In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts. Hyperparameter optimization determines the set of hyperparameters that yields an optimal model which minimizes a predefined loss function on a given data set. The objective function takes a set of hyperparameters and returns the associated loss. Cross-validation is often used to estimate this generalization performance, and therefore choose the set of values for hyperparameters that maximize it. == Approaches == === Grid search === The traditional method for hyperparameter optimization has been grid search, or a parameter sweep, which is simply an exhaustive searching through a manually specified subset of the hyperparameter space of a learning algorithm. A grid search algorithm must be guided by some performance metric, typically measured by cross-validation on the training set or evaluation on a hold-out validation set. Since the parameter space of a machine learner may include real-valued or unbounded value spaces for certain parameters, manually set bounds and discretization may be necessary before applying grid search. For example, a typical soft-margin SVM classifier equipped with an RBF kernel has at least two hyperparameters that need to be tuned for good performance on unseen data: a regularization constant C and a kernel hyperparameter γ. Both parameters are continuous, so to perform grid search, one selects a finite set of "reasonable" values for each, say C ∈ { 10 , 100 , 1000 } {\displaystyle C\in \{10,100,1000\}} γ ∈ { 0.1 , 0.2 , 0.5 , 1.0 } {\displaystyle \gamma \in \{0.1,0.2,0.5,1.0\}} Grid search then trains an SVM with each pair (C, γ) in the Cartesian product of these two sets and evaluates their performance on a held-out validation set (or by internal cross-validation on the training set, in which case multiple SVMs are trained per pair). Finally, the grid search algorithm outputs the settings that achieved the highest score in the validation procedure. Grid search suffers from the curse of dimensionality, but is often embarrassingly parallel because the hyperparameter settings it evaluates are typically independent of each other. === Random search === Random Search replaces the exhaustive enumeration of all combinations by selecting them randomly. This can be simply applied to the discrete setting described above, but also generalizes to continuous and mixed spaces. A benefit over grid search is that random search can explore many more values than grid search could for continuous hyperparameters. It can outperform Grid search, especially when only a small number of hyperparameters affects the final performance of the machine learning algorithm. In this case, the optimization problem is said to have a low intrinsic dimensionality. Random Search is also embarrassingly parallel, and additionally allows the inclusion of prior knowledge by specifying the distribution from which to sample. Despite its simplicity, random search remains one of the important base-lines against which to compare the performance of new hyperparameter optimization methods. === Bayesian optimization === Bayesian optimization is a global optimization method for noisy black-box functions. Applied to hyperparameter optimization, Bayesian optimization builds a probabilistic model of the function mapping from hyperparameter values to the objective evaluated on a validation set. By iteratively evaluating a promising hyperparameter configuration based on the current model, and then updating it, Bayesian optimization aims to gather observations revealing as much information as possible about this function and, in particular, the location of the optimum. It tries to balance exploration (hyperparameters for which the outcome is most uncertain) and exploitation (hyperparameters expected close to the optimum). In practice, Bayesian optimization has been shown to obtain better results in fewer evaluations compared to grid search and random search, due to the ability to reason about the quality of experiments before they are run. === Gradient-based optimization === For specific learning algorithms, it is possible to compute the gradient with respect to hyperparameters and then optimize the hyperparameters using gradient descent. The first usage of these techniques was focused on neural networks. Since then, these methods have been extended to other models such as support vector machines or logistic regression. A different approach in order to obtain a gradient with respect to hyperparameters consists in differentiating the steps of an iterative optimization algorithm using automatic differentiation. A more recent work along this direction uses the implicit function theorem to calculate hypergradients and proposes a stable approximation of the inverse Hessian. The method scales to millions of hyperparameters and requires constant memory. In a different approach, a hypernetwork is trained to approximate the best response function. One of the advantages of this method is that it can handle discrete hyperparameters as well. Self-tuning networks offer a memory efficient version of this approach by choosing a compact representation for the hypernetwork. More recently, Δ-STN has improved this method further by a slight reparameterization of the hypernetwork which speeds up training. Δ-STN also yields a better approximation of the best-response Jacobian by linearizing the network in the weights, hence removing unnecessary nonlinear effects of large changes in the weights. Apart from hypernetwork approaches, gradient-based methods can be used to optimize discrete hyperparameters also by adopting a continuous relaxation of the parameters. Such methods have been extensively used for the optimization of architecture hyperparameters in neural architecture search. === Evolutionary optimization === Evolutionary optimization is a methodology for the global optimization of noisy black-box functions. In hyperparameter optimization, evolutionary optimization uses evolutionary algorithms to search the space of hyperparameters for a given algorithm. Evolutionary hyperparameter optimization follows a process inspired by the biological concept of evolution: Create an initial population of random solutions (i.e., randomly generate tuples of hyperparameters, typically 100+) Evaluate the hyperparameter tuples and acquire their fitness function (e.g., 10-fold cross-validation accuracy of the machine learning algorithm with those hyperparameters) Rank the hyperparameter tuples by their relative fitness Replace the worst-performing hyperparameter tuples with new ones generated via crossover and mutation Repeat steps 2-4 until satisfactory algorithm performance is reached or is no longer improving. Evolutionary optimization has been used in hyperparameter optimization for statistical machine learning algorithms, automated machine learning, typical neural network and deep neural network architecture search, as well as training of the weights in deep neural networks. === Population-based === Population Based Training (PBT) learns both hyperparameter values and network weights. Multiple learning processes operate independently, using different hyperparameters. As with evolutionary methods, poorly performing models are iteratively replaced with models that adopt modified hyperparameter values and weights based on the better performers. This replacement model warm starting is the primary differentiator between PBT and other evolutionary methods. PBT thus allows the hyperparameters to evolve and eliminates the need for manual hypertuning. The process makes no assumptions regarding model architecture, loss functions or training procedures. PBT and its variants are adaptive methods: they update hyperparameters during the training of the models. On the contrary, non-adaptive methods have the sub-optimal strategy to assign a constant set of hyperparameters for the whole training. === Early stopping-based === A class of early stopping-based hyperparameter optimization algorithms is purpose-built for large search spaces of continuous and discrete hyperparameters, particularly when the computational cost to evaluate the performance of a set of hyperparameters is high. Irace implements the iterated racing algorithm, that focuses the search around the most promising configurations, using statistical tests to discard the ones that perform poorly. Another early stopping hyperparameter optimization algorithm is successive halving (SHA), which begins as a random search but periodically prunes low-performing models, thereby focusing computational resources on more promising models. Asynchronous successive halving (ASHA) further improves upon SHA's resource utilization profile by removing the need to synchronously evaluate a
Machine vision
Machine vision is the technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, process control, and robot guidance, usually in industry. Machine vision refers to many technologies, software and hardware products, integrated systems, actions, methods and expertise. Machine vision as a systems engineering discipline can be considered distinct from computer vision, a form of computer science. It attempts to integrate existing technologies in new ways and apply them to solve real world problems. The term is the prevalent one for these functions in industrial automation environments but is also used for these functions in other environment vehicle guidance. The overall machine vision process includes planning the details of the requirements and project, and then creating a solution. During run-time, the process starts with imaging, followed by automated analysis of the image and extraction of the required information. == Definition == Definitions of the term "Machine vision" vary, but all include the technology and methods used to extract information from an image on an automated basis, as opposed to image processing, where the output is another image. The information extracted can be a simple good-part/bad-part signal, or more a complex set of data such as the identity, position and orientation of each object in an image. The information can be used for such applications as automatic inspection and robot and process guidance in industry, for security monitoring and vehicle guidance. This field encompasses a large number of technologies, software and hardware products, integrated systems, actions, methods and expertise. Machine vision is practically the only term used for these functions in industrial automation applications; the term is less universal for these functions in other environments such as security and vehicle guidance. Machine vision as a systems engineering discipline can be considered distinct from computer vision, a form of basic computer science; machine vision attempts to integrate existing technologies in new ways and apply them to solve real world problems in a way that meets the requirements of industrial automation and similar application areas. The term is also used in a broader sense by trade shows and trade groups such as the Automated Imaging Association and the European Machine Vision Association. This broader definition also encompasses products and applications most often associated with image processing. The primary uses for machine vision are automatic inspection and industrial robot/process guidance. In more recent times the terms computer vision and machine vision have converged to a greater degree. See glossary of machine vision. == Imaging based automatic inspection and sorting == The primary uses for machine vision are imaging-based automatic inspection and sorting and robot guidance.; in this section the former is abbreviated as "automatic inspection". The overall process includes planning the details of the requirements and project, and then creating a solution. This section describes the technical process that occurs during the operation of the solution. === Methods and sequence of operation === The first step in the automatic inspection sequence of operation is acquisition of an image, typically using cameras, lenses, and lighting that has been designed to provide the differentiation required by subsequent processing. MV software packages and programs developed in them then employ various digital image processing techniques to extract the required information, and often make decisions (such as pass/fail) based on the extracted information. === Equipment === The components of an automatic inspection system usually include lighting, a camera or other imager, a processor, software, and output devices. === Imaging === The imaging device (e.g. camera) can either be separate from the main image processing unit or combined with it in which case the combination is generally called a smart camera or smart sensor. Inclusion of the full processing function into the same enclosure as the camera is often referred to as embedded processing. When separated, the connection may be made to specialized intermediate hardware, a custom processing appliance, or a frame grabber within a computer using either an analog or standardized digital interface (Camera Link, CoaXPress). MV implementations also use digital cameras capable of direct connections (without a framegrabber) to a computer via FireWire, USB or Gigabit Ethernet interfaces. While conventional (2D visible light) imaging is most commonly used in MV, alternatives include multispectral imaging, hyperspectral imaging, imaging various infrared bands, line scan imaging, 3D imaging of surfaces and X-ray imaging. Key differentiations within MV 2D visible light imaging are monochromatic vs. color, frame rate, resolution, and whether or not the imaging process is simultaneous over the entire image, making it suitable for moving processes. Though the vast majority of machine vision applications are solved using two-dimensional imaging, machine vision applications utilizing 3D imaging are a growing niche within the industry. The most commonly used method for 3D imaging is scanning based triangulation which utilizes motion of the product or image during the imaging process. A laser is projected onto the surfaces of an object. In machine vision this is accomplished with a scanning motion, either by moving the workpiece, or by moving the camera & laser imaging system. The line is viewed by a camera from a different angle; the deviation of the line represents shape variations. Lines from multiple scans are assembled into a depth map or point cloud. Stereoscopic vision is used in special cases involving unique features present in both views of a pair of cameras. Other 3D methods used for machine vision are time of flight and grid based. One method is grid array based systems using pseudorandom structured light system as employed by the Microsoft Kinect system circa 2012. === Image processing === After an image is acquired, it is processed. Central processing functions are generally done by a CPU, a GPU, a FPGA or a combination of these. Deep learning training and inference impose higher processing performance requirements. Multiple stages of processing are generally used in a sequence that ends up as a desired result. A typical sequence might start with tools such as filters which modify the image, followed by extraction of objects, then extraction (e.g. measurements, reading of codes) of data from those objects, followed by communicating that data, or comparing it against target values to create and communicate "pass/fail" results. Machine vision image processing methods include; Stitching/Registration: Combining of adjacent 2D or 3D images. Filtering (e.g. morphological filtering) Thresholding: Thresholding starts with setting or determining a gray value that will be useful for the following steps. The value is then used to separate portions of the image, and sometimes to transform each portion of the image to simply black and white based on whether it is below or above that grayscale value. Pixel counting: counts the number of light or dark pixels Segmentation: Partitioning a digital image into multiple segments to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Edge detection: finding object edges Color Analysis: Identify parts, products and items using color, assess quality from color, and isolate features using color. Blob detection and extraction: inspecting an image for discrete blobs of connected pixels (e.g. a black hole in a grey object) as image landmarks. Neural network / deep learning / machine learning processing: weighted and self-training multi-variable decision making Circa 2019 there is a large expansion of this, using deep learning and machine learning to significantly expand machine vision capabilities. The most common result of such processing is classification. Examples of classification are object identification,"pass fail" classification of identified objects and OCR. Pattern recognition including template matching. Finding, matching, and/or counting specific patterns. This may include location of an object that may be rotated, partially hidden by another object, or varying in size. Barcode, Data Matrix and "2D barcode" reading Optical character recognition: automated reading of text such as serial numbers Gauging/Metrology: measurement of object dimensions (e.g. in pixels, inches or millimeters) Comparison against target values to determine a "pass or fail" or "go/no go" result. For example, with code or bar code verification, the read value is compared to the stored target value. For gauging, a measurement is compared against the proper value and tolerances. For verification of alpha-numberic codes, the
Deluxe Media
Deluxe Media Inc., also known simply as Deluxe and formerly Deluxe Entertainment Services Group, Inc., is an American multinational multimedia and entertainment service provisions company owned by Platinum Equity, founded in 1915 by Hungarian-born American film producer William Fox and headquartered in Burbank, California. The company services multiple clients in the film, television, digital content and advertising industries across the globe, and has been recognized with 10 Academy Awards for scientific and technical achievements, including developments in CinemaScope pictures (as part of 20th Century Fox) and more recently for a process of creating archival separations from digital image data. == History == Deluxe began as a film processing laboratory established in 1915 by William Fox under the name De Luxe as part of his eponymous film conglomerate corporation in Fort Lee, New Jersey. In 1916, Fox Film Corporation opened its studio in Hollywood on 13 acres at Sunset and Western. The first Deluxe film laboratory on the west coast was built on the south side of the lot (Fernwood and Serrano), and the laboratory was moved to the new Fox studios building on Manhattan's west side in 1919, where it remained for over 40 years. The "business manager" (later president) of the laboratory was Alan E. Freedman, who guided the company into the 1960s. In 1927, Fox (Deluxe) received a patent for sound-on-film, the Fox Movietone system. In 1927, "Sunrise: A Song of Two Humans," an early Movietone film, opened. Fox Movietone News, ran weekly in theaters until 1963. During the Great Depression, Fox Film Corporation encountered financial difficulties. Among the actions taken to maintain liquidity, Fox sold the laboratories in 1932 to Freedman, who renamed the operation Deluxe. Under Freedman's leadership, Deluxe added two more plants in Chicago and Toronto. In January 1934, Fox was granted an option to rebuy DeLuxe before December 31, 1938. On 31 May 1935, under Sidney Kent, Fox merged his film company with Twentieth Century Pictures to form The Twentieth Century-Fox Film Corporation following a bank-infused reorganisation. The merged company then exercised this option in July 1936, with Freedman remaining as president. In 1953, Deluxe developed the widescreen format CinemaScope. Titles included "There's No Business Like Show Business" (1954) and "The Seven Year Itch" (1955). Other innovations included the processing and sound striping of CinemaScope, and were patented and/or received Academy awards. In 1962 Freedman retired. In the 1960s, Deluxe closed its New York plant, followed by its plants in Chicago and Toronto, as motion picture production declined on the East Coast. In 1972, Deluxe began large volume videocassette production, with a billion by 1996. In 1990, The Rank Organisation acquired Deluxe from Fox. In 2000, Deluxe began large volume DVD production. In 2006, The Rank Organisation sold Deluxe Film Group to MacAndrews & Forbes, renamed Deluxe Entertainment Services Group. On 9 February 2012, Deluxe acquired Hong Kong–based visual effects and post-production company, Centro Digital Pictures, with its founder John Chu remaining as president while reporting to Alaric McAusland, managing director for Deluxe in Australia. In May 2014, Deluxe shut down its Los Angeles plant at Sunset & Western Studios complex, where other studios themselves were demolished way back in 1971. Also that same year, Deluxe closed the Hollywood film labs, and they gave thousands of orphaned film elements to the Academy Film Archive. The Deluxe Laboratories Collection at the Academy Film Archive consists of over 7,500 35mm and 16mm film elements of various motion pictures dating back to the early 1960s. On 22 April 2015, Deluxe and its longtime competitor, Technicolor S.A., announced that they had entered into a binding agreement to create a new joint venture known as Deluxe Technicolor Digital Cinema which will specialize in cinema mastering, distribution and management services. Deluxe got acquired on 4 September 2019 by creditors in a debt-for-equity swap to avoid bankruptcy. On 3 October 2019, Deluxe filed for bankruptcy, pending in the Southern District of New York. The same month on the 24th, the company received court approval to emerge from bankruptcy with a comprehensive restructuring plan. On July 1, 2020, Platinum Equity agreed to acquire the distribution division of Deluxe and re-unite with former CEO Cyril Drabinsky who would merge CineVizion, a film distribution company he founded after leaving Deluxe in 2016, into it. The companies Company 3 and Method Studios which formed the creative divisions of Deluxe were sold to Framestore in November 2020.
Digital cinema
Digital cinema is the digital technology used within the film industry to distribute or project motion pictures as opposed to the historical use of reels of motion picture film, such as 35 mm film. Whereas film reels have to be shipped to movie theaters, a digital movie can be distributed to cinemas in a number of ways: over the Internet or dedicated satellite links, or by sending hard drives or optical discs such as Blu-ray discs, then projected using a digital video projector instead of a film projector. Typically, digital movies are shot using digital movie cameras or in animation transferred from a file and are edited using a non-linear editing system (NLE). The NLE is often a video editing application installed in one or more computers that may be networked to access the original footage from a remote server, share or gain access to computing resources for rendering the final video, and allow several editors to work on the same timeline or project. Alternatively a digital movie could be a film reel that has been digitized using a motion picture film scanner and then restored, or, a digital movie could be recorded using a film recorder onto film stock for projection using a traditional film projector. Digital cinema is distinct from high-definition television and does not necessarily use traditional television or other traditional high-definition video standards, aspect ratios, or frame rates. In digital cinema, resolutions are represented by the horizontal pixel count, usually 2K (2048×1080 or 2.2 megapixels) or 4K (4096×2160 or 8.8 megapixels). The 2K and 4K resolutions used in digital cinema projection are often referred to as DCI 2K and DCI 4K. DCI stands for Digital Cinema Initiatives. As digital cinema technology improved in the early 2010s, most theaters across the world converted to digital video projection. Digital cinema technology has continued to develop over the years with RealD 3D, IMAX, RPX, 4DX, Dolby Cinema, and ScreenX, allowing moviegoers more immersive experiences. == History == The transition from film to digital video was preceded by cinema's transition from analog to digital audio, with the release of the Dolby Digital (AC-3) audio coding standard in 1991. Its main basis is the modified discrete cosine transform (MDCT), a lossy audio compression algorithm. It is a modification of the discrete cosine transform (DCT) algorithm, which was first proposed by Nasir Ahmed in 1972 and was originally intended for image compression. The DCT was adapted into the MDCT by J.P. Princen, A.W. Johnson and Alan B. Bradley at the University of Surrey in 1987, and then Dolby Laboratories adapted the MDCT algorithm along with perceptual coding principles to develop the AC-3 audio format for cinema needs. Cinema in the 1990s typically combined analog photochemical images with digital audio. Digital media playback of high-resolution 2K files has at least a 20-year history. Early video data storage units (RAIDs) fed custom frame buffer systems with large memories. In early digital video units, the content was usually restricted to several minutes of material. Transfer of content between remote locations was slow and had limited capacity. It was not until the late 1990s that feature-length films could be sent over the "wire" (Internet or dedicated fiber links). On October 23, 1998, Digital light processing (DLP) projector technology was publicly demonstrated with the release of The Last Broadcast, the first feature-length movie, shot, edited and distributed digitally. In conjunction with Texas Instruments, the movie was publicly demonstrated in five theaters across the United States (Philadelphia, Portland (Oregon), Minneapolis, Providence, and Orlando). === Foundations === In the United States, on June 18, 1999, Texas Instruments' DLP Cinema projector technology was publicly demonstrated on two screens in Los Angeles and New York for the release of Lucasfilm's Star Wars Episode I: The Phantom Menace. In Europe, on February 2, 2000, Texas Instruments' DLP Cinema projector technology was publicly demonstrated, by Philippe Binant, on one screen in Paris for the release of Toy Story 2. From 1997 to 2000, the JPEG 2000 image compression standard was developed by a Joint Photographic Experts Group (JPEG) committee chaired by Touradj Ebrahimi (later the JPEG president). In contrast to the original 1992 JPEG standard, which is a DCT-based lossy compression format for static digital images, JPEG 2000 is a discrete wavelet transform (DWT) based compression standard that could be adapted for motion imaging video compression with the Motion JPEG 2000 extension. JPEG 2000 technology was later selected as the video coding standard for digital cinema in 2004. In 1992, Hughes-JVC was founded by JVC and Hughes Electronics to develop ILA (Image Light Amplifer) digital video projectors for commercial movie theaters using liquid crystal on silicon (LCOS) technology. In 1997, JVC introduced D-ILA (Direct-Drive ILA) technology with a 2K resolution digital video projector. In 2000, JVC introduced a 4K resolution video projector using D-ILA technology. === Initiatives === On January 19, 2000, the Society of Motion Picture and Television Engineers, in the United States, initiated the first standards group dedicated to developing digital cinema. By December 2000, there were 15 digital cinema screens in the United States and Canada, 11 in Western Europe, 4 in Asia, and 1 in South America. Digital Cinema Initiatives (DCI) was formed in March 2002 as a joint project of many motion picture studios (Disney, Fox, MGM, Paramount, Sony Pictures, Universal and Warner Bros.) to develop a system specification for digital cinema. The same month it was reported that the number of cinemas equipped with digital projectors had increased to about 50 in the US and 30 more in the rest of the world. In April 2004, in collaboration with the American Society of Cinematographers, DCI created standard evaluation material (the ASC/DCI StEM material) for testing of 2K and 4K playback and compression technologies. DCI selected JPEG 2000 as the basis for the compression in the system the same year. Initial tests with JPEG 2000 produced bit rates of around 75–125 Mbit/s for 2K resolution and 100–200 Mbit/s for 4K resolution. === Worldwide deployment === In China, in June 2005, an e-cinema system called "dMs" was established and was used in over 15,000 screens spread across China's 30 provinces. DMs estimated that the system would expand to 40,000 screens in 2009. In 2005, the UK Film Council Digital Screen Network launched in the UK by Arts Alliance Media creating a chain of 250 2K digital cinema systems. The roll-out was completed in 2006. This was the first mass roll-out in Europe. AccessIT/Christie Digital also started a roll-out in the United States and Canada. By mid-2006, about 400 theaters were equipped with 2K digital projectors with the number increasing every month. In August 2006, the Malayalam digital movie Moonnamathoral, produced by Benzy Martin, was distributed via satellite to cinemas, thus becoming the first Indian digital cinema. This was done by Emil and Eric Digital Films, a company based at Thrissur using the end-to-end digital cinema system developed by Singapore-based DG2L Technologies. In January 2007, Guru became the first Indian film mastered in the DCI-compliant JPEG 2000 Interop format and also the first Indian film to be previewed digitally, internationally, at the Elgin Winter Garden in Toronto. This film was digitally mastered at Real Image Media Technologies in India. In 2007, the UK became home to Europe's first DCI-compliant fully digital multiplex cinemas; Odeon Hatfield and Odeon Surrey Quays (in London), with a total of 18 digital screens, were launched on 9 February 2007. By March 2007, with the release of Disney's Meet the Robinsons, about 600 screens had been equipped with digital projectors. In June 2007, Arts Alliance Media announced the first European commercial digital cinema Virtual Print Fee (VPF) agreements (with 20th Century Fox and Universal Pictures). In March 2009, AMC Theatres announced that it closed a $315 million deal with Sony to replace all of its movie projectors with 4K HDR digital projectors starting in the second quarter of 2009; it was anticipated that this replacement would be finished by 2012. As digital cinema technology improved in the early 2010s, most theaters across the world converted to digital video projection. In January 2011, the total number of digital screens worldwide was 36,242, up from 16,339 at end 2009 or a growth rate of 121.8 percent during the year. There were 10,083 d-screens in Europe as a whole (28.2 percent of global figure), 16,522 in the United States and Canada (46.2 percent of global figure) and 7,703 in Asia (21.6 percent of global figure). Worldwide progress was slower as in some territories, particularly Latin America and Africa. As of 31 March 2015, 38,719 screens (out of a total of 3
Ambient awareness
Ambient awareness (AmA) is a term used by social scientists to describe a form of peripheral social awareness through social media. This awareness is propagated from relatively constant contact with one's friends and colleagues via social networking platforms on the Internet. The term essentially defines the sort of omnipresent knowledge one experiences by being a regular user of these media outlets that allow a constant connection with one's social circle. According to Clive Thompson of The New York Times, ambient awareness is "very much like being physically near someone and picking up on mood through the little things; body language, sighs, stray comments". Academic Andreas Kaplan defines ambient awareness as "awareness created through regular and constant reception, and/or exchange of information fragments through social media". Two friends who regularly follow one another's digital information can already be aware of each other's lives without actually being physically present to have had a conversation. == Social == Socially speaking, ambient awareness and social media are products of the new generations who are being born or growing up in the digital age, starting circa 1998 and running to current times. Social media is personal media (what you're doing in the moment, how you feel, a picture of where you are) combined with social communication. Social media is the lattice work for ambient awareness. Without social media the state of ambient awareness cannot exist. Artificial Social Networking Intelligence (ASNI) refers to the application of artificial intelligence within social networking services and social media platforms. It encompasses various technologies and techniques used to automate, personalize, enhance, improve, and synchronize user's interactions and experiences within social networks. ASNI is expected to evolve rapidly, influencing how we interact online and shaping their digital experiences. Transparency, ethical considerations, media influence bias, and user control over data will be crucial to ensure responsible development and positive impact. A significant feature of social media is that it is created by those who also consume it. Mostly, those participating in this phenomenon are adolescents, college age, or young adult professionals. According to Dr. Mimi Ito, a cultural anthropologist and Professor in Residence at the University of California at Irvine, the mobile device is the greatest proxy device used to create and distribute Social Media. She reportedly states that "teenagers capture and produce their own media, and stay in constant ambient contact with each other..." using mobile devices. Usually while doing this they are consuming other forms of media such as music or video content via their smart phones, tablets, or other similar devices. Effectively this has led social scientists to believe that learning and multitasking will have a new face as the products of the digital generation enter the work force and begin to integrate their learning methods into the standard preexisting business models of today. Professors Kaplan and Haenlein see ambient awareness as one of the major reasons for the success of such microblogging sites as Twitter. == Origins == The earliest available technology that could be used for constant social contact is the cell phone. For the first time, people could be contacted readily and at will beyond the confines of their work or homes. Then later, with the additional service of texting, one can see the somewhat primitive form of the status update. Since the text message only allows for 160 characters to transmit pertinent information it paved the way for the status update as we know it today. The transition from only having a few points of regular long distance contact, to being constantly available via cell phone, is what primed society for social networking websites. Perhaps the first instance where these websites created the possibility of larger scale ambient awareness was when Facebook installed the news feed. The news feed automatically sends compiled information on all of a users contacts activities directly to them so that they can access all of the happenings in their world from one location. For the first time, becoming someone's Facebook friend was the equivalent of subscribing to a feed of their daily minutiae. Since this innovation, a new wave of micro-blogging services have emerged, such as Twitter or Tumblr. Although these services have often been criticized as containing seemingly meaningless snippets of information, when a follower gathers a certain amount of information, they begin to obtain an ambient understanding of who they are following. This has led to the mass usage of social media as not only a social tool but also as a marketing and business tool. == Uses in marketing == Websites such as Twitter, YouTube, Facebook, and Myspace, among many others, have been used by people in all forms of business to create a closer digital/ambient bond with their clientele base. This is most notably seen in the music industry where social media networking has become the mainstay of all advertising for independent and major artists. The effect of this type of ambient marketing is that the consumer begins to get a sense of the artist's life style and personality. In this way social media outlets and ambient awareness have managed to tighten the gap between consumers and producers in all areas of business. == Uses in business processes == As web-based collaboration tools and social project management suites proliferate, the addition of activity streams to those products help to create business context-specific ambient awareness, and produce a new class of products, such as social project management platforms.
Magisto
Magisto provided an online video editing tool (both as a web application and a mobile app) for automated video editing and production. In 2019, the company was acquired by Vimeo for an estimated US$200 million. The Magisto app contained a library of music. The music, largely by independent artists, was sorted by mood and is licensed for in-app use. Magisto had a freemium business model where users can create basic video clips for free. In addition, advanced business, professional and personal service tiers are available via various subscription plans, unlocking more features; such as longer videos, HD, premium themes, customization, and control features. == History == Magisto was founded in 2009 as SightEra (LTD) by Oren Boiman (CEO) and Alex Rav-Acha (CTO). Boiman, frustrated with the amount of time it took editing together videos of his daughter, wanted an easier to use application to capture and share videos. Boiman, a computer scientist that graduated from Tel Aviv University, followed with graduate work in computer vision at the Weizmann Institute of Science. Boiman developed several patent-pending image analysis technologies that analyze unedited videos to identify the most interesting parts. The system recognized faces, animals, landscapes, action sequences, movements and other important content within the video, as well as analyzing speech and audio. These scenes are then edited together, along with music and effects. Magisto was launched publicly on September 20, 2011, as a video editing software web application through which users could upload unedited video footage, choose a title and soundtrack and have their video edited for them automatically. On the following day, Magisto was added to YouTube Create's collection of video production applications. The Magisto iPhone app was launched publicly at the 2012 International Consumer Electronics Show (CES) in Las Vegas. At CES, the company was also awarded first place in the 2012 CES Mobile App Showdown. In August 2012, Magisto launched the Android app on Google Play. In September 2012, Magisto launched a Google Chrome App and announced Google Drive integration. In March 2013, Magisto claimed it had 5 million users. Google listed Magisto as an "Editors’ Choice" on its list of "Best Apps of 2013". In September 2013, the company claimed that 10 million users had downloaded the App. In February 2014, Magisto claimed that they had 20 million users, with 2 million new users per month. The company also confirmed investment from Mail.Ru. In September 2014, Magisto rolled out a feature called 'Instagram Ready' which allowed users to upload 15 second clips that are automatically formatted for Instagram. In the same month, Magisto launched a feature for iOS and Android users, called 'Surprise Me', which created video from still photography on users’ smartphones. In October 2014, Magisto was placed 9th on the 2014 Deloitte Israel Technology Fast 50 list and named as a finalist in the Red Herring's Top 100 Europe award. In July 2015, Magisto released an editing theme dedicated to Jerry Garcia. In April 2019, the company was acquired by Vimeo, the IAC-owned platform for hosting, sharing and monetizing streamed video, for an estimated $200 million. === Financing === In 2011, the company received more than $5.5 million in a Series B venture round funding from Magma Venture Partners and Horizons Ventures. In September 2011, at the same time as the public launch of their web application, Magisto announced a $5.5 million Series B funding round led by Li Ka-shing’s Horizons Ventures. Li Ka-Shing is known for making early-stage investments in companies like Facebook, Spotify, SecondMarket and Siri. In October 2013, the company received $13 million in funding from Qualcomm and Sandisk. In 2014, the company received $2 million in Venture Funding from Magma Venture Partners, Qualcomm Ventures, Horizons Ventures and the Mail.Ru Group. == Awards == Magisto won first place at Technonomy3, an annual Internet Technology start-up competition in Israel. Judges of the competition included Jeff Pulver, TechCrunch editor Mike Butcher, investor Yaron Samid, Bessemer Venture Partners Israel partner Adam Fisher and Brad McCarty of The Next Web. Magisto won first place at CES 2012 Mobile app competition, during the launch of Magisto iOS mobile app. Magisto was awarded twice the Google Play Editor's Choice and was part of iPhone App Store Best App awards for 2013 and 2014, and Wired Essential iPad Apps. Magisto was declared by Deloitte as the 7th fastest growing company in Europe, the Middle East, and Africa in 2016.
M-DISC
M-DISC (Millennial Disc) is a write-once optical disc technology introduced in 2009 by Millenniata, Inc. and available as DVD and Blu-ray discs. == Overview == M-DISC's design is intended to provide archival media longevity. M-Disc claims that properly stored M-DISC DVD recordings will last up to 1000 years. The M-DISC DVD looks like a standard disc, except it is almost transparent with later DVD and BD-R M-Disks having standard and inkjet printable labels. The patents protecting the M-DISC technology assert that the data layer is a glassy carbon material that is substantially inert to oxidation and has a melting point of 200–1000 °C (392–1832 °F). M-Discs are readable by most regular DVD players made after 2005 and Blu-Ray and BDXL disc drives and writable by most made after 2011. Available recording capacities conform to standard DVD/Blu-ray sizes: 4.7 GB DVD+R to 25 GB BD-R, 50 GB BD-R and 100 GB BDXL. == History == M-DISC developer Millenniata, Inc. was co-founded by Brigham Young University professors Barry Lunt, Matthew Linford, CEO Henry O'Connell and CTO Doug Hansen. The company was incorporated on May 13, 2010, in American Fork, Utah. Millenniata, Inc. officially went bankrupt in December 2016. Under the direction of CEO Paul Brockbank, Millenniata had issued convertible debt. When the obligation for conversion was not satisfied, the company defaulted on the debt payment and the debt holders took possession of all of the company's assets. The debt holders subsequently started a new company, Yours.co, to sell M-DISCs and related services. As of the 2020s, there are only 2 licensed manufacturers of M-Discs: Ritek, sold under the Ritek and RiDATA brands, and Verbatim with co-branded discs, marketed as the "Verbatim M-DISC". 128 GB BDXL never made it to market due to the 2016 bankruptcy. Early in 2022, Verbatim changed the formulation of their M-DISC branded Blu-rays. These new discs could be written at a faster rate than the previous ones – 6× speed instead of 4×. The new discs also had different colouration and markings compared with older version. Later in the year customers accused Verbatim of selling an inferior product and deceptive marketing. Verbatim responded that the new discs were a further development of the older discs and should have the same longevity, and that the technical changes therein were responsible for the altered appearance and higher write speeds. The updated M-DISC currently sold on the market uses the same metal ablative layer (MABL) metal oxide inorganic recording layer used in many of Verbatim's regular Blu-ray products. == Durability claims == The original M-DISC DVD+R was tested according to ISO/IEC 10995:2011 and ECMA-379 with a projected rated lifespan of several hundred years in archival use. The glassy carbon layers, in theory if preserved correctly in an environment like a salt mine, could store the data for over 10,000 years before going outside of readable specifications. However, the polycarbonate plastics, which are commonly used by almost all optical media and heavily in CBRN and ballistic protective equipment due to their optical, physical impact and chemical resistant properties, have a lifespan rating of only around 1000 years before degradation. Verbatim Japan claims that M-DISCs now use a titanium layer to prevent moisture ingression and to provide environmental stability. M-DISCs sold in Japan are advertised to have a projected lifespan of 100 years or more based on internal ISO/IEC 16963 testing, while other regional Verbatim websites claim that M-DISCs have a projected lifespan of "several hundred years" based on ISO/IEC 16963 testing. == Durability testing == In 2009, testing was done by the US Department of Defense (DoD) producing the China Lake Report testing Millenniata's M-Disk DVD to current market offerings from Delkin, MAM-A, Mitsubishi, Taiyo Yuden and Verbatim with all brands using organic dyes failing to pass the series of accelerated aging tests. From 2010 to 2012, the French National Laboratory of Metrology and Testing (LNE) used high-temperature accelerated aging testing, at 90 °C (194 °F) and 85% relative humidity inside a CLIMATS Excal 5423-U, for 250 to 1000 hours with a mix of inorganic DVD+R discs from MPO, Verbatim, Maxell, Syylex and DataTresor. The summary of the tests states that Syylex Glass Master Disc was rated for 1000+ hours, DataTresor Disc 250 hours+ and M-Disk under 250 hours. The Syylex disc was a custom-ordered product that could not be burned in a consumer player when they were still purchaseable from Syylex before their bankruptcy, so it was not truly in the same category as the others. In 2016, a consumer Mol Smith did real world stress testing on the 25 GB BD-R M-Disc alongside TDK's standard BD-R 25 GB disc using a copied movie, which demonstrated the reliability of M-Disc's molding compared to standard discs; after 60 days of outdoor direct exposure the M-Disk was played without error, while the TDK disc was physically destroyed. In 2022, the NIST Interagency Report NIST IR 8387 listed the M-Disc as an acceptable archival format rated for 100+ years, citing the aforementioned 2009 and 2012 tests by the US Department of Defense and French National Laboratory of Metrology and Testing as sources. == Commercial support == While recorded discs are readable in conventional DVD and BD drives, M-disc DVDs can only be burned by drives with firmware that supports the slightly higher power mode that M-Disk requires for burning its inorganic layers, as such writing speed is typically 2× speed. Blu-ray M-discs can be both written and read in most standard Blu-ray drives and are certified by the Blu-ray Disc Association to meet all current standard specifications as of 2019. Typically, the M-Discs cost 1.5–3× the price of standard Blu-Ray discs with DVD M-Discs now having sparse availability. With the first-generation DVD M-DISCs, it was difficult to determine which was the writable side of the disc due to being near fully translucent, until coloring and later labels similar to that on standard DVD discs was added to discs to help distinguish the sides preventing user error. Asus, LG Electronics, Lite-On, Pioneer, Buffalo Technology, and Hitachi-LG produce drives that can record M-DISC media while Verbatim and Ritek produce M-DISC discs. == Adoption == The regional government of the U.S. state of Utah has used M-Disc since 2011. Some consumers and avid datahoarders have adopted the format for cold digital data storage. == Alternative technologies == === Optical === Syylex Glass Master Disc: these discs use etched glass and are only typically degradable by physical or chemical damage, but not by normal ageing inside an archival environment. Current BD 25 GB, BD-R DL 50 GB & BDXL 100 GB (three layer) and Sony's BDXL 128 GB (four layer) discs are rated for up to 50 years (Standard inorganic HTL discs). Sony's Optical Disc Archive, is an optical competitor to the LTO tape-based data storage system, currently with up to 5.5 TB cartridges of dual-sided 120mm discs, with desktop readers and automated rackmount standard archival systems allowing for large scale archival and data retrieval rated for an estimated 100+ years. Pioneer DM for Archive is a disc media and drive combination developed by Pioneer to meet the requirements laid out by the Japanese government for preservation of financial data for a minimum of 100 years. The discs use a MABL type recording layer and are manufactured with tight tolerances. Although burnable in any BD Writer, when burned in Pioneers DM for Archive writers using the DM Archiver software the media and burn quality meet ISO/IEC 18630 which defines the testing methods needed for ensuring media and burn quality. === Magnetic === Linear Tape-Open (LTO) is rated for up to 30 years in a climate-controlled environment and is currently in use by most industries, including broadcast and corporate digital data systems. The latest generation released in 2026 is LTO-10, it defines two unique cartridge types which can hold 30 TB or 40 TB each Hard disk drives are currently available up to 30 TB (HDD) capacity in 3.5-inch format and 5 TB in 2.5-inch laptop format. However, unlike optical media, they are limited to 5–25 years of operation lifespan due to inevitable mechanical failure or magnetic instability. == Gallery ==