AI Data Center Financing Surge

AI Data Center Financing Surge — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Likewise, Inc.

    Likewise, Inc.

    Likewise, Inc., is an American technology startup company which provides a social networking service for finding and saving content recommendations for movies, TV shows, books, and podcasts. A team of ex-Microsoft employees founded Likewise in October 2017 with financial investment from Microsoft co-founder Bill Gates. The company is led by CEO Ian Morris and as of 2020 had a team of about 35 employees. Its headquarters operates in Bellevue, Washington. As of July 2020, 1 million users had joined the platform. == History == === Ideation (October 2017) === In 2017, former Microsoft Communications Chief Larry Cohen came up with the idea for Likewise in Bill Gates’ private office, Gates Ventures. Cohen currently serves as Gates Ventures’ CEO and managing partner. Cohen collaborated with colleagues Michael Dix and Ian Morris to co-found what would become Likewise, with Morris as its CEO. Gates funded the company's early development. The company developed its platform in stealth mode before launching publicly in October 2018. === Release (October 2018) === Likewise officially released its platform in the US and Canada on October 3, 2018. === Growth (2020 COVID-19 pandemic) === Likewise experienced accelerated growth alongside the COVID-19 pandemic. From March 2020 to July 2020, the platform's monthly active users tripled in numbers. The company reached one million users in July 2020. == Applications == === Mobile === Likewise is available as a mobile app for the Android and iOS mobile operating systems. Users receive recommendations from the Likewise algorithm, people they follow, and the Likewise editorial team. === Likewise TV === In October 2019, the company launched its Apple TV app called Likewise TV. The television app organizes shows across streaming services under one watchlist. On July 20, 2020, Likewise TV expanded to Android TV and Amazon Fire TV users.

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  • Line detection

    Line detection

    In image processing, line detection is an algorithm that takes a collection of n edge points and finds all the lines on which these edge points lie. The most popular line detectors are the Hough transform and convolution-based techniques. == Hough transform == The Hough transform can be used to detect lines and the output is a parametric description of the lines in an image, for example ρ = r cos(θ) + c sin(θ). If there is a line in a row and column based image space, it can be defined ρ, the distance from the origin to the line along a perpendicular to the line, and θ, the angle of the perpendicular projection from the origin to the line measured in degrees clockwise from the positive row axis. Therefore, a line in the image corresponds to a point in the Hough space. The Hough space for lines has therefore these two dimensions θ and ρ, and a line is represented by a single point corresponding to a unique set of these parameters. The Hough transform can then be implemented by choosing a set of values of ρ and θ to use. For each pixel (r, c) in the image, compute r cos(θ) + c sin(θ) for each values of θ, and place the result in the appropriate position in the (ρ, θ) array. At the end, the values of (ρ, θ) with the highest values in the array will correspond to strongest lines in the image == Convolution-based technique == In a convolution-based technique, the line detector operator consists of a convolution masks tuned to detect the presence of lines of a particular width n and a θ orientation. Here are the four convolution masks to detect horizontal, vertical, oblique (+45 degrees), and oblique (−45 degrees) lines in an image. a) Horizontal mask(R1) (b) Vertical (R3) (C) Oblique (+45 degrees)(R2) (d) Oblique (−45 degrees)(R4) In practice, masks are run over the image and the responses are combined given by the following equation: R(x, y) = max(|R1 (x, y)|, |R2 (x, y)|, |R3 (x, y)|, |R4 (x, y)|) If R(x, y) > T, then discontinuity As can be seen below, if mask is overlay on the image (horizontal line), multiply the coincident values, and sum all these results, the output will be the (convolved image). For example, (−1)(0)+(−1)(0)+(−1)(0) + (2)(1) +(2)(1)+(2)(1) + (−1)(0)+(−1)(0)+(−1)(0) = 6 pixels on the second row, second column in the (convolved image) starting from the upper left corner of the horizontal lines. page 82 == Example == These masks above are tuned for light lines against a dark background, and would give a big negative response to dark lines against a light background. == Code example == The code was used to detect only the vertical lines in an image using Matlab and the result is below. The original image is the one on the top and the result is below it. As can be seen on the picture on the right, only the vertical lines were detected

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  • Nuance Communications

    Nuance Communications

    Nuance Communications, Inc. was an American multinational computer software technology corporation, headquartered in Burlington, Massachusetts, that markets speech recognition and artificial intelligence software. Nuance merged with its competitor in the commercial large-scale speech application business, ScanSoft, in October 2005. ScanSoft was a Xerox spin-off that was bought in 1999 by Visioneer, a hardware and software scanner company, which adopted ScanSoft as the new merged company name. The original ScanSoft had its roots in Kurzweil Computer Products. In April 2021, Microsoft announced it would buy Nuance Communications. The deal is an all-cash transaction of $19.7 billion, including company debt, or $56 per share. The acquisition was completed in March 2022. == History == The Speech Technology and Research (STAR) Laboratory at SRI International began the journey that, in 1994, resulted in a spin-off company; Corona Corporation (later renamed to Nuance Communications ). Nuance Communications (NUAN) went public on the Nasdaq Stock Market in 1995. Nuance focused on commercializing advanced speech recognition technologies. Nuance was an early spinoff of SRI's Speech Technology and Research (STAR) Laboratory, a world leader in audio processing, speech and speaker analytics and spoken language research. The technology that served as the foundation of Nuance's speech recognition solution started at the STAR Lab and helped launch Nuance more than 20 years ago. In 1995, The SRI Language Modeling Toolkit (SRILM) was developed. This provides the tools to build and apply statistical language models (LMs), primarily for use in speech recognition, statistical tagging and segmentation, and machine translation. In terms of commercialization of natural automated speech recognition, SRI's natural language speech recognition software was the first to be deployed by a major corporation. In 1996, Charles Schwab & Co., Inc., used Nuance's speech recognition technology to allow customers to receive stock quotes over the telephone. One of the key features of the ‘Schwab Discount Brokerage system’, was the ability to recognize English words even when spoken by customers with accents. In 1997, Nuance Communications developed the first large scale commercial dialog system for United Parcel Services (UPS). UPS used the voice recognition platform to handle very large numbers of inquiries about package status. The company that would later merge with Nuance Communications started life as Visioneer, incorporated in 1992. In 1999, Visioneer acquired ScanSoft, Inc. (SSFT), and the combined company became known as ScanSoft. In September 2005, ScanSoft Inc. acquired and merged with Nuance Communications (NUAN), a natural language DOD-project spinoff from SRI International. The resulting company adopted the Nuance name. During the prior decade, the two companies competed in the commercial large-scale speech application business. === Data breach === Between 2014 and 2017, Nuance exposed over 45,000 patient records. == Solutions == Customer service virtual assistants Speech recognition — for people Speech recognition — for business Speech recognition — for physicians Accessibility Power PDF Managed Print Services Transcription === ScanSoft origins === In 1974, Raymond Kurzweil founded Kurzweil Computer Products, Inc. to develop the first omni-font optical character-recognition system – a computer program capable of recognizing text written in any normal font. In 1980, Kurzweil sold his company to Xerox. The company became known as Xerox Imaging Systems (XIS), and later ScanSoft. In March 1992, a new company called Visioneer, Inc. was founded to develop scanner hardware and software products, such as a sheetfed scanner called PaperMax and the document management software PaperPort. Visioneer eventually sold its hardware division to Primax Electronics, Ltd. in January 1999. Two months later, in March, Visioneer acquired ScanSoft from Xerox to form a new public company with ScanSoft as the new company-wide name. Prior to 2001, ScanSoft focused primarily on desktop imaging software such as TextBridge, PaperPort and OmniPage. Beginning with the December 2001 acquisition of Lernout & Hauspie assets, the company moved into the speech recognition business and began to compete with Nuance. Lernout & Hauspie had acquired speech recognition company Dragon Systems in June 2001, shortly before becoming bankrupt in October. Scansoft acquired speech recognition company SpeechWorks in 2003. === Partnership with Siri and Apple Inc. === In 2013, Nuance confirmed that its natural language processing algorithms supported Apple's Siri voice assistant. === Focus on health care === In 2019, Nuance spun off its automotive division as the company Cerence, allowing it to focus on health care applications. === Acquisition by Microsoft === On April 12, 2021, Microsoft announced that it would buy Nuance Communications for $19.7 billion, or $56 a share, a 22% increase over the previous closing price. Nuance's CEO, Mark Benjamin, stayed with the company. This was Microsoft's second-biggest acquisition up to that point, after its purchase of LinkedIn for $24 billion (~$30.7 billion in 2024) in 2016. Shortly after the deal, the Competition and Markets Authority, a UK regulatory body, stated it was looking into the deal on the basis of antitrust concerns. In December 2021, it was reported that the deal would be approved by the European Union. The acquisition was completed on March 4, 2022. In May 2023, Nuance announced an unspecified number of layoffs.

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  • Fyuse

    Fyuse

    Fyuse is a spatial photography app which lets users capture and share interactive 3D images. By tilting or swiping one's smartphone, one can view such "fyuses" from various angles — as if one were walking around an object or subject. The app blends photography and video to create an interactive medium and was first published for iOS in April 2014. The Android version was released at the end of 2014. == The app == Fyuse lets users capture panoramas, selfies, and full 360° views of objects and allows one to view captured moments from different angles. It has its own personal gallery, social network and standalone web integration. With the app, Fyusion also created a social networking platform similar to Instagram. Fyuses can be shared, commented on, liked and re-shared to one's followers (called Echoes). One can build a network of followers and with engagement tracking, one can see how many times an image has been interacted with The images can also be saved for private, offline view, or shared to other social networks, like Facebook or Twitter, or embedded on a website where the images can be interacted with by desktop users via dragging the mouse. Furthermore, in the compass tab other fyuses can be discovered using the app's system of tags and categories. One's Fyuse feed is prepopulated with top users, and one can follow people to see when they post a new fyuse. The app will also find one's friends if one signs up with Facebook or connects it with one's Twitter account. To create a fyuse one moves around a person or object with one's phone's camera in one direction or moving/tilting one's phone around while holding one's finger on the screen. By combining photography and video the app allows one to capture moments that one may not have otherwise been able to capture by recording not one moment in time but stitched together little moments. According to Fyusion CEO Radu Rusu, a photo freezes a moment in time, while a video captures moments in a linear timeline — both still flat, when viewed. A fyuse image captures a moment in space, where one can not only see one side of something, but also around it. When it is done rendering, fyuses can also be edited – one can trim the fyuse for length and edit the brightness, contrast, exposure, saturation and sharpness. One can also add a vignette and apply a filters, with options to adjust their intensity. After editing, one can write a description, add hashtags, and tag parts of the fyuse before one can (voluntarily) publish and share it. Version 1.0 has been described as "alpha prototype" and version 2.0 was released on 17 December 2014. Version 3.0 introduced 3D tagging by which users can layer 3D graphic that animate accordingly with each interaction to add some context to the content. Version 4.0 was released on December 21, 2016 for iOS. Since January 2016 (v3.2) the app allows the export of fyuses as Live Photos. The app has also been described as a more sophisticated version of 3D stickers and flip images. == Applications == The app has many applications for e-commerce such as for fashion designers who want to showcase a garment from every angle, or real estate listings and Airbnb-type sites that want to make their rental properties seem as enticing as possible. The app can also be used for interactive art, 360° panoramas and selfies. == History == San Francisco-based Fyusion Inc.'s three founders — Radu B. Rusu, CTO Stefan Holzer, and VP of Engineering Stephen Miller — worked together at Willow Garage, the robotics research lab started by early Google employee Scott Hassan in the area of "personal robotics" — Hassan decided to turn the lab into more of an incubator, suggesting that the members spin off their technologies into consumer-facing enterprises. Rusu first set out with an open-source 3D perception software startup called Open Perception. Fyusion was officially founded in 2013, and soon after Rusu and his cofounders patented the technology for spatial photography. The company closed a seed funding round at the end of May, raising $3.35 million from investors, including an angel investment from Sun Microsystems cofounder Andreas Bechtolsheim. In 2014 the Fyuse team consisted of 13 employees, mostly engineers and designers, recruited from around the globe. In March 2015 the team displayed their app at Katy Perry's premiere for the movie "Prismatic World Tour on Epix" where Perry also took Fyuse for a test run. == Augmented reality == In September 2016 Fyusion unveiled its platform for creating augmented reality content using ones smartphone. It takes the images from ones smartphone and converts them into 3D holographic images, which one can then view on an AR headset. According to Rusu "by making it easy for people to capture their surroundings on any mobile device, [Fyusion is] revolutionizing the way that people view the world around them" and also states that for "AR to be successful, anyone should be able to create content for it" opposed to the current "small number of content creators and an even smaller number of hardware players". According to him "the applications of [Fyusion's] technology for consumers and businesses are incredibly limitless". The platform uses the company's patented 3D spatio-temporal platform that uses advanced sensor fusion, machine learning and computer vision algorithms and part of the platform is built into the Fyuse app. Before committing to releasing a separate consumer product the company intends to wait until the HoloLens device becomes available to the public. Until then any Fyuse representation created using Fyuse is AR ready and will be able to be shown in HoloLens in the future. == Fyuse - Point of No Return == Fyuse - Point of No Return is a science fiction short advert for Fyuse 3.0 in which Fyuse's digital medium is extrapolated into the future. In the film a woman uses a mini scanning-drone to 3D scan a tree with Fyuse and later recreate it as an augmented reality object at another place.

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  • Film-out

    Film-out

    Film-out is the process in the computer graphics, video production and filmmaking disciplines of transferring images or animation from videotape or digital files to a traditional film print. Film-out is a broad term that encompasses the conversion of frame rates, color correction, as well as the actual printing, also called scannior recording. The film-out process is different depending on the regional standard of the master videotape in question – NTSC, PAL, or SECAM – or likewise on the several emerging region-independent formats of high definition video (HD video); thus each type is covered separately, taking into account regional film-out industries, methods and technical considerations. == Live action video == Many modern documentaries and low-budget films are shot on videotape or other digital video media, instead of film stock, and completed as digital video. Video production means substantially lower costs than 16 mm or 35 mm film production on all levels. Until recently, the relatively low cost of video ended when the issue of a theatrical presentation was raised, which required a print for film projection. With the growing presence of digital projection, this is becoming less of a factor. === Standard definition (SD) video === Film-out of standard-definition video – or any source that has an incompatible frame rate – is the up-conversion of video media to film for theatrical viewing. The video-to-film conversion process consists of two major steps: first, the conversion of video into digital film frames which are then stored on a computer or on HD videotape; and secondly, the printing of these digital film frames onto actual film. To understand these two steps, it is important to understand how video and film differ. Film (sound film, at least) has remained unchanged for almost a century and creates the illusion of moving images through the rapid projection of still images, frames, upon a screen, typically 24 per second. Traditional interlaced SD video has no real frame rate, (though the term frame is applied to video, it has a different meaning). Instead, video consists of a very fast succession of horizontal lines that continually cascade down the television screen – streaming top to bottom, before jumping back to the top and then streaming down to the bottom again, repeatedly, almost 60 alternating screen-fulls every second for NTSC, or exactly 50 such screen-fulls per second for PAL and SECAM. Since visual movement in video is infused in this continuous cascade of scan lines, there is no discrete image or real frame that can be identified at any one time. Therefore, when transferring video to film, it is necessary to invent individual film frames, 24 for every second of elapsed time. The bulk of the work done by a film-out company is this first step, creating film frames out of the stream of interlaced video. Each company employs its own (often proprietary) technology for turning interlaced video into high-resolution digital video files of 24 discrete images every second, called 24 progressive video or 24p. The technology must filter out all the visually unappealing artifacting that results from the inherent mismatch between video and film movement. Moreover, the conversion process usually requires human intervention at every edit point of a video program, so that each type of scene can be calibrated for maximum visual quality. The use of archival footage in video especially calls for extra attention. Step two, the scanning to film, is the rote part of the process. This is the mechanical step where lasers print each of the newly created frames of the 24p video, stored on computer files or HD videotape, onto rolls of film. Most companies that do film-out, do all the stages of the process themselves for a lump sum. The job includes converting interlaced video into 24p and often a color correction session – (calibrating the image for theatrical projection), before scanning to physical film, (possibly followed by color correction of the film print made from the digital intermediary) – is offered. At the very least, film-out can be understood as the process of converting interlaced video to 24p and then scanning it to film. ==== NTSC video ==== NTSC is the most challenging of the formats when it comes to standards conversion and, specifically, converting to film prints. NTSC runs at the approximate rate of 29.97 video frames (consisting of two interlaced screen-fulls of scan lines, called fields, per frame) per second. In this way, NTSC resolves actual live action movement at almost – but not quite – 60 alternating half-resolution images every second. Because of this 29.97 rate, no direct correlation to film frames at 24 frames per second can be achieved. NTSC is hardest to reconcile with film, thus motivating its own unique processes. ==== PAL and SECAM video ==== PAL and SECAM run at 25 interlaced video frames per second, which can be slowed down or frame-dropped, then deinterlaced, to correlate frame for frame with film running at 24 actual frames per second. PAL and SECAM are less complex and demanding than NTSC for film-out. PAL and SECAM conversions do agitate, though, with the unpleasant choice between slowing down video (and audio pitch, noticeably) by four percent, from 25 to 24 frames per second, in order to maintain a 1:1 frame match, slightly changing the rhythm and feel of the program; or maintaining original speed by periodically dropping frames, thereby creating jerkiness and possible loss of vital detail in fast-moving action or precise edits. === High definition (HD) digital video === High definition digital video can be shot at a variety of frame rates, including 29.97 interlaced (like NTSC) or progressive; or 25 interlaced (like PAL) or progressive; or even 24-progressive (just like film). HD, if shot in 24-progressive, scans nearly perfectly to film without the need for a frame or field conversion process. Other issues remain though, based on the different resolutions, color spaces, and compression schemes that exist in the high-definition video world. == Computer graphics and animation == Artists working with CGI-Computer-generated imagery animation computers create pictures frame by frame. Once the finished product is done, the frames are outputted, normally in a DPX file. These picture data files can then be put on to film using a film recorder for film out. SGI computers started the high-end CGI-Computer-generated imagery animation systems, but with faster computers and the growth of Linux-based systems, many others are on the market now. Movies fully rendered and animated in CGI such as Toy Story, and Antz utilize the film-out method to produce 35mm copies for archival and release prints. Most CGI work is done in 2K Display resolution files (about the size of QXGA) and then output to the Film-out device for creation of 35 mm elements. With 4K Display resolution digital intermediates on the rise, newer types of film-out recorders are being developed to accept 4k resolution files. A 2K movie requires a Storage Area Network storage several terabytes in size to be properly stored and played out. Computer graphics files are handled the same way but in single frames and may use DPX, TIFF or other file formats. == Digital intermediates == Film-out-recording is the last step of digital intermediate workflow. DPX files that were scanned on a motion picture film scanner are stored on a storage area network (often abbreviated as SAN). The scanned DPX footage is edited and composited-FX on workstations, then mastered back on film. Film restoration is also done this way. A "film intermediate" is an analog variation of a digital intermediate, where a project shot on digital video is printed onto film stock and transferred back to digital video to emulate film. The term was coined after it was used on the Oscar-winning 2012 short film "Curfew". The process was also used on the films Dune (2021) and The Batman (2022). == Images for graphic design and print industries == The days of newspapers and magazines shooting 35mm film are almost gone. Digital cameras can now shoot all the images needed, storing them as files (e.g. JPEG, DPX or another format) that are readily edited prior to use. Once the final copy is approved, it can be filmed out for publishing. Digital stills are not the only way to get pictures used in the graphic design and print industries. Film scanners and computer graphics programs are also common sources for graphic design and print industries. == Types of devices == The following devices are used in film-out processes: CRT recorder. Camera and a special TV display Kinescope – early type Electronic Video Recording or EVR – early type EBR Electron Beam Film Recorder 16 mm by 3M Laser film recorder, like Kodak's high-end Lightning II recorder and Arri's Arrilaser. DLP Film recorder, like Cinevation's real-time Cinevator. == History == Lately it has become possible to transfer video images, inclu

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  • Read Along

    Read Along

    Read Along, formerly known as Bolo, is an Android language-learning app for children developed by Google for the Android operating system. The application was released on the Play Store on March 7, 2019. It features a character named Diya helping children learn to read through illustrated stories. It has the facility to learn English and Indian major languages i.e. Hindi, Bengali, Tamil, Telugu, Marathi and Urdu, as well as Spanish, Portuguese and Arabic. == Technology == The app uses text-to-speech technology, through which the character named Dia reads the story, as well as speech-to-text technology, which mechanically identifies the matches between the text and the reading of the user. The story of Chhota Bheem and Katha Kids was added in September 2019. In April 2020, a new version of the application was released. In September 2020, it added Arabic language to its language option. A web version was launched in August 2022.

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  • Open-source robotics

    Open-source robotics

    Open-source robotics is a branch of robotics where robots are developed with open-source hardware and free and open-source software, publicly sharing blueprints, schematics, and source code. It is thus closely related to the open design movement, the maker movement and open science. == Requirements == Open source robotics means that information about the hardware is easily discerned, so that others can easily rebuild it. In turn, this requires design to use only easily available standard subcomponents and tools, and for the build process to be documented in detail including a bill of materials and detailed ('Ikea style') step-by-step building and testing instructions. (A CAD file alone is not sufficient, as it does not show the steps for performing or testing the build). These requirements are standard to open source hardware in general, and are formalised by various licences, certifications, especially those defined by the peer-reviewed journals Journal of Open Hardware and HardwareX. Licensing requirements for software are the same as for any open source software. But in addition, for software components to be of practical use in real robot systems, they need to be compatible with other software, usually as defined by some robotics middleware community standard. == Hardware systems == Applications to date include: Robot arms, e.g. PARA or Thor Wheeled mobile robots. e.g. OpenScout Four-legged robots such as the Open Dynamic Robot Initiative UAV quadcopters (drones) such as Agilicious Humanoid robots, e.g. iCub, Berkeley Humanoid Lite Self-driving cars, e.g. OpenPodcar (→ Personal rapid transit) Submersible robots, eg. OpenFish Laboratory robotics such as chemical liquid handling Vertical farming Swarm robots, e.g. HeRoSwarm Domestic tasks: vacuum cleaning, floor washing and grass mowing Robot sports including robot combat and autonomous racing Education == Hardware subcomponents == Most open source hardware definitions allow non-open subcomponents to be used in modular design, as long as they are easily available. However many designs try to push openness down into as many subcomponents as possible, with the aim of ultimately reaching fully open designs. Open hardware manual-drive vehicles and their subcomponents, such as from Open Source Ecology, are often used as starting points and extended with automation systems. Open subcomponents can include open-source computing hardware as subcomponents, such as Arduino and RISC-V, as well as open source motors and drivers such as the Open Source Motor Controller and ODrive. Open hardware robotics interface boards can simplify interfacing between middleware software and physical hardware. == Software subcomponents == === Middleware === Robotics middleware is software which links multiple other software components together. In robotics, this specifically means real-time communication systems with standardized message passing protocols. The predominant open source middleware is ROS2, the robot operating system, now as version 2. Other alternatives include ROS1, YARP — used in the iCub, URBI, and Orca. Open source middleware is usually run on an open source operating system, especially the Ubuntu distribution of Linux. === Driver software === Most robot sensors and actuators require software drivers. There is little standardization of open source software at this level, because each hardware device is different. Creating open drivers for closed hardware is difficult as it requires both low level programming and reverse engineering. === Simulation software === Open source robotics simulators include Gazebo, MuJoCo and Webots. Open source 3D game engines such as Godot are also sometimes used as simulators, when equipped with suitable middleware interfaces. === Automation software === At the level of AI, many standard algorithms have open source software implementations, mostly in ROS2. Major components include: Machine vision systems such as the YOLO object detector. 3D photogrammetry Navigation including SLAM and planning such as nav2 Arm inverse kinematics such as moveIt2 == Community == The first signs of the increasing popularity of building and sharing robot designs were found with the maker culture community. What began with small competitions for remote operated vehicles (e.g. Robot combat), soon developed to the building of autonomous telepresence robots such as Sparky and then true robots (being able to take decisions themselves) as the Open Automaton Project. Several commercial companies now also produce kits for making simple robots. The community has adopted open source hardware licenses, certifications, and peer-reviewed publications, which check that source has been made correctly and permanently available under community definitions, and which validate that this has been done. These processes have become critically important due to many historical projects claiming to be open source but them reverting on the promise due to commercialisation or other pressures. As with other forms of open source hardware, the community continues to debate precise criteria for 'ease of build'. A common standard is that designs should be buildable by a technical university student, in a few days, using typical fablab tools, but definitions of all of these subterms can also be debated. Compared to other forms of open source hardware, open source robotics typically includes a large software element, so involves software as well as hardware engineers. Open source concepts are more established in open source software than hardware, so robotics is a field in which those concepts can be shared and transferred from software to hardware. While the community in open source robotics is multi-faceted with a wide range of backgrounds, a sizable sub-community uses the ROS middleware and meets at the ROSCon conferences to discuss development of ROS itself and automation components built on it.

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  • Procreate (software)

    Procreate (software)

    Procreate is a raster graphics editor app for digital painting developed and published by the Australian company Savage Interactive for iOS and iPadOS. It was launched on the App Store in 2011. == Versions == === Procreate === Procreate for iPad was first released in 2011 by the Tasmanian software company Savage Interactive. In June 2013, Savage launched Procreate 2 in conjunction with iOS 7, adding new features such as higher resolution capabilities and more brush options. In 2016, Procreate became one of the top ten best-selling iPad apps on the App Store. In 2018, Procreate became the overall best selling iPad app. With iOS 26, Procreate adapted Liquid Glass into its software. As of March 2026, the most recent version of Procreate for the iPad is 5.4.9. === Procreate Pocket === Procreate Pocket was released to the App Store in December 2014. In 2018, Savage launched Procreate Pocket 2.0 to the App Store. In December 2018, Procreate Pocket received Apple's "App of the Year" award. As of September 2025, the most recent version of Procreate Pocket (for the iPhone) is 4.0.15. === Procreate Dreams === Procreate Dreams, their more recent app focused on 2D animation, was released on the App Store on November 22, 2023. While the application is commended for its intuitive interface and accessibility, some reviewers have noted that it may lack some key animations features, such as reference layers. In June 2024, Procreate Dreams received the 2024 Apple Design Award for Innovation. In December 2025, Savage Interactive released Procreate Dreams 2, a long awaited update and redesign to Procreate Dreams. == Features == The current versions of Procreate use Valkyrie, a proprietary graphics engine to allow customisable brush options and importing brushes from Adobe Photoshop. Procreate offers known features like layers, masks, and blending mode. Its biggest standout compared to other professional drawing software is its simple UI and comparatively easy learning curve. The app also allows for animation. Savage expanded upon Procreate's animation features with a companion app dedicated to 2D animation called Procreate Dreams, released in November 2023. On August 2024, Procreate announced that it would not be incorporating generative artificial intelligence into its software. Savage offers a free internet forum called Procreate Discussions in which users can ask for help, suggest ideas, and share user-generated content on the marketplace or the resources board. == Notable users == Concept artist Doug Chiang creates robot, vehicle, and creature designs for Star Wars in Procreate. Professional artists have also used Procreate to create the posters for Stranger Things, Logan, and Blade Runner 2049, as well as several covers for The New Yorker. It has also been professionally adopted at Marvel Comics, DC Comics, Disney Animation, and Pixar.

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  • Prism Video Converter

    Prism Video Converter

    Prism is a multi-format video converter developed by NCH Software for Windows and Mac OS. It offers converting tools for instant media conversions. Prism Video Converter can handle large and high-quality resolution media files. It provides built-in compressor and adjuster settings, allowing users to customize and optimize their videos according to their needs. The software also includes features such as previewing videos and adding effects. Prism offers a free version for non-commercial use as well as a premium version. == Features == Prism Video File Converter supports a wide range of file formats. It enables users to convert videos into formats like AVI, ASF, WMV, MP4, 3GP, etc. It offers the ability to convert DVDs into various formats. It provides tools for adjusting colour and filter options. Prism Video File Converter provides several customizable options for tweaking the output files during the conversion process. Users can adjust compression/encoder rates, set the resolution and frame rate, and specify the desired output file size. The software also offers various effects like video rotation, captions, watermarks, and text overlay. It also includes a built-in preview feature, that enables users to view their videos before and after the conversion process. It supports batch conversion and running conversion in background. == Controversy == Previously, Prism and certain other NCH Software products were bundled with optional browser plugins, including the Google Chrome toolbar and the Conduit toolbar. This resulted in user complaints and raised concerns from antivirus software companies like Norton and McAfee, which flagged them as potential malware. NCH Software has since removed all toolbars, browsers, and third-party app offerings in all Prism versions.

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  • Cups (app)

    Cups (app)

    Cups (stylized as CUPS) was a mobile app launched in New York City in April 2014. It was a mobile payment and discovery platform for independent coffee shops nearby. The app was active in more than 400 cafes in New York, San Francisco, Philadelphia, Nashville, Minneapolis and Saint Paul, and other U.S. cities. == History == Cups was founded in Israel in 2012 by Gilad Rotem and four other co-founders, who were all high school friends. The company ran a limited beta pilot in Tel Aviv and Jerusalem, featuring 80 locations, from September 2012 until September 2014. Customers received all-you-can-drink coffee at certain coffee shops in Tel Aviv for approximately $45 a month. In October 2013, the founders relocated to New York. Cups participated in the Entrepreneur's Roundtable Accelerator program and went live in New York in 2014, initially working with 50 small coffee shops in Manhattan and Brooklyn. In early 2016, the company launched 30 locations in Philadelphia in February, followed by 40 more locations in San Francisco in March. == Functionality == The Cups app gave the user a list of the nearest participating coffee shops to their current location. The app user can order a drink using the app and pay the cashier with their phone. The cashier would enter a code that entered the purchase into the app's system. The app also allowed for onboard tipping and food purchases. The company reimbursed the coffee shop and kept a portion of their sales. In early 2016, the Cups Café Network was launched, using bulk purchasing power to land discounts with service providers which would normally be reserved for larger chains. In this way, the company aimed to help its café partners compete with the larger coffee chains.

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  • ActivTrak

    ActivTrak

    ActivTrak is an American company that produces workforce analytics and productivity software. The company was founded in 2009 by Birch Grove Software and is headquartered in Austin, Texas. The company has raised US$77.5 million in funding and is backed by Sapphire Ventures and Elsewhere Partners. == History == ActivTrak was founded in 2009 by Herb Axilrod and Anton Seidler in Dallas, Texas. ActivTrak's first on-demand software product launched in 2012, and the workforce analytics platform launched in 2015. It uses data sourced from more than 9,500 customers and 900,000 users. In 2019, ActivTrak raised $20 million in a Series A round of funding with Elsewhere Partners, a growth-stage venture capital firm that principally invests in B2B startups. Rita Selvaggi assumed the role of CEO. In 2020, ActivTrak raised $50M in a Series B round of funding with Sapphire Ventures and Elsewhere Partners. The company also introduced the ActivTrak Productivity Lab, an online resource about workforce productivity research, industry benchmark data, and best practices. == Product == ActivTrak is a workforce analytics and productivity platform that uses reports, dashboards, and data analysis. The platform uses machine learning (AI) to collect and analyze user activity data and produce reports about workforce productivity. The software runs on Microsoft Windows, Mac, Chrome, Terminal Services, and VDI. It includes the ActivTrak Agent, which runs in the background and collects data. It responds to user activity, sensing mouse and keyboard movement in the active window(s) of the user's device. This data is collected and stored in a database that aggregates the data based on the user's request. ActivTrak does not utilize keystroke logging, content scraping, camera access, video recording or mobile device monitoring. The database leverages data analytics to generate account and team benchmarks, and identify productivity patterns and outliers. == Awards == Built In, 100 Best Midsize Places to Work in Austin, 2025 G2, Winter: Best Estimated ROI, High Performer, Best Relationship, Best Support, Users Most Likely to Recommend, Easiest Setup, Easiest Admin, Best Meets Requirements, Users Love Us, 2025 TrustRadius, Buyer’s Choice, 2025 Deloitte Technology Fast 500, No. 468 Fastest-Growing Company, 2024 Product Marketing Alliance, AI Marketing Innovation, 2024 Fortune Best Workplaces in Technology™, 2024 Inc. 5000, No. 2335 of America’s Fastest-Growing Private Companies, 2024 Fortune Best Workplaces in Texas™, 2024 Reworked IMPACT Gold Award: Most Innovative Workplace Productivity Solution, 2024 TrustRadius, Most Loved, 2024 Great Place To Work-Certified™, 2024 Inc. 5000 Regionals: Southwest, 2024 Brandon Hall Group, Best Advance in HR Predictive Analytics Technology, 2024

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  • Faceu

    Faceu

    FaceU (Chinese: 激萌) is a camera app for smartphones running Android or Apple iOS that edits portrait photographs, typically selfies. This app uses AR technology to allow users to add stickers or effects in real-time when taking selfies and videos. It was launched in 2016 and had 250 million registered users in 2017. Most of the users of Faceu are females from 15 to 35 years old. In February 2018, Faceu was acquired by Chinese media startup Toutiao, which is worth about $300 million. The app was banned in India (along with other Chinese apps) on 2 September 2020 by the government, the move came amid the 2020 China-India skirmish. == Online marketing == FaceU is one of several selfie camera apps in China, including MeituPic, Pitu, and Camera360. The app includes social functions such as instant messaging and video chat. Photos and short videos are deleted after a short period. . FaceU has worked with brands to create themed stickers for social media campaigns. In 2016, Faceu collaborated with MeituPic's Meipai and launched a rainbow effect. In October 2017, during the Mid-Autumn Festival and National Day, FaceU released a feature that applied historical or military costumes to selfies. The app has also worked with various social media personalities and celebrities, who have posted content using FaceU effects. Faceu group engages users' emotions utilizing key opinion leaders (KOL) and posters on social media. == Usage and Demographics == FaceU had a large user base. According to industry sources, the app had more than 90 million monthly active users (MAU) and over 11 million daily active users (DAU) at certain points. Most of the users were under 30 and mainly women. The app was especially popular in major Chinese cities like Beijing, Shanghai, and Guangzhou. FaceU also caught on in other parts of East Asia, particularly Japan and South Korea. Some app stores claim the app had hundreds of millions of users worldwide, but these numbers mostly come from the company’s marketing materials and have not been confirmed by independent sources. == Product Features == FaceU includes face recognition and live augmented reality (AR) effects. It allows users to add filters and stickers in real time while they are recording, rather than having to apply them later. The app integrates beauty filters, tools to create emojis and GIFs, and follow-video functionality that automatically tracks the face and movements as it records. Studies and market reports indicate that augmented reality (AR) filters and beautification tools are now common in smartphone photography. These features have influenced the way people take photos and what they expect photos to look like when shared online. Adding AR filters and beautification options has become a standard feature that most mobile photography apps now include.

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  • AIXI

    AIXI

    AIXI is a theoretical mathematical formalism for artificial general intelligence. It combines Solomonoff induction with sequential decision theory. AIXI was first proposed by Marcus Hutter in 2000 and several results regarding AIXI are proved in Hutter's 2005 book Universal Artificial Intelligence. AIXI is a reinforcement learning (RL) agent. It maximizes the expected total rewards received from the environment. Intuitively, it simultaneously considers every computable hypothesis (or environment). In each time step, it looks at every possible program and evaluates how many rewards that program generates depending on the next action taken. The promised rewards are then weighted by the subjective belief that this program constitutes the true environment. This belief is computed from the length of the program: longer programs are considered less likely, in line with Occam's razor. AIXI then selects the action that has the highest expected total reward in the weighted sum of all these programs. == Etymology == According to Hutter, the word "AIXI" can have several interpretations. AIXI can stand for AI based on Solomonoff's distribution, denoted by ξ {\displaystyle \xi } (which is the Greek letter xi), or e.g. it can stand for AI "crossed" (X) with induction (I). There are other interpretations. == Definition == AIXI is a reinforcement learning agent that interacts with some stochastic and unknown but computable environment μ {\displaystyle \mu } . The interaction proceeds in time steps, from t = 1 {\displaystyle t=1} to t = m {\displaystyle t=m} , where m ∈ N {\displaystyle m\in \mathbb {N} } is the lifespan of the AIXI agent. At time step t, the agent chooses an action a t ∈ A {\displaystyle a_{t}\in {\mathcal {A}}} (e.g. a limb movement) and executes it in the environment, and the environment responds with a "percept" e t ∈ E = O × R {\displaystyle e_{t}\in {\mathcal {E}}={\mathcal {O}}\times \mathbb {R} } , which consists of an "observation" o t ∈ O {\displaystyle o_{t}\in {\mathcal {O}}} (e.g., a camera image) and a reward r t ∈ R {\displaystyle r_{t}\in \mathbb {R} } , distributed according to the conditional probability μ ( o t r t | a 1 o 1 r 1 . . . a t − 1 o t − 1 r t − 1 a t ) {\displaystyle \mu (o_{t}r_{t}|a_{1}o_{1}r_{1}...a_{t-1}o_{t-1}r_{t-1}a_{t})} , where a 1 o 1 r 1 . . . a t − 1 o t − 1 r t − 1 a t {\displaystyle a_{1}o_{1}r_{1}...a_{t-1}o_{t-1}r_{t-1}a_{t}} is the "history" of actions, observations and rewards. The environment μ {\displaystyle \mu } is thus mathematically represented as a probability distribution over "percepts" (observations and rewards) which depend on the full history, so there is no Markov assumption (as opposed to other RL algorithms). Note again that this probability distribution is unknown to the AIXI agent. Furthermore, note again that μ {\displaystyle \mu } is computable, that is, the observations and rewards received by the agent from the environment μ {\displaystyle \mu } can be computed by some program (which runs on a Turing machine), given the past actions of the AIXI agent. The only goal of the AIXI agent is to maximize ∑ t = 1 m r t {\displaystyle \sum _{t=1}^{m}r_{t}} , that is, the sum of rewards from time step 1 to m. The AIXI agent is associated with a stochastic policy π : ( A × E ) ∗ → A {\displaystyle \pi :({\mathcal {A}}\times {\mathcal {E}})^{}\rightarrow {\mathcal {A}}} , which is the function it uses to choose actions at every time step, where A {\displaystyle {\mathcal {A}}} is the space of all possible actions that AIXI can take and E {\displaystyle {\mathcal {E}}} is the space of all possible "percepts" that can be produced by the environment. The environment (or probability distribution) μ {\displaystyle \mu } can also be thought of as a stochastic policy (which is a function): μ : ( A × E ) ∗ × A → E {\displaystyle \mu :({\mathcal {A}}\times {\mathcal {E}})^{}\times {\mathcal {A}}\rightarrow {\mathcal {E}}} , where the ∗ {\displaystyle } is the Kleene star operation. In general, at time step t {\displaystyle t} (which ranges from 1 to m), AIXI, having previously executed actions a 1 … a t − 1 {\displaystyle a_{1}\dots a_{t-1}} (which is often abbreviated in the literature as a < t {\displaystyle a_{ Read more →

  • Human–robot interaction

    Human–robot interaction

    Human–robot interaction (HRI) is the study of interactions between humans and robots. Human–robot interaction is a multidisciplinary field with contributions from human–computer interaction, artificial intelligence, robotics, natural language processing, design, psychology and philosophy. A subfield known as physical human–robot interaction (pHRI) has tended to focus on device design to enable people to safely interact with robotic systems. == Origins == Human–robot interaction has been a topic of both science fiction and academic speculation even before any robots existed. Because much of active HRI development depends on natural language processing, many aspects of HRI are continuations of human communications, a field of research which is much older than robotics. The origin of HRI as a discrete problem was stated by 20th-century author Isaac Asimov in 1941, in his novel I, Robot. Asimov coined Three Laws of Robotics, namely: A robot may not injure a human being or, through inaction, allow a human being to come to harm. A robot must obey the orders given it by human beings except where such orders would conflict with the First Law. A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws. These three laws provide an overview of the goals engineers and researchers hold for safety in the HRI field, although the fields of robot ethics and machine ethics are more complex than these three principles. However, generally human–robot interaction prioritizes the safety of humans that interact with potentially dangerous robotics equipment. Solutions to this problem range from the philosophical approach of treating robots as ethical agents (individuals with moral agency), to the practical approach of creating safety zones. These safety zones use technologies such as lidar to detect human presence or physical barriers to protect humans by preventing any contact between machine and operator. Although initially robots in the human–robot interaction field required some human intervention to function, research has expanded this to the extent that fully autonomous systems are now far more common than in the early 2000s. Autonomous systems include from simultaneous localization and mapping systems which provide intelligent robot movement to natural-language processing and natural-language generation systems which allow for natural, human-esque interaction which meet well-defined psychological benchmarks. Anthropomorphic robots (machines which imitate human body structure) are better described by the biomimetics field, but overlap with HRI in many research applications. Examples of robots which demonstrate this trend include Willow Garage's PR2 robot, the NASA Robonaut, and Honda ASIMO. However, robots in the human–robot interaction field are not limited to human-like robots: Paro and Kismet are both robots designed to elicit emotional response from humans, and so fall into the category of human–robot interaction. Goals in HRI range from industrial manufacturing through Cobots, medical technology through rehabilitation, autism intervention, and elder care devices, entertainment, human augmentation, and human convenience. Future research therefore covers a wide range of fields, much of which focuses on assistive robotics, robot-assisted search-and-rescue, and space exploration. == The goal of friendly human–robot interactions == Robots are artificial agents with capacities of perception and action in the physical world often referred by researchers as workspace. Their use has been generalized in factories but nowadays they tend to be found in the most technologically advanced societies in such critical domains as search and rescue, military battle, mine and bomb detection, scientific exploration, law enforcement, entertainment and hospital care. These new domains of applications imply a closer interaction with the user, sharing the workspace but also goals in terms of task achievement. The subfield of physical human–robot interaction (pHRI) has largely focused on device design to enable people to safely interact with robotic systems but is increasingly developing algorithmic approaches in an attempt to support fluent and expressive interactions between humans and robotic systems. With the advance in AI, the research is focusing on one part towards the safest physical interaction but also on a socially correct interaction, dependent on cultural criteria. The goal is to build an intuitive, and easy communication with the robot through speech, gestures, and facial expressions. Kerstin Dautenhahn refers to friendly Human–robot interaction as "Robotiquette" defining it as the "social rules for robot behaviour (a 'robotiquette') that is comfortable and acceptable to humans" The robot has to adapt itself to our way of expressing desires and orders and not the contrary. But every day environments such as homes have much more complex social rules than those implied by factories or even military environments. Thus, the robot needs perceiving and understanding capacities to build dynamic models of its surroundings. It needs to categorize objects, recognize and locate humans and further recognize their emotions. The need for dynamic capacities pushes forward every sub-field of robotics. Furthermore, by understanding and perceiving social cues, robots can enable collaborative scenarios with humans. For example, with the rapid rise of personal fabrication machines such as desktop 3D printers, laser cutters, etc., entering our homes, scenarios may arise where robots can collaboratively share control, co-ordinate and achieve tasks together. Industrial robots have already been integrated into industrial assembly lines and are collaboratively working with humans. The social impact of such robots have been studied and has indicated that workers still treat robots and social entities, rely on social cues to understand and work together. On the other end of HRI research the cognitive modelling of the "relationship" between human and the robots benefits the psychologists and robotic researchers the user study are often of interests on both sides. This research endeavours part of human society. For effective human – humanoid robot interaction numerous communication skills and related features should be implemented in the design of such artificial agents/systems. == General HRI research == HRI research spans a wide range of fields, some general to the nature of HRI. === Methods for perceiving humans === Methods for perceiving humans in the environment are based on sensor information. Research on sensing components and software led by Microsoft provide useful results for extracting the human kinematics (see Kinect). An example of older technique is to use colour information for example the fact that for light skinned people the hands are lighter than the clothes worn. In any case a human modelled a priori can then be fitted to the sensor data. The robot builds or has (depending on the level of autonomy the robot has) a 3D mapping of its surroundings to which is assigned the humans locations. Most methods intend to build a 3D model through vision of the environment. The proprioception sensors permit the robot to have information over its own state. This information is relative to a reference. Theories of proxemics may be used to perceive and plan around a person's personal space. A speech recognition system is used to interpret human desires or commands. By combining the information inferred by proprioception, sensor and speech the human position and state (standing, seated). In this matter, natural-language processing is concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural-language data. For instance, neural-network architectures and learning algorithms that can be applied to various natural-language processing tasks including part-of-speech tagging, chunking, named-entity recognition, and semantic role labeling. === Methods for motion planning === Motion planning in dynamic environments is a challenge that can at the moment only be achieved for robots with 3 to 10 degrees of freedom. Humanoid robots or even 2 armed robots, which can have up to 40 degrees of freedom, are unsuited for dynamic environments with today's technology. However lower-dimensional robots can use the potential field method to compute trajectories which avoid collisions with humans. === Cognitive models and theory of mind === Humans exhibit negative social and emotional responses as well as decreased trust toward some robots that closely, but imperfectly, resemble humans; this phenomenon has been termed the "Uncanny Valley". However recent research in telepresence robots has established that mimicking human body postures and expressive gestures has made the robots likeable and engaging in a remote setting. Further, the presence o

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  • Frameserver

    Frameserver

    A frameserver is any program that acts as a media source in the process called frameserving, which transfers digital video data from one computer program to another without intermediate files. The program that receives the data – the frameclient – could be any type of video application. The process is controlled by the frameclient: the frameclient requests audio/video frames and the frameserver serves them. The client can request frames in any order, allowing it to pause or jump to an arbitrary frame, just as a media player does with a file on disk. The client is most commonly a media encoder, a non-linear editing system, or a media player. == Frameservers == AviSynth VirtualDub VapourSynth Debugmode FrameServer

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