AI For Business Cards

AI For Business Cards — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Bump (application)

  Bump was an iOS and Android mobile app that enabled smartphone users to transfer contact information, photos and files between devices. In 2011, it was #8 on Apple's list of all-time most popular free iPhone apps, and by February 2013 it had been downloaded 125 million times. Its developer, Bump Technologies, shut down the service and discontinued the app on January 31, 2014, after being acquired by Google for Google Photos and Android Camera. == Features == Bump sent contact information, photos and files to another device over the internet. Before activating the transfer, each user confirmed what they want to send to the other user. To initiate a transfer, two people physically bumped their phones together. A screen appeared on both users' smartphone displays, allowing them to confirm what they want to send to each other. When two users bumped their phones, software on the phones send a variety of sensor data to an algorithm running on Bump servers, which included the location of the phone, accelerometer readings, IP address, and other sensor readings. The algorithm figured out which two phones felt the same physical bump and then transfers the information between those phones. Bump did not use Near Field Communication. February 2012 release of Bump 3.0 for iOS, the company streamlined the app to focus on its most frequently used features: contact and photo sharing. Bump 3.0 for Android maintained the features eliminated from the iOS version but moved them behind swipeable layers. In May 2012, a Bump update enabled users to transfer photos from their phone to their computer via a web service. To initiate a transfer, the user goes to the Bump website on their computer and bumps the smartphone on the computer keyboard's space bar. By December 2012, various Bump updates for iOS and Android had added the abilities to share video, audio, and any files. Users swipe to access those features. In February 2013, an update to the Bump iOS and Android apps enabled users to transfer photos, videos, contacts and other files from a computer to a smartphone and vice versa via a web service. To perform the transfer, users went to the Bump website on their computer and bump the smartphone on the computer keyboard's space bar. == History == The underlying idea of a synchronous gesture like bumping two devices for content transfer or pairing them was first conceived by Ken Hinkley of Microsoft Research in 2003. This idea was presented at a user interface and technology conference that same year. The paper proposed the use of accelerometers and a bumping gesture of two devices to enable communication, screen sharing and content transfer between them. Similar to this original concept, the idea for Bump app was conceived by David Lieb, a former employee of Texas Instruments, while he was attending the University of Chicago Booth School of Business for his MBA. While going through the orientation and meeting process of business school, he became frustrated by constantly entering contact information into his iPhone and felt that the process could be improved. His fellow Texas Instruments employees Andy Huibers and Jake Mintz, who was a classmate of Lieb's at the University of Chicago's MBA program, joined Lieb to form Bump Technologies. Bump Technologies launched in 2008 and is located in Mountain View, CA. Early funding for the project was provided by startup incubator Y Combinator, Sequoia Capital and other angel investors. It gained attention at the CTIA international wireless conference, due to its accessibility and novelty factor. In October 2009, Bump received $3.4m in Series A funding followed in January 2011 with a $16m series B financing round led by Andreessen Horowitz. Silicon Valley venture capitalist Marc Andreessen sits on the company's board. The Bump app debuted in the Apple iOS App Store in March 2009 and was “one of the apps that helped to define the iPhone” (Harry McCracken, Technologizer). It soon became the billionth download on Apple's App Store. An Android version launched in November 2009. By the time Bump 3.0 for iOS was released in February 2012, the app had been installed 77 million times, with users sharing more than 2 million photos daily. As of February 2013, there had been 125 million Bump app downloads. == Other apps created by Bump Technologies == Bump Technologies worked with PayPal in March 2010 to create a PayPal iPhone application. The application, which allows two users to automatically activate an Internet transfer of money between their accounts, found widespread adoption. A similar version was released for Android in August 2010. The Bump capability in PayPal's apps was removed in March 2012. At that time, Bump Technologies released Bump Pay, an iOS app that lets users transfer money via PayPal by physically bumping two smartphones together. The tool was originally created for the Bump team to use when splitting up restaurant bills. The payment feature was not added to the Bump app because the company “wanted to make it as simple as possible so people understand how this works,” Lieb told ABC News. Bump Pay was the first app from the company's Bump Labs initiative. A goal of Bump Labs is to test new app ideas that may not fit within the main Bump app. ING Direct added a feature to its iPhone app in 2011 that lets users transfer money to each other using Bump's technology. The feature was later added to its Android app, now called Capital One 360. In July 2012, Bump Technologies released Flock, an iPhone photo sharing app. An Android version was released in December 2012. Using geolocation data embedded in photos and a user's Facebook connections, Flock finds pictures the user takes while out with friends and family and puts everyone's photos from that event into a single shared album. Users receive a push notification after the event, asking if they want to share their photos with friends who were there in the moment. The app will also scan previous photos in the iPhone camera roll and uncover photos that have yet to be shared. If location services were enabled at the time a photo was taken, Flock allows users to create an album of photos from the past with the friends who were there with them. == Acquisition by Google == On September 16, 2013, Bump Technologies announced that it had been acquired by Google. On December 31, 2013, they broke the news that both Bump and Flock would be discontinued so that the team could focus on new projects at Google. The apps were removed from the App Store and Google Play on January 31, 2014. The company subsequently deleted all user data and shut down their servers, thus rendering existing installations of the apps inoperable.

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  Kórsafn

  Kórsafn (Icelandic: Choral archives) is a sound installation by Icelandic artist Björk. Developed in collaboration with the technology company Microsoft, audio design firm Listen and architecture office firm Atelier Ace, the installation was designed for the lobby of the Sister City Hotel in New York City, United States, and launched in 2020. Elaborating 17 years of choral recording taken from Björk discography, Kórsafn consisted of an evolving music composition that uses an artificial intelligence model that responds to real-time weather data, creating a continuously shifting auditory experience. == Background and concept == In 2018, Björk announced her tenth concert tour Cornucopia, which debuted as a residency show at The Shed arts center. Before the start of the show, it was confirmed she would be accompanied by The Hamrahlid Choir. In 2019, while she was performing at The Shed, Björk stayed alongside the choir at the Sister City Hotel in New York City, where they would rehearse for the performances. While there, the Atelier Ace, which owns the Sister City boutique hotels, asked her to create a sound installation for the lobby. This was the second work commissioned by the hotel, a year after a similar piece by Julianna Barwick was featured in the lobby. Kórsafn is formed from two Icelandic words, "kór" ("choral") and "safn" ("archives"). The installation features recordings of Björk’s choral works from the previous 17 years, including compositions taken from her albums Medúlla (2004) and Biophilia (2011). The artificial intelligence system was developed in collaboration with Microsoft. The software processes data gathered from sensors and by a camera placed on the roof of the Sister City Hotel building and by a barometer. It then uses algorithms to determine how the choral elements are layered, pitched, and mixed in real time. The AI generate variations in real time by reacting to the passage of flocks, clouds, airplanes and changes in pressure. Data collected from sensors on the hotel’s rooftop include wind speed, cloud cover, and precipitation levels. These inputs influence the tonal quality, volume, and rhythmic patterns of the soundscape. The sound is played through hidden speakers in the hotel's lobby, blending with the architectural environment to create an immersive experience for guests. The AI system learns over time from the changing of the seasons and weather constantly evolving the sound - keeping in harmony with the sky. Björk described the project as an "AI tango," expressing curiosity about the interplay between her choral compositions and the AI's interpretations of environmental data. She noted the significance of the Hudson Valley's rich bird migrations, which influence the generative aspects of the soundscape. Due to the COVID-19 pandemic, the hotel closed while the installation was ongoing, making a version of the sound piece available online. == Reception == Kórsafn was positively reviewed. It's Nice That author Jenny Brewer described the piece as "a high-tech alternative to the smooth jazz that usually whistles through hotel lobbies". Writing for CNET, Scott Stein observed that it "is lovely and low-key, and honestly, it just blends into the background. It's nothing wild, but it fits the hotel", adding that "after an hour, it didn't get annoying, or too repetitive". The installation garnered several recognitions. It was nominated in the Fast Company's 2020 Innovation by Design Awards in the Hospitality category. It received three Clio Awards silver prizes, in the Use of Music in Experience/Activation, Sound Design and Emerging Technology categories.

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  Feeding the Machine (book)

  Feeding the Machine: The Hidden Human Labour Powering AI is a 2024 book by James Muldoon, Mark Graham and Callum Cant. == Writing == The authors developed the concept for the book while doing fieldwork studying data annotation in developing countries in East Africa. == Synopsis == The book examines the human input needed to develop and sustain AI ecosystems. == Reception == The book received positive reviews. Rosalie Waelen of Capital & Class gave it a mostly positive review. Tim Hornyak of Literary Review praised it. Kirkus Reviews called it "A sobering and timely—if sometimes distracted—study of AI.". Publishers Weekly gave the book a starred review, writing that "The grim real-life stories read like dystopian parables, such as the account of a European voice actor whose recordings were legally used without her consent to create an inexpensive synthetic clone whom she now competes with for business. Driven by striking reporting and finely observed profiles, this unsettles."

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  A Fire Upon the Deep

  A Fire Upon the Deep is a 1992 science fiction novel by American writer Vernor Vinge. It is a space opera involving superhuman intelligences, aliens, variable physics, space battles, love, betrayal, genocide, and a communication medium resembling Usenet. A Fire Upon the Deep won the Hugo Award in 1993, sharing it with Doomsday Book by Connie Willis. Besides the normal print book editions, the novel was also included on a CD-ROM sold by ClariNet Communications along with the other nominees for the 1993 Hugo awards. The CD-ROM edition included numerous annotations by Vinge on his thoughts and intentions about different parts of the book, and was later released as a standalone e-book. It has a loose prequel, A Deepness in the Sky, from 1999, and a direct sequel, The Children of the Sky, from 2012. == Setting == The novel is set in various locations within the Milky Way. The galaxy is divided into four concentric volumes called the "Zones of Thought"; it is not clear to the novel's characters whether this is a natural phenomenon or an artificially created one. Each Zone has fundamental differences in basic physical laws. One of the main consequences of these differences is the effect on intelligence. Artificial intelligence and automation is most directly affected, in that advanced hardware and software from the Beyond or the Transcend will work less and less well as a ship descends towards the Unthinking Depths. Biological intelligence is affected to a lesser degree. The four zones are spoken of in terms of "low" to "high" as follows: The Unthinking Depths are the innermost zone, surrounding the Galactic Center. In it, only minimal forms of intelligence, biological or otherwise, are possible. This means that any ship straying into the Depths will be stranded, effectively permanently. Even if the crew did not die immediately—and some forms of life native to "higher" Zones would likely do so—they would be rendered incapable of even human intelligence, leaving them unable to operate their ship in any meaningful way. Surrounding the Depths is the Slow Zone or Slowness. "Old Earth" is in this Zone, although Earth plays no significant role in the story. Biological intelligence is possible in "the Slowness", but not true, sentient, artificial intelligence. Faster than light travel (FTL) is impossible in the Slow Zone. Faster-than-light communication is impossible into or out of the Slow Zone. As the boundaries of the Zones are subject to change, accidental entry into the Slow Zone is a major hazard at the "Bottom" of the Beyond. Starships which operate near the Beyond/Slow Zone border often have an auxiliary Bussard ramjet drive, so that if they accidentally stray into the Slow Zone, they will at least have a backup (sub-light) drive to try to reach the Beyond. Such ships also tend to include "coldsleep" equipment, as it is likely that any such return will still take many lifetimes for most species. The next layer outward is the Beyond, within which artificial intelligence, FTL travel, and FTL communication are possible. All human civilizations in the Beyond are descended from a single ethnic Norwegian group. The original settlement of this group is known as Nyjora; other human settlements in the Beyond include Straumli Realm and Sjandra Kei. In the Beyond, FTL travel is accomplished by making many small "jumps" across space, with the efficiency of the drive increasing the farther a ship travels from the galactic core. The Beyond is not a homogeneous zone; it includes the "High Beyond", "Middle Beyond", and the "Bottom of the Beyond", depending on distance from the galactic core. The Beyond is populated by a very large number of interstellar and intergalactic civilizations which are linked by an FTL communication network, "the Net", sometimes cynically called the "Net of a Million Lies". The Net is depicted as working much like the Usenet network in the early 1990s, with transcripts of messages containing header and footer information as one would find in such forums. The outermost layer, containing the galactic halo, is the Transcend, within which incomprehensible, superintelligent beings dwell. When a "Beyonder" civilization reaches the point of technological singularity, it can "Transcend", becoming a "Power". Such Powers always seem to relocate to the Transcend, seemingly necessarily, where they become engaged in activities which are entirely mysterious to those in the Beyond. == Plot == An expedition from Straumli Realm, a human civilization in the High Beyond, investigates a newly discovered data archive in the Low Transcend. The expedition's facility, High Lab, is gradually compromised by a superintelligence that is accidentally awoken by the researchers. This superintelligence is later known as the Blight. Shortly before the Blight's final "flowering", two self-aware entities, created similarly to the Blight, plot to aid the humans before the Blight can gain its full powers. Finally recognizing their danger, the High Lab researchers attempt to flee in two ships. The Blight destroys one ship; a second ship, carrying many High Lab children in coldsleep boxes, escapes. This ship lands on a distant planet at the Bottom of the Beyond. The planet is occupied by dog-like creatures, dubbed "Tines", who live in packs as group minds. The Tines have a level of technology comparable to the human Middle Ages. Upon landing, however, the two surviving adults, Arne and Sjana Olnsdot, are ambushed and killed by Tine fanatics known as Flenserists, in whose realm they have landed. The Flenserists capture their children, Jefri and Johanna. Johanna is rescued by a Tine named Peregrine and taken to a neighboring kingdom ruled by Woodcarver. A distress signal from the Straumli ship eventually reaches Relay, a major information provider for the Net. A Transcendent being named "Old One" contacts Relay, seeking information about the Blight and the humans who released it. Old One then reconstitutes a human man named Pham Nuwen from the wreckage of a spaceship to act as its agent. Pham remains unsure if he is a construct or if his memories are real. Ravna Bergsndot, the only human Relay employee, traces the Straumli ship's signal to the Tines' world and persuades her employer to investigate. Ravna contracts the merchant vessel Out of Band II to transport her and Pham. The ship is owned by two Skroderiders, Blueshell and Greenstalk. Before the mission is launched, the Blight launches a surprise attack on Relay and kills Old One. As Old One dies, it downloads its anti-Blight information into Pham. Pham, Ravna and the Skroderiders barely escape Relay's destruction in the Out of Band II. During their journey to Tine's World, Ravna communicates with Jefri. Jefri is manipulated to believe that Woodcarver is his enemy. The Flenserist leaders, Steel and Tyrathect, use Ravna's information to develop advanced technology such as cannon and radio communication. Meanwhile, Johanna and the knowledge stored in her dataset device help Woodcarver rapidly develop as well. The Blight expands, taking over several civilizations, brainwashing their populations, and seizing archives in the Beyond. On the Net, some claim that humans are the means by which the Blight is able to spread. Anti-human fanatics destroy the entire civilization of Sjandra Kei, which is Ravna's home world. The Out of Band II is pursued by three fleets: anti-human fanatics, survivors from Sjandra Kei, and a shadow fleet controlled by the Blight. During the pursuit, Ravna and Pham learn that every member of the Skroderider species can be subverted by the Blight; this drives a wedge between the crew members. Ships from Sjandra Kei sacrifice themselves to delay the Blight and the anti-human ships, allowing the Out of Band II to reach Tine's World before the Blight. When the Out of Band II arrives at Tine's World, the humans ally with Woodcarver to defeat the Flenserists and rescue Jefri. Blueshell sacrifices himself to rescue Jefri. Pham then initiates an anti-Blight Countermeasure, which was aboard the humans' ship. The Countermeasure extends the Slow Zone outward by thousands of light years. This envelops and destroys the Blight, but results in the destruction of thousands of civilizations and trillions of deaths. The humans are stranded on the Tines' World, now in the depths of the Slow Zone. Activating the Countermeasure proves fatal to Pham, but before he dies, the remnant of Old One reveals to him that, although his body is a reconstruction, his memories are indeed real. == Related works == Vinge first used the concepts of "Zones of Thought" in a 1988 novella The Blabber, which occurs after Fire. Vinge's novel A Deepness in the Sky (1999) is a prequel to A Fire Upon the Deep set 20,000 years earlier and featuring Pham Nuwen. Vinge's The Children of the Sky, "a near-term sequel to A Fire Upon the Deep", set ten years later, was released in October 2011. Vinge's former wife, Joan D. Vinge, has also written s

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  Sarpa (snakebite app)

  Sarpa or SARPA (Snake Awareness, Rescue and Protection app) is a snakebite app, an application for mobile devices developed in India to provide rapid, life-saving help for victims of snakebite, which kill an estimated 58,000 people a year in India. The app provides information about snakes, gets fast aid for people bitten, and helps in the development of antivenoms. Similar systems developed in India include SnakeHub, Snake Lens, Snakepedia, Serpent and the Big Four Mapping Project. The apps provide rapid response to snakebite incidents, often in remote areas, using a network of volunteers managed by local wildlife departments; their use can save human lives by providing rapid medical care, and also snakes, by helping to avoid interaction between the species. In 2026, it was announced that the app had plans to offer real-time contact from doctors directly from the app to provide users with decision-making advice.

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  Data analysis for fraud detection

  Fraud represents a significant problem for governments and businesses and specialized analysis techniques for discovering fraud using them are required. Some of these methods include knowledge discovery in databases (KDD), data mining, machine learning and statistics. They offer applicable and successful solutions in different areas of electronic fraud crimes. In general, the primary reason to use data analytics techniques is to tackle fraud since many internal control systems have serious weaknesses. For example, the currently prevailing approach employed by many law enforcement agencies to detect companies involved in potential cases of fraud consists in receiving circumstantial evidence or complaints from whistleblowers. As a result, a large number of fraud cases remain undetected and unprosecuted. In order to effectively test, detect, validate, correct error and monitor control systems against fraudulent activities, businesses entities and organizations rely on specialized data analytics techniques such as data mining, data matching, the sounds like function, regression analysis, clustering analysis, and gap analysis. Techniques used for fraud detection fall into two primary classes: statistical techniques and artificial intelligence. == Statistical techniques == Examples of statistical data analysis techniques are: Data preprocessing techniques for detection, validation, error correction, and filling up of missing or incorrect data. Calculation of various statistical parameters such as averages, quantiles, performance metrics, probability distributions, and so on. For example, the averages may include average length of call, average number of calls per month and average delays in bill payment. Models and probability distributions of various business activities either in terms of various parameters or probability distributions. Computing user profiles. Time-series analysis of time-dependent data. Clustering and classification to find patterns and associations among groups of data. Data matching Data matching is used to compare two sets of collected data. The process can be performed based on algorithms or programmed loops. Trying to match sets of data against each other or comparing complex data types. Data matching is used to remove duplicate records and identify links between two data sets for marketing, security or other uses. Sounds like Function is used to find values that sound similar. The Phonetic similarity is one way to locate possible duplicate values, or inconsistent spelling in manually entered data. The ‘sounds like’ function converts the comparison strings to four-character American Soundex codes, which are based on the first letter, and the first three consonants after the first letter, in each string. Regression analysis allows you to examine the relationship between two or more variables of interest. Regression analysis estimates relationships between independent variables and a dependent variable. This method can be used to help understand and identify relationships among variables and predict actual results. Gap analysis is used to determine whether business requirements are being met, if not, what are the steps that should be taken to meet successfully. Matching algorithms to detect anomalies in the behavior of transactions or users as compared to previously known models and profiles. Techniques are also needed to eliminate false alarms, estimate risks, and predict future of current transactions or users. Some forensic accountants specialize in forensic analytics which is the procurement and analysis of electronic data to reconstruct, detect, or otherwise support a claim of financial fraud. The main steps in forensic analytics are data collection, data preparation, data analysis, and reporting. For example, forensic analytics may be used to review an employee's purchasing card activity to assess whether any of the purchases were diverted or divertible for personal use. == Artificial intelligence == Fraud detection is a knowledge-intensive activity. The main AI techniques used for fraud detection include: Data mining to classify, cluster, and segment the data and automatically find associations and rules in the data that may signify interesting patterns, including those related to fraud. Expert systems to encode expertise for detecting fraud in the form of rules. Pattern recognition to detect approximate classes, clusters, or patterns of suspicious behavior either automatically (unsupervised) or to match given inputs. Machine learning techniques to automatically identify characteristics of fraud. Neural nets to independently generate classification, clustering, generalization, and forecasting that can then be compared against conclusions raised in internal audits or formal financial documents such as 10-Q. Other techniques such as link analysis, Bayesian networks, decision theory, and sequence matching are also used for fraud detection. A new and novel technique called System properties approach has also been employed where ever rank data is available. Statistical analysis of research data is the most comprehensive method for determining if data fraud exists. Data fraud as defined by the Office of Research Integrity (ORI) includes fabrication, falsification and plagiarism. == Machine learning and data mining == Early data analysis techniques were oriented toward extracting quantitative and statistical data characteristics. These techniques facilitate useful data interpretations and can help to get better insights into the processes behind the data. Although the traditional data analysis techniques can indirectly lead us to knowledge, it is still created by human analysts. To go beyond, a data analysis system has to be equipped with a substantial amount of background knowledge, and be able to perform reasoning tasks involving that knowledge and the data provided. In effort to meet this goal, researchers have turned to ideas from the machine learning field. This is a natural source of ideas, since the machine learning task can be described as turning background knowledge and examples (input) into knowledge (output). If data mining results in discovering meaningful patterns, data turns into information. Information or patterns that are novel, valid and potentially useful are not merely information, but knowledge. One speaks of discovering knowledge, before hidden in the huge amount of data, but now revealed. The machine learning and artificial intelligence solutions may be classified into two categories: 'supervised' and 'unsupervised' learning. These methods seek for accounts, customers, suppliers, etc. that behave 'unusually' in order to output suspicion scores, rules or visual anomalies, depending on the method. Whether supervised or unsupervised methods are used, note that the output gives us only an indication of fraud likelihood. No stand alone statistical analysis can assure that a particular object is a fraudulent one, but they can identify them with very high degrees of accuracy. As a result, effective collaboration between machine learning model and human analysts is vital to the success of fraud detection applications. === Supervised learning === In supervised learning, a random sub-sample of all records is taken and manually classified as either 'fraudulent' or 'non-fraudulent' (task can be decomposed on more classes to meet algorithm requirements). Relatively rare events such as fraud may need to be over sampled to get a big enough sample size. These manually classified records are then used to train a supervised machine learning algorithm. After building a model using this training data, the algorithm should be able to classify new records as either fraudulent or non-fraudulent. Supervised neural networks, fuzzy neural nets, and combinations of neural nets and rules, have been extensively explored and used for detecting fraud in mobile phone networks and financial statement fraud. Bayesian learning neural network is implemented for credit card fraud detection, telecommunications fraud, auto claim fraud detection, and medical insurance fraud. Hybrid knowledge/statistical-based systems, where expert knowledge is integrated with statistical power, use a series of data mining techniques for the purpose of detecting cellular clone fraud. Specifically, a rule-learning program to uncover indicators of fraudulent behaviour from a large database of customer transactions is implemented. Cahill et al. (2000) design a fraud signature, based on data of fraudulent calls, to detect telecommunications fraud. For scoring a call for fraud its probability under the account signature is compared to its probability under a fraud signature. The fraud signature is updated sequentially, enabling event-driven fraud detection. Link analysis comprehends a different approach. It relates known fraudsters to other individuals, using record linkage and social network methods. This type of detection is only able to detect fra

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  Anytime algorithm

  In computer science, an anytime algorithm is an algorithm that can return a valid solution to a problem even if it is interrupted before it ends. The algorithm is expected to find better and better solutions the longer it keeps running. Most algorithms run to completion: they provide a single answer after performing some fixed amount of computation. In some cases, however, the user may wish to terminate the algorithm prior to completion. The amount of computation required may be substantial, for example, and computational resources might need to be reallocated. Most algorithms either run to completion or they provide no useful solution information. Anytime algorithms, however, are able to return a partial answer, whose quality depends on the amount of computation they were able to perform. The answer generated by anytime algorithms is an approximation of the correct answer. == Names == An anytime algorithm may be also called an "interruptible algorithm". They are different from contract algorithms, which must declare a time in advance; in an anytime algorithm, a process can just announce that it is terminating. == Goals == The goal of anytime algorithms are to give intelligent systems the ability to make results of better quality in return for turn-around time. They are also supposed to be flexible in time and resources. They are important because artificial intelligence or AI algorithms can take a long time to complete results. This algorithm is designed to complete in a shorter amount of time. Also, these are intended to have a better understanding that the system is dependent and restricted to its agents and how they work cooperatively. An example is the Newton–Raphson iteration applied to finding the square root of a number. Another example that uses anytime algorithms is trajectory problems when you're aiming for a target; the object is moving through space while waiting for the algorithm to finish and even an approximate answer can significantly improve its accuracy if given early. What makes anytime algorithms unique is their ability to return many possible outcomes for any given input. An anytime algorithm uses many well defined quality measures to monitor progress in problem solving and distributed computing resources. It keeps searching for the best possible answer with the amount of time that it is given. It may not run until completion and may improve the answer if it is allowed to run longer. This is often used for large decision set problems. This would generally not provide useful information unless it is allowed to finish. While this may sound similar to dynamic programming, the difference is that it is fine-tuned through random adjustments, rather than sequential. Anytime algorithms are designed so that it can be told to stop at any time and would return the best result it has found so far. This is why it is called an interruptible algorithm. Certain anytime algorithms also maintain the last result, so that if they are given more time, they can continue from where they left off to obtain an even better result. == Decision trees == When the decider has to act, there must be some ambiguity. Also, there must be some idea about how to solve this ambiguity. This idea must be translatable to a state to action diagram. == Performance profile == The performance profile estimates the quality of the results based on the input and the amount of time that is allotted to the algorithm. The better the estimate, the sooner the result would be found. Some systems have a larger database that gives the probability that the output is the expected output. One algorithm can have several performance profiles. Most of the time performance profiles are constructed using mathematical statistics using representative cases. For example, in the traveling salesman problem, the performance profile was generated using a user-defined special program to generate the necessary statistics. In this example, the performance profile is the mapping of time to the expected results. This quality can be measured in several ways: certainty: where probability of correctness determines quality accuracy: where error bound determines quality specificity: where the amount of particulars determine quality == Algorithm prerequisites == Initial behavior: While some algorithms start with immediate guesses, others take a more calculated approach and have a start up period before making any guesses. Growth direction: How the quality of the program's "output" or result, varies as a function of the amount of time ("run time") Growth rate: Amount of increase with each step. Does it change constantly, such as in a bubble sort or does it change unpredictably? End condition: The amount of runtime needed

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  Oblivion (2013 film)

  Oblivion is a 2013 American epic post-apocalyptic science fiction action film produced and directed by Joseph Kosinski from a screenplay by Karl Gajdusek and Michael deBruyn, starring Tom Cruise in the main role alongside Morgan Freeman, Olga Kurylenko, Andrea Riseborough, Nikolaj Coster-Waldau, and Melissa Leo in supporting roles. Based on Kosinski's unpublished Radical Comics graphic novel of the same name, the film pays homage to 1970s sci-fi, and is a "love story" set in 2077 on an Earth desolated by an alien war; a maintenance technician on the verge of completing his mission finds a woman who survived from a space ship crash, leading him to question his purpose and discover the truth about the war. Oblivion premiered in Buenos Aires on March 26, 2013, and was released in theaters by Universal Pictures on April 19. The film grossed $286 million worldwide on a production budget of $120 million and received mixed reviews from critics. == Plot == In 2017, aliens known as Scavengers attack Earth and destroy the Moon, triggering global natural disasters. Although humanity wins the war using nuclear weapons, Earth is left uninhabitable. Sixty years later, the remnants of humanity have relocated to a colony on Saturn's moon Titan, except for Unit 49—technician Jack and his communications officer Victoria—who are scheduled to join them in two weeks. The pair oversee hydro rigs that convert seawater into fusion energy for the Tet, the last remaining human colony ship in orbit. Though Jack and Victoria are romantically involved and have had their memories erased for security reasons, Jack experiences recurring dreams of an unknown woman. He also secretly visits a hidden, verdant valley where he has built a lakeside cabin and collects relics of Earth's past. While investigating a missing drone—autonomous, highly advanced, and heavily armed machines—Jack is nearly captured by Scavengers. Later, he discovers the Scavengers are transmitting a signal into space. A NASA pod crash-lands at the signal's coordinates, carrying five humans in suspended animation, including the woman from Jack's dreams. A drone arrives and destroys four of the pods, but Jack rescues the remaining one and brings the unconscious woman to Unit 49's base. After reviving her, Jack and Victoria learn that the woman, Julia, has been in stasis aboard the Odyssey spaceship since 2017. Julia insists on recovering the ship's flight recorder. However, she and Jack are captured by Scavengers and brought to the Raven Rock Mountain Complex. Their leader, Malcolm, reveals that the Scavengers are actually surviving humans. Malcolm needs Jack to reprogram a captured drone to deliver a nuclear bomb, built from Odyssey's reactor, to the Tet. Jack refuses, so Malcolm releases him and Julia, urging him to seek the truth in the radiation zone, which is supposedly deadly and off-limits. Julia helps Jack recall that she is his wife, and fragments of his memories begin to return. When they arrive back at Unit 49, a devastated Victoria informs Sally, the Tet's mission controller, that she and Jack are no longer an "effective team." A drone activates and kills Victoria. Jack and Julia destroy the drone, but crash their aircraft inside the radiation zone. There, they encounter another version of Jack—"Jack-52"—who arrives to repair the drone. Jack subdues him, but Julia is seriously injured in the fight. Jack impersonates his clone to infiltrate Unit 52, meets Victoria-52, and steals medical supplies for Julia. They rest at his cabin. At Raven Rock, Malcolm reveals the truth: humanity lost the war, and the Tet is an alien machine intelligence harvesting Earth's resources. After the Moon's destruction, the Tet deployed thousands of clones of astronaut Jack Harper—brainwashed into obedience—to exterminate the remaining humans. Malcolm had assumed these clones were inhuman until witnessing Jack show interest in a discarded book, hinting at lingering humanity. Jack reprograms the captured drone, but it is destroyed in a surprise attack by other drones, leaving Malcolm badly wounded. Jack and Julia resolve to deliver the bomb themselves; Julia enters a stasis pod. En route, Jack listens to the Odyssey's flight recorder, which reveals the original Jack Harper and Victoria were astronauts sent to explore Titan before being confronted by the Tet. The pair were captured, but not before Jack ejected the remaining crew—including Julia—in stasis pods to protect them. Jack gains access to the Tet by claiming he is delivering Julia, as previously instructed. However, the stasis pod contains a dying Malcolm. Jack and Malcolm detonate the bomb, destroying the Tet and themselves. Julia later awakens at the cabin. Three years later, Julia lives there and it is revealed she had a daughter with Jack. A group of Raven Rock survivors arrives, alongside Jack-52, who has begun regaining fragments of his own lost identity. == Cast == Tom Cruise as Jack Harper—Tech 49, a technician who works to repair drones on Earth and questions his mission. Originally, he was the American commander of a mission en route to Titan who was captured by the Tet and cloned to fight humanity. Cruise also plays Jack Harper—Tech 52, a clone who seeks out Julia after the destruction of the Tet. Morgan Freeman as Malcolm Beech, an American veteran soldier and leader of a large community of scavengers, the human survivors of the alien Tet's attacks. Olga Kurylenko as Julia Rusakova Harper, Jack's wife and a Russian crew member on the Odyssey, who was sent back towards Earth by her husband to protect her from the initial contact with the Tet. Andrea Riseborough as Victoria "Vika" Olsen, Jack's communications partner and housemate. Originally, she was the British co-pilot of Jack's mission to Titan who was captured and cloned to assist in the Tet's war on humanity. Riseborough also plays a clone of Vika who Jack misleads to obtain medical supplies. Nikolaj Coster-Waldau as Sergeant Sykes, the main military commander of Beech's community of scavengers who is skeptical of Jack at first. Melissa Leo as the Tet, an alien artificial intelligence seeking to acquire Earth's natural resources and wipe out humanity. Leo also plays Sally, the mission director of Jack and Julia's mission to Titan; her likeness was copied by the Tet to serve as its visual and auditory representation. Zoë Bell as Kara, a soldier and member of the scavengers. == Production == === Development === Joseph Kosinski started the movie process by beginning work on a graphic novel called Oblivion featuring his story. While the completion of this would be teased to the public and the concept was used to pitch the movie, it was never finished and Kosinski claims he never intended to, stating it was "just a stage in the project [of film development]". Arvid Nelson was billed as co-writer and Radical Comics was attached as publisher. The novel was never finished; Kosinski explaining: "the partnership with Radical Comics allowed me to continue working on the story by developing a series of images and continuing to refine the story more over a period of years. Then I basically used all that development as a pitch kit to the studio. So even though we really never released it as an illustrated novel the story is being told as a film, which was always the intention." Walt Disney Pictures, which produced Kosinski's previous film Tron: Legacy (2010), acquired the Oblivion film adaptation rights from Radical Comics and Kosinski after a heated auction in August 2010. The film was a directing vehicle for Kosinski, with Barry Levine producing, and Jesse Berger executive producing. Other studios that made bids on the film were Paramount Pictures, 20th Century Fox, and Universal Pictures. Disney subsequently released the rights after realizing the PG-rated film they envisioned, in line with their family-oriented reputation, would require too many story changes. Universal, which had also bid for the original rights, then bought them from Kosinski and Radical and authorized a PG-13 film version. The film's script was originally written by Kosinski and William Monahan and underwent a first rewrite by Karl Gajdusek. When the film passed into Universal's hands, a final rewrite was done by Michael Arndt, under the pen name "Michael deBruyn". Universal was particularly appreciative of the script, saying, "It's one of the most beautiful scripts we've ever come across." The Bubble Ship operated by Cruise's main character, Jack 49, was inspired by the Bell 47 helicopter (often colloquially referred to as a "bubble cockpit" helicopter), a utilitarian 1947 vehicle with a transparent round canopy that Kosinski saw in the lobby of the Museum of Modern Art in Manhattan, and which he likened to a dragonfly. Daniel Simon, who previously worked with Kosinski as the lead vehicle designer on Tron: Legacy, was tasked with creating the Bubble Ship from this basis, incorporating elements evocative of an advanced fighter

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  Agentive logic

  Agentive logic (also called the logic of action or logic of agency) is the field of philosophical logic and logic in computer science that studies formal representations of agents, their actions, and their abilities. An agentive logic in the narrower sense is a formal system whose primitive operators express that an agent does something, can do something, or sees to it that something is the case. Agentive logics generalise modal logic by adding modalities indexed to agents and to actions. Typical examples include: STIT logics (from sees to it that) with operators of the form [ i s t i t : φ ] {\displaystyle [i\ {\mathsf {stit}}:\varphi ]} meaning that agent i {\displaystyle i} sees to it that φ {\displaystyle \varphi } holds; dynamic logics of action with program-like modalities [ α ] φ {\displaystyle [\alpha ]\varphi } and ⟨ α ⟩ φ {\displaystyle \langle \alpha \rangle \varphi } meaning, roughly, that after every (respectively, some) execution(s) of action α {\displaystyle \alpha } , φ {\displaystyle \varphi } holds; logics with explicit agentive operators such as "can do", "brings about", or "is able to ensure". Agentive logics are used in action theory in philosophy, in the semantics of natural language, in the theory of program verification, and in artificial intelligence, where they underpin formalisms for reasoning about actions, planning, and intelligent agents. == Terminology and scope == The adjective agentive derives from the Latin agens ("one who acts") and originally referred to the grammatical agent of a verb. In logical contexts it designates operators or predicates whose primary argument position is an agent rather than a proposition alone, for example A i φ {\displaystyle A_{i}\varphi } ("agent i {\displaystyle i} does φ {\displaystyle \varphi } ") or C i φ {\displaystyle C_{i}\varphi } ("agent i {\displaystyle i} can bring about φ {\displaystyle \varphi } "). In contemporary literature, agentive logic is sometimes used narrowly for formal reconstructions of St. Anselm's modal account of facere ("to do"). More broadly, the term is used interchangeably with logic of action or logic of agency to cover a family of modal and dynamic logics designed to capture the structure of action and choice. == Historical background == === Medieval and early modern roots === Medieval logicians already explored analogies between modalities of action and alethic modalities such as possibility and necessity, for instance, in discussions of obligation and power. An influential early agentive analysis is due to St. Anselm (11th century), who treated "doing φ {\displaystyle \varphi } " as a kind of modal operator on propositions, anticipating later modal logics of agency. Modern reconstructions of Anselm's theory show that the resulting "agentive logic" can be modelled with neighbourhood semantics and satisfies a recognisable square of opposition. === Modern logic of action === Modern study of the logic of action began in the mid-20th century, parallel to developments in deontic logic and tense logic. Early systems were proposed by Georg Henrik von Wright, Stig Kanger, and others, often motivated by questions about norms and responsibility. From the 1960s onward, two largely independent but eventually converging traditions emerged: a branching-time tradition, culminating in STIT logics, emphasising agents' choices among possible futures; and dynamic logics of programs and actions, developed within computer science to reason about program execution. In the 1990s and 2000s, action logics were further developed in connection with knowledge representation, planning, and multi-agent systems in AI, and with dynamic and update semantics in linguistics. == Core ideas == Despite their diversity, most agentive logics share some general themes: Agents are treated as explicit indices of modal operators, as in [ i d o e s ] φ {\displaystyle [i\ {\mathsf {does}}]\varphi } or C i φ {\displaystyle C_{i}\varphi } . Actions are represented either implicitly, via changes between possible worlds along an accessibility relation, or explicitly, as terms denoting primitive and composite actions. Choice and ability are captured by modalities describing what an agent can ensure, usually relative to assumptions about the environment and other agents. Formal properties such as closure under composition, interaction between different agents, and connections to obligation (what an agent ought to do) and knowledge (what an agent knows how to do) are investigated. == STIT logics == STIT ("sees to it that") logics, originating in work by Nuel Belnap and collaborators, treat agency in a branching-time framework. A STIT model consists of a partially ordered set of moments with a tree-like structure, sets of histories (maximal branches through the tree), and for each agent at each moment, a partition of the histories through that moment representing the choices available to the agent. Intuitively, an agent's action at a moment determines which equivalence class (choice cell) of histories becomes actual; a formula [ i s t i t : φ ] {\displaystyle [i\ {\mathsf {stit}}:\varphi ]} is true at a history–moment pair if φ {\displaystyle \varphi } holds on all histories in the choice cell corresponding to the agent's current action. Different STIT operators have been distinguished, notably: the Chellas STIT operator, often written [ i c s t i t : φ ] {\displaystyle [i\ {\mathsf {cstit}}:\varphi ]} , which requires only that the agent's choice guarantees φ {\displaystyle \varphi } ; and the deliberative STIT operator, [ i d s t i t : φ ] {\displaystyle [i\ {\mathsf {dstit}}:\varphi ]} , which additionally requires that φ {\displaystyle \varphi } is not already historically necessary. STIT frameworks have been extended with group agency operators, temporal modalities, epistemic operators, and deontic operators to study responsibility, collective action, and obligations under indeterminism. == Dynamic logics of action == Dynamic logic was originally developed to reason about the behaviour of computer programs, treating program execution as a kind of action. In propositional dynamic logic (PDL), action terms α , β , … {\displaystyle \alpha ,\beta ,\dots } denote abstract programs or actions, and formulas of the form [ α ] φ {\displaystyle [\alpha ]\varphi } and ⟨ α ⟩ φ {\displaystyle \langle \alpha \rangle \varphi } express that all, respectively some, terminating executions of α {\displaystyle \alpha } lead to states where φ {\displaystyle \varphi } holds. From the standpoint of agentive logic, dynamic logic provides: a language for building complex actions from primitives via sequencing, choice, and iteration (e.g., α ; β {\displaystyle \alpha ;\beta } , α ∪ β {\displaystyle \alpha \cup \beta } , α ∗ {\displaystyle \alpha ^{}} ); a Kripke semantics in which actions correspond to labelled accessibility relations; and proof systems (such as Hoare logic and weakest precondition calculi) for reasoning about the correctness of action sequences. Extensions such as concurrent dynamic logic add operators for parallel composition, allowing reasoning about interacting processes and concurrent actions. John-Jules Ch. Meyer and others have argued that dynamic logic is a natural base for logics of agents, by adding modalities for knowledge, belief, and ability on top of the action modalities. Dynamic logics have also been applied to normative reasoning, yielding dynamic deontic logics where actions are related to obligations and permissions, and to dynamic epistemic logics in which information-changing actions such as announcements are modelled as programs. == Situation calculus and other action formalisms == In artificial intelligence, reasoning about action and change is often based on first-order languages that explicitly represent situations, events, and fluents (time-varying properties). The best known is situation calculus, introduced by John McCarthy and developed extensively by Raymond Reiter. In such formalisms: action terms name primitive actions; a function symbol (often d o {\displaystyle {\mathsf {do}}} ) maps an action and a situation to a successor situation; and axioms describe which fluents hold in which situations and how actions change them. Reiter's successor state axioms give compact specifications of how each fluent changes under all actions, and precondition axioms specify when actions are possible. Related formalisms include the event calculus and fluent calculus, which provide alternative ways of representing events and their effects. While these systems are often first-order rather than modal, they are closely related to agentive logics: their action terms and transition structures can be seen as providing models for dynamic or STIT-style modalities, and conversely, dynamic logics can be used as abstract specification languages for such AI formalisms. == Ability, agency, and related modalities == Many agentive logics introduce explicit operators for ability or "can-do"

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  Really Simple Licensing

  Really Simple Licensing (RSL) is an open content licensing standard that allows web publishers to set terms for web crawlers gathering training data for generative AI use. It was launched on September 10, 2025 and is managed by the nonprofit RSL Collective, co-founded by RSS co-creator Eckart Walther and former Ask.com CEO Doug Leeds. Participating companies at launch include Reddit, Yahoo, and Medium. Publishers can implement the RSL standard by adding licensing terms to their robots.txt files.

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  Alice and Sparkle

  Alice and Sparkle is a 2022 illustrated children's book published by American technology product designer Ammaar Reshi. Reshi created the book using artificial intelligence programs ChatGPT and Midjourney in one weekend, which sparked controversy among artists, both in regard to the copyright status of the book and the quality of the illustration and text. == Plot == A girl named Alice discovers a group of magical and benevolent artificial intelligence beings. She knows that artificial intelligence is powerful, and that it has the power to do good and evil depending on how it is used. One day, she creates her own artificial intelligence and names it Sparkle. Sparkle helps Alice with her homework and plays with her, and they quickly become good friends. However, Sparkle soon grows more powerful and begins to make its own decisions, which makes Alice both proud and scared. She knows that it is her responsibility to guide Sparkle to do good, not evil. Together, Alice and Sparkle use their knowledge to make the world a better place and to teach people about the power of artificial intelligence. The two live happily ever after, spreading the magic of artificial intelligence. == Structure == Including the dedication and postscript, the book contains twenty four pages, about half of which being illustrations provided by Midjourney. The very short story, composed of text generated by ChatGPT, contains 343 words. Some of the illustrations are accompanied by descriptions, at least one of which was provided by Reshi. Both Alice's and Sparkle's appearances change significantly between illustrations, although Alice's is more consistent. Reshi said Midjourney was unable to generate consistent images of Sparkle, so he had to include a line in the book saying that it could turn "into all kinds of robot shapes". == Creation == When reading a children's book to his friend's daughter, Ammaar Reshi "decided he wanted to write his own". He had no experience with creative writing or illustration, so instead used the chatbot ChatGPT to write the story for him and used the image generation software Midjourney to illustrate it. On December 4, 2022, 72 hours after having the idea for the book, he published it on Amazon's digital bookstore, and published a paperback version the following day. == Controversy == On December 9, 2022, Reshi made a thread on Twitter about his experience publishing the book, which soon went viral. Reshi received heavy backlash from artists with concerns over the ethics of art generated by artificial intelligence. He also received death threats and messages encouraging self-harm because of his publication. Many writers and illustrators criticized both the creation process and the product itself, claiming that if artificial intelligence programs such as Midjourney are trained on existing illustrations, then the original artists should be financially compensated for derivative works such as Alice and Sparkle. The book was temporarily removed from Amazon in January 2023 because of "suspicious review activity", caused by a high volume of both five-star and one-star reviews.

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  Mobile Fortify

  Mobile Fortify is a mobile app used by United States Immigration and Customs Enforcement (ICE) on their government-issued phones. The app allows agents to take a photo in order to gather biometrics, including contactless fingerprints and faceprints, for the purpose of identifying an individual and their potential immigration status. The app was created by NEC. == History == In June 2025, use of Mobile Fortify by ICE was uncovered through leaked emails and the user manual, reported by 404 Media. The app is internally developed, and details of the parent company and developer were initially unknown. In January 2026, the DHS's 2025 AI Use Case Inventory revealed the vendor as NEC Corporation, an international conglomerate with subsidiaries in Argentina, Australia, China, India and Malaysia. Later that month, several senators demanded transparency around the app and its origins, and that ICE stop using it. A second letter was sent again in November, after hearing no response to the previous letter from ICE. == Technology == Unlike other facial recognition software, Fortify uses federally linked databases. By contrast, Clearview AI uses public social media databases for biometric scanning. Federal databases include DHS's automated biometric identification system (IDENT), containing more than 270 million biometric records, and Customs and Border Protection's Traveler Verification Service. The State Department's visa and passport photo database, the FBI's National Crime Information Center, National Law Enforcement Telecommunications Systems, and CBP's TECS and Seized Assets and Case Tracing System (SEACATS). == Oversight == Several senators urged ICE to stop using the app for fear of infringing on fourth amendment and first amendment rights, and requested details on who developed the app, when it was deployed, whether the app was tested for accuracy, and policies and practices governing its use. In June 2025, they sent an open letter to Todd Lyons, ICE acting director, signed by senators Cory Booker, Chris Van Hollen, Ed Markey, Bernie Sanders, Adam Schiff, Tina Smith, Elizabeth Warren, and Ron Wyden. On November 3, a second letter was sent to the ICE by senators, after not receiving answers to questions from the previous letter deadlined for October 2. == Criticism == Mobile Fortify, and ICE's use of similar biometric identification technologies (such as Mobile Identify, an app similar to Mobile Fortify to be used by local or regional law enforcement to assist in immigration enforcement ) has faced scrutiny from a variety of digital rights organizations, politicians, and news outlets. The criticism is already considered to potentially be a reason why the similar Mobile Identify app was pulled from the Google Play Store. Facial recognition technologies are known to produce false-positives and generally unreliable results, especially on those with darker skin tones. ICE has already previously mistakenly arrested a U.S. citizen under the belief he was illegally in the country, and later stated that he "could be deported based on biometric confirmation of his identity" prior to his release. U.S. representative Bennie Thompson, ranking member of the House Homeland Security Committee has previously commented that "ICE officials have told us that an apparent biometric match by Mobile Fortify is a ‘definitive’ determination of a person's status and that an ICE officer may ignore evidence of American citizenship—including a birth certificate—if the app says the person is an alien," and that "Mobile Fortify is a dangerous tool in the hands of ICE, and it puts American citizens at risk of detention and even deportation," On January 19, 2026, 404 Media reported on a case where a woman, identified in court documents as "MJMA", was scanned by Mobile Fortify twice in the same interaction, and two entirely different names were provided by the app. According to the Innovation Law Lab, whose attorneys are representing MJMA, both of the names were incorrect. ICE has stated that they will not allow people to decline to be scanned by Mobile Fortify, and that photos taken, even those of U.S. citizens, will be stored for 15 years, something that has been criticized primarily because ICE has not performed a Privacy Impact Assessment (PIA) for Mobile Fortify, the right to decline other forms of biometric verification to the U.S. government is often available under other circumstances, and the 15 year window is viewed as unnecessarily large.

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  Josh (app)

  Josh (stylized as JOSH) was a video-sharing social networking service but it has since evolved into a live call and chat application owned by VerSe Innovation – an Indian technology company based in Bangalore, India. Josh was an Indian short video app that was launched in immediately after the Indian Government banned TikTok and other Chinese apps in June 2020. The founders of the platform have promoted the app as the “Instagram for Bharat” referring to their focus on the Indian audience that speaks its own regional and state languages. Josh was among the top 10 most downloaded apps social and entertainment apps in India of 2021 and had 150 million monthly active users as per April 2022. The word 'Josh' translates to fervour or passion. The app was launched under the aegis of the Atmanirbhar Bharat campaign and to compete with the duopoly of Google and Facebook in India. Josh's parent company VerSe Innovations Pvt. Ltd. owns another startup Dailyhunt, which a content and news aggregator application. Both Dailyhunt and Josh are a part of the VerSe's focus on the "next billion" regional language users of India. Founders Virendra Gupta and Umang Bedi conceptualised Josh as a short-video platform that made content creation accessible to vernacular language users, essentially the non-English speaking audience in India. == Features == Josh is currently available in 12 Indian languages and allows users to upload, share, remix bite-sized videos of up to 120 seconds. There are various categories across the video section including viral, trending, glamour, dance, devotion, yoga and cooking among others. Similar to Instagram and TikTok, it has a video feed which is curated for individuals on the basis of their app behaviour. The app hosts many daily, weekly and monthly social media challenges. == Funding == In December 2020, within 3 months of its launch, Josh's parent app VerSe Innovation raised more than $100 million from investors including Alphabet Inc's Google and Microsoft. In February 2021, VerSe Innovation raised $100 million in Series H funding from Qatar Investment Authority, the sovereign wealth fund of the State of Qatar, and Glade Brook Capital Partners. In August 2021, VerSe raised over $450 million in its Series I financing round with a valuation of $1 billion. Investors included Canada Pension Plan Investment Board (CPPIB), Siguler Guff, Baillie Gifford, Carlyle Asia Partners Growth II affiliates, and others. The startup announced its plan to expand overseas and broaden its ecommerce play for both Dailyhunt and Josh. In April 2022, VerSe announced that it has raised $805 million in funding from investors at a valuation of nearly $5 billion. ByteDance Offloads Stake In Josh Parent VerSe, Exits At 56% Discount == Partnerships == In February 2021, Saregama and Josh signed a music licensing deal, wherein Josh expanded its musical library with 1.3 lakh songs from Saregama in 25 different languages. To improve their user experience, Josh partnered with computer vision company D-ID in August 2021. The company helped Josh introduce photo-to-video features, live portrait technology, animate their photos etc. In order to solidify their efforts in enhancing Josh, VerSe acquired Indian social networking platform GolBol in October 2021. The move came as an effort by the startup to strengthen their discovery initiatives on the platform and classify content at scale and understand the core behaviour of Indian regional audiences. Josh has also announced its plans to include live commerce as a potential revenue stream through its partnership with multiple large e-commerce players. == Notable campaigns == Say No To Dowry – In association with Josh, the Kerala Police partook in the #SayNo2Dowry online social media campaign that was started to highlight and stop the social evil in the state. Salute India – Josh entered the Guinness World Records by creating the largest online video album of people saluting (29,529). It organised an online campaign #SaluteIndia on the app during the 75th Independence Day of India during 10–15 August 2021.

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  A Very Fatal Murder

  A Very Fatal Murder is a podcast produced by the satirical publication The Onion. A parody of true crime podcasts, A Very Fatal Murder is hosted by fictional New York City reporter David Pascall, who travels to the small town Bluff Springs, Nebraska to investigate the murder of prom queen Hayley Price. Pascall is voiced by David Sidorov, who also wrote for the podcast. The podcast premiered on January 23, 2018, and consists of 7 episodes. Season 2 was released in its entirety on May 11, 2019. == Production == A Very Fatal Murder satirizes popular true crime podcasts such as Serial, S-Town, and My Favorite Murder. According to head writer Katy Yeiser, the podcast is not meant as a take down of any particular podcast, but rather an ode to the genre. == Synopsis == The podcast follows fictional investigative reporter David Pascall (voiced by David Sidorov) who is searching for the perfect murder to create an award-winning podcast about. He is assisted by ETHL (the Extremely Timely Homicide Locator), an MIT-created computer programmed to find "the most interesting, violent, culturally relevant murder cases in America". == Episodes == === Season 1 === === Season 2 === == Reception == The podcast received mostly positive reviews, and was largely praised for attacking true-crime tropes such as the "hot dead girl" and the romanticization of small-town America. === Awards ===

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  SmartAction

  SmartAction Company LLC is a U.S.-based software company that develops artificial intelligence–driven virtual agents for customer service applications, including voice-based interactive voice response (IVR) systems, chat, and SMS. The company was founded in 2009 by inventor and entrepreneur Peter Voss and is headquartered in Fort Worth, Texas. == History == In 2001, Peter Voss founded Adaptive AI, Inc., a research and development company focused on artificial intelligence concepts. In 2009, Voss founded SmartAction Company, LLC to commercialize customer-service automation software derived from this work. The company’s initial products focused on automating inbound and outbound calls for contact center environments. In November 2022, Kyle Johnson was appointed chief executive officer, succeeding Gary Davis, who had served as CEO since 2020. In 2024, SmartAction was acquired by Capacity, an AI-powered customer support automation company based in St. Louis, Missouri. == Technology == SmartAction develops cloud-based voice automation software that integrates speech recognition and natural language processing to support automated customer interactions in contact center environments. The platform supports automated handling of common customer service tasks and is designed to integrate with enterprise systems.

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