WriterDuet is a screenwriting software for writing and editing screenplays and other forms of mass media. == History == WriterDuet was founded in 2013 by Guy Goldstein. In April 2015, WriterDuet acquired the domain for Scripped.com after they closed, citing a serious technical failure. In August 2016, WriterDuet released a localized version of its software in China. In May 2018, WriterDuet included Bechdel test analysis functions to address issues of gender diversity in the screenwriting industry. In 2018, WriterDuet published WriterSolo, an offline version of their app that runs on the browser and opens/saves files on the computer, Dropbox, Google Drive, and iCloud. In July 2019, WriterDuet made the WriterSolo browser app and desktop app available as pay-what-you-want under the web address FreeScreenwriting.com. == Features == WriterDuet is primarily used to outline, write, and format screenplays to the standards recommended by the AMPAS. It also supports formats for theater, novels, and video games. The software is powered by Firebase allowing users to write together in real-time from multiple devices. WriterDuet's main competitors in the screenwriting industry are Final Draft, Celtx, and Movie Magic Screenwriter.
Tresorit
Tresorit is a Swiss company providing end-to-end encrypted cloud storage and secure content collaboration services. Founded in 2011, the company primarily serves businesses and organizations with elevated data protection and compliance requirements. Since 2021, Tresorit has been part of Swiss Post's digital business services, which, under the name 'Swiss Post Digital' offer secure communication platforms and connectable software solutions for SMEs, public authorities, and the healthcare sector, among others. == History == Tresorit was founded in 2011 by Hungarian software developers Istvan Lam, Szilveszter Szebeni and Gyorgy Szilagyi with the aim of providing a secure alternative to traditional cloud storage solutions. The company developed a cloud collaboration platform based on client-side end-to-end encryption and a zero-knowledge architecture. In its early years, Tresorit gained attention through a public security challenge inviting researchers to attempt to compromise its encryption system. The initiative received coverage in technology and cybersecurity media. The company initially positioned itself as a secure alternative to conventional cloud storage services and gradually expanded its offering toward enterprise-focused collaboration tools. In 2021, Swiss Post Communications Services acquired a majority stake in Tresorit. The company is now part of Swiss Post, and continues to operate independently within Swiss Post’s digital division, while benefiting from the broader infrastructure and institutional framework of its parent organization. Tresorit has offices in Zurich, Munich, and Budapest. == Products and Services == Tresorit provides a cloud-based platform for secure file storage and collaboration. Its services include encrypted file sharing, email encryption, electronic signatures, and encrypted data rooms for managing sensitive documents and workflows. The platform is available on Windows, macOS, Linux, Android, and iOS. == Technology == Tresorit uses client-side end-to-end encryption based on a zero-knowledge model. Files are encrypted on the user’s device before being uploaded to company servers. According to the company, encryption keys remain under user control, meaning that Tresorit and third parties cannot access the content of stored files. == Security challenge == Between 2013 and 2014, Tresorit organized a public challenge inviting security researchers to attempt to compromise the service's encryption implementation. The challenge received coverage in technology and cybersecurity media. == Acquisition by Swiss Post == In 2021, Swiss Post Communications Services acquired a majority stake in Tresorit as part of Swiss Post’s broader digital services strategy. The company is now part of Swiss Post. == Reception == Tresorit has been covered by international technology and business publications in the context of secure cloud storage and encrypted collaboration services. TechCrunch described the company as an early European provider of end-to-end encrypted cloud services, while The New York Times included it in discussions of secure file-sharing tools. Other publications such as TechRadar and ITPro have reviewed Tresorit in the context of enterprise security and confidential data handling.
Virtual influencer
A virtual influencer, sometimes described as a virtual persona or virtual model, is a computer-generated fictional character that can be used for a variety of marketing-related purposes, but most frequently for social media marketing, in lieu of online human "influencers". Most virtual influencers are designed using computer graphics and motion capture technology to resemble real people in realistic situations. Common derivatives of virtual influencers include VTubers, which broadly refer to online entertainers and YouTubers who represent themselves using virtual avatars instead of their physical selves. == History == Virtual influencers are fundamentally synonymous with virtual idols, which originate from Japan's anime and Japanese idol culture that dates back to the 1980s. The first virtual idol created was Lynn Minmay, a fictional singer and main character of the anime television series Super Dimension Fortress Macross (1982) and the animated film adaptation Macross: Do You Remember Love? (1984). Minmay's success led to the production of more Japanese virtual idols, such as EVE from the Japanese cyberpunk anime Megazone 23 (1985), and Sharon Apple in Macross Plus (1994). Virtual idols were not always well received – in 1995, Japanese talent agency Horipro created Kyoko Date, which was inspired by the Macross franchise and dating sim games such as Tokimeki Memorial (1994). Date failed to gain commercial success despite drawing headlines for her debut as a CGI idol, largely due to technical limitations leading to issues such as unnatural movements, an issue also known as the uncanny valley. Since their inception, many virtual idols created have achieved continual success, with notable names including the Vocaloid singer Hatsune Miku, and the VTuber Kizuna AI. Technological advancements have also enabled production teams to use artificial intelligence and advanced techniques to customize the personalities and behavior of virtual idols. Due to modern-day advancements in technology, many virtual idols have held real-life tours and events. Notable ones include Hatsune Miku's titular tour Miku Expo and Hololive's concerts with many of their idols from their English, Japanese and Indonesian branches. Some notable events including virtual singers and influencers have included: Hatsune Miku opening for Lady Gaga in 2014 and Hoshimachi Suisei's concerts at the famous Budokan venue in Japan and her addition to the Forbes Japan list of '30 Under 30' individuals who are changing the world in their respective fields. == Benefits and criticism == From a branding perspective, virtual influencers are perceived to be much less likely to be mired in scandals. In China, celebrities caught in bad publicity such as singer Wang Leehom and entertainer Kris Wu have heightened the appeal of virtual influencers, since their existence relies entirely on computer-generated imagery and they are therefore unlikely to cause any damage to a brand's image by association. Some studies have also suggested that Generation Z consumers have a unique appetite for virtual idols and influencers, since they grew up in the age of the internet. Studies also show that human-like appearance of virtual influencers show higher message credibility than anime-like virtual influencers. Scholars and commentators have also questioned the ethics and cultural impact of virtual influencers, arguing that computer-generated personas can entrench unrealistic beauty standards while diffusing accountability for labor, identity, and consent. Business and marketing analysts have also warned that disclosure and governance remain inconsistent, recommending clearer guardrails and transparency when brands deploy synthetic spokespeople. In 2025, reporting highlighted concerns that AI-driven "virtual humans" could displace human creators and sales workers, intensifying debates over the future of creative labor and authenticity online. == Notable examples == === Virtual bands === Eternity - A South Korean virtual idol group formed by Pulse9. Gorillaz - A virtual band formed in 1998. K/DA - A virtual K-pop girl group created as part of the League of Legends video game franchise. MAVE: - A South Korean virtual girl group formed in 2023 by Metaverse Entertainment. Pentakill - A virtual heavy metal band created as part of the League of Legends video game franchise. Plave (band) - A South Korean virtual boy band formed by VLast. Squid Sisters and Off the Hook - Two virtual pop idol duos as part of the Splatoon series. Studio Killers - A Finnish-Danish-British virtual band formed in 2011. === Vocaloids === Hatsune Miku (modeled after Saki Fujita) Kagamine Rin/Len (modeled after Asami Shimoda) Megurine Luka (modeled after Yū Asakawa) Meiko (modeled after Meiko Haigō) Kaito (modeled after Naoto Fūga) === VTubers === Kano Kizuna AI Neuro-sama VShojo Ironmouse Projekt Melody Nijisanji Hololive Akai Haato Gawr Gura Hoshimachi Suisei Natsuiro Matsuri === Other examples === Ami Yamato Crazy Frog FN Meka IA Kuki AI Kyoko Date Kyra Miquela Naevis Shudu Gram
SocialIQ
Social IQ (formerly Soovox Inc.) was a San Diego-based influencer marketing platform that measured users' online social influence and connected them with brands for word-of-mouth marketing campaigns. The company was founded in 2009 by Akram Benmbarek and was headquartered in San Diego, California. == History == Akram Benmbarek, who had previously worked in technology finance at Advanced Equities Financial Corp and in wealth management at Morgan Stanley, Merrill Lynch, and UBS, founded the company in mid-2009 under the name Soovox. In October 2011, Benmbarek rebranded the company as SocialIQ. At that time, the company was seeking a Series A round of venture capital, having raised under $1 million in angel seed funding. == Similar metrics == Klout PeerIndex
InfiniBand
InfiniBand (IB) is a computer networking standard used in high-performance computing that features very high throughput and very low latency. It is used for data interconnect both among and within computers. InfiniBand is also used as either a direct or switched interconnect between servers and storage systems, as well as an interconnect between storage systems. It is designed to be scalable and uses a switched fabric network topology. Between 2014 and June 2016, it was the most commonly used interconnect in the TOP500 list of supercomputers. Mellanox (acquired by Nvidia) manufactures InfiniBand host bus adapters and network switches, which are used by large computer system and database vendors in their product lines. As a computer cluster interconnect, IB competes with Ethernet, Fibre Channel, and Intel Omni-Path. The technology is promoted by the InfiniBand Trade Association. == History == InfiniBand originated in 1999 from the merger of two competing designs: Future I/O and Next Generation I/O (NGIO). NGIO was led by Intel, with a specification released in 1998, and joined by Sun Microsystems and Dell. Future I/O was backed by Compaq, IBM, and Hewlett-Packard. This led to the formation of the InfiniBand Trade Association (IBTA), which included both sets of hardware vendors as well as software vendors such as Microsoft. At the time it was thought some of the more powerful computers were approaching the interconnect bottleneck of the PCI bus, in spite of upgrades like PCI-X. Version 1.0 of the InfiniBand Architecture Specification was released in 2000. Initially the IBTA vision for IB was simultaneously a replacement for PCI in I/O, Ethernet in the machine room, cluster interconnect and Fibre Channel. IBTA also envisaged decomposing server hardware on an IB fabric. Mellanox had been founded in 1999 to develop NGIO technology, but by 2001 shipped an InfiniBand product line called InfiniBridge at 10 Gbit/second speeds. Following the burst of the dot-com bubble there was hesitation in the industry to invest in such a far-reaching technology jump. By 2002, Intel announced that instead of shipping IB integrated circuits ("chips"), it would focus on developing PCI Express, and Microsoft discontinued IB development in favor of extending Ethernet. Sun Microsystems and Hitachi continued to support IB. In 2003, the System X supercomputer built at Virginia Tech used InfiniBand in what was estimated to be the third largest computer in the world at the time. The OpenIB Alliance (later renamed OpenFabrics Alliance) was founded in 2004 to develop an open set of software for the Linux kernel. By February, 2005, the support was accepted into the 2.6.11 Linux kernel. In November 2005 storage devices finally were released using InfiniBand from vendors such as Engenio. Cisco, desiring to keep technology superior to Ethernet off the market, adopted a "buy to kill" strategy. Cisco successfully killed InfiniBand switching companies such as Topspin via acquisition. Of the top 500 supercomputers in 2009, Gigabit Ethernet was the internal interconnect technology in 259 installations, compared with 181 using InfiniBand. In 2010, market leaders Mellanox and Voltaire merged, leaving just one other IB vendor, QLogic, primarily a Fibre Channel vendor. At the 2011 International Supercomputing Conference, links running at about 56 gigabits per second (known as FDR, see below), were announced and demonstrated by connecting booths in the trade show. In 2012, Intel acquired QLogic's InfiniBand technology, leaving only one independent supplier. By 2014, InfiniBand was the most popular internal connection technology for supercomputers, although within two years, 10 Gigabit Ethernet started displacing it. In 2016, it was reported that Oracle Corporation (an investor in Mellanox) might engineer its own InfiniBand hardware. In 2019 Nvidia acquired Mellanox, the last independent supplier of InfiniBand products. == Specification == Specifications are published by the InfiniBand trade association. === Performance === Original names for speeds were single-data rate (SDR), double-data rate (DDR) and quad-data rate (QDR) as given below. Subsequently, other three-letter initialisms were added for even higher data rates. Notes Each link is duplex. Links can be aggregated: most systems use a 4 link/lane connector (QSFP). HDR often makes use of 2x links (aka HDR100, 100 Gb link using 2 lanes of HDR, while still using a QSFP connector). NDR introduced OSFP connectors which host one or two links at 2x (NDR200) or 4x (NDR400). They are not logically configured as a single 8x link, even when connecting switches together with an OSFP cable. InfiniBand provides remote direct memory access (RDMA) capabilities for low CPU overhead. === Topology === InfiniBand uses a switched fabric topology, as opposed to early shared medium Ethernet. All transmissions begin or end at a channel adapter. Each processor contains a host channel adapter (HCA) and each peripheral has a target channel adapter (TCA). These adapters can also exchange information for security or quality of service (QoS). === Messages === InfiniBand transmits data in packets of up to 4 KB that are taken together to form a message. A message can be: a remote direct memory access read or write a channel send or receive a transaction-based operation (that can be reversed) a multicast transmission an atomic operation === Physical interconnection === In addition to a board form factor connection, it can use both active and passive copper (up to 10 meters) and optical fiber cable (up to 10 km). QSFP connectors are used. The InfiniBand Association also specified the CXP connector system for speeds up to 120 Gbit/s over copper, active optical cables, and optical transceivers using parallel multi-mode fiber cables with 24-fiber MPO connectors. === Software interfaces === Mellanox operating system support is available for Solaris, FreeBSD, Red Hat Enterprise Linux, SUSE Linux Enterprise Server (SLES), Windows, HP-UX, VMware ESX, and AIX. InfiniBand has no specific standard application programming interface (API). The standard only lists a set of verbs such as ibv_open_device or ibv_post_send, which are abstract representations of functions or methods that must exist. The syntax of these functions is left to the vendors. Sometimes for reference this is called the verbs API. The de facto standard software is developed by OpenFabrics Alliance and called the Open Fabrics Enterprise Distribution (OFED). It is released under two licenses GPL2 or BSD license for Linux and FreeBSD, and as Mellanox OFED for Windows (product names: WinOF / WinOF-2; attributed as host controller driver for matching specific ConnectX 3 to 5 devices) under a choice of BSD license for Windows. It has been adopted by most of the InfiniBand vendors, for Linux, FreeBSD, and Microsoft Windows. IBM refers to a software library called libibverbs, for its AIX operating system, as well as "AIX InfiniBand verbs". The Linux kernel support was integrated in 2005 into the kernel version 2.6.11. === Ethernet over InfiniBand === Ethernet over InfiniBand, abbreviated to EoIB, is an Ethernet implementation over the InfiniBand protocol and connector technology. EoIB enables multiple Ethernet bandwidths varying on the InfiniBand (IB) version. Ethernet's implementation of the Internet Protocol Suite, usually referred to as TCP/IP, is different in some details compared to the direct InfiniBand protocol in IP over IB (IPoIB).
Labeled data
Labeled data is a group of samples that have been tagged with one or more labels. Labeling typically takes a set of unlabeled data and augments each piece of it with informative tags called judgments. For example, a data label might indicate whether a photo contains a horse or a cow, which words were uttered in an audio recording, what type of action is being performed in a video, what the topic of a news article is, what the overall sentiment of a tweet is, or whether a dot in an X-ray is a tumor. Labels can be obtained by having humans make judgments about a given piece of unlabeled data. Labeled data is significantly more expensive to obtain than the raw unlabeled data. The quality of labeled data directly influences the performance of supervised machine learning models in operation, as these models learn from the provided labels. == Crowdsourced labeled data == In 2006, Fei-Fei Li, the co-director of the Stanford Human-Centered AI Institute, initiated research to improve the artificial intelligence models and algorithms for image recognition by significantly enlarging the training data. The researchers downloaded millions of images from the World Wide Web and a team of undergraduates started to apply labels for objects to each image. In 2007, Li outsourced the data labeling work on Amazon Mechanical Turk, an online marketplace for digital piece work. The 3.2 million images that were labeled by more than 49,000 workers formed the basis for ImageNet, one of the largest hand-labeled database for outline of object recognition. == Automated data labelling == After obtaining a labeled dataset, machine learning models can be applied to the data so that new unlabeled data can be presented to the model and a likely label can be guessed or predicted for that piece of unlabeled data. == Challenges == === Data-driven bias === Algorithmic decision-making is subject to programmer-driven bias as well as data-driven bias. Training data that relies on bias labeled data will result in prejudices and omissions in a predictive model, despite the machine learning algorithm being legitimate. The labeled data used to train a specific machine learning algorithm needs to be a statistically representative sample to not bias the results. For example, in facial recognition systems underrepresented groups are subsequently often misclassified if the labeled data available to train has not been representative of the population,. In 2018, a study by Joy Buolamwini and Timnit Gebru demonstrated that two facial analysis datasets that have been used to train facial recognition algorithms, IJB-A and Adience, are composed of 79.6% and 86.2% lighter skinned humans respectively. === Human error and inconsistency === Human annotators are prone to errors and biases when labeling data. This can lead to inconsistent labels and affect the quality of the data set. The inconsistency can affect the machine learning model's ability to generalize well. === Domain expertise === Certain fields, such as legal document analysis or medical imaging, require annotators with specialized domain knowledge. Without the expertise, the annotations or labeled data may be inaccurate, negatively impacting the machine learning model's performance in a real-world scenario.
Social television
Social television is the union of television and social media. Millions of people now share their TV experience with other viewers on social media such as Twitter and Facebook using smartphones and tablets. TV networks and rights holders are increasingly sharing video clips on social platforms to monetise engagement and drive tune-in. The social TV market covers the technologies that support communication and social interaction around TV as well as companies that study television-related social behavior and measure social media activities tied to specific TV broadcasts – many of which have attracted significant investment from established media and technology companies. The market is also seeing numerous tie-ups between broadcasters and social networking players such as Twitter and Facebook. The market is expected to be worth $256bn by 2017. Social TV was named one of the 10 most important emerging technologies by the MIT Technology Review on Social TV in 2010. And in 2011, David Rowan, the editor of Wired magazine, named Social TV at number three of six in his peek into 2011 and what tech trends to expect to get traction. Ynon Kreiz, CEO of the Endemol Group told the audience at the Digital Life Design (DLD) conference in January 2011: "Everyone says that social television will be big. I think it's not going to be big—it's going to be huge". Much of the investment in the earlier years of social TV went into standalone social TV apps. The industry believed these apps would provide an appealing and complimentary consumer experience which could then be monetized with ads. These apps featured TV listings, check-ins, stickers and synchronised second-screen content but struggled to attract users away from Twitter and Facebook. Most of these companies have since gone out of business or been acquired amid a wave of consolidation and the market has instead focused on the activities of the social media channels themselves – such as Twitter Amplify, Facebook Suggested Videos and Snapchat Discover – and the technologies that support them. == Twitter == Twitter and Facebook are both helping users connect around media, which can provoke strong debate and engagement. Both social platforms want to be the 'digital watercooler' and host conversation around TV because the engagement and data about what media people consume can then be used to generate advertising revenue. As an open platform, conversation on Twitter is closely aligned with real-time events. In May 2013, it launched Twitter Amplify – an advertising product for media and consumer brands. With Amplify, Twitter runs video highlights from major live broadcasts, with advertisers' names and messages playing before the clip. By February 2014, all four major U.S. TV networks had signed up to the Amplify program, bringing a variety of premium TV content onto the social platform in the form of in-tweet real-time video clips. In June 2014, Twitter acquired its Twitter Amplify partner in the U.S. SnappyTV, a company that was helping broadcasters and rights holders to share video content both organically across social and via Twitter's Amplify program. Twitter continues to rely on Grabyo, which has also struck numerous deals with some of the largest broadcasters and rights holders in Europe and North America to share video content across Facebook and Twitter. == Facebook == Facebook made significant changes to its platform in 2014 including updates to its algorithm to enhance how it serves video in users' feeds. It also launched video autoplay to get users to watch the videos in their feeds. It rapidly surpassed Twitter and by the end of 2014 it was enjoying three billion video views a day on its platform and had announced a partnership with the NFL, one of Twitter's most active Twitter Amplify partners. In April 2015, at its F8 Developer Conference, it revealed it was working with Grabyo among other technology partners to bring video onto its platform. Then in July it announced it would be launching Facebook Suggested Videos, bringing related videos and ads to anyone that clicks on a video – a move that not only competed with Twitter's commercial video offering but also put it in direct competition with YouTube. == TV Time == TV Time is a television dedicated social network that allows users to keep track of the television series they watch, as well as films. It also allows them to express their reaction to the media they have seen with episode specific voting for favorite characters and emotional reaction to episodes, as well as commenting in episode restrictive pages. This way users are able to avoid spoilers while also finding a precise audience and community for each of their interactions, as opposed to bigger, non-television dedicated social medias such as Facebook and Twitter where the likelihood of unintentionally reading spoilers is much higher. TV Time offers an analytics service called "TVLytics" where the votes and reactions collected from users can be studied for research and television production purposes. == Advertising == According to Businessinsider.com, there are variety of applications for social TV, including support for TV ad sales, optimizing TV ad buys, making ad buys more efficient, as a complement to audience measurement, and eventually, audience forecasting and real-time optimization. Social TV data can ease access to focus groups and may create a positive feedback loop for generating ultra-sticky TV programming and multi-screen ad campaigns. == In numbers == Viewers share their TV experience on social media in real-time as events unfold: between 88-100m Facebook users login to the platform during the primetime hours of 8pm – 11pm in the US. The volume of social media engagement in TV is also rising – according to Nielsen SocialGuide, there was a 38% increase in tweets about TV in 2013 to 263m. For the 2014 Super Bowl, Twitter reported that a record 24.9 million tweets about the game were sent during the telecast, peaking at 381,605 tweets per minute. Facebook reported that 50 million people discussed the Super Bowl, generating 185 million interactions. The 2014 Oscars generated 5m tweets, viewed by an audience of 37m unique Twitter users and delivering 3.3bn impressions globally as conversation and key moments were shared virally across the platform. In 2014 the All England Lawn Tennis Club (AELTC), hosts of Wimbledon, used Grabyo to share video content across social. The videos were viewed 3.5 million times across Facebook and Twitter. In partnered with Grabyo again in 2015 and the videos generated over 48 million views across Facebook and Twitter. == Television shows with social integration == Here are some examples of how TV executives are integrating social elements with TV shows: C-SPAN streamed tweets from US Senators and Representatives during the quorum call The Voice had the judges of the program tweet during the show and the posts scrolls on the bottom of the screen. The use of Twitter also led to an increase in viewers. "Glee" Entertainment Weekly created a second screen viewing platform for the Glee season 3 premiere. == Related publications == Erika Jonietz. "Making TV Social, Virtually" MIT Technology Review. (January 11, 2010) AmigoTV (Alcatel-Lucent; Coppens et al.) – 2004 www.ist-ipmedianet.org/Alcatel_EuroiTV2004_AmigoTV_short_paper_S4-2.pdf Nextream (MIT Media Lab, Martin et al.) – 2010 Social Interactive Television: Immersive Shared Experiences and Perspectives (P. Cesar, D. Geerts, and K. Chorianopoulos (eds.)) – 2009 Social TV and the Emergence of Interactive TV – Multimedia Research Group – November 2010 Interactive Social TV on Service Oriented Environments: Challenges and Enablers (May 2011) == Systems == Boxee – acquired by Samsung GetGlue – acquired by i.TV Grabyo KIT digital Miso TV Tank Top TV WiO Xbox Live