Digital scrapbooking is the term for the creation of a new 2D artwork by re-combining various graphic elements. It is a form of scrapbooking that is done using a personal computer, digital or scanned photos and computer graphics software. It is a relatively new form of the traditional print scrapbooking. Recent advances in technology now enable the craft to be pursued on tablets and smart devices utilising imaging apps as well as hobby specific apps, some of which have been created specifically by brands for use with their own image products. Digital scrapbooking kits are available to purchase and download at many websites that specialize in the craft. Kits contain graphics and word-art and are usually themed and color-coordinated. They usually consist of a mix of background images and "cut out" [extracted] images containing alpha channels. Once a kit has been downloaded to the computer or device, it can then be used over and over again to make new scrapbook pages (scrapbook layouts) within the software program that one chooses to use, often in combination with the users's own family photographs, scanned keepsakes and other unique personal elements scanned on a flatbed scanner. Scanning is usually done at 300dpi, to make the resulting images suitable for print. == Licensing and Copyright == Kits are sometimes licensed differently from other forms of traditional royalty-free stock images that may be purchased per-item or in sets at online stock photography sites. Some kit packs will be wholly royalty-free, but some kit makers may restrict usage to non-commercial work only. Some may specifically forbid the use of their work in projects for commercial gain, for example greetings cards and gift tags that may be made with their kits. Licensing often varies from kit to kit, even from the same maker. Some kits include derivative works of public domain material. In contrast to stock, creators of digital scrapbooking kits often require a credit or byline to indicate that their image elements have been used in a new creation. == Uses == Some artistic individuals combine digital scrapbooking with traditional scrapbooking to create what's known as hybrid scrapbooking projects. Hybrid scrapbooking involves creating layouts on the computer using digital supplies that will then be printed and combined with traditional supplies such as buttons, ribbons and other elements. Conversely, a hybrid scrapbook project may also be created using traditional paper supplies and augmented with digital elements that have been printed and cut out specifically for use on the project. Journaling may be done within the software programs to accompany images and to create digital storybooks, or scrapbooks, which are then published in photo books via various popular print-on-demand services, printed and added to traditional scrapbooks, burned to CDs or posted on the Web. Digital Scrapbooking may also be done online by uploading photos to a specialist scrapbooking website and utilising their custom built platforms and decorative image elements to complete the projects for print to finished products, for example photo books and holiday greeting cards. == Market Size == The traditional scrapbooking market appeared to decline somewhat in the USA since 2010, probably due to the 2008 financial crisis, and the digital scrapbooking market (being potentially a much cheaper form of scrapbooking) may have increased accordingly. Both markets currently appear to have recovered lost ground and expanded since the beginning of the COVID-19 pandemic as many people sought to productively fill their time during lockdowns, quarantines and self-isolation / stay at home directions. == Digital scrapbooking software == The main software programs that are typically used are Adobe Photoshop, Adobe Photoshop Elements, paint.net (freeware), Filter Forge, Corel Paintshop Pro, and GIMP. Additionally Adobe offer the Photoshop iOS product using the same code base as the desktop version to drive the app version. == Digital scrapbooking supplies == Digital scrapbooking supplies are downloaded from the Internet and then stored on a computer or external hardrive, DVD or CD media, SD cards, or in the cloud, to be used as needed. Both paid and free digital scrapbooking supplies available from numerous designers on their blogs or in e-commerce stores either as solo designers or as part of a wide cohort of designers working cooperatively in large full service e-commerce websites. Usually designed at 300ppi image resolution, digital scrapbooking product offerings and supplies often include: Full coordinated kits containing digital background “papers”, decorative alphabets, and diverse embellishments generally containing a mixture of .JPG and .PNG files; "Quick pages", flattened files containing a completed page layout with transparent photo windows in .PNG file format; Digital templates, fully layered layouts i.e. pages that have had the composition pre-designed ready for use in an imaging program or app, fully customizable for color schemes, kit choices, photographs and other embellishments, generally supplied in either .PSD or .TIF file format; Hybrid “quick pages”, i.e. layouts that are both fully designed and fully layered for customization, generally supplied in either .PSD or .TIF file format; Adobe Photoshop actions, brushes, custom shapes, paths and styles, saved in their respective native Photoshop file formats; and Corel PaintShop Pro equivalent tools.
Box blur
A box blur (also known as a box linear filter) is a spatial domain linear filter in which each pixel in the resulting image has a value equal to the average value of its neighboring pixels in the input image. It is a form of low-pass ("blurring") filter. A 3 by 3 box blur ("radius 1") can be written as matrix 1 9 [ 1 1 1 1 1 1 1 1 1 ] . {\displaystyle {\frac {1}{9}}{\begin{bmatrix}1&1&1\\1&1&1\\1&1&1\end{bmatrix}}.} Due to its property of using equal weights, it can be implemented using a much simpler accumulation algorithm, which is significantly faster than using a sliding-window algorithm. Box blurs are frequently used to approximate a Gaussian blur. By the central limit theorem, repeated application of a box blur will approximate a Gaussian blur. In the frequency domain, a box blur has zeros and negative components. That is, a sine wave with a period equal to the size of the box will be blurred away entirely, and wavelengths shorter than the size of the box may be phase-reversed, as seen when two bokeh circles touch to form a bright spot where there would be a dark spot between two bright spots in the original image. == Extensions == Gwosdek, et al. has extended Box blur to take a fractional radius: the edges of the 1-D filter are expanded with a fraction. It makes slightly better gaussian approximation possible due to the elimination of integer-rounding error. Mario Klingemann has a "stack blur" that tries to better emulate gaussian's look in one pass by stacking weights: 1 9 [ 1 2 3 2 1 ] {\displaystyle {\frac {1}{9}}{\begin{bmatrix}1&2&3&2&1\end{bmatrix}}} The triangular impulse response it forms decomposes to two rounds of box blur. Stacked Integral Image by Bhatia et al. takes the weighted average of a few box blurs to fit the gaussian response curve. == Implementation == The following pseudocode implements a 3x3 box blur. The example does not handle the edges of the image, which would not fit inside the kernel, so that these areas remain unblurred. In practice, the issue is better handled by: Introducing an alpha channel to represent the absence of colors; Extending the boundary by filling in values, ranked by quality: Fill in a mirrored image at the border Fill in a constant color extending from the last pixel Pad in a fixed color A number of optimizations can be applied when implementing the box blur of a radius r and N pixels: The box blur is a separable filter, so that only two 1D passes of averaging 2 r + 1 pixels will be needed, one horizontal and one vertical, for each pixel. This lowers the complexity from O(Nr2) to O(Nr). In digital signal processing terminology, each pass is a moving-average filter. Accumulation. Instead of discarding the sum for each pixel, the algorithm re-uses the previous sum, and updates it by subtracting away the old pixel and adding the new pixel in the blurring range. A summed-area table can be used similarly. This lowers the complexity from O(Nr) to O(N). When being used in multiple passes to approximate a Gaussian blur, the cascaded integrator–comb filter construction allows for doing the equivalent operation in a single pass.
Application software
Application software is software that is intended for end-user use – not operating, administering or programming a computer. It includes programs such as word processors, web browsers, media players, and mobile applications used in daily tasks. An application (app, application program, software application) is any program that can be categorized as application software. Application is a subjective classification that is often used to differentiate from system and utility software. Application software represents the user-facing layer of computing systems, designed to translate complex system capabilities into task-oriented, goal-driven workflows. Unlike system software, which focuses on hardware orchestration and resource management, application software is centered on problem abstraction, user interaction, and domain-specific functionality. The abbreviation app became popular with the 2008 introduction of the iOS App Store, to refer to applications for mobile devices such as smartphones and tablets. Later, with the release of the Mac App Store in 2010 and the Windows Store in 2011, it began to be used to refer to end-user software in general, regardless of platform. Applications may be bundled with the computer and its system software or published separately. Applications may be proprietary or open-source. == Terminology == === Meaning program and software === When used as an adjective, application can have a broader meaning than that described in this article. For example, concepts such as application programming interface (API), application server, application virtualization, application lifecycle management and portable application refer to programs and software in general. === Distinction between system and application software === The distinction between system and application software is subjective and has been the subject of controversy. For example, one of the key questions in the United States v. Microsoft Corp. antitrust trial was whether Microsoft's Internet Explorer web browser was part of its Windows operating system or a separate piece of application software. As another example, the GNU/Linux naming controversy is, in part, due to disagreement about the relationship between the Linux kernel and the operating systems built over this kernel. In some types of embedded systems, the application software and the operating system software may be indistinguishable by the user, as in the case of software used to control a VCR, DVD player, or microwave oven. The above definitions may exclude some applications that may exist on some computers in large organizations. For an alternative definition of an app: see Application Portfolio Management. === Killer application === A killer application (killer app, coined in the late 1980s) is an application that is so popular that it causes demand for its host platform to increase. For example, VisiCalc was the first modern spreadsheet software for the Apple II and helped sell the then-new personal computers into offices. For the BlackBerry, it was its email software. === Software suite === As software suite consists of multiple applications bundled together. They usually have related functions, features, and user interfaces, and may be able to interact with each other, e.g. open each other's files. Business applications often come in suites, e.g. Microsoft Office, LibreOffice and iWork, which bundle together a word processor, a spreadsheet, etc.; but suites exist for other purposes, e.g. graphics or music. == Ways to classify == As there so many applications and since their attributes vary so dramatically, there are many different ways to classify them. === By legal aspects === Proprietary software is protected under an exclusive copyright, and a software license grants limited usage rights. Such applications may allow add-ons from third parties. Free and open-source software (FOSS) can be run, distributed, sold, and extended for any purpose. FOSS software released under a free license may be perpetual and also royalty-free. Perhaps, the owner, the holder or third-party enforcer of any right (copyright, trademark, patent, or ius in re aliena) are entitled to add exceptions, limitations, time decays or expiring dates to the license terms of use. Public-domain software is a type of FOSS that is royalty-free and can be run, distributed, modified, reversed, republished, or created in derivative works without any copyright attribution and therefore revocation. It can even be sold, but without transferring the public domain property to other single subjects. Public-domain software can be released under a (un)licensing legal statement, which enforces those terms and conditions for an indefinite duration (for a lifetime, or forever). === By platform === An application can be categorized by the host platform on which it runs. Notable platforms include operating system (native), web browser, cloud computing and mobile. For example a web application runs in a web browser whereas a more traditional, native application runs in the environment of a computer's operating system. There has been a contentious debate regarding web applications replacing native applications for many purposes, especially on mobile devices such as smartphones and tablets. Web apps have indeed greatly increased in popularity for some uses, but the advantages of applications make them unlikely to disappear soon, if ever. Furthermore, the two can be complementary, and even integrated. === Horizontal vs. vertical === Application software can be seen as either horizontal or vertical. Horizontal applications are more popular and widespread, because they are general purpose, for example word processors or databases. Vertical applications are niche products, designed for a particular type of industry or business, or department within an organization. Integrated suites of software will try to handle every specific aspect possible of, for example, manufacturing or banking worker, accounting, or customer service. === By purpose === There are many types of application software: Enterprise Addresses the needs of an entire organization's processes and data flows, across several departments, often in a large distributed environment. Examples include enterprise resource planning systems, customer relationship management (CRM) systems, data replication engines, and supply chain management software. Departmental Software is a sub-type of enterprise software with a focus on smaller organizations or groups within a large organization. (Examples include travel expense management and IT Helpdesk.) Enterprise infrastructure Provides common capabilities needed to support enterprise software systems. (Examples include databases, email servers, and systems for managing networks and security.) Application platform as a service (aPaaS) A cloud computing service that offers development and deployment environments for application services. Knowledge worker Lets users create and manage information, often for and individual media editors may aid in multiple information worker tasks. Content access Used primarily to access content without editing, but may include software that allows for content editing. Such software addresses the needs of individuals and groups to consume digital entertainment and published digital content. (Examples include media players, web browsers, and help browsers.) Educational Related to content access software, but has the content or features adapted for use by educators or students. For example, it may deliver evaluations (tests), track progress through material, or include collaborative capabilities. Simulation Simulates physical or abstract systems for either research, training, or entertainment purposes. Media development Generates print and electronic media for others to consume, most often in a commercial or educational setting. This includes graphic-art software, desktop publishing software, multimedia development software, HTML editors, digital-animation editors, digital audio and video composition, and many others. Engineering Used in developing hardware and software products. This includes computer-aided design (CAD), computer-aided engineering (CAE), computer language editing and compiling tools, integrated development environments, and application programmer interfaces. Entertainment Refers to video games, screen savers, programs to display motion pictures or play recorded music, and other forms of entertainment which can be experienced through the use of a computing device. == Taxonomy == This section is a taxonomy of kinds of applications. This organization is but one of many different ways to organize them. A kind is included in only one category even if it logically fits in multiple. === General-purpose === Calculator Spreadsheet Web browser Web mapping E-commerce Social media === Communication === Chat Email Presentation software Phone Messages Networking software Web conferencing === Documentation === Desktop
Grammar induction
Grammar induction (or grammatical inference) is the process in machine learning of learning a formal grammar (usually as a collection of re-write rules or productions or alternatively as a finite-state machine or automaton of some kind) from a set of observations, thus constructing a model which accounts for the characteristics of the observed objects. More generally, grammatical inference is that branch of machine learning where the instance space consists of discrete combinatorial objects such as strings, trees and graphs. == Grammar classes == Grammatical inference has often been very focused on the problem of learning finite-state machines of various types (see the article Induction of regular languages for details on these approaches), since there have been efficient algorithms for this problem since the 1980s. Since the beginning of the century, these approaches have been extended to the problem of inference of context-free grammars and richer formalisms, such as multiple context-free grammars and parallel multiple context-free grammars. Other classes of grammars for which grammatical inference has been studied are combinatory categorial grammars, stochastic context-free grammars, contextual grammars and pattern languages. == Learning models == The simplest form of learning is where the learning algorithm merely receives a set of examples drawn from the language in question: the aim is to learn the language from examples of it (and, rarely, from counter-examples, that is, example that do not belong to the language). However, other learning models have been studied. One frequently studied alternative is the case where the learner can ask membership queries as in the exact query learning model or minimally adequate teacher model introduced by Angluin. == Methodologies == There is a wide variety of methods for grammatical inference. Two of the classic sources are Fu (1977) and Fu (1982). Duda, Hart & Stork (2001) also devote a brief section to the problem, and cite a number of references. The basic trial-and-error method they present is discussed below. For approaches to infer subclasses of regular languages in particular, see Induction of regular languages. A more recent textbook is de la Higuera (2010), which covers the theory of grammatical inference of regular languages and finite state automata. D'Ulizia, Ferri and Grifoni provide a survey that explores grammatical inference methods for natural languages. === Induction of probabilistic grammars === There are several methods for induction of probabilistic context-free grammars. === Grammatical inference by trial-and-error === The method proposed in Section 8.7 of Duda, Hart & Stork (2001) suggests successively guessing grammar rules (productions) and testing them against positive and negative observations. The rule set is expanded so as to be able to generate each positive example, but if a given rule set also generates a negative example, it must be discarded. This particular approach can be characterized as "hypothesis testing" and bears some similarity to Mitchel's version space algorithm. The Duda, Hart & Stork (2001) text provide a simple example which nicely illustrates the process, but the feasibility of such an unguided trial-and-error approach for more substantial problems is dubious. === Grammatical inference by genetic algorithms === Grammatical induction using evolutionary algorithms is the process of evolving a representation of the grammar of a target language through some evolutionary process. Formal grammars can easily be represented as tree structures of production rules that can be subjected to evolutionary operators. Algorithms of this sort stem from the genetic programming paradigm pioneered by John Koza. Other early work on simple formal languages used the binary string representation of genetic algorithms, but the inherently hierarchical structure of grammars couched in the EBNF language made trees a more flexible approach. Koza represented Lisp programs as trees. He was able to find analogues to the genetic operators within the standard set of tree operators. For example, swapping sub-trees is equivalent to the corresponding process of genetic crossover, where sub-strings of a genetic code are transplanted into an individual of the next generation. Fitness is measured by scoring the output from the functions of the Lisp code. Similar analogues between the tree structured lisp representation and the representation of grammars as trees, made the application of genetic programming techniques possible for grammar induction. In the case of grammar induction, the transplantation of sub-trees corresponds to the swapping of production rules that enable the parsing of phrases from some language. The fitness operator for the grammar is based upon some measure of how well it performed in parsing some group of sentences from the target language. In a tree representation of a grammar, a terminal symbol of a production rule corresponds to a leaf node of the tree. Its parent nodes corresponds to a non-terminal symbol (e.g. a noun phrase or a verb phrase) in the rule set. Ultimately, the root node might correspond to a sentence non-terminal. === Grammatical inference by greedy algorithms === Like all greedy algorithms, greedy grammar inference algorithms make, in iterative manner, decisions that seem to be the best at that stage. The decisions made usually deal with things like the creation of new rules, the removal of existing rules, the choice of a rule to be applied or the merging of some existing rules. Because there are several ways to define 'the stage' and 'the best', there are also several greedy grammar inference algorithms. These context-free grammar generating algorithms make the decision after every read symbol: Lempel-Ziv-Welch algorithm creates a context-free grammar in a deterministic way such that it is necessary to store only the start rule of the generated grammar. Sequitur and its modifications. These context-free grammar generating algorithms first read the whole given symbol-sequence and then start to make decisions: Byte pair encoding and its optimizations. === Distributional learning === A more recent approach is based on distributional learning. Algorithms using these approaches have been applied to learning context-free grammars and mildly context-sensitive languages and have been proven to be correct and efficient for large subclasses of these grammars. === Learning of pattern languages === Angluin defines a pattern to be "a string of constant symbols from Σ and variable symbols from a disjoint set". The language of such a pattern is the set of all its nonempty ground instances i.e. all strings resulting from consistent replacement of its variable symbols by nonempty strings of constant symbols. A pattern is called descriptive for a finite input set of strings if its language is minimal (with respect to set inclusion) among all pattern languages subsuming the input set. Angluin gives a polynomial algorithm to compute, for a given input string set, all descriptive patterns in one variable x. To this end, she builds an automaton representing all possibly relevant patterns; using sophisticated arguments about word lengths, which rely on x being the only variable, the state count can be drastically reduced. Erlebach et al. give a more efficient version of Angluin's pattern learning algorithm, as well as a parallelized version. Arimura et al. show that a language class obtained from limited unions of patterns can be learned in polynomial time. === Pattern theory === Pattern theory, formulated by Ulf Grenander, is a mathematical formalism to describe knowledge of the world as patterns. It differs from other approaches to artificial intelligence in that it does not begin by prescribing algorithms and machinery to recognize and classify patterns; rather, it prescribes a vocabulary to articulate and recast the pattern concepts in precise language. In addition to the new algebraic vocabulary, its statistical approach was novel in its aim to: Identify the hidden variables of a data set using real world data rather than artificial stimuli, which was commonplace at the time. Formulate prior distributions for hidden variables and models for the observed variables that form the vertices of a Gibbs-like graph. Study the randomness and variability of these graphs. Create the basic classes of stochastic models applied by listing the deformations of the patterns. Synthesize (sample) from the models, not just analyze signals with it. Broad in its mathematical coverage, pattern theory spans algebra and statistics, as well as local topological and global entropic properties. == Applications == The principle of grammar induction has been applied to other aspects of natural language processing, and has been applied (among many other problems) to semantic parsing, natural language understanding, example-based translation, language acquisition, grammar-based compre
Text-to-video model
A text-to-video model is a form of generative artificial intelligence that uses a natural language description as input to produce a video relevant to the input text. Advancements during the 2020s in the generation of high-quality, text-conditioned videos have largely been driven by the development of video diffusion models. == Models == There are different models, including open source models. Chinese-language input CogVideo is the earliest text-to-video model "of 9.4 billion parameters" to be developed, with its demo version of open source codes first presented on GitHub in 2022. That year, Meta Platforms released a partial text-to-video model called "Make-A-Video", and Google's Brain (later Google DeepMind) introduced Imagen Video, a text-to-video model with 3D U-Net. === 2023 === In February 2023, Runway released Gen-1 and Gen-2, among the first commercially available text-to-video and video-to-video models accessible to the public through a web interface. Gen-1, initially released as a video-to-video model, allowed users to transform existing video footage using text or image prompts. Gen-2, introduced in March 2023 and made publicly available in June 2023, added text-to-video capabilities, enabling users to generate videos from text prompts alone. In March 2023, a research paper titled "VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation" was published, presenting a novel approach to video generation. The VideoFusion model decomposes the diffusion process into two components: base noise and residual noise, which are shared across frames to ensure temporal coherence. By utilizing a pre-trained image diffusion model as a base generator, the model efficiently generated high-quality and coherent videos. Fine-tuning the pre-trained model on video data addressed the domain gap between image and video data, enhancing the model's ability to produce realistic and consistent video sequences. In the same month, Adobe introduced Firefly AI as part of its features. === 2024 === In January 2024, Google announced development of a text-to-video model named Lumiere which is anticipated to integrate advanced video editing capabilities. Matthias Niessner and Lourdes Agapito at AI company Synthesia work on developing 3D neural rendering techniques that can synthesise realistic video by using 2D and 3D neural representations of shape, appearances, and motion for controllable video synthesis of avatars. In June 2024, Luma Labs launched its Dream Machine video tool. That same month, Kuaishou extended its Kling AI text-to-video model to international users. In July 2024, TikTok owner ByteDance released Jimeng AI in China, through its subsidiary, Faceu Technology. By September 2024, the Chinese AI company MiniMax debuted its video-01 model, joining other established AI model companies like Zhipu AI, Baichuan, and Moonshot AI, which contribute to China's involvement in AI technology. In December 2024 Lightricks launched LTX Video as an open source model. === 2025 === Alternative approaches to text-to-video models include Google's Phenaki, Hour One, Colossyan, Runway's Gen-3 Alpha, and OpenAI's Sora, Several additional text-to-video models, such as Plug-and-Play, Text2LIVE, and TuneAVideo, have emerged. FLUX.1 developer Black Forest Labs has announced its text-to-video model SOTA. Google was preparing to launch a video generation tool named Veo for YouTube Shorts in 2025. In May 2025, Google launched the Veo 3 iteration of the model. It was noted for its impressive audio generation capabilities, which were a previous limitation for text-to-video models. In July 2025 Lightricks released an update to LTX Video capable of generating clips reaching 60 seconds, and in October 2025 it released LTX-2, with audio capabilities built in. === 2026 === In February 2026, ByteDance released Seedance 2.0, it was noted for its impressive realistic generation, motion and camera control and 15 second generation, however the model faced huge critiscism from Motion Picture Association for copyright infringement. After viewing a viral clip of a fight between actors Brad Pitt and Tom Cruise, Rhett Reese, who is the co-writer of Deadpool & Wolverine and Zombieland announced that on social media "I hate to say it. It’s likely over for us," further stating that "In next to no time, one person is going to be able to sit at a computer and create a movie indistinguishable from what Hollywood now releases." == Architecture and training == There are several architectures that have been used to create text-to-video models. Similar to text-to-image models, these models can be trained using Recurrent Neural Networks (RNNs) such as long short-term memory (LSTM) networks, which has been used for Pixel Transformation Models and Stochastic Video Generation Models, which aid in consistency and realism respectively. An alternative for these include transformer models. Generative adversarial networks (GANs), Variational autoencoders (VAEs), — which can aid in the prediction of human motion — and diffusion models have also been used to develop the image generation aspects of the model. Text-video datasets used to train models include, but are not limited to, WebVid-10M, HDVILA-100M, CCV, ActivityNet, and Panda-70M. These datasets contain millions of original videos of interest, generated videos, captioned-videos, and textual information that help train models for accuracy. Text-video datasets used to train models include, but are not limited to PromptSource, DiffusionDB, and VidProM. These datasets provide the range of text inputs needed to teach models how to interpret a variety of textual prompts. The video generation process involves synchronizing the text inputs with video frames, ensuring alignment and consistency throughout the sequence. This predictive process is subject to decline in quality as the length of the video increases due to resource limitations. The Will Smith Eating Spaghetti test is a benchmark for models. == Limitations == Despite the rapid evolution of text-to-video models in their performance, a primary limitation is that they are very computationally heavy which limits its capacity to provide high quality and lengthy outputs. Additionally, these models require a large amount of specific training data to be able to generate high quality and coherent outputs, which brings about the issue of accessibility. Moreover, models may misinterpret textual prompts, resulting in video outputs that deviate from the intended meaning. This can occur due to limitations in capturing semantic context embedded in text, which affects the model's ability to align generated video with the user's intended message. Various models, including Make-A-Video, Imagen Video, Phenaki, CogVideo, GODIVA, and NUWA, are currently being tested and refined to enhance their alignment capabilities and overall performance in text-to-video generation. Another issue with the outputs is that text or fine details in AI-generated videos often appear garbled, a problem that stable diffusion models also struggle with. Examples include distorted hands and unreadable text. == Ethics == The deployment of text-to-video models raises ethical considerations related to content generation. These models have the potential to create inappropriate or unauthorized content, including explicit material, graphic violence, misinformation, and likenesses of real individuals without consent. Ensuring that AI-generated content complies with established standards for safe and ethical usage is essential, as content generated by these models may not always be easily identified as harmful or misleading. The ability of AI to recognize and filter out NSFW or copyrighted content remains an ongoing challenge, with implications for both creators and audiences. == Impacts and applications == Text-to-video models offer a broad range of applications that may benefit various fields, from educational and promotional to creative industries. These models can streamline content creation for training videos, movie previews, gaming assets, and visualizations, making it easier to generate content. During the Russo-Ukrainian war, fake videos made with artificial intelligence were created as part of a propaganda war against Ukraine and shared in social media. These included depictions of children in the Ukrainian Armed Forces, fake ads targeting children encouraging them to denounce critics of the Ukrainian government, or fictitious statements by Ukrainian President Volodymyr Zelenskyy about the country's surrender, among others. === Movies === Kaur vs Kore is the first Indian feature film made using generative AI which features dual role for the AI character of Sunny Leone, set to release in 2026. Chiranjeevi Hanuman – The Eternal is an Indian movie made entirely using Generative AI created by Vijay Subramaniam which is set for theatrical release in 2026. The movie was widely criticised by the Film makers in the Bollywood industr
Period-tracking app
Period-tracking apps are mobile applications used to track the menstrual cycle. They may be used to predict menstruation, to plan fertility, and to track health. Examples include Clue, Glow, and Flo. == Function == Users enter their dates of menstruation, and frequently other experiences such as vaginal discharge and spotting; premenstrual syndrome; changes in mood; menstrual cramps and other pain; and other symptoms such as appetite changes, bloating, and acne. The apps predict the date of users' next period, and often also their ovulation and fertile window. Some apps have additional features such as contraceptive reminders, educational content, tracking modes for use during pregnancy, or the ability to share one's menstrual cycle data with a partner. == Privacy == Period-tracking apps collect personal health data, potentially raising concerns about privacy. Researchers have warned that data may be transferred to third parties and used for consumer profiling and targeted advertising, used for employment and health insurance discrimination, or used to prosecute users for seeking abortions. After the 2022 decision by the United States Supreme Court to overturn Roe v. Wade, and the bans and restrictions on abortion in many US states that followed, many American women uninstalled the apps amidst fear that the data could be accessed by law enforcement and used to prosecute users. WIRED published a ranking of several period-tracking apps by data privacy.
Pandorabots
Pandorabots, Inc. is an artificial intelligence company that runs a web service for building and deploying chatbots. Pandorabots implements and supports development of the Artificial Intelligence Markup Language and makes portions of its code accessible for free. The Pandorabots Platform is "one of the oldest and largest chatbot hosting services in the world", allowing creation of virtual agents to hold human-like text or voice chats with consumers. The platform is written in Allegro Common LISP. == Use Cases == Common use cases include advertising, virtual assistance, e-learning, entertainment and education. The platform has also been used by academics and universities use the platform for teaching and research.