AI Face Combiner

AI Face Combiner — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Security.txt

security.txt is an accepted standard for website security information that allows security researchers to report security vulnerabilities easily. The standard prescribes a text file named security.txt in the well known location, similar in syntax to robots.txt but intended to be machine and human readable, for those wishing to contact a website's owner about security issues. security.txt files have been adopted by Google, GitHub, LinkedIn, and Facebook. == History == The Internet Draft was first submitted by Edwin Foudil in September 2017. At that time it covered four directives, "Contact", "Encryption", "Disclosure" and "Acknowledgement". Foudil expected to add further directives based on feedback. In addition, web security expert Scott Helme said he had seen positive feedback from the security community while use among the top 1 million websites was "as low as expected right now". In 2019, the Cybersecurity and Infrastructure Security Agency (CISA) published a draft binding operational directive that requires all US federal agencies to publish a security.txt file within 180 days. The Internet Engineering Steering Group (IESG) issued a Last Call for security.txt in December 2019 which ended on January 6, 2020. A study in 2021 found that over ten percent of top-100 websites published a security.txt file, with the percentage of sites publishing the file decreasing as more websites were considered. The study also noted a number of discrepancies between the standard and the content of the file. In April 2022 the security.txt file has been accepted by Internet Engineering Task Force (IETF) as RFC 9116. == File format == security.txt files can be served under the /.well-known/ directory (i.e. /.well-known/security.txt) or the top-level directory (i.e. /security.txt) of a website. The file must be served over HTTPS and in plaintext format.
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Fuzzy number

A fuzzy number is a generalization of a regular real number in the sense that it does not refer to one single value but rather to a connected set of possible values, where each possible value has its own weight between 0 and 1. This weight is called the membership function. A fuzzy number is thus a special case of a convex, normalized fuzzy set of the real line. Just like fuzzy logic is an extension of Boolean logic (which uses absolute truth and falsehood only, and nothing in between), fuzzy numbers are an extension of real numbers. Calculations with fuzzy numbers allow the incorporation of uncertainty on parameters, properties, geometry, initial conditions, etc. The arithmetic calculations on fuzzy numbers are implemented using fuzzy arithmetic operations, which can be done by two different approaches: (1) interval arithmetic approach; and (2) the extension principle approach. A fuzzy number is equal to a fuzzy interval. The degree of fuzziness is determined by the a-cut which is also called the fuzzy spread.
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Futuresport

Futuresport is a 1998 American made-for-television sports film directed by Ernest Dickerson, starring Dean Cain, Vanessa Williams, and Wesley Snipes. It originally aired on ABC in October 1998, was released on VHS and DVD in March 1999 and then distributed outside of the U.S. by Minerva Pictures. == Plot == The film is set in 2025, and centers on a sport called "Futuresport" (a combination of basketball, baseball and hockey that uses hoverboards and rollerblades) created as a non-lethal way to reduce gang warfare. Tre Ramzey (Dean Cain) along with his ex-girlfriend Alex Torres (Vanessa Williams) and his old coach Obike Fixx (Wesley Snipes) must prevent an all out war between the North American Alliance and the Pan-Pacific Commonwealth (The Com). At stake is who rules over the Hawaiian Islands—which are being terrorized by Eric Sythe (JR Bourne) and his gang the Hawaiian Liberation Organization (Hilo). It takes a revolutionary sport to stop a revolution. == Cast ==
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Overwatch

Overwatch (abbreviated as OW) is a multimedia franchise centered on a series of multiplayer first-person shooter (FPS) video games developed by Blizzard Entertainment. Overwatch was released in 2016. Overwatch 2 was released in 2022 and the original game was taken offline upon its release, though Blizzard renamed it back to Overwatch in 2026. Overwatch features hero-based combat between two teams of players fighting over various objectives, along with other traditional gameplay modes. Released in 2016, Overwatch lacked a traditional story mode. Instead, Blizzard employed a transmedia storytelling strategy to disseminate lore regarding the game's characters, releasing comics and other literary media, as well as animated media that includes short films. The game enjoyed both critical and commercial success, and garnered a devoted following. The fan community around the franchise has produced a large amount of content including art, cosplay, fan fiction, anime-influenced music videos, Internet memes, and pornography. Blizzard helped launch and promote an esports scene surrounding the game, including an annual Overwatch World Cup, Overwatch League a minor league, and the Overwatch Champions Series which borrowed elements found in traditional American sports leagues. == Gameplay == Both games in the Overwatch series are team-based hero shooters. Players select a hero character from a large roster (52 as of Season 2), divided among three class types. These are: Tanks, who have higher health and generally meant to help protect their teammates from damage, but are larger and easier to hit; Damage, who act as the team's offensive leads; and Support, who heal, provide buffs for teammates, or de-buff the opposing team. Each role also features sub-roles with extra passives. These sub-roles include 'Initiator', 'Stalwart', and 'Bruiser' for Tank. 'Specialist', 'Flanker', 'Recon', and 'Sharpshooter' for Damage. 'Medic', 'Tactician', and 'Survivor' for Support. Players are generally free to change to different heroes while inside their spawn room during the course of a match in response to the current tactics employed by other players. As of the development of Overwatch 2, a standard game features one tank player, two damage players and two support players, a change from having two of each class in its predecessor. Players choose their class before the match, and can only pick characters within that class for the duration of the game. There are different styles of game modes, however, that allow players to choose characters from any class throughout the game. Each hero has a skill kit that includes a primary attack, active skills that require a cooldown period before they can be used again, passive skills that remain active at all times, and an Ultimate skill that can only be used once they fill their Ultimate meter either by damaging opponents, mitigating damage, healing teammates or by passively generating it over time. An update in 2025 saw each hero receive a total of four unique abilities known as perks. Each hero has two minor and two major perks; minor perks consist of smaller changes to a hero's kit, while major perks are intended to affect the match more significantly. At the beginning of each match, all heroes are set to level 1 for each player. As the match progresses, players can individually level up their respective heroes, minor perks are unlocked at level 2, and major perks are unlocked at the maximum level 3. When perks become available, players may only select one of each type of perk; a selected perk becomes irreversibly attached to the current hero for the remainder of the match. If a player switches to another hero mid-match, the previously selected hero retains their level and perk progress. Game types of Overwatch are split between standard matches, competitive play, custom games, and arcade modes. Standard matches have matchmaking based loosely on the player's skill level as measured by the game. Competitive mode uses more strict matchmaking based on a player's current rank on the competitive ladder, with their rank increasing or decreasing when they win or lose a game, respectively. Arcade modes do not use matchmaking and are generally more experimental modes compared to standard and competitive modes. Custom games are created via the workshop and can be utilised to make game modes that are very different from the base game. The workshop, is the software in Overwatch which creates the game using either presets and settings or rules and conditions made by code. These game modes can be published directly onto Overwatch’s custom browse tab or shared off platform using a 5 digit alphanumeric code. Standard and competitive game modes are randomly selected at the start of each match, and are objective based, requiring teams to control a fixed objective point for a duration of time, or escort a payload to a target zone before match time expires. These modes include: Assault (introduced in Overwatch): Also known as 2 Capture Points (or 2CP), Assault has the attacking team tasked with capturing two target points in sequence on the map, while the defending team must stop them. Assault-style maps were removed from main gameplay rotation after Overwatch 2 released but available in the game's arcade mode. It is still available in the game's custom game modes. Since Season 2, Assault-style maps are available in Arcade Mode daily routines. Escort (introduced in Overwatch): Also known as "Payload" by the community, The attacking team is tasked with escorting a payload to a certain delivery point before time runs out, while the defending team must stop them. The payload vehicle moves along a fixed track when any player on the attacking team is close to it, increasing in speed if multiple attackers are present, the increase capping at 3, but will stop if a defending player is nearby; should no attacker be near the vehicle, it will start to move backwards along the track. The payload will also heal any attacking players by 10 health per second while they are near the payload. Passing specific checkpoints will extend the match time and prevent the payload from moving backwards from that point. Hybrid (Assault/Escort) (introduced in Overwatch): The attacking team has to capture the payload (as if it were a target point from Assault) and escort it to its destination, while the defending team tries to hold them back. Control (introduced in Overwatch): Each team tries to capture and maintain a common control point until their capture percentage reaches 100%. This game mode is played in a best-of-three format. Control maps are laid out in a symmetric fashion so no team has an intrinsic position advantage. Push (introduced in Overwatch 2's launch): Each team attempts to secure control of a large robot that pushes one of two barriers to the opposing team's side of the map, whilst being escorted by at least one team member, stopping when enemy players are nearby, similar to the payload movement system in Escort. The team that pushes the payload fully to the other side, or furthest into the enemy territory before the time runs out, wins the match. Flashpoint (introduced in Overwatch 2 in 2023): Similar to Control, each team attempts to capture and maintain a common control point until their capture percentage reaches 100%. This game mode takes place on significantly larger maps with five separate control points, which take a shorter amount of time to capture as compared to a standard Control map. A central control point is always activated first; after it is secured by one team, the remaining four are activated in a random order. The first team to secure three control points wins. Clash (introduced in Overwatch 2 in 2024): Clash maps feature symmetrical maps with five control points. Teams initially vie for control of the central point, with the winning team progressing to the next control point, towards the opponent's base. Opponents can push back by winning control points and shifting the next point away from their base. If a team captures the point closest to the opponent's base, they win. Otherwise the match plays out until one team wins control five times. Arcade modes may include variations of the above modes with experimental rules, and can also include modes like Deathmatch and Capture the Flag. Other common arcade modes include: Elimination (introduced in Overwatch in 2016): Two teams face off in a series of rounds, attempting to wipe out the other team; once a player is killed they remain out of the game until the next round, though they can be revived by Mercy's 'Resurrect' ability. If no team has won a round by a certain time, then the winners are decided by the team that can first take a neutral control point. Players cannot change heroes until the next round. Some of these can be played in "lockout" mode, in which the heroes selected by the winning team for a round are "locked" and cannot be selected in future rounds. Total Mayhem (i
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Pixlr

Pixlr is a group of SaaS creative tools including Pixlr.com, Designs.ai and Vectr.com. Pixlr.com is a cloud-based set of image editing tools and utilities, including AI image generation and enhancements. The Pixlr suite targets users who require subjectively simple, or more advanced, photo editing as well as graphic design. It features a freemium business model with subscription plans—Plus, Premium and Teams. The platform can be used on desktop and also smartphones and tablets. Pixlr is compatible with various image formats such as JPEG, PNG, WEBP, GIF, PSD (Photoshop Document) and PXZ (native Pixlr document format). Designs.ai lets users create content using AI, with a goal of being within two minutes, across different media types including videos, text, banners and audio. Vectr.com was acquired in 2017 before being spun out into Pixlr Group in 2023. == History == Pixlr was founded in 2008 and built on Macromedia Flash. On 19 July 2011, Autodesk announced that they had acquired the Pixlr suite. In 2013, Time listed Pixlr as one of the top 50 websites of the year. In 2017, Pixlr was acquired from Autodesk. It was subsequently rebuilt and relaunched in HTML5 in 2019. In September 2023, Pixlr was awarded as the Top 13 GenAi Web Product by the world's top venture firm Andreessen Horowitz. In November 2023, Pixlr, Designs.ai and Vectr were combined as a new business group named Pixlr Group focusing on generative AI and creative software solutions. In May 2024, Pixlr was featured as one of the top 18 progressive web applications highlighted on Google I/O. == Versions == Pixlr.com rebranded itself as a full creative suite in 2019 by introducing Pixlr X, Pixlr E and Pixlr M. The platform introduced more features in December 2021 with a new logo and added tools which included: Brushes, the 'Heal tool', Animation, and Batch upload. The brush feature enables the creation of hand-drawn effects. The Heal tool allows users to remove unwanted objects from their images whereas the Animation feature can be used to include movements into their edits. Users can also utilize Batch upload to edit up to 50 images simultaneously. In November 2022, Pixlr 2023 was launched, adding more tools such as "AI smart resize", colorization, text wrapping and other additional effects. In November 2023, Pixlr 2024 was launched with Pixlr Designer and new AI-powered updates which includes AI image generation, AI infill, AI inpainting and more.
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Google AI Studio

Google AI Studio is a web-based integrated development environment developed by Google for prototyping applications using generative AI models. Released in December 2023 alongside the Gemini API, the platform provides access to Google's Gemini family of models and related tools for image, video, and audio generation. The service targets both developers and non-technical users for testing prompts and generating code for the Gemini API. == History == Google launched AI Studio on December 13, 2023, as the successor to Google MakerSuite. MakerSuite, introduced at Google I/O in May 2023, had provided similar functionality for Google's PaLM language models. The AI Studio was launched alongside the public release of the Gemini API. == Features == AI Studio's interface consists of a central prompt area and a settings panel for model selection and parameter adjustment. The platform supports chat prompts for multi-turn conversations and includes system instructions for defining model behavior, tone, or specific rules. Users can employ zero-shot and few-shot prompting techniques to guide the model's output format. The platform processes various media types including video, audio, and documents, and can generate images through Imagen models, videos through Veo models, and audio through text-to-speech functionality. Additional tools include real-time streaming for screen sharing and live analysis, code execution in a sandboxed Python environment, grounding with Google Search for current information, URL context for analyzing specific web pages, and a thinking mode for complex reasoning tasks. == Available models == The platform provides access to several Google AI models including the Gemini language models, Imagen for image generation, Veo for video generation, LearnLM for educational applications, and Gemma, Google's open-source model family. == Privacy and data usage == Google AI Studio's data handling differs between free and paid users. For free tier users, Google uses submitted prompts, uploaded files, and generated responses to improve its products and services, with human reviewers potentially reading and annotating the data after disconnection from user accounts. Google advises against submitting sensitive information on the free tier. Users who enable Google Cloud Billing are considered paid service users, and their data is not used for product improvement. Data is processed according to Google's Data Processing Addendum and retained temporarily for abuse monitoring. == Availability == The platform is available at no cost, with API usage subject to a free tier with daily and per-minute rate limits. Access is restricted to users aged 18 and older in specific countries and territories. The service was initially unavailable in the United Kingdom and European Economic Area due to regulatory concerns, which drew user complaints. == Reception == Reviews have noted the platform's accessibility and integration with Gemini models, with features such as real-time screen sharing and large context windows cited as notable capabilities. However, reviewers have raised concerns about the privacy implications for free tier users, whose data is used for model training. Some users have reported inconsistent performance with features like screen streaming and issues with folder uploads for large datasets. The initial geographic restrictions were a point of criticism among developers in affected regions.
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Global Artificial Intelligence Summit & Awards

The Global Artificial Intelligence Summit & Awards (GAISA) is an international conference on Artificial Intelligence organized annually by AICRA. Since its inception in 2019, GAISA has been held at various locations each year. The 5th Edition of GAISA will be Scheduled on April 11-12, 2024, at Bharat Mandapam. GAISA 2025 features a distinguished lineup of speakers, including leading experts, researchers, and executives from top global tech companies. These thought leaders are at the forefront of AI innovation, with deep expertise in areas such as machine learning, robotics, and ethical AI. Their diverse backgrounds span academia, industry, and entrepreneurship, offering unique insights into how AI is reshaping sectors like healthcare, finance, transportation, and more. Attendees can expect thought-provoking discussions on the future of AI, its societal impact, and the transformative potential of emerging technologies in solving complex global challenges Few Speakers are listed below:- Shri Nitin Gadkari, Rao Inderjit Singh, Piyush Goyal, Admiral R Hari Kumar PVSM, AVSM, ADC, Samir V Kamat, Narayan Tatu Rane, Prof. K. Vijay Raghavan and many others. == History == The conference was launched first in 2019 as Vigyan Bhawan New Delhi by AICRA with an objective of discussion and exploring artificial intelligence in engrossed sectors.
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Refik Anadol

Refik Anadol (born November 7, 1985) is a Turkish American media artist and the co-founder of Refik Anadol Studio and Dataland. Recognized as a pioneer in the aesthetics of data visualization and AI arts, his work merges art, technology, science, and architecture. Through media embedded into existing architecture, live audio-visual performances, immersive rooms, exhibitions, AI data paintings and sculptures, and digital collections, Anadol explores collective memories, humanity's relationship to nature, the perception of space and time, and human-machine collaborations. His work has been exhibited in more than seventy cities on six continents. == Early life and education == Anadol was born and raised in Istanbul and grew up in a family of teachers. He taught himself basic programming on a Commodore 64 when he was eight. His connection to machines began with coding and video games. Anadol saw Blade Runner for the first time when he was eight; his mother said the way he perceived his surroundings shifted the day after he saw the film. He was fascinated with its futuristic depiction of downtown Los Angeles, and transfixed by as a scene during which a replicant discovers that her memories are an implanted component of her machine mind, In a 2024 interview with the Financial Times, he said: "Since that moment, one of my inspirations has been that question: 'What can a machine do with someone else's memories?" Anadol attended Istanbul Bilgi University, where he received a BA in photography and video in 2009 and an MFA in visual communication in 2011. In 2014 he earned an MFA in design media arts at UCLA. He was mentored by Casey Reas, Jennifer Steinkamp, and Christian Moeller. == Career and selected works == === 2008–2012: Data painting, Quadrature and Quadrangle, Istanbul Biennial === As an undergraduate, Anadol read a paper by Lev Manovich on augmented space. Manovich's assertion that collaborations between architects and artists could make the "invisible flow of data visible" triggered Anadol's imagination, and in 2008, he altered built space for the first time. Bringing a projector outside, he projected large-scale images onto a concrete to create the illusion of movement. Coining the term "data painting," the piece inspired Anadol to use light as material and data as pigment. In 2010 he created Quadrature with Alican Aktürk, a fellow graduate student, at the SantralIstanbul Art and Culture Center's main gallery building. A live audio-visual performance that examined the relationship between architecture and media, Quadrature used video projection techniques to manipulate footage of quadrilaterals. He followed Quadrature with Quadrangle at SANAA School of Design in Essen, Germany, using the entire 360 degrees of the building as a canvas. In 2011, he was invited to create a media installation at the Istanbul Biennial on the heavily trafficked İstiklal Avenue. He created a site-specific large-scale interpretation of sounds he recorded during different times of day, and used nine projectors to project reinterpreted images. The work was titled Augmented Structures v1.0. Anadol's first solo exhibition, Sceptical Interventions, was held at the Piveneli Gallery in Istanbul in early 2012. Later that year he moved to Los Angeles to attend UCLA's Design Media Arts program. The first place he went after his arrival was downtown Los Angeles. [6] === 2013–2016: Visions of America: Amériques, Infinity Room, Google AMI === In 2013, at Microsoft Research's annual Design Expo, Anadol presented his idea to use the external walls of Walt Disney Concert Hall as a canvas. His presentation brought him to the attention of Gehry Technologies, and with the support of Gehry and his team, Anadol was offered the use of the original 3D model of the concert hall. For his 2014 thesis project, with assistance from architects and UCLA researchers, he created a site-specific architectural video installation inside the concert hall that accompanied a Los Angeles Philharmonic performance of Edgard Varèse's Amérique. Titled Visions of America: Amériques, Anadol used algorithmic sound analysis to listen and respond to the music in real-time. He tracked conductor Esa-Pekka Salonen's heartbeat with a sensor and used a 3-D camera system to integrate Salonen's movements. He created Infinity Room at the Zorlu PSM for the 2015 Istanbul Biennial. Rather than creating an illusion only with mirrors, Anadol used pixel and 3D projection mapping to transform every surface of the room into an abstract infinite moving space. A temporary immersive environment, Infinity Room was also exhibited at events including South by Southwest in Austin, Texas, the New Zealand Festival in Wellington, New Zealand, and Jeffrey Deitch in Los Angeles. In 2016, Anadol was awarded the first Google Artists and Machine Intelligence Artist Residency; it was just after a team at Google opened up the algorithm for DeepDream, a computer vision program that prompted Anadol's realization that if a machine could learn, it could remember, dream, and hallucinate. === 2017–2018: Winds of Boston, Archive Dreaming, Melting Memories, WDCH Dreams === In 2017, he created the data painting Winds of Boston, a 6' x 13' foot video installation in the lobby of a Boston office building, using software he created to read, analyze and visualize wind speed, direction, and gust patterns along with time and temperature at 20-second intervals recorded over a one-year period at Logan International Airport. Later in the year, he used AI to generate infinite new outputs based on a massive dataset for Archive Dreaming, an immersive installation at Salt Research, a contemporary gallery and library in Istanbul. Inspired by his idea of consciousness and its context within AI, as well as Jorge Luis Borges' The Library of Babel, Anadol used AI and machine learning to look at and discover interactions and correlations between 1.7 million items culled from 40,000 publications covering Turkish contemporary and modern art, architecture, and economics from 1997 to 2010. Archive Dreaming, which could be controlled by users with a joystick, dreamed of unexpected correlations among documents when idle. In 2018, after his uncle was diagnosed with Alzheimer's, Anadol created Melting Memories. Working with scientists from the neuroscape laboratory at the University of California, San Francisco, he used academic data from the neuroscience archives and EEG scans of an anonymous Alzheimer's disease dataset to create AI-generated visuals related to memory, health, degeneration, and decay.Melting Memories was projected on the walls of Pilevneli Gallery; visitors to the exhibition could watch as millions of pixels reconstructed people's memories. Anadol won the Lumen Prize Gold Award for Melting Memories. Anadol was commissioned by the Los Angeles Philharmonic to create an installation to celebrate the orchestra's centennial anniversary in 2018. He worked with Google's Kenric MacDowell to create WDCH Dreams, using algorithmic visualizations of data to mimic the process of human dreaming. Projected across the exterior walls of Walt Disney Concert Hall using 42 large-scale projectors with 50K visual resolution, 8-channel sound, and 1.2M luminance, Anadol painted with data points culled from the orchestra's archives, including 587,763 images, 1,880 videos, 1,483 metadata files, and 17,773 audio files. Because Gehry gave him access to the 3D architectural files of Walt Disney Concert Hall, Anadol knew the exact contours of the building. WDCH Dreams debuted in September 2018. A 12-minute performance in three parts staged every 30 minutes over ten nights, "Centennial Memories,” the first piece, used 44.5 terabytes of historical data from the Phil's archives. It was followed by "Consciousness", which processed every note the orchestra has ever recorded, using billions of data points to generate connections; and "Dream," which merged "Centennial Memories" and "Consciousness" to create hallucinations that were described in the New York Times as "a sort of combinatorial Fantasia. === 2019–2021: Machine Hallucinations: NYC, Machine Hallucinations: Nature Dreams, Machine Memories: Space, Quantum Memories === In 2019, Refik Anadol presented Latent History at Fotografiska Stockholm. The site specific installation transformed photographic archives of Stockholm into a large scale, machine generated visual projection displayed in the museum’s main exhibition hall. Drawing on thousands of archival images spanning approximately 150 years, the work used artificial intelligence to reinterpret the city’s historical imagery as a continuously evolving visual narrative.. Anadol began thinking about the work that would become the Machine Hallucinations series while in residence at Google. In 2019, he completed the first work in the series, Machine Hallucinations: NYC, which used 300 million photos of New York City and 113 million additional data points, including subway sounds, ra
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Automated machine learning

Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML. AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment. AutoML was proposed as an artificial intelligence-based solution to the growing challenge of applying machine learning. The high degree of automation in AutoML aims to allow non-experts to make use of machine learning models and techniques without requiring them to become experts in machine learning. Automating the process of applying machine learning end-to-end additionally offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models. Common techniques used in AutoML include hyperparameter optimization, meta-learning and neural architecture search. == Comparison to the standard approach == In a typical machine learning application, practitioners have a set of input data points to be used for training. The raw data may not be in a form that all algorithms can be applied to. To make the data amenable for machine learning, an expert may have to apply appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods. After these steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their model. If deep learning is used, the architecture of the neural network must also be chosen manually by the machine learning expert. Each of these steps may be challenging, resulting in significant hurdles to using machine learning. AutoML aims to simplify these steps for non-experts, and to make it easier for them to use machine learning techniques correctly and effectively. AutoML plays an important role within the broader approach of automating data science, which also includes challenging tasks such as data engineering, data exploration and model interpretation and prediction. == Targets of automation == Automated machine learning can target various stages of the machine learning process. Steps to automate are: Data preparation and ingestion (from raw data and miscellaneous formats) Column type detection; e.g., Boolean, discrete numerical, continuous numerical, or text Column intent detection; e.g., target/label, stratification field, numerical feature, categorical text feature, or free text feature Task detection; e.g., binary classification, regression, clustering, or ranking Feature engineering Feature selection Feature extraction Meta-learning and transfer learning Detection and handling of skewed data and/or missing values Model selection - choosing which machine learning algorithm to use, often including multiple competing software implementations Ensembling - a form of consensus where using multiple models often gives better results than any single model Hyperparameter optimization of the learning algorithm and featurization Neural architecture search Pipeline selection under time, memory, and complexity constraints Selection of evaluation metrics and validation procedures Problem checking Leakage detection Misconfiguration detection Analysis of obtained results Creating user interfaces and visualizations == Challenges and Limitations == There are a number of key challenges being tackled around automated machine learning. A big issue surrounding the field is referred to as "development as a cottage industry". This phrase refers to the issue in machine learning where development relies on manual decisions and biases of experts. This is contrasted to the goal of machine learning which is to create systems that can learn and improve from their own usage and analysis of the data. Basically, it's the struggle between how much experts should get involved in the learning of the systems versus how much freedom they should be giving the machines. However, experts and developers must help create and guide these machines to prepare them for their own learning. To create this system, it requires labor intensive work with knowledge of machine learning algorithms and system design. Additionally, other challenges include meta-learning and computational resource allocation.
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Iron Man 2020 (event)

"Iron Man 2020" is a storyline published by Marvel Comics in 2020 which follows the character Arno Stark as he attempts to take over Stark Industries and the mantle of his estranged brother Tony Stark (Iron Man). The crossover characters of two different brands meeting up in one storyline received mixed reviews from critics. == Publication history == Marvel Comics released the teaser for the event at New York Comic Con in November 2019. It was also alluded to in December 2019's Incoming! In the original checklist released for the event, 2020 Force Works was originally titled Force Works 2020, while 2020 Machine Man was previously named Machine Man 2020, and so on. Additionally, 2020 Wolverine was going to be called Weapon.EXE 2020. The publication of this event was intended to span from January to June 2020, however, due to the COVID-19 pandemic, Diamond Comic Distributors suspended the distribution of new print titles between April 1 and May 27, which also caused digital releases by Marvel Entertainment to be postponed. The rescheduling of the postponed issues to new dates pushed the event's conclusion to August, and certain issues, namely 2020 Force Works #3 and 2020 Ironheart #1–2, were released exclusively in a digital format. == Main plot == Arno Stark wakes up from a nightmare involving the Extinction Entity, a monstrous amalgamation of alien and machine. He dreams that the Extinction Entity is going to come to Earth in a matter of weeks and create an artificial intelligence (A.I.) army to consume humanity. After eating breakfast with duplicates of Howard Stark and Maria Stark, Arno suits up as Iron Man and saves a construction worker from a hostage situation involving several Nick Fury Life Model Decoys, which represent the A.I. army trying to liberate construction robots. Over different news outlets, the media wonders about the whereabouts of Tony Stark, who declared himself as nothing more than a simulation of the real, late Tony Stark. At the A.I. army's base, Machine Man is commanding the robots' moves when Arno appears, having planned for the A.I. army's leader to show himself. Machine Man activates the bomb, forcing Arno to fly it away so it explodes somewhere safe while he escapes. Machine Man reaches the Thirteenth Floor, a dimensional-shunted plane of existence made of solid light, and a haven for robotkind that humans cannot access or comprehend. Aaron meets with the leader of the A.I. army and creator of Thirteenth Floor: Tony Stark -- who is now going by the name Mark One, having embraced his nature as artificial intelligence. Also in the A.I. army are Albert, Awesome Android, H.E.R.B.I.E., Machinesmith, and Quasimodo. The A.I. army continues its efforts to liberate artificial life forms by raiding places where robots are being subjugated. Iron Man intercepts an attack on a Futura Motors testing site by Quasimodo and H.E.R.B.I.E. and manages to recover an Un-Inhibitor allowing him to take control of all A.I.s. On the Thirteenth Floor, Mark One receives a transmission from a mole inside Baintronics -- codenamed Ghost in the Machine --revealing that Arno used the submission code on Jocasta, who received a new body, making her entirely compliant. Stark plans to upload the submission code to the internet to instantly infect robots. With only three hours before the code is transmitted to Stark Unlimited's satellite network, Mark One devises a heist on Bain Tower to tamper with the code before launch. Having discovered the secret behind the Thirteenth Floor, Arno shuts out the A.I. army, uses Jocasta to lure Machine Man away from the tower, infects Machinesmith with the submission code, and confronts Mark One. H.E.R.B.I.E., Awesome Android, and Machinesmith escape from Bain Tower and call for help to every robot in New York City. Mark One is left to fight Iron Man and is defeated. Meanwhile, Sunset Bain confronts and fires Andy Bhang under the accusation of working as a mole inside Stark Unlimited and feeding Bethany Cabe information to relay to the A.I. army. Arno takes Mark One inside Bain Tower to meet Howard and Maria Stark and asks Tony to join him, but he refuses and dismisses his rationale as lunacy. The robotic mob assembled by Machine Man reaches Bain Tower, giving Mark a distraction which allows him to fly off and disable the transmission dish from which Arno intends to broadcast the obedience O.S. to subjugate every robot. Tony manages to stop the upload and make the antenna unusable. In retaliation, Arno fires all of his armor's firepower at Tony as he falls to the ground. Tony Stark's remaining allies escape with his body as Arno attacks the robot protesters. Tony wakes up inside the Thirteenth Floor and is greeted by F.R.I.D.A.Y., who had plucked Tony's consciousness from his body during his fall. In the streets, Arno Stark tracks down Howard and Maria, who die from an illness inherited from Arno. When Sunset Bain objects to Arno creating new bodies for his parents and trying to control people, he reveals she is an A.I., a duplicate of the real Bain whom Arno replaced back when she solicited him to heal a scar on her face. He makes new bodies for Howard and Maria by recreating the Arsenal and Mistress bodies from the eScape. After learning of Arno's new plan, Dr. Shapiro (who is the actual mole) sneaks into a computer and warns F.R.I.D.A.Y. about it. When F.R.I.D.A.Y. relays that only Tony Stark can stop Arno, Tony insists that he is not the real Tony Stark, but is confronted by holographic manifestations of himself in different points of his life, until they all merge into him and he acknowledges that he has always been Tony. As Arno Stark sets off to the Stark Space Station to install his mind-controlling device to enslave all of humanity, Tony Stark's allies assault the Stark Unlimited HQ, confronting Sunset Bain's duplicate and Arno's Iron Legion. Jocasta uploads a submission code to Bain and they place Tony's body inside a bio-pod that restores his body to normalcy, uploads his consciousness back into his body. Using the Thirteenth Floor's access mechanisms, Tony and his allies reach the Stark Space Station from one of the elevators within. Employing his new Virtual Armor, Tony defeats Arno in combat. When Arno prepares to activate his mind-controlling device, the Extinction Entity suddenly appears. Arno ultimately defeats the Extinction Entity by willingly assimilating with it, causing it to explode. The entity is revealed to be a delusion caused by Arno's terminal disease, of which he would die by the end of 2020. Unable to stop Arno, Tony placed him in a simulation where he successfully stopped the entity. Afterwards, Jocasta uses the submission code to force Sunset Bain's duplicate to confess all of Baintronics' crimes, also claiming responsibility for tricking Tony into thinking he was an artificial intelligence and pulling the strings of the A.I. Army, putting an end to the robot revolution. Tony gives up Stark Unlimited to Bhang Robotics and he flies off in a new armor, reasserting himself as Iron Man. == Issues involved == === Main issues === Iron Man 2020 (vol. 2) #1–6 === Tie-In issues === 2020 Force Works #1–3 2020 Iron Age #1 2020 Ironheart #1–2 2020 Machine Man #1–2 2020 Rescue #1–2 2020 iWolverine #1–2 == Critical reception == According to Comic Book Roundup, the entire crossover received an average score of 6.4 out of 10 based on 36 reviews. William Tucker from ButWhyTho Podcast stated "Iron Man 2020 #6 is an initially exciting end to a great event that eventually feels deflated. There is absolutely nothing wrong with the art, Woods has been incredible throughout, but the ending that Slott and Gage chose to round out an epic tale like this left me feeling cold. And while there were loads of enjoyable cameos, their involvement ultimately didn't seem important to the story as a whole. Which is disappointing, as the rest of the event really was a fun and exciting ride." Anthony Wendel from MonkeysFightingRobots wrote "The 2020 event seems like it is taking some big risk, and it doesn't inspire a lot of confidence from the start. Iron Man 2020 #1 has set the stakes and shown some very intense players on both sides of the board. Sadly, if it doesn't unfold just the right way, many may feel cheated about defending the path characters are taking." == Collected editions ==
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Squirrel AI

Squirrel Ai Learning is an international educational technology company that specializes in intelligent adaptive learning and was one of the first companies in the world to offer large scale AI-powered adaptive education solutions. == Methodology == Squirrel Ai Learning uses artificial intelligence to tailor lesson plans to each individual student. The company's AI researchers have access to the world's largest student databases, which are used to train the AI algorithms. Squirrel Ai Learning works with teachers to identify the most fine-grained possible concepts ("knowledge points") for a course in order to precisely target learning gaps. For example, middle school mathematics is broken into over 10,000 points such as rational numbers, the properties of a triangle, and the Pythagorean theorem. Each point is linked to related items, forming a "knowledge graph". Each knowledge point is addressed by videos, examples and practice problems. A textbook might address 3,000 points; ALEKS, another adaptive learning platform, uses 1,000. Each student begins with a diagnostic test to identify where to begin their learning. The system continues to refine its graph as more students proceed. Learning is not student-directed. The system decides the order of topics. == History and milestones == Squirrel Ai Learning was founded by Derek Haoyang Li in 2014. In March, 2017, The Squirrel Ai Intelligent Adaptive Learning System (IALS) was launched. IALS utilizes artificial intelligence to customize lessons, practice and evaluations for each individual student. In 2018, Squirrel Ai Learning established a joint research lab of AI adaptive learning with the institute of Automation of the Chinese Academy of Sciences. By 2019, Squirrel Ai Learning had opened 2,000 learning centers in 200 cities and registered over a million students in Asia. In 2019, Squirrel Ai Learning opened a research lab in partnership with Carnegie Mellon University. As of 2019, Squirrel Ai Learning had raised over $180 million in funding and in 2018 it surpassed $1 billion in valuation. In 2020, Squirrel Ai Learning launched the $1 million AAAI Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity in partnership with AAAI. The inaugural award was given to Regina Barzilay for her work developing machine learning models to address drug synthesis and early-stage breast cancer diagnosis. In 2020, Squirrel Ai Learning established strategic partnership with DingTalk, Alibaba Group. As of 2021, Squirrel Ai Learning had served over 60,000 public schools, in over 1200 cities in Asia. Squirrel Ai plans to start offering its services in the United States in 2026. The American arm is separate from the Chinese company to avoid regulatory hurdles. As of January 2026, it had set up an "independent technology platform" in the US. == Recognition == Squirrel Ai Learning has gained recognition both in Asia and internationally including: Squirrel Ai Learning was named one of the World's Top 30 AI application case in the 2018 Synced Machine Intelligence Awards. In June 2019, Squirrel Ai Learning was named as one of the 50 smartest companies in China by MIT technology review. Squirrel Ai Learning won the GITEX 2019 Best Education Technology Award. In 2020, Squirrel Ai Learning won the UNESCO AI Innovation Award. Squirrel Ai Learning was listed in the 2020 CB Insight's AI 100, CB Insights' annual ranking of the 100 most promising AI startups in the world. Squirrel Ai Learning won Edtech Review's Best AI in Education Company of the Year award 2020.
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Distributed artificial intelligence

Distributed Artificial Intelligence (DAI) (also called Decentralized Artificial Intelligence) is a melding of artificial intelligence with distributed computing. From artificial intelligence comes the theory and technology for constructing or analyzing an intelligent system. But where artificial intelligence uses psychology as a source of ideas, inspiration, and metaphor, DAI uses sociology, economics, and management science for inspiration. Where the focus of artificial intelligence is on the individual, the focus of DAI is on the group. Distributed computing provides the computational substrate on which this group focus can occur. Using techniques from artificial intelligence, communication theory, control theory, and interaction theory, it produces a cooperative solution to problems by a decentralized group of computational entities (agents). DAI is closely related to and a predecessor of the field of multi-agent systems. They are distinguished generally by multi-agent systems being open, where the entities might arise from different interests and have individual goals, and distributed artificial-intelligence systems, where the entities have common goals. There are numerous applications and tools. == Definition == Distributed Artificial Intelligence (DAI) is an approach to solving complex learning, planning, and decision-making problems. It is embarrassingly parallel, thus able to exploit large scale computation and spatial distribution of computing resources. These properties allow it to solve problems that require the processing of very large data sets. DAI systems consist of autonomous learning processing nodes (agents), that are distributed, often at a very large scale. DAI nodes can act independently, and partial solutions are integrated by communication between nodes, often asynchronously. By virtue of their scale, DAI systems are robust and elastic, and by necessity, loosely coupled. Furthermore, DAI systems are built to be adaptive to changes in the problem definition or underlying data sets due to the scale and difficulty in redeployment. DAI systems do not require all the relevant data to be aggregated in a single location, in contrast to monolithic or centralized Artificial Intelligence systems, which have tightly coupled and geographically close processing nodes. Therefore, DAI systems often operate on sub-samples or hashed impressions of very large datasets. In addition, the source dataset may change or be updated during the course of the execution of a DAI system. == Development == In 1975 distributed artificial intelligence emerged as a subfield of artificial intelligence that dealt with interactions of intelligent agents. As a scientific discipline, it progressed through a series of workshops in the USA (International Workshop on Distributed Artificial Intelligence, held in 13 editions from 1978 - 1994), Europe (Workshop on Modelling Autonomous Agents in a Multi-Agent World https://link.springer.com/conference/maamaw), and Asia (Multi-Agent and Cooperative Computation Workshop (MACC) https://sites.google.com/view/sig-macc/macc-workshop?authuser=0). Distributed artificial intelligence systems were conceived as a group of intelligent entities, called agents, that interacted by cooperation, by coexistence, or by competition. DAI is categorized into multi-agent systems and distributed problem solving. In multi-agent systems the main focus is how agents coordinate their knowledge and activities. For distributed problem solving the major focus is how the problem is decomposed and the solutions are synthesized. == Goals == The objectives of Distributed Artificial Intelligence are to solve the reasoning, planning, learning and perception problems of artificial intelligence, especially if they require large data, by distributing the problem to autonomous processing nodes (agents). To reach the objective, DAI requires: A distributed system with robust and elastic computation on unreliable and failing resources that are loosely coupled Coordination of the actions and communication of the nodes Subsamples of large data sets and online machine learning There are many reasons for wanting to distribute intelligence or cope with multi-agent systems. Mainstream problems in DAI research include the following: Parallel problem solving: mainly deals with how classic artificial intelligence concepts can be modified, so that multiprocessor systems and clusters of computers can be used to speed up calculation. Distributed problem solving (DPS): the concept of agent, autonomous entities that can communicate with each other, was developed to serve as an abstraction for developing DPS systems. See below for further details. Multi-Agent Based Simulation (MABS): a branch of DAI that builds the foundation for simulations that need to analyze not only phenomena at macro level but also at micro level, as it is in many social simulation scenarios. == Approaches == Two types of DAI has emerged: In Multi-agent systems agents coordinate their knowledge and activities and reason about the processes of coordination. Agents are physical or virtual entities that can act, perceive their environment, and communicate with other agents. An agent is autonomous and has skills to achieve goals. The agents change the state of their environment by their actions. There are a number of different coordination techniques. In distributed problem solving the work is divided among nodes and the knowledge is shared. The main concerns are task decomposition and synthesis of the knowledge and solutions. DAI can apply a bottom-up approach to AI, similar to the subsumption architecture as well as the traditional top-down approach of AI. In addition, DAI can also be a vehicle for emergence. === Challenges === The challenges in Distributed AI are: How to carry out communication and interaction of agents and which communication language or protocols should be used. How to ensure the coherency of agents. How to synthesise the results among 'intelligent agents' group by formulation, description, decomposition and allocation. == Applications and tools == Areas where DAI have been applied are: Electronic commerce, e.g. for trading strategies the DAI system learns financial trading rules from subsamples of very large samples of financial data Networks, e.g. in telecommunications the DAI system controls the cooperative resources in a WLAN network Routing, e.g. model vehicle flow in transport networks Scheduling, e.g. flow shop scheduling where the resource management entity ensures local optimization and cooperation for global and local consistency Search engines, e.g. in LLM federated search like Ithy where document retrieval and analysis are distributed to DAI agents before aggregation Multi-Agent systems, e.g. artificial life, the study of simulated life Electric power systems, e.g. Condition Monitoring Multi-Agent System (COMMAS) applied to transformer condition monitoring, and IntelliTEAM II Automatic Restoration System DAI integration in tools has included: ECStar is a distributed rule-based learning system. == Agents == === Systems: Agents and multi-agents === Notion of Agents: Agents can be described as distinct entities with standard boundaries and interfaces designed for problem solving. Notion of Multi-Agents: Multi-Agent system is defined as a network of agents which are loosely coupled working as a single entity like society for problem solving that an individual agent cannot solve. === Software agents === The key concept used in DPS and MABS is the abstraction called software agents. An agent is a virtual (or physical) autonomous entity that has an understanding of its environment and acts upon it. An agent is usually able to communicate with other agents in the same system to achieve a common goal, that one agent alone could not achieve. This communication system uses an agent communication language. A first classification that is useful is to divide agents into: reactive agent – A reactive agent is not much more than an automaton that receives input, processes it and produces an output. deliberative agent – A deliberative agent in contrast should have an internal view of its environment and is able to follow its own plans. hybrid agent – A hybrid agent is a mixture of reactive and deliberative, that follows its own plans, but also sometimes directly reacts to external events without deliberation. Well-recognized agent architectures that describe how an agent is internally structured are: ASMO (emergence of distributed modules) BDI (Believe Desire Intention, a general architecture that describes how plans are made) InterRAP (A three-layer architecture, with a reactive, a deliberative and a social layer) PECS (Physics, Emotion, Cognition, Social, describes how those four parts influences the agents behavior). Soar (a rule-based approach)
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AltStore

AltStore is an alternative app store for the iOS and iPadOS[1] mobile operating systems, which allows users to download applications that are not available on the App Store, most commonly tweaked apps, jailbreak apps, and apps including paid apps on the app store. It was publicly announced on September 25, 2019, and launched on September 28. == History == Riley Testut is an American developer who began to work on AltStore after Apple declined to allow his Nintendo emulator Delta on the App Store. Since Xcode allowed him to temporarily install his Delta app to his iOS device for 7 days of testing, he created AltStore in 2019 to replicate this functionality, which could be extended to other .ipa files. As of 2022, AltStore had been downloaded 1.5 million times. In the following years, AltStore expanded beyond its initial sideloading functionality. The platform was founded by Testut, with Shane Gill later joining as co-founder. AltStore was initially supported through Patreon contributions from its user community, and later saw increased adoption following regulatory developments in the European Union that enabled broader third-party app distribution. The project has also been involved in notable industry collaborations, including a partnership with Epic Games. == Features == AltStore exploits a loophole in the Xcode developer platform, which allows developers to sideload their own apps which they are working on without needing to jailbreak. Sideloaded apps are signed like a developer project for testing and will expire after 7 days with a free account or one year with a paid developer account, by which they will need to be refreshed or reinstalled.
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The Machine That Won the War (short story)

"The Machine That Won the War" is a science fiction short story by American writer Isaac Asimov. The story first appeared in the October 1961 issue of The Magazine of Fantasy & Science Fiction, and was reprinted in the collections Nightfall and Other Stories (1969) and Robot Dreams (1986). It was also printed in a contemporary edition of Reader's Digest, illustrated. It is one of a loosely connected series of such stories concerning a fictional supercomputer called Multivac. == Plot summary == Three influential leaders of the human race meet in the aftermath of a successful war against the Denebians. Discussing how the vast and powerful Multivac computer was a decisive factor in the war, each of the men admits that in fact, he falsified his part of the decision process because he felt that the situation was too complex to follow normal procedures. John Henderson, Multivac's Chief Programmer, admits that he altered the data being fed to Multivac, since the populace could not be trusted to report accurate information in the current situation. Max Jablonski then admits that he altered the data that Multivac produced, since he knew that Multivac was not in good working order due to manpower and spare parts shortage. Finally, Lamar Swift, executive director of the Solar Federation, reveals that he had not trusted the reports produced by Multivac, and had made the final decisions purely on the toss of a coin.
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SQLf

SQLf is a SQL extended with fuzzy set theory application for expressing flexible (fuzzy) queries to traditional (or ″Regular″) Relational Databases. Among the known extensions proposed to SQL, at the present time, this is the most complete, because it allows the use of diverse fuzzy elements in all the constructions of the language SQL. SQLf is the only known proposal of flexible query system allowing linguistic quantification over set of rows in queries, achieved through the extension of SQL nesting and partitioning structures with fuzzy quantifiers. It also allows the use of quantifiers to qualify the quantity of search criteria satisfied by single rows. Several mechanisms are proposed for query evaluation, the most important being the one based on the derivation principle. This consists in deriving classic queries that produce, given a threshold t, a t-cut of the result of the fuzzy query, so that the additional processing cost of using a fuzzy language is diminished. == Basic block == The fundamental querying structure of SQLf is the multi-relational block. The conception of this structure is based on the three basic operations of the relational algebra: projection, cartesian product and selection, and the application of fuzzy sets’ concepts. The result of a SQLf query is a fuzzy set of rows that is a fuzzy relation instead of a regular relation. A basic block in SQLf consists of a SELECT clause, a FROM clause and an optional WHERE clause. The semantic of this query structure is: The SELECT clause corresponds to the projection. It specifies the relations’ attributes (or attribute expressions) that will be selected. The resulting table is a fuzzy set and it is given in decreasing ordered of satisfaction degree. The SELECT clause specifies also a calibration that is intended to restrict the set of rows retrieved. There are two kinds of calibrations: quantitative and qualitative. In quantitative calibration the user specifies the number of results to be retrieved, so that the query will retrieve the rows with highest membership degrees up to the number of required answers. In qualitative calibration the user specifies a minim level of satisfaction that must have any retrieved row. The FROM clause corresponds to the Cartesian Product. The consult is made on the Cartesian Product of the relations that are specified in this clause. The WHERE clause corresponds to the selection. It specifies the condition for which the satisfaction degree will be calculated. Rows that do not satisfy at all the condition are rejected. This condition is a fuzzy predicate that may involve any attribute of the relations. The following is an example of a SELECT query that returns a list of hotels that are cheap. The query retrieves all rows from the Hotels table that satisfice the fuzzy predicate cheap defined by the fuzzy set μ=(∞, ∞, 25, 30). The result is sorted in descending order by the membership degree of the query.
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