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  Zolostays

  Zolostays is a real-tech co-living focused startup that provides ready-to-move rooms/beds. It was founded in 2015 by Nikhil Sikri, Akhil Sikri and Sneha Choudhry. == Overview == During the pandemic, Zolo provided 75 of rent-free accommodation to those who lost their jobs. Zolo uses bulk inventory in usually residential township and ties up with real estate companies to make the rooms/beds available. Zolostays has both revenue sharing and leased model. == History == Zolostays was founded in 2015 to solve the problem of students and young professionals who would move to temporarily go to other cities to study and work and look for affordable housing. In 2020, it was operating in 10 Indian cities. It has four round of funding, with total $98 million.

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  Rifts (role-playing game)

  Rifts is a multi-genre role-playing game created by Kevin Siembieda in August 1990 and published continuously by Palladium Books since then. It takes place in a post-apocalyptic future, deriving elements from cyberpunk, science fiction, fantasy, horror, western, mythology and many other genres. Rifts serves as a cross-over environment for a variety of other Palladium games with different universes connected through "rifts" on Earth that lead to different spaces, times, and realities that Palladium calls the "Rifts Megaverse". Rifts describes itself as an "advanced" role-playing game and not an introduction for those new to the concept. Palladium continues to publish books for the Rifts series, with about 80 books published between 1990 and 2011. Rifts Ultimate Edition was released in August 2005 and designed to update the game with Palladium's incremental changes to its system, changes in the game world, and additional information and character types. The web site is quick to point out that this is not a second edition but an improvement and expansion of the original role playing game. == Background == The RPG had the tentative title Boomers, named after the original name for the Glitter Boy power armor until Kevin Siembieda changed the name after finding out it was in use for Bubblegum Crisis. == Setting == The Rifts world is Earth, but hundreds of years into the future. Ley lines, lines of magic energy, criss-cross the earth forming supernatural geographic areas such as the Bermuda Triangle. Points where Ley Lines intersect, called a nexus, are places of powerful magic, such as the Pyramids of Giza and Stonehenge. If a Ley Line nexus energy surges or is purposely activated, the fabric of space and time can be torn, creating a rift - a hole in space-time leading to another place, time, or dimension. Ley lines contain magical energy called Potential Psychic Energy (PPE), which is found in various places, objects, and animals and is particularly strong in children. An adult's level of PPE can vary based on other factors. PPE also allows Psionics which uses energy known as Inner Strength Points or ISP. Psychic phenomenon (more commonly called psionics) can also vary from individuals, ranging from none at all to Master level abilities. Psychic abilities can manifest in virtually any way imaginable. Some psychics develop differently, such as psi-stalkers; human mutants that feed on psychic energy. === Earth === Rifts begins with two future-historical premises: first, a golden age of humanity occurs, with tremendous advances in science, technology, military, and society. Humanity as a whole is at peace as a majority of Earth's nations decide to cease world war and begin to share ideas and technology freely. Much of the Solar System is conquered, humanity's wars will end, and harmony will reign. This golden age is followed by an unknown cause near the winter solstice and a rare planetary alignment, causing a disaster that cascades into tremendous destruction via a ripple effect. The cataclysm begins with unprecedented storms, earthquakes, tsunamis, and volcanic eruptions, which kill millions of people. The Ley Line networks that crisscross the globe are energized, causing rifts to open both on Earth and throughout the Megaverse. For hundreds of years after the holocaust, many creatures, both mythical beasts and aliens, come through the Rifts to wreak havoc. The old world gone, a new Dark Age dawns and humanity's shrinking population is reduced, due to catastrophe and domestic failure, immeasurably. This period is covered in Palladium's Rifts Chaos Earth spin-off series. Rifts initially takes place in 101 P.A. (equivalent to the year 2387) 289 years after this event. The "Post-Apocalypse" calendar was established by the formation of the Coalition States in 2286. By this time, most of the disasters have quieted down, though Earth is still bathed in PPE. The planet's mystical energy has attracted aliens from other dimensions, who continue to arrive through the Rifts both accidentally and deliberately. The humanoid creatures that arrive on Earth are referred to as Dimensional Beings (called D-Bees). Some resemble familiar fantasy races, such as elves and dwarfs, while others were created specifically for the game setting. Non-humanoid creatures have also arrived, including monstrous creatures and mystical demons. To cope with these natural, supernatural, and alien menaces, the human race has adapted in a variety of ways, many of them borrowed from the technological developments of the lost Golden Age. Powered armor suits and giant vehicles are frequently used to combat the dangers of Rifts, but more invasive augmentation is common. This has three basic categories: "Juicers" augment themselves chemically, the "Borgs" augment themselves mechanically, and "Crazies" use performance-enhancing brain implants. All such augmentations boost strength, speed, endurance, and dexterity to superhuman levels. However, all come at great cost. Chemicals cause the body to wear out faster, decreasing life span to a few years. Mechanical Borg augmentation causes a loss of humanity when those with multiple limb and organ replacements become more machine than human. Brain implants cause mental instability ranging from mild phobias to crippling neurosis or psychosis. ==== North America ==== The strongest power in North America is the Coalition States (CS), which is based in the arcological city of Chi-Town and lays claim to northern Illinois, all of Iowa, the Texas Panhandle, Missouri, and the eastern half of Ontario, Canada. The second greatest power is Free Quebec, a former Coalition State that seceded following a civil war with the other Coalition States. Mexico is ruled by a group of vampire kingdoms, who treat humans as little more than food. North of the Rio Grande, west of Texas and roaming most of the American Southwest are large nomadic bands/tribes of bandits who collectively form the Pecos Empire, consisting of El Paso, Los Alamos, and Houstown. Much of the western United States has more or less willingly reverted to a mix of modern and past technology akin to the Wild West. The Royal Canadian Mounted Police managed to survive the great cataclysm, though Canada itself did not. The Mounties have become an independent law enforcement force called the Tundra Rangers, patrolling the northern wilderness. The Midwest, both upper and central, is home to most of North America's population. The Manistique Imperium and Northern Gun in Michigan's Upper Peninsula, both Coalition allies, are among the largest weapons manufacturing areas on the continent. New Lazlo is one of the largest cities in Michigan's southern portion. Chillicothe in Missouri is a large supplier of Coalition food processing and growing. Missouri's southern half, home to the city-states of Whykin (Poplar Bluff) and Kingsdale (West Plains) are in constant opposition to the CS and claim independence. Arkansas is home to the independent CS ally El Dorado. Southern Illinois and the Ohio Valley is home to the Federation of Magic. Also in the Ohio Valley is Psyscape, a city-state founded by psychics. Tolkeen was a major city in the former Minneapolis region in early Rifts books; the city welcomed users of magic. A military campaign made by the Coalition States (which is the primary event of 109 PA) resulted in the magic-user kingdom being wiped off the map. In the Northeast, the city-state of Lazlo, named after supernatural researcher and writer Victor Lazlo, was built upon the ruins of Toronto. This major center of civilization is well known as a melting pot of humans, D-Bees and other beings, and is the home of Techno-Wizardry. Mad Haven is the name given to the ruins of Manhattan; tectonic forces during the cataclysm have moved it into the coast, creating a peninsula. It is seen by most denizens of Rifts Earth as a refuge of demons and madness. ==== South America ==== The return of Atlantis caused the Amazon River basin to flood most of western South America, giving it the nickname The Land of a Thousand Islands. The Empire of the Sun, consisting of Cuzco, Nazca, Arequipa and Lima, created a wide range of technology and magic, including magic derived from the Nazca lines. In Argentina, the Silver River Republics of Cordoba (the South American Chi-Town), Santiago (one of the most tolerant human nations on Rifts Earth), Achilles (a nation founded by mutants), and New Babylon, a nation where humans and aliens coexist) have thrived and created nations whose strength rivals that of the CS. In Bolivia, freed Human and D-Bees formed the Megaversal Legion: a mercenary company with one of the highest levels of technology on Rifts Earth. ==== Europe ==== England has become a vast wilderness again, broken up by the occasional giant Millennium Tree or feudal kingdom, complete with a New Camelot and a new King Arthur, partially being manipulated by an alien intelligence disguised as Merlin. Also the magic of

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  Noise-based logic

  Noise-based logic (NBL) is a class of multivalued deterministic logic schemes, developed in the twenty-first century, where the logic values and bits are represented by different realizations of a stochastic process. The concept of noise-based logic and its name was created by Laszlo B. Kish. In its foundation paper it is noted that the idea was inspired by the stochasticity of brain signals and by the unconventional noise-based communication schemes, such as the Kish cypher. == The noise-based logic space and hyperspace == The logic values are represented by multi-dimensional "vectors" (orthogonal functions) and their superposition, where the orthogonal basis vectors are independent noises. By the proper combination (products or set-theoretical products) of basis-noises, which are called noise-bit, a logic hyperspace can be constructed with D(N) = 2N number of dimensions, where N is the number of noise-bits. Thus N noise-bits in a single wire correspond to a system of 2N classical bits that can express 22N different logic values. Independent realizations of a stochastic process of zero mean have zero cross-correlation with each other and with other stochastic processes of zero mean. Thus the basis noise vectors are orthogonal not only to each other but they and all the noise-based logic states (superpositions) are orthogonal also to any background noises in the hardware. Therefore, the noise-based logic concept is robust against background noises, which is a property that can potentially offer a high energy-efficiency. == The types of signals used in noise-based logic == In the paper, where noise-based logic was first introduced, generic stochastic-processes with zero mean were proposed and a system of orthogonal sinusoidal signals were also proposed as a deterministic-signal version of the logic system. The mathematical analysis about statistical errors and signal energy was limited to the cases of Gaussian noises and superpositions as logic signals in the basic logic space and their products and superpositions of their products in the logic hyperspace (see also. In the subsequent brain logic scheme, the logic signals were (similarly to neural signals) unipolar spike sequences generated by a Poisson process, and set-theoretical unifications (superpositions) and intersections (products) of different spike sequences. Later, in the instantaneous noise-based logic schemes and computation works, random telegraph waves (periodic time, bipolar, with fixed absolute value of amplitude) were also utilized as one of the simplest stochastic processes available for NBL. With choosing unit amplitude and symmetric probabilities, the resulting random-telegraph wave has 0.5 probability to be in the +1 or in the −1 state which is held over the whole clock period. == The noise-based logic gates == Noise-based logic gates can be classified according to the method the input identifies the logic value at the input. The first gates analyzed the statistical correlations between the input signal and the reference noises. The advantage of these is the robustness against background noise. The disadvantage is the slow speed and higher hardware complexity. The instantaneous logic gates are fast, they have low complexity but they are not robust against background noises. With either neural spike type signals or with bipolar random-telegraph waves of unity absolute amplitude, and randomness only in the sign of the amplitude offer very simple instantaneous logic gates. Then linear or analog devices unnecessary and the scheme can operate in the digital domain. However, whenever instantaneous logic must be interfaced with classical logic schemes, the interface must use correlator-based logic gates for an error-free signal. == Universality of noise-based logic == All the noise-based logic schemes listed above have been proven universal. The papers typically produce the NOT and the AND gates to prove universality, because having both of them is a satisfactory condition for the universality of a Boolean logic. == Computation by noise-based logic == The string verification work over a slow communication channel shows a powerful computing application where the methods is inherently based on calculating the hash function. The scheme is based on random telegraph waves and it is mentioned in the paper that the authors intuitively conclude that the intelligence of the brain is using similar operations to make a reasonably good decision based on a limited amount of information. The superposition of the first D(N) = 2N integer numbers can be produced with only 2N operations, which the authors call "Achilles ankle operation" in the paper. == Computer chip realization of noise-based logic == Preliminary schemes have already been published to utilize noise-based logic in practical computers. However, it is obvious from these papers that this young field has yet a long way to go before it will be seen in everyday applications.

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  Transhuman Space

  Transhuman Space (THS) is a role-playing game by David Pulver, published by Steve Jackson Games as part of the "Powered by GURPS" (Generic Universal Role-Playing System) line. Set in the year 2100, humanity has begun to colonize the Solar System. The pursuit of transhumanism is now in full swing, as more and more people reach fully posthuman states. In 2002, the Transhuman Space adventure "Orbital Decay" received an Origins Award nomination for Best Role-Playing Game Adventure. Transhuman Space won the 2003 Grog d'Or Award for Best Role-playing Game, Game Line or RPG Setting. == Setting == The game assumes that no cataclysm — natural or human-induced — swept Earth in the 21st century. Instead, constant developments in information technology, genetic engineering, nanotechnology and nuclear physics generally improved condition of the average human life. Plagues of the 20th century (like cancer or AIDS) have been suppressed, the ozone layer is being restored and Earth's ecosystems are recovering (although thermal emission by fusion power plants poses an environmental threat—albeit a much lesser one than previous sources of energy). Thanks to modern medicine humans live biblical timespans surrounded by various artificially intelligent helper applications and robots (cybershells), sensory experience broadcasts (future TV) and cyberspace telepresence. Thanks to cheap and clean fusion energy humanity has power to fuel all these wonders, restore and transform its home planet and finally settle on other heavenly bodies. Human genetic engineering has advanced to the point that anyone—single individuals, same-sex couples, groups of three or more—can reproduce. The embryos can be allowed to be developed naturally, or they can undergo three levels of tinkering: 1. Genefixing, which corrects defects; 2. Upgrades, which boost natural abilities (Ishtar Upgrades are slightly more attractive than usual, Metanoia Upgrades are more intelligent, etc.); and... 3. Full transition to parahuman status (Nyx Parahumans only need a few hours of sleep per week, Aquamorphs can live underwater, etc.) Another type of human genetic engineering, far more controversial, is the creation of bioroids, fully sentient slave races. People can "upload" by recording the simulation of their brains on computer disks. The emulated individual then becomes a ghost, an infomorph very easily confused with "sapient artificial intelligence". However, this technology has several problems as the solely available "brainpeeling" technique is fatal to the original biological lifeform being simulated, has a significant failure rate and the philosophical questions regarding personal identity remain equivocal. Any infomorph, regardless of its origin, can be plugged into a "cybershell" (robotic or cybernetic body), or a biological body, or "bioshell". Or, the individual can illegally make multiple "xoxes", or copies of themselves, and scatter them throughout the system, exponentially increasing the odds that at least one of them will live for centuries more, if not forever. This is also a time of space colonization. First, humanity (specifically China, followed by the United States and others) colonized Mars in a fashion resembling that outlined in the Mars Direct project. The Moon, Lagrangian points, inner planets and asteroids soon followed. In the late 21st century even some of Saturn's moons have been settled as a base for that planet's Helium-3 scooping operations. Transhuman Space's setting is neither utopia nor dystopia, however: several problems have arisen from these otherwise beneficial developments. The generation gap has become a chasm as lifespans increase. No longer do the elite fear death, and no longer can the young hope to replace them. While it seemed that outworld colonies would offer accommodation and work for those young ones, they are being replaced by genetically tailored bioroids and AI-powered cybershells. The concept of humanity is no longer clear in a world where even some animals speak of their rights and the dead haunt both cyberspace and reality (in form of infomorph-controlled bioshells or cybershells). And the wonders of high science are not universally shared — some countries merely struggle with informatization while others suffer from nanoplagues, defective drugs, implants and software tested on their populace. In some poor countries high-tech tyrants oppress their backward people. And in outer space all sort of modern crime thrives, barely suppressed by military forces. == Publication history == After the initial set of GURPS books that were published using the GURPS Lite, later publications such as Transhuman Space by David Pulver were labelled simply "Powered by GURPS" without using the name "GURPS" in the book title. Transhuman Space received a significant amount of supporting publications, and was the largest original background setting that Steve Jackson Games produced in 15 years. Shannon Appelcline noted that by its inclusion of posthuman characters, the book began to show the limits of the GURPS system as it was, which is something that Pulver would address soon thereafter. Steve Jackson Games has not updated the core book (GURPS Transhuman Space) to 4th edition, although the supplement Transhuman Space: Changing Times provides a path for migrating to 4th edition. It has produced several 4th edition supplements for the setting: Transhuman Space: Bioroid Bazaar, Transhuman Space: Cities on the Edge, Transhuman Space: Martial Arts 2100, Transhuman Space: Personnel Files 2-5, Transhuman Space: Shell-Tech, GURPS Spaceships 8: Transhuman Spacecraft, Transhuman Space: Transhuman Mysteries, and Transhuman Space: Wings of the Rising Sun. == Reception == In a review of Transhuman Space in Black Gate, William Stoddard said "Transhuman Space was a richly detailed setting; if it had imperfections, it had enough depth to make up for them. I think it has the potential to become a classic in its field. Perhaps a campaign set in its default start year of 2100 could leave the early twenty-first century blurry enough to avoid obvious incongruities." == Reviews == Review in Vol. 20, No. 1 of Prometheus, the journal of the Libertarian Futurist Society.

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  Alice AI (AI model family)

  Alice AI is a neural network family developed by the Russian company Yandex LLC. Alice AI can create and revise texts, generate new ideas and capture the context of the conversation with the user. Alice AI is trained using a dataset which includes information from books, magazines, newspapers and other open sources available on the internet. The neural network may get facts wrong and hallucinate, but as it learns, it will produce increasingly accurate answers. == Usage == YandexGPT is integrated into virtual assistant Alice (an analog of Siri and Alexa) and is available in Yandex services and applications. The company gives businesses access to the neural network’s API through the public cloud platform Yandex Cloud and develops its own B2B solutions on its basis. Since July 2023, 800 companies have participated in the closed testing of YandexGPT. IT developers, banks, retail businesses, and companies from other industries can use the technology in two modes — API and Playground (an interface in the Yandex Cloud console for testing models and hypotheses). Two model versions are available to businesses: one works in asynchronous mode and is better able to handle complex tasks, while the other is suitable for creating quick responses in real time. As a result, YandexGPT has been tested in dozens of scenarios such as content tasks, tech support, creating chatbots, virtual assistants, etc. == History == In February 2023, Yandex announced that it was working on its own version of the ChatGPT generative neural network while developing a language model from the YaLM (Yet another Language Model) family. The project was tentatively named YaLM 2.0, which was later changed to YandexGPT. On May 17, the company unveiled a neural network called YandexGPT (YaGPT) and enabled its virtual assistant Alice to interact with the new language model. On June 15, 2023, Yandex added the YandexGPT language model to the image generation application Shedevrum. This enabled its users to create fully-fledged posts complete with a title, text, and relevant illustration. In July 2023, YandexGPT launched new features enabling businesses to create virtual assistants and chatbots, as well as generate and structure texts. On September 7, 2023, Yandex presented a new version of the language model, YandexGPT 2, at the Practical ML Conf. Compared to the previous one, the new version is able to perform more types of tasks, and the quality of answers has improved. The developers claimed that YandexGPT 2 answered user questions better than the first version in 67% of cases. From October 6, 2023, YandexGPT can create short retellings of online Russian-language videos on the Internet. It can summarize videos that are from two minutes to four hours long and contain speech.

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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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  Big Mechanism

  Big Mechanism is a $45 million DARPA research program, begun in 2014, aimed at developing software that will read cancer research papers, integrate them into a cancer model and frame new hypotheses by the end of 2017 through the automated collection of big data and integrating across various disciplines such as knowledge-based NLP, curation and ontology, systems and mathematical biology by reading research abstracts and papers to extract pieces of causal mechanisms. == Ras gene == The program focuses on mutations in the Ras gene family, which underlie some one-third of human cancers. Currently, a rough road map shows interaction sequences among proteins affecting cell replication and death. However, the causal relations are poorly understood. == Plan == The program is to occur in three stages. The first is to read literature and convert it into formal representations. Second is to integrate the knowledge into computational models. Third is to produce experimentally testable explanations and predictions. Research teams are developing four separate systems targeting all three tasks. In February 2015, an evaluation meeting reviewed progress on the first stage. Multiple tasks were considered. One was extraction of experimental procedure details and evaluating statements such as "we demonstrate" and "we suggest." Another worked to map sentence meaning and relationships. The best machine-reading system extracted 40% of relevant information from a small corpus and correctly determined how each passage related to the model. The second stage is to become active in summer 2015, when members attempt to produce a single reference model. The third stage is the most challenging, because the artificial intelligence community has had limited success at developing hypothesis generators. Molecular biology may be more amenable, because most domain knowledge is technical and available in written form.

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  Competitions and prizes in artificial intelligence

  There are a number of competitions and prizes to promote research in artificial intelligence. == General machine intelligence == The David E. Rumelhart Prize is an annual award for making a "significant contemporary contribution to the theoretical foundations of human cognition". The prize is $100,000. The Human-Competitive Award is an annual challenge started in 2004 to reward results "competitive with the work of creative and inventive humans". The prize is $10,000. Entries are required to use evolutionary computing. The Intel AI Global Impact Festival is an international annual competition held by Intel Corporation for school, and college students with prizes upwards of $15,000. It is about artificial intelligence technology. There are two age brackets in this competition, 13-18 Age Group, and 18 and Above Age Group. The IJCAI Award for Research Excellence is a biannual award given at the International Joint Conference on Artificial Intelligence (IJCAI) to researchers in artificial intelligence as a recognition of excellence of their career. The 2011 Federal Virtual World Challenge, advertised by The White House and sponsored by the U.S. Army Research Laboratory's Simulation and Training Technology Center, held a competition offering a total of US$52,000 in cash prize awards for general artificial intelligence applications, including "adaptive learning systems, intelligent conversational bots, adaptive behavior (objects or processes)" and more. The Machine Intelligence Prize is awarded annually by the British Computer Society for progress towards machine intelligence. The Kaggle – "the world's largest community of data scientists compete to solve most valuable problems". == Conversational behaviour == The Loebner prize is an annual competition to determine the best Turing test competitors. The winner is the computer system that, in the judges' opinions, demonstrates the "most human" conversational behaviour, they have an additional prize for a system that in their opinion passes a Turing test. This second prize has not yet been awarded. == Automatic control == === Pilotless aircraft === The International Aerial Robotics Competition is a long-running event begun in 1991 to advance the state of the art in fully autonomous air vehicles. This competition is restricted to university teams (although industry and governmental sponsorship of teams is allowed). Key to this event is the creation of flying robots which must complete complex missions without any human intervention. Successful entries are able to interpret their environment and make real-time decisions based only on a high-level mission directive (e.g., "find a particular target inside a building having certain characteristics which is among a group of buildings 3 kilometers from the aerial robot launch point"). In 2000, a $30,000 prize was awarded during the 3rd Mission (search and rescue), and in 2008, $80,000 in prize money was awarded at the conclusion of the 4th Mission (urban reconnaissance). === Driverless cars === The DARPA Grand Challenge is a series of competitions to promote driverless car technology, aimed at a congressional mandate stating that by 2015 one-third of the operational ground combat vehicles of the US Armed Forces should be unmanned. While the first race had no winner, the second awarded a $2 million prize for the autonomous navigation of a hundred-mile trail, using GPS, computers and a sophisticated array of sensors. In November 2007, DARPA introduced the DARPA Urban Challenge, a sixty-mile urban area race requiring vehicles to navigate through traffic. In November 2010 the US Armed Forces extended the competition with the $1.6 million prize Multi Autonomous Ground-robotic International Challenge to consider cooperation between multiple vehicles in a simulated-combat situation. Roborace will be a global motorsport championship with autonomously driving, electric vehicles. The series will be run as a support series during the Formula E championship for electric vehicles. This will be the first global championship for driverless cars. == Data-mining and prediction == The Netflix Prize was a competition for the best collaborative filtering algorithm that predicts user ratings for films, based on previous ratings. The competition was held by Netflix, an online DVD-rental service. The prize was $1,000,000. The Pittsburgh Brain Activity Interpretation Competition will reward analysis of fMRI data "to predict what individuals perceive and how they act and feel in a novel Virtual Reality world involving searching for and collecting objects, interpreting changing instructions, and avoiding a threatening dog." The prize in 2007 was $22,000. The Face Recognition Grand Challenge (May 2004 to March 2006) aimed to promote and advance face recognition technology. The American Meteorological Society's artificial intelligence competition involves learning a classifier to characterise precipitation based on meteorological analyses of environmental conditions and polarimetric radar data. == Cooperation and coordination == === Robot football === The RoboCup and Federation of International Robot-soccer Association (FIRA) are annual international robot soccer competitions. The International RoboCup Federation challenge is by 2050 "a team of fully autonomous humanoid robot soccer players shall win the soccer game, comply with the official rule of the FIFA, against the winner of the most recent World Cup." == Logic, reasoning and knowledge representation == The Herbrand Award is a prize given by Conference on Automated Deduction (CADE) Inc. to honour persons or groups for important contributions to the field of automated deduction. The prize is $1000. The CADE ATP System Competition (CASC) is a yearly competition of fully automated theorem provers for classical first order logic associated with the Conference on Automated Deduction (CADE) and International Joint Conference on Automated Reasoning (IJCAR). The competition was part of the Alan Turing Centenary Conference in 2012, with total prizes of 9000 GBP given by Google. The SUMO prize is an annual prize for the best open source ontology extension of the Suggested Upper Merged Ontology (SUMO), a formal theory of terms and logical definitions describing the world. The prize is $3000. The Hutter Prize for lossless compression of human knowledge is a cash prize which rewards compression improvements on a specific 100 MB English text file. The prize awards 500 euros for each one percent improvement, up to €50,000. The organizers believe that text compression and AI are equivalent problems and 3 prizes have been given, at around € 2k. The Cyc TPTP Challenge is a competition to develop reasoning methods for the Cyc comprehensive ontology and database of everyday common sense knowledge. The prize is 100 euros for "each winner of two related challenges". The Eternity II challenge was a constraint satisfaction problem very similar to the Tetravex game. The objective is to lay 256 tiles on a 16x16 grid while satisfying a number of constraints. The problem is known to be NP-complete. The prize was US$2,000,000. The competition ended in December 2010. == Games == The World Computer Chess Championship has been held since 1970. The International Computer Games Association continues to hold an annual Computer Olympiad which includes this event plus computer competitions for many other games. The Ing Prize was a substantial money prize attached to the World Computer Go Congress, starting from 1985 and expiring in 2000. It was a graduated set of handicap challenges against young professional players with increasing prizes as the handicap was lowered. At the time it expired in 2000, the unclaimed prize was 400,000 NT dollars for winning a 9-stone handicap match. The AAAI General Game Playing Competition is a competition to develop programs that are effective at general game playing. Given a definition of a game, the program must play it effectively without human intervention. Since the game is not known in advance the competitors cannot especially adapt their programs to a particular scenario. The prize in 2006 and 2007 was $10,000. The General Video Game AI Competition (GVGAI) poses the problem of creating artificial intelligence that can play a wide, and in principle unlimited, range of games. Concretely, it tackles the problem of devising an algorithm that is able to play any game it is given, even if the game is not known a priori. Additionally, the contests poses the challenge of creating level and rule generators for any game is given. This area of study can be seen as an approximation of General Artificial Intelligence, with very little room for game dependent heuristics. The competition runs yearly in different tracks: single player planning, two-player planning, single player learning, level and rule generation, and each track prizes ranging from 200 to 500 US dollars for winners and runner-ups. The 2007 Ultimate Computer Ches

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  DevOps toolchain

  A DevOps toolchain is a set or combination of tools that aid in the delivery, development, and management of software applications throughout the systems development life cycle, as coordinated by an organization that uses DevOps practices. Generally, DevOps tools fit into one or more activities, which supports specific DevOps initiatives: Plan, Create, Verify, Package, Release, Configure, Monitor, and Version Control. == Toolchains == In software, a toolchain is the set of programming tools that is used to perform a complex software development task or to create a software product, which is typically another computer program or a set of related programs. In general, the tools forming a toolchain are executed consecutively so the output or resulting environment state of each tool becomes the input or starting environment for the next one, but the term is also used when referring to a set of related tools that are not necessarily executed consecutively. As DevOps is a set of practices that emphasizes the collaboration and communication of both software developers and other information technology (IT) professionals, while automating the process of software delivery and infrastructure changes, its implementation can include the definition of the series of tools used at various stages of the lifecycle; because DevOps is a cultural shift and collaboration between development and operations, there is no one product that can be considered a single DevOps tool. Instead a collection of tools, potentially from a variety of vendors, are used in one or more stages of the lifecycle. == Stages of DevOps == === Plan === Plan consists of two elements: "define" and "plan". This activity refers to the business value and application requirements. Specifically "Plan" activities include: Production metrics, objects and feedback Requirements Business metrics Update release metrics Release plan, timing and business case Security policy and requirement A combination of the IT personnel will be involved in these activities: business application owners, software development, software architects, continual release management, security officers and the organization responsible for managing the production of IT infrastructure. === Create === Create consists of the building, coding, and configuring of the software development process. The specific activities are: Design of the software and configuration Coding including code quality and performance Software build and build performance Release candidate Tools and vendors in this category often overlap with other categories. Because DevOps is about breaking down silos, this is reflective in the activities and product solutions. === Verify === Verify is directly associated with ensuring the quality of the software release; activities designed to ensure code quality is maintained and the highest quality is deployed to production. The main activities in this are: Acceptance testing Regression testing Security and vulnerability analysis Performance Configuration testing Solutions for verify-related activities generally fall under four main categories: Test automation, Static analysis, Test Lab, and Security. === Package === Package refers to the activities involved once the release is ready for deployment, often also referred to as staging or Preproduction / "preprod". This often includes tasks and activities such as: Approval/preapprovals Package configuration Triggered releases Release staging and holding === Release === Release related activities include schedule, orchestration, provisioning and deploying software into production and targeted environment. The specific Release activities include: Release coordination Deploying and promoting applications Fallbacks and recovery Scheduled/timed releases Solutions that cover this aspect of the toolchain include application release automation, deployment automation and release management. === Configure === Configure activities fall under the operation side of DevOps. Once software is deployed, there may be additional IT infrastructure provisioning and configuration activities required. Specific activities including: Infrastructure storage, database and network provisioning and configuring Application provision and configuration. The main types of solutions that facilitate these activities are continuous configuration automation, configuration management, and infrastructure as code tools. === Monitor === Monitoring is an important link in a DevOps toolchain. It allows IT organization to identify specific issues of specific releases and to understand the impact on end-users. A summary of Monitor related activities are: Performance of IT infrastructure End-user response and experience Production metrics and statistics Information from monitoring activities often impacts Plan activities required for changes and for new release cycles. === Version Control === Version Control is an important link in a DevOps toolchain and a component of software configuration management. Version Control is the management of changes to documents, computer programs, large web sites, and other collections of information. A summary of Version Control related activities are: Non-linear development Distributed development Compatibility with existent systems and protocols Toolkit-based design Information from Version Control often supports Release activities required for changes and for new release cycles.

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  Micah Xavier Johnson

  Micah Xavier Johnson (July 2, 1991 – July 8, 2016) was an American Army reserve Afghan war veteran, black nationalist, and mass murderer who perpetrated the 2016 shooting of Dallas police officers during a Black Lives Matter protest. He ambushed and killed five officers and wounded eleven others in Downtown, Dallas, Texas. He was killed by police during a standoff after expressing anger over police killings of black men. The shootings were the second-deadliest targeted attack on law enforcement officers in U.S. history, surpassed only by the September 11 attacks. == Early life == Micah Xavier Johnson was born in Magee, Mississippi, on July 2, 1991, and he was raised in Mesquite, Texas. When he was four years old, his parents divorced. At 17, Johnson enrolled at John Horn High School, where he joined the Junior Reserve Officers' Training Corps, as reported by the Mesquite Independent school district. He faced academic challenges, graduating in 2009 with a 1.98 GPA and ranking 430th out of 453 students in his class. In Spring 2011, Johnson registered for four courses at Richland college but did not complete any. Evidence suggests his enrollment at Richland gave him access to El Centro College, due to his pre-planned and coordinated movements throughout Building B during his standoff with police in 2016. == Military service == === Enlistment and early service === Micah Xavier Johnson enlisted in the U.S. Army Reserve in March 2009 at the age of 18, shortly after graduating high school in Mesquite, Texas. His initial service was primarily stateside, where he trained as a carpentry and masonry specialist (military occupational specialty 51B). This role involved engineering tasks such as construction and repair in support of military operations. During his reserve tenure, Johnson served part-time while living at home, and he was described by family and friends as initially idealistic about the military, even aspiring to become a police officer. === Deployment to Afghanistan === In September 2013, Johnson was activated for full-time duty and deployed to Afghanistan as part of the 420th Engineer Brigade, a unit based in Seagoville, Texas. His tour began in November 2013 and lasted approximately eight months, ending in July 2014. During this period, he performed non-combat engineering duties, though the stresses of serving in a combat zone were noted by those close to him. Associates from his service later suggested he experienced significant psychological strain, including the loss of friends and general disillusionment with military life, which contrasted with his pre-deployment enthusiasm. His mother later reflected that "the military was not what Micah thought it would be." === Sexual harassment allegation and early return === About six months into his deployment, in May 2014, Johnson faced a serious accusation of sexual harassment from a higher-ranking female soldier. She filed for a military protective order against him, prompting an investigation. As a result, his chain of command recommended an "other than honorable" discharge—the second (more severe is a dishonorable discharge, which does not require a court martial) most severe administrative separation short of a court-martial—and he was sent back to the United States ahead of schedule. Despite this, Johnson was not court-martialed, and the case did not lead to criminal charges. A military lawyer who represented him described the handling as unusual, noting that "someone really screwed up" in allowing him to avoid harsher consequences. === Post-deployment and discharge === Upon returning stateside in August 2014, Johnson resumed reserve duties with his engineering brigade until April 2015. He was honorably discharged at the rank of private first class (E-3), a relatively low junior enlisted rank after six years of service, which military sources attributed partly to the unresolved harassment allegation impacting his promotions and evaluations. Friends and family observed a marked change in his demeanor post-deployment: he became more reclusive, resentful toward the government, and withdrawn, with some speculating that the Afghanistan experience and the scandal contributed to a "small breakdown." In July 2016, following the Dallas shooting, the U.S. Army launched an internal review of his service record, including the harassment claims, to assess whether all misconduct allegations had been fully investigated. == Shootings == On July 7, 2016, a peaceful Black Lives Matter protest marched through downtown Dallas, Texas, drawing about 800 demonstrators. The event responded to the recent police killings of Alton Sterling in Baton Rouge, Louisiana, on July 5, and Philando Castile in Falcon Heights, Minnesota, on July 6—both black men shot during encounters captured on video. Around 100 officers monitored the march, which passed near El Centro College without incident until gunfire erupted around 8:45 p.m. Johnson arrived in a dark SUV, armed with an SKS semi-automatic rifle, a handgun, extra ammunition, and ballistic vests. He parked near the protest's end, chatted briefly with two officers, then opened fire on police from an elevated position on Lamar Street (now Botham Jean Boulevard). He shot from behind barriers, through windows, and while moving, targeting white officers specifically. The ambush killed five officers and wounded seven more, plus two civilians. Gunfire scattered protesters in panic as Johnson used military-style tactics, like quick position changes, to prolong the assault. === Standoff and Johnson's end === Johnson fled into El Centro College's Building C, then Building B, navigating pre-planned routes with familiarity from prior enrollment at nearby Richland College. He barricaded in a parking garage, wounding more officers in close-range fights. During two-hour negotiations, he taunted police via phone—laughing, singing, asking kill counts, and claiming planted bombs (none found). He admitted solo action, rage at White officers, and no group ties. At 2:30 a.m. on July 8, SWAT ended the standoff by detonating a bomb via remote-controlled robot in the garage, killing Johnson. This marked the first U.S. police use of such a tactic. === Victims and investigation findings === The slain officers were: Brent Thompson (Transit Authority, 36), Patrick Zamarripa (Dallas PD, 33), Michael Krol (Dallas PD, 40), Lorne Ahrens (Dallas PD, 48), and Michael Smith (Dallas PD, 55). Wounded officers included Sheik Smith, John Mitchell, and others; civilians She Tamara El-Sobky and Hillary Castro. Searches of Johnson's home revealed bomb-making materials, rifles, vests, and notes on tactics, suggesting plans for a larger attack. He had practiced explosions and honed skills post-discharge, including marksmanship. === Aftermath and impact === Dallas mourned with vigils and memorials, while national protests against police violence continued amid grief. President Barack Obama, the first African American president of the United States, called Johnson a "demented individual" and formed a task force on race and policing. The incident fueled debates on gun control, race relations, and veteran mental health—Johnson had sought VA treatment for stress and anxiety but showed no prior violent signs to friends. El Centro College canceled all classes on July 8. Police barricaded the perimeter and began canvassing the crime scene. The explosion that killed Johnson also destroyed the school's servers, further delaying reopening. The school partially reopened on July 20, with staff returning that day and students on the following day. Buildings A, B, and C remained closed pending the FBI investigation. == Motive == An investigation into his online activities uncovered his interest in black nationalist groups. The Southern Poverty Law Center (SPLC), and news outlets reported that Johnson "liked" the Facebook pages of black nationalist organizations such as the New Black Panther Party (NBPP), Nation of Islam, and Black Riders Liberation Party, three groups which are listed by the SPLC as hate groups. On Facebook, Johnson posted an angry and "disjointed" post against White people on July 2, several days before the attack. NBPP head Quanell X said after the shooting that Johnson had been a member of the NBPP's Houston chapter for about six months, several years before. Quanell X added that Johnson had been "asked to leave" the group for violating the organization's "chain of command" and espousing dangerous rhetoric, such as asking the NBPP why they had not purchased more weapons and ammunition, and expressing his desire to harm black church preachers because he believed they were more interested in money than God. Following the shooting, a national NBPP leader distanced the group from Johnson, saying that he "was not a member of" the party. Further investigation into his digital footprint showed that Johnson visited the sites of Marxist Leninist groups associated with "Revolutionary Black Nationalism",

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  HYPO CBR

  HYPO is a computer program, an expert system, that models reasoning with cases and hypotheticals in the legal domain. It is the first of its kind and the most sophisticated of the case-based legal reasoners, which was designed by Kevin Ashley for his Ph.D dissertation in 1987 at the University of Massachusetts Amherst under the supervision of Edwina Rissland. HYPO's design represents a hybrid generalization/comparative evaluation method appropriate for a domain with a weak analytical theory and applies to tasks that rarely involve just one right answer. The domain covers US trade secret law, and is substantially a common law domain. Since Anglo-American common law operates under the doctrine of precedent, the definitive way of interpreting problems is of necessity and case-based. Thus, HYPO did not involve the analysis of a statute, as required by the Prolog program. Rissland and Ashley (1987) envisioned HYPO as employing the key tasks performed by lawyers when analyzing case law for precedence to generate arguments for the prosecution or the defence. HYPO was a successful example of a general category of legal expert systems (LESs), it applies artificial intelligence (A.I.) techniques to the domain of legal reasoning in patent law, implementing a case-based reasoning (CBR) system, in contrast to rule based systems like MYCIN, or mixed-paradigm systems integrating CBR with rule-based or model-based reasoning like IKBALS II. A legal case-based reasoning essentially reasons from prior tried cases, comparing the contextual information in the current input case with that of cases previously tried and entered into the system. As noted by Ashley and Rissland (1988) CBR is used to "... capture expertise in domains where rules are ill-defined, incomplete or inconsistent". The HYPO project set out to model the creation of hypotheticals in law, where no case matches well enough. HYPO uses hypotheticals for a variety of tasks necessary for good interpretation: "to redefine old situations in terms of new dimensions, to create new standard cases when an appropriate one doesn’t exist, to explore and test the limits of a concept, to refocus a case by excluding some issues and to organize or cluster cases". Hypotheticals can include facts that support two conflicting lines of reasoning. So, it makes and responds to arguments from competing viewpoints about who should win the dispute. HYPO use heuristics such as making a case weaker or stronger, making a case extreme, enabling a near-miss, disabling a near-hit to generate hypotheticals in the context of an argument by using the dimensions mechanism. Dimensions have a range of values, along which the supportive strength that may shift from one side to the other. What differentiated this expert system from others was its facility not only to return a primary to best-case response but to return near-best-fit responses also. == Components == Legal knowledge in HYPO is contained in: the case-knowledge-base (CKB) and the library of dimensions. The CKB contains HYPO's base of known cases that are highly structured objects and sub-objects both real and hypothetical in the area of trade secret law. Each case is represented as a hierarchical set of frames whose slots are important facets of the case (e.g. Plaintiff, defendant, secret knowledge, employer/employee data).Ashley’s HYPO system used a database of thirty cases in the area indexed by thirteen dimensions. A key mechanism in HYPO is a dimension i.e. a mechanism to allow retrieval from the CKB, in order to represent legal cases. Ashley's dimensions are composed of (i) prerequisites, which are a set of factual predicates that must be satisfied for the dimension to apply (ii) focal slots, which accommodate one or two of the dimension's prerequisites designated as being indicative of the case's strength along that dimension and (iii) range information, which tells how a change in focal slot value effects the strength of a party's case along a given dimension. Dimensions focus attention on important aspects of cases. In HYPO's domain of misappropriation of trade secrets the dimension called “secrets voluntary disclosed” captures the idea that the more disclosures the plaintiff has made of his/her putative secret, the less convincing is his/her argument that the defendant is responsible for letting the secret. HYPO, like any other CBR system has also the following components: Similarity/relevancy metrics: that is, standards by which to evaluate the closeness of cases, judge their relevancy to the instant case, and select “most on point” cases. Half-Order Theory of the Application Domain: that is, hierarchies and taxonomies of knowledge, especially regarding the application domain. Precedent-based argumentation abilities: that is, capabilities to generate and evaluate precedent-based arguments. Knowledge to generate hypotheticals: that is, the ability to generate hypothetical cases to deal with various circumstances, like testing the validity of an interpretation or argument by providing gedanken experiments such as test cases or to fill in a weak CKB. == Functions == HYPO's method of creating an argument and justifying a solution or position has several steps. HYPO begins its processing with the current fact situation (cfs) which is direct input by the user into HYPO's representation framework. Once the user inputs the case, HYPO begins its legal analysis. The cfc is analyzed for relevant factors. Based on these factors HYPO selects the relevant cases and produces a case-analysis-record that records which dimensions apply to the cfc and which nearly apply (i.e. are "near misses"). The combined list of applicable and near miss dimensions is called the D-list. At this point the fact gathered module may request additional information from the user in order to draw a legal conclusion. Once all the facts are in the case-positioner module it uses the case-analysis record to create the claim lattice. This is a technique that organizes the relevant retrieved cases from the point of view of the cfc and makes it easy for HYPO to ascertain the most-on point cases (mopc) and to least on-point-cases. HYPO's arguments are 3ply, leading to the construction of the skeleton of an argument: it makes a point for one side, drawing the analogy between the problem and the precedent, responds with an argument for the opponent side, endeavoring to differentiate the cited case and citing other cases as counterarguments. Then it makes a final rebuttal, attempting to differentiate the counterarguments. The claim lattice also enables the HYPO-generator module to produce legally hypotheticals. With its use of dimension-based heuristics, the HYPO-generator does a heuristic search of the space of all possible cases. Lastly, the Explanation module expands upon the argument skeleton and provides explanation and justification for the different lines of analysis and cases found by HYPO. == An intelligent legal tutoring system == Legal expert systems are specifically designed to teach an area of law and are useful for pedagogical purposes. Ashley's work was mainly concerned to build tools to help students understand legal reasoning. Explanation and argument are the bases of the case method used in many professional schools in the U.S., first introduced by the Dean of the Harvard Law School, Christopher Columbus Langdell in 1870. The case method focuses on close readings of cases and principles; it involves students in pointed Socratic dialogue and makes strong use of hypotheticals (hypos). Thus, CATO (Aleven 1997) was a research project to device and test an intelligent, case-based tutorial program for teaching law students how to argue with cases implementing the HYPO program. Within the tutor system, Ashley and Aleven (1991) proposed to leverage an understanding of legal reasoning against the standard case-based tutoring methodology. What makes this tutoring system stand out is the additional levels of abstraction involved in its results. The system presents exercises, including the facts of a problem and a set of on-line cases and instructions to make, or respond to, a legal argument about the problem. The student/user will have a set of tools to analyze the problem and fashion an answer comparing it to other cases. Instead of simply generating precedent cases, the system works to interpret student responses, comparing them against a list of possibilities and responding to student entries, for example, by citing counterexamples, and providing feedback on a student's problem solving activities with explanations of correctness or giving further hints as to what may be wrong with evaluating a student's ability to perform legal reasoning and argument, examples and follow-up assignments by employing HYPO's model of case-based structure. == HYPO’s progeny == The quality of HYPO's results speak for themselves, in that a number of sequent legal reasoning systems are either directly based upon H

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  Whisper (speech recognition system)

  Whisper is a machine learning model for speech recognition and transcription, created by OpenAI and first released as open-source software in September 2022. It is capable of transcribing speech in English and multiple other languages, and can translate several non-English languages into English. Whisper is a weakly-supervised deep learning acoustic model, made using an encoder-decoder transformer architecture. OpenAI claims that the combination of different training data and post-training filtering used in its development has led to improved recognition of accents, background noise, and jargon compared to previous approaches. While the model does not outperform larger, more specialized models and still experiences AI hallucination, it has been showed to be useful for general sound recognition and has many applications across different industries. == Background == Speech recognition has had a long history in research; the first approaches made use of statistical methods, such as dynamic time warping, and later hidden Markov models. At around the 2010s, deep neural network approaches became more common for speech recognition models, which were enabled by the availability of large datasets ("big data") and increased computational performance. Early approaches to deep learning in speech recognition included convolutional neural networks, which were limited due to their inability to capture sequential data, which later led to developments of Seq2seq approaches, which include recurrent neural networks, which made use of long short-term memory. Transformers, introduced in 2017 by Google, displaced many prior state-of-the-art approaches across a wide range in machine learning, and started becoming the core neural architecture in fields such as language modeling and computer vision. Weakly-supervised approaches to training acoustic models were recognized in the early 2020s as promising for speech recognition approaches using deep neural networks. According to a NYT report, in 2021 OpenAI believed they exhausted sources of higher-quality data to train their large language models and decided to complement scraped web text with transcriptions of YouTube videos and podcasts, and developed Whisper to solve this task. Whisper Large V2 was released on December 8, 2022, followed by Whisper Large V3 being released in November 2023, during the OpenAI Dev Day. In March 2025, OpenAI released new transcription models based on GPT-4o and GPT-4o mini, both of which have lower error rates than Whisper. == Architecture == The Whisper architecture is based on an encoder-decoder transformer. Input audio is resampled to 16,000 Hertz (Hz) and converted to an 80-channel Log-magnitude Mel spectrogram using 25 ms windows with a 10 ms stride. The spectrogram is then normalized to a [-1, 1] range with near-zero mean. The encoder takes this Mel spectrogram as input and processes it. It first passes through two convolutional layers. Sinusoidal positional embeddings are added. It is then processed by a series of Transformer encoder blocks (with pre-activation residual connections). The encoder's output is layer normalized. The decoder is a standard transformer decoder. It has the same width and Transformer blocks as the encoder. It uses learned positional embeddings and tied input-output token representations (using the same weight matrix for both the input and output embeddings). It uses a byte-pair encoding tokenizer, of the same kind as used in GPT-2. English-only models use the GPT-2 vocabulary, while multilingual models employ a re-trained multilingual vocabulary with the same number of words. Special tokens are used to allow the decoder to perform multiple tasks: Tokens that denote language (one unique token per language). Tokens that specify task (<|transcribe|> or <|translate|>). Tokens that specify if no timestamps are present (<|notimestamps|>). If the token is not present, then the decoder predicts timestamps relative to the segment, and quantized to 20 ms intervals. <|nospeech|> for voice activity detection. <|startoftranscript|>, and <|endoftranscript|> . Any text that appears before <|startoftranscript|> is not generated by the decoder, but given to the decoder as context. Loss is only computed over non-contextual parts of the sequence, i.e. tokens between these two special tokens. == Training data == The training dataset consists of 680,000 hours of labeled audio-transcript pairs sourced from the internet using semi-supervised learning. This includes 117,000 hours in 96 non-English languages and 125,000 hours of X→English translation data, where X stands for any non-English language. Preprocessing involved standardization of transcripts, filtering to remove machine-generated transcripts using heuristics (e.g., punctuation, capitalization), language identification and matching with transcripts, fuzzy deduplication, and deduplication with evaluation datasets to avoid data contamination. Speechless segments were also included to allow voice activity detection training. For the files still remaining after the filtering process, audio files were then broken into 30-second segments paired with the subset of the transcript that occurs within that time. If this predicted spoken language differed from the language of the text transcript associated with the audio, that audio-transcript pair was not used for training the speech recognition models, but instead for training translation. The model was trained using the AdamW optimizer with gradient norm clipping and a linear learning rate decay with warmup, with batch size 256 segments. Training proceeded for 1 million updates (approximately 2-3 epochs). No data augmentation or regularization, except for the Large V2 model, which used SpecAugment, Stochastic Depth, and BPE Dropout. The training used data parallelism with float16, dynamic loss scaling, and activation checkpointing. === Post-training filtering === After training the first model, researchers ran it on different subsets of the training data, each representing a distinct source. Data sources were ranked by a combination of their error rate and size. Manual inspection of the top-ranked sources (high error, large size) helped determine if the source was low quality (e.g., partial transcriptions, inaccurate alignment). After training, it was fine-tuned to suppress the prediction of speaker names and low-quality sources were then removed. == Capacity == While Whisper does not outperform models which specialize in the LibriSpeech dataset, when tested across many datasets, it is more robust and makes 55.2% fewer errors than other models. Whisper has a differing error rate with respect to transcribing different languages, with a higher word error rate in languages not well-represented in the training data. The authors found that multi-task learning improved overall performance compared to models specialized to one task. They conjectured that the best Whisper model trained is still underfitting the dataset, and larger models and longer training can result in better models. Third-party evaluations have found varying levels of AI hallucination. A study of transcripts of public meetings found hallucinations in eight out of every 10 transcripts, while an engineer discovered hallucinations in "about half" of 100 hours of transcriptions and a developer identified them in "nearly every one" of 26,000 transcripts. A study of 13,140 short audio segments (averaging 10 seconds) found 187 hallucinations (1.4%), 38% of which generated text that could be harmful because it inserted false references to things like race, non-existent medications, or violent events that were not in the audio. == Applications == The model has been used as the base for many applications, such as a unified model for speech recognition and more general sound recognition. Whisper has also been integrated into the workflow of biomedical research. In 2025, a study on Alzheimer's disease detection used the model to transcribe spontaneous speech recordings. The transcripts that were generated by the model were combined with LLM vector embeddings and traditional classifiers to help classify the patients' health. Another application is when OVALYTICS incorporated Whisper to transcribe YouTube videos and automate content moderation systems, which improved its detection of offensive content. The model has also been used in academic libraries and cultral heritage institutions to generate transcripts and captions for their digitized audiovisual collections. In a 2025 case study, Emory University Libraries found that Whisper reduced the labor used in transcription by around 30-35%, shifting work from text creation to text correction. However, human review is still necessary to make sure accuracy, formatting, and accessibility are all standard.

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  Learning to rank

  Learning to rank (LTR) or machine-learned ranking (MLR) is the application of machine learning, often supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval and recommender systems. Training data may, for example, consist of lists of items with some partial order specified between items in each list. This order is typically induced by giving a numerical or ordinal score or a binary judgment (e.g. "relevant" or "not relevant") for each item. The goal of constructing the ranking model is to rank new, unseen lists in a similar way to rankings in the training data. == Applications == === In information retrieval === Ranking is a central part of many information retrieval problems, such as document retrieval, collaborative filtering, sentiment analysis, and online advertising. A possible architecture of a machine-learned search engine is shown in the accompanying figure. Training data consists of queries and documents matching them together with the relevance degree of each match. It may be prepared manually by human assessors (or raters, as Google calls them), who check results for some queries and determine relevance of each result. It is not feasible to check the relevance of all documents, and so typically a technique called pooling is used — only the top few documents, retrieved by some existing ranking models are checked. This technique may introduce selection bias. Alternatively, training data may be derived automatically by analyzing clickthrough logs (i.e. search results which got clicks from users), query chains, or such search engines' features as Google's (since-replaced) SearchWiki. Clickthrough logs can be biased by the tendency of users to click on the top search results on the assumption that they are already well-ranked. Training data is used by a learning algorithm to produce a ranking model which computes the relevance of documents for actual queries. Typically, users expect a search query to complete in a short time (such as a few hundred milliseconds for web search), which makes it impossible to evaluate a complex ranking model on each document in the corpus, and so a two-phase scheme is used. First, a small number of potentially relevant documents are identified using simpler retrieval models which permit fast query evaluation, such as the vector space model, Boolean model, weighted AND, or BM25. This phase is called top- k {\displaystyle k} document retrieval and many heuristics were proposed in the literature to accelerate it, such as using a document's static quality score and tiered indexes. In the second phase, a more accurate but computationally expensive machine-learned model is used to re-rank these documents. === In other areas === Learning to rank algorithms have been applied in areas other than information retrieval: In machine translation for ranking a set of hypothesized translations; In computational biology for ranking candidate 3-D structures in protein structure prediction problems; In recommender systems for identifying a ranked list of related news articles to recommend to a user after he or she has read a current news article. == Feature vectors == For the convenience of MLR algorithms, query-document pairs are usually represented by numerical vectors, which are called feature vectors. Such an approach is sometimes called bag of features and is analogous to the bag of words model and vector space model used in information retrieval for representation of documents. Components of such vectors are called features, factors or ranking signals. They may be divided into three groups (features from document retrieval are shown as examples): Query-independent or static features — those features, which depend only on the document, but not on the query. For example, PageRank or document's length. Such features can be precomputed in off-line mode during indexing. They may be used to compute document's static quality score (or static rank), which is often used to speed up search query evaluation. Query-dependent or dynamic features — those features, which depend both on the contents of the document and the query, such as TF-IDF score or other non-machine-learned ranking functions. Query-level features or query features, which depend only on the query. For example, the number of words in a query. Some examples of features, which were used in the well-known LETOR dataset: TF, TF-IDF, BM25, and language modeling scores of document's zones (title, body, anchors text, URL) for a given query; Lengths and IDF sums of document's zones; Document's PageRank, HITS ranks and their variants. Selecting and designing good features is an important area in machine learning, which is called feature engineering. == Evaluation measures == There are several measures (metrics) which are commonly used to judge how well an algorithm is doing on training data and to compare the performance of different MLR algorithms. Often a learning-to-rank problem is reformulated as an optimization problem with respect to one of these metrics. Examples of ranking quality measures: Mean average precision (MAP); DCG and NDCG; Precision@n, NDCG@n, where "@n" denotes that the metrics are evaluated only on top n documents; Mean reciprocal rank; Kendall's tau; Spearman's rho. DCG and its normalized variant NDCG are usually preferred in academic research when multiple levels of relevance are used. Other metrics such as MAP, MRR and precision, are defined only for binary judgments. Recently, there have been proposed several new evaluation metrics which claim to model user's satisfaction with search results better than the DCG metric: Expected reciprocal rank (ERR); Yandex's pfound. Both of these metrics are based on the assumption that the user is more likely to stop looking at search results after examining a more relevant document, than after a less relevant document. == Approaches == Learning to Rank approaches are often categorized using one of three approaches: pointwise (where individual documents are ranked), pairwise (where pairs of documents are ranked into a relative order), and listwise (where an entire list of documents are ordered). Tie-Yan Liu of Microsoft Research Asia has analyzed existing algorithms for learning to rank problems in his book Learning to Rank for Information Retrieval. He categorized them into three groups by their input spaces, output spaces, hypothesis spaces (the core function of the model) and loss functions: the pointwise, pairwise, and listwise approach. In practice, listwise approaches often outperform pairwise approaches and pointwise approaches. This statement was further supported by a large scale experiment on the performance of different learning-to-rank methods on a large collection of benchmark data sets. In this section, without further notice, x {\displaystyle x} denotes an object to be evaluated, for example, a document or an image, f ( x ) {\displaystyle f(x)} denotes a single-value hypothesis, h ( ⋅ ) {\displaystyle h(\cdot )} denotes a bi-variate or multi-variate function and L ( ⋅ ) {\displaystyle L(\cdot )} denotes the loss function. === Pointwise approach === In this case, it is assumed that each query-document pair in the training data has a numerical or ordinal score. Then the learning-to-rank problem can be approximated by a regression problem — given a single query-document pair, predict its score. Formally speaking, the pointwise approach aims at learning a function f ( x ) {\displaystyle f(x)} predicting the real-value or ordinal score of a document x {\displaystyle x} using the loss function L ( f ; x j , y j ) {\displaystyle L(f;x_{j},y_{j})} . A number of existing supervised machine learning algorithms can be readily used for this purpose. Ordinal regression and classification algorithms can also be used in pointwise approach when they are used to predict the score of a single query-document pair, and it takes a small, finite number of values. === Pairwise approach === In this case, the learning-to-rank problem is approximated by a classification problem — learning a binary classifier h ( x u , x v ) {\displaystyle h(x_{u},x_{v})} that can tell which document is better in a given pair of documents. The classifier shall take two documents as its input and the goal is to minimize a loss function L ( h ; x u , x v , y u , v ) {\displaystyle L(h;x_{u},x_{v},y_{u,v})} . The loss function typically reflects the number and magnitude of inversions in the induced ranking. In many cases, the binary classifier h ( x u , x v ) {\displaystyle h(x_{u},x_{v})} is implemented with a scoring function f ( x ) {\displaystyle f(x)} . As an example, RankNet adapts a probability model and defines h ( x u , x v ) {\displaystyle h(x_{u},x_{v})} as the estimated probability of the document x u {\displaystyle x_{u}} has higher quality than x v {\displaystyle x_{v}} : P u , v ( f ) = CDF ( f ( x u ) − f ( x v ) ) , {\displaystyle P_{u,v}(f)={\text{CDF}

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  Sora (text-to-video model)

  Sora was a text-to-video model and social media app developed by OpenAI. Using artificial intelligence, the model generated short video clips based on prompts, and could also extend existing short videos. In February 2024, OpenAI previewed examples of its output to the public, with the first generation of Sora released publicly for ChatGPT Plus and ChatGPT Pro users in the United States and Canada in December 2024. The second generation of Sora was released to select users in the US and Canada at the end of September 2025. Sora 2 integrated social media features into the app. The app was shut down on April 26, 2026 and the application programming interface (API) is planned to be discontinued on September 24, 2026, marking the end of the Sora AI brand as a whole. By default, the generator used copyrighted material in its videos, unless copyright holders actively opt out of having their content included. Videos contained a visible, moving digital watermark to prevent misuse, but a week after Sora 2's release, third-party programs became available which could remove the watermark. == Background == Several other models capable of generating video from text had been created prior to Sora, including Meta's Make‑A‑Video, Runway's Gen‑2 and Google Veo. OpenAI, the company behind Sora, had released DALL·E 3, the third of its DALL-E text-to-image models, in September 2023. == History == === Initial release === The team that developed Sora named it after the Japanese word for 'sky' to signify its "limitless creative potential". On February 15, 2024, OpenAI first previewed Sora by releasing multiple clips of high-definition videos that it had created, including an SUV driving down a mountain road, an animation of a "short fluffy monster" next to a candle, two people walking through Tokyo in the snow, and fake historical footage of the California gold rush. OpenAI stated that it was able to generate videos as long as one minute. The company then shared a technical report that highlighted the methods used to train the model. OpenAI CEO Sam Altman also posted a series of tweets responding to Twitter users' prompts with Sora-generated videos of the prompts. As of December 9, 2024, OpenAI had gradually made Sora available to the public for ChatGPT Pro and ChatGPT Plus users in the U.S. and Canada. Prior to this, the company had provided limited access to a small "red team", including experts in misinformation and bias, to perform adversarial testing on the model. The company also shared Sora with a small group of creative professionals, including video makers and artists, to seek feedback on its usefulness in creative fields. In February 2025, OpenAI announced plans to integrate Sora into ChatGPT by letting users generate Sora videos from the chatbot. === Sora 2 === Sora 2 was unveiled on September 30, 2025, with an iOS app at the same time, as well as an Android app two months later. All videos generated by the model feature a visible, moving watermark to prevent misuse of the tool. The previous version of Sora also added a safety watermark to allow viewers to distinguish between real and fictional content. On October 7, 404 Media reported that third-party programs that could remove the watermark from Sora 2 videos had become prevalent. Many outlets, such as Wired magazine, have noted that the Sora 2 app is overtly similar to TikTok in style and features. === Discontinuation === On March 24, 2026, OpenAI announced on X that it was discontinuing Sora in both the mobile app and the API. The Sora app was shut down on April 26, 2026, while the API is planned to be shut down on September 24, 2026. OpenAI's partnership with Disney, which included a licensing agreement allowing Disney characters to be used within Sora, was also coming to an end. The decision prompted British technology news website The Register to label OpenAI a "product-killer", following in the footsteps of other technology companies such as Google, Amazon Web Services, Broadcom, Cloud Software Group, and Netscape. OpenAI did not provide a specific reason for discontinuing Sora in its shutdown notice. The reports that emerged regarding this discontinuity linked the decision to computation shortages, cost pressures, and a broader shift toward core enterprise products. Following its public launch, Sora's worldwide users peaked at around a million before declining to fewer than 500,000, while the service cost an estimated $1 million per day to operate due to the computational demands of video generation. == Legal regulation == In November 2024, an API key for Sora access was leaked by a group of testers on Hugging Face who posted a manifesto stating that they were protesting that Sora was used for "art washing". OpenAI revoked all access three hours after the leak was made public and stated that "hundreds of artists" have shaped the development and that "participation is voluntary". At the time of its launch, Sora 2 allowed copyrighted content by default unless copyright holders contacted OpenAI to restrict the generation of their content on the platform. On October 3, 2025, OpenAI stated that a future update to Sora 2 would give copyright holders "more granular control" over the generation of copyrighted content, but the company did not state whether existing content would be removed. On October 6, the chairman of the MPA criticized OpenAI's approach to copyright with Sora 2. On December 11, 2025, the Walt Disney Company announced that it would invest $1 billion in OpenAI to allow users to generate more than 200 of its copyrighted characters on Sora 2. These characters include those from Disney Animation, Pixar, Marvel Studios, and Star Wars. == Capabilities and limitations == The technology behind Sora is an adaptation of the technology behind DALL-E 3. According to OpenAI, Sora is a diffusion transformer, a denoising latent diffusion model with one transformer as its denoiser. A video is generated in latent space by denoising 3D "patches", then transformed to standard space by a video decompressor. Recaptioning is employed to augment training data by using a video-to-text model to create detailed captions for videos. OpenAI trained the model using publicly available videos as well as copyrighted videos licensed for the purpose, but did not reveal the number or the exact source of the videos. Upon its release, OpenAI acknowledged some of Sora's shortcomings, including its limited capacity to simulate complex physics, to understand causality and to differentiate left from right. OpenAI also stated that, in adherence to the company's existing safety practices, Sora will restrict text prompts for sexual, violent, hateful or celebrity imagery, as well as content featuring existing intellectual property. Sora researcher Tim Brooks stated that the model learned how to create 3D graphics from its dataset alone, while fellow Sora researcher Bill Peebles said that the model automatically created different video angles without being prompted. According to OpenAI, Sora-generated videos are also tagged with C2PA metadata to indicate that they are AI-processed. === Comparison with other models === The Artificial Analysis have placed Sora 2 pro lower than other text-to-video AI generators in the market on its leaderboard. Other models, such as Seedance 2.0 from ByteDance, Runaway 4.5 from Runaway, and Kling 3.0 from KlingAI, have ranked higher than Sora 2.0. == Reception == === Positive === In 2024, Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", but noted that they must have been cherry-picked and may not be representative of Sora's typical output. Lisa Lacy of CNET called its example videos "remarkably realistic – except perhaps when a human face appears close up or when sea creatures are swimming". In October 2025, The New York Times remarked that the release of the Sora 2 app in September 2025 was "jaw-dropping (for better and worse)" though also remarked that the app was a "social network in disguise" and "the type of product that companies like Meta and X have sought to build: a way to bring A.I. to the masses that people can share." The article expressed concern regarding the product's potential impact on society and its potential use to promote misinformation, disinformation, and scams. A 2025 study in Science Advances found that generative AI tools can lower barriers to entry in creative work. It enables users with diverse skill sets, including people with less formal artistic training and technical skills, to act on their creative and imaginative ideas. The lower barrier to entry allows such users previously locked out of the creative industry to produce content and easily act on their creative ideas. === Negative === Some internet users and online content creators, such as Hank Green, called the mobile app "SlopTok," a reference to both the mobile app TikTok and the term AI slop. Filmmaker Tyler Perry announced he would be putting a planned

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  Sword Art Online

  Sword Art Online (Japanese: ソードアート・オンライン, Hepburn: Sōdo Āto Onrain) is a Japanese light novel series written by Reki Kawahara and illustrated by abec. The series takes place in the 2020s and focuses on protagonists Kazuto "Kirito" Kirigaya and Asuna Yuuki as they play through various virtual reality MMORPG worlds, and later their involvement in the matters of a simulated civilization. Kawahara originally released the series as a web novel on his website from 2002 to 2008. The light novels began publication on ASCII Media Works' Dengeki Bunko imprint from April 10, 2009, with a spin-off series launching in October 2012. The series has spawned twelve manga adaptations published by ASCII Media Works and Kadokawa. The novels and the manga adaptations have been licensed for release in North America by Yen Press. An anime television series produced by A-1 Pictures, known simply as Sword Art Online, aired in Japan between July and December 2012, with a television film Sword Art Online: Extra Edition airing on December 31, 2013, and a second season, titled Sword Art Online II, airing between July and December 2014. An animated film titled Sword Art Online the Movie: Ordinal Scale, featuring an original story by Kawahara, premiered in Japan and Southeast Asia on February 18, 2017, and was released in the United States on March 9, 2017. A spin-off anime series titled Sword Art Online Alternative: Gun Gale Online premiered in April 2018, while a third season titled Sword Art Online: Alicization aired from October 2018 to September 2020. An anime film adaptation of Sword Art Online: Progressive titled Sword Art Online Progressive: Aria of a Starless Night premiered on October 30, 2021. A second film titled Sword Art Online Progressive: Scherzo of Deep Night premiered on October 22, 2022. Many video games based on the series have been released for consoles, PC, and mobile devices. Sword Art Online has achieved widespread commercial success, with the light novels having over 30 million copies sold worldwide. The anime series has received mixed to positive reviews, with praise for its animation, musical score, and exploration of the psychological aspects of virtual reality, but it has also been met with criticisms for its pacing and writing. == Synopsis == === Setting === The light novel series spans several virtual reality worlds, beginning with the game, Sword Art Online (SAO), which is set in a world known as Aincrad. Each world is built on a game engine called Cardinal system, which was initially developed specifically for SAO by Akihiko Kayaba, but was later duplicated for Alfheim Online (ALO), and a consolidated package is later given to Kirito in the form of the World Seed, who had it leaked online with the successful intention of reviving the virtual reality industry. A third world known as Gun Gale Online (GGO) appears in the third arc and is stylized as a first-person shooter game instead of a role-playing game, and is the main setting of Alternative Gun Gale Online. It was created using the World Seed by an American company. A fourth world appears in the fourth arc known as the Underworld (UW). The world itself was created using the World Seed as a base, but it is as realistic as the real world due to using many powerful government resources to keep it running. === Plot === In 2022, a virtual reality massively multiplayer online role-playing game (VRMMORPG) called Sword Art Online (SAO) was released. With the NerveGear, a helmet that stimulates the user's five senses via their brain, players can experience and control their in-game characters with their minds. Both the game and the NerveGear were created by Akihiko Kayaba. On November 6, 10,000 players log into SAO's mainframe cyberspace for the first time, only to discover that they are unable to log out. Kayaba appears and tells the players that they must beat all 100 floors of Aincrad, a steel castle which is the setting of SAO, if they wish to be free. He also states that those who suffer in-game deaths or forcibly remove the NerveGear out-of-game will suffer real-life deaths. A player named Kazuto "Kirito" Kirigaya is one of 1,000 testers in the game's previous closed beta. With the advantage of previous VR gaming experience and a drive to protect other beta testers from discrimination, he isolates himself from the greater groups and plays the game alone, bearing the mantle of "beater", a portmanteau of "beta tester" and "cheater". As the players progress through the game Kirito eventually befriends a young woman named Asuna Yuuki, forming a relationship with and later marrying her in-game. After the duo discover the identity of Kayaba's secret ID, who was playing as "Heathcliff", the leader of the guild Asuna joined in, they confront and destroy him, freeing themselves and the other players from the game. In the real world, Kazuto discovers that 300 SAO players, including Asuna, remain trapped in their NerveGear. As he goes to the hospital to see Asuna, he meets Asuna's father Shouzou Yuuki who is asked by an associate of his, Nobuyuki Sugou, to make a decision, which Sugou later reveals to be his marriage with Asuna, angering Kazuto. Several months later, he is informed by Agil, another SAO survivor, that a figure similar to Asuna was spotted on "The World Tree" in another VRMMORPG cyberspace called Alfheim Online (ALO). Assisted in-game by his cousin and adoptive sister Suguha "Leafa" Kirigaya and Yui, a navigation pixie (originally an AI from SAO), he quickly learns that the trapped players in ALO are part of a plan conceived by Sugou to perform illegal experiments on their minds. The goal is to create the perfect mind-control for financial gain and to subjugate Asuna, whom he intends to marry in the real world, to assume control of her family's corporation. Kirito eventually stops the experiment and rescues the remaining 300 SAO players, foiling Sugou's plans. Before leaving ALO to see Asuna, Kayaba, who has uploaded his mind to the Internet using an experimental, destructively high-powered version of NerveGear at the cost of his life, entrusts Kirito with The Seed – a package program designed to create virtual worlds. Kazuto eventually reunites with Asuna in the real world after thwarting an attack from Sugou and The Seed is released onto the Internet, reviving Aincrad as other VRMMORPGs begin to thrive. One year after the events of SAO, at the prompting of a government official investigating strange occurrences in VR, Kazuto takes on a job to investigate a series of murders involving another VRMMORPG called Gun Gale Online (GGO), the AmuSphere (the successor of the NerveGear), and a player called Death Gun. Aided by a female player named Shino "Sinon" Asada, he participates in a gunfight tournament called the Bullet of Bullets (BoB) and discovers the truth behind the murders, which originated with a player who participated in a player-killing guild in SAO. Through his and Sinon's efforts, two suspects are captured, though the third suspect, Johnny Black, escapes. Kazuto is later recruited to test an experimental FullDive machine, Soul Translator (STL), which has an interface far more realistic and complex than the previous machine he had played, to help RATH, a research and development organization under the Ministry of Defense (MOD), develop an artificial intelligence named A.L.I.C.E. He tests the STL by entering the Underworld (UW), a virtual reality cyberspace created with The Seed package. In the UW, the flow of time proceeds a thousand times faster than in the real world, and Kirito's memories of what happens inside are restricted. However, when Johnny Black ambushes and mortally wounds Kazuto with suxamethonium chloride, RATH recovers Kazuto and places him back into the STL to preserve his mind while attempts are made to save him. During his time in Underworld, Kirito befriends Eugeo, a carver in a small village of Rulid, and helps him on a journey to save Alice Zuberg, his friend who was taken by a group of highly skilled warriors known as the Integrity Knights for accidentally breaking a rule of the Axiom Church, the leaders of the Human Empire. He and Eugeo soon find themselves uncovering the secrets of the Axiom Church, led by a woman only known as "The Administrator", and the true purpose of Underworld itself, while unbeknownst to them, a war against the opposing Dark Territory is brewing on the horizon. They meet Alice, now an Integrity Knight, and though she does not remember them, Kirito helps her remember her true identity: a form of true artificial intelligence known as A.L.I.C.E. In the battle against the Administrator, Kirito manages to slay her, though Eugeo dies in the process, to Kirito's dismay. Meanwhile, in the real world, conflict escalates as American forces raid RATH's facility in the Ocean Turtle in an effort to take A.L.I.C.E. for purposes unknown. Two of the attackers - Gabriel "Vecta" Miller and Vassago "Prince of Hell" Cassals - take contr

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