Flux (also known as FLUX.1 and FLUX.2) is a text-to-image model developed by Black Forest Labs (BFL), based in Freiburg im Breisgau, Germany. Black Forest Labs was founded by former employees of Stability AI. As with other text-to-image models, Flux generates images from natural language descriptions, called prompts. == History == Black Forest Labs (BFL) was founded in 2024 by Robin Rombach, Andreas Blattmann, and Patrick Esser, former employees of Stability AI. All three founders had previously researched the artificial intelligence image generation at LMU Munich as research assistants under Björn Ommer. They published their research results on image generation in 2022, which resulted in creation of Stable Diffusion. Investors in BFL included venture capital firm Andreessen Horowitz, Brendan Iribe, Michael Ovitz, Garry Tan, and Vladlen Koltun. The company received an initial investment of US$31 million. In August 2024, Flux was integrated into the Grok chatbot developed by xAI and made available as part of premium feature on X (formerly Twitter). Grok later switched to its own text-to-image model Aurora in December 2024. On 18 November 2024, Mistral AI announced that its Le Chat chatbot had integrated Flux Pro as its image generation model. On 21 November 2024, BFL announced the release of Flux.1 Tools, a suite of editing tools designed to be used on top of existing Flux models. The tools consisting of Flux.1 Fill for inpainting and outpainting, Flux.1 Depth for control based on extracted depth map of input images and prompts, Flux.1 Canny for control based on extracted canny edges of input images and prompts, and Flux.1 Redux for mixing existing input images and prompts. Each tools are available in both Pro and Dev models. In January 2025, BFL announced a partnership with Nvidia for inclusion of Flux models as foundation models for Nvidia's Blackwell microarchitecture. The company also announced the release of Flux Pro Finetuning API, designed for customisation and fine-tuning of Flux-generated images and a partnership with German media company Hubert Burda Media for usage of Flux Pro as part of content creation. On 29 May 2025, BFL announced Flux.1 Kontext, a suite of models that enable in-context image generation and editing, allowing users to prompt with both text and images. Alongside this, BFL Playground, an interface for testing Flux models was released. On 31 July 2025, BFL announced Flux.1 Krea Dev, a model developed in collaboration with Krea AI that trained to achieve better performance, more varied aesthetics, and better realism compared to existing text-to-image models. In September 2025, Adobe Inc. announced that Photoshop (beta) users can use Flux.1 Kontext Pro as a model for its generative fill tool. BFL collaborated with Meta on Vibes, a video-generation app. On 25 November 2025, BFL announced the release of Flux.2 model series, consisting of Pro, Flex, Dev, and Apache 2.0-licensed Klein (meaning Little or Small in German language) models along with Flux.2 variational autoencoder which also released as open-source software under Apache 2.0 licence. This series claimed improvements for image reference, photorealism, typography, and prompt understanding. == Models == Flux is a series of text-to-image models. The models are based on rectified flow transformer blocks scaled to 12 billion parameters. Flux.1 models were released under different licences with Schnell (meaning Fast or Quick in German language) released as open-source software under Apache License, Dev released as source-available software under a non-commercial licence (users can obtain a self-serving commercial licence for Dev from BFL), and Pro released as proprietary software and only available as API that can be licensed by third-party users. Users retained the ownership of resulting output regardless of models used. An improved flagship model, Flux 1.1 Pro was released on 2 October 2024. Two additional modes were added on 6 November, Ultra which can generate image at four times higher resolution and up to 4 megapixel without affecting generation speed and Raw which can generate hyper-realistic image in the style of candid photography. Flux.1 Kontext is a series with in-context image generation and editing capabilities. It is available in Max, Pro, and Dev models. Max is the highest quality model and can be used to iteratively modify an existing image by using prompt while Pro is optimized to balance quality and speed of generation. Dev is an open-weight model released under non-commercial license, same as Flux.1 Dev. Flux.2 models are based on latent flow matching architecture with Mistral AI's Mistral-3 model (24 billion parameters) for its vision-language model. As with Flux.1, Flux.2 models were also released under different licences with Klein released as open-source software under Apache License, Dev released as source-available software under a non-commercial licence (users can obtain a self-serving commercial licence from BFL), and both Flex and Pro released as proprietary software and only available as API. The models can be used either online or locally by using generative AI user interfaces such as ComfyUI, Recraft Studio and Stable Diffusion WebUI Forge (a fork of Automatic1111 WebUI). Related to Flux is a text-to-video model by Black Forest Labs, under development as of February 2026. == Reception == According to a test performed by Ars Technica, the outputs generated by Flux.1 Dev and Flux.1 Pro are comparable with DALL-E 3 in terms of prompt fidelity, with the photorealism closely matched Midjourney 6 and generated human hands with more consistency over previous models such as Stable Diffusion XL. Flux has been criticised for its very realistic generated images. According to media reports, depictions ranged from an image of Donald Trump posing with guns to disturbing scenes, which triggered discussions about ethical implications of Flux models. After the release of the model, social media platform X was flooded with Flux-generated images. Black Forest Labs has not provided exact details of the data used to train the model. Ars Technica suspected that Flux is based on a large, unauthorised collection of images scraped from the internet, a controversial practice with potential legal consequences. According to a test performed by Japanese technology news website Gigazine for Flux.1 Kontext, the model series has a good understanding of the English language and can easily transfer style of the image from photorealistic into anime-style according to prompts given by the user; however, its ability to understand Japanese is quite poor. == Availability == In addition to the official BFL Playground on its website, the Flux models are also widely available through various third-party platforms for creative and professional use. These include repositories on platforms like Hugging Face and Replicate. == Further readings == FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space (29 May 2025) FLUX.2: Analyzing and Enhancing the Latent Space of FLUX – Representation Comparison (25 November 2025)
Second-order co-occurrence pointwise mutual information
In computational linguistics, second-order co-occurrence pointwise mutual information (SOC-PMI) is a method used to measure semantic similarity, or how close in meaning two words are. The method does not require the two words to appear together in a text. Instead, it works by analyzing the "neighbor" words that typically appear alongside each of the two target words in a large body of text (corpus). If the two target words frequently share the same neighbors, they are considered semantically similar. For example, the words "cemetery" and "graveyard" may not appear in the same sentence often, but they both frequently appear near words like "buried," "dead," and "funeral." SOC-PMI uses this shared context to determine that they have a similar meaning. The method is called "second-order" because it doesn't look at the direct co-occurrence of the target words (which would be first-order), but at the co-occurrence of their neighbors (a second level of association). The strength of these associations is quantified using pointwise mutual information (PMI). == History == The method builds on earlier work like the PMI-IR algorithm, which used the AltaVista search engine to calculate word association probabilities. The key advantage of a second-order approach like SOC-PMI is its ability to measure similarity between words that do not co-occur often, or at all. The British National Corpus (BNC) has been used as a source for word frequencies and contexts for this method. == Methodology == The SOC-PMI algorithm measures the similarity between two words, w 1 {\displaystyle w_{1}} and w 2 {\displaystyle w_{2}} , in several steps. === Step 1: Score neighboring words with PMI === First, for each target word ( w 1 {\displaystyle w_{1}} and w 2 {\displaystyle w_{2}} ), the algorithm identifies its "neighbor" words within a certain text window (e.g., within 5 words to the left or right) across a large corpus. The strength of the association between a target word t i {\displaystyle t_{i}} and its neighbor w {\displaystyle w} is calculated using pointwise mutual information (PMI). A higher PMI value means the two words appear together more often than would be expected by chance. The PMI between a target word t i {\displaystyle t_{i}} and a neighbor word w {\displaystyle w} is calculated as: f pmi ( t i , w ) = log 2 f b ( t i , w ) × m f t ( t i ) f t ( w ) {\displaystyle f^{\text{pmi}}(t_{i},w)=\log _{2}{\frac {f^{b}(t_{i},w)\times m}{f^{t}(t_{i})f^{t}(w)}}} where: f b ( t i , w ) {\displaystyle f^{b}(t_{i},w)} is the number of times t i {\displaystyle t_{i}} and w {\displaystyle w} appear together in the context window. f t ( t i ) {\displaystyle f^{t}(t_{i})} is the total number of times t i {\displaystyle t_{i}} appears in the corpus. f t ( w ) {\displaystyle f^{t}(w)} is the total number of times w {\displaystyle w} appears in the corpus. m {\displaystyle m} is the total number of tokens (words) in the corpus. === Step 2: Create a semantic 'signature' for each word === For each target word ( w 1 {\displaystyle w_{1}} and w 2 {\displaystyle w_{2}} ), the algorithm creates a list of its most significant neighbors. This is done by taking the top β {\displaystyle \beta } neighbor words, sorted in descending order by their PMI score with the target word. This list of top neighbors, X w {\displaystyle X^{w}} , acts as a semantic "signature" for the word w {\displaystyle w} . X w = { X i w } {\displaystyle X^{w}=\{X_{i}^{w}\}} , for i = 1 , 2 , … , β {\displaystyle i=1,2,\ldots ,\beta } The size of this list, β {\displaystyle \beta } , is a parameter of the method. === Step 3: Compare the signatures === The algorithm then compares the signatures of w 1 {\displaystyle w_{1}} and w 2 {\displaystyle w_{2}} . It looks for words that are present in both signatures. The similarity of w 1 {\displaystyle w_{1}} to w 2 {\displaystyle w_{2}} is calculated by summing the PMI scores of w 2 {\displaystyle w_{2}} with every word in w 1 {\displaystyle w_{1}} 's signature list. The β {\displaystyle \beta } -PMI summation function defines this score. The score for w 1 {\displaystyle w_{1}} with respect to w 2 {\displaystyle w_{2}} is: f ( w 1 , w 2 , β ) = ∑ i = 1 β ( f pmi ( X i w 1 , w 2 ) ) γ {\displaystyle f(w_{1},w_{2},\beta )=\sum _{i=1}^{\beta }(f^{\text{pmi}}(X_{i}^{w_{1}},w_{2}))^{\gamma }} This sum only includes terms where the PMI value is positive. The exponent γ {\displaystyle \gamma } (with a value > 1) is used to give more weight to neighbors that are more strongly associated with w 2 {\displaystyle w_{2}} . This calculation is done in both directions: The similarity of w 1 {\displaystyle w_{1}} with respect to w 2 {\displaystyle w_{2}} : f ( w 1 , w 2 , β 1 ) = ∑ i = 1 β 1 ( f pmi ( X i w 1 , w 2 ) ) γ {\displaystyle f(w_{1},w_{2},\beta _{1})=\sum _{i=1}^{\beta _{1}}(f^{\text{pmi}}(X_{i}^{w_{1}},w_{2}))^{\gamma }} The similarity of w 2 {\displaystyle w_{2}} with respect to w 1 {\displaystyle w_{1}} : f ( w 2 , w 1 , β 2 ) = ∑ i = 1 β 2 ( f pmi ( X i w 2 , w 1 ) ) γ {\displaystyle f(w_{2},w_{1},\beta _{2})=\sum _{i=1}^{\beta _{2}}(f^{\text{pmi}}(X_{i}^{w_{2}},w_{1}))^{\gamma }} === Step 4: Calculate final similarity score === Finally, the total semantic similarity is the average of the two scores from the previous step. S i m ( w 1 , w 2 ) = f ( w 1 , w 2 , β 1 ) β 1 + f ( w 2 , w 1 , β 2 ) β 2 {\displaystyle \mathrm {Sim} (w_{1},w_{2})={\frac {f(w_{1},w_{2},\beta _{1})}{\beta _{1}}}+{\frac {f(w_{2},w_{1},\beta _{2})}{\beta _{2}}}} This score can be normalized to fall between 0 and 1. For example, using this method, the words cemetery and graveyard achieve a high similarity score of 0.986 (with specific parameter settings).
Mashup (web application hybrid)
A mashup (computer industry jargon), in web development, is a web page or web application that uses content from more than one source to create a single new service displayed in a single graphical interface. For example, a user could combine the addresses and photographs of their library branches with a Google map to create a map mashup. The term implies easy, fast integration, frequently using open application programming interfaces (open API) and data sources to produce enriched results that were not necessarily the original reason for producing the raw source data. The term mashup originally comes from creating something by combining elements from two or more sources. The main characteristics of a mashup are combination, visualization, and aggregation. It is important to make existing data more useful, for personal and professional use. To be able to permanently access the data of other services, mashups are generally client applications or hosted online. In the past years, more and more Web applications have published APIs that enable software developers to easily integrate data and functions the SOA way, instead of building them by themselves. Mashups can be considered to have an active role in the evolution of social software and Web 2.0. Mashup composition tools are usually simple enough to be used by end-users. They generally do not require programming skills and rather support visual wiring of GUI widgets, services and components together. Therefore, these tools contribute to a new vision of the Web, where users are able to contribute. The term "mashup" is not formally defined by any standard-setting body. == History == The broader context of the history of the Web provides a background for the development of mashups. Under the Web 1.0 model, organizations stored consumer data on portals and updated them regularly. They controlled all the consumer data, and the consumer had to use their products and services to get the information. The advent of Web 2.0 introduced Web standards that were commonly and widely adopted across traditional competitors and which unlocked the consumer data. At the same time, mashups emerged, allowing mixing and matching competitors' APIs to develop new services. The first mashups used mapping services or photo services to combine these services with data of any kind and therefore to produce visualizations of data. In the beginning, most mashups were consumer-based, but recently the mashup is to be seen as an interesting concept useful also to enterprises. Business mashups can combine existing internal data with external services to generate new views on the data. There was also the free Yahoo! Pipes to build mashups for free using the Yahoo! Query Language. == Types of mashup == There are many types of mashup, such as business mashups, consumer mashups, and data mashups. The most common type of mashup is the consumer mashup, aimed at the general public. Business (or enterprise) mashups define applications that combine their own resources, application and data, with other external Web services. They focus data into a single presentation and allow for collaborative action among businesses and developers. This works well for an agile development project, which requires collaboration between the developers and customer (or customer proxy, typically a product manager) for defining and implementing the business requirements. Enterprise mashups are secure, visually rich Web applications that expose actionable information from diverse internal and external information sources. Consumer mashups combine data from multiple public sources in the browser and organize it through a simple browser user interface. (e.g.: Wikipediavision combines Google Map and a Wikipedia API) Data mashups, opposite to the consumer mashups, combine similar types of media and information from multiple sources into a single representation. The combination of all these resources create a new and distinct Web service that was not originally provided by either source. === By API type === Mashups can also be categorized by the basic API type they use but any of these can be combined with each other or embedded into other applications. ==== Data types ==== Indexed data (documents, weblogs, images, videos, shopping articles, jobs ...) used by metasearch engines Cartographic and geographic data: geolocation software, geovisualization Feeds, podcasts: news aggregators ==== Functions ==== Data converters: language translators, speech processing, URL shorteners... Communication: email, instant messaging, notification... Visual data rendering: information visualization, diagrams Security related: electronic payment systems, ID identification... Editors == Mashup enabler == In technology, a mashup enabler is a tool for transforming incompatible IT resources into a form that allows them to be easily combined, in order to create a mashup. Mashup enablers allow powerful techniques and tools (such as mashup platforms) for combining data and services to be applied to new kinds of resources. An example of a mashup enabler is a tool for creating an RSS feed from a spreadsheet (which cannot easily be used to create a mashup). Many mashup editors include mashup enablers, for example, Presto Mashup Connectors, Convertigo Web Integrator or Caspio Bridge. Mashup enablers have also been described as "the service and tool providers, [sic] that make mashups possible". === History === Early mashups were developed manually by enthusiastic programmers. However, as mashups became more popular, companies began creating platforms for building mashups, which allow designers to visually construct mashups by connecting together mashup components. Mashup editors have greatly simplified the creation of mashups, significantly increasing the productivity of mashup developers and even opening mashup development to end-users and non-IT experts. Standard components and connectors enable designers to combine mashup resources in all sorts of complex ways with ease. Mashup platforms, however, have done little to broaden the scope of resources accessible by mashups and have not freed mashups from their reliance on well-structured data and open libraries (RSS feeds and public APIs). Mashup enablers evolved to address this problem, providing the ability to convert other kinds of data and services into mashable resources. === Web resources === Of course, not all valuable data is located within organizations. In fact, the most valuable information for business intelligence and decision support is often external to the organization. With the emergence of rich web applications and online Web portals, a wide range of business-critical processes (such as ordering) are becoming available online. Unfortunately, very few of these data sources syndicate content in RSS format and very few of these services provide publicly accessible APIs. Mashup editors therefore solve this problem by providing enablers or connectors. == Mashups versus portals == Mashups and portals are both content aggregation technologies. Portals are an older technology designed as an extension to traditional dynamic Web applications, in which the process of converting data content into marked-up Web pages is split into two phases: generation of markup "fragments" and aggregation of the fragments into pages. Each markup fragment is generated by a "portlet", and the portal combines them into a single Web page. Portlets may be hosted locally on the portal server or remotely on a separate server. Portal technology defines a complete event model covering reads and updates. A request for an aggregate page on a portal is translated into individual read operations on all the portlets that form the page ("render" operations on local, JSR 168 portlets or "getMarkup" operations on remote, WSRP portlets). If a submit button is pressed on any portlet on a portal page, it is translated into an update operation on that portlet alone (processAction on a local portlet or performBlockingInteraction on a remote, WSRP portlet). The update is then immediately followed by a read on all portlets on the page. Portal technology is about server-side, presentation-tier aggregation. It cannot be used to drive more robust forms of application integration such as two-phase commit. Mashups differ from portals in the following respects: The portal model has been around longer and has had greater investment and product research. Portal technology is therefore more standardized and mature. Over time, increasing maturity and standardization of mashup technology will likely make it more popular than portal technology because it is more closely associated with Web 2.0 and lately Service-oriented Architectures (SOA). New versions of portal products are expected to eventually add mashup support while still supporting legacy portlet applications. Mashup technologies, in contrast, are not expected to provide support for portal standards. == Business mashups
Haul video
A haul video is a video recording posted to the Internet in which a person discusses items that they recently purchased, sometimes going into detail about their experiences during the purchase and the cost of the items they bought. The posting of haul videos (or hauls) was a growing trend between 2008 and 2016. Often the items bought are books, clothing, groceries, household goods, makeup, or jewellery. == Details == The posting of haul videos grew as a trend between 2008 and 2016. By late 2010, nearly a quarter of a million haul videos had been shared on the website YouTube alone. Certain videos have each received tens of millions of views. Many young adults (mostly women) have displayed their shopping hauls, while including their beauty and design commentary in the narration. The videos are often grouped by store name or by the type of product (cosmetics, accessories, shoes, postage stamps, etc.). Before haul videos became an online trend, millions of people spent time watching other people, in technical product videos unbox their latest new gadgets and technology. The trend of "unboxing videos" had emerged during 2006. Haul videos have led to celebrity status for some people. Other haul video bloggers have entered sponsorship deals and advertising programs from major brands. The videos are rarely negative about the products being reviewed. This aspect of the genre of haul videos makes sponsorship by brand advertisers particularly appealing. Brands including J.C. Penney contacted haulers as part of their marketing efforts for Back to School 2010. Haul videos also convinced three San Francisco Bay Area area natives to launch HaulBlog–a parody site that creates fake haul videos which poke fun at the phenomenon. The site is also home to the original monthly web series "The Haul Monitor" a humorous commentary show that features haul videos from around the community. == Fashion media == Sarah Sykes and John Zimmerman of Carnegie Mellon University, HCII and School of Design wrote an article "Making Sense of Haul Videos: Self-created Celebrities Fill a Fashion Media Gap". They discuss their analysis and research project examining what makes video bloggers so popular on YouTube, as well as how it affects fashion media through the production of haul videos. == Federal Trade Commission == The United States Federal Trade Commission recently enacted laws to regulate many types of online publishers and content creators. The posted information includes blogging and podcasting in text, images, audio, and video. While any publishers (including the haul-video creators) are allowed to accept free merchandise and advertising, the gifts or payments must be fully (and clearly) disclosed to reveal being paid by a brand name, as a sponsor, to review a product. The Canadian Radio-television and Telecommunications Commission is also closely monitoring such Internet activities.
DVD
DVD (digital video disc or digital versatile disc) is a digital optical disc data storage format. It was invented and developed in 1995 and first released on November 1, 1996, in Japan. The medium can store any kind of digital data and has been widely used to store video programs (watched using DVD players), software and other computer files. DVDs offer significantly higher storage capacity than compact discs (CD) while having the same dimensions. A standard single-layer DVD can store up to 4.7 GB of data, a dual-layer DVD up to 8.5 GB. Dual-layer, double-sided DVDs can store up to a maximum of 17.08 GB. Prerecorded DVDs are mass-produced using molding machines that physically stamp data onto the DVD. Such discs are a form of DVD-ROM because data can only be read and not written or erased. Blank recordable DVD discs (DVD-R and DVD+R) can be recorded once using a DVD recorder and then function as a DVD-ROM. Rewritable DVDs (DVD-RW, DVD+RW, and DVD-RAM) can be recorded and erased many times. DVDs are used in DVD-Video consumer digital video format and less commonly in DVD-Audio consumer digital audio format, as well as for authoring DVD discs written in a special AVCHD format to hold high definition material (often in conjunction with AVCHD format camcorders). DVDs containing other types of information may be referred to as DVD data discs. == Etymology == The Oxford English Dictionary comments that, "In 1995, rival manufacturers of the product initially named digital video disc agreed that, in order to emphasize the flexibility of the format for multimedia applications, the preferred abbreviation DVD would be understood to denote digital versatile disc." The OED also states that in 1995, "The companies said the official name of the format will simply be DVD. Toshiba had been using the name 'digital video disc', but that was switched to 'digital versatile disc' after computer companies complained that it left out their applications." "Digital versatile disc" is the explanation provided in a DVD Forum Primer from 2000 and in the DVD Forum's mission statement, which the purpose is to promote broad acceptance of DVD products on technology, across entertainment, and other industries. Because DVDs became highly popular for the distribution of movies in the 2000s, the term DVD became popularly used in English as a noun to describe specifically a full-length movie released on the format; for example the phrase "to watch a DVD" describes watching a movie on DVD. == History == === Development and launch === Released in 1987, CD Video used analog video encoding on optical discs matching the established standard 120 mm (4.7 in) size of audio CDs. Video CD (VCD) became one of the first formats for distributing digitally encoded films in this format, in 1993. In the same year, two new optical disc storage formats were being developed. One was the Multimedia Compact Disc (MMCD), backed by Philips and Sony (developers of the CD and CD-i), and the other was the Super Density (SD) disc, supported by Toshiba, Time Warner, Matsushita Electric, Hitachi, Mitsubishi Electric, Pioneer, Thomson, and JVC. By the time of the press launches for both formats in January 1995, the MMCD nomenclature had been dropped, and Philips and Sony were referring to their format as Digital Video Disc (DVD). On May 3, 1995, an ad hoc industry technical group formed from five computer companies (IBM, Apple, Compaq, Hewlett-Packard, and Microsoft) issued a press release stating that they would only accept a single format. The group voted to boycott both formats unless the two camps agreed on a single, converged standard. They recruited Lou Gerstner, president of IBM, to pressure the executives of the warring factions. In one significant compromise, the MMCD and SD groups agreed to adopt proposal SD 9, which specified that both layers of the dual-layered disc be read from the same side—instead of proposal SD 10, which would have created a two-sided disc that users would have to turn over. Philips/Sony strongly insisted on the source code, EFMPlus, that Kees Schouhamer Immink had designed for the MMCD, because it makes it possible to apply the existing CD servo technology. Its drawback was a loss from 5 to 4.7 Gigabytes of capacity. As a result, the DVD specification provided a storage capacity of 4.7 GB (4.38 GiB) for a single-layered, single-sided disc and 8.5 GB (7.92 GiB) for a dual-layered, single-sided disc. The DVD specification ended up similar to Toshiba and Matsushita's Super Density Disc, except for the dual-layer option. MMCD was single-sided and optionally dual-layer, whereas SD was two half-thickness, single-layer discs which were pressed separately and then glued together to form a double-sided disc. Philips and Sony decided that it was in their best interests to end the format war, and on September 15, 1995 agreed to unify with companies backing the Super Density Disc to release a single format, with technologies from both. After other compromises between MMCD and SD, the group of computer companies won the day, and a single format was agreed upon. The computer companies also collaborated with the Optical Storage Technology Association (OSTA) on the use of their implementation of the ISO-13346 file system (known as Universal Disk Format) for use on the new DVDs. The format's details were finalized on December 8, 1995. In November 1995, Samsung announced it would start mass-producing DVDs by September 1996. The format launched on November 1, 1996, in Japan, mostly with music video releases. The first major releases from Warner Home Video arrived on December 20, 1996, with four titles being available. The format's release in the U.S. was delayed multiple times, from August 1996, to October 1996, November 1996, before finally settling on early 1997. Players began to be produced domestically that winter, with March 24, 1997, as the U.S. launch date of the format proper in seven test markets. Approximately 32 titles were available on launch day, mainly from the Warner Bros., MGM, and New Line libraries, with the notable inclusion of the 1996 film Twister. However, the launch was planned for the following day (March 25), leading to a distribution change with retailers and studios to prevent similar violations of breaking the street date. The nationwide rollout for the format happened on August 22, 1997. DTS announced in late 1997 that they would be coming onto the format. The sound system company revealed details in a November 1997 online interview, and clarified it would release discs in early 1998. However, this date would be pushed back several times before finally releasing their first titles at the 1999 Consumer Electronics Show. In 2001, blank DVD recordable discs cost the equivalent of $27.34 US dollars in 2022. === Adoption === Movie and home entertainment distributors adopted the DVD format to replace the ubiquitous VHS tape as the primary consumer video distribution format. Immediately following the formal adoption of a unified standard for DVD, two of the four leading video game console companies (Sega and The 3DO Company) said they already had plans to design a gaming console with DVDs as the source medium. Sony stated at the time that they had no plans to use DVD in their gaming systems, despite being one of the developers of the DVD format and eventually the first company to actually release a DVD-based console. Game consoles such as the PlayStation 2, Xbox, and Xbox 360 use DVDs as their source medium for games and other software. Contemporary games for Windows were also distributed on DVD. Early DVDs were mastered using DLT tape, but using DVD-R DL or +R DL eventually became common. TV DVD combos, combining a standard definition CRT TV or an HD flat panel TV with a DVD mechanism under the CRT or on the back of the flat panel, and VCR/DVD combos were also available for purchase. For consumers, DVD soon overtook VHS as the favored choice for home movie releases. In 2001, DVD players outsold VCRs for the first time in the United States. At that time, one in four American households owned a DVD player. By 2007, about 80% of Americans owned a DVD player, a figure that had surpassed VCRs; it was also higher than personal computers or cable television. == Specifications == The DVD specifications created and updated by the DVD Forum are published as so-called DVD Books (e.g. DVD-ROM Book, DVD-Audio Book, DVD-Video Book, DVD-R Book, DVD-RW Book, DVD-RAM Book, DVD-AR (Audio Recording) Book, DVD-VR (Video Recording) Book, etc.). DVD discs are made up of two discs; normally one is blank, and the other contains data. Each disc is 0.6 mm thick, and they are glued together to form a DVD disc. The gluing process must be done carefully to make the disc as flat as possible to avoid both birefringence and "disc tilt", which is when the disc is not perfectly flat, preventing it from being read. Some specifications for mechanical, physical and optical characteristics of DV
SimSimi
SimSimi is an artificial intelligence conversation program created in 2002 by ISMaker. It grows its artificial intelligence day by day assisted by a feature that allows users to teach it to respond correctly. SimSimi, pronounced as "shim-shimi", is from a Korean word simsim (심심) which means "bored". It has an application designed for Android, Windows Phone and iOS. The application was banned in Thailand in 2012 after users taught it to make responses containing profanity, and to criticise leading politicians. In April 2018, SimSimi was suspended in Brazil due to accusations of sending inappropriate messages, such as sexual language, bullying and even death threats, being labeled as "dangerous" mainly due to its popularity among children, and according to its developer, the suspension of the app in the country "was inevitable because the SimSimi app, at least in the last few days, had a significant negative social impact in Brazil.”
ISO/IEC JTC 1/SC 6
ISO/IEC JTC 1/SC 6 Telecommunications and information exchange between systems is a standardization subcommittee of the Joint Technical Committee ISO/IEC JTC 1. It is part of the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), which develops and facilitates standards within the field of telecommunications and information exchange between systems. ISO/IEC JTC 1/SC 6 was established in 1964, following the creation of a Special Working Group under ISO/TC 97 on Data Link Control Procedures and Modem Interfaces. The international secretariat of ISO/IEC JTC 1/SC 6 is the Korean Agency for Technology and Standards (KATS), located in South Korea. == Scope == The scope of ISO/IEC JTC 1/SC 6 is “Standardization in the field of telecommunications dealing with the exchange of information between open systems including system functions, procedures, parameters as well as the conditions for their use. The standardization encompasses protocols and services of lower layers, including physical, data link, network, and transport as well as those of upper layers including but not limited to Directory and ASN.1.” Future Network has recently been added as an important work scope. A considerable part of the work is done in effective cooperation with ITU-T and other standardization bodies including IEEE 802 and Ecma International. == Structure == ISO/IEC JTC 1/SC 6 has three active working groups (WGs), each of which carries out specific tasks in standards development within the field of telecommunications and information exchange between systems. The focus of each working group is described in the group’s terms of reference. Working groups can be established if new working areas arise, or disbanded if the group’s working area is no longer relevant to standardization needs. Active working groups of ISO/IEC JTC 1/SC 6 are: == Collaborations == ISO/IEC JTC 1/SC 6 works in close collaboration with a number of other organizations or subcommittees, both internal and external to ISO or IEC. Organizations internal to ISO or IEC that collaborate with or are in liaison with ISO/IEC JTC 1/SC 6 include: ISO/IEC JTC 1/WG 7, Sensor networks ISO/IEC JTC 1/SC 17, Cards and personal identification ISO/IEC JTC 1/SC 25, Interconnection of information technology equipment ISO/IEC JTC 1/SC 27, IT security techniques ISO/IEC JTC 1/SC 29, Coding of audio, picture, multimedia and hypermedia information ISO/IEC JTC 1/SC 31, Automatic identification and data capture techniques ISO/IEC JTC 1/SC 38, Distributed application platforms & services (DAPS) ISO/TC 68, Financial services ISO/TC 122, Packaging ISO/TC 184/SC 5, Interoperability, integration, and architectures for enterprise systems and automation applications ISO/TC 215, Health Informatics IEC/SC 46A, Coaxial cables IEC/SC 46C, Wires and symmetric cables IEC/TC 48, Electrical connectors and mechanical structures for electrical and electronic equipment IEC/SC 48B, Electrical connectors IEC/TC 65, Industrial-process measurement, control and automation IEC/SC 65C, Industrial networks IEC/TC 86, Fibre optics IEC/SC 86C, Fibre optic systems and active devices IEC/TC 93, Design automation Some organizations external to ISO or IEC that collaborate with or are in liaison to ISO/IEC JTC 1/SC 6 include: European Conference of Postal and Telecommunications Administrations (CEPT) European Organization for Nuclear Research (CERN) European Commission (EC) European Telecommunications Standards Institute (ETSI) Ecma International International Civil Aviation Organization (ICAO) IEEE 802 LMSC (LAN/MAN Standards Committee) Internet Society (ISOC) International Telecommunications Satellite Organization (ITSO) ITU-T Organization for the Advancement of Structured Information Standards (OASIS) NFC Forum MFA Forum United Nations Conference on Trade and Development (UNCTAD) United Nations Economic Commission for Europe (UNECE) Universal Postal Union (UPU) World Meteorological Organization (WMO) CEN/TC 247/WG 4 == Member countries == Countries pay a fee to ISO to be members of subcommittees. The 19 "P" (participating) members of ISO/IEC JTC 1/SC 6 are: Austria, Belgium, Canada, China, Czech Republic, Finland, Germany, Greece, Jamaica, Japan, Kazakhstan, Republic of Korea, Netherlands, Russian Federation, Spain, Switzerland, Tunisia, United Kingdom, and United States. The 31 "O" (observing) members of ISO/IEC JTC 1/SC 6 are: Argentina, Bosnia and Herzegovina, Colombia, Cuba, Cyprus, France, Ghana, Hong Kong, Hungary, Iceland, India, Indonesia, Islamic Republic of Iran, Ireland, Italy, Kenya, Luxembourg, Malaysia, Malta, New Zealand, Norway, Philippines, Poland, Romania, Saudi Arabia, Serbia, Singapore, Slovenia, Thailand, Turkey, and Ukraine. == Published standards == There are 365 published standards under the direct responsibility of ISO/IEC JTC 1/SC 6. Published standards by ISO/IEC JTC 1/SC 6 include: