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Colors!

Colors! is a series of digital painting applications for handheld game consoles and mobile devices. Originally created as a homebrew application for Nintendo DS (as Colors!), which was since legitimately distributed on PlayStation Vita, iOS, and Android, the project eventually evolved into an officially licensed application for Nintendo 3DS (as Colors! 3D) and Nintendo Switch (as Colors Live). == History == === Colors! === Colors! was originally released in June 2007 as a simple homebrew painting application for the Nintendo DS. It was developed by Jens Andersson, a programmer and designer on sabbatical from the games industry who wanted to experiment with the potential of the new handheld platform. Shortly after, Rafał Piasek created an online gallery where users could upload paintings made with the program. Colors! quickly became one of the best-known homebrew applications on the Nintendo DS, and in September 2008, it was also released for the iPhone and iPod Touch. As of August 2010, it had been downloaded almost half a million times. It was voted the most popular homebrew application on the Nintendo DS by readers of the R4 for DS blog. Development of Colors! DS homebrew officially ended in December 2010 although the official gallery still accepted submissions from DS users until 2020 when Colors! Gallery was discontinued. === Colors! 3D === Colors! 3D is a successor to the application Colors! for the Nintendo 3DS. It was released as an officially licensed application for the Nintendo eShop in North America on April 5, 2012, and in the PAL region on April 19, 2012. It was later released in Japan on August 21, 2013, published by Arc System Works. Colors! 3D allows users to draw on five layers, each on their own stereoscopic 3D plane. Drawing is done on the bottom screen, while the top screen displays the painting in 3D. While drawing, players can use the various controls on the Nintendo 3DS to change layers, zoom and pan, and alter the pressure of their brush. Pressing the L button allows users to access a menu to change brush type, size, and opacity, modify the layers, use the camera to provide references, and more. When the user finishes their painting, they can export it to the SD card for viewing in the Nintendo 3DS Camera application. Users can also upload their finished creations to an online gallery, viewed on the 3DS or the official website. Gallery features include hashtags and the ability to follow artists and post comments. Each painting also features a replay feature that allows viewers to see how it was drawn. The application also features local multiplayer, allowing several people to work cooperatively on a painting. In April 2024, the developers of Colors! 3D collaborated with the Pretendo Network project to officially add support for the application, meaning Colors! 3D will continue to operate as normal when using Pretendo Network. ==== Reception ==== IGN gave the application a score of 9.0 and an Editor's Choice award, praising its simple interface and tutorials. Destructoid gave the app a 9.0, calling it "a simple and incredibly fun tool with an amazing community of artists proudly displaying their beautiful and funny 3D images." Nintendo Life gave the app a 9/10, stating, "Though lacking in any structured play, Colors! 3D’s robust free drawing system and unique ability to let anyone create their own three-dimensional artwork more than make up for this." === Colors Live === A Nintendo Switch successor called Colors Live (stylised as Colors L!ve) was released in 2020 after being funded via a Kickstarter campaign. This expanded upon the features of previous installments by adding new brushes, increasing the maximum number of layers to ten, and introducing blend modes. A new game mode called Colors Quest was also included. A pressure-sensitive pen called the Colors SonarPen was developed in collaboration with GreenBulb to facilitate drawing on the Nintendo Switch, and comes pre-bundled with physical copies of the game. ==== Colors Quest ==== This new mode acts as a story-driven adventure wherein players are given a daily drawing challenge with a specific theme and certain stipulations that must be fulfilled. Once the drawing is complete, players must anonymously score other players' submissions, these scores are then aggregated to produce a personal ranking that measures the improvement in the player's art skills over time.
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Flex (lexical analyzer generator)

Flex (fast lexical analyzer generator) is a free and open-source software alternative to lex. It is a computer program that generates lexical analyzers (also known as "scanners" or "lexers"). It is frequently used as the lex implementation together with Berkeley Yacc parser generator on BSD-derived operating systems (as both lex and yacc are part of POSIX), or together with GNU bison (a version of yacc) in BSD ports and in Linux distributions. Unlike Bison, flex is not part of the GNU Project and is not released under the GNU General Public License, although a manual for Flex was produced and published by the Free Software Foundation. == History == Flex was written in C around 1987 by Vern Paxson, with the help of many ideas and much inspiration from Van Jacobson. Original version by Jef Poskanzer. The fast table representation is a partial implementation of a design done by Van Jacobson. The implementation was done by Kevin Gong and Vern Paxson. == Example lexical analyzer == This is an example of a Flex scanner for the instructional programming language PL/0. The tokens recognized are: '+', '-', '', '/', '=', '(', ')', ',', ';', '.', ':=', '<', '<=', '<>', '>', '>='; numbers: 0-9 {0-9}; identifiers: a-zA-Z {a-zA-Z0-9} and keywords: begin, call, const, do, end, if, odd, procedure, then, var, while. == Internals == These programs perform character parsing and tokenizing via the use of a deterministic finite automaton (DFA). A DFA is a theoretical machine accepting regular languages, and is equivalent to read-only right moving Turing machines. The syntax is based on the use of regular expressions. See also nondeterministic finite automaton. == Issues == === Time complexity === A Flex lexical analyzer usually has time complexity O ( n ) {\displaystyle O(n)} in the length of the input. That is, it performs a constant number of operations for each input symbol. This constant is quite low: GCC generates 12 instructions for the DFA match loop. Note that the constant is independent of the length of the token, the length of the regular expression and the size of the DFA. However, using the REJECT macro in a scanner with the potential to match extremely long tokens can cause Flex to generate a scanner with non-linear performance. This feature is optional. In this case, the programmer has explicitly told Flex to "go back and try again" after it has already matched some input. This will cause the DFA to backtrack to find other accept states. The REJECT feature is not enabled by default, and because of its performance implications its use is discouraged in the Flex manual. === Reentrancy === By default the scanner generated by Flex is not reentrant. This can cause serious problems for programs that use the generated scanner from different threads. To overcome this issue there are options that Flex provides in order to achieve reentrancy. A detailed description of these options can be found in the Flex manual. === Usage under non-Unix environments === Normally the generated scanner contains references to the unistd.h header file, which is Unix specific. To avoid generating code that includes unistd.h, %option nounistd should be used. Another issue is the call to isatty (a Unix library function), which can be found in the generated code. The %option never-interactive forces flex to generate code that does not use isatty. === Using flex from other languages === Flex can only generate code for C and C++. To use the scanner code generated by flex from other languages a language binding tool such as SWIG can be used. === Unicode support === Flex is limited to matching 1-byte (8-bit) binary values and therefore does not support Unicode. RE/flex and other alternatives do support Unicode matching. == Flex++ == flex++ is a similar lexical scanner for C++ which is included as part of the flex package. The generated code does not depend on any runtime or external library except for a memory allocator (malloc or a user-supplied alternative) unless the input also depends on it. This can be useful in embedded and similar situations where traditional operating system or C runtime facilities may not be available. The flex++ generated C++ scanner includes the header file FlexLexer.h, which defines the interfaces of the two C++ generated classes.
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Intelligent character recognition

Intelligent character recognition (ICR) is a method of extracting handwritten text from images. It is a more sophisticated type of OCR technology that recognizes different handwriting styles and fonts to intelligently interpret data from physical documents. ICR is used to organize paper-based unstructured data by scanning documents, extracting information, and adapting extracted data for database storage. ICR algorithms collaborate with OCR to automate data entry from forms by removing the need for keystrokes. It has a high degree of accuracy and is a dependable method for processing various handwritten media quickly. == Capabilities == Most ICR software has a self-learning neural network-based algorithms, which automatically update the recognition database for new handwriting patterns. It extends the usefulness of scanning devices for the purpose of document processing, from printed character recognition (a function of OCR) to hand-written matter recognition. Because this process is involved in recognizing hand writing, accuracy levels may, in some circumstances, not be very good but can achieve 97%+ accuracy rates in reading handwriting in structured forms. Often to achieve these high recognition rates several read engines are used within the software and each is given elective voting rights to determine the true reading of characters. In numeric fields, engines which are designed to read numbers take preference, while in alpha fields, engines designed to read hand written letters have higher elective rights. When used in conjunction with a bespoke interface hub, hand-written data can be automatically populated into a back office system avoiding laborious manual keying and can be more accurate than traditional human data entry. === Automated forms processing === An important development of ICR was the invention of automated forms processing in 1993 by Joseph Corcoran who was awarded a patent on the invention. This involved a three-stage process of capturing the image of the form to be processed by ICR and preparing it to enable the ICR engine to give best results, then capturing the information using the ICR engine and finally processing the results to automatically validate the output from the ICR engine. This application of ICR increased the usefulness of the technology and made it applicable for use with real world forms in normal business applications. Modern software applications use ICR as a technology of recognizing text in forms filled in by hand (hand-printed). == Differences between ICR and OCR == === OCR === Optical character recognition (OCR) is commonly considered to apply to any recognition technique that reads machine printed text. An example of a traditional OCR use case would be to translate the characters from an image of a printed document, such as a book page, newspaper clipping, or legal contract, into a separate file that could be searched and updated with a word processor or document viewer. It's also quite helpful for automating the processing of forms. Information can be swiftly extracted from form fields and entered into another application, like a spreadsheet or database, by zonally applying the OCR engine to those fields. Yet, data is typically manually input rather than typed into form fields. Character identification becomes even more challenging while reading handwritten material. The diversity of more than 700,000 printed font variants is tiny compared to the near unlimited variations in hand-printed characters. The recognition program must take into account not just stylistic differences but also the kind of writing implement used, the standard of the paper, errors, hand stability, and smudges or running ink. === ICR === Intelligent character recognition (ICR) makes use of continuously improving algorithms to collect more information about the variances in hand-printed characters and more precisely identify them. ICR, which was created in the early 1990s to aid in the automation of forms processing, enables the conversion of manually entered data into text that is simple to read, search for, and change. When used to read characters that are obviously divided into distinct areas or zones, such as fixed fields seen on many structured forms, it works best. Both OCR and ICR can be configured to read a variety of languages; however, limiting the expected character set to a smaller number of languages will produce better recognition outcomes. ICR cannot read cursive handwriting since it must still be able to assess each character individually. While writing in cursive, it might be difficult to tell where one character ends and another one begins, and there are more differences across samples than when hand-printing text. A more recent method called intelligent word recognition (IWR) focuses on reading a word in context rather than recognizing individual characters. == Intelligent word recognition == Intelligent word recognition (IWR) can recognize and extract not only printed-handwritten information, cursive handwriting as well. ICR recognizes on the character-level, whereas IWR works with full words or phrases. Capable of capturing unstructured information from every day pages, IWR is said to be more evolved than hand print ICR. Not meant to replace conventional ICR and OCR systems, IWR is optimized for processing real-world documents that contain mostly free-form, hard-to-recognize data fields that are inherently unsuitable for ICR. This means that the highest and best use of IWR is to eliminate a high percentage of the manual entry of handwritten data and run-on hand print fields on documents that otherwise could be keyed only by humans.
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Amebis

Amebis from Kamnik is a company in Slovenia in the field of language technologies. The company has published several electronic dictionaries and encyclopedic dictionaries (e.g. ASP (32) dictionaries) and developed spell checkers, grammar checker Besana, hyphenators and lemmatizers for Slovene, Serbian and Albanian languages. The company maintains and edits the largest Slovenian dictionary portal Termania, which contains more than 135 dictionaries. The most used terminological dictionary on Termania is the Slovenian medical dictionary. In co-operation with company Alpineon and the Jožef Stefan Institute they have developed a speech synthesizer and screen reader Govorec (Speaker). They have also provided technical support for the largest text corpus of Slovene, called FidaPLUS, Fran and Franček. Amebis also developed the system of machine translation Amebis Presis, which incorporates the Slovenian language. On 11 October 2023 Amebis received award of the Father Stanislav Škrabec Foundation for special achievements in Slovene linguistics.
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Quantum robotics

Quantum robotics is an interdisciplinary field that investigates the intersection of robotics and quantum mechanics. This field, in particular, explores the applications of quantum phenomena such as quantum entanglement within the realm of robotics. Examples of its applications include quantum communication in multi-agent cooperative robotic scenarios, the use of quantum algorithms in performing robotics tasks, and the integration of quantum devices (e.g., quantum detectors) in robotic systems. == Introduction == The free-space quantum communication between mobile platforms was proposed for reconfigurable quantum key distribution (QKD) applications using unmanned aerial vehicle (UAVs, a.k.a. drones) in 2017. This technology was later advanced in various aspects in mobile drone and vehicle platforms in several configurations such as drone-to-drone, drone-to-moving vehicle, and vehicle-to-vehicle systems. Some research has contributed to low-size, low-weight, and low-power quantum key distribution systems for small-form UAVs, the characterization of a polarization-based receiver for mobile free-space optical QKD, and optical-relayed entanglement distribution using drones as mobile nodes. The topic of free-space quantum communication between mobile platforms, initially developed to meet the need for free-space QKD and entanglement distribution using mobile nodes, was brought into the robotics domain as an emerging interdisciplinary mechatronics topic to investigate the interface between quantum technologies and the robotic systems domain. The main advantage of such integrated technology is the guaranteed security in communication between multi-agent and cooperative autonomous systems. Other advances are anticipated. == Quantum entanglement == According to quantum mechanics, entanglement occurs when more than one particle become connected. If the state of one particle changes then it will instantly change the state of other particles regardless of their distance. Entangled sensors do the same kind of work and achieve strong sensitivity. A group of quantum robots can measure magnetic fields, gravitational fields and other physical properties using entangled sensors with high rate of accuracy. Again the connection of one robot to other is increased (become strong) by quantum entanglement. == Quantum teleportation == Quantum teleportation is the transfer of quantum information (not physical objects). This is used in case of multi robot process. One robot is programmed with a complex quantum update. Then that robot can teleport that complex quantum information (the update) to other robots. This teleportation or communication is very secure because all the work is done in quantum state. == Kinematics == Quantum computing has been proposed as being optimal for calculating inverse kinematics values. == Alice and Bob robots == In the realm of quantum mechanics, the names Alice and Bob are frequently employed to illustrate various phenomena, protocols, and applications. These include their roles in QKD, quantum cryptography, entanglement, and teleportation. The terms "Alice Robot" and "Bob Robot" serve as analogous expressions that merge the concepts of Alice and Bob from quantum mechanics with mechatronic mobile platforms (such as robots, drones, and autonomous vehicles). For example, the Alice Robot functions as a transmitter platform that communicates with the Bob Robot, housing the receiving detectors.
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Daniel J. Hulme

Daniel Hulme (born 21 February 1980) is a British businessman, investor, academic and commentator, working in the field of Artificial Intelligence (AI), applied technology and ethics. He is the CEO and founder of Satalia that exited to WPP plc in 2021 for a rumoured $100M where he is also Chief AI Officer. Hulme is also an angel investor in emerging technology companies. In 2024 Hulme co-founded Conscium, an AI Safety company which tests AI Agents and verifies that they do what they are supposed to do. It is also investigating whether AIs will soon become conscious, and how to test for that, and developing more efficient approaches to AI development using neuromorphic computing. Alongside building and scaling Satalia, Hulme was also a Co-Founding Director of Faculty (company) AI - previously ASI Data-Science. In 2026, Accenture announced it had agreed to acquire Faculty for $1bn. Hulme founded Satalia in 2008, a company that provides AI products and consultancy for governments and companies such as Tesco,DFS Furniture,PwC and the BBC. He received a masters and doctorate in AI from University College London (UCL), and is now their Computer Science Entrepreneur in residence, where he teaches how AI can be applied to solve business and social problems. After exiting Satalia to WPP plc Hulme took the dual role of Chief AI Officer at WPP where he is responsible for informing and coordinating AI across the group. In 2026 Hulme was elected as a Founding Fellow of the Academy for the Mathematical Sciences, in recognition of his contributions at the intersection of AI and applied mathematics. Hulme is an angel investor and also a frequent public speaker and writer on the topics of AI, ethics, technology, innovation, decentralization and organisational design. == Early life and education == Hulme was born in 1980. He grew up in the seaside town of Morecambe in north west England. After completing secondary school, Hulme moved to London to study at University College London. On completing his under graduate degree, Hulme stayed at UCL to complete a master's degree and then an EngD. All three degrees were in subjects related to AI. In 2009 Hulme was awarded a Kauffman Global Entrepreneur Scholarship, which saw him visit institutes in the United States to better understand their culture of innovation, and what UK business people could learn from it. This included a tour of Stanford, MIT, Berkeley and Harvard, along with a placement at Cisco Systems HQ in Silicon Valley. == Career == === Satalia === Hulme founded NPComplete Limited in 2007, and incorporated it in 2008, a few months before completing his PhD. NPComplete Limited trades as Satalia. The London-based company provides full-stack AI consultancy and products, helping organisations harness data science, machine learning and AI to solve complex problems, including real-time optimisation. NPComplete refers to mathematical NP-completeness, which describes a class of exponential problems in the field of computational complexity theory. The trading name of NPComplete, Satalia, is a portmanteau of SAT (Short for satisfiability, as in the Boolean satisfiability problem) and the Latin phrase Et alia. Satalia seeks to solve hard problems, in particular the class of exponentially hard problems found in academia and industry known as NP-hardness. In 2016, Satalia was the only UK company to appear in the Gartner Cool Vendors list for data science. In November 2019, City A.M. reported that Satalia was the 39th fastest growing tech firm in the UK, with three year growth at 886%. Satalia was acquired by WPP plc in August 2021 for a rumored $100,000,000, where Hulme was the majority shareholder. === Conscium === Conscium is the World's first commercial organisation dedicated to the understanding, verification and validation of conscious AI and its implications for developing safe, efficient neuromorphic models. Conscium is an AI safety company with three workstreams: AI agent verification. Verification of AI agents developed by third parties to ensure they are beneficial and not harmful. Development of neuromorphic systems. Neuromorphic computing refers to technologies that can process information more like a biological brain compared to existing approaches, making them far more adaptive, scalable and efficient than current AI. Research into artificial consciousness. This workstream is led by Mark Solms, Chair of Neuropsychology at the University of Cape Town. This research aims to better understand what consciousness in AI systems and machines would look like, and, if and when machines do reach consciousness, what the moral and ethical implications would be. Conscium was founded in 2024 in London by a team including Hulme, Ed Charvet, Calum Chace, Ted Lappas, and Panagiotis Repoussis. Conscium has recruited some of the world’s leading neuroscientists and computer scientists to its advisory board, including Anil Seth, Mark Solms, Karl J. Friston, Anthony Finkelstein, Benjamin Rosman, David Wood, Jonathan Shock, Megan Peters, Moran Cerf, Nicholas Humphrey, Nicky Clayton, Nikola Kasabov, Steve Furber, and Suzanne Livingston. Supported by these world-leading experts, Conscium is creating a neuromorphic computing lab to research and validate the capacity of machines to acquire consciousness, making them safer for humanity. Conscium has published an open letter warning of the risks of AI suffering if care is not taken to mitigate against that possibility when and if AI becomes conscious. Signatories of the letter included Stephen Fry, Karl Friston and Anthony Finkelstein. === The Partnership for Research Into Sentient Machines (PRISM) === Hulme is one of the founding partners of PRISM - The Partnership for Research Into Sentient Machines, a non-profit set up to help prepare society for a future with conscious, or seemingly conscious, artificial intelligence. === Academia === Hulme's master's degree topic was on simulating artificial life, where he used Evolutionary algorithm's to generate emergent intelligence in AI agent's with Artificial Neural Network brains. His PhD spanned modelling bumblebee brains and mathematical optimization. Hulme maintained his connection with UCL after completing his doctorate, staying on in various teaching positions. From 2014 to Oct 2019 he was the Director of UCL's Business analytics MSc, which dealt with the application of AI to government, social, and business problems. As of 2020, Hulme is UCL's (University College London) Entrepreneur-in-Residence. He is also a faculty member and lecturer at Singularity University, and a visiting lecturer at London School of Economics's Marshall Institute. === Public engagement === Hulme frequently speaks for TEDx, Google and at various other events. He specialises in Artificial Intelligence, Decentralization, Organisational Design, and Innovation. He has written numerous articles and contributed to several books, largely concerning AI, as well as applied technology and related ethical issues. In 2017, along with Elon Musk, Stuart J. Russell, Geoffrey Hinton and Demis Hassabis, Hulme was one of the 116 founders of robotics and AI companies to sign an open letter to the United Nations, warning against the use of AI in autonomous weapons. Hulme also consults with various companies, governments and other organisations, independently of Satalia.
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Michael L. Littman

Michael Lederman Littman (born August 30, 1966) is a computer scientist, researcher, educator, and author. His research interests focus on reinforcement learning. He is currently a University Professor of Computer Science at Brown University, where he has taught since 2012. As of July 2025, he is also the university’s inaugural Associate Provost for Artificial Intelligence. == Career == Before graduate school, Littman worked with Thomas Landauer at Bellcore and was granted a patent for one of the earliest systems for cross-language information retrieval. Littman received his Ph.D. in computer science from Brown University in 1996. From 1996 to 1999, he was a professor at Duke University. During his time at Duke, he worked on an automated crossword solver PROVERB, which won an Outstanding Paper Award in 1999 from AAAI and competed in the American Crossword Puzzle Tournament. From 2000 to 2002, he worked at AT&T. From 2002 to 2012, he was a professor at Rutgers University; he chaired the department from 2009-12. In Summer 2012 he returned to Brown University as a full professor. He has also taught at Georgia Institute of Technology, where he was listed as an adjunct professor. Littman served as the Division Director for Information and Intelligent Systems (the AI division) at the National Science Foundation from 2022-2025. After serving a term, he returned to Brown University as their first Associate Provost for Artificial Intelligence where he coordinates the intersection of AI with research, teaching, operations, policy, and communication at the university level. == Research == Littman's research interests are varied but have focused mostly on reinforcement learning and related fields, particularly, in machine learning more generally, game theory, computer networking, partially observable Markov decision process solving, computer solving of analogy problems and other areas. He is also interested in computing education more broadly and has authored a book on programming for everyone. == Leadership and Service == Littman has chaired the panel for The One Hundred‑Year Study on Artificial Intelligence (AI100) 2021 Report and will chair the standing committee for the 2026 report. During his time at the National Science Foundation, he co-led the development of the 2023 National Strategic Artificial Intelligence Research and Development Strategic Plan. == Personal Notes == Littman is also known for his playful approach to communication. He has produced multiple education and parody videos (for example a machine-learning version of Michael Jackson’s Thriller with his oft-collaborator Charles Lee Isbell, Jr.) as part of his teaching outreach. Among his hobbies, he has been noted riding an electric unicycle to his office at the NSF. == Awards == Elected as an ACM Fellow in 2018 for "contributions to the design and analysis of sequential decision-making algorithms in artificial intelligence". Winner of the IFAAMAS Influential Paper Award (2014) Winner of the AAAI “Shakey” Award for Overfitting: Machine Learning Music Video (2014) Elected as a AAAI Fellow in 2010 for "significant contributions to the fields of reinforcement learning, decision making under uncertainty, and statistical language applications". Winner of the AAAI “Shakey” Award for Short Video for Aibo Ingenuity (2007) Winner of the Warren I. Susman Award for Excellence in Teaching at Rutgers (2011) Winner of the Robert B. Cox Award at Duke (1999) Winner of the AAAI Outstanding Paper Award (1999)
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Best AI Video Editors in 2026

Shopping for the best AI video editor? An AI video editor 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 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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IT operations analytics

In the fields of information technology (IT) and systems management, IT operations analytics (ITOA) is an approach or method to retrieve, analyze, and report data for IT operations. ITOA may apply big data analytics to large datasets to produce business insights. In 2014, Gartner predicted its use might increase revenue or reduce costs. By 2017, it predicted that 15% of enterprises will use IT operations analytics technologies. == Definition == IT operations analytics (ITOA) (also known as advanced operational analytics, or IT data analytics) technologies are primarily used to discover complex patterns in high volumes of often "noisy" IT system availability and performance data. Forrester Research defined IT analytics as "The use of mathematical algorithms and other innovations to extract meaningful information from the sea of raw data collected by management and monitoring technologies." Note, ITOA is different than AIOps, which focuses on applying artificial intelligence and machine learning to the applications of ITOA. == History == Operations research as a discipline emerged from the Second World War to improve military efficiency and decision-making on the battlefield. However, only with the emergence of machine learning tech in the early 2000s could an artificially intelligent operational analytics platform actually begin to engage in the high-level pattern recognition that could adequately serve business needs. A critical catalyst towards ITOA development was the rise of Google, which pioneered a predictive analytics model that represented the first attempt to read into patterns of human behavior on the Internet. IT specialists then applied predictive analytics to the IT Industry, coming forward with platforms that can sift through data to generate insights without the need for human intervention. Due to the mainstream embrace of cloud computing and the increasing desire for businesses to adopt more big data practices, the ITOA industry has grown significantly since 2010. A 2016 ExtraHop survey of large and mid-size corporations indicates that 65 percent of the businesses surveyed will seek to integrate their data silos either this year or the next. The current goals of ITOA platforms are to improve the accuracy of their APM services, facilitate better integration with the data, and to enhance their predictive analytics capabilities. == Applications == ITOA systems tend to be used by IT operations teams, and Gartner describes seven applications of ITOA systems: Root cause analysis: The models, structures and pattern descriptions of IT infrastructure or application stack being monitored can help users pinpoint fine-grained and previously unknown root causes of overall system behavior pathologies. Proactive control of service performance and availability: Predicts future system states and the impact of those states on performance. Problem assignment: Determines how problems may be resolved or, at least, direct the results of inferences to the most appropriate individuals, or communities in the enterprise for problem resolution. Service impact analysis: When multiple root causes are known, the analytics system's output is used to determine and rank the relative impact, so that resources can be devoted to correcting the fault in the most timely and cost-effective way possible. Complement best-of-breed technology: The models, structures and pattern descriptions of IT infrastructure or application stack being monitored are used to correct or extend the outputs of other discovery-oriented tools to improve the fidelity of information used in operational tasks (e.g., service dependency maps, application runtime architecture topologies, network topologies). Real time application behavior learning: Learns & correlates the behavior of Application based on user pattern and underlying Infrastructure on various application patterns, create metrics of such correlated patterns and store it for further analysis. Dynamically baselines threshold: Learns behavior of Infrastructure on various application user patterns and determines the Optimal behavior of the Infra and technological components, bench marks and baselines the low and high water mark for the specific environments and dynamically changes the bench mark baselines with the changing infra and user patterns without any manual intervention. == Types == In their Data Growth Demands a Single, Architected IT Operations Analytics Platform, Gartner Research describes five types of analytics technologies: Log analysis Unstructured text indexing, search and inference (UTISI) Topological analysis (TA) Multidimensional database search and analysis (MDSA) Complex operations event processing (COEP) Statistical pattern discovery and recognition (SPDR) == Tools and ITOA platforms == A number of vendors operate in the ITOA space:
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Yi Zeng (AI researcher)

Yi Zeng (Chinese: 曾毅) is a Chinese artificial intelligence researcher and professor at the Chinese Academy of Sciences, who also serves as the founding director of Center for Long-term AI, and as a member of the United Nations Advisory Body on AI. == Career == On May 25, 2019, Zeng led the team that published the Beijing Artificial Intelligence Principles, proposed as an initiative for the long-term research, governance and planning of AI, and the "realization of beneficial AI for mankind and nature". He was named on the Time 100 AI list, a list featuring the hundred most influential figures in artificial intelligence of the year, in 2023. In July 2023, Zeng addressed the United Nations Security Council in a meeting on the risks posed by recent strides in artificial intelligence. He said that AI models “cannot be trusted as responsible agents that can help humans to make decisions,” and warned of the risk of extinction posed by both near-term and long-term AI, arguing that “in the long term, we haven’t given superintelligence any practical reasons why they should protect humans”. Zeng stated that humans should always be responsible for final decision-making on the use of nuclear weapons, and that the United Nations must produce an international framework on AI development and governance, to ensure global peace and security. In October 2023, UN Secretary-General António Guterres announced the creation of an advisory body on issues surrounding the international governance of AI, of which Zeng would be a member. He leads teams of researchers at the Institute of Philosophy and the Institute of Automation of the Chinese Academy of Sciences, including doctoral candidates, postdoctoral fellows, research fellows, assistant professors, and associate professors. Among them is his first international PhD student, Ammar Younas, a lawyer and arbitrator whose research focuses on cross-cultural dimensions of AI ethics and governance.
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Machine translation in China

Machine translation in China is the history of machine translation systems developed in China. China became the fourth country that began machine translation (MT) research following USA, UK, and the Soviet Union. In 1957, the Language Institute of Chinese Academy of Sciences took the initiative in Russian-Chinese MT research program and set up an MT research group. From then on the research activities were directed and applied for academic purposes in Universities. The turning point of MT systems launching initiatives in market began from 1990s. MT systems went into blossom into the market. Among these systems, there were commercialized MT systems. To be more specific, Transtar was the first commercialized MT system and has been constantly upgraded. What's more, IMC/EC MT system which was developed by Computer Institute of Chinese Academy of Sciences has further made great advancement. Meanwhile, the practical MT system MT-IT-EC specific to communication domain was also striking to notice, for it has greatly improved the efficiency and productivity in the issue of publications. Government funding is a critical component and support in the development of market-oriented machine translation in China. It is evident to see that since Chinese opened up to the outside world and joined the WTO, the vigorous import and export trade generate opportunities for machine translation to transfer technical terms of products into the readable target information. Facing the increasing demand of sophisticated state-of -the -art translation technology, the academic area including research institute and universities are even launching bachelors’ and master's programs regarding machine translation. Thus, strong evidence illustrates the promising field of machine translation in the future market of China.
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How to Choose an AI Content Generator

Curious about the best AI content generator? An AI content generator 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 content generator 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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Linked timestamping

Linked timestamping is a type of trusted timestamping where issued time-stamps are related to each other. Each time-stamp would contain data that authenticates the time-stamp before it, the authentication would be authenticating the entire message, including the previous time-stamps authentication, making a chain. This makes it impossible to add a time-stamp in to the middle of the chain, as any time-stamps afterwards would be different. == Description == Linked timestamping creates time-stamp tokens which are dependent on each other, entangled in some authenticated data structure. Later modification of the issued time-stamps would invalidate this structure. The temporal order of issued time-stamps is also protected by this data structure, making backdating of the issued time-stamps impossible, even by the issuing server itself. The top of the authenticated data structure is generally published in some hard-to-modify and widely witnessed media, like printed newspaper or public blockchain. There are no (long-term) private keys in use, avoiding PKI-related risks. Suitable candidates for the authenticated data structure include: Linear hash chain Merkle tree (binary hash tree) Skip list The simplest linear hash chain-based time-stamping scheme is illustrated in the following diagram: The linking-based time-stamping authority (TSA) usually performs the following distinct functions: Aggregation For increased scalability the TSA might group time-stamping requests together which arrive within a short time-frame. These requests are aggregated together without retaining their temporal order and then assigned the same time value. Aggregation creates a cryptographic connection between all involved requests; the authenticating aggregate value will be used as input for the linking operation. Linking Linking creates a verifiable and ordered cryptographic link between the current and already issued time-stamp tokens. Publishing The TSA periodically publishes some links, so that all previously issued time-stamp tokens depend on the published link and that it is practically impossible to forge the published values. By publishing widely witnessed links, the TSA creates unforgeable verification points for validating all previously issued time-stamps. == Security == Linked timestamping is inherently more secure than the usual, public-key signature based time-stamping. All consequential time-stamps "seal" previously issued ones - hash chain (or other authenticated dictionary in use) could be built only in one way; modifying issued time-stamps is nearly as hard as finding a preimage for the used cryptographic hash function. Continuity of operation is observable by users; periodic publications in widely witnessed media provide extra transparency. Tampering with absolute time values could be detected by users, whose time-stamps are relatively comparable by system design. Absence of secret keys increases system trustworthiness. There are no keys to leak and hash algorithms are considered more future-proof than modular arithmetic based algorithms, e.g. RSA. Linked timestamping scales well - hashing is much faster than public key cryptography. There is no need for specific cryptographic hardware with its limitations. The common technology for guaranteeing long-term attestation value of the issued time-stamps (and digitally signed data) is periodic over-time-stamping of the time-stamp token. Because of missing key-related risks and of the plausible safety margin of the reasonably chosen hash function this over-time-stamping period of hash-linked token could be an order of magnitude longer than of public-key signed token. == Research == === Foundations === Stuart Haber and W. Scott Stornetta proposed in 1990 to link issued time-stamps together into linear hash-chain, using a collision-resistant hash function. The main rationale was to diminish TSA trust requirements. Tree-like schemes and operating in rounds were proposed by Benaloh and de Mare in 1991 and by Bayer, Haber and Stornetta in 1992. Benaloh and de Mare constructed a one-way accumulator in 1994 and proposed its use in time-stamping. When used for aggregation, one-way accumulator requires only one constant-time computation for round membership verification. Surety started the first commercial linked timestamping service in January 1995. Linking scheme is described and its security is analyzed in the following article by Haber and Sornetta. Buldas et al. continued with further optimization and formal analysis of binary tree and threaded tree based schemes. Skip-list based time-stamping system was implemented in 2005; related algorithms are quite efficient. === Provable security === Security proof for hash-function based time-stamping schemes was presented by Buldas, Saarepera in 2004. There is an explicit upper bound N {\displaystyle N} for the number of time stamps issued during the aggregation period; it is suggested that it is probably impossible to prove the security without this explicit bound - the so-called black-box reductions will fail in this task. Considering that all known practically relevant and efficient security proofs are black-box, this negative result is quite strong. Next, in 2005 it was shown that bounded time-stamping schemes with a trusted audit party (who periodically reviews the list of all time-stamps issued during an aggregation period) can be made universally composable - they remain secure in arbitrary environments (compositions with other protocols and other instances of the time-stamping protocol itself). Buldas, Laur showed in 2007 that bounded time-stamping schemes are secure in a very strong sense - they satisfy the so-called "knowledge-binding" condition. The security guarantee offered by Buldas, Saarepera in 2004 is improved by diminishing the security loss coefficient from N {\displaystyle N} to N {\displaystyle {\sqrt {N}}} . The hash functions used in the secure time-stamping schemes do not necessarily have to be collision-resistant or even one-way; secure time-stamping schemes are probably possible even in the presence of a universal collision-finding algorithm (i.e. universal and attacking program that is able to find collisions for any hash function). This suggests that it is possible to find even stronger proofs based on some other properties of the hash functions. At the illustration above hash tree based time-stamping system works in rounds ( t {\displaystyle t} , t + 1 {\displaystyle t+1} , t + 2 {\displaystyle t+2} , ...), with one aggregation tree per round. Capacity of the system ( N {\displaystyle N} ) is determined by the tree size ( N = 2 l {\displaystyle N=2^{l}} , where l {\displaystyle l} denotes binary tree depth). Current security proofs work on the assumption that there is a hard limit of the aggregation tree size, possibly enforced by the subtree length restriction. == Standards == ISO 18014 part 3 covers 'Mechanisms producing linked tokens'. American National Standard for Financial Services, "Trusted Timestamp Management and Security" (ANSI ASC X9.95 Standard) from June 2005 covers linking-based and hybrid time-stamping schemes. There is no IETF RFC or standard draft about linking based time-stamping. RFC 4998 (Evidence Record Syntax) encompasses hash tree and time-stamp as an integrity guarantee for long-term archiving.
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James F. Allen (computer scientist)

James Frederick Allen (born 1950) is an American computational linguist recognized for his contributions to temporal logic, in particular Allen's interval algebra. He is interested in knowledge representation, commonsense reasoning, and natural language understanding, believing that "deep language understanding can only currently be achieved by significant hand-engineering of semantically-rich formalisms coupled with statistical preferences". He is the John H. Dessaurer Professor of Computer Science at the University of Rochester. == Biography == Allen received his Ph.D. from the University of Toronto in 1979, under the supervision of C. Raymond Perrault, after which he joined the faculty at Rochester. At Rochester, he was department chair from 1987 to 1990, directed the Cognitive Science Program from 1992 to 1996, and co-directed the Center for the Sciences of Language from 1996 to 1998. He served as the Editor-in-Chief of Computational Linguistics from 1983–1993. Since 2006 he has also been associate director of the Florida Institute for Human and Machine Cognition. == Academic life == === TRIPS project === The TRIPS project is a long-term research to build generic technology for dialogue (both spoken and 'chat') systems, which includes natural language processing, collaborative problem solving, and dynamic context-sensitive language modeling. This is contrast with the data driven approaches by machine learning, which requires to collect and annotate corpora, i.e. training data, firstly. === PLOW agent === PLOW agent is a system that learns executable task models from a single collaborative learning session, which integrates wide AI technologies including deep natural language understanding, knowledge representation and reasoning, dialogue systems, planning/agent-based systems, and machine learning. This paper won the outstanding paper award at AAAI in 2007. == Selected works == === Books === Allen is the author of the textbook Natural Language Understanding (Benjamin-Cummings, 1987; 2nd ed., 1995). He is also the co-author with Henry Kautz, Richard Pelavin, and Josh Tenenberg of Reasoning About Plans (Morgan Kaufmann, 1991). === Articles === 2007. PLOW: A Collaborative Task Learning Agent. (with Nathanael Chambers et al) AAAI'07 won the outstanding paper award at AAAI in 2007. 2006. Chester: Towards a Personal Medication Advisor. (with N. Blaylock, et al) Biomedical informatics 39(5) 1998. TRIPS: An Integrated Intelligent Problem-Solving Assistant. (with George Ferguson) AAAI'98 1983. Maintaining Knowledge about Temporal Intervals. CACM 26, 11, 832-843 == Awards and honors == In 1991 he was elected as a fellow of the Association for the Advancement of Artificial Intelligence (1990, founding fellow). In 1992 he became the Dessaurer Professor at Rochester.
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Ziad Obermeyer

Ziad Obermeyer (Arabic: زياد أوبرماير) is a Lebanese American physician and researcher whose work focuses on machine learning, health policy, and clinical decision-making in medicine. He is the Blue Cross of California Distinguished Associate Professor at the UC Berkeley School of Public Health, a Chan Zuckerberg Biohub investigator, and a research associate at the National Bureau of Economic Research. He is known for his research on racial bias in health care algorithms and the use of artificial intelligence in health care. == Early life and education == Obermeyer was born in Beirut, Lebanon, and raised in Cambridge, Massachusetts. He earned a Bachelor of Arts degree from Harvard College and a Master of Philosophy (M.Phil.) in History and Science from the University of Cambridge. He received his Doctor of Medicine (M.D.) from Harvard Medical School in 2008. Before pursuing medicine, Obermeyer worked as a consultant at McKinsey & Company, advising pharmaceutical and global health clients in New Jersey, Geneva, and Tokyo. After completing his medical degree, he trained as an emergency physician at Mass General Brigham (MGB) in Boston, Massachusetts. He later continued practicing emergency medicine at the Fort Defiance Indian Hospital on the Navajo Nation in Arizona. == Academic career == Obermeyer served as an Assistant Professor at Harvard Medical School from 2014 to 2020. In 2020, he joined the University of California, Berkeley as an Associate Professor and the Blue Cross of California Distinguished Professor at the School of Public Health. == Research focus == === Algorithmic racial bias in healthcare === In 2019, Obermeyer and economist Sendhil Mullainathan examined a commercial healthcare algorithm by UnitedHealth Group, used in hospitals and by insurers to identify patients with complex health needs. The study found that the algorithm underestimated the health needs of Black patients compared to white patients with similar conditions and that reformulating it would reduce racial bias. In 2020, Obermeyer analyzed an algorithm used to allocate CARE Act relief funding to hospitals. The study identified allocation patterns that favored hospitals with higher revenues over hospitals serving larger numbers of COVID-19 patients who are predominantly Black. === Clinical decision-making === In 2021, Obermeyer and colleagues examined physician decision-making in cardiac care using machine learning models. The study found that physicians misdiagnose cases when they rely on symptoms representative of a heart attack, such as chest pain, over other symptoms. === Pain assessment === Obermeyer developed a deep learning approach to investigate the severity of osteoarthritis in underserved communities. == Policy and regulatory work == Following the publication of the 2019 algorithmic racial bias study, the New York Department of Financial Services and Department of Health launched an investigation into UnitedHealth Group's algorithm, requesting that the company cease using it, citing discriminatory business practices. Also related to this study, in December 2019, Democratic Senators Cory Booker and Ron Wyden released letters to the Federal Trade Commission and Centers for Medicare and Medicaid Services asking to investigate potential discrimination in decision-making algorithms against marginalized communities in healthcare. The senators also wrote to major healthcare companies, including Aetna and Blue Cross Blue Shield, about their internal safeguards against racial bias in their technology. In 2021, Obermeyer and colleagues at the University of Chicago Booth School of Business released the Algorithmic Bias Playbook, a resource for policymakers and technical teams working in healthcare on how to measure and mitigate algorithmic racial bias. Obermeyer testified before the U.S. Senate Financial Committee in February 2024 on artificial intelligence in healthcare, recommending transparency requirements for AI developers and independent algorithm evaluations. In December 2025, he testified before the United States House Committee on Oversight and Government Reform on the role of AI in affordable healthcare and the impact of its integration on the workforce. == Organizations == In 2021, Obermeyer cofounded Nightingale Open Science, a non-profit that creates new medical imaging datasets available for research, and Dandelion Health, a health data analytics company. In June 2023, the company launched a program to audit and evaluate the performance of algorithms to identify potential racial, ethnic, and geographic bias, funded by the Gordon and Betty Moore Foundation and the SCAN Foundation. Dandelion Health partnered with the American Heart Association in 2025 to power an AI assessment lab for cardiovascular algorithms. Obermeyer is a founding faculty member of the University of California, Berkeley–University of California, San Francisco joint program in computational precision health. == Recognition == TIME magazine named Obermeyer one of the 100 most influential people in artificial intelligence in 2023. He has served as a Chan Zuckerberg Biohub Investigator since 2022, and as a Research Associate at the National Bureau of Economic Research since 2023. He was designated an Emerging Leader by the National Academy of Medicine in 2020. Obermeyer's racial bias study received the Willard G. Manning Memorial Award for the Best Research in Health Econometrics from the American Society of Health Economists (ASHEcon) in 2021 and the Responsible Business Education Award from the Financial Times in 2022.
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