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Embodied cognitive science

Embodied cognitive science is an interdisciplinary field of research, the aim of which is to explain the mechanisms underlying intelligent behavior. It comprises three main methodologies: the modeling of psychological and biological systems in a holistic manner that considers the mind and body as a single entity; the formation of a common set of general principles of intelligent behavior; and the experimental use of robotic agents in controlled environments. == Contributors == Embodied cognitive science borrows heavily from embodied philosophy and the related research fields of cognitive science, psychology, neuroscience and artificial intelligence. Contributors to the field include: From the perspective of neuroscience, Gerald Edelman of the Neurosciences Institute at La Jolla, Francisco Varela of CNRS in France, and J. A. Scott Kelso of Florida Atlantic University From the perspective of psychology, Lawrence Barsalou, Michael Turvey, Vittorio Guidano and Eleanor Rosch From the perspective of linguistics, Gilles Fauconnier, George Lakoff, Mark Johnson, Leonard Talmy and Mark Turner From the perspective of language acquisition, Eric Lenneberg and Philip Rubin at Haskins Laboratories From the perspective of anthropology, Edwin Hutchins, Bradd Shore, James Wertsch and Merlin Donald. From the perspective of autonomous agent design, early work is sometimes attributed to Rodney Brooks or Valentino Braitenberg From the perspective of artificial intelligence, Understanding Intelligence by Rolf Pfeifer and Christian Scheier or How the Body Shapes the Way We Think, by Rolf Pfeifer and Josh C. Bongard From the perspective of philosophy, Andy Clark, Dan Zahavi, Shaun Gallagher, and Evan Thompson In 1950, Alan Turing proposed that a machine may need a human-like body to think and speak: It can also be maintained that it is best to provide the machine with the best sense organs that money can buy, and then teach it to understand and speak English. That process could follow the normal teaching of a child. Things would be pointed out and named, etc. Again, I do not know what the right answer is, but I think both approaches should be tried. == Traditional cognitive theory == Embodied cognitive science is an alternative theory to cognition in which it minimizes appeals to computational theory of mind in favor of greater emphasis on how an organism's body determines how and what it thinks. Traditional cognitive theory is based mainly around symbol manipulation, in which certain inputs are fed into a processing unit that produces an output. These inputs follow certain rules of syntax, from which the processing unit finds semantic meaning. Thus, an appropriate output is produced. For example, a human's sensory organs are its input devices, and the stimuli obtained from the external environment are fed into the nervous system which serves as the processing unit. From here, the nervous system is able to read the sensory information because it follows a syntactic structure, thus an output is created. This output then creates bodily motions and brings forth behavior and cognition. Of particular note is that cognition is sealed away in the brain, meaning that mental cognition is cut off from the external world and is only possible by the input of sensory information. == The embodied cognitive approach == Embodied cognitive science differs from the traditionalist approach in that it denies the input-output system. This is chiefly due to the problems presented by the Homunculus argument, which concluded that semantic meaning could not be derived from symbols without some kind of inner interpretation. If some little man in a person's head interpreted incoming symbols, then who would interpret the little man's inputs? Because of the specter of an infinite regress, the traditionalist model began to seem less plausible. Thus, embodied cognitive science aims to avoid this problem by defining cognition in three ways. === Physical attributes of the body === The first aspect of embodied cognition examines the role of the physical body, particularly how its properties affect its ability to think. This part attempts to overcome the symbol manipulation component that is a feature of the traditionalist model. Depth perception, for instance, can be better explained under the embodied approach due to the sheer complexity of the action. Depth perception requires that the brain detect the disparate retinal images obtained by the distance of the two eyes. In addition, body and head cues complicate this further. When the head is turned in a given direction, objects in the foreground will appear to move against objects in the background. From this, it is said that some kind of visual processing is occurring without the need of any kind of symbol manipulation. This is because the objects appearing to move the foreground are simply appearing to move. This observation concludes then that depth can be perceived with no intermediate symbol manipulation necessary. A more poignant example exists through examining auditory perception. Generally speaking the greater the distance between the ears, the greater the possible auditory acuity. Also relevant is the amount of density in between the ears, for the strength of the frequency wave alters as it passes through a given medium. The brain's auditory system takes these factors into account as it process information, but again without any need for a symbolic manipulation system. This is because the distance between the ears for example does not need symbols to represent it. The distance itself creates the necessary opportunity for greater auditory acuity. The amount of density between the ears is similar, in that it is the actual amount itself that simply forms the opportunity for frequency alteration. Thus under consideration of the physical properties of the body, a symbolic system is unnecessary and an unhelpful metaphor. === The body's role in the cognitive process === The second aspect draws heavily from George Lakoff's and Mark Johnson's work on concepts. They argued that humans use metaphors whenever possible to better explain their external world. Humans also have a basic stock of concepts in which other concepts can be derived from. These basic concepts include spatial orientations such as up, down, front, and back. Humans can understand what these concepts mean because they can directly experience them from their own bodies. For example, because human movement revolves around standing erect and moving the body in an up-down motion, humans innately have these concepts of up and down. Lakoff and Johnson contend this is similar with other spatial orientations such as front and back too. As mentioned earlier, these basic stocks of spatial concepts are the basis in which other concepts are constructed. Happy and sad for instance are seen now as being up or down respectively. When someone says they are feeling down, what they are really saying is that they feel sad for example. Thus the point here is that true understanding of these concepts is contingent on whether one can have an understanding of the human body. So the argument goes that if one lacked a human body, they could not possibly know what up or down could mean, or how it could relate to emotional states. [I]magine a spherical being living outside of any gravitational field, with no knowledge or imagination of any other kind of experience. What could UP possibly mean to such a being? While this does not mean that such beings would be incapable of expressing emotions in other words, it does mean that they would express emotions differently from humans. Human concepts of happiness and sadness would be different because human would have different bodies. So then an organism's body directly affects how it can think, because it uses metaphors related to its body as the basis of concepts. === Interaction of local environment === A third component of the embodied approach looks at how agents use their immediate environment in cognitive processing. Meaning, the local environment is seen as an actual extension of the body's cognitive process. The example of a personal digital assistant (PDA) is used to better imagine this. Echoing functionalism (philosophy of mind), this point claims that mental states are individuated by their role in a much larger system. So under this premise, the information on a PDA is similar to the information stored in the brain. So then if one thinks information in the brain constitutes mental states, then it must follow that information in the PDA is a cognitive state too. Consider also the role of pen and paper in a complex multiplication problem. The pen and paper are so involved in the cognitive process of solving the problem that it seems ridiculous to say they are somehow different from the process, in very much the same way the PDA is used for information like the brain. Another example examines how humans control and manipulate their environment
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Blended artificial intelligence

Blended artificial intelligence (blended AI) refers to the blending of different artificial intelligence techniques or approaches to achieve more robust and practical solutions. It involves integrating multiple AI models, algorithms, and technologies to leverage their respective strengths and compensate for their weaknesses. == Background == In the context of machine learning, blended AI can involve using different types of models, such as generative AI, decision trees, neural networks, and support vector machines. By combining their results, predictions are more accurate and reliable. This blending of models can be done through techniques like ensemble learning, where multiple models are trained independently and their predictions are combined to make a final decision. Blended AI can also involve combining different AI techniques or technologies, such as natural language processing, computer vision, and expert systems, to tackle complex problems that require a multi-dimensional approach. For example, in a sales scenario AI could be used for lead generation and gathering information from social media such as LinkedIn posts, or understanding a prospect's hobbies and interests. Another blended AI could achieve customer profiling including past interactions and purchasing habits, by them, their industry and growth areas. Blended AI could be used to do predictive analytics to look at historical sales data, market trends, and external factors to generate accurate sales forecasts. This method is critical to gauge and increase "efficiency, revenue, and productivity". Lastly, another could integrate all the information into the CRM to build and maintain better prospect and customer profiles. Blended AI aims to leverage the strengths of different AI techniques and technologies, allowing them to complement each other and create more powerful and comprehensive AI solutions. By combining multiple approaches, blended AI aims to achieve better performance, higher accuracy, improved robustness, and enhanced capabilities in solving diverse and challenging problems.
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AI art

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
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Supreme Commander (video game)

Supreme Commander (sometimes SupCom) is a 2007 real-time strategy video game designed by Chris Taylor and developed by his company, Gas Powered Games. The game is considered to be a spiritual successor, not a direct sequel, to Taylor's 1997 game Total Annihilation. First announced in the August 2005 edition of PC Gamer magazine, the game was released in Europe on February 16, 2007, and in North America on February 20. The standalone expansion Supreme Commander: Forged Alliance was released on November 6 of the same year. The sequel, Supreme Commander 2, was released in 2010. Nowadays, the original Supreme Commander is played through the community client called Forged Alliance Forever; the game has been further developed and balanced, and offers a wide variety of community mods. The gameplay of Supreme Commander focuses on using a giant bipedal mech called an Armored Command Unit (ACU), the so-called "Supreme Commander", to build a base, upgrading units to reach higher technology tiers, and conquering opponents. The player can command one of three factions: the Aeon Illuminate, the Cybran Nation, or the United Earth Federation (UEF). The expansion game added the Seraphim faction. Supreme Commander was highly anticipated in pre-release previews, and was well received by critics, with a Metacritic average of 86 out of 100. == Gameplay == Supreme Commander, like its spiritual predecessors, Total Annihilation and Spring, begins with the player solely possessing a single, irreplaceable construction unit called the "Armored Command Unit," or ACU, the titular Supreme Commander. Normally the loss of this unit results in the loss of the game (Skirmish missions can be set for a variety of victory conditions). These mech suits are designed to be transported through quantum gateways across the galaxy and contain all the materials and blueprints necessary to create an army from a planet's native resources in hours. All standard units except Commanders and summoned Support Commanders (sACU) are self-sufficient robots. All units and structures belong to one of four technology tiers, or "Tech" levels, each tier being stronger and/or more efficient than the previous. Certain lower-tier structures can be upgraded into higher ones without having to rebuild them. The first tier is available at the start of the game and consists of small, relatively weak units and structures. The second tier expands the player's abilities greatly, especially in terms of stationary weapons and shielding, and introduces upgraded versions of tier one units. The third tier level has very powerful assault units designed to overcome the fortifications of the most entrenched player. The fourth tier is a limited range of "experimental" technology. These are usually massive units which take a lot of time and energy to produce, but provide a significant tactical advantage. Supreme Commander features a varied skirmish AI. The typical Easy' and Normal modes are present, but the Hard difficulty level has four possible variants. Horde AI will swarm the player with hordes of lower level units, Tech AI will upgrade its units as fast as possible and assault the player with advanced units, the Balanced AI attempts to find a balance between the two, and the Supreme AI decides which of the three hard strategies is best for the map. The single player campaign consists of eighteen missions, six for each faction. The player is an inexperienced Commander who plays a key role in their faction's campaign to bring the "Infinite War" to an end. Despite the low number of campaign missions, each mission can potentially last hours. At the start of a mission, objectives are assigned for the player to complete. Once the player accomplishes them, the map is expanded, sometimes doubling or tripling in size, and new objectives are assigned. As the mission is commonly divided into three segments, the player will often have to overcome several enemy positions to achieve victory. === Resource management === Because humans have developed replication technology, making advanced use of rapid prototyping and nanotechnology, only two types of resources are required to wage war: Energy and Mass. Energy is obtained by constructing power generators on any solid surface (except fuel generators, which can only be built on fuel deposits), while Mass is obtained either by placing mass extractors on limited mass deposit spots (the most efficient method, although it requires map control) or by building mass fabricators to convert energy into mass. Constructor units can gather energy by "reclaiming" it from organic debris such as trees and mass from rocks and wrecked units. Each player has a certain amount of resource storage, which can be expanded by the construction of storage structures. This gives the player reserves in times of shortage or allows them to stockpile resources. If the resource generation exceeds the player's capacity, the material is wasted. On the contrary, if the storages are depleted and the demand of one of the resources exceeds the production, then all the productions speed is reduced. In addition, if an energy deficit occurs, shields will stop working. An adjacency system allows certain structures to benefit from being built directly adjacent to others. Energy-consuming structures will use less energy when built adjacent to power generators and power generators will produce more energy when built adjacent to power storage structures. The same applies to their mass-producing equivalents. Likewise, factories will consume less energy and mass when built adjacent to power generators and mass fabricators/extractors, respectively. However, by placing structures in close proximity, they become more vulnerable to collateral damage if an adjacent structure is destroyed. Furthermore, most resource generation structures can cause chain reactions when destroyed (especially Tier III structures, which produce large amounts of resources but often have large detonations that can wipe out a nearby army). === Warfare === Supreme Commander uses a "strategic zoom" system that allows the player to seamlessly zoom from a detailed close up view of an individual unit all the way out to a view of the entire map, at which point it resembles a fullscreen version of the minimap denoting individual units with icons. The camera also has a free movement mode and can be slaved to track a selected unit and there is a split screen mode which also supports multiple monitors. This system allows Supreme Commander to use vast maps up to 80 km x 80 km, with players potentially controlling a thousand units each. Units in Supreme Commander are built to scale as they would be in the real world. For example, battleships dwarf submarines. Late into the game, the larger "experimental" units, such as the Cybran Monkeylord, an enormous spider-shaped assault unit, can actually crush smaller enemy units by stepping on them. Because of the wide range of planets colonized by humanity in the setting, the theatres of war range from desert to arctic, and all battlespaces are employed. Technologies emerging in modern warfare are frequently employed in Supreme Commander. For example, stealth technology and both tactical and strategic missile and missile defense systems can be used. Supreme Commander introduced several innovations designed to reduce the amount of micromanagement inherent in many RTS games. Engineers units have the command "assist", that will help follow other engineers and help them finish their orders or improve production rate of factories. In addition, engineers with the order "patrol" will repair units, buildings and recycle wrecks in their along their patrol route. Holding the shift key causes any orders given to a unit (or group of units) to be queued. In this manner a unit may be ordered to attack several targets in succession, or to make best speed to a given point on the map and then attack towards a specified location engaging any hostiles it encounters along the way. After orders have been issued, holding the shift key causes all issued orders to be displayed on the map where they can be subsequently modified to accommodate a change of plan. Further, when a unit is ordered to attack a target, the player can issue an order to perform a coordinated attack to another unit. This order coordinates the arrival time of the units at the target automatically by adjusting the speed of the units involved. As in other RTS games, air transports can be used to convey units to specified destinations, in Supreme Commander though by shift queuing orders a transport containing several units can be ordered to drop specific units at subsequent waypoints. An air transport can also be ordered to create a ferry route, an airbridge wherein any land units ordered to the start of the ferry route will be conveyed by the air transport to the specified destination. The output from a production factory can be routed to a ferry route causing all units co
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EfficientNet

EfficientNet is a family of convolutional neural networks (CNNs) for computer vision published by researchers at Google AI in 2019. Its key innovation is compound scaling, which uniformly scales all dimensions of depth, width, and resolution using a single parameter. EfficientNet models have been adopted in various computer vision tasks, including image classification, object detection, and segmentation. == Compound scaling == EfficientNet introduces compound scaling, which, instead of scaling one dimension of the network at a time, such as depth (number of layers), width (number of channels), or resolution (input image size), uses a compound coefficient ϕ {\displaystyle \phi } to scale all three dimensions simultaneously. Specifically, given a baseline network, the depth, width, and resolution are scaled according to the following equations: depth multiplier: d = α ϕ width multiplier: w = β ϕ resolution multiplier: r = γ ϕ {\displaystyle {\begin{aligned}{\text{depth multiplier: }}d&=\alpha ^{\phi }\\{\text{width multiplier: }}w&=\beta ^{\phi }\\{\text{resolution multiplier: }}r&=\gamma ^{\phi }\end{aligned}}} subject to α ⋅ β 2 ⋅ γ 2 ≈ 2 {\displaystyle \alpha \cdot \beta ^{2}\cdot \gamma ^{2}\approx 2} and α ≥ 1 , β ≥ 1 , γ ≥ 1 {\displaystyle \alpha \geq 1,\beta \geq 1,\gamma \geq 1} . The α ⋅ β 2 ⋅ γ 2 ≈ 2 {\displaystyle \alpha \cdot \beta ^{2}\cdot \gamma ^{2}\approx 2} condition is such that increasing ϕ {\displaystyle \phi } by a factor of ϕ 0 {\displaystyle \phi _{0}} would increase the total FLOPs of running the network on an image approximately 2 ϕ 0 {\displaystyle 2^{\phi _{0}}} times. The hyperparameters α {\displaystyle \alpha } , β {\displaystyle \beta } , and γ {\displaystyle \gamma } are determined by a small grid search. The original paper suggested 1.2, 1.1, and 1.15, respectively. Architecturally, they optimized the choice of modules by neural architecture search (NAS), and found that the inverted bottleneck convolution (which they called MBConv) used in MobileNet worked well. The EfficientNet family is a stack of MBConv layers, with shapes determined by the compound scaling. The original publication consisted of 8 models, from EfficientNet-B0 to EfficientNet-B7, with increasing model size and accuracy. EfficientNet-B0 is the baseline network, and subsequent models are obtained by scaling the baseline network by increasing ϕ {\displaystyle \phi } . == Variants == EfficientNet has been adapted for fast inference on edge TPUs and centralized TPU or GPU clusters by NAS. EfficientNet V2 was published in June 2021. The architecture was improved by further NAS search with more types of convolutional layers. It also introduced a training method, which progressively increases image size during training, and uses regularization techniques like dropout, RandAugment, and Mixup. The authors claim this approach mitigates accuracy drops often associated with progressive resizing.
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YouNoodle

YouNoodle, Inc. is a San Francisco-based company, with offices in Barcelona and Santiago, founded in 2010, building a platform for entrepreneurship competitions all over the world. YouNoodle matches entrepreneurs with competitions, accelerators, and startup programs, and provides a judging and voting SaaS platform to university, non-profit, government and enterprise clients organizing innovation challenges and competitions. Stanford's BASES, UC Berkeley LAUNCH, Start-Up Chile, Amazon Startup Challenge, and NASA are all running one or more competitions on YouNoodle's platform. == History and structure == YouNoodle was founded by Rebeca Hwang and Torsten Kolind in 2010. The company was spun off a project started by Bob Goodson (Quid) and Kirill Makharinsky (Enki) in 2007 with support from Peter Thiel (Founders Fund), Max Levchin (PayPal) and Charles Lho (Amicus Group), founding investor and Chairman of YouNoodle today. This project also spawned Quid (Goodson) and indirectly Ostrovok (Makharinsky). Although also named YouNoodle, this project/company was discontinued in 2010, when the three new entities started operations. The founders of the 2007-2010 entity were Goodson and Makharinsky, both former students of the University of Oxford. Goodson had studied medieval English literature before moving from Oxford to California when Levchin, the co-founder of PayPal, invited him to join a start-up there. Makharinsky's degree was in applied mathematics, and he was also encouraged to pursue opportunities in the United States by Levchin. Other significant employees included Hwang (co-founder of today's YouNoodle), a Stanford University doctoral student whose research is into social network theory. == Startup predictor == YouNoodle's now discontinued "Startup predictor", part of the 2007-2010 entity and developed by Makharinsky and Hwang, used mathematical models to predict the success of new businesses. The user fills in a questionnaire, which takes about half an hour to complete and concentrates on the business concept, finances, founders and advisers. Because the procedure was designed for new companies, questions on revenue and traffic are not included. The site then provided an estimate of what the company's value will be after three years and a score from 1 to 1000 representing its value as an investment. The service was free for the startups themselves, but YouNoodle intended to charge third parties for access to the results. The level of detail required by the questionnaire makes it difficult for people without inside knowledge of a company to provide the data for a prediction on their own. The company's founders have declined to explain the algorithm in detail, but state that it takes into account the entrepreneurs' experience, networks and mutual relations. Information provided by companies which use the site's networking features is used to improve the algorithm. As of August 2008, the algorithm was based on data from 3,000 startups. In the same month the company had four patents pending on the technology.
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Tempos Modernos

Tempos Modernos (English: Modern Times) is a Brazilian telenovela produced and broadcast by TV Globo. It premiered on 11 January 2010, replacing Caras & Bocas, and ended on 16 July 2010, replaced by Ti Ti Ti. The series is written by Bosco Brasil, with the collaboration of Izabel de Oliveira, Maria Elisa Berredo, Mário Teixeira and Patrícia Moretzsohn. It stars Fernanda Vasconcellos, Thiago Rodrigues, Antônio Fagundes, and Eliane Giardini. Priscila Fantin, Danton Mello, Marcos Caruso, Regiane Alves, Vivianne Pasmanter, Otávio Muller, Felipe Camargo, and Malu Galli also star in main roles. == Cast == Fernanda Vasconcellos as Cornélia Cordeiro Santos Reis "Nelinha" Thiago Rodrigues as José Carlos Pimenta Cordeiro "Zeca" Antônio Fagundes as Leal Cordeiro Eliane Giardini as Hélia Pimenta Priscila Fantin as Nara Nolasco Marcos Caruso as Otto Niemann Vivianne Pasmanter as Regiane Cordeiro Mourão Regiane Alves as Goretti Cordeiro Bodanski "Gô" Otávio Muller as Altemir Assunção da Paz Bodanski (Bodanski) Felipe Camargo as Vinícius Porto de Mello "Portinho" Danton Mello as Renato Vieira de Mattos Alessandra Maestrini as Benedita Kusnezov Piñon "Dita'" Leonardo Medeiros as Ramon Piñon Guilherme Weber as Albano Mourão Grazi Massafera as Deodora Madureira Niemann / N. Anne Malu Galli as Iolanda Paranhos Guilherme Leicam as Led Piñon Aline Peixoto as Jannis Piñon Caroline Abras as Katrina João Baldasserini as Túlio Osório Débora Duarte as Tertuliana "Tertu" Otávio Augusto as Faustaço Lumbriga Selma Egrei as Tamara Palumbo Genézio de Barros as Pasquale Paula Possani as Maureen Lobianco Ricardo Blat as Fidélio Pascoal da Conceição as Zuppo Tuna Dwek as Justine Jairo Mattos as Gaulês "Jean Paul" Luciana Borghi as Bárbara Lee Cris Vianna as Tita Bicalho Edmilson Barros as Lindomar Mariano Assunção Cláudia Missura as Lavínia Palumbo Victor Pecoraro as Ricardo Maurício "Maurição" Naruna Costa as Dolores Damasceno Antônio Fragoso as Zapata Fabrício Boliveira as Nabuco Mota Eliana Pittman as Miranda Paranhos Márcio Seixas as Frankenstein "Frank" (voice) Joana Lerner as Heloísa "Helô" Darlan Cunha as João Carlos Paranhos "Joca" Janaína Ávila as Milena Morgado Anderson Lau as Okuda Alexandra Martins as Dulcinólia Lumbriga "Duba" Paulo Leal de Melo as Raulzão "Ducha Fria" Cássio Inácio as Tartana Gilberto Miranda as Madrugadinha Rafa Martins as Max do Cavaco Isabel Lobo as Thaís Trancoso Alexandre Cioletti as Valvênio Xandy Britto as Nelsinho Pallotti Polliana Aleixo as Maria Eunice Cordeiro Bodanski Ana Karolina Lannes as Maria Eugênia Cordeiro Bodanski Rebeca Orestein as Maria Helena Cordeiro Bodanski Jenifer de Oliveira Andrade as Maria Clara Cordeiro Bodanski
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Ordered weighted averaging

In applied mathematics, specifically in fuzzy logic, the ordered weighted averaging (OWA) operators provide a parameterized class of mean type aggregation operators. They were introduced by Ronald R. Yager. Many notable mean operators such as the max, arithmetic average, median and min, are members of this class. They have been widely used in computational intelligence because of their ability to model linguistically expressed aggregation instructions. == Definition == An OWA operator of dimension n {\displaystyle \ n} is a mapping F : R n → R {\displaystyle F:\mathbb {R} ^{n}\rightarrow \mathbb {R} } that has an associated collection of weights W = [ w 1 , … , w n ] {\displaystyle \ W=[w_{1},\ldots ,w_{n}]} lying in the unit interval and summing to one and with F ( a 1 , … , a n ) = ∑ j = 1 n w j b j {\displaystyle F(a_{1},\ldots ,a_{n})=\sum _{j=1}^{n}w_{j}b_{j}} where b j {\displaystyle b_{j}} is the jth largest of the a i {\displaystyle a_{i}} . By choosing different W one can implement different aggregation operators. The OWA operator is a non-linear operator as a result of the process of determining the bj. == Notable OWA operators == F ( a 1 , … , a n ) = max ( a 1 , … , a n ) {\displaystyle \ F(a_{1},\ldots ,a_{n})=\max(a_{1},\ldots ,a_{n})} if w 1 = 1 {\displaystyle \ w_{1}=1} and w j = 0 {\displaystyle \ w_{j}=0} for j ≠ 1 {\displaystyle j\neq 1} F ( a 1 , … , a n ) = min ( a 1 , … , a n ) {\displaystyle \ F(a_{1},\ldots ,a_{n})=\min(a_{1},\ldots ,a_{n})} if w n = 1 {\displaystyle \ w_{n}=1} and w j = 0 {\displaystyle \ w_{j}=0} for j ≠ n {\displaystyle j\neq n} F ( a 1 , … , a n ) = a v e r a g e ( a 1 , … , a n ) {\displaystyle \ F(a_{1},\ldots ,a_{n})=\mathrm {average} (a_{1},\ldots ,a_{n})} if w j = 1 n {\displaystyle \ w_{j}={\frac {1}{n}}} for all j ∈ [ 1 , n ] {\displaystyle j\in [1,n]} == Properties == The OWA operator is a mean operator. It is bounded, monotonic, symmetric, and idempotent, as defined below. == Characterizing features == Two features have been used to characterize the OWA operators. The first is the attitudinal character, also called orness. This is defined as A − C ( W ) = 1 n − 1 ∑ j = 1 n ( n − j ) w j . {\displaystyle A-C(W)={\frac {1}{n-1}}\sum _{j=1}^{n}(n-j)w_{j}.} It is known that A − C ( W ) ∈ [ 0 , 1 ] {\displaystyle A-C(W)\in [0,1]} . In addition A − C(max) = 1, A − C(ave) = A − C(med) = 0.5 and A − C(min) = 0. Thus the A − C goes from 1 to 0 as we go from Max to Min aggregation. The attitudinal character characterizes the similarity of aggregation to OR operation(OR is defined as the Max). The second feature is the dispersion. This defined as H ( W ) = − ∑ j = 1 n w j ln ⁡ ( w j ) . {\displaystyle H(W)=-\sum _{j=1}^{n}w_{j}\ln(w_{j}).} An alternative definition is E ( W ) = ∑ j = 1 n w j 2 . {\displaystyle E(W)=\sum _{j=1}^{n}w_{j}^{2}.} The dispersion characterizes how uniformly the arguments are being used. == Type-1 OWA aggregation operators == The above Yager's OWA operators are used to aggregate the crisp values. Can we aggregate fuzzy sets in the OWA mechanism? The Type-1 OWA operators have been proposed for this purpose. So the type-1 OWA operators provides us with a new technique for directly aggregating uncertain information with uncertain weights via OWA mechanism in soft decision making and data mining, where these uncertain objects are modelled by fuzzy sets. The type-1 OWA operator is defined according to the alpha-cuts of fuzzy sets as follows: Given the n linguistic weights { W i } i = 1 n {\displaystyle \left\{{W^{i}}\right\}_{i=1}^{n}} in the form of fuzzy sets defined on the domain of discourse U = [ 0 , 1 ] {\displaystyle U=[0,\;\;1]} , then for each α ∈ [ 0 , 1 ] {\displaystyle \alpha \in [0,\;1]} , an α {\displaystyle \alpha } -level type-1 OWA operator with α {\displaystyle \alpha } -level sets { W α i } i = 1 n {\displaystyle \left\{{W_{\alpha }^{i}}\right\}_{i=1}^{n}} to aggregate the α {\displaystyle \alpha } -cuts of fuzzy sets { A i } i = 1 n {\displaystyle \left\{{A^{i}}\right\}_{i=1}^{n}} is given as Φ α ( A α 1 , … , A α n ) = { ∑ i = 1 n w i a σ ( i ) ∑ i = 1 n w i | w i ∈ W α i , a i ∈ A α i , i = 1 , … , n } {\displaystyle \Phi _{\alpha }\left({A_{\alpha }^{1},\ldots ,A_{\alpha }^{n}}\right)=\left\{{{\frac {\sum \limits _{i=1}^{n}{w_{i}a_{\sigma (i)}}}{\sum \limits _{i=1}^{n}{w_{i}}}}\left|{w_{i}\in W_{\alpha }^{i},\;a_{i}}\right.\in A_{\alpha }^{i},\;i=1,\ldots ,n}\right\}} where W α i = { w | μ W i ( w ) ≥ α } , A α i = { x | μ A i ( x ) ≥ α } {\displaystyle W_{\alpha }^{i}=\{w|\mu _{W_{i}}(w)\geq \alpha \},A_{\alpha }^{i}=\{x|\mu _{A_{i}}(x)\geq \alpha \}} , and σ : { 1 , … , n } → { 1 , … , n } {\displaystyle \sigma :\{\;1,\ldots ,n\;\}\to \{\;1,\ldots ,n\;\}} is a permutation function such that a σ ( i ) ≥ a σ ( i + 1 ) , ∀ i = 1 , … , n − 1 {\displaystyle a_{\sigma (i)}\geq a_{\sigma (i+1)},\;\forall \;i=1,\ldots ,n-1} , i.e., a σ ( i ) {\displaystyle a_{\sigma (i)}} is the i {\displaystyle i} th largest element in the set { a 1 , … , a n } {\displaystyle \left\{{a_{1},\ldots ,a_{n}}\right\}} . The computation of the type-1 OWA output is implemented by computing the left end-points and right end-points of the intervals Φ α ( A α 1 , … , A α n ) {\displaystyle \Phi _{\alpha }\left({A_{\alpha }^{1},\ldots ,A_{\alpha }^{n}}\right)} : Φ α ( A α 1 , … , A α n ) − {\displaystyle \Phi _{\alpha }\left({A_{\alpha }^{1},\ldots ,A_{\alpha }^{n}}\right)_{-}} and Φ α ( A α 1 , … , A α n ) + , {\displaystyle \Phi _{\alpha }\left({A_{\alpha }^{1},\ldots ,A_{\alpha }^{n}}\right)_{+},} where A α i = [ A α − i , A α + i ] , W α i = [ W α − i , W α + i ] {\displaystyle A_{\alpha }^{i}=[A_{\alpha -}^{i},A_{\alpha +}^{i}],W_{\alpha }^{i}=[W_{\alpha -}^{i},W_{\alpha +}^{i}]} . Then membership function of resulting aggregation fuzzy set is: μ G ( x ) = ∨ α : x ∈ Φ α ( A α 1 , ⋯ , A α n ) α ⁡ α {\displaystyle \mu _{G}(x)=\mathop {\vee } _{\alpha :x\in \Phi _{\alpha }\left({A_{\alpha }^{1},\cdots ,A_{\alpha }^{n}}\right)_{\alpha }}\alpha } For the left end-points, we need to solve the following programming problem: Φ α ( A α 1 , ⋯ , A α n ) − = min W α − i ≤ w i ≤ W α + i A α − i ≤ a i ≤ A α + i ∑ i = 1 n w i a σ ( i ) / ∑ i = 1 n w i {\displaystyle \Phi _{\alpha }\left({A_{\alpha }^{1},\cdots ,A_{\alpha }^{n}}\right)_{-}=\min \limits _{\begin{array}{l}W_{\alpha -}^{i}\leq w_{i}\leq W_{\alpha +}^{i}A_{\alpha -}^{i}\leq a_{i}\leq A_{\alpha +}^{i}\end{array}}\sum \limits _{i=1}^{n}{w_{i}a_{\sigma (i)}/\sum \limits _{i=1}^{n}{w_{i}}}} while for the right end-points, we need to solve the following programming problem: Φ α ( A α 1 , ⋯ , A α n ) + = max W α − i ≤ w i ≤ W α + i A α − i ≤ a i ≤ A α + i ∑ i = 1 n w i a σ ( i ) / ∑ i = 1 n w i {\displaystyle \Phi _{\alpha }\left({A_{\alpha }^{1},\cdots ,A_{\alpha }^{n}}\right)_{+}=\max \limits _{\begin{array}{l}W_{\alpha -}^{i}\leq w_{i}\leq W_{\alpha +}^{i}A_{\alpha -}^{i}\leq a_{i}\leq A_{\alpha +}^{i}\end{array}}\sum \limits _{i=1}^{n}{w_{i}a_{\sigma (i)}/\sum \limits _{i=1}^{n}{w_{i}}}} Zhou et al. presented a fast method to solve two programming problem so that the type-1 OWA aggregation operation can be performed efficiently. == OWA for committee voting == Amanatidis, Barrot, Lang, Markakis and Ries present voting rules for multi-issue voting, based on OWA and the Hamming distance. Barrot, Lang and Yokoo study the manipulability of these rules.
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Abillion

abillion was a mobile application helping users to find vegan and sustainable products. The platform allowed users to review plant-based, cruelty-free and sustainable products, while donating between 0.10 and $1 to nonprofit organisations for each review written. As of May 2023, the company claimed to have donated over $2.8M to various nonprofit organisations including Sea Shepherd and Mercy for Animals. The main objective of the company was to reach the number of one billion people following a vegan diet and lifestyle by 2030. == History == The American entrepreneur Vikas Garg founded the company in Singapore and the app was officially launched in May 2018. The start-up was first named 'abillionveg' and changed its name in 2020 to shorten it to 'abillion'. In 2019, the company raised $3M in its first round of funding (pre-Series A). In 2021, it raised $10M in its Series A funding. In February 2023, the company announced the launch of a community investment round, using the crowdfunding platform Wefunder, which reached a total of $500 000. In May 2023, it celebrated its 5th anniversary and reaching 1M downloads. In March 2026, the company announced that they would be closing down by the end of the month. == Awards == Using data from the reviews published by its users, abillion was awarding the most liked vegan products and brands. In May 2023, the company published a world Top 10 Best Plant Based Burgers, among the winning brands were Beyond Meat, NotCo and Sojasun.
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The Eye of Mexico

The Eye of Mexico (Spanish: El Ojo de México) is an outdoor sculpture in Mexico City. It is located in Ampliación Granada, Miguel Hidalgo, at the mixed-use development Neuchâtel Polanco, developed by the Canadian real estate company Ivanhoé Cambridge. The artwork was created by the Turkish artist Ferdi Alıcı and it was selected from among 350 proposals from artists from 35 countries. The project for The Eye of Mexico was developed by MIRA, a real estate investment and development company, and MASSIVart, a creative consulting agency. According to MIRA, upon its inauguration it became the first artwork in Latin America to use artificial intelligence (AI). The sculpture can read environmental and urban data using AI algorithms and transform the results into videos related to arts, science and technology. The ring was inaugurated on 20 May 2022 and it is 10 meters (33 ft) high and 3 meters (9.8 ft) wide.
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Tempos Modernos

Tempos Modernos (English: Modern Times) is a Brazilian telenovela produced and broadcast by TV Globo. It premiered on 11 January 2010, replacing Caras & Bocas, and ended on 16 July 2010, replaced by Ti Ti Ti. The series is written by Bosco Brasil, with the collaboration of Izabel de Oliveira, Maria Elisa Berredo, Mário Teixeira and Patrícia Moretzsohn. It stars Fernanda Vasconcellos, Thiago Rodrigues, Antônio Fagundes, and Eliane Giardini. Priscila Fantin, Danton Mello, Marcos Caruso, Regiane Alves, Vivianne Pasmanter, Otávio Muller, Felipe Camargo, and Malu Galli also star in main roles. == Cast == Fernanda Vasconcellos as Cornélia Cordeiro Santos Reis "Nelinha" Thiago Rodrigues as José Carlos Pimenta Cordeiro "Zeca" Antônio Fagundes as Leal Cordeiro Eliane Giardini as Hélia Pimenta Priscila Fantin as Nara Nolasco Marcos Caruso as Otto Niemann Vivianne Pasmanter as Regiane Cordeiro Mourão Regiane Alves as Goretti Cordeiro Bodanski "Gô" Otávio Muller as Altemir Assunção da Paz Bodanski (Bodanski) Felipe Camargo as Vinícius Porto de Mello "Portinho" Danton Mello as Renato Vieira de Mattos Alessandra Maestrini as Benedita Kusnezov Piñon "Dita'" Leonardo Medeiros as Ramon Piñon Guilherme Weber as Albano Mourão Grazi Massafera as Deodora Madureira Niemann / N. Anne Malu Galli as Iolanda Paranhos Guilherme Leicam as Led Piñon Aline Peixoto as Jannis Piñon Caroline Abras as Katrina João Baldasserini as Túlio Osório Débora Duarte as Tertuliana "Tertu" Otávio Augusto as Faustaço Lumbriga Selma Egrei as Tamara Palumbo Genézio de Barros as Pasquale Paula Possani as Maureen Lobianco Ricardo Blat as Fidélio Pascoal da Conceição as Zuppo Tuna Dwek as Justine Jairo Mattos as Gaulês "Jean Paul" Luciana Borghi as Bárbara Lee Cris Vianna as Tita Bicalho Edmilson Barros as Lindomar Mariano Assunção Cláudia Missura as Lavínia Palumbo Victor Pecoraro as Ricardo Maurício "Maurição" Naruna Costa as Dolores Damasceno Antônio Fragoso as Zapata Fabrício Boliveira as Nabuco Mota Eliana Pittman as Miranda Paranhos Márcio Seixas as Frankenstein "Frank" (voice) Joana Lerner as Heloísa "Helô" Darlan Cunha as João Carlos Paranhos "Joca" Janaína Ávila as Milena Morgado Anderson Lau as Okuda Alexandra Martins as Dulcinólia Lumbriga "Duba" Paulo Leal de Melo as Raulzão "Ducha Fria" Cássio Inácio as Tartana Gilberto Miranda as Madrugadinha Rafa Martins as Max do Cavaco Isabel Lobo as Thaís Trancoso Alexandre Cioletti as Valvênio Xandy Britto as Nelsinho Pallotti Polliana Aleixo as Maria Eunice Cordeiro Bodanski Ana Karolina Lannes as Maria Eugênia Cordeiro Bodanski Rebeca Orestein as Maria Helena Cordeiro Bodanski Jenifer de Oliveira Andrade as Maria Clara Cordeiro Bodanski
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Abu Dhabi Autonomous Racing League

The Abu Dhabi Autonomous Racing League (A2RL) is an autonomous racing league based in Abu Dhabi and organized by ASPIRE, part of the UAE government's Advanced Technology Research Council. It has three distinct categories: the "car race", the drone race, and the buggy race. The first car race was held on 27 April 2024 at the Yas Marina Circuit, marking the first major autonomous formula race outside the US since the now-folded Roborace championship. The first drone race was held on 11 and 12 April 2025. == Formats == A2RL has three distinct formats, the formula racing format (dubbed the Car Race), the quadcopter drone racing format (dubbed the Drone Race), and the off-road dune buggy racing format (dubbed the Buggy Race). === Car Race === A2RL's main event, the car race is a standard formula racing format with self-driving formula cars. The cars are made by Dallara and are modified versions of Super Formula cars with Yokohama tires. These cars had the CPUs of their AIs mounted where the driver's seat is on a non-modified chassis, as well as hydraulic actuators for AI control of the vehicle, multiple sensor systems including LIDAR and GPS, and a large LED indicator showing the status of the AI. The first car race was held on 27 April 2024. This race was marked by the cars' subpar performance: Out of four cars that qualified, only two finished the race - the other two did not. The next race was held on 15 November 2025, with 11 teams. ==== Technical specifications ==== The full list of technical specifications are as follows: Chassis: Dallara EAV24 (modified Dallara SF23) Forward suspension: Pushrod type, torsion bar spring, adjustable dampers, third element Rear suspension: Pushrod type, torsion bar, coil springs, adjustable dampers, third element Tires: Yokohama Advan Drive-by-wire system: Provided by Meccanica 42, the DBW system consists of steering and brake actuators, with a central ECU that coordinates the driving actions and reacts to any critical situation in real-time. Brakes: Brembo calipers, Brembo carbon discs, electro-hydraulically activated Engine: 4 Piston Racing K20C1 (based on Honda 2.0l; turbocharged 4-cylinder engine) Gearbox: 3MO 6-speed gearbox Sensor suite: 7x Sony IMX728 cameras, 4x ZF ProWave radar units, 3x Seyond Falcon Kinetic lidar units Main computer: Neousys RGS-8805GC ==== Races held ==== === Drone Race === Created in partnership with the Drone Champions' League, the drone race is the quadcopter drone racing aerial format of the A2RL. The first race was held on 11/12 April 2025 at the ADNEC Marina Hall. 10 teams are scheduled to take part. === Buggy Race === The buggy race will be the off-road format of the A2RL using self-driving dune buggies. No date or number of teams has been announced for the first race. === Other events === A2RL is known to host AI vs AI and Human vs AI events, in Abu Dhabi and abroad. One such event took place at the Suzuka Circuit in Japan. The Human vs AI race was precluded due to AI car "Yalla" crashing into the wall during the formation lap. == Team lists ==
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Screen generator

A screen generator, also known as a screen painter, screen mapper, or forms generator is a software package (or component thereof) which enables data entry screens to be generated declaratively, by "painting" them on the screen WYSIWYG-style, or through filling-in forms, rather than requiring writing of code to display them manually. 4GLs commonly incorporate a screen generator feature. They are also commonly found bundled with database systems, especially entry-level databases. A screen generator is one aspect of an application generator, which can also include other functions such as report generation and a data dictionary. The earliest screen generators were character-based; by the 1990s, GUI support became common, and then support for generating HTML forms as well. Some screen generators work by generating code to display the screen in a high-level language (for example, COBOL); others store the screen definition in a data file or in database tables, and then have a runtime component responsible for actually displaying the form and receiving and validating user input. == Examples == Examples of screen generators include: IBM Screen Definition Facility II: generates screens for CICS BMS, IMS MFS, ISPF, GDDM and CSP/AD. Performix for Informix. Microsoft Visual Basic the forms component of Microsoft Access Oracle Developer, in particular its Oracle Forms component the QDesign component of PowerHouse SystemBuilder/SB+ the Screen Painter component of SAP's ABAP Workbench the FoxView component of FoxPro. FoxView was originally developed by Luis Castro as a dBASE screen generator named ViewGen; Fox purchased it and bundled it with FoxPro 1.0. Later, Fox replaced Castro's code with their own screen painter code. dBASE included a built-in screen generator in dBASE IV onwards; in dBASE III and earlier, third party screen generators were available, including the already mentioned ViewGen DPS 1100 for UNIVAC 1100 series mainframes.
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Pippit

Pippit (Chinese: 小云雀; pinyin: Xiǎoyúnquè) is an artificial intelligence content creation platform developed by the Chinese technology company ByteDance. The platform, powered by CapCut leverages multimodal AI technology to streamline professional-grade video and image production, specifically targeting small and medium-sized enterprisesand social media creators. == History == In May 2025, ByteDance officially launched Pippit, which is positioned as an AI video and picture creation tool. In early 2026, Pippit underwent a major architectural overhaul with the integration of the Dreamina seedance 2.0. This technical milestone introduced the "Short Drama Agent" functionality, which enables the end-to-end conversion of scripts up to 100,000 words into fully rendered video productions.
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Autonomic computing

Autonomic computing (AC) is distributed computing resources with self-managing characteristics, adapting to unpredictable changes while hiding intrinsic complexity to operators and users. Initiated by IBM in 2001, this initiative ultimately aimed to develop computer systems capable of self-management, to overcome the rapidly growing complexity of computing systems management, and to reduce the barrier that complexity poses to further growth. == Description == The AC system concept is designed to make adaptive decisions, using high-level policies. It will constantly check and optimize its status and automatically adapt itself to changing conditions. An autonomic computing framework is composed of autonomic components (AC) interacting with each other. An AC can be modeled in terms of two main control schemes (local and global) with sensors (for self-monitoring), effectors (for self-adjustment), knowledge and planner/adapter for exploiting policies based on self- and environment awareness. This architecture is sometimes referred to as Monitor-Analyze-Plan-Execute (MAPE). Driven by such vision, a variety of architectural frameworks based on "self-regulating" autonomic components has been recently proposed. A similar trend has recently characterized significant research in the area of multi-agent systems. However, most of these approaches are typically conceived with centralized or cluster-based server architectures in mind and mostly address the need of reducing management costs rather than the need of enabling complex software systems or providing innovative services. Some autonomic systems involve mobile agents interacting via loosely coupled communication mechanisms. Autonomy-oriented computation is a paradigm proposed by Jiming Liu in 2001 that uses artificial systems imitating social animals' collective behaviours to solve difficult computational problems. For example, ant colony optimization could be studied in this paradigm. == Problem of growing complexity == Forecasts suggested that the computing devices in use would grow at 38% per year and the average complexity of each device was increasing. This volume and complexity was managed by highly skilled humans; but the demand for skilled IT personnel was already outstripping supply, with labour costs exceeding equipment costs by a ratio of up to 18:1. Computing systems have brought great benefits of speed and automation but there is now an overwhelming economic need to automate their maintenance. In a 2003 IEEE Computer article, Kephart and Chess warn that the dream of interconnectivity of computing systems and devices could become the "nightmare of pervasive computing" in which architects are unable to anticipate, design and maintain the complexity of interactions. They state the essence of autonomic computing is system self-management, freeing administrators from low-level task management while delivering better system behavior. A general problem of modern distributed computing systems is that their complexity, and in particular the complexity of their management, is becoming a significant limiting factor in their further development. Large companies and institutions are employing large-scale computer networks for communication and computation. The distributed applications running on these computer networks are diverse and deal with multiple tasks, ranging from internal control processes to presenting web content to customer support. Additionally, mobile computing is pervading these networks at an increasing speed: employees need to communicate with their companies while they are not in their office. They do so by using laptops, personal digital assistants, or mobile phones with diverse forms of wireless technologies to access their companies' data. This creates an enormous complexity in the overall computer network which is hard to control manually by human operators. Manual control is time-consuming, expensive, and error-prone. The manual effort needed to control a growing networked computer-system tends to increase quickly. 80% of such problems in infrastructure happen at the client specific application and database layer. Most 'autonomic' service providers guarantee only up to the basic plumbing layer (power, hardware, operating system, network and basic database parameters). == Characteristics of autonomic systems == A possible solution could be to enable modern, networked computing systems to manage themselves without direct human intervention. The Autonomic Computing Initiative (ACI) aims at providing the foundation for autonomic systems. It is inspired by the autonomic nervous system of the human body. This nervous system controls important bodily functions (e.g. respiration, heart rate, and blood pressure) without any conscious intervention. In a self-managing autonomic system, the human operator takes on a new role: instead of controlling the system directly, he/she defines general policies and rules that guide the self-management process. For this process, IBM defined the following four types of property referred to as self-star (also called self-, self-x, or auto-) properties. Self-configuration: Automatic configuration of components; Self-healing: Automatic discovery, and correction of faults; Self-optimization: Automatic monitoring and control of resources to ensure the optimal functioning with respect to the defined requirements; Self-protection: Proactive identification and protection from arbitrary attacks. Others such as Poslad and Nami and Sharifi have expanded on the set of self-star as follows: Self-regulation: A system that operates to maintain some parameter, e.g., Quality of service, within a reset range without external control; Self-learning: Systems use machine learning techniques such as unsupervised learning which does not require external control; Self-awareness (also called Self-inspection and Self-decision): System must know itself. It must know the extent of its own resources and the resources it links to. A system must be aware of its internal components and external links in order to control and manage them; Self-organization: System structure driven by physics-type models without explicit pressure or involvement from outside the system; Self-creation (also called Self-assembly, Self-replication): System driven by ecological and social type models without explicit pressure or involvement from outside the system. A system's members are self-motivated and self-driven, generating complexity and order in a creative response to a continuously changing strategic demand; Self-management (also called self-governance): A system that manages itself without external intervention. What is being managed can vary dependent on the system and application. Self -management also refers to a set of self-star processes such as autonomic computing rather than a single self-star process; Self-description (also called self-explanation or Self-representation): A system explains itself. It is capable of being understood (by humans) without further explanation. IBM has set forth eight conditions that define an autonomic system: The system must know itself in terms of what resources it has access to, what its capabilities and limitations are and how and why it is connected to other systems; be able to automatically configure and reconfigure itself depending on the changing computing environment; be able to optimize its performance to ensure the most efficient computing process; be able to work around encountered problems by either repairing itself or routing functions away from the trouble; detect, identify and protect itself against various types of attacks to maintain overall system security and integrity; adapt to its environment as it changes, interacting with neighboring systems and establishing communication protocols; rely on open standards and cannot exist in a proprietary environment; anticipate the demand on its resources while staying transparent to users. Even though the purpose and thus the behaviour of autonomic systems vary from system to system, every autonomic system should be able to exhibit a minimum set of properties to achieve its purpose: Automatic: This essentially means being able to self-control its internal functions and operations. As such, an autonomic system must be self-contained and able to start-up and operate without any manual intervention or external help. Again, the knowledge required to bootstrap the system (Know-how) must be inherent to the system. Adaptive: An autonomic system must be able to change its operation (i.e., its configuration, state and functions). This will allow the system to cope with temporal and spatial changes in its operational context either long term (environment customisation/optimisation) or short term (exceptional conditions such as malicious attacks, faults, etc.). Aware: An autonomic system must be able to monitor (sense) its operational context as well as its internal state in order to be able to asses
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