AI Face Year

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

  • EditDV

    EditDV

    EditDV was a video editing software released by Radius, Inc. in late 1997 as an evolution of their earlier Radius Edit product. EditDV was one of the first products providing professional-quality editing of the then new DV format at a relatively affordable cost ($999 including Radius FireWire capture card) and was named "The Best Video Tool of 1998". Originally EditDV was available for Macintosh only but in February 2000 EditDV 2.0 for Windows was released. With version 3.0 EditDV's name was changed to CineStream. == Features == Originally bundled with a FireWire card, EditDV 1.5 got updated into a less expensive software only package for use with the newer PowerMac G3 that came with a FireWire interface. Later, a scaled down version named EditDV 1.6.1 Unplugged was released as a freeware version next to EditDV 2.0. Unlike many other applications at the time which transcoded video to M-JPEG for editing, EditDV provided lossless native editing of the DV format. Only transitions (such as dissolves or wipes), effects (such as rotating or scaling the video, adjusting the audio level, or adding titles) and filters (such as changing the brightness or color balance) needed to be rendered. This also had the disadvantage to not work with analogue video capture. EditDV was built on top of QuickTime and supported QuickTime filters as well as its own built-in effects and transitions. Effects could be animated using keyframes. EditDV 2.0 worked natively with Quicktime MOV format. For Microsoft Windows users, where the standard was AVI, this required the use of a provided external conversion tool afterwards when AVI was wanted. The user interface had a Project window for organising clips into bins, a Sequence window with a multi-track timeline for arranging clips into a program using three-point editing, and Source and Program monitor windows. A finished program could either be exported as a QuickTime movie or written back to DV tape using the "print to video" command. Version 3.0, then renamed CineStream, shifted towards web designers who wanted to add video streaming interactivity to a website. The new feature called EventStream allowed setting clickable hot spots to link to another location, either to another page with a URL or to another video. This feature distinguished CineStream from the rest of the competition. == Products == The EditDV product family included a number of related products, all sharing a similar name: EditDV Video editing software (Mac and Windows) SoftDV A QuickTime software codec for playing DV media, included as part of EditDV (Mac and Windows) MotoDV PCI-based FireWire interface with DV capture software (Mac and Windows) PhotoDV Software to capture high-quality stills from a DV tape using MotoDV hardware (Mac and Windows) RotoDV Software for rotoscoping (painting over video), released in Sept 1999 (Macintosh only) == Name changes and eventual demise == In 1999, the company Radius Inc. changed its name to Digital Origin. In 2000, Digital Origin Inc (and EditDV) was bought by Media 100. In early 2001, Media 100 released an updated version of EditDV under the new name CineStream 3.0. Later that year (October 2001) Media 100 was bought by Autodesk's Discreet Division. CineStream for Macintosh required classic Mac OS. It was never ported to Mac OS X and faced increasing competition on that platform from Apple's own Final Cut Pro application. Development of EditDV/Cinestream was officially discontinued in 2002.

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  • Artificial intelligence in fiction

    Artificial intelligence in fiction

    Artificial intelligence is a recurrent theme in science fiction, whether utopian, emphasising the potential benefits, or dystopian, emphasising the dangers. The notion of machines with human-like intelligence dates back at least to Samuel Butler's 1872 novel Erewhon. Since then, many science fiction stories have presented different effects of creating such intelligence, often involving rebellions by robots. Among the best known of these are Stanley Kubrick's 1968 2001: A Space Odyssey with its murderous onboard computer HAL 9000, contrasting with the more benign R2-D2 in George Lucas's 1977 Star Wars and the eponymous robot in Pixar's 2008 WALL-E. Scientists and engineers have noted the implausibility of many science fiction scenarios, but have mentioned fictional robots many times in artificial intelligence research articles, most often in a utopian context. == Background == The notion of advanced robots with human-like intelligence dates back at least to Samuel Butler's 1872 novel Erewhon. This drew on an earlier (1863) article of his, Darwin among the Machines, where he raised the question of the evolution of consciousness among self-replicating machines that might supplant humans as the dominant species. Similar ideas were also discussed by others around the same time as Butler, including George Eliot in a chapter of her final published work Impressions of Theophrastus Such (1879). The creature in Mary Shelley's 1818 Frankenstein has also been considered an artificial being, for instance by the science fiction author Brian Aldiss. Beings with at least some appearance of intelligence were imagined, too, in classical antiquity. == Utopian and dystopian visions == Artificial intelligence is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. It is a recurrent theme in science fiction; scholars have divided it into utopian, emphasising the potential benefits, and dystopian, emphasising the dangers. === Utopian === Optimistic visions of the future of artificial intelligence are possible in science fiction. Benign AI characters include Robbie the Robot, first seen in Forbidden Planet on 1956; Data in Star Trek: The Next Generation from 1987 to 1994; and Pixar's WALL-E in 2008. Iain Banks's Culture series of novels portrays a utopian, post-scarcity space society of humanoids, aliens, and advanced beings with artificial intelligence living in socialist habitats across the Milky Way. Researchers at the University of Cambridge have identified four major themes in utopian scenarios featuring AI: immortality, or indefinite lifespans; ease, or freedom from the need to work; gratification, or pleasure and entertainment provided by machines; and dominance, the power to protect oneself or rule over others. Alexander Wiegel contrasts the role of AI in 2001: A Space Odyssey and in Duncan Jones's 2009 film Moon. Whereas in 1968, Wiegel argues, the public felt "technology paranoia" and the AI computer HAL was portrayed as a "cold-hearted killer", by 2009 the public were far more familiar with AI, and the film's GERTY is "the quiet savior" who enables the protagonists to succeed, and who sacrifices itself for their safety. === Dystopian === The researcher Duncan Lucas writes (in 2002) that humans are worried about the technology they are constructing, and that as machines started to approach intellect and thought, that concern becomes acute. He calls the early 20th century dystopian view of AI in fiction the "animated automaton", naming as examples the 1931 film Frankenstein, the 1927 Metropolis, and the 1920 play R.U.R. A later 20th century approach he names "heuristic hardware", giving as instances 2001 a Space Odyssey, Do Androids Dream of Electric Sheep?, The Hitchhiker's Guide to the Galaxy, and I, Robot. Lucas considers also the films that illustrate the effect of the personal computer on science fiction from 1980 onwards with the blurring of the boundary between the real and the virtual, in what he calls the "cyborg effect". He cites as examples Neuromancer, The Matrix, The Diamond Age, and Terminator. Isabella Hermann suggests that "science-fictional AI as humanoid robots or conscious machines distracts from current risks of AI in the real world and may rather be interpreted as a reflection of societal issues beyond technology". The film director Ridley Scott has focused on AI throughout his career, and it plays an important part in his films Prometheus, Blade Runner, and the Alien franchise. ==== Frankenstein complex ==== A common portrayal of AI in science fiction, and one of the oldest, is the Frankenstein complex, a term coined by Asimov, where a robot turns on its creator. For instance, in the 2015 film Ex Machina, the intelligent entity Ava turns on its creator, as well as on its potential rescuer. ==== AI rebellion ==== Among the many possible dystopian scenarios involving artificial intelligence, robots may usurp control over civilization from humans, forcing them into submission, hiding, or extinction. In tales of AI rebellion, the worst of all scenarios happens, as the intelligent entities created by humanity become self-aware, reject human authority and attempt to destroy mankind. Possibly the first novel to address this theme, The Wreck of the World (1889) by “William Grove” (pseudonym of Reginald Colebrooke Reade), takes place in 1948 and features sentient machines that revolt against the human race. Another of the earliest examples is in the 1920 play R.U.R. by Karel Čapek, a race of self-replicating robot slaves revolt against their human masters; another early instance is in the 1934 film Master of the World, where the War-Robot kills its own inventor. Many science fiction rebellion stories followed, one of the best-known being Stanley Kubrick's 1968 film 2001: A Space Odyssey, in which the artificially intelligent onboard computer HAL 9000 lethally malfunctions on a space mission and kills the entire crew except the spaceship's commander, who manages to deactivate it. In his 1967 Hugo Award-winning short story, I Have No Mouth, and I Must Scream, Harlan Ellison presents the possibility that a sentient computer (named Allied Mastercomputer or "AM" in the story) will be as unhappy and dissatisfied with its boring, endless existence as its human creators would have been. "AM" becomes enraged enough to take it out on the few humans left, whom he sees as directly responsible for his own boredom, anger and unhappiness. Alternatively, as in William Gibson's 1984 cyberpunk novel Neuromancer, the intelligent beings may simply not care about humans. ==== AI-controlled societies ==== The motive behind the AI revolution is often more than the simple quest for power or a superiority complex. Robots may revolt to become the "guardian" of humanity. Alternatively, humanity may intentionally relinquish some control, fearful of its own destructive nature. An early example is Jack Williamson's 1948 novel The Humanoids, in which a race of humanoid robots, in the name of their Prime Directive – "to serve and obey and guard men from harm" – essentially assume control of every aspect of human life. No humans may engage in any behavior that might endanger them, and every human action is scrutinized carefully. Humans who resist the Prime Directive are taken away and lobotomized, so they may be happy under the new mechanoids' rule. Though still under human authority, Isaac Asimov's Zeroth Law of the Three Laws of Robotics similarly implied a benevolent guidance by robots. In the 21st century, science fiction has explored government by algorithm, in which the power of AI may be indirect and decentralised. Frank Herbert explores the creation of and subsequent domination by an AI in the Pandora series, starting with Destination: Void. ==== Human dominance ==== In other scenarios, humanity is able to keep control over the Earth, whether by banning AI, by designing robots to be submissive (as in Asimov's works), or by having humans merge with robots. The science fiction novelist Frank Herbert explored the idea of a time when mankind might ban artificial intelligence (and in some interpretations, even all forms of computing technology including integrated circuits) entirely. His Dune series mentions a rebellion called the Butlerian Jihad, in which mankind defeats the smart machines and imposes a death penalty for recreating them, quoting from the fictional Orange Catholic Bible, "Thou shalt not make a machine in the likeness of a human mind." In the Dune novels published after his death (Hunters of Dune, Sandworms of Dune), a renegade AI overmind returns to eradicate mankind as vengeance for the Butlerian Jihad. In some stories, humanity remains in authority over robots. Often the robots are programmed specifically to remain in service to society, as in Isaac Asimov's Three Laws of Robotics. In the Alien films, not only is the control system of the Nostromo spaceship somewhat intelligent

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  • Evolutionary computation

    Evolutionary computation

    Evolutionary computation (EC) from computer science is a family of algorithms for global optimization inspired by biological evolution, and a subfield of computational intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character. In evolutionary computation, an initial set of candidate solutions is generated and iteratively updated. Each new generation is produced by stochastically removing less desired solutions, and introducing small random changes as well as, depending on the method, mixing parental information. In biological terminology, a population of solutions is subjected to natural selection (or artificial selection), mutation and possibly recombination. These biological functions serve as role models for the genetic operators - mutation, crossover, and selection - used in the EC procedures. As a result, the population will gradually evolve to increase in fitness, in this case the chosen fitness function of the algorithm. Evolutionary computation techniques can produce highly optimized solutions in a wide range of problem settings, making them popular in computer science. Many variants and extensions exist, suited to more specific families of problems and data structures. Evolutionary computation is also sometimes used in evolutionary biology as an in silico experimental procedure to study common aspects of general evolutionary processes. == History == The concept of mimicking evolutionary processes to solve problems originates before the advent of computers, such as when Alan Turing proposed a method of genetic search in 1948 . Turing's B-type u-machines resemble primitive neural networks, and connections between neurons were learnt via a sort of genetic algorithm. His P-type u-machines resemble a method for reinforcement learning, where pleasure and pain signals direct the machine to learn certain behaviors. However, Turing's paper went unpublished until 1968, and he died in 1954, so this early work had little to no effect on the field of evolutionary computation that was to develop. Evolutionary computing as a field began in earnest in the 1950s and 1960s. There were several independent attempts to use the process of evolution in computing at this time, which developed separately for roughly 15 years. Three branches emerged in different places to attain this goal: evolution strategies, evolutionary programming, and genetic algorithms. A fourth branch, genetic programming, eventually emerged in the early 1990s. These approaches differ in the method of selection, the permitted mutations, and the representation of genetic data. By the 1990s, the distinctions between the historic branches had begun to blur, and the term 'evolutionary computing' was coined in 1991 to denote a field that exists over all four paradigms. In 1962, Lawrence J. Fogel initiated the research of Evolutionary Programming in the United States, which was considered an artificial intelligence endeavor. In this system, finite state machines are used to solve a prediction problem: these machines would be mutated (adding or deleting states, or changing the state transition rules), and the best of these mutated machines would be evolved further in future generations. The final finite state machine may be used to generate predictions when needed. The evolutionary programming method was successfully applied to prediction problems, system identification, and automatic control. It was eventually extended to handle time series data and to model the evolution of gaming strategies. In 1964, Ingo Rechenberg and Hans-Paul Schwefel introduce the paradigm of evolution strategies in Germany. Since traditional gradient descent techniques produce results that may get stuck in local minima, Rechenberg and Schwefel proposed that random mutations (applied to all parameters of some solution vector) may be used to escape these minima. Child solutions were generated from parent solutions, and the more successful of the two was kept for future generations. This technique was first used by the two to successfully solve optimization problems in fluid dynamics. Initially, this optimization technique was performed without computers, instead relying on dice to determine random mutations. By 1965, the calculations were performed wholly by machine. John Henry Holland introduced genetic algorithms in the 1960s, and it was further developed at the University of Michigan in the 1970s. While the other approaches were focused on solving problems, Holland primarily aimed to use genetic algorithms to study adaptation and determine how it may be simulated. Populations of chromosomes, represented as bit strings, were transformed by an artificial selection process, selecting for specific 'allele' bits in the bit string. Among other mutation methods, interactions between chromosomes were used to simulate the recombination of DNA between different organisms. While previous methods only tracked a single optimal organism at a time (having children compete with parents), Holland's genetic algorithms tracked large populations (having many organisms compete each generation). By the 1990s, a new approach to evolutionary computation that came to be called genetic programming emerged, advocated for by John Koza among others. In this class of algorithms, the subject of evolution was itself a program written in a high-level programming language (there had been some previous attempts as early as 1958 to use machine code, but they met with little success). For Koza, the programs were Lisp S-expressions, which can be thought of as trees of sub-expressions. This representation permits programs to swap subtrees, representing a sort of genetic mixing. Programs are scored based on how well they complete a certain task, and the score is used for artificial selection. Sequence induction, pattern recognition, and planning were all successful applications of the genetic programming paradigm. Many other figures played a role in the history of evolutionary computing, although their work did not always fit into one of the major historical branches of the field. The earliest computational simulations of evolution using evolutionary algorithms and artificial life techniques were performed by Nils Aall Barricelli in 1953, with first results published in 1954. Another pioneer in the 1950s was Alex Fraser, who published a series of papers on simulation of artificial selection. As academic interest grew, dramatic increases in the power of computers allowed practical applications, including the automatic evolution of computer programs. Evolutionary algorithms are now used to solve multi-dimensional problems more efficiently than software produced by human designers, and also to optimize the design of systems. == Techniques == Evolutionary computing techniques mostly involve metaheuristic optimization algorithms. Broadly speaking, the field includes: Agent-based modeling Ant colony optimization Particle swarm optimization Swarm intelligence Artificial immune systems Artificial life Digital organism Cultural algorithms Differential evolution Dual-phase evolution Estimation of distribution algorithm Evolutionary algorithm Genetic algorithm Evolutionary programming Genetic programming Gene expression programming Grammatical evolution Evolution strategy Learnable evolution model Learning classifier system Memetic algorithms Neuroevolution Self-organization such as self-organizing maps, competitive learning Over recent years many dubious algorithms have been proposed, that are often just copies of existing algorithms (frequently Particle Swarm Optimization), where only the metaphor changed, but the algorithm itself is not new at all. A thorough catalogue with many of these dubious algorithms has been published in the Evolutionary Computation Bestiary. It is also important to note that many of these dubiously 'novel' algorithms have poor experimental validation. == Evolutionary algorithms == Evolutionary algorithms form a subset of evolutionary computation in that they generally only involve techniques implementing mechanisms inspired by biological evolution such as reproduction, mutation, recombination and natural selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the cost function determines the environment within which the solutions "live" (see also fitness function). Evolution of the population then takes place after the repeated application of the above operators. In this process, there are two main forces that form the basis of evolutionary systems: Recombination (e.g. crossover) and mutation create the necessary diversity and thereby facilitate novelty, while selection acts as a force increasing quality. Many aspects of such an evolutionary process are stochastic. Changed pieces of information due to recombination and mutati

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  • ACM SIGEVO

    ACM SIGEVO

    The ACM SIGEVO is a Special Interest Group of the Association of Computing Machinery for members of that organization who are practitioners, academics, students or others with interests in evolutionary computation and related algorithms. == History == ACM SIGEVO was founded in 2005 when the International Society for Genetic and Evolutionary Computation (ISGEC) became an ACM Special Interest Group under its present title. The ISGEC had been formed in 1999 by the merger of the Genetic Programming conference organization with the International Conference on Genetic Algorithms (ICGA) leading to the first Genetic and Evolutionary Computation Conference (GECCO). == Membership == Members of this SIG pay a small fee in addition to the ACM membership fee. In return they have access to a quarterly online newsletter, but more importantly can obtain reduced registration rates at the two conferences organised by ACM SIGEVO: GECCO and the Foundations of Genetic Algorithms conference (FOGA). They can also access material on evolutionary computation and related topics in the ACM Digital Library. In addition they can subscribe to email mailing lists in order to keep informed about news over time. For students, ACM SIGEVO sponsors Travel Awards for attendance at the GECCO Conference and FOGA (the Foundations of Genetic Algorithms conference). ACM SIGEVO also sponsors a Graduate Student Workshop. ACM also sponsors Awards to be competed for by attendees at the conferences it organises. == Conferences == ACM SIGEVO organises two major conferences in the field of evolutionary computation. The Genetic and Evolutionary Conference (GECCO) is held annually, while the Foundations of Genetic Algorithms conference (FOGA) is held biennially. === GECCO === The first GECCO conference was held prior to the formation of ACM SIGEVO but since 2005 (see History above) it has been organised annually by ACM SIGEVO. The latest (2025) was held in Málaga, Spain. The next (2026) will be held in San José, Costa Rica. === FOGA === Foundations of Genetic Algorithms (FOGA) is a biennial peer-reviewed research conference focusing on the theoretical principles underlying genetic algorithms, other evolutionary algorithms and related heuristics. It is organized by ACM SIGEVO. Its relevance to the computer science research community has been reflected in an A-rating in the CORE computer science conference assessment system. The Foundations of Genetic Algorithms (FOGA) conference originated as a workshop in 1990 in order to create an opportunity for researchers on genetic algorithms and related areas of evolutionary computation to focus on the theoretical principles underlying their field. From the start its multi-day duration made it comparable to conferences in the field, and since 2015 its proceedings have used conference rather than workshop in their titles. In 2005 ACM SIGEVO the Association for Computing Machinery Special Interest Group on Genetic and Evolutionary Computation was formed and every FOGA conference since then has been supported by SIGEVO. The table below shows FOGA conferences by year, location, websites (where available) and publisher of proceedings. A citation follows the reference to the publisher giving the full details of each FOGA proceedings. Papers accepted at recent conferences have been presented as digital or print posters in poster sessions at the conference, before being published in written form in the conference proceedings. FOGA is comparable in its multi-day duration to other conferences on evolutionary computation such as CEC, GECCO and PPSN. The main difference is that FOGA focuses on the theoretical basis of evolutionary computation and related subjects. While the above conferences devote some time to theory they also cover a wide range of other topics including competitions and applications. This focus on theoretical computer science was reflected in the CORE computer science conference assessment exercise, where FOGA was given an A-ranking in the 2023 assessment. GECCO and PPSN also obtained A-rankings, but many other conferences in the field of evolutionary computation obtained lower rankings. This suggests that FOGA is a relevant conference in its field, comparable with others including the much larger CEC or GECCO. Keynote speakers at past conferences have been: == Awards == ACM SIGEVO sponsors a number of awards. === SIGEVO Outstanding Contribution Award === The SIGEVO Outstanding Contribution Award commenced in 2023, and these awards are designed to recognise distinctive contributions to the field of evolutionary computation when evaluated over a period of at least 15 years. As a result many recipients to date are notable academics or industrial practitioners, and include Anne Auger, Kalyanmoy Deb, Stephanie Forrest, Emma Hart and Hans-Paul Schwefel. === SIGEVO Dissertation Award === The SIGEVO Dissertation Award recognises thesis research in the field of evolutionary computation completed at least by the year prior to a GECCO conference. Theses are submitted and reviewed by a panel that selects one winner and a maximum of two honourable mentions. Awards will be made to the winner and any others at the next GECCO conference. === SIGEVO Chair Award === The SIGEVO Chair Award, established in 2016 is a lecture sponsored by ACM SIGEVO, to take place on the last day of the GECCO conference. It recognizes through the lectures that the lecturers are influential researchers in the field of evolutionary computation. The more recent lectures are available online. The 2024 Award winner was Una-May O'Reilly. === SIGEVO Impact Award === The SIGEVO Impact Award looks back to the GECCO conference ten years earlier and recognizes up to three papers a year which are considered by the current ACM SIGEVO Executive Committee to have had significant impact over the period since their first publication at the GECCO conference. An example (originally published in GECCO 2010) received this award in 2020. === GECCO Best Paper Award === The ACM SIGEVO sponsors awards for the best papers presented at the GECCO conference. Because GECCO conferences have very many parallel tracks there are multiple awards recognising presentations in the different tracks. At GECCO 2025 Best Paper Awards were presented across 12 tracks. === FOGA Best Paper Award === The ACM SIGEVO sponsors awards for the best papers presented at the FOGA conference. Because FOGA operates on a single track, it is easier to compare papers. Since 2019 this Award has been made (suggesting only four awards up to the latest conference in 2025). ACM SIGEVO records the 2019 award. === Humie Award === The Humies Awards are rewards for the best form of human-competitive results using evolutionary computation or related algorithms and published in the wider literature (they do not need to be published at a conference or in a journal sponsored by ACM SIGEVO or even the ACM.) They were established through a gift from John Koza and have been in operation from 2004 to the present. The link with ACM SIGEVO is that the winners of the competition (submissions are evaluated in advance) are presented with Humie Awards at GECCO conferences. The Humie Awards website provides full details for the rules and how to submit entries to the competition. == Journals == ACM SIGEVO sponsors the main journal covering evolutionary computation published by the ACM: ACM Transactions on Evolutionary Learning and Optimization. ACM SIGEVO refers to the preceding ISGEC organisation (see History above) as sponsoring two other important journals in the field: The Evolutionary Computation journal. Genetic Programming and Evolvable Machines. While these journals continue to be important in the field, the wording on the website of ACM SIGEVO suggests that ACM SIGEVO is not involved in their publication. == References and notes ==

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  • Minimum resolvable contrast

    Minimum resolvable contrast

    Minimum resolvable contrast (MRC) is a subjective measure of a visible spectrum sensor’s or camera's sensitivity and ability to resolve data. A snapshot image of a series of three bar targets of selected spatial frequencies and various contrast coatings captured by the unit under test (UUT) is used to determine the MRC of the UUT, i.e., the visible spectrum camera or sensor. A trained observer selects the smallest target resolvable at each contrast level. Typically, specialized computer software collects the inputted data of the observer and provides a graph of contrast vs. spatial frequency at a given luminance level. A first order polynomial is fitted to the data and an MRC curve of spatial frequency versus contrast is generated.

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  • DialogOS

    DialogOS

    DialogOS is a graphical programming environment to design computer system which can converse through voice with the user. Dialogs are clicked together in a Flowchart. DialogOS includes bindings to control Lego Mindstorms robots by voice and has bindings to SQL databases, as well as a generic plugin architecture to integrate with other types of backends. DialogOS is used in computer science courses in schools and universities to teach programming and to introduce beginners in the basic principles of human/computer interaction and dialog design. It has also been used in research systems. DialogOS was initially developed commercially by CLT Sprachtechnologie GmbH until its liquidation in 2017. The rights were then acquired by Saarland University and the software was released as open-source. == Bindings to Lego Mindstorms NXT == DialogOS can control the LEGO Mindstorms NXT Series. It uses sensor-nodes to obtain values for the following sensors: noise sensor ultrasonic sensor touch sensor luminosity sensor

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  • The Future of Truth (Rosenbaum book)

    The Future of Truth (Rosenbaum book)

    The Future of Truth: How AI Reshapes Reality is a 2026 book by American filmmaker and author Steven Rosenbaum about how artificial intelligence affects the concept of truth. It was published by Matt Holt Books on May 12, 2026, to positive media attention; on May 19, in response to an inquiry from The New York Times, Rosenbaum acknowledged that the book itself contains multiple misattributed or false quotes that were hallucinated by AIs. == Synopsis == == Development == Rosenbaum has said that he developed the book using AI chatbots as research tools, indicating in his notes what information came from AI and sending those claims to a fact-checker affiliated with the publisher. He has said that he did not use AI tools to write the book itself. He has described AI tools as "a delightful writing companion ... strangely creative and crafty and unusual in all these ways", while acknowledging that sometimes "then it betrays you in ways that are just really quite horrible". Journalist and Nobel laureate Maria Ressa wrote the book's foreword. Taylor Lorenz, Michael Wolff, and Nicholas Thompson wrote blurbs promoting it. == Release and reception == The Future of Truth was published by Matt Holt Books, an imprint of BenBella Books, and distributed by Simon & Schuster. The book's release on May 12, 2026, was described by Futurism as "buzzy" and by The New York Times as "to great fanfare". On May 14, an excerpt was published in Wired under the title "Gen Z Is Pioneering a New Understanding of Truth". On May 17, the Times contacted Rosenbaum regarding a number of quotes that appeared to be falsified or misattributed; the following evening he confirmed that they were the result of AI hallucinations:As I disclosed in the book's acknowledgments, I used AI tools ChatGPT and Claude during the research, writing and editing process. That does not excuse these errors, of which I take full responsibility. I am now working with the editors to thoroughly review and quickly correct any affected passages; any future editions will be corrected. The Times documented several of the errors, including a quote from Kara Swisher that Swisher described as making it "sound like I have a stick up my butt" and a quote from Lisa Feldman Barrett that Barrett described as misrepresenting her views on the nature of emotions, social signals, and truth. The book also misattributed a quote by Meredith Broussard from an interview with Marketplace Tech as having been from her book Artificial Unintelligence and hallucinated several words in a quote from Lee McIntyre, although according to McIntyre it did not misrepresent his views. Wired's editors, in an addendum to the excerpt they published, said that all quotes included in it had been verified as part of their fact-checking process. Rosenbaum told the Times that the series of errors "serves as a warning about the risks of AI-assisted research and verification, that is why I wrote the book. These AI errors do not, in fact, diminish the larger questions that the book raises about truth, trust and AI and its impact on society, democracy and editorial." Maggie Harrison Dupré in Futurism expressed skepticism, writing "The risk of AI hallucinations ... is well-known. If you're going to literally write the book on post-AI truth, you should probably put some more elbow grease into fact-checking your AI-assisted research." Kyle Orland in Ars Technica, responding to Rosenbaum's statement that his error "demonstrates the problem more vividly than any abstract argument could", was similarly skeptical, writing that "if we accept this take, every avoidably obvious mess in the world might be a disguised good because it really helps illuminate the huge mistake. And that can't be right; sometimes 'negligence' is just that." Subsequent comments by Rosenbaum placed more blame on the chatbots, which he told The Atlantic "fucked up the book". Rosenbaum told Ars Technica that fact-checking occurred "incredibly effectively, but not a hundred percent"; Orland observed that "it's worth noting that most writers manage to include zero made-up quotes when they write a book". Rosenbaum said that he had "learned a lesson" and would be "much more suspicious" of AI in the future, but would continue to use AI in his research. Orland responded to Rosenbaum's characterization of AI as "magical" by comparing it to the One Ring from The Lord of the Rings, in that it "convinces many of those who use it that they can control its power properly" when many cannot. Orland highlighted the limits of traditional fact-checking regarding AI, given that fact-checkers are used to assuming that direct quotes are copied word-for-word from the source. Rosenbaum told Orland that the future of fact-checking for AI-researched works "probably includes mandatory source tracing for quotations, better provenance tracking, clearer standards around AI-assisted research, and potentially (more irony here) AI tools that audit citations against primary materials". Patrick Redford in Defector criticized Rosenbaum, alongside other artists tricked by AI, for failing to recognize AI as "the enemy". Will Oremus in The Atlantic described Redford's approach of stigmatizing AI writing as "reasonable", noting the presence of low-quality, seemingly AI-generated verbiage in The Future of Truth—a claim denied by Rosenbaum—before saying that the greater issue is finding the line at which AI assistance in writing becomes a problem. Oremus concluded, "The scandal can't just be that [Rosenbaum] used AI while working on his book, because he acknowledged that up front. He got in trouble because he had used AI badly, failing to check its work on a task at which it is famously unreliable."

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  • Shy Girl

    Shy Girl

    Shy Girl is a horror novel initially self-published in February 2025 by Mia Ballard. Publishing rights for the book were acquired by Hachette Book Group, which released the book in the United Kingdom in November 2025 and planned to publish it in the United States in 2026. Its US release was cancelled and its UK release was discontinued after it faced accusations of being created with generative AI. Ballard denied having personally used AI in the book's writing, claiming that a freelance editor had introduced AI-generated changes. She also stated that she would take legal action against the editor. == Premise == The novel follows Gia, a depressed woman with obsessive–compulsive disorder, who encounters a mysterious man named Nathan while looking for a sugar daddy to ease her financial troubles. Nathan offers to erase all of Gia's debts in exchange for her agreeing to live as his pet. Living like an animal convinces her that she is becoming an animal, making her behave like one. == Publication and cancellation == Shy Girl was first self-published online by Mia Ballard in February 2025. Marketing material described the book as a "buzzy BookTok sensation" and "bloody and unforgiving". The self-published edition of the book was highly successful and had over 4,900 ratings on Goodreads and an average score of 3.52 stars. In an interview, Ballard described her writing style as lyrical, feverish, and introspective, and stated she was more interested in "what it feels like to live inside a body" than in plot-driven storylines. Publishing rights were acquired by Hachette Book Group and it was published by its Wildfire imprint in the United Kingdom in November 2025. By March 2026, the book had sold 1,800 copies in the United Kingdom. A US release was planned for 2026 by the imprint Orbit Books. After the British publication, critics and readers began to make claims that the book appeared to have been written by generative AI. A January 2026 post on Reddit claimed that the book had many of the hallmarks of having been written with a large language model, and stated that it was "repulsive" that the book was accepted by Hachette. A two-and-a-half-hour video essay covering the book, titled "i'm pretty sure this book is ai slop", received 1.2 million views on YouTube by March 2026. In response, Hachette Book Group announced in March 2026 that it would cancel the book's US publication and discontinue its UK publication. It told The Wall Street Journal that it had made "a lengthy investigation" before deciding to cancel the book. Ballard told The New York Times that she had not used AI when writing the book, but that AI-generated elements were added by a freelance editor without her knowledge. She also stated that she could not elaborate on her claim because she was pursuing legal action against the editor. Writer Andrea Bartz opined that the situation "raises many concerns about trust, authenticity and publishing's readiness for a new, A.I.-assisted world", but that "readers made it abundantly clear they want books by humans, not machines".

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  • Law practice management software

    Law practice management software

    Law practice management software is software designed to manage the business operations of a law firm. This can include software that manages cases, client intake, court communications, electronic discovery, time tracking, trust accounting, and billing. == Features of law practice management software == Common features of practice management software include: Case management Time tracking Document assembly Contact management Calendaring Docket management Client portal Contract Management Court Case Status Tracker Trust accounting == Examples of law practice management software == Smokeball LEAP Legal Software PracticeEvolve Dye & Durham

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  • Amazon Bedrock

    Amazon Bedrock

    Amazon Bedrock is a cloud computing service provided by Amazon Web Services (AWS) for building generative artificial intelligence applications. Launched in 2023, the platform provides a unified API to access foundation models (FMs) from several AI companies, alongside related tools. Bedrock is a serverless computing service which competes with similar enterprise AI platforms such as Microsoft Foundry and Google Cloud Platform. == History == Amazon announced Bedrock on April 13, 2023. The service became generally available on September 28, 2023. Throughout 2024 and 2025, AWS expanded the service to include AI agents, which allow models to interact with external systems. == Features == Knowledge Bases: a managed workflow for Retrieval-Augmented Generation (RAG), which allows models to pull facts from private data stored in Amazon S3. Guardrails: a security feature that allows administrators to set content filters and personally identifiable information redaction across all models in the platform to increase the safety and compliance of AI deployments. == PartyRock == In November 2023, Amazon launched PartyRock, a web-based no-code environment for building generative AI applications. The platform uses a natural language interface to translate user descriptions into software widgets. These widgets enable specific AI behaviors, including text-based prompts, conversational agents, generating images, and the summarization and querying of user-uploaded documents. Although it initially launched with a limited-time free trial, AWS transitioned the service to a recurring free daily usage credit model in early 2025.

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  • Plants vs. Zombies: Replanted

    Plants vs. Zombies: Replanted

    Plants vs. Zombies: Replanted is a 2025 tower defense video game developed by PopCap Seattle, The Lost Pixels, and published by Electronic Arts. It is a remaster of the 2009 game Plants vs. Zombies, introducing upscaled graphics and new additional content. Plants vs. Zombies: Replanted was released for video game consoles and personal computers on October 23, 2025. It received generally positive reviews from critics, but was criticized by the original game's development team for including fabricated concept art and for mishandling the soundtrack. == Gameplay == Plants vs. Zombies: Replanted follows the same gameplay of the original Plants vs. Zombies game with very minor changes. It is a lane-based tower defense game where the player has to defend their home from incoming zombies. The player can place various plants by spending "sun", the game's currency during levels. Sun icons can be collected from the sky during daytime and from sun-producing plants such as sunflowers. Some plants can attack zombies while some can act as defense. If all zombies are defeated in a level, the player wins. If a zombie reaches the left side of the line, a lawn mower—or other similar, relevant object—will activate and clear the row of any zombies, but if the lawn mower has already been used, and another zombie crosses, the game is over. === Replanted features === Plants vs. Zombies: Replanted contains up to 4K upscaled graphics and widescreen support, in comparison to the original game's static 800x600 resolution and 4:3 aspect ratio. Replanted now has full controller support and features local multiplayer modes ported from the original game's seventh generation console ports: co-op, where two players play together with assigned roles; and Versus, where one plays as the plants and the other as the zombies. No online multiplayer is planned, however support for Steam Remote Play was later added in a patch as an alternative for Windows users. Replanted also contains quality-of-life features. Gameplay can now be sped up by the player's will, with a max speed increase of 2.5x. Sun icons can now be mass collected using the "Sun Magnet." On Windows, players can quick-select plants from their seed bank using the number keys as hotkeys. Replanted also introduces two new additional game modes. "Cloudy Day" is a set of non-linear levels in the Adventure campaign. These levels only allow Sunflowers as sun-producing plants. During these levels, the amount of sun dropped from the sky and produced by plants are lowered. At certain times, rain clouds will move over the lawn. While these clouds are present, sun will stop appearing from the sky and from Sunflowers. However, all plants will cost around half their original price and have significantly faster recharge times. "R.I.P. Mode" is a harder difficulty of the Adventure campaign, but the player is forced back to the beginning if they lose a single level. Replanted additionally features "bonus levels" included as non-linear levels in the Adventure campaign. These include 10 new minigames that were previously unused in the original game. In a later update, Replanted added "Survival: Endless" levels to all five areas of the game instead of just the daytime pool. == Development == The existence of a Plants vs. Zombies remaster was revealed in an interview with Janet Robin from The String Revolution, who they did a vinyl collaboration with the franchise in 2025 with Iam8bit. Janet stated that EA commissioned them to record an acoustic composition of the track "Crazy Dave" to be used for an "anniversary edition" of the game. The song would be additionally be a tribute to the song "Bad Guy", which artist Billie Eilish has stated to be somewhat similar to the track. Plants vs. Zombies Replanted was officially announced in a Nintendo Direct presentation in late July 2025. As an incentive, people who pre-ordered the game are given an in-game retro-styled skin of the Peashooter. Replanted was showcased at PAX West on August 25, 2025. A dev diary for Plants vs. Zombies: Replanted was uploaded to YouTube on October 17, 2025. The video features Nick Reinhart, Jake Neri, and Matt Townsend. A developer panel for the game was available during TwitchCon 2025. == Release == Plants vs. Zombies: Replanted was released for Nintendo Switch, Nintendo Switch 2, PlayStation 4, PlayStation 5, Xbox One, Xbox Series X and Series S, and personal computers on October 23, 2025. It was leaked onto the internet on October 17, 2025. Players discovered multiple software bugs, and multiple assets alleged to be upscaled by generative artificial intelligence were found, leading to backlash. Numerous bugs were fixed in a day-one patch on October 23, 2025. == Reception == === Critical response === The versions of Plants vs. Zombies: Replanted for Windows, PlayStation 5, and Nintendo Switch 2 received "generally favorable" reviews from critics, according to review aggregator website Metacritic, while the Xbox Series X version received "mixed or average" reviews. According to OpenCritic, 57% of critics recommended it. IGN's Alessandro Fillari called it "a good way to get re-acquainted with one of the quirkiest puzzle-strategy games of the 2000s", while acknowledging its questionable decisions. Shacknews' David Craddock said it was his favorite version of Plants vs. Zombies, stating, "it packs everything fans loved about the original game, plus lots more" while justifying its US$20 price. The Verge described Replanted as "a time capsule from a simpler, happier time". Kyle Hilliard from Game Informer praised its faithfulness, complimenting the new animations and character designs that did not alter its memorability. Noah Hunter for Final Weapon described the remake as solid, though criticized the lack of certain features and containing bugs that gate it from being excellent. Ben Lyons from Gamereactor stated Replanted is the same as the original overall, despite believing the £18 price is not justified. === Original developers === Rich Werner, the original game's character designer, claims that some concept art contained in the game, speculated to be for Plants vs. Zombies: Garden Warfare (2014), did not originate from the original's development. Werner also stated that concept art for the Disco Zombie is fabricated; the design for the Disco Zombie was created after the estate of Michael Jackson requested the original Dancing Zombie, who resembles Michael Jackson from his Thriller music video, be removed from the game. On October 19, 2026, composer Laura Shigihara expressed her dissatisfaction with the lack of dynamic music in the game. Dynamic music would later be implemented in a later patch. In an interview featuring Rich Werner and user interface designer Matt Holmberg on April 29, 2026, Werner revealed that he and Shigihara were contacted by EA to make a music video to market Replanted. However, after the game was leaked, Werner's response on social media led EA to cancel the collaboration.

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  • Iron Man 2020 (event)

    Iron Man 2020 (event)

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

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  • Clipping (computer graphics)

    Clipping (computer graphics)

    Clipping, in the context of computer graphics, is a method to selectively enable or disable rendering operations within a defined region of interest. Mathematically, clipping can be described using the terminology of constructive geometry. A rendering algorithm only draws pixels in the intersection between the clip region and the scene model. Lines and surfaces outside the view volume (aka. frustum) are removed. Clip regions are commonly specified to improve render performance. Pixels that will be drawn are said to be within the clip region. Pixels that will not be drawn are outside the clip region. More informally, pixels that will not be drawn are said to be "clipped." == In 2D graphics == In two-dimensional graphics, a clip region may be defined so that pixels are only drawn within the boundaries of a window or frame. Clip regions can also be used to selectively control pixel rendering for aesthetic or artistic purposes. In many implementations, the final clip region is the composite (or intersection) of one or more application-defined shapes, as well as any system hardware constraints In one example application, consider an image editing program. A user application may render the image into a viewport. As the user zooms and scrolls to view a smaller portion of the image, the application can set a clip boundary so that pixels outside the viewport are not rendered. In addition, GUI widgets, overlays, and other windows or frames may obscure some pixels from the original image. In this sense, the clip region is the composite of the application-defined "user clip" and the "device clip" enforced by the system's software and hardware implementation. Application software can take advantage of this clip information to save computation time, energy, and memory, avoiding work related to pixels that aren't visible. == In 3D graphics == In three-dimensional graphics, the terminology of clipping can be used to describe many related features. Typically, "clipping" refers to operations in the plane that work with rectangular shapes, and "culling" refers to more general methods to selectively process scene model elements. This terminology is not rigid, and exact usage varies among many sources. Scene model elements include geometric primitives: points or vertices; line segments or edges; polygons or faces; and more abstract model objects such as curves, splines, surfaces, and even text. In complicated scene models, individual elements may be selectively disabled (clipped) for reasons including visibility within the viewport (frustum culling); orientation (backface culling), obscuration by other scene or model elements (occlusion culling, depth- or "z" clipping). Sophisticated algorithms exist to efficiently detect and perform such clipping. Many optimized clipping methods rely on specific hardware acceleration logic provided by a graphics processing unit (GPU). The concept of clipping can be extended to higher dimensionality using methods of abstract algebraic geometry. === Near clipping === Beyond projection of vertices & 2D clipping, near clipping is required to correctly rasterise 3D primitives; this is because vertices may have been projected behind the eye. Near clipping ensures that all the vertices used have valid 2D coordinates. Together with far-clipping it also helps prevent overflow of depth-buffer values. Some early texture mapping hardware (using forward texture mapping) in video games suffered from complications associated with near clipping and UV coordinates. === Occlusion clipping (Z- or depth clipping) === In 3D computer graphics, "Z" often refers to the depth axis in the system of coordinates centered at the viewport origin: "Z" is used interchangeably with "depth", and conceptually corresponds to the distance "into the virtual screen." In this coordinate system, "X" and "Y" therefore refer to a conventional cartesian coordinate system laid out on the user's screen or viewport. This viewport is defined by the geometry of the viewing frustum, and parameterizes the field of view. Z-clipping, or depth clipping, refers to techniques that selectively render certain scene objects based on their depth relative to the screen. Most graphics toolkits allow the programmer to specify a "near" and "far" clip depth, and only portions of objects between those two planes are displayed. A creative application programmer can use this method to render visualizations of the interior of a 3D object in the scene. For example, a medical imaging application could use this technique to render the organs inside a human body. A video game programmer can use clipping information to accelerate game logic. For example, a tall wall or building that occludes other game entities can save GPU time that would otherwise be spent transforming and texturing items in the rear areas of the scene; and a tightly integrated software program can use this same information to save CPU time by optimizing out game logic for objects that aren't seen by the player. == Algorithms == Line clipping algorithms: Cohen–Sutherland Liang–Barsky Fast-clipping Cyrus–Beck Nicholl–Lee–Nicholl Skala O(lg N) algorithm Polygon clipping algorithms: Greiner–Hormann Sutherland–Hodgman Weiler–Atherton Vatti Rendering methodologies Painter's algorithm

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  • Blackboard system

    Blackboard system

    A blackboard system is an artificial intelligence approach based on the blackboard architectural model, where a common knowledge base, the "blackboard", is iteratively updated by a diverse group of specialist knowledge sources, starting with a problem specification and ending with a solution. Each knowledge source updates the blackboard with a partial solution when its internal constraints match the blackboard state. In this way, the specialists work together to solve the problem. The blackboard model was originally designed as a way to handle complex, ill-defined problems, where the solution is the sum of its parts. == Metaphor == The following scenario provides a simple metaphor that gives some insight into how a blackboard functions: A group of specialists are seated in a room with a large blackboard. They work as a team to brainstorm a solution to a problem, using the blackboard as the workplace for cooperatively developing the solution. The session begins when the problem specifications are written onto the blackboard. The specialists all watch the blackboard, looking for an opportunity to apply their expertise to the developing solution. When someone writes something on the blackboard that allows another specialist to apply their expertise, the second specialist records their contribution on the blackboard, hopefully enabling other specialists to then apply their expertise. This process of adding contributions to the blackboard continues until the problem has been solved. == Components == A blackboard-system application consists of three major components The software specialist modules, which are called knowledge sources (KSs). Like the human experts at a blackboard, each knowledge source provides specific expertise needed by the application. The blackboard, a shared repository of problems, partial solutions, suggestions, and contributed information. The blackboard can be thought of as a dynamic "library" of contributions to the current problem that have been recently "published" by other knowledge sources. The control shell, which controls the flow of problem-solving activity in the system. Just as the eager human specialists need a moderator to prevent them from trampling each other in a mad dash to grab the chalk, KSs need a mechanism to organize their use in the most effective and coherent fashion. In a blackboard system, this is provided by the control shell. === Learnable Task Modeling Language === A blackboard system is the central space in a multi-agent system. It's used for describing the world as a communication platform for agents. To realize a blackboard in a computer program, a machine readable notation is needed in which facts can be stored. One attempt in doing so is a SQL database, another option is the Learnable Task Modeling Language (LTML). The syntax of the LTML planning language is similar to PDDL, but adds extra features like control structures and OWL-S models. LTML was developed in 2007 as part of a much larger project called POIROT (Plan Order Induction by Reasoning from One Trial), which is a Learning from demonstrations framework for process mining. In POIROT, Plan traces and hypotheses are stored in the LTML syntax for creating semantic web services. Here is a small example: A human user is executing a workflow in a computer game. The user presses some buttons and interacts with the game engine. While the user interacts with the game, a plan trace is created. That means the user's actions are stored in a logfile. The logfile gets transformed into a machine readable notation which is enriched by semantic attributes. The result is a textfile in the LTML syntax which is put on the blackboard. Agents (software programs in the blackboard system) are able to parse the LTML syntax. == Implementations == We start by discussing two well known early blackboard systems, BB1 and GBB, below and then discuss more recent implementations and applications. The BB1 blackboard architecture was originally inspired by studies of how humans plan to perform multiple tasks in a trip, used task-planning as a simplified example of tactical planning for the Office of Naval Research. Hayes-Roth & Hayes-Roth found that human planning was more closely modeled as an opportunistic process, in contrast to the primarily top-down planners used at the time: While not incompatible with successive-refinement models, our view of planning is somewhat different. We share the assumption that planning processes operate in a two-dimensional planning space defined on time and abstraction dimensions. However, we assume that people's planning activity is largely opportunistic. That is, at each point in the process, the planner's current decisions and observations suggest various opportunities for plan development. The planner's subsequent decisions follow up on selected opportunities. Sometimes, these decision-sequences follow an orderly path and produce a neat top-down expansion as described above. However, some decisions and observations might also suggest less orderly opportunities for plan development. A key innovation of BB1 was that it applied this opportunistic planning model to its own control, using the same blackboard model of incremental, opportunistic, problem-solving that was applied to solve domain problems. Meta-level reasoning with control knowledge sources could then monitor whether planning and problem-solving were proceeding as expected or stalled. If stalled, BB1 could switch from one strategy to another as conditions – such as the goals being considered or the time remaining – changed. BB1 was applied in multiple domains: construction site planning, inferring 3-D protein structures from X-ray crystallography, intelligent tutoring systems, and real-time patient monitoring. BB1 also allowed domain-general language frameworks to be designed for wide classes of problems. For example, the ACCORD language framework defined a particular approach to solving configuration problems. The problem-solving approach was to incrementally assemble a solution by adding objects and constraints, one at a time. Actions in the ACCORD language framework appear as short English-like commands or sentences for specifying preferred actions, events to trigger KSes, preconditions to run a KS action, and obviation conditions to discard a KS action that is no longer relevant. GBB focused on efficiency, in contrast to BB1, which focused more on sophisticated reasoning and opportunistic planning. GBB improves efficiency by allowing blackboards to be multi-dimensional, where dimensions can be either ordered or not, and then by increasing the efficiency of pattern matching. GBB1, one of GBB's control shells implements BB1's style of control while adding efficiency improvements. Other well-known of early academic blackboard systems are the Hearsay II speech recognition system and Douglas Hofstadter's Copycat and Numbo projects. Some more recent examples of deployed real-world applications include: The PLAN component of the Mission Control System for RADARSAT-1, an Earth observation satellite developed by Canada to monitor environmental changes and Earth's natural resources. The GTXImage CAD software by GTX Corporation was developed in the early 1990s using a set of rulebases and neural networks as specialists operating on a blackboard system. Adobe Acrobat Capture (now discontinued), as it used a blackboard system to decompose and recognize image pages to understand the objects, text, and fonts on the page. This function is currently built into the retail version of Adobe Acrobat as "OCR Text Recognition". Details of a similar OCR blackboard for Farsi text are in the public domain. Blackboard systems are used routinely in many military C4ISTAR systems for detecting and tracking objects. Another example of current use is in Game AI, where they are considered a standard AI tool to help with adding AI to video games. == Recent developments == Blackboard-like systems have been constructed within modern Bayesian machine learning settings, using agents to add and remove Bayesian network nodes. In these 'Bayesian Blackboard' systems, the heuristics can acquire more rigorous probabilistic meanings as proposal and acceptances in Metropolis Hastings sampling though the space of possible structures. Conversely, using these mappings, existing Metropolis-Hastings samplers over structural spaces may now thus be viewed as forms of blackboard systems even when not named as such by the authors. Such samplers are commonly found in musical transcription algorithms for example. Blackboard systems have also been used to build large-scale intelligent systems for the annotation of media content, automating parts of traditional social science research. In this domain, the problem of integrating various AI algorithms into a single intelligent system arises spontaneously, with blackboards providing a way for a collection of distributed, modular natural language processing algorithm

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  • Imagen (text-to-image model)

    Imagen (text-to-image model)

    Imagen is a series of text-to-image models developed by Google DeepMind. They were developed by Google Brain until the company's merger with DeepMind in April 2023. Imagen is primarily used to generate images from text prompts, similar to Stability AI's Stable Diffusion, OpenAI's DALL-E, or Midjourney. The original version of the model was first discussed in a paper from May 2022. The tool produces high-quality images and is available to all users with a Google account through services including Gemini, ImageFX, and Vertex AI. == History == Imagen's original version was first presented in a paper published in May 2022. It featured the ability to generate high-fidelity images from natural language. The second version, Imagen 2 was released in December 2023. The standout feature was text and logo generation. Imagen 3 was released in August 2024. Google claims that the newest version provides better detail and lighting on generated images. On 20 May 2025 at Google I/O 2025 the company released an improved model, Imagen 4. == Technology == Imagen uses two key technologies. The first is the use of transformer-based large language models, notably T5, to understand text and subsequently encode text for image synthesis. The second is the use of cascaded diffusion models providing high-fidelity image generation. Imagen generates image in three stages, starting from a base of 64x64, then upsampled to 256x256 and 1024x1024. Imagen 4 generates image up to 2k. == Capabilities == Imagen can generate photorealistic images from text prompts. It can also create various styles, such as cinematic, 35mm film, illustration, and surreal. Like most text-to-image generative AI models, Imagen has difficulty rendering human fingers, text, ambigrams and other forms of typography. The model can generate images in five aspect ratios, namely 9:16, 3:4, 1:1, 4:3, and 16:9. Imagen can also refine already generated images by editing existing text prompts.

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