardi aims at debunking the myth of computer science as a man-oriented and "inflexible" discipline. She is the founder of the project "NERD? Non e' roba per donne?" (translated from Italian: "NERD? Is it not stuff for women?"). This project was launched by Velardi in 2012 in the Department of Computer Science at Sapienza University. Since 2013 the project has been carried out in partnership with IBM Italy, which later created a spin-off of the project. The goal of the project is two-fold: (1) conveying computer science as creative, interdisciplinary and problem-solving-oriented science, and (2) encouraging young female students in studying computer science by, for instance, developing apps for smartphones. She has been the program chair of the 19th ACM celebration of Women in Computing. She is the creator and coordinator of the G4GRETA, an educational project that involves students of the third and fourth grades of Rome and Lazio. The project combines the development of IT skills with the themes of environmental sustainability and soft skills (teambuilding, pitching, social networking, etc.) Velardi is also involved in scientific dissemination. In 2020 and 2021 she cooperated with RaiCultura, the cultural division of RAI, the national broadcasting company.
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Data exploration

Data exploration is an approach similar to initial data analysis, whereby a data analyst uses visual exploration to understand what is in a dataset and the characteristics of the data, rather than through traditional data management systems. These characteristics can include size or amount of data, completeness of the data, correctness of the data, possible relationships amongst data elements or files/tables in the data. Data exploration is typically conducted using a combination of automated and manual activities. Automated activities can include data profiling or data visualization or tabular reports to give the analyst an initial view into the data and an understanding of key characteristics. This is often followed by manual drill-down or filtering of the data to identify anomalies or patterns identified through the automated actions. Data exploration can also require manual scripting and queries into the data (e.g. using languages such as SQL or R) or using spreadsheets or similar tools to view the raw data. All of these activities are aimed at creating a mental model and understanding of the data in the mind of the analyst, and defining basic metadata (statistics, structure, relationships) for the data set that can be used in further analysis. Once this initial understanding of the data is had, the data can be pruned or refined by removing unusable parts of the data (data cleansing), correcting poorly formatted elements and defining relevant relationships across datasets. This process is also known as determining data quality. Data exploration can also refer to the ad hoc querying or visualization of data to identify potential relationships or insights that may be hidden in the data and does not require to formulate assumptions beforehand. Traditionally, this had been a key area of focus for statisticians, with John Tukey being a key evangelist in the field. Today, data exploration is more widespread and is the focus of data analysts and data scientists; the latter being a relatively new role within enterprises and larger organizations. == Interactive Data Exploration == This area of data exploration has become an area of interest in the field of machine learning. This is a relatively new field and is still evolving. As its most basic level, a machine-learning algorithm can be fed a data set and can be used to identify whether a hypothesis is true based on the dataset. Common machine learning algorithms can focus on identifying specific patterns in the data. Many common patterns include regression and classification or clustering, but there are many possible patterns and algorithms that can be applied to data via machine learning. By employing machine learning, it is possible to find patterns or relationships in the data that would be difficult or impossible to find via manual inspection, trial and error or traditional exploration techniques. == Software == Trifacta – a data preparation and analysis platform Paxata – self-service data preparation software Alteryx – data blending and advanced data analytics software Microsoft Power BI - interactive visualization and data analysis tool OpenRefine - a standalone open source desktop application for data clean-up and data transformation Tableau software – interactive data visualization software
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Erkki Oja

Erkki Oja (born 22 March 1948) is a Finnish computer scientist and Aalto Distinguished Professor in the Department of Information and Computer Science at Aalto University School of Science. He is recognized for developing Oja's rule, which is a model of how neurons in the brain or in artificial neural networks learn over time. == Early life and education == Oja was born in Helsinki and studied at Helsinki University of Technology, where he received his diploma engineer in 1972, licentiate in technology in 1975 and Doctor of Technology in 1977. == Career == Oja was a research associate at the Center for Cognitive Science at Brown University between 1977 and 1978 and a research fellow at the Academy of Finland from 1976 to 1981. Since 1981, he took up a professorship in applied mathematics at Kuopio University (now University of Eastern Finland). He was a visiting research scholar at Tokyo Institute of Technology from 1983 to 1984. From 1987 to 1993, he was a professor in computer science at the Lappeenranta University of Technology. He moved back to the Helsinki University of Technology (now Aalto University) from 1993 as a professor in computer science. He retired in 2015. == Honors and awards == Oja is a Fellow of the International Association for Pattern Recognition and the IEEE, and a member of the Finnish Academy of Sciences. He served as chairman of the European Neural Network Society between 2000 and 2005, and as the chairman of the Academy of Finland’s Research Council for Natural Sciences and Engineering between 2007 and 2012. He was awarded the Frank Rosenblatt Award for his contributions to artificial intelligence research in 2019. Oja was a member of the Board of Governors for the International Neural Network Society (INNIS) in 2003. He received honorary doctorates from Uppsala University and Lappeenranta University of Technology in 2008.
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AI Content Generators Reviews: What Actually Works in 2026

In search of the best AI content generator? An AI content 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 content generator 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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Deborah Raji

Inioluwa Deborah Raji (born 1995/1996) is a Nigerian-Canadian computer scientist and socio-tech leader who works on algorithmic bias, AI accountability, and algorithmic auditing. A current Mozilla fellow, she has been recognized by MIT Technology Review and Forbes as one of the world's top young innovators. Raji started her work with racial bias in technology during her internship with Clarifai when she recognized that people of color were more often tagged for NSFW compared to white people. Raji has previously worked with Joy Buolamwini, Timnit Gebru, and the Algorithmic Justice League on researching gender and racial bias in facial recognition technology. Her work on racial bias in facial recognition has forced companies to ultimately change their practices. She has also worked with Google’s Ethical AI team and been a research fellow at the Partnership on AI and AI Now Institute at New York University working on how to operationalize ethical considerations in machine learning engineering practice. She was working on a computer vision model that would help clients flag inappropriate images as NSFW. == Early life and education == Raji was born in Port Harcourt, Nigeria, and moved to Mississauga, Ontario, Canada, when she was four years old. Eventually her family moved to Ottawa. She attended Colonel By Secondary School and completed the International Baccalaureate programme. She studied Engineering Science at the University of Toronto, graduating in 2019. In 2015, she founded Project Include, a nonprofit providing increased student access to engineering education, mentorship, and resources in low income and immigrant communities in the Greater Toronto Area. She started a Doctor of Philosophy - PhD, in Computer Science from the University of California, Berkeley in Aug 2021. == Career and research == Raji worked with Joy Buolamwini at the MIT Media Lab and Algorithmic Justice League, where she audited commercial facial recognition technologies from Microsoft, Amazon, IBM, Face++, and Kairos. They found that these technologies were significantly less accurate for darker-skinned women than for white men. With support from other top AI researchers and increased public pressure and campaigning, their work led IBM and Amazon to agree to support facial recognition regulation and later halt the sale of their product to police for at least a year. Raji also interned at machine learning startup Clarifai, where she worked on a computer vision model for flagging images. She participated in a research mentorship program at Google and worked with their Ethical AI team on creating model cards, a documentation framework for more transparent machine learning model reporting. She also co-led the development of internal auditing practices at Google. Her contributions at Google were separately presented and published at the AAAI conference and ACM Conference on Fairness, Accountability, and Transparency. In 2019, Raji was a summer research fellow at The Partnership on AI working on setting industry machine learning transparency standards and benchmarking norms. Raji was a Tech Fellow at the AI Now Institute worked on algorithmic and AI auditing. Currently, she is a fellow at the Mozilla Foundation researching algorithmic auditing and evaluation. Raji's work on bias in facial recognition systems has been highlighted in the 2020 documentary Coded Bias directed by Shalini Kantayya. She also took part in the 2026 documentary The AI Doc: Or How I Became an Apocaloptimist directed by Daniel Roher. == Awards == 2019 Venture Beat AI Innovations Award in category AI for Good (received with Joy Buolamwini and Timnit Gebru) 2020 MIT Technology Review 35 Under 35 Innovator Award 2020 EFF Pioneer Award (received with Buolamwini and Gebru) 2021 Forbes 30 Under 30 Award in Enterprise Technology 2021 100 Brilliant Women in AI Ethics Hall of Fame Honoree 2023 Time magazine 100 Most Influential People in AI
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Score bug

A score bug is a digital on-screen graphic which is displayed in a broadcast of a sporting event, displaying the current score and other statistics. It is similar in function to a scoreboard, and is usually placed at either the top or lower third of the television screen. == History == The concept of a persistent score bug was devised by Sky Sports head David Hill, who was dissatisfied over having to wait to see what the score was after tuning into a football match in-progress. The score bug was introduced when Sky launched its coverage of the then newly-formed English Premier League in August 1992. Hill's boss repeatedly demanded that the graphic be removed, describing it as the "stupidest thing [he] had ever seen". Hill defied the boss's demands and kept the graphic in place. ITV introduced a score bug at the start of the 1993–94 football season, and the BBC introduced a score bug towards the end of 1993. The concept was introduced to the United States by ABC Sports and ESPN during coverage of the 1994 FIFA World Cup. Their justification for the graphic was to provide a location for a rotating series of sponsor logos, in order to allow matches to air without commercial interruption. With the acquisition of rights to the National Football League (NFL) by BSkyB's American sibling Fox (a fellow venture of Rupert Murdoch), Hill became the first president of Fox Sports. Under Hill's leadership, Fox introduced a version of the score bug branded as the "Fox Box", which was part of its inaugural season of NFL coverage in 1994. Variety criticized it as an "annoying see-through clock and score graphic" and expressed concern for people "who actually watched the beginning of the game and would rather have their screen clear of graphics". Hill even received a death threat from an irate viewer, with a specific emphasis on him being a "foreigner", but the score bug soon became a ubiquitous feature for American football broadcasts, along with almost all American sports broadcasts in the years that followed. Dick Ebersol of NBC Sports initially opposed the idea of a score bug, as he thought that fans would dislike seeing more graphics on the screen and would change the channel from blowout games if the score was constantly being displayed. Since the 2010s, the on-air design and positioning of some score bugs have been influenced by the needs of Internet video (especially when viewing an event on devices with smaller screens), including bugs noticeably larger than prior iterations designed with television viewing in mind, or designs primarily kept towards the bottom-center of the screen (easing the ability for the bug to remain visible when highlights are cropped for square videos posted on social media). == Details == Score bugs used in team sports typically include the names of both teams, an abbreviation of the team's name, and/or the team's logo; for individual sports, they include the names of individual competitors. In sports where a game clock or playing periods are used, those are generally also displayed as part of the score bug. Some broadcasts also include teams' win-loss records. In 2024, ESPN experimented with adding a persistent win probability meter to its bug in Major League Baseball, which was based on input from its statisticians. === Variations === In addition to the above information, score bugs in some sports include additional information: In baseball, score bugs display the current inning, number of outs, the pitch clock if applicable, and a graphic displaying which bases are occupied; and usually include names of the current pitcher and batter, the pitcher's pitch count, and the number of balls and strikes accrued by the batter. In basketball, score bugs generally include the shot clock, the number of fouls accrued by each team, and whether a team is in the bonus. In cricket, score bugs often take the form of larger dashboards across the bottom of the screen, displaying the current team up and their number of runs, wickets, and overs, a display showing the runs scored and number of balls faced by the current batting partnership, and statistics for the opposing team's bowler (including the number of wickets scored and runs given up). In American football, score bugs usually include the play clock and the down and distance of the current play; they also incorporate graphics indicating when a penalty flag has been thrown. In ice hockey, score bugs display when a penalty or power play is in effect, and often include the number of shots on goal accrued by each team. In golf, Fox popularized the display of a persistent leaderboard graphic in the bottom-right of the screen, usually displaying the top 5. ==== Racing ==== Telecasts of automobile races often include a score bug with the current positions of participants, statistics such as distance behind the leader, and the remaining distance or number of laps. In the mid-2010s, NASCAR broadcasters such as Fox began to transition from horizontal tickers to vertical leaderboards (also referred to as "pylons", in reference to the physical scoring pylons at). The CW differentiated itself by using a horizontal display that divides the field into multiple columns along the bottom of the screen.
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How to Choose an AI Code Generator

Shopping for the best AI code generator? An AI code 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 code generator 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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Synchronizing word

In computer science, more precisely, in the theory of deterministic finite automata (DFA), a synchronizing word or reset sequence is a word in the input alphabet of the DFA that sends any state of the DFA to one and the same state. That is, if an ensemble of copies of the DFA are each started in different states, and all of the copies process the synchronizing word, they will all end up in the same state. Not every DFA has a synchronizing word; for instance, a DFA with two states, one for words of even length and one for words of odd length, can never be synchronized. == Existence == Given a DFA, the problem of determining if it has a synchronizing word can be solved in polynomial time using a theorem due to Ján Černý. A simple approach considers the power set of states of the DFA, and builds a directed graph where nodes belong to the power set, and a directed edge describes the action of the transition function. A path from the node of all states to a singleton state shows the existence of a synchronizing word. This algorithm is exponential in the number of states. A polynomial algorithm results however, due to a theorem of Černý that exploits the substructure of the problem, and shows that a synchronizing word exists if and only if every pair of states has a synchronizing word. == Length == The problem of estimating the length of synchronizing words has a long history and was posed independently by several authors, but it is commonly known as the Černý conjecture. In 1969, Ján Černý conjectured that (n − 1)2 is the upper bound for the length of the shortest synchronizing word for any n-state complete DFA (a DFA with complete state transition graph). If this is true, it would be tight: in his 1964 paper, Černý exhibited a class of automata (indexed by the number n of states) for which the shortest reset words have this length. The best upper bound known is 0.1654n3, far from the lower bound. For n-state DFAs over a k-letter input alphabet, an algorithm by David Eppstein finds a synchronizing word of length at most 11n3/48 + O(n2), and runs in time complexity O(n3+kn2). This algorithm does not always find the shortest possible synchronizing word for a given automaton; as Eppstein also shows, the problem of finding the shortest synchronizing word is NP-complete. However, for a special class of automata in which all state transitions preserve the cyclic order of the states, he describes a different algorithm with time O(kn2) that always finds the shortest synchronizing word, proves that these automata always have a synchronizing word of length at most (n − 1)2 (the bound given in Černý's conjecture), and exhibits examples of automata with this special form whose shortest synchronizing word has length exactly (n − 1)2. == Road coloring == The road coloring problem is the problem of labeling the edges of a regular directed graph with the symbols of a k-letter input alphabet (where k is the outdegree of each vertex) in order to form a synchronizable DFA. It was conjectured in 1970 by Benjamin Weiss and Roy Adler that any strongly connected and aperiodic regular digraph can be labeled in this way; their conjecture was proven in 2007 by Avraham Trahtman. == Related: transformation semigroups == A transformation semigroup is synchronizing if it contains an element of rank 1, that is, an element whose image is of cardinality 1. A DFA corresponds to a transformation semigroup with a distinguished generator set.
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