Artificial intelligence visual art, or AI art, is visual artwork generated or enhanced through the implementation of artificial intelligence (AI) programs, most commonly using text-to-image models. The process of automated art-making has existed since antiquity. The field of artificial intelligence was founded in the 1950s, and artists began to create art with artificial intelligence shortly after the discipline's founding. A select number of these creations have been showcased in museums and have been recognized with awards. Throughout its history, AI has raised many philosophical questions related to the human mind, artificial beings, and the nature of art in human–AI collaboration. During the AI boom of the 2020s, text-to-image models such as Midjourney, DALL-E and Stable Diffusion became widely available to the public, allowing users to quickly generate imagery with little effort. Commentary about AI art in the 2020s has often focused on issues related to copyright, deception, defamation, and its impact on more traditional artists, including technological unemployment. In August 2023, the US Supreme Court ruled that AI art is ineligible for copyright due to failure to meet human authorship. In March 2026, it declined to hear a case over whether AI-generated art can be subject to copyright. == History == === Early history === Automated art dates back at least to the automata of ancient Greek civilization, when inventors such as Daedalus and Hero of Alexandria were described as designing machines capable of writing text, generating sounds, and playing music. Creative automatons have flourished throughout history, such as Maillardet's automaton, created around 1800 and capable of creating multiple drawings and poems. Also in the 19th century, Ada Lovelace, wrote that "computing operations" could potentially be used to generate music and poems. In 1950, Alan Turing's paper "Computing Machinery and Intelligence" focused on whether machines can mimic human behavior convincingly. Shortly after, the academic discipline of artificial intelligence was founded at a research workshop at Dartmouth College in 1956. Since its founding, AI researchers have explored philosophical questions about the nature of the human mind and the consequences of creating artificial beings with human-like intelligence; these issues have previously been explored by myth, fiction, and philosophy since antiquity. === Artistic history === Since the founding of AI in the 1950s, artists have used artificial intelligence to create artistic works. These works were sometimes referred to as algorithmic art, computer art, digital art, or new media art. One of the first significant AI art systems is AARON, developed by Harold Cohen beginning in the late 1960s at the University of California at San Diego. AARON uses a symbolic rule-based approach to generate technical images in the era of GOFAI programming, and it was developed by Cohen with the goal of being able to code the act of drawing. AARON was exhibited in 1972 at the Los Angeles County Museum of Art. From 1973 to 1975, Cohen refined AARON during a residency at the Artificial Intelligence Laboratory at Stanford University. In 2024, the Whitney Museum of American Art exhibited AI art from throughout Cohen's career, including re-created versions of his early robotic drawing machines. Karl Sims has exhibited art created with artificial life since the 1980s. He received an M.S. in computer graphics from the MIT Media Lab in 1987 and was artist-in-residence from 1990 to 1996 at the supercomputer manufacturer and artificial intelligence company Thinking Machines. In both 1991 and 1992, Sims won the Golden Nica award at Prix Ars Electronica for his videos using artificial evolution. In 1997, Sims created the interactive artificial evolution installation Galápagos for the NTT InterCommunication Center in Tokyo. Sims received an Emmy Award in 2019 for outstanding achievement in engineering development. In 1999, Scott Draves and a team of several engineers created and released Electric Sheep as a free software screensaver. Electric Sheep is a volunteer computing project for animating and evolving fractal flames, which are distributed to networked computers that display them as a screensaver. The screensaver used AI to create an infinite animation by learning from its audience. In 2001, Draves won the Fundacion Telefónica Life 4.0 prize for Electric Sheep. In 2014, Stephanie Dinkins began working on Conversations with Bina48. For the series, Dinkins recorded her conversations with BINA48, a social robot that resembles a middle-aged black woman. In 2019, Dinkins won the Creative Capital award for her creation of an evolving artificial intelligence based on the "interests and culture(s) of people of color." In 2015, Sougwen Chung began Mimicry (Drawing Operations Unit: Generation 1), an ongoing collaboration between the artist and a robotic arm. In 2019, Chung won the Lumen Prize for her continued performances with a robotic arm that uses AI to attempt to draw in a manner similar to Chung. In 2018, an auction sale of artificial intelligence art was held at Christie's in New York where the AI artwork Edmond de Belamy sold for US$432,500, which was almost 45 times higher than its estimate of US$7,000–10,000. The artwork was created by Obvious, a Paris-based collective. In 2024, Japanese film generAIdoscope was released. The film was co-directed by Hirotaka Adachi, Takeshi Sone, and Hiroki Yamaguchi. All video, audio, and music in the film were created with artificial intelligence. In 2025, the Japanese anime television series Twins Hinahima was released. The anime was produced and animated with AI assistance during the process of cutting and conversion of photographs into anime illustrations and later retouched by art staff. Most of the remaining parts such as characters and logos were hand-drawn with various software. === Technical history === Deep learning, characterized by its multi-layer structure that attempts to mimic the human brain, first came about in the 2010s, causing a significant shift in the world of AI art. During the deep learning era, there are mainly these types of designs for generative art: autoregressive models, diffusion models, GANs, normalizing flows. In 2014, Ian Goodfellow and colleagues at Université de Montréal developed the generative adversarial network (GAN), a type of deep neural network capable of learning to mimic the statistical distribution of input data such as images. The GAN uses a "generator" to create new images and a "discriminator" to decide which created images are considered successful. Unlike previous algorithmic art that followed hand-coded rules, generative adversarial networks could learn a specific aesthetic by analyzing a dataset of example images. In 2015, a team at Google released DeepDream, a program that uses a convolutional neural network to find and enhance patterns in images via algorithmic pareidolia. The process creates deliberately over-processed images with a dream-like appearance reminiscent of a psychedelic experience. Later, in 2017, a conditional GAN learned to generate 1000 image classes of ImageNet, a large visual database designed for use in visual object recognition software research. By conditioning the GAN on both random noise and a specific class label, this approach enhanced the quality of image synthesis for class-conditional models. Autoregressive models were used for image generation, such as PixelRNN (2016), which autoregressively generates one pixel after another with a recurrent neural network. Immediately after the Transformer architecture was proposed in Attention Is All You Need (2018), it was used for autoregressive generation of images, but without text conditioning. The website Artbreeder, launched in 2018, uses the models StyleGAN and BigGAN to allow users to generate and modify images such as faces, landscapes, and paintings. In the 2020s, text-to-image models, which generate images based on prompts, became widely used, marking yet another shift in the creation of AI-generated artworks. In 2021, using the influential large language generative pre-trained transformer models that are used in GPT-2 and GPT-3, OpenAI released a series of images created with the text-to-image AI model DALL-E 1. It is an autoregressive generative model with essentially the same architecture as GPT-3. Along with this, later in 2021, EleutherAI released the open source VQGAN-CLIP based on OpenAI's CLIP model. Diffusion models, generative models used to create synthetic data based on existing data, were first proposed in 2015, but they only became better than GANs in early 2021. Latent diffusion model was published in December 2021 and became the basis for the later Stable Diffusion (August 2022), developed through a collaboration between Stability AI, CompVis Group at LMU Munich, and Runway. In 2022, Midjourney was released, followed by Google Brain's Imagen and Pa
Anderson's rule (computer science)
In the field of computer security, Anderson's rule refers to a principle formulated by Ross J. Anderson: systems that handle sensitive personal information involve a trilemma of security, functionality, and scale, of which you can choose any two. A system that has information on many data subjects and to which many people require access is hard to secure unless its functionality is severely restricted. If it has rich functionality, you may have to restrict the number of people with access, or accept that some information will leak.
Preferential entailment
Preferential entailment is a non-monotonic logic based on selecting only models that are considered the most plausible. The plausibility of models is expressed by an ordering among models called a preference relation, hence the name preference entailment. Formally, given a propositional formula F {\displaystyle F} and an ordering over propositional models ≤ {\displaystyle \leq } , preferential entailment selects only the models of F {\displaystyle F} that are minimal according to ≤ {\displaystyle \leq } . This selection leads to a non-monotonic inference relation: F ⊨ pref G {\displaystyle F\models _{\text{pref}}G} holds if and only if all minimal models of F {\displaystyle F} according to ≤ {\displaystyle \leq } are also models of G {\displaystyle G} . Circumscription can be seen as the particular case of preferential entailment when the ordering is based on containment of the sets of variables assigned to true (in the propositional case) or containment of the extensions of predicates (in the first-order logic case).
Type–token distinction
The type–token distinction is the difference between a type of objects (analogous to a class) and the individual tokens of that type (analogous to instances). Since each type may be instantiated by multiple tokens, there are generally more tokens than types of an object. For example, the sentence "A rose is a rose is a rose" contains three word types: three word tokens of the type a, two word tokens of the type is, and three word tokens of the type rose. The distinction is important in disciplines such as logic, linguistics, metalogic, typography, and computer programming. == Overview == The type–token distinction separates types (abstract descriptive concepts) from tokens (objects that instantiate concepts). For example, in the sentence "the bicycle is becoming more popular" the word bicycle represents the abstract concept of bicycles and this abstract concept is a type, whereas in the sentence "the bicycle is in the garage", it represents a particular object and this particular object is a token. Similarly, the word type 'letter' uses only four letter types: L, E, T and R. Nevertheless, it uses both E and T twice. One can say that the word type 'letter' has six letter tokens, with two tokens each of the letter types E and T. Whenever a word type is inscribed, the number of letter tokens created equals the number of letter occurrences in the word type. Some logicians consider a word type to be the class of its tokens. Other logicians counter that the word type has a permanence and constancy not found in the class of its tokens. The type remains the same while the class of its tokens is continually gaining new members and losing old members. == Typography == In typography, the type–token distinction is used to determine the presence of a text printed by movable type: The defining criteria which a typographic print has to fulfill is that of the type identity of the various letter forms which make up the printed text. In other words: each letter form which appears in the text has to be shown as a particular instance ("token") of one and the same type which contains a reverse image of the printed letter. == Charles Sanders Peirce == The distinctions between using words as types or tokens were first made by American logician and philosopher Charles Sanders Peirce in 1906 using terminology that he established. Peirce's type–token distinction applies to words, sentences, paragraphs and so on: to anything in a universe of discourse of character-string theory, or concatenation theory. Peirce's original words are the following: A common mode of estimating the amount of matter in a ... printed book is to count the number of words. There will ordinarily be about twenty 'thes' on a page, and, of course, they count as twenty words. In another sense of the word 'word,' however, there is but one word 'the' in the English language; and it is impossible that this word should lie visibly on a page, or be heard in any voice .... Such a ... Form, I propose to term a Type. A Single ... Object ... such as this or that word on a single line of a single page of a single copy of a book, I will venture to call a Token. .... In order that a Type may be used, it has to be embodied in a Token which shall be a sign of the Type, and thereby of the object the Type signifies.
Rohit Chadda
Rohit Chadda (born 26 August 1982) is an Indian investment banker and entrepreneur, who is the President & COO of Times Network. He leads the tech business portfolio and AI transformation of Times Group covering verticals like media tech, OTT, fintech, health tech, edu tech, ecommerce, gaming and sports. Previously, CEO of the digital business at Essel Group (Zee Entertainment, Zee Media and DNA), he was the co-founder of online food ordering platform Foodpanda. He is also the founder of omni-channel digital payments platform PayLo. He has been attributed for the turnaround of Zee Digital driving 4x growth in 2 years and bringing Zee's digital business to the second position on ComScore from ninth position making Zee the second largest digital media group in India. He has been featured among Top Tech CEOs of the decade (2010–2020) in India and was featured among Fortune 40 under 40 in 2015. == Education and early career == Chadda graduated from Delhi Technological University (formerly Delhi College of Engineering) with a degree in computer engineering and worked as a software engineer for Computer Sciences Corporation. In 2007 he joined Indian Institute of Management Calcutta to do his MBA after which he worked at Merrill Lynch as an investment banker in United Kingdom. He took an internal transfer to India in 2011. == Career == === Foodpanda === Chadda began his career in 2012 when he co-founded foodpanda. foodpanda expanded to around 40 countries before being bought by Delivery Hero. Before foodpanda got popular, he joked that he delivered pizza for a living. foodpanda had raised a total investment of over US$300 million till 2015. Chadda in the middle of 2015 stepped down from day-to-day responsibilities at Foodpanda to launch his digital payments startup. Foodpanda was acquired by its global competitor Delivery Hero in 2016. === Paylo === In 2015, he launched an omni-channel digital payments platform PayLo which acquired the in-restaurant payments app Ruplee in March 2016 for an undisclosed sum. PayLo was successful in the wake of demonetisation in India and expanded pan-India before being acquired by Immortal Technologies. Chadda believes that execution is more important than the idea to make a startup successful and the key challenge for experienced professionals to work in a startup environment is to unlearn what they have previously learned. PayLo acquired Ruplee before being itself acquired by Immortal Technologies. === Zee Group === Chadda took over as CEO of digital publishing of Zee Group in May 2019. Since 2017, he had led global product and strategy for Zee Group launching ZEE5, the flagship OTT of Zee Entertainment, across 170+ countries. Since June 2019, Zee Digital, the online arm of the Zee group, has registered the highest growth year-on-year among the top media publishers in India. Times Internet Limited, Network 18 Group, and India Today Group have grown by 45%, 21%, and 22% respectively from June 2020 over June 2019 while Zee Digital witnessed a growth of 123% over the same period. Zee Digital achieved its first milestone in September 2019 by crossing 100 million unique monthly visitors and was ranked 6th in the news and information category on ComScore India rankings at the time. Later in the month of March 2020 it crossed 150 million unique monthly visitors mark moving to 4th position. Further in May 2020 Zee Digital moved to 3rd position by crossing 185 million unique monthly visitors mark before finally ranking 2nd position in June 2020 in the ComScore rankings among all digital media groups in India. Chadda has led the transformation of the business of Zee Digital by scaling it to over 200 million users from 60 million users making it the second-largest digital media group in India. He attributes the growth from rank 9 to rank 2 in one year to the data and technology driven approach to content and the focus on vernacular languages. During his tenure, Zee Digital launched 8 new brand websites and 3 new languages to expand the product portfolio to 20 brands and 12 languages. During the US elections in November 2020, Zee Digital launched the English global news channel WION through a digital first approach across Asia Pacific, Middle East, UK and North America. Chadda launched Zee's UGC short video platform HiPi in the midst of the TikTok ban in India. Hipi was first launched within ZEE5 app ecosystem to capitalise on the reach of the OTT platform. After the success of the POC, he launched a standalone app for HiPi. HiPi is a short video platform that provides a complete video creation ecosystem along with news avenues of monetisation to content creators. He plans to use Zee's network reach of 600 million broadcast viewers and 300 million digital users to get creators on HiPi. HiPi launched India's first digital star hunt to allow users to audition for ZEE5 original shows through the short video platform. === Times Group === Chadda took over as President & COO of Times Network in September 2022. Leading the digital transformation of the group Chadda launched 11 new products in 18 months expanding the group's presence to various verticals in the tech business like fintech, health tech, edu tech, auto tech, OTT, ecommerce and gaming while extending the news vertical into business news, tech news and various vernacular languages. Within 4 months of his stint, in January 2023 he launched the digital platform for ET Now, targeting Gen Z, early jobbers and first time investors and laying the foundation for the fintech expansion for the brand. Since then, the product has expended to Hindi language targeting the larger Indian audience through the launch of ET Now Swadesh and further expanding to fintech business by launching ET Now Advisor, a distribution business focussing to upselling of cards, loans etc. to consumers by educating them and enabling them to make the right choices. ET Now reached 10 million users within the first 20 days of launch and became the No.1 business news channel on YouTube with 200 million views in April and May 2024. Expanding to health-tech, he launched AI powered daily health companion Health & Me in the presence of actor & fitness enthusiast Milind Soman. Chadda unveiled the auto-tech platform for Times Drive together with Union Minister of Road Transport and Highways, Nitin Gadkari showcasing the AI assisted platform that helps consumers make the right decisions when it comes to their automotive needs. In order to expand the group's presence into tech and gaming, Chadda acquired India's largest and most popular tech magazine Digit along with their digital platforms Digit.in and Skoar.gg in June 2024. Within a year, he was able to turnaround Digit's business with Digit.in becoming the No.1 Tech news platform in India in April 2025. Times Network launched college discovery platform unilist.in to enable students and parents search for the right course and institute for their higher education needs. With a focus on sports and gaming, Chadda launched India's first Inter-college esports championship under the brand of SKOAR College Gaming Championship. Times Network launched its OTT app Times Play under his leadership. The platform expanded its presence in the US through a partnership with Sling TV. He launched Pickleball Now which is the World's first TV channel focussed on the sport of Pickleball covering tournaments and leagues across the World. The channel has presence on TV and digital platforms and is being distributed to global markets through partnerships with BOTIM, Distro TV, Yupp TV and Rumble. In India, the channel is available on Jio TV, Jio TV+, Airtel Xtream Play, OTT Play, Dailyhunt. Times Group has launched India's Official Pickleball League affiliated with Indian Pickleball Association and Global Pickelball Federation which shall also be streamed live on Pickleball Now from 1st to 7th Dec 2025. === Investing and speaking === Chadda is a mentor at Esselerator, a Startup accelerator by Subhash Chandra Foundation. Esselerator is an initiative by Subhash Chandra, a billionaire Media baron, to promote and support tech entrepreneurs in domains like Media, Fintech and Education. Its powered by TiE Mumbai. Chadda is an angel investor in multiple technology startups like online school aggregator platform SchoolForSure.com. In 2019, he spoke at DPS to students on starting a business. At the time he remained CEO of Zee group's digital business division. == Philanthropy == Chadda organised a £1 mliion charity bike ride in aid of the British Asian Trust which saw participation by the Prince of Wales. Chadda presented the Prince of Wales with a cycling vest, which was said to be for his grandchildren. Chadda supports a non-profit organisation Mukkamaar founded by Bollywood actress Ishita Sharma that works towards fighting crime against women by teaching free self defence to young girls. He is helping the organisation launch their digital program through a WhatsApp-based chatbot. == A
Distributed concurrency control
Distributed concurrency control is the concurrency control of a system distributed over a computer network (Bernstein et al. 1987, Weikum and Vossen 2001). In database systems and transaction processing (transaction management) distributed concurrency control refers primarily to the concurrency control of a distributed database. It also refers to the concurrency control in a multidatabase (and other multi-transactional object) environment (e.g., federated database, grid computing, and cloud computing environments. A major goal for distributed concurrency control is distributed serializability (or global serializability for multidatabase systems). Distributed concurrency control poses special challenges beyond centralized one, primarily due to communication and computer latency. It often requires special techniques, like distributed lock manager over fast computer networks with low latency, like switched fabric (e.g., InfiniBand). The most common distributed concurrency control technique is strong strict two-phase locking (SS2PL, also named rigorousness), which is also a common centralized concurrency control technique. SS2PL provides both the serializability and strictness. Strictness, a special case of recoverability, is utilized for effective recovery from failure. For large-scale distribution and complex transactions, distributed locking's typical heavy performance penalty (due to delays, latency) can be saved by using the atomic commitment protocol, which is needed in a distributed database for (distributed) transactions' atomicity.
MindSpore
MindSpore is an open-source software framework for deep learning, machine learning and artificial intelligence developed by Huawei. == Overview == MindSpore provides support for Python by allowing users to define models, control flow, and custom operators using native Python syntax. Unlike graph-based frameworks that require users to learn DSL or complex APIs, MindSpore adopts a source-to-source (S2S) automatic differentiation approach, allowing Python code to be automatically transformed into optimized computational graphs. It has support for custom OpenHarmony-based HarmonyOS NEXT single core framework system built for HarmonyOS, includes an AI system stack that comes with Huawei's built LLM model called PanGu-Σ with full MindSpore framework support. Alongside, OpenHarmony Native device-side AI support for training interface and ArkTS programming interface for its NNRt (Neural Network Runtime) backend configurations via MindSpore Lite AI framework codebase introduced in API 11 Beta 1 of OpenHarmony 4.1. MindSpore platform runs on Ascend AI chips and Kirin alongside other HiSilicon NPU chips. CANN (Compute Architecture of Neural Networks), heterogeneous computing architecture for AI developed by Huawei. With CANN backend in OpenCV DNN, giving developers ability to run created AI models on the Ascend, Kirin and other HiSilicon NPU enabled chips. It supports cross platform development such as Android, iOS, Windows, global OpenHarmony-based distro, Eclipse Oniro, Linux-based EulerOS alongside OpenEuler Huawei's server OS platforms, macOS and Linux. == History == On April 24, 2024, Huawei's MindSpore 2.3.RC1 was released to open source community with Foundation Model Training, Full-Stack Upgrade of Foundation Model Inference, Static Graph Optimization, IT Features and new MindSpore Elec MT (MindSpore-powered magnetotelluric) Intelligent Inversion Model.