AI Generator Sora

AI Generator Sora — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Cognitive philology

    Cognitive philology

    Cognitive philology is the science that studies written and oral texts as the product of human mental processes. Studies in cognitive philology compare documentary evidence emerging from textual investigations with results of experimental research, especially in the fields of cognitive and ecological psychology, neurosciences and artificial intelligence. "The point is not the text, but the mind that made it". Cognitive Philology aims to foster communication between literary, textual, philological disciplines on the one hand and researches across the whole range of the cognitive, evolutionary, ecological and human sciences on the other. Cognitive philology: investigates transmission of oral and written text, and categorization processes which lead to classification of knowledge, mostly relying on the information theory; studies how narratives emerge in so called natural conversation and selective process which lead to the rise of literary standards for storytelling, mostly relying on embodied semantics; explores the evolutive and evolutionary role played by rhythm and metre in human ontogenetic and phylogenetic development and the pertinence of the semantic association during processing of cognitive maps; Provides the scientific ground for multimedia critical editions of literary texts. Among the founding thinkers and noteworthy scholars devoted to such investigations are: Alan Richardson: Studies Theory of Mind in early-modern and contemporary literature. Anatole Pierre Fuksas Benoît de Cornulier David Herman: Professor of English at North Carolina State University and an adjunct professor of linguistics at Duke University. He is the author of "Universal Grammar and Narrative Form" and the editor of "Narratologies: New Perspectives on Narrative Analysis". Domenico Fiormonte François Recanati Gilles Fauconnier, a professor in Cognitive science at the University of California, San Diego. He was one of the founders of cognitive linguistics in the 1970s through his work on pragmatic scales and mental spaces. His research explores the areas of conceptual integration and compressions of conceptual mappings in terms of the emergent structure in language. Julián Santano Moreno Luca Nobile Manfred Jahn in Germany Mark Turner Paolo Canettieri

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  • Kialo

    Kialo

    Kialo is an online structured debate platform with argument maps in the form of debate trees. It is a collaborative reasoning tool for thoughtful discussion, understanding different points of view, and collaborative decision-making, showing arguments for and against claims underneath user-submitted theses or questions. The deliberative discourse platform is designed to present hundreds of supporting or opposing arguments in a dynamic argument tree and is streamlined for rational civil debate on topics such as philosophical questions, policy deliberations, entertainment, ethics, science questions, and unsolved problems or subjects of disagreement in general. Argument-boxes are structured into hierarchical branches where the root is the main thesis (or theses) of the debate, enabling deliberation and navigable debates between opposing perspectives. A debate is divided into Pro (supporting) and Con (refuting or devaluing) columns where registered users can add arguments and rate the impact on the weight or validity of the parent claim. The arguments are sorted according to the rating average. Its argument tree structure enables detailed scrutiny of claims at all levels of the tree and allows users to for example quickly understand why a decision was made or which of the aggregated arguments swayed it this way. Newcomers can join a debate at any time and look back at the structured discussion history, and then weigh in at the right place with their new argument or their comment on a specific argument. The design presets a structure on debates "that allows participants to easily see, process, and ultimately assess the many facets of competing claims". The word Kialo is Esperanto for "reason". The platform is the world's largest argument mapping and structured debate site. == Overview == Users can comment on every Pro or Con, for example for requesting sources or expansions. Recent activities of a debate are shown in a panel on the right side of the respective debate. Debates can be found through the search or on the Explore page through their descriptions and topic-tags. Mere comments that do not make a constructive point (a self-contained argument backed by reasoning) are not allowed and are picked up by other users and moderators. "Civil language and sensible observations from opposing perspectives" can be seen also in debates about controversial topics. The site by-design incentivizes fair, rigorous, open-minded dialogue. Contributors making claims often also write counterpoints to their own contribution. Claims need to be shorter than 500 characters and can link to external sources. Debate trees can also start off with multiple theses – such as different policy options or hypotheses. Claims can link to related debates or include segments of them. In the discussion tab of each claim, users can make edit proposals (e.g. for accuracy, improving sources, or changing scope), decide if the argument should be moved or copied to another branch, call for archiving a claim, and ask for extra evidence or clarification. Debates can grow large and complex for which a sunburst diagram visualization of the topology of the debate and the search functionality can be useful. Each debate also has a chat-box. In cases where e.g. a "Con" is a point against multiple in the "Pros", users – through moderators – can link these arguments at the respective places to avoid duplication of content and allowing a clean chain for people to understand which points are arguments against each other. Contributions of users are tracked, enabling a board of thought-leaders for every debate. Other gamification elements include a feature to thank users for their contributions. The "Perspectives" feature allows users to see 'Impact' ratings of supporters and opposers of a thesis as well as of the debate's moderators and individual contributors. It thereby enables participants to see a debate from other participants' perspectives and to sort by them. In Kialo Edu, this feature lets teachers view votes for a whole class, individuals, or supporters/opponents of a specific thesis. Users in both versions of Kialo can vote on the overall debate topic as well as on individual claims to express their perspectives or conclusions, with the rationale (i.e. the main causal arguments) why they voted on the veracity of the thesis as they did not being captured. Voting can be done by any registered user while navigating through any debate that has voting enabled or via using the Guided Voting wizard user interface that automatically walks through branches. As of 2021, Kialo doesn't have a mobile app. == Contents == A 2018 report stated the collaborative argument platform hosts more than 10,000 debates in various languages. It also hosts private debates. The website claims that it has over 18,000 public debates as of July 2023, as well as over 1 million votes and over 720,000 claims. Debates can be found via the site's internal search and up to six tags per debate. Preprint studies have scraped public debates on over 1.4K issues with over 130K statements as of October 2019 and 1628 debates, related to over 1120 categories, with 124,312 unique claims as of June 26, 2020. == Kialo Inc. == The site is run by Kialo Inc. It was founded by German-born entrepreneur and London School of Economics and Political Science graduate Errikos Pitsos in August 2017 and is based in Brooklyn and Berlin. According to a 2018 report, the site does not show advertisements and does not sell user's data. The for-profit company was founded in 2011, Pitsos began to develop the concept in 2012 and described various specifics of the system in 2014. In 2018, he stated that they intend to make money by selling the platform to companies as a deliberation and decision-making tool. The site is free to use for the public and in education. According to the site, as of 2023 Kialo.com is a non-revenue generating site with no ads and no reselling of user data. == Applications and adoption == === Adopted applications === Applications of its content or the platform in society include: Teachers and professors, especially in high schools – including the universities Harvard and Princeton, are using Kialo for class discussions and exercises in critical thinking and reasoning, as consolidating understanding of materials covered in recent classes, more useful and engaging learning experiences, for remote/e-learning, for clearing up misconceptions, teaching logical fallacies and rational argumentation, for academic dialogue, teaching media literacy, and for teaching to sufficiently reflect or research before posting online. Like for debaters of the main site, access for schools and universities is free. Kialo Edu is the custom version of Kialo specifically designed for classroom use where debates are private and locked to invited students. Kialo allows teachers to provide feedback to students on their ideas, argument structure, and research quality while it is left to other students to rate the impacts of their peers' arguments. Students can be allowed to contribute anonymously which may be useful for controversial issues as well as for safeguarding privacy in education. Students are or can be encouraged to back up their claims with evidence which can foster digital literacy and research skills. Students and teachers can use it to arrange their thoughts when structuring an essay or project. The site's name was decided on internally using the software. === Prototypical and theoretical applications === Potential, theoretical, prototypical or little-used applications include: Education Improving critical thinking skills of society at large as well as facilitating deep or efficient thinking and deepening research and debates where e.g. discussions are less shallow and the well-known or many arguments have already been made and in many cases aren't unreasonably over- or underrated. Pitsos claimed that "we're training students to be very good test-takers instead of critical thinkers", suggesting teaching people to think things through may be more important or neglected compared to essay writing skills. Many young people and adults are "submerged into a sea of dispersed information", "[b]rowsing and engaging in superficial thinking activities". Kialo could counteract this issue and help people develop good sane reasoning. Academia, R&D and policy Three scholars from three prestigious U.S. universities outlined possible benefits in this domain, including applications beyond higher education such as for academic communication. They suggest the debate platform could be used for structuring the communication of open peer-review by helping those giving feedback to "hone in on[sic] core arguments and pieces of evidence in an even more direct way" than annotated commenting. It could be used to evaluate extracted argument structures and sequences from raw texts, as in a Semantic Web for arguments. Such "argument mining", to which Kialo is the lar

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  • Course of Action Display and Evaluation Tool

    Course of Action Display and Evaluation Tool

    Course of Action Display and Evaluation Tool (CADET) was a research program, and the eponymous prototype software system, that applied knowledge-based techniques of Artificial Intelligence to the problem of battle planning. CADET was also known as Course of Action Display and Elaboration Tool. It was considered an early example of such systems and was funded by the United States Army and by the Defense Advanced Research Projects Agency (DARPA). CADET influenced a later DARPA program called RAID which in turn produced a technology adopted by the United States Army and the United States Marine Corps. == History == The development of Course of Action Display and Evaluation Tool (CADET) began in 1996, at the Carnegie Group, Inc., Pittsburgh PA, funded under the Small Business Innovation Research (SBIR) program. The goal of the first phase SBIR project was to produce “...a live storyboard of [Course of Action] COA development, wargaming, animation, and assessment.” In 1997, the United States Army awarded the Carnegie Group Inc. $750K for SBIR Phase II. The intent was to develop “...a war-gaming modeling and analysis Decision Support System (DSS), … CADET will consist of a combination of Knowledge-Based and decision analytic tools and technologies to provide fast nimble COA war-gaming modeling, simulation, and animation under direct control of the commander and staff. ...Phase II will result in an operations prototype (OP) suitable for use and evaluation in field exercises.” In 2000, CADET was integrated and experimentally evaluated within the framework of the Integrated Course of Action Critiquing and Elaboration System (ICCES) experiment, conducted by the Battle Command Battle Laboratory – Leavenworth (BCBL-L) within the program Concept Experimentation Program (CEP) sponsored by TRADOC. In 2000-2002, DARPA applied CADET in the program titled Command Post of the Future (CPoF) as a tool to generate a course of action. Under the umbrella of the CPoF program, CADET was integrated with the FOX GA system to provide a detailed planner, coupled with COA generation capability. In the same period, Battle Command Battle Lab-Huachuca (BCBL-H) performed an integration CADET with the system called All Source Analysis System-Light (ASAS-L); here CADET was intended to generate plans for intelligence assets, and conduct wargames of different COAs, enemy versus friendly. From 1996 through 2002, work on CADET was performed by the Carnegie Group, Inc., and supported by funding from the US Army CECOM (CADET SBIR Phase I, CADET SBIR Phase II and CADET Enhancements); DARPA (Command Post of the Future); and TRADOC BCBL-H. == Operation == CADET was intended to be used by the staff of the United States Army Brigade, within the Military Decision Making Process (MDMP). In particular, CADET helped produce, automatically or semi-automatically, the products generated within the step of MDMP called Course of Action (COA) Development and the following step of MDMP called COA Analysis and Wargaming. CADET software resided on a laptop computer. Using the computer, the staff officers entered the input to CADET, or alternatively this input arrived at CADET from upstream computer systems. The input consisted of: Order of Battle, i.e., the units constituting the friendly brigade and the enemy units participating in the battle, and their various characteristics; primary activities of the Course of Action, where each activity is typically linked to one or more geographic areas or a route, and sometimes to a major unit executing the activity; digital map of the region where the battle was to take place, including the digital description of significant features such as locations of friendly and enemy units, roads, assembly areas, objectives, and axes of attacks. Taking this input, CADET automatically performed the following tasks (not sequentially): Planning and scheduling the low-level tasks necessary for a given COA Allocating tasks to various units and assets constituting the brigade Assigning suitable locations and routes Estimating the battle losses (attrition) of friendly and enemy forces, and consumption of resources (e.g., fuel and ammunition) Predicting enemy actions or reactions. CADET produced the following outputs: Synchronization matrix, directly editable and printable; synchronization matrix is a kind of Gantt chart that shows assignments of activities to units, to locations/routes and to time periods Map overlays in PPT or JPG formats Animation output XML formally-encoded plan Textual Operation Plan (OPLAN) draft E-mail messages with attachments: XML and text versions of OPLAN == Design == The core algorithm is a planning algorithm where CADET uses a knowledge-based approach of the hierarchical-task-network type. Each task class is associated with a model of more detailed subtasks that should be performed in order to accomplish the higher-level task. Algorithms selected (heuristically) a task and then decomposes it into subtasks. Although similar to hierarchical-task-network planning algorithm, CADET’s algorithm includes elements of adversarial reasoning. After adding a subtask, the algorithm uses rules to determine the enemy’s probable actions and reactions as well as friendly counteractions This approximated the action-reaction-counteraction technique of manual wargaming used by the United States Army. When a task involves movements of a unit, the algorithm performs routing, i.e., finds a route for the movement that minimizes the time required for the movement as well as exposure to the enemy attacks. Each added tasks (subtask) normally requires a unit which would execute the task, and a time period when the task would be executed. Therefore, when a certain number of subtasks is added by the planning process, the algorithm also performs the allocation of the newly added subtasks to units and to time periods (i.e., scheduling). allocation and scheduling of tasks relies on both domain-specific and constraint-guided heuristics. A tasks may also require expenditures of fuel and ammunition. If the tasks involves engagement with the enemy, the performing units will experience lossesof personnel and weapon systems (attrition). CADET’s algorithm includes estimates of consumption of different types of consumables, and also attrition. Depending on the degree of attrition and consumption, CADET adds tasks that are needed to refuel or reconstitute the units. The algorithm continually interleaves incremental steps of planning, routing, scheduling, and attrition and consumption estimates. == Evaluation == Two evaluation experiments are described in literature. The first experiment called ICCES took three days. The subjects were Army officers from combat arms branches, with 11 to 23 years of active service, in the ranks of majors and lieutenant colonels, a total of 8. Each officer was given 4 hours of training learning to operate CADET and related computer tools. Officers were divided into two groups and given a tactical scenario. One group (the control group) used the traditional, manual process; the other used the system called ICCES, the automated core of which was CADET. Each group produced three COA sketches and statements and one COA synchronization matrix. Then, the experiment was repeated with another scenario but the control group became the automated group and vice versa. The users were generally satisfied with the quality of the ICCES-generated products. The group using ICCES made only a few changes to the product that was automatically generated, indicating that they agreed with the majority of the plan that ICCES produced. The second experiment was reminiscent of Turing test. The experiment involved one user, nine judges (active-duty officers, mainly colonels and lieutenant colonels), and five scenarios obtained from several US Army exercises. For each scenario, experimenters obtained synchronization matrices that were produced in earlier exercises, typically by a team of four to five officers in three to four hours, spending approximately 16 person-hours in total. Using these scenarios and COAs, the user had CADET generate automatically detailed plans and express them as synchronization matrices. The user, a retired US Army officer, reviewed and slightly edited the matrices. The entire process took less than two minutes of computations by and approximately 20 minutes of review and post-editing, approximately 0.4 person-hour in total per product. The experimenters gave the resulting matrices the same visual style as those produced by humans. The judges, who did not know whether a planning product was a traditional product of humans, or with computerized aids, were asked to grade the products. The result was that the average grades for manual products and CADET-generated products were statistically indistinguishable, even though CADET-generated products required far less time to produce. == Legacy == CADET served as “...an example of how even relatively basic A

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  • Evolving intelligent system

    Evolving intelligent system

    In computer science, an evolving intelligent system is a fuzzy logic system which improves the own performance by evolving rules. The technique is known from machine learning, in which external patterns are learned by an algorithm. Fuzzy logic based machine learning works with neuro-fuzzy systems. Intelligent systems have to be able to evolve, self-develop, and self-learn continuously in order to reflect a dynamically evolving environment. The concept of Evolving Intelligent Systems (EISs) was conceived around the turn of the century with the phrase EIS itself coined for the first time by Angelov and Kasabov in a 2006 IEEE newsletter and expanded in a 2010 text. EISs develop their structure, functionality and internal knowledge representation through autonomous learning from data streams generated by the possibly unknown environment and from the system self-monitoring. EISs consider a gradual development of the underlying (fuzzy or neuro-fuzzy) system structure and differ from evolutionary and genetic algorithms which consider such phenomena as chromosomes crossover, mutation, selection and reproduction, parents and off-springs. The evolutionary fuzzy and neuro systems are sometimes also called "evolving" which leads to some confusion. This was more typical for the first works on this topic in the late 1990s. == Implementations == EISs can be implemented, for example, using neural networks or fuzzy rule-based models. The first neural networks which consider an evolving structure were published in. These were later expanded by N. Kasabov and P. Angelov for the neuro-fuzzy models. P. Angelov introduced the evolving fuzzy rule-based systems (EFSs) as the first mathematical self-learning model that can dynamically evolve its internal structure and is human interpretable and coined the phrase EFS. Contemporarily, the offline incremental approach for learning an EIS, namely, EFuNN, was proposed by N. Kasabov. P. Angelov, D. Filev, N. Kasabov and O. Cordon organised the first IEEE Symposium on EFSs in 2006 (the proceedings of the conference can be found in). EFSs include a formal (and mathematically sound) learning mechanism to extract it from streaming data. One of the earliest and the most widely cited comprehensive survey on EFSs was done in 2008. Later comprehensive surveys on EFS methods with real applications were done in 2011 and 2016 by E. Lughofer. Other works that contributed further to this area in the following years expanded it to evolving participatory learning, evolving grammar, evolving decision trees, evolving human behaviour modelling, self-calibrating (evolving) sensors (eSensors), evolving fuzzy rule-based classifiers, evolving fuzzy controllers, autonomous fault detectors. More recently, the stability of the evolving fuzzy rule-based systems that consist of the structure learning and the fuzzily weighted recursive least square parameter update method has been proven by Rong. Generalized EFS, which allow rules to be arbitrarily rotated in the feature space and thus to improve their data representability, have been proposed in with significant extensions in towards 'smartness' of the rule bases (thus, termed as "Generalized Smart EFS"), allowing more interpretability and reducing curse of dimensionality. The generalized rule structure was also successfully used in the context of evolving neuro-fuzzy systems. Several facets and challenges for achieving more transparent and understandable rule bases in EFS have been discussed by E. Lughofer in. EISs form the theoretical and methodological basis for the Autonomous Learning Machines (ALMA) and autonomous multi-model systems (ALMMo) as well as of the Autonomous Learning Systems. Evolving Fuzzy Rule-based classifiers, in particular, is a very powerful new concept that offers much more than simply incremental or online classifiers – it can cope with new classes being added or existing classes being merged. This is much more than just adapting to new data samples being added or classification surfaces being evolved. Fuzzy rule-based classifiers are the methodological basis of a new approach to deep learning that was until now considered as a form of multi-layered neural networks. Deep Learning offers high precision levels surpassing the level of human ability and grabbed the imagination of the researchers, industry and the wider public. However, it has a number of intrinsic constraints and limitations. These include: The "black box", opaque internal structure which has millions of parameters and involves ad hoc decisions on the number of layers and algorithm parameters. The requirement for a huge amount of training data samples, computational resources (usually requiring GPUs and/or HPC) and time (usually requiring many hours of training). Iterative search. Requires retraining for new situations (is not evolving). Does not have proven convergence and stability. Most, if not all, of the above limitations can be avoided with the use of the Deep (Fuzzy) Rule-based Classifiers, which were recently introduced based on ALMMo, while achieving similar or even better performance. The resulting prototype-based IF...THEN...models are fully interpretable and dynamically evolving (they can adapt quickly and automatically to new data patterns or even new classes). They are non-parametric and, therefore, their training is non-iterative and fast (it can take few milliseconds per data sample/image on a normal laptop which contrasts with the multiple hours the current deep learning methods require for training even when they use GPUs and HPC). Moreover, they can be trained incrementally, online, or in real-time. Another aspect of Evolving Fuzzy Rule-based classifiers has been proposed in, which, in case of multi-class classification problems, achieves the reduction of class imbalance by cascadability into class sub-spaces and an increased flexibility and performance for adding new classes on the fly from streaming samples.

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  • Netomi

    Netomi

    Netomi, formerly msg.ai, is an American artificial intelligence company and developer of chatbot technologies. == History == msg.ai was founded in May 2015 by Puneet Mehta. msg.ai worked with Sony Pictures to launch a chat bot on Facebook Messenger for a $100M film, Goosebumps and subsequently joined Y Combinator as a member of the Winter 2016 class. Later that year and in 2017, msg.ai completed two rounds of seed funding, led by Y Combinator and Index Ventures. In 2018, the company changed its name to Netomi. In 2019, the company raised $14.7 million in a Series A funding round also led by Index Ventures. In 2021, the company raised $30 million in a Series B funding round led by WndrCo LLC.

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  • A Very Fatal Murder

    A Very Fatal Murder

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

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  • Fuzzy electronics

    Fuzzy electronics

    Fuzzy electronics is an electronic technology that uses fuzzy logic, instead of the two-state Boolean logic more commonly used in digital electronics. Fuzzy electronics is fuzzy logic implemented on dedicated hardware. This is to be compared with fuzzy logic implemented in software running on a conventional processor. Fuzzy electronics has a wide range of applications, including control systems and artificial intelligence. == History == The first fuzzy electronic circuit was built by Takeshi Yamakawa et al. in 1980 using discrete bipolar transistors. The first industrial fuzzy application was in a cement kiln in Denmark in 1982. The first VLSI fuzzy electronics was by Masaki Togai and Hiroyuki Watanabe in 1984. In 1987, Yamakawa built the first analog fuzzy controller. The first digital fuzzy processors came in 1988 by Togai (Russo, pp. 2–6). In the early 1990s, the first fuzzy logic chips were presented to the public. Two companies which are Omron and NEC have announced the development of dedicated fuzzy electronic hardware in the year 1991. Two years later, the Japanese Omron Cooperation has shown a working fuzzy chip during a technical fair.

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  • Fuzzy differential inclusion

    Fuzzy differential inclusion

    Fuzzy differential inclusion is the extension of differential inclusion to fuzzy sets introduced by Lotfi A. Zadeh. x ′ ( t ) ∈ [ f ( t , x ( t ) ) ] α {\displaystyle x'(t)\in [f(t,x(t))]^{\alpha }} with x ( 0 ) ∈ [ x 0 ] α {\displaystyle x(0)\in [x_{0}]^{\alpha }} Suppose f ( t , x ( t ) ) {\displaystyle f(t,x(t))} is a fuzzy valued continuous function on Euclidean space. Then it is the collection of all normal, upper semi-continuous, convex, compactly supported fuzzy subsets of R n {\displaystyle \mathbb {R} ^{n}} . == Second order differential == The second order differential is x ″ ( t ) ∈ [ k x ] α {\displaystyle x''(t)\in [kx]^{\alpha }} where k ∈ [ K ] α {\displaystyle k\in [K]^{\alpha }} , K {\displaystyle K} is trapezoidal fuzzy number ( − 1 , − 1 / 2 , 0 , 1 / 2 ) {\displaystyle (-1,-1/2,0,1/2)} , and x 0 {\displaystyle x_{0}} is a trianglular fuzzy number (-1,0,1). == Applications == Fuzzy differential inclusion (FDI) has applications in Cybernetics Artificial intelligence, Neural network, Medical imaging Robotics Atmospheric dispersion modeling Weather forecasting Cyclone Pattern recognition Population biology

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  • Gallery software

    Gallery software

    Gallery software is software that helps the user publish or share photos, pictures, videos or other digital media. Most galleries are located on Web servers, where users are allowed to register and publish their pictures. Gallery software usually features automatic image resizing, allows digital media be categorized into sets, and allows comments. == Types == Early digital media publishing and sharing was done with imageboards. The boards are by topics, sometimes called "chan". Each discussion in a "chan" are started with a piece of digital media, and follow-up discussions can contain another piece too. Software works in this way: Futallaby, Danbooru. Traditionally, galleries are managed. An administrator maintains a set of or hierarchy of albums. The users can upload their digital media in one of the existing albums defined by an administrator, or create their own albums. The users with sufficient permission can re-categorise the digital media others uploaded. Often, the site's administrator can define which album the users are allowed to categorise their media into, or delete other user's content. Examples are open source galleries Coppermine, Gallery Project. There are decentralised gallery software that does not have an administrator for managing contents. Pinterest, Flickr and DeviantArt has been successful with this model. Open source gallery software MediaGoblin works in this way. Each user can create their own "collections", to categorise theirs or other users' media. However users cannot put media into other user's collections. Each user's category is separate. There is no centralised theme or hierarchy for the media.

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  • Murderbot (TV series)

    Murderbot (TV series)

    Murderbot is an American science fiction action comedy television series created by Paul Weitz and Chris Weitz for Apple TV+. It is based on All Systems Red, the first book of the series The Murderbot Diaries by Martha Wells, who serves as a consulting producer. The series stars Alexander Skarsgård as the titular character. The first season premiered on May 16, 2025 and received positive reviews. In July 2025, the series was renewed for a second season. == Premise == A media-obsessed private security construct (manufactured from cloned human tissue and mechanical parts) calling itself Murderbot must hide its newly acquired autonomy while completing dangerous assignments and being simultaneously drawn to humans, and appalled by their weakness. == Cast and characters == === Main === Alexander Skarsgård as Murderbot Noma Dumezweni as Ayda Mensah, a terraforming specialist, the President of Preservation Alliance and the leader of the science team protected by Murderbot David Dastmalchian as Gurathin, a tech expert and augmented human Sabrina Wu as Pin-Lee, a scientist and legal counsel to the team Akshay Khanna as Ratthi, a wormhole expert Tamara Podemski as Bharadwaj, a geochemist Tattiawna Jones as Arada, a biologist === Recurring === Cast of show-within-a-show The Rise and Fall of Sanctuary Moon John Cho as Eknie Jef Chem (playing Captain Hossein) Jack McBrayer as Breiller MocJac (playing Navigation Officer Hordööp-Sklanch) Clark Gregg as Arletty (playing Lieutenant Kullervv) DeWanda Wise as Pordron Bretney III Roche (playing NawBot 337 Alt 66) === Guest === Anna Konkle as Leebeebee, a member of another survey team on the planet. The character does not appear in the novella. Amanda Brugel as GrayCris Blue Leader David Reale as GrayCris Yellow == Episodes == == Production == The book series was optioned in the late 2010s, and its film adaptation was considered. In 2021, book series author Martha Wells said that a potential TV series adaptation was in development and that she had read the script and was "really excited about it". The series was green lit by Apple TV+ in 2022, with Wells serving as a consulting producer. The production design team, led by Sue Chan, started work in the autumn. Tommy Arnold, the Murderbot Diaries special edition illustrator, created the concept art for the show. After the casting was delayed by the 2023 SAG-AFTRA strike, in December 2023 it was announced that Alexander Skarsgård would produce and star in the series. He developed the character and the world of Murderbot with the showrunners. In February 2024, David Dastmalchian and Noma Dumezweni joined the cast. In March, Sabrina Wu, Tattiawna Jones, Akshay Khanna, and Tamara Podemski joined the cast. On July 10, 2025, the series was renewed for a second season. Showrunners Chris and Paul Weitz suggested the second season would combine the next three books of the series and will have longer episodes. === Filming === Principal photography for the first season took place from March–June 2024, in Toronto and parts of Ontario, Canada. Most of the filming was done on location, with the Sanctuary Moon scenes filmed on a virtual production stage. Principal photography for the second season began in mid-2026, in Madrid, Spain. It is planned to last 71 days, with Martha Wells also visiting the set. == Release == The first two episodes of Murderbot premiered on Apple TV+ on May 16, 2025, with subsequent episodes released weekly. The first season consists of ten episodes. == Reception == Even before the release of the show, numerous media sources had commented on the titular character as being coded as autistic and agender. On the review aggregator website Rotten Tomatoes, Murderbot has an approval rating of 96% with an average score of 7.5/10, based on 76 critics' reviews. The website's critical consensus states, "Alexander Skarsgård's superbly dry wit brings a lot of heart to Murderbot, making for a refreshingly jaunty sci-fi saga about finally coming out of one's shell". Metacritic, which uses a weighted average, assigned a score of 70 out of 100, based on 28 critics, indicating "generally favorable" reviews. Some reviewers have criticized Murderbot's changes to Wells' original books. Angela Watercutter of Wired noted that the series has significant tonal differences from the books and noted the show's changes to characters, particularly Murderbot and Dr. Mensah, and Wells' social commentary. === Accolades === Murderbot was a finalist for the 2025 Dragon Award for Best Science Fiction or Fantasy TV Series. Tommy Arnold won the 2025 Concept Art Association Award in the category of Live-Action Series Character Art for his work on Murderbot. Alexander Skarsgård was nominated for a Critics' Choice Award for Best Actor in a Comedy Series. Carrie Grace and Laura Jean Shannon were nominated for a Costume Designers Guild Award in the category of Excellence in Sci-Fi/Fantasy Television for their work on FreeCommerce. Amanda Jones was nominated for a Composers & Lyricists Award for Outstanding Original Title Sequence for a Television Production.

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  • Noise-based logic

    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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  • Full Dive

    Full Dive

    Full Dive, short for Full Dive: This Ultimate Next-Gen Full Dive RPG Is Even Shittier than Real Life! (Japanese: 究極進化したフルダイブRPGが現実よりもクソゲーだったら, Hepburn: Kyūkyoku Shinka shita Furu Daibu RPG ga Genjitsu yori mo Kusogē Dattara), is a Japanese light novel series written by Light Tuchihi and illustrated by Youta. Media Factory has published four volumes since August 2020 under their MF Bunko J imprint. A manga adaptation with art by Kino was serialized in Media Factory's seinen manga magazine Monthly Comic Alive from January 2021 to January 2022. An anime television series adaptation by ENGI aired from April to June 2021. == Plot == Hiroshi Yuki, with the player name of Hiro, is a high school boy who loves to play virtual reality MMORPGs (VRMMORPG) in order to escape reality. When a game store manager named Reona Kisaragi tricks him into buying the game Kiwame Quest, he soon discovers that it is not what it seems. Unlike regular games, it is a game that tries to pursue realism to a fanatical point. As such, Hiroshi struggles to eke out a niche. Despite the disadvantages, he is determined to complete the game. == Characters == === Main characters === Hiroshi Yuki (結城宏, Yūki Hiroshi) Voiced by: Daiki Yamashita, Riho Sugiyama (young) (Japanese); Johnny Yong Bosch, Michele Knotz (young) (English) Hiroshi is a high school student who is tricked into buying Kiwame Quest by game store manager, Reona Kisaragi. He is a former member of the track team who quit following an unfortunate incident and he likes to play VRMMORPGs in order to escape reality. His player name is Hiro. Reona Kisaragi (如月玲於奈, Kisaragi Reona) Voiced by: Ayana Taketatsu (Japanese); Natalie Van Sistine (English) Reona is a game store manager who tricks Hiroshi into buying Kiwame Quest. She likes to tease him and her in-game avatar is that of a fairy. Alicia (アリシア, Arishia) Voiced by: Fairouz Ai (Japanese); Kayli Mills (English) Alicia is one of Hiroshi's childhood friends in Kiwame Quest. She has an older brother named Martin in-game. Mizarisa (ミザリサ) Voiced by: Shiori Izawa (Japanese); Sarah Anne Williams (English) Mizarisa is the town inquisitor in Kiwame Quest. Kaede Yuki (結城楓, Yūki Kaede) Voiced by: Aoi Koga (Japanese); Kate Bristol (English) Kaede is Hiroshi's younger sister. She used to look up to her older brother, but their relationship has been strained ever since he quit the track team. === NPCs === Martin (マーチン, Māchin) Voiced by: Haruki Ishiya, Natsumi Fujiwara (young) (Japanese); Ben Lepley, Krystal LaPorte (young) (English) Martin is one of Hiroshi's childhood friends in Kiwame Quest. He is also Alicia's older brother in-game. Tesla (テスラ, Tesura) Voiced by: Satoshi Hino (Japanese); Jason Liebrecht (English) Tesla is the captain of the City Guard in Kiwame Quest. Govern (ガバン, Gaban) Voiced by: Shizuka Itō (Japanese); Lisa Ortiz (English) Govern is the queen of Ted in Kiwame Quest. === Other characters === Ginji (ギンジ) Voiced by: Katsuyuki Konishi (Japanese); Brent Mukai (English) Ginji is a veteran player of Kiwame Quest. Soichiro Kamui (神居宗一郎, Kamui Sōichirō) Voiced by: Yoshitsugu Matsuoka (Japanese); Samuel Drake (English) Kamui is the only known player who has successfully completed Kiwame Quest. == Media == === Light novels === Light Tuchihi launched the light novel series, with illustrations by Youta, under Media Factory's MF Bunko J label on August 25, 2020. ==== Volumes ==== === Manga === A manga adaptation by Kino was serialized in Media Factory's Monthly Comic Alive magazine from January 27, 2021, to January 27, 2022. Two tankōbon volumes were released from May 21, 2021, to January 21, 2022. ==== Volumes ==== === Anime === An anime television series adaptation was announced on December 4, 2020. The series was animated by ENGI and directed by Kazuya Miura, with Kenta Ihara writing the series' scripts, and Yūta Kevin Kenmotsu designing the characters. It ran from April 7 to June 23, 2021, on AT-X, Tokyo MX, SUN, KBS Kyoto, and BS11. Mayu Maeshima performed the opening theme "Answer", while Ayana Taketatsu, Fairouz Ai, Shiori Izawa, and Aoi Koga performed the ending theme "Kisuida!". It ran for 12 episodes. Funimation licensed and streamed the series. On June 8, 2021, Funimation announced that the series would receive an English dub, which premiered the following day. Following Sony's acquisition of Crunchyroll, the series was moved to Crunchyroll. ==== Episodes ====

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  • Outline of databases

    Outline of databases

    The following is provided as an overview of and topical guide to databases: Database – organized collection of data, today typically in digital form. The data are typically organized to model relevant aspects of reality (for example, the availability of rooms in hotels), in a way that supports processes requiring this information (for example, finding a hotel with vacancies). == What type of things are databases? == Databases can be described as all of the following: Information – sequence of symbols that can be interpreted as a message. Information can be recorded as signs, or transmitted as signals. Data – values of qualitative or quantitative variables, belonging to a set of items. Data in computing (or data processing) are often represented by a combination of items organized in rows and multiple variables organized in columns. Data are typically the results of measurements and can be visualised using graphs or images. Computer data – information in a form suitable for use with a computer. Data is often distinguished from programs. A program is a sequence of instructions that detail a task for the computer to perform. In this sense, data is everything in software that is not program code. == Types of databases == Active database – Database with event driven features Animation database – Database for storing and reusing animation fragments or motion capture data Back-end database – Organized collection of data in computingPages displaying short descriptions of redirect targets Bibliographic database – database of bibliographic records, an organized digital collection of references to published literature, including journal and newspaper articles, conference proceedings, reports, government and legal publications, patents, books, etc. Centralized database – database located and maintained in one location, unlike a distributed database. Cloud database – Database running on a cloud computing platform Collection database – collection catalog of a museum or archive implemented using a computerized database, in which the institution's objects or material are catalogued. Collective Optimization Database – open repository to enable sharing of benchmarks, data sets and optimization cases from the community, provide web services and Plug-in (computing)|plugins to analyze optimization data and predict program transformations or better hardware designs for multi-objective optimizations based on statistical and machine learning techniques provided there is enough information collected in the repository from multiple users. Configuration management database – Database used to store info on hardware and software assets Cooperative database – holds information on customers and their transactions. Current database – conventional database that stores data that is valid now. Directory – repository or database of information which is optimized for reading, under the assumption that data updates are very rare compared to data reads. Commonly, a directory supports search and browsing in addition to simple lookups. Distributed database – database in which storage devices are not all attached to a common CPU. Document-oriented database – computer program designed for storing, retrieving, and managing document-oriented, or Semi-structured model|semi structured data, information. EDA database – database specialized for the purpose of electronic design automation. Endgame tablebase – computerized database that contains precalculated exhaustive analysis of a chess endgame position. Food composition database (FCDB) – provides detailed information on the nutritional composition of foods. Full-text database – database that contains the complete text of books, dissertations, journals, magazines, newspapers or other kinds of textual documents. Also called a "complete-text database". Government database – collects personal information for various reasons (mass surveillance, Schengen Information System in the European Union, social security, statistics, etc.). Graph database – uses graph structures with nodes, edges, and properties to represent and store data. Knowledge base – special kind of database for knowledge management. A knowledge base provides a means for information to be collected, organised, shared, searched and utilised. Mobile database – can be connected to by a mobile computing device over a mobile network. Navigational database – database in which objects (or records) in it are found primarily by following references from other objects. Non-native speech database – speech database of non-native pronunciations of English. Online database – database accessible from a network, including from the Internet. Operational database – accessed by an Operational System to carry out regular operations of an organization. Parallel database – improves performance through parallelization of various operations, such as loading data, building indexes and evaluating queries. Probabilistic database – uncertain database in which the possible worlds have associated probabilities. Real-time database – processing system designed to handle workloads whose state is constantly changing (Buchmann). Relational database – collection of data items organized as a set of formally described tables from which data can be accessed easily. Spatial database – database that is optimized to store and query data that is related to objects in space, including points, lines and polygons. Temporal database – database with built-in time aspects, for example a temporal data model and a temporal version of Structured Query Language (SQL). Time series database – a time series is an associative array of numbers indexed by a datetime or a datetime range. These time series are often called profiles or curves, depending upon the market. A time series of stock prices might be called a price curve, or a time series of energy consumption might be called a load profile. Despite the disparate naming, the operations performed on them are sufficiently common as to demand special database treatment. Triplestore – purpose-built database for the storage and retrieval of triples, a triple being a data entity composed of subject-predicate-object, like "Bob is 35" or "Bob knows Fred". Very large database (VLDB) – contains an extremely high number of tuples (database rows), or occupies an extremely large physical filesystem storage space. Vulnerability database – platform aimed at collecting, maintaining, and disseminating information about discovered vulnerabilities targeting real computer systems. XLDB – Stands for "eXtremely Large Data Base". XML database – data stored in XML format, where it can be queried, exported and serialized into the desired format. == History of databases == History of databases – History of database management systems –: == Database use == Database usage requirements – Database theory – encapsulates a broad range of topics related to the study and research of the theoretical realm of databases and database management systems. Database machine – or is a computer or special hardware that stores and retrieves data from a database. Also called a "back end processor" Database server – computer program that provides database services to other computer programs or computers, as defined by the client-server model. Database application – computer program whose primary purpose is entering and retrieving information from a computer-managed database. Database management system (DBMS) – software package with computer programs that control the creation, maintenance, and use of a database. Database connection – facility in computer science that allows client software to communicate with database server software, whether on the same machine or not. Datasource – name given to the connection set up to a database from a server. The name is commonly used when creating a query to the database. The Database Source Name (DSN) does not have to be the same as the filename for the database. For example, a database file named "friends.mdb" could be set up with a DSN of "school". Then DSN "school" would then be used to refer to the database when performing a query. Data Source Name (DSN) – are data structures used to describe a connection to a data source. Sometimes known as a database source name though data sources are not limited to databases. Database administrator (DBA) – is a person responsible for the installation, configuration, upgrade, administration, monitoring and maintenance of physical databases. Lock – Comparison of database tools – (provides tables for comparing general and technical information for a number of available database administrator tools.) Database-centric architecture – software architectures in which databases play a crucial role. Also called "data-centric architecture". Intelligent database – was put forward as a system that manages information (rather than data) in a way that appears natural to users and which goes beyond simple record keeping. Two-phase locking (2PL) – is a

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  • IJCAI Award for Research Excellence

    IJCAI Award for Research Excellence

    The IJCAI Award for Research Excellence is a biannual award before given at the IJCAI conference to researcher in artificial intelligence as a recognition of excellence of their career. Beginning in 2016, the conference is held annually and so is the award. == Laureates == The recipients of this award have been: John McCarthy (1985) Allen Newell (1989) Marvin Minsky (1991) Raymond Reiter (1993) Herbert A. Simon (1995) Aravind Joshi (1997) Judea Pearl (1999) Donald Michie (2001) Nils Nilsson (2003) Geoffrey E. Hinton (2005) Alan Bundy (2007) Victor R. Lesser (2009) Robert Kowalski (2011) Hector Levesque (2013) Barbara Grosz (2015) for her pioneering research in Natural Language Processing and in theories and applications of Multiagent Collaboration. Michael I. Jordan (2016) for his groundbreaking and impactful research in both the theory and application of statistical machine learning. Andrew Barto (2017) for his pioneering work in the theory of reinforcement learning. Jitendra Malik (2018) Yoav Shoham (2019) Eugene Freuder (2020) Richard S. Sutton (2021) Stuart J. Russell (2022) Sarit Kraus (2023) for her pioneering work of the study of interactions among self-interested agents, creating the field of automated negotiation, and developing methods for coalition formation and teamwork, both as formal models and real-world implementations. == Winners of also Turing Award == John McCarthy (1971) Allen Newell (1975) Marvin Minsky (1969) Herbert A. Simon (1975) Judea Pearl (2011) Geoffrey Hinton (2018) Andrew Barto (2024) Richard S. Sutton (2024)

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  • Terminator (franchise)

    Terminator (franchise)

    Terminator is an American media franchise created by James Cameron and Gale Anne Hurd. It is considered to be of the cyberpunk subgenre of science fiction. The franchise primarily focuses on the events leading to a future post-apocalyptic war between a synthetic intelligence known as Skynet, and a surviving resistance of humans led by John Connor. In this future, Skynet uses an arsenal of cyborgs known as Terminators, designed to mimic humans and infiltrate the resistance. Much of the franchise takes place in time periods prior to the Skynet takeover, with both humans and Terminators using time travel to attempt to alter the past and change the outcome of the future. A prominent Terminator model throughout the films is the T-800, commonly known as "the Terminator", with instances of this model portrayed by Arnold Schwarzenegger. The franchise began with the 1984 film The Terminator, written and directed by Cameron, with Hurd as producer. They would return for the 1991 sequel Terminator 2: Judgment Day (or T2). Both films were critical and commercial successes. Terminator 3: Rise of the Machines (or T3) was released in 2003 to positive reviews, followed by Terminator Salvation in 2009 to more negative reviews. Salvation was intended as the first in a new trilogy, which was later scrapped after the film rights were sold. Cameron was consulted for the 2015 film Terminator Genisys, a reboot branching off from the timeline of the original film. It was negatively received and performed poorly at the box-office. Cameron had a larger role as a producer of the 2019 film Terminator: Dark Fate, a direct sequel to T2 that ignores the three preceding films. As with Salvation, both Genisys and Dark Fate were planned as first installments of new trilogies, with the plans scrapped each time due to the films' poor box-office performances. Outside of the theatrical films, Cameron co-directed T2-3D: Battle Across Time, a 1996 theme park film-based attraction. It was produced as the original sequel to T2 and reunited its main cast. A television series, Terminator: The Sarah Connor Chronicles, was developed without Cameron's involvement and aired for two seasons in 2008 and 2009. It was also produced as a T2 sequel, taking place in an alternate timeline that ignores the third film and subsequent events. Terminator Zero, an anime series, premiered in August 2024. The franchise has also inspired several lines of comic books since 1988, and numerous video games since 1991. By 2010, the franchise had generated $3 billion in revenue. == Themes and setting == The central theme of the franchise is the battle for survival between the nearly-extinct human race and the world-spanning, synthetic intelligence that is Skynet. Skynet is positioned in the first film, The Terminator (1984), as a U.S. strategic "Global Digital Defense Network" computer system by Cyberdyne Systems which becomes self-aware. Shortly after activation, Skynet seemingly perceives all humans as a threat to its existence and formulates a plan to systematically wipe out humanity itself. The system initiates a nuclear first strike against Russia, thereby ensuring a devastating second strike and a nuclear holocaust which wipes out much of humanity in the resulting nuclear war. In the post-apocalyptic aftermath, Skynet later builds up its own autonomous machine-based military capability which includes the Terminators used against individual human targets and thereafter proceeds to wage a persistent total war against the surviving elements of humanity, some of whom have militarily organized themselves into a Resistance. At some point in this future, Skynet develops the capability of time travel and both it and the Resistance seek to use this technology in order to win the war; either by altering or accelerating past events or by preventing the apocalyptic timeline. === Judgment Day === In the franchise, Judgment Day (a reference to the biblical Day of Judgment) is the date on which Skynet becomes self-aware, in which case its creators panic and attempt to deactivate the network. As a result, Skynet perceives humanity as a threat and attempts to exterminate them. Skynet launches an all-out nuclear attack on Russia in order to provoke a nuclear counter-strike against the United States, knowing this will eliminate its human enemies. Due to time travel and the consequent ability to change the future, several differing dates are given for Judgment Day. In Terminator 2: Judgment Day (1991), Sarah Connor states that Judgment Day will occur on August 29, 1997. However, this date is delayed following the attack on Cyberdyne Systems in the same film. Judgment Day has various different dates in different timelines of the subsequent films, as well as the television series, creating a multiverse of temporal phenomena. In Terminator 3: Rise of the Machines (2003) and Terminator Salvation (2009), Judgment Day was postponed to July 2003. In Terminator: The Sarah Connor Chronicles (2008–2009), the attack on Cyberdyne Systems in the second film delayed Judgment Day to April 21, 2011. In Terminator Genisys (2015), the fifth film in the franchise, Judgment Day was postponed to an unspecified day in October 2017, attributed to altered events in both the future and the past. Sarah and Kyle Reese travel through time to the year 2017 and seemingly defeat Skynet, but the system core, contained inside a subterranean blast shelter, survives unknown to them, thus further delaying, rather than preventing, Judgment Day. In Terminator: Dark Fate (2019), the direct sequel to Terminator 2: Judgment Day, a date is not given for the new Judgment Day though it is named as such by Grace. Since Grace is a ten-year-old in 2020 and shown as a teenager in the post-Judgment Day world in flash-forwards throughout the film, Judgment Day occurs sometime in the early 2020s in this timeline. == Franchise rights == Before the first film was created, director James Cameron sold the rights for $1 to Gale Anne Hurd, his future wife, who produced the film, under the strict provision that he be allowed to direct it. Hemdale Film Corporation also became a 50-percent owner of the franchise rights, until its share was sold in 1990 to Carolco Pictures, a company founded by Andrew G. Vajna and Mario Kassar. Terminator 2: Judgment Day was released a year later. Carolco filed for bankruptcy in 1995 and its library was subsequently acquired by StudioCanal, which continues to own the franchise today. However, the rights to future Terminator films were ultimately put up for auction. By that time, Cameron had become interested in making a Terminator 3 film. The rights were ultimately auctioned to Vajna in 1997, for $8 million. Vajna and Kassar spent another $8 million to purchase Hurd's half of the rights in 1998, becoming the full owners of the franchise. Hurd was initially opposed to the sale of the rights, while Cameron had lost interest in the franchise and a third film. After the 2003 release of Terminator 3: Rise of the Machines, the franchise rights were sold in 2007 for about $25 million to The Halcyon Company, which produced Terminator Salvation in 2009. Later that year, the company faced legal issues and filed for bankruptcy, putting the franchise rights up for sale. The rights were valued at about $70 million. In 2010, the rights were sold for $29.5 million to Pacificor, a hedge fund that was Halcyon's largest creditor. In 2012, the rights were sold to Megan Ellison and her production company Annapurna Pictures for less than $20 million, a lower price than what was previously offered. The low price was because of the possibility of Cameron regaining the rights in 2019, as a result of new North American copyright laws. Megan's brother David Ellison and Skydance Productions produced Terminator Genisys in 2015. Cameron worked together with David Ellison to produce the 2019 film Terminator: Dark Fate. As the film neared its release, Hurd filed to terminate a copyright grant made 35 years earlier. Under this move, Hurd would again become a 50-percent owner of the rights with Cameron and Skydance could lose the rights to make any additional Terminator films beginning in November 2020, unless a new deal is worked out. Skydance responded that it had a deal in place with Cameron and that it "controls the rights to the Terminator franchise for the foreseeable future". == Films == === The Terminator (1984) === The Terminator is a 1984 science fiction action film released by Orion Pictures, co-written and directed by James Cameron and starring Arnold Schwarzenegger, Linda Hamilton and Michael Biehn. It is the first work in the Terminator franchise. In the film, robots take over the world in the near future, directed by the artificial intelligence Skynet. With its sole mission to completely annihilate humanity, it develops android assassins called Terminators that outwardly appear human. A man named John Connor starts the Tech-Com resistance to fight the machi

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