AI Generator Of Trump

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

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  Adobe Presenter Video Express

  Adobe Presenter Video Express is screencasting and video editing software developed by Adobe Systems. == Description == Adobe Presenter Video Express is primarily used as a software by video creators, to record and mix webcam and screen video feeds. It allows users to simultaneously record video from their webcam and the screen, and easily mix the 2 tracks with a simple user interface. Users can change the background in their recorded video without needing equipment like a green screen. This is unlike other video tools which rely on chroma keying technology, and only work with green or blue screens. They can also add annotations and quizzes to their content and publish the video to MP4 or HTML5 formats. == List of notable features == === Record and mix, screen and webcam === Support for simultaneous recording of screen and webcam video feeds, with a simple editing interface to mix the two video streams. This lets the author rapidly create screencasts, software demos, etc. === Make my background awesome === This feature allows authors to change the background of their webcam recording without needing a green screen, provided they use a solid-colored backdrop which contrasts well against them. Authors can select images, videos or even the screen recording as their background. === In-video quizzing === Authors can insert quizzes within their video content. On success/failure attempts, the author can decide what message to display, and can also configure the video to jump to a certain point and play. Quizzes are published as part of the interactive HTML 5 player, which cannot be hosted on YouTube and Vimeo. === LMS Reporting === Authors can publish to any SCORM compliant LMS (Learning Management System) for quiz reporting, or to Adobe Captivate Prime. === In-app assets and branding === Adobe Presenter Video Express ships with a large number of branding videos, backgrounds and video filters to help authors create studio quality videos. === MP4 and HTML5 Output === The tool publishes a single MP4 video file containing all the video content, within an HTML 5 wrapper that contains the interactive player. The interactive HTML 5 player can be hosted on any website. == Common uses == === Screencasting === Screencasting is the process of recording one's computer screen as a video, usually with an audio voice over, to create a software demonstration, tutorial, presentation, etc. Adobe Presenter Video Express supports simultaneous recording of full screen video and microphone audio for creating screencasts. === Product marketing and demos === The ability to record the webcam video in addition to everything that is visible on the screen in Adobe Presenter Video Express, allows the author to add their personality to their screencasts. Features like video mixing and 'make my background awesome' further enhance the presentation, allowing effortless creation of marketing and demo videos. === Education === Adobe Presenter Video Express supports in-video quizzes and LMS reporting, along with screencasting and webcam recording. These features make it a powerful tool for creating educational content. == Differences from Adobe Presenter and Adobe Captivate == Adobe Presenter is a Microsoft PowerPoint plug-in for converting PowerPoint slides into interactive eLearning content, available only on Windows. Starting with Adobe Presenter 8, the video creation tool Adobe Presenter Video Express was bundled with every purchase of Adobe Presenter. From September 2015, Adobe Presenter Video Express 11 was also made available as a stand-alone product on Windows and Mac. A subscription license for Adobe Presenter Video Express, valid on Windows and Mac, is available for $9.99/month. Adobe Presenter Video Express continues to be bundled with purchases of Adobe Presenter on Windows as well. Adobe Captivate is an authoring tool for creating numerous forms of interactive eLearning content. Unlike Adobe Presenter, it uses a proprietary editing interface instead of Microsoft PowerPoint. While it is possible to create screen captures with Adobe Captivate, you cannot record the webcam feed. Adobe Captivate does not bundle Adobe Presenter or Adobe Presenter Video Express.

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  Linguatec

  The Linguatec Sprachtechnologien GmbH is a language technology provider, specialized in the field of machine translation, speech synthesis and speech recognition. Linguatec was founded in Munich in 1996 and its headquarters are in Pasing. Linguatec has won the European Information Society Technologies Prize three times. On their website, they are now using the online service Voice Reader Web, so that the information can be read out in every language by means of a text-to-speech function. == Core areas == Machine translation The different versions of Personal Translator (seven language pairs) can be used "for home use" or for professional business use in the company network. In addition to this, specialist dictionaries are offered to broaden standard vocabulary. Speech synthesis The Voice Reader text-to-speech program reads in twelve languages: German, British English, American English, French, Quebec French, Spanish, Mexican Spanish, Italian, Dutch, Portuguese, Czech, Chinese. Speech recognition Voice Pro is based on ViaVoice technology from IBM. There are special software programs for doctors and lawyers. == Patents == 2005 pending patent application for a newly developed hybrid technology that uses the intelligence of neural networks for machine translation. == Awards == 2004 European IT Prize for Beyond Babel 2004 test winner Stiftung Warentest – best voice recognition 1998 European IT Prize – applied voice recognition 1996 European IT Prize – automated translation == Studies == 2005 University of Regensburg: Voice Reader user test 2002 Fraunhofer Institute for Industrial Engineering and Organization IAO: user study on the efficiency of machine translation

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  Showbox.com

  Showbox is an online video streaming platform that enables users to stream and download many videos, commonly movies and TV shows, for free. == History == The company opened the platforms to users who registered from its beta in late 2015. The platform was officially launched in February 2016, enabling any visitor to sign up and create videos online. In April 2016, Showbox was featured on the Product Hunt website, coming to the top of the website's lists for that day and week with over 1400 upvotes from the Product Hunt community. Also in April 2016, Showbox partnered with YouTube's leading multi-channel networks, including Fullscreen, BroadbandTV, StyleHaul, AwesomenessTV, and BuzzMyVideos, to enable their communities of creators to access the platform. In June 2016, the company launched Showbox For Brands, a business-oriented video creation platform, enabling companies to create video content in-house and with their communities and influencers. In March 2017, the company launched Showbox Engage, a use case of its B2B product launched in 2016, enabling companies to launch user-generated content campaigns with their communities. In April 2017, Showbox and the United Nations announced a partnership around the 70th anniversary of the declaration of human rights, with an annual, ongoing global campaign in 135 languages, inviting people worldwide to create their part of the declaration in a video from anywhere around the world. In November 2017, Showbox partnered with the Ad:tech and Digital Marketing World Forum conferences (DMWF) in New York to provide their users and communities with a User Generated Content video solution. == Technology == Showbox's video creation technology includes an online green screen feature, proprietary computer vision algorithms, deep learning technology to support the automatic creation of videos in the cloud, and advanced video composition, including special effects. == Coverage and awards == In March 2015, Showbox was nominated as one of the 10 Israeli startups to take over our TV screens this year. In July 2016, Showbox won the Publicis90 award as part of Publicis' "global initiative to foster digital entrepreneurship". In March 2017, Showbox was chosen as one of The Culture Trip's 10 startups to watch for in 2017.

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  Normalization (image processing)

  In image processing, normalization is a process that changes the range of pixel intensity values, a kind of intensity mapping. Applications include photographs with poor contrast due to glare, for example. A typical case is contrast stretching. In more general fields of data processing, such as digital signal processing, it is referred to as dynamic range expansion. The purpose of dynamic range expansion in the various applications is usually to bring the image, or other type of signal, into a range that is more familiar or normal to the senses, hence the term normalization. Often, the motivation is to achieve consistency in dynamic range for a set of data, signals, or images to avoid mental distraction or fatigue. For example, a newspaper will strive to make all of the images in an issue share a similar range of grayscale. Auto-normalization in image processing software typically normalizes to the full dynamic range of the number system specified in the image file format. == Definition == Normalization transforms an n-dimensional grayscale image I : { X ⊆ R n } → { Min , . . , Max } {\displaystyle I:\{\mathbb {X} \subseteq \mathbb {R} ^{n}\}\rightarrow \{{\text{Min}},..,{\text{Max}}\}} with intensity values in the range ( Min , Max ) {\displaystyle ({\text{Min}},{\text{Max}})} , into a new image I N : { X ⊆ R n } → { newMin , . . , newMax } {\displaystyle I_{N}:\{\mathbb {X} \subseteq \mathbb {R} ^{n}\}\rightarrow \{{\text{newMin}},..,{\text{newMax}}\}} with intensity values in the range ( newMin , newMax ) {\displaystyle ({\text{newMin}},{\text{newMax}})} . The linear normalization of a grayscale digital image is performed according to the formula I N = ( I − Min ) newMax − newMin Max − Min + newMin {\displaystyle I_{N}=(I-{\text{Min}}){\frac {{\text{newMax}}-{\text{newMin}}}{{\text{Max}}-{\text{Min}}}}+{\text{newMin}}} For example, if the intensity range of the image is 50 to 180 and the desired range is 0 to 255 the process entails subtracting 50 from each of pixel intensity, making the range 0 to 130. Then each pixel intensity is multiplied by 255/130, making the range 0 to 255. Normalization might also be non-linear, as the relationship between I {\displaystyle I} and I N {\displaystyle I_{N}} may not be linear. An example of non-linear normalization is when the normalization follows a sigmoid function, in which case the normalized image is computed according to the formula I N = ( newMax − newMin ) 1 1 + e − I − β α + newMin {\displaystyle I_{N}=({\text{newMax}}-{\text{newMin}}){\frac {1}{1+e^{-{\frac {I-\beta }{\alpha }}}}}+{\text{newMin}}} Where α {\displaystyle \alpha } defines the width of the input intensity range, and β {\displaystyle \beta } defines the intensity around which the range is centered. Gamma correction (log/inverse log) is also a common transformation function. === Colorspace === Intensity operations generally operate on a colorspace that maps to the human perception of lightness without intentionally changing the other properties. This can be done, for example, by operating on the L component of the CIELAB color space, or approximately by operating on the Y component of YCbCr. It is also possible to operate on each of the RGB color channels, though the result will not always make sense. == Contrast stretching == This is the most significant and essential technique of spatial-based image enhancement. The basic intent of this contrast enhancement technique is to adjust the local contrast in the image so as to bring out the clear regions or objects in the image. Low-contrast images often result from poor or non-uniform lighting conditions, a limited dynamic range of the imaging sensor, or improper settings of the lens aperture. This operation tries to change the intensity of the pixel in the image, particularly in the input image, to obtain an enhanced image. It is based on the number of techniques, namely local, global, dark and bright levels of contrast. The contrast enhancement is considered as the amount of color or gray differentiation that lies among the different features in an image. The contrast enhancement improves the quality of image by increasing the luminance difference between the foreground and background. A contrast stretching transformation can be achieved by: Stretching the dark range of input values into a wider range of output values: This involves increasing the brightness of the darker areas in the image to enhance details and improve visibility. Shifting the mid-range of input values: This involves adjusting the brightness levels of the mid-tones in the image to improve overall contrast and clarity. Compressing the bright range of input values: This process involves reducing the brightness of the brighter areas in the image to prevent overexposure resulting in a more balanced and visually appealing image. It can be described as the following piecewise funciton: I N = { s 1 r 1 I if I < r 1 s 2 − s 1 r 1 − r 2 ( I − r 1 ) if r 1 ≤ I ≤ r 2 1 − s 2 1 − r 2 ( I − r 2 ) if I > r 2 {\displaystyle I_{N}={\begin{cases}{\frac {s_{1}}{r_{1}}}I&{\text{if }}Ir_{2}\end{cases}}} Where: ( r 1 , s 1 ) {\displaystyle (r_{1},s_{1})} defines the transition point between the "dark" range to the "main" range. ( r 2 , s 2 ) {\displaystyle (r_{2},s_{2})} defines the transition point between the "main" range to the "bright" range. A typical linear stretch is obtained when ( r 1 , s 1 ) = ( r min , 0 ) {\displaystyle (r_{1},s_{1})=(r_{\text{min}},0)} and ( r 2 , s 2 ) = ( r max , 1 ) {\displaystyle (r_{2},s_{2})=(r_{\text{max}},1)} , where r min {\displaystyle r_{\text{min}}} and r max {\displaystyle r_{\text{max}}} denote the minimum and maximum levels in the source image. === Global contrast stretching === Global Contrast Stretching considers all color palate ranges at once to determine the maximum and minimum values for the entire RGB color image. This approach utilizes the combination of RGB colors to derive a single maximum and minimum value for contrast stretching across the entire image. === Local contrast stretching === Local contrast stretching (LCS) is an image enhancement method that focuses on locally adjusting each pixel's value to improve the visualization of structures within an image, particularly in both the darkest and lightest portions. It operates by utilizing sliding windows, known as kernels, which traverse the image. The central pixel within each kernel is adjusted using the following formula: I p ( x , y ) = 255 × [ I 0 ( x , y ) − m i n ] ( m a x − m i n ) {\displaystyle I_{p}(x,y)=255\times {\frac {[I_{0}(x,y)-min]}{(max-min)}}} Where: Ip(x,y) is the color level for the output pixel (x,y) after the contrast stretching process. I0(x,y) is the color level input for data pixel (x, y). max is the maximum value for color level in the input image within the selected kernel. min is the minimum value for color level in the input image within the selected kernel. A piecewise form (see above) may also be used. LCS can be applied to the three color channels of an image separately.

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  Multi-agent reinforcement learning

  Multi-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple learning agents that coexist in a shared environment. Each agent is motivated by its own rewards, and does actions to advance its own interests; in some environments these interests are opposed to the interests of other agents, resulting in complex group dynamics. Multi-agent reinforcement learning is closely related to game theory and especially repeated games, as well as multi-agent systems. Its study combines the pursuit of finding ideal algorithms that maximize rewards with a more sociological set of concepts. While research in single-agent reinforcement learning is concerned with finding the algorithm that gets the biggest number of points for one agent, research in multi-agent reinforcement learning evaluates and quantifies social metrics, such as cooperation, reciprocity, equity, social influence, language and discrimination. == Definition == Similarly to single-agent reinforcement learning, multi-agent reinforcement learning is modeled as some form of a Markov decision process (MDP). Fix a set of agents I = { 1 , . . . , N } {\displaystyle I=\{1,...,N\}} . We then define: A set S {\displaystyle S} of environment states. One set A i {\displaystyle {\mathcal {A}}_{i}} of actions for each of the agents i ∈ I = { 1 , … , N } {\displaystyle i\in I=\{1,\dots ,N\}} . P a → ( s , s ′ ) = Pr ( s t + 1 = s ′ ∣ s t = s , a → t = a → ) {\displaystyle P_{\vec {a}}(s,s')=\Pr(s_{t+1}=s'\mid s_{t}=s,{\vec {a}}_{t}={\vec {a}})} is the probability of transition (at time t {\displaystyle t} ) from state s {\displaystyle s} to state s ′ {\displaystyle s'} under joint action a → {\displaystyle {\vec {a}}} . R → a → ( s , s ′ ) {\displaystyle {\vec {R}}_{\vec {a}}(s,s')} is the immediate joint reward after the transition from s {\displaystyle s} to s ′ {\displaystyle s'} with joint action a → {\displaystyle {\vec {a}}} . In settings with perfect information, such as the games of chess and Go, the MDP would be fully observable. In settings with imperfect information, especially in real-world applications like self-driving cars, each agent would access an observation that only has part of the information about the current state. In the partially observable setting, the core model is the partially observable stochastic game in the general case, and the decentralized POMDP in the cooperative case. == Cooperation vs. competition == When multiple agents are acting in a shared environment their interests might be aligned or misaligned. MARL allows exploring all the different alignments and how they affect the agents' behavior: In pure competition settings, the agents' rewards are exactly opposite to each other, and therefore they are playing against each other. Pure cooperation settings are the other extreme, in which agents get the exact same rewards, and therefore they are playing with each other. Mixed-sum settings cover all the games that combine elements of both cooperation and competition. === Pure competition settings === When two agents are playing a zero-sum game, they are in pure competition with each other. Many traditional games such as chess and Go fall under this category, as do two-player variants of video games like StarCraft. Because each agent can only win at the expense of the other agent, many complexities are stripped away. There is no prospect of communication or social dilemmas, as neither agent is incentivized to take actions that benefit its opponent. The Deep Blue and AlphaGo projects demonstrate how to optimize the performance of agents in pure competition settings. One complexity that is not stripped away in pure competition settings is autocurricula. As the agents' policy is improved using self-play, multiple layers of learning may occur. === Pure cooperation settings === MARL is used to explore how separate agents with identical interests can communicate and work together. Pure cooperation settings are explored in recreational cooperative games such as Overcooked, as well as real-world scenarios in robotics. In pure cooperation settings all the agents get identical rewards, which means that social dilemmas do not occur. In pure cooperation settings, oftentimes there are an arbitrary number of coordination strategies, and agents converge to specific "conventions" when coordinating with each other. The notion of conventions has been studied in language and also alluded to in more general multi-agent collaborative tasks. === Mixed-sum settings === Most real-world scenarios involving multiple agents have elements of both cooperation and competition. For example, when multiple self-driving cars are planning their respective paths, each of them has interests that are diverging but not exclusive: Each car is minimizing the amount of time it's taking to reach its destination, but all cars have the shared interest of avoiding a traffic collision. Zero-sum settings with three or more agents often exhibit similar properties to mixed-sum settings, since each pair of agents might have a non-zero utility sum between them. Mixed-sum settings can be explored using classic matrix games such as prisoner's dilemma, more complex sequential social dilemmas, and recreational games such as Among Us, Diplomacy and StarCraft II. Mixed-sum settings can give rise to communication and social dilemmas. == Social dilemmas == As in game theory, much of the research in MARL revolves around social dilemmas, such as prisoner's dilemma, chicken and stag hunt. While game theory research might focus on Nash equilibria and what an ideal policy for an agent would be, MARL research focuses on how the agents would learn these ideal policies using a trial-and-error process. The reinforcement learning algorithms that are used to train the agents are maximizing the agent's own reward; the conflict between the needs of the agents and the needs of the group is a subject of active research. Various techniques have been explored in order to induce cooperation in agents: Modifying the environment rules, adding intrinsic rewards, and more. === Sequential social dilemmas === Social dilemmas like prisoner's dilemma, chicken and stag hunt are "matrix games". Each agent takes only one action from a choice of two possible actions, and a simple 2x2 matrix is used to describe the reward that each agent will get, given the actions that each agent took. In humans and other living creatures, social dilemmas tend to be more complex. Agents take multiple actions over time, and the distinction between cooperating and defecting is not as clear cut as in matrix games. The concept of a sequential social dilemma (SSD) was introduced in 2017 as an attempt to model that complexity. There is ongoing research into defining different kinds of SSDs and showing cooperative behavior in the agents that act in them. == Autocurricula == An autocurriculum (plural: autocurricula) is a reinforcement learning concept that's salient in multi-agent experiments. As agents improve their performance, they change their environment; this change in the environment affects themselves and the other agents. The feedback loop results in several distinct phases of learning, each depending on the previous one. The stacked layers of learning are called an autocurriculum. Autocurricula are especially apparent in adversarial settings, where each group of agents is racing to counter the current strategy of the opposing group. The Hide and Seek game is an accessible example of an autocurriculum occurring in an adversarial setting. In this experiment, a team of seekers is competing against a team of hiders. Whenever one of the teams learns a new strategy, the opposing team adapts its strategy to give the best possible counter. When the hiders learn to use boxes to build a shelter, the seekers respond by learning to use a ramp to break into that shelter. The hiders respond by locking the ramps, making them unavailable for the seekers to use. The seekers then respond by "box surfing", exploiting a glitch in the game to penetrate the shelter. Each "level" of learning is an emergent phenomenon, with the previous level as its premise. This results in a stack of behaviors, each dependent on its predecessor. Autocurricula in reinforcement learning experiments are compared to the stages of the evolution of life on Earth and the development of human culture. A major stage in evolution happened 2-3 billion years ago, when photosynthesizing life forms started to produce massive amounts of oxygen, changing the balance of gases in the atmosphere. In the next stages of evolution, oxygen-breathing life forms evolved, eventually leading up to land mammals and human beings. These later stages could only happen after the photosynthesis stage made oxygen widely available. Similarly, human culture could not have gone through the Industrial Revolution in the 18th century without the resources and insights gaine

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  BigDog

  BigDog is a dynamically stable quadruped military robot platform that was created in 2005 by Boston Dynamics with the Harvard University Concord Field Station. It was funded by the U.S. Defense Advanced Research Projects Agency (DARPA), but the project was shelved after the BigDog's gas engine was deemed too loud for combat. == History == BigDog was funded by the Defense Advanced Research Projects Agency (DARPA) in the hopes that it would be able to serve as a mechanic pack mule to accompany soldiers in terrain too rough for conventional vehicles. Instead of wheels or treads, BigDog uses four legs for movement, allowing it to move across surfaces that would be difficult for wheels. The legs contain a variety of sensors, including joint position and ground contact. BigDog also features a laser gyroscope and a stereo vision system. BigDog is 3 feet (0.91 m) long, stands 2.5 feet (0.76 m) tall, and weighs 240 pounds (110 kg), making it about the size of a small mule. It is capable of traversing difficult terrain, running at four miles per hour (6.4 km/h), carrying 340 pounds (150 kg), and climbing a 35 degree incline. Locomotion is controlled by an onboard computer that receives input from the robot's various sensors. Navigation and balance are also managed by the control system. BigDog's walking pattern is controlled through four legs, each equipped with four low-friction hydraulic cylinder actuators that power the joints. BigDog's locomotion behaviors can vary greatly. It can stand up, sit down, walk with a crawling gait that lifts one leg at a time, walk with a trotting gait lifting diagonal legs, or trot with a running gait. The travel speed of BigDog varies from a 0.62 mph (1 km/h) crawl to a 3.3 mph (5.3 km/h) trot. The BigDog project was headed by Dr. Martin Buehler, who received the Joseph Engelberger Award from the Robotics Industries Association in 2012 for the work. Dr. Buehler while previously a professor at McGill University, headed the robotics lab there, developing four-legged walking and running robots. Built onto the actuators are sensors for joint position and force, and movement is ultimately controlled through an onboard computer which manages the sensors. Approximately 50 sensors are located on BigDog. These measure the attitude and acceleration of the body, motion, and force of joint actuators as well as engine speed, temperature and hydraulic pressure inside the robot's internal engine. Low-level control, such as position and force of the joints, and high-level control such as velocity and altitude during locomotion, are both controlled through the onboard computer. BigDog was featured in episodes of Web Junk 20 and Hungry Beast, and in articles in New Scientist, Popular Science, Popular Mechanics, and The Wall Street Journal. In September 2011 Boston Dynamics released video footage of a new generation of BigDog known as AlphaDog. The footage shows AlphaDog's ability to walk on rough terrain and recover its balance when kicked from the side. The refined equivalent has been designed by Boston Dynamics to exceed the BigDog in terms of capabilities and use to dismounted soldiers. In February 2012, with further DARPA support, the militarized Legged Squad Support System (LS3) variant of BigDog demonstrated its capabilities during a hike over a rough terrain. Starting in the summer of 2012, DARPA planned to complete the overall development of the system and refine its key capabilities in 18 months, ensuring its worth to dismounted warfighters before it is rolled out to squads operating in-theatre. BigDog must be able to demonstrate its ability to complete a 20-mile (32 km) trail in 24 hours, without refuelling, while carrying a 325-pound (150 kg) load. A refinement of its vision sensors will also be conducted. At the end of February 2013, Boston Dynamics released video footage of a modified BigDog with an arm. The arm could pick up objects and throw them. The robot is relying on its legs and torso to help power the motions of the arm. It is believed that it can lift weights around 55 pounds (25 kg). This work was funded by the United States Army Research Laboratory and paved the way for integrating manipulators with quadrupeds as found on Spot, the spiritual successor of BigDog. === Discontinuation === At the end of December 2013, the BigDog project was discontinued. Despite hopes that it would one day work like a pack mule for US soldiers in the field, the gasoline-powered engine was deemed too noisy for use in combat, and it could be heard from hundreds of meters away. A similar project for an all-electric robot named Spot in 2016 was much quieter, but could only carry 45 pounds (20 kg). Both projects are no longer in progress, but the Spot was only released in 2020. == Hardware == BigDog is powered by a small two-stroke, one-cylinder, 15-brake-horsepower (11 kW) engine operating at 9,000 RPM. The engine drives a hydraulic pump, which in turn drives the hydraulic leg actuators. Each leg has four actuators (two for the hip joint, and two each for the knee and ankle joints), for a total of 16. Each actuator unit consists of a hydraulic cylinder, servo valve, position sensor, and force sensor. Onboard computing power is a ruggedized PC/104 board stack with two computers, one running a Pentium M processor running QNX (used for sensor data processing) and another running a Core Duo processor (used for visual data processing). == Gallery ==

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  ACLU Mobile Justice

  ACLU Mobile Justice was a video live streaming application developed for smartphones by various state chapters of the American Civil Liberties Union. It was intended to allow instant, secure video recording and transmission of interactions with, and perceived abuses by, law enforcement officers. Since its release by the ACLU of California for California residents, other versions of the app have been released for 16 other states and the District of Columbia by their ACLU chapters. It was discontinued in February 2025.

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  Flexidraw

  Flexidraw is a 1985 graphics computer program published by Inkwell Systems. == Gameplay == Flexidraw is a graphics program that allows users to produce drawings using a light pen and print them. == Reception == Roy Wagner reviewed the product for Computer Gaming World, and stated that "Of the many graphics programs available Flexidraw is certainly the best supported by it's [sic] parent company."

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  Cross-entropy method

  The cross-entropy (CE) method is a Monte Carlo method for importance sampling and optimization. It is applicable to both combinatorial and continuous problems, with either a static or noisy objective. The method approximates the optimal importance sampling estimator by repeating two phases: Draw a sample from a probability distribution. Minimize the cross-entropy between this distribution and a target distribution to produce a better sample in the next iteration. Reuven Rubinstein developed the method in the context of rare-event simulation, where tiny probabilities must be estimated, for example in network reliability analysis, queueing models, or performance analysis of telecommunication systems. The method has also been applied to the traveling salesman, quadratic assignment, DNA sequence alignment, max-cut and buffer allocation problems. == Estimation via importance sampling == Consider the general problem of estimating the quantity ℓ = E u [ H ( X ) ] = ∫ H ( x ) f ( x ; u ) d x {\displaystyle \ell =\mathbb {E} _{\mathbf {u} }[H(\mathbf {X} )]=\int H(\mathbf {x} )\,f(\mathbf {x} ;\mathbf {u} )\,{\textrm {d}}\mathbf {x} } , where H {\displaystyle H} is some performance function and f ( x ; u ) {\displaystyle f(\mathbf {x} ;\mathbf {u} )} is a member of some parametric family of distributions. Using importance sampling this quantity can be estimated as ℓ ^ = 1 N ∑ i = 1 N H ( X i ) f ( X i ; u ) g ( X i ) {\displaystyle {\hat {\ell }}={\frac {1}{N}}\sum _{i=1}^{N}H(\mathbf {X} _{i}){\frac {f(\mathbf {X} _{i};\mathbf {u} )}{g(\mathbf {X} _{i})}}} , where X 1 , … , X N {\displaystyle \mathbf {X} _{1},\dots ,\mathbf {X} _{N}} is a random sample from g {\displaystyle g\,} . For positive H {\displaystyle H} , the theoretically optimal importance sampling density (PDF) is given by g ∗ ( x ) = H ( x ) f ( x ; u ) / ℓ {\displaystyle g^{}(\mathbf {x} )=H(\mathbf {x} )f(\mathbf {x} ;\mathbf {u} )/\ell } . This, however, depends on the unknown ℓ {\displaystyle \ell } . The CE method aims to approximate the optimal PDF by adaptively selecting members of the parametric family that are closest (in the Kullback–Leibler sense) to the optimal PDF g ∗ {\displaystyle g^{}} . == Generic CE algorithm == Choose initial parameter vector v ( 0 ) {\displaystyle \mathbf {v} ^{(0)}} ; set t = 1. Generate a random sample X 1 , … , X N {\displaystyle \mathbf {X} _{1},\dots ,\mathbf {X} _{N}} from f ( ⋅ ; v ( t − 1 ) ) {\displaystyle f(\cdot ;\mathbf {v} ^{(t-1)})} Solve for v ( t ) {\displaystyle \mathbf {v} ^{(t)}} , where v ( t ) = argmax v ⁡ 1 N ∑ i = 1 N H ( X i ) f ( X i ; u ) f ( X i ; v ( t − 1 ) ) log ⁡ f ( X i ; v ) {\displaystyle \mathbf {v} ^{(t)}=\mathop {\textrm {argmax}} _{\mathbf {v} }{\frac {1}{N}}\sum _{i=1}^{N}H(\mathbf {X} _{i}){\frac {f(\mathbf {X} _{i};\mathbf {u} )}{f(\mathbf {X} _{i};\mathbf {v} ^{(t-1)})}}\log f(\mathbf {X} _{i};\mathbf {v} )} If convergence is reached then stop; otherwise, increase t by 1 and reiterate from step 2. In several cases, the solution to step 3 can be found analytically. Situations in which this occurs are When f {\displaystyle f\,} belongs to the natural exponential family When f {\displaystyle f\,} is discrete with finite support When H ( X ) = I { x ∈ A } {\displaystyle H(\mathbf {X} )=\mathrm {I} _{\{\mathbf {x} \in A\}}} and f ( X i ; u ) = f ( X i ; v ( t − 1 ) ) {\displaystyle f(\mathbf {X} _{i};\mathbf {u} )=f(\mathbf {X} _{i};\mathbf {v} ^{(t-1)})} , then v ( t ) {\displaystyle \mathbf {v} ^{(t)}} corresponds to the maximum likelihood estimator based on those X k ∈ A {\displaystyle \mathbf {X} _{k}\in A} . == Continuous optimization—example == The same CE algorithm can be used for optimization, rather than estimation. Suppose the problem is to maximize some function S {\displaystyle S} , for example, S ( x ) = e − ( x − 2 ) 2 + 0.8 e − ( x + 2 ) 2 {\displaystyle S(x)={\textrm {e}}^{-(x-2)^{2}}+0.8\,{\textrm {e}}^{-(x+2)^{2}}} . To apply CE, one considers first the associated stochastic problem of estimating P θ ( S ( X ) ≥ γ ) {\displaystyle \mathbb {P} _{\boldsymbol {\theta }}(S(X)\geq \gamma )} for a given level γ {\displaystyle \gamma \,} , and parametric family { f ( ⋅ ; θ ) } {\displaystyle \left\{f(\cdot ;{\boldsymbol {\theta }})\right\}} , for example the 1-dimensional Gaussian distribution, parameterized by its mean μ t {\displaystyle \mu _{t}\,} and variance σ t 2 {\displaystyle \sigma _{t}^{2}} (so θ = ( μ , σ 2 ) {\displaystyle {\boldsymbol {\theta }}=(\mu ,\sigma ^{2})} here). Hence, for a given γ {\displaystyle \gamma \,} , the goal is to find θ {\displaystyle {\boldsymbol {\theta }}} so that D K L ( I { S ( x ) ≥ γ } ‖ f θ ) {\displaystyle D_{\mathrm {KL} }({\textrm {I}}_{\{S(x)\geq \gamma \}}\|f_{\boldsymbol {\theta }})} is minimized. This is done by solving the sample version (stochastic counterpart) of the KL divergence minimization problem, as in step 3 above. It turns out that parameters that minimize the stochastic counterpart for this choice of target distribution and parametric family are the sample mean and sample variance corresponding to the elite samples, which are those samples that have objective function value ≥ γ {\displaystyle \geq \gamma } . The worst of the elite samples is then used as the level parameter for the next iteration. This yields the following randomized algorithm that happens to coincide with the so-called Estimation of Multivariate Normal Algorithm (EMNA), an estimation of distribution algorithm. === Pseudocode === // Initialize parameters μ := −6 σ2 := 100 t := 0 maxits := 100 N := 100 Ne := 10 // While maxits not exceeded and not converged while t < maxits and σ2 > ε do // Obtain N samples from current sampling distribution X := SampleGaussian(μ, σ2, N) // Evaluate objective function at sampled points S := exp(−(X − 2) ^ 2) + 0.8 exp(−(X + 2) ^ 2) // Sort X by objective function values in descending order X := sort(X, S) // Update parameters of sampling distribution via elite samples μ := mean(X(1:Ne)) σ2 := var(X(1:Ne)) t := t + 1 // Return mean of final sampling distribution as solution return μ == Related methods == Simulated annealing Genetic algorithms Harmony search Estimation of distribution algorithm Tabu search Natural Evolution Strategy Ant colony optimization algorithms

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  Uniphore

  Uniphore is an American software company that develops artificial intelligence platforms for business use. The company is headquartered in Palo Alto, California, with offices in the United States, United Kingdom, Spain, Israel, United Arab Emirates, and India. Uniphore is known for its "Business AI Cloud," an enterprise AI platform that combines data, knowledge, models, and software agents for use in sales, marketing, and service. The company has also acquired firms in video emotion AI, AI agents, low-code automation, knowledge automation, voice and screen capture, customer data platforms, and data engineering. == History == Uniphore Software Systems was founded by Umesh Sachdev and Ravi Saraogi in 2008 and was incubated at IIT Madras. The company received an initial grant of $100,000 from the National Research Development Corporation. Early work focused on speech technologies for emerging markets. Uniphore partnered with companies that specialized in English and European languages, and adapting the technology for Indian languages and dialects. In 2014, Uniphore released its first flagship products, auMina, along with two other products, Akeira and amVoice. Uniphore raised series A funding, led by Kris Gopalakrishnan (cofounder of Infosys), in April 2015. The next month, Uniphore received additional investment from IDG Ventures. With input from its investors, Uniphore changed its business model from license fee-based income to a software as a service-based subscription fee model in 2015. By June 2016, it had added more than 70 global languages and expanded its services to Southeast Asia, the Middle East, and the United States. The company opened operations in Singapore in October 2016. The company raised Series B funding in October 2017, led by John Chambers and existing investors. Series C funding of $51 million was announced in August 2019 and led by March Capital. Uniphore acquired an exclusive third-party license for robotic process automation technology from NTT DATA in October 2020. In January 2021, Uniphore acquired Emotion Research Lab, a startup based in Spain that uses artificial intelligence and machine learning to analyze video and interpret emotions. The company received $140 million in Series D funding, led by Sorenson Capital Partners, in March 2021, bringing total funding to $210 million. In January 2021, Uniphore acquired Emotion Research Lab. In July 2021, it agreed to acquire Jacada, a provider of low-code/no-code automation; the transaction closed in October 2021. On February 16, 2022, Uniphore announced a $400 million Series E financing led by NEA, which valued the company at $2.5 billion. Hilarie Koplow-McAdams, an NEA venture partner and former Salesforce/New Relic executive, joined Uniphore's board in 2022. Uniphore's board has also included former Cisco CEO John Chambers, former Convergys CEO Andrea J. Ayers, and CrowdStrike CFO Burt Podbere (appointed January 2021). In February 2023, Uniphore acquired UK-based Red Box, a platform for capturing voice and screen recordings used in regulated and large-scale environments. It also acquired France-based Hexagone, a behavioral analytics firm combining computer vision and natural-language techniques. On December 5, 2024, Uniphore announced agreements to acquire ActionIQ, a customer data platform (CDP) vendor, and Infoworks, an enterprise data engineering platform. Uniphore launched the Business AI Cloud on June 9, 2025. The Business AI Cloud consists of a single, unified platform that includes data, knowledge, AI models, and AI agents. Uniphore announced in August 2025 that it had acquired Orby AI and intended to acquire Autonom8 to extend multi-agent and workflow automation capabilities. As of September 2025, Uniphore's customers included the United States Coast Guard, Singapore Police Force, London Underground, DirecTV, JPMorgan Chase, LG, DHL, UPS, Vodafone, Verizon, NTT Data, and as of May 2021, Firstsource. In October 2025, Uniphore raised $260 million in a Series F round at a reported valuation of $2.5 billion. Investors included March Capital, NEA, Nvidia, AMD, Snowflake, and Databricks. In January 2026, KPMG and Uniphore announced a collaboration focused on deploying AI agents powered by specialized small language models. The announcement was made at the World Economic Forum held in Davos. Cognizant and Uniphore announced a partnership in February 2026 to develop industry-specific AI tools for regulated sectors, which would initially focus on life sciences and finance. Uniphore and Rackspace also announced a partnership in March 2026. This partnership was announced in order to create an "Infrastructure-to-Agents" architecture, focusing on Business AI as a private cloud service. == Products == As of 2025, Uniphore's core offering is the Business AI Cloud and Business AI Suite of agentic AI applications. === Business AI Cloud === Uniphore’s Business AI Cloud is a full-stack platform that organizes enterprise data and knowledge for agentic AI applications. The platform enables deployment across clouds and existing data sources. Key layers and capabilities include the following. Agentic layer: Includes prebuilt agents, a natural-language agent builder, and orchestration based on Business Process Model and Notation (BPMN) to run AI workflows across business units. Model layer: Supports an open, interoperable mix of closed and open-source large language models (LLMs). Models can be orchestrated, governed, and replaced as needed. Knowledge layer: Organizes raw data into structured knowledge used for retrieval, explainability, and fine-tuning of small language models (SLMs). Data layer: Connects to data across multiple platforms and clouds through a zero-copy, composable fabric, enabling in-place preparation and supporting data residency and sovereignty requirements. === Business AI Suite === The Uniphore Business AI Suite has various prebuilt AI agents that can be used in customer service, sales, marketing, and human resources. The Uniphore Business AI Suite includes several LOBs (Lines of Business) for business functions with intelligent agents that are prebuilt, but composable. Built on the Uniphore Business AI Cloud, each application combines agentic automation and fine-tuned models. Marketing AI, Customer Service AI, Sales AI, and People AI (for human resources) are included. Competitors include Palantir, Microsoft Azure, Amazon Bedrock, Google's Vertex AI, Databricks, and Snowflake. == Recognition == Deloitte Technology Fast 50 India identified Uniphore as the 17th fastest-growing technology company in India in 2012 and one of the top 500 fastest growing companies in the Asia-Pacific region in 2014. In 2016, Time included Sachdev on its list of "10 millennials who are changing the world" for “building a phone that can understand almost any language”. NASSCOM named Uniphore to its "League of 10" emerging Indian technology companies in 2017. In 2020, the San Francisco Business Times ranked Uniphore as No. 7 among small companies in its list of the best places to work in the San Francisco Bay Area. In 2022, the company was featured on the Forbes AI 50 list. Uniphore was mentioned in the Deloitte Technology Fast 500 list in 2023, 2024, and 2025. In 2025, Inc. included Uniphore in its Best in Business program.

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  Educational robotics

  Educational robotics teaches the design, analysis, application and operation of robots. Robots include articulated robots, mobile robots or autonomous vehicles. Educational robotics can be taught from elementary school to graduate programs. Robotics may also be used to motivate and facilitate the instruction other, often foundational, topics such as computer programming, artificial intelligence or engineering design. == Education and training == Robotics engineers design robots, maintain them, develop new applications for them, and conduct research to expand the potential of robotics. Robots have become a popular educational tool in some middle and high schools, as well as in numerous youth summer camps, raising interest in programming, artificial intelligence and robotics among students. First-year computer science courses at several universities now include programming of a robot in addition to traditional software engineering-based coursework. == Category of Educational robotics == The categories of educational robots seen as having more than one category. It can be alienated into different categories based on their physical design and coding method. Generally they are categorised as arm robots, wheeled mobile robots and humanoid robots. Tangibly, coded robots uses a physical means of coding instead of the screens coding. === Initiatives in schools === Leachim, was a robot teacher programmed with the class curricular, as well as certain biographical information on the 40 students whom it was programmed to teach. Leachim could synthesize human speech using Diphone synthesis. It was invented by Michael J. Freeman in 1974 and was tested in a fourth grade classroom in the Bronx, New York. === Post-secondary degree programs === From approximately 1960 through 2005, robotics education at post-secondary institutions took place through elective courses, thesis experiences and design projects offered as part of degree programs in traditional academic disciplines, such as mechanical engineering, electrical engineering, industrial engineering or computer science. Since 2005, more universities have begun granting degrees in robotics as a discipline in its own right, often under the name "Robotic Engineering". Based on a 2015 web-based survey of robotics educators, the degree programs and their estimates annual graduates are listed alphabetically below. Note that only official degree programs where the word "robotics" appears on the transcript or diploma are listed here; whereas degree programs in traditional disciplines with course concentrations or thesis topics related to robotics are deliberately omitted. === Certification === The Robotics Certification Standards Alliance (RCSA) is an international robotics certification authority that confers various industry- and educational-related robotics certifications. === Summer robotics camp === Several summer camp programs include robotics as part of their core curriculum. In addition, youth summer robotics programs are frequently offered by celebrated museums such as the American Museum of Natural History and The Tech Museum of Innovation in Silicon Valley, CA, just to name a few. There are of benefits that come from attending robotics camps. It teaches students how to use teamwork, resilience and motivation, and decision-making. Students learn teamwork because most camps involve exciting activities requiring teamwork. Resilience and motivation is expected because by completing the challenging programs, students feel talented and accomplished after they complete the program. Also students are given unique situations making them make decisions to further their situation. === Educational robotics in special education === Educational robotics can be a useful tool in early and special education. According to a journal on new perspectives in science education, educational robotics can help to develop abilities that promote autonomy and assist their integration into society. Social and personal skills can also be developed through educational robotics. Using Lego Mindstorms NXT, schoolteachers were able to work with middle school aged children in order to develop programs and improve the children's social and personal skills. Additionally, problem solving skills and creativity were utilized through the creation of artwork and scenery to house the robots. Other studies show the benefits of educational robotics in special education as promoting superior cognitive functions, including executive functions. This can lead to an increased ability in "problem solving, reasoning and planning in typically developing preschool children." Through eight weeks of weekly forty-five-minute group sessions using the Bee-Bot, an increase in interest, attention, and interaction between both peers and adults was found in the school and preschool-aged children with Down Syndrome. This study suggests that educational robotics in the classroom can also lead to an improvement in visuo-spatial memory and mental planning. Furthermore, executive functions seemed to be possible in one child during this study.

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  Artbreeder

  Artbreeder, formerly known as Ganbreeder, is a collaborative, machine learning-based art website. Using the models StyleGAN and BigGAN, the website allows users to generate and modify images of faces, landscapes, and paintings, among other categories. == Overview == On Artbreeder, users mainly interact through the remixing - referred to as 'breeding' - of other users' images found in the publicly accessible database of images. The creation of new variations can be done by tweaking sliders on an image's page, known as "genes", which in the "Portraits" model can range from color balance to gender, facial hair, and glasses. Additionally, any image can be "crossbred" with other publicly viewable images from the database, using a slider to control how much of each image should influence the resulting "child". The site also allows for uploading new images, which the model will attempt to convert into the latent space of the network. == Notable usages == The similarly AI-driven text adventure game AI Dungeon uses Artbreeder to generate profile pictures for its users, and The Static Age's Andrew Paley has used Artbreeder to create the visuals for his music videos. Artbreeder has been used to create portraits of characters from popular novels such as Harry Potter and Twilight. They have also been used to add realistic features to ancient portraits. Artbreeder was used to create characters in the sequel to Ben Drowned with the titular villain, an AI-construct itself, created entirely using the website. == Changes to Artbreeder == ArtBreeder underwent an overhaul, introducing several features to enhance the user experience. Among these updates is the integration SD-XL, developed by stability.ai. Additionally, ArtBreeder also added a functionality known as ControlNet, which enables users to create images based on specific poses. With ControlNet, users can incorporate various poses into their AI Artworks. More features that were introduced into Artbreeder, are Pattern, which creates AI Pattern Images, Outpainting or Uncropping was also an added feature to Artbreeder, that allows the user to expand the image beyond the normal dimensions of the image. == Reception == The artwork generated by users of the website has been described as "beautiful" and "surreal," drawing comparisons to "weird, incomprehensible dreams" that "somehow touch the deep, unconscious parts of [the] mind". However, the generated faces were noted as "creepy and 'off'", and still nowhere near the quality attained by actual digital artists. Additionally, the site faced criticism for perceived confusing aspects of the AI's behavior. Jonathan Bartlett of Mind Matters News noted that "As is always the case with AI, sometimes the [gene] knobs don't work as expected and sometimes the results are... strange," while conceding that Artbreeder was still "probably the start of a new future of made-to-order stock images." Writers from Hyperallergic also took issue with perceived racial biases in the Portraits model, citing a comment from a user who faced difficulty from the neural network while attempting to darken the skin of a portrait to match a source image.

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  Blobotics

  Blobotics is a term describing research into chemical-based computer processors based on ions rather than electrons. Andrew Adamatzky, a computer scientist at the University of the West of England, Bristol used the term in an article in New Scientist March 28, 2005 [1]. The aim is to create 'liquid logic gates' which would be 'infinitely reconfigurable and self-healing'. The process relies on the Belousov–Zhabotinsky reaction, a repeating cycle of three separate sets of reactions. Such a processor could form the basis of a robot which, using artificial sensors, interact with its surroundings in a way which mimics living creatures. The coining of the term was featured by ABC radio in Australia [2].

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  Lenna

  Lenna (or Lena) is a standard test image used in the field of digital image processing, starting in 1973. It is a picture of the Swedish model Lena Forsén, shot by photographer Dwight Hooker and cropped from the centerfold of the November 1972 issue of Playboy magazine. Lenna has attracted controversy because of its subject matter. Starting in the mid-2010s, many journals have deemed it inappropriate and discouraged its use, while others have banned it from publication outright. Forsén herself has called for it to be retired, saying "It's time I retired from tech." The spelling "Lenna" came from the model's desire to encourage the proper pronunciation of her name. "I didn't want to be called Leena [English: ]," she explained. == History == Before Lenna, the first use of a Playboy magazine image to illustrate image processing algorithms was in 1961. Lawrence G. Roberts used two cropped six-bit grayscale facsimile scanned images from Playboy's July 1960 issue featuring Playmate Teddi Smith, in his master's thesis on image dithering at Massachusetts Institute of Technology. Lenna was originally intended for high resolution color image processing study. Its history was described in the May 2001 newsletter of the IEEE Professional Communication Society, in an article by Jamie Hutchinson: Alexander Sawchuk estimates that it was in June or July of 1973 when he, then an assistant professor of electrical engineering at the University of Southern California Signal and Image Processing Institute (SIPI), along with a graduate student and the SIPI lab manager, was hurriedly searching the lab for a good image to scan for a colleague's conference paper. They got tired of their stock of usual test images, dull stuff dating back to television standards work in the early 1960s. They wanted something glossy to ensure good output dynamic range, and they wanted a human face. Just then, somebody happened to walk in with a recent issue of Playboy. The engineers tore away the top third of the centerfold so they could wrap it around the drum of their Muirhead wirephoto scanner, which they had outfitted with analog-to-digital converters (one each for the red, green, and blue channels) and a Hewlett Packard 2100 minicomputer. The Muirhead had a fixed resolution of 100 lines per inch and the engineers wanted a 512×512 image, so they limited the scan to the top 5.12 inches of the picture, effectively cropping it at the subject's shoulders. The image's reach was limited in the 1970s and 80s, which is reflected in it initially only appearing in .org domains, but in July 1991, the image featured on the cover of Optical Engineering alongside Peppers, another popular test image. This drew the attention of Playboy to the potential copyright infringement. The peak of image hits on the internet was in 1995. The scan became one of the most used images in computer history. The use of the photo in electronic imaging has been described as "clearly one of the most important events in [its] history". The image spread to over 100 different domains, particularly .com and .edu. In a 1999 issue of IEEE Transactions on Image Processing "Lena" was used in three separate articles, and the picture continued to appear in scientific journals throughout the beginning of the 21st century. Lenna is so widely accepted in the image processing community that Forsén was a guest at the 50th annual Conference of the Society for Imaging Science and Technology (IS&T) in 1997. In 2015, Lena Forsén was also guest of honor at the banquet of IEEE ICIP 2015. After delivering a speech, she chaired the best paper award ceremony. To explain why the image became a standard in the field, David C. Munson, editor-in-chief of IEEE Transactions on Image Processing, stated that it was a good test image because of its detail, flat regions, shading, and texture. He also noted that "the Lena image is a picture of an attractive woman. It is not surprising that the (mostly male) image processing research community gravitated toward an image that they found attractive." While Playboy often cracks down on illegal uses of its material and did initially send a notice to the publisher of Optical Engineering about its unauthorized use in that publication, over time it has decided to overlook the wide use of Lena. Eileen Kent, VP of new media at Playboy, said, "We decided we should exploit this, because it is a phenomenon." == Criticism == The use of the image has produced controversy because Playboy is "seen (by some) as being degrading to women". In a 1999 essay on reasons for the male predominance in computer science, applied mathematician Dianne P. O'Leary wrote: Suggestive pictures used in lectures on image processing ... convey the message that the lecturer caters to the males only. For example, it is amazing that the "Lena" pin-up image is still used as an example in courses and published as a test image in journals today. A 2012 paper on compressed sensing used a photo of the model Fabio Lanzoni as a test image to draw attention to this issue. The use of the test image at the magnet school Thomas Jefferson High School for Science and Technology in Fairfax County, Virginia, provoked a guest editorial by a senior in The Washington Post in 2015 about its detrimental impact on aspiring female students in computer science. In 2017, the Journal of Modern Optics published an editorial titled "On alternatives to Lenna" suggesting three images (Pirate, Cameraman, and Peppers) that "are reasonably close to Lenna in feature space". In 2018, the Nature Nanotechnology journal announced that they would no longer consider articles using Lenna. In the same year SPIE, the publishers of Optical Engineering, also announced that they "strongly discourage" the use of Lenna, and would no longer consider new submissions containing the image "without convincing scientific justification for its use". They noted that aside from the copyright and ethical issues, that it was also no longer useful as a standard image: "In today's age of high-resolution digital image technology, it seems difficult to argue that a 512 × 512 image produced with a 1970s-era analog scanner is the best we have to offer as an image quality test standard". Forsén stated in the 2019 documentary film Losing Lena, "I retired from modeling a long time ago. It's time I retired from tech, too... Let's commit to losing me." The Institute of Electrical and Electronics Engineers (IEEE) announced that, starting April 1, 2024, it will no longer allow use of Lenna in its publications.

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  Augment (app)

  Augment is an augmented reality SaaS platform that allows users to visualize their products in 3D in real environment and in real-time through tablets or smartphones. The software can be used for retail, e-commerce, architecture, and other purposes. Augment created a mobile app of the same name, used to visualize 3D models in augmented reality and a web application called Augment Manager for 3D content management. The company is based in Paris, France, and was founded in October 2011 by Jean-François Chianetta, Cyril Champier, and Mickaël Jordan. In March 2016, Augment announced €3 million in its series-A round from Salesforce Ventures, which bringing the total funding since launch to $4.7 million. Augment lets businesses and 3D professionals visualize projects in their actual size and environment, on iPhone, iPad, and Android, using the power of augmented reality. Users can print the Augment tracker or create their own tracker to place the 3D models in space and at scale in real time. Common uses of the technology include product presentations, interactive print campaigns and e-Commerce product visualization. Augment has just released its augmented reality SDK solutions for retail and augmented commerce. The SDK solutions, available for both native mobile app and web integrations, allow companies to embed augmented reality product visualization in their existing eCommerce platforms. == Technology == Augment uses the following 3D technologies: Vuforia Augmented Reality SDK OpenGL == Customer cases == Companies such as Coca-Cola, Siemens, Nokia, Nestle, and Boeing are using Augment's solutions. == History == Augment was first created by Jean-François Chianetta in October 2011. Chianetta later teamed up with Cyril Champier and Mickaël Jordan for further development. The co-founding team was among the 12 startups of Season 3 of French accelerator Le Camping. The team raised one million euros (US$1,300,000) in April 2013 and moved its office to Paris. In March 2016, Augment raised US$3M Series A funding from Salesforce and other investors. In 2013, Augment's first service, Boost Business Catalog, was made available to help businesses catalogue and display their product models. Customers can rotate the images in 3D and view augmented content before deciding what to buy. == Awards == "Best Innovation" at Ecommerce Mag Trophy 2013

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