AI For Business Isb

AI For Business Isb — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Luma (video)

    Luma (video)

    In video, luma ( Y ′ {\displaystyle Y'} ) represents the brightness in an image (the "black-and-white" or achromatic portion of the image). Luma is typically paired with chroma. Luma represents the achromatic image, while the chroma components represent the color information. Converting R′G′B′ sources (such as the output of a three-CCD camera) into luma and chroma allows for chroma subsampling: because human vision has finer spatial sensitivity to luminance ("black and white") differences than chromatic differences, video systems can store and transmit chromatic information at lower resolution, optimizing perceived detail at a particular bandwidth. == Luma versus relative luminance == Luma is the weighted sum of gamma-compressed R′G′B′ components of a color video—the prime symbols ′ denote gamma compression. The word was proposed to prevent confusion between luma as implemented in video engineering and relative luminance as used in color science (i.e. as defined by CIE). Relative luminance is formed as a weighted sum of linear RGB components, not gamma-compressed ones. Even so, luma is sometimes erroneously called luminance. SMPTE EG 28 recommends the symbol Y ′ {\displaystyle Y'} to denote luma and the symbol Y {\displaystyle Y} to denote relative luminance. === Use of relative luminance === While luma is more often encountered, relative luminance is sometimes used in video engineering when referring to the brightness of a monitor. The formula used to calculate relative luminance uses coefficients based on the CIE color matching functions and the relevant standard chromaticities of red, green, and blue (e.g., the original NTSC primaries, SMPTE C, or Rec. 709). For the Rec. 709 (and sRGB) primaries, the linear combination, based on pure colorimetric considerations and the definition of relative luminance is: Y = 0.2126 R + 0.7152 G + 0.0722 B {\displaystyle Y=0.2126R+0.7152G+0.0722B} The formula used to calculate luma in the Rec. 709 spec arbitrarily also uses these same coefficients, but with gamma-compressed components: Y ′ = 0.2126 R ′ + 0.7152 G ′ + 0.0722 B ′ , {\displaystyle Y'=0.2126R'+0.7152G'+0.0722B',} where the prime symbol ′ denotes gamma compression. == Rec. 601 luma versus Rec. 709 luma coefficients == For digital formats following CCIR 601 (i.e. most digital standard definition formats), luma is calculated with this formula: Y 601 ′ = 0.299 R ′ + 0.587 G ′ + 0.114 B ′ {\displaystyle Y'_{\text{601}}=0.299R'+0.587G'+0.114B'} Formats following ITU-R Recommendation BT. 709 (i.e. most digital high definition formats) use a different formula: Y 709 ′ = 0.2126 R ′ + 0.7152 G ′ + 0.0722 B ′ {\displaystyle Y'_{\text{709}}=0.2126R'+0.7152G'+0.0722B'} Modern HDTV systems use the 709 coefficients, while transitional 1035i HDTV (MUSE) formats may use the SMPTE 240M coefficients: Y 240 ′ = 0.212 R ′ + 0.701 G ′ + 0.087 B ′ = Y 145 ′ {\displaystyle Y'_{\text{240}}=0.212R'+0.701G'+0.087B'=Y'_{\text{145}}} These coefficients correspond to the SMPTE RP 145 primaries (also known as "SMPTE C") in use at the time the standard was created. The change in the luma coefficients is to provide the "theoretically correct" coefficients that reflect the corresponding standard chromaticities ('colors') of the primaries red, green, and blue. However, there is some controversy regarding this decision. The difference in luma coefficients requires that component signals must be converted between Rec. 601 and Rec. 709 to provide accurate colors. In consumer equipment, the matrix required to perform this conversion may be omitted (to reduce cost), resulting in inaccurate color. == Luma and luminance errors == As well, the Rec. 709 luma coefficients may not necessarily provide better performance. Because of the difference between luma and relative luminance, luma does not exactly represent the luminance in an image. As a result, errors in chroma can affect luminance. Luma alone does not perfectly represent luminance; accurate luminance requires both accurate luma and chroma. Hence, errors in chroma "bleed" into the luminance of an image. Note the bleeding in lightness near the borders. Due to the widespread usage of chroma subsampling, errors in chroma typically occur when it is lowered in resolution/bandwidth. This lowered bandwidth, coupled with high frequency chroma components, can cause visible errors in luminance. An example of a high frequency chroma component would be the line between the green and magenta bars of the SMPTE color bars test pattern. Error in luminance can be seen as a dark band that occurs in this area.

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  • Statistical semantics

    Statistical semantics

    In linguistics, statistical semantics applies the methods of statistics to the problem of determining the meaning of words or phrases, ideally through unsupervised learning, to a degree of precision at least sufficient for the purpose of information retrieval. == History == The term statistical semantics was first used by Warren Weaver in his well-known paper on machine translation. He argued that word-sense disambiguation for machine translation should be based on the co-occurrence frequency of the context words near a given target word. The underlying assumption that "a word is characterized by the company it keeps" was advocated by J. R. Firth. This assumption is known in linguistics as the distributional hypothesis. Emile Delavenay defined statistical semantics as the "statistical study of the meanings of words and their frequency and order of recurrence". "Furnas et al. 1983" is frequently cited as a foundational contribution to statistical semantics. An early success in the field was latent semantic analysis. == Applications == Research in statistical semantics has resulted in a wide variety of algorithms that use the distributional hypothesis to discover many aspects of semantics, by applying statistical techniques to large corpora: Measuring the similarity in word meanings Measuring the similarity in word relations Modeling similarity-based generalization Discovering words with a given relation Classifying relations between words Extracting keywords from documents Measuring the cohesiveness of text Discovering the different senses of words Distinguishing the different senses of words Subcognitive aspects of words Distinguishing praise from criticism == Related fields == Statistical semantics focuses on the meanings of common words and the relations between common words, unlike text mining, which tends to focus on whole documents, document collections, or named entities (names of people, places, and organizations). Statistical semantics is a subfield of computational semantics, which is in turn a subfield of computational linguistics and natural language processing. Many of the applications of statistical semantics (listed above) can also be addressed by lexicon-based algorithms, instead of the corpus-based algorithms of statistical semantics. One advantage of corpus-based algorithms is that they are typically not as labour-intensive as lexicon-based algorithms. Another advantage is that they are usually easier to adapt to new languages or noisier new text types from e.g. social media than lexicon-based algorithms are. However, the best performance on an application is often achieved by combining the two approaches.

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  • We Appreciate Power

    We Appreciate Power

    "We Appreciate Power" is a song by Canadian musician Grimes, featuring American musician Hana. It was released on November 29, 2018, billed as the lead single from her fifth studio album Miss Anthropocene, however it is only available on the Japanese and deluxe releases. The song was written and produced by Grimes, Poppy (originally), Hana and Chris Greatti. == Background and release == The song was supposed to be one of two collaborations between Grimes and American singer Poppy, for the latter's second studio album Am I a Girl?. In an interview, Poppy mentioned that she wrote two songs with Grimes; one about "destroying things" and another about "power". The other song, "Play Destroy", was featured on the album. Grimes shared a lyric of the song with a photo of her with Poppy on Twitter in May 2018. Following feuds between the two singers, the song was released by Grimes featuring singer Hana instead. On November 26, Grimes announced she would be releasing new music on November 29. Two days later, she revealed that the single is titled "We Appreciate Power" and features Hana, and shared the artwork. The release of the song was accompanied by a lyric video directed by Grimes and her brother Mac Boucher. == Music and lyrics == "We Appreciate Power" is an industrial rock, nu metal, and techno-industrial song. The track is regarded as a further step into Grimes's experimentation with guitars that started on 2015's Art Angels. The track was compared to the works of Nine Inch Nails; Jillian Mapes of Pitchfork described the song as "an immediate onslaught of mutilated noise—distorted metal guitar chug, bloody screams, a guitar loop that conjures fear and demands worship. Flashes of Nine Inch Nails' Pretty Hate Machine reverberate through the drum programming and synths." Brendan Klinkenberg of Rolling Stone placed the song "somewhere between power pop and straightforward industrial (with an extended bridge reminiscent of the most sweeping moments in a Final Fantasy score)" and "a distinctly 2018 take on Nine Inch Nails-esque hard-edged rock." A press release stated that the song was inspired by the North Korean band Moranbong and was written "from the perspective of a Pro-A.I. Girl Group Propaganda machine who use song, dance, sex and fashion to spread goodwill towards Artificial Intelligence." In addition Grimes stated that by simply listening to the song you will be reducing your risk of ending up on any future AI overlord's hit list when it reigns supreme, mirroring the Roko's basilisk theory. Lyrically, the song touches on transhumanist ideas such as the betterment and future of the human race, the possibilities of merging consciousness with machines to extend life indefinitely through mind uploading, and the idea that reality may be simulated. The song's chorus generated a spike in interest in the word "capitulate". == Critical reception == Pitchfork critic Jillian Mapes wrote: "If "Freak on a Leash" isn't a dealbreaker, then the supervillain allure of "We Appreciate Power" might pull you in (it legitimately slaps), but it just as well may leave you weighed down by Grimes' commitment to the absolute darkest timeline." Billboard's Gil Kaufman described the song as "a dystopian, aggressive dive into a more rock-leaning sound." Similarly, Brendan Klinkenberg of Rolling Stone called it "the most aggressive single Grimes has released to date" Noisey called the song "an absolute motherfucker of a single" and opined it sounds "like a K-pop band covering nu-metal". Justin Kamp of Paste described the track as a "glitchy empowerment anthem that chugs along on screeching synths and Grimes' repeated exultations of power." == Personnel == Credits adapted from Tidal. Grimes – vocals, guitar, production, engineering Hana – vocals, guitar, additional production Chris Greatti – guitar, keyboards, production, engineering Zakk Cervini – mixing == Track listing == == Charts ==

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  • Shy Girl

    Shy Girl

    Shy Girl is a horror novel initially self-published in February 2025 by Mia Ballard. Publishing rights for the book were acquired by Hachette Book Group, which released the book in the United Kingdom in November 2025 and planned to publish it in the United States in 2026. Its US release was cancelled and its UK release was discontinued after it faced accusations of being created with generative AI. Ballard denied having personally used AI in the book's writing, claiming that a freelance editor had introduced AI-generated changes. She also stated that she would take legal action against the editor. == Premise == The novel follows Gia, a depressed woman with obsessive–compulsive disorder, who encounters a mysterious man named Nathan while looking for a sugar daddy to ease her financial troubles. Nathan offers to erase all of Gia's debts in exchange for her agreeing to live as his pet. Living like an animal convinces her that she is becoming an animal, making her behave like one. == Publication and cancellation == Shy Girl was first self-published online by Mia Ballard in February 2025. Marketing material described the book as a "buzzy BookTok sensation" and "bloody and unforgiving". The self-published edition of the book was highly successful and had over 4,900 ratings on Goodreads and an average score of 3.52 stars. In an interview, Ballard described her writing style as lyrical, feverish, and introspective, and stated she was more interested in "what it feels like to live inside a body" than in plot-driven storylines. Publishing rights were acquired by Hachette Book Group and it was published by its Wildfire imprint in the United Kingdom in November 2025. By March 2026, the book had sold 1,800 copies in the United Kingdom. A US release was planned for 2026 by the imprint Orbit Books. After the British publication, critics and readers began to make claims that the book appeared to have been written by generative AI. A January 2026 post on Reddit claimed that the book had many of the hallmarks of having been written with a large language model, and stated that it was "repulsive" that the book was accepted by Hachette. A two-and-a-half-hour video essay covering the book, titled "i'm pretty sure this book is ai slop", received 1.2 million views on YouTube by March 2026. In response, Hachette Book Group announced in March 2026 that it would cancel the book's US publication and discontinue its UK publication. It told The Wall Street Journal that it had made "a lengthy investigation" before deciding to cancel the book. Ballard told The New York Times that she had not used AI when writing the book, but that AI-generated elements were added by a freelance editor without her knowledge. She also stated that she could not elaborate on her claim because she was pursuing legal action against the editor. Writer Andrea Bartz opined that the situation "raises many concerns about trust, authenticity and publishing's readiness for a new, A.I.-assisted world", but that "readers made it abundantly clear they want books by humans, not machines".

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  • Automated Mathematician

    Automated Mathematician

    The Automated Mathematician (AM) is one of the earliest successful discovery systems. It was created by Douglas Lenat in Lisp, and in 1977 led to Lenat being awarded the IJCAI Computers and Thought Award. AM worked by generating and modifying short Lisp programs which were then interpreted as defining various mathematical concepts; for example, a program that tested equality between the length of two lists was considered to represent the concept of numerical equality, while a program that produced a list whose length was the product of the lengths of two other lists was interpreted as representing the concept of multiplication. The system had elaborate heuristics for choosing which programs to extend and modify, based on the experiences of working mathematicians in solving mathematical problems. == Controversy == Lenat claimed that the system was composed of hundreds of data structures called "concepts", together with hundreds of "heuristic rules" and a simple flow of control: "AM repeatedly selects the top task from the agenda and tries to carry it out. This is the whole control structure!" Yet the heuristic rules were not always represented as separate data structures; some had to be intertwined with the control flow logic. Some rules had preconditions that depended on the history, or otherwise could not be represented in the framework of the explicit rules. What's more, the published versions of the rules often involve vague terms that are not defined further, such as "If two expressions are structurally similar, ..." (Rule 218) or "... replace the value obtained by some other (very similar) value..." (Rule 129). Another source of information is the user, via Rule 2: "If the user has recently referred to X, then boost the priority of any tasks involving X." Thus, it appears quite possible that much of the real discovery work is buried in unexplained procedures. Lenat claimed that the system had rediscovered both Goldbach's conjecture and the fundamental theorem of arithmetic. Later critics accused Lenat of over-interpreting the output of AM. In his paper Why AM and Eurisko appear to work, Lenat conceded that any system that generated enough short Lisp programs would generate ones that could be interpreted by an external observer as representing equally sophisticated mathematical concepts. However, he argued that this property was in itself interesting—and that a promising direction for further research would be to look for other languages in which short random strings were likely to be useful. == Successor == This intuition was the basis of AM's successor Eurisko, which attempted to generalize the search for mathematical concepts to the search for useful heuristics.

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  • Fuzzy relation

    Fuzzy relation

    A fuzzy relation is the cartesian product of mathematical fuzzy sets. Two fuzzy sets are taken as input, the fuzzy relation is then equal to the cross product of the sets which is created by vector multiplication. Usually, a rule base is stored in a matrix notation which allows the fuzzy controller to update its internal values. From a historical perspective, the first fuzzy relation was mentioned in the year 1971 by Lotfi A. Zadeh. A practical approach to describe a fuzzy relation is based on a 2d table. At first, a table is created which consists of fuzzy values from 0..1. The next step is to apply the if-then-rules to the values. The resulting numbers are stored in the table as an array. Fuzzy relations can be utilized in fuzzy databases.

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  • Digital fashion

    Digital fashion

    Digital fashion is a field of fashion design that relies on 3D software or artificial intelligence to produce hyper-realistic, data-intensive digital 3D garment simulations that are digital-only products or digital models for physical products. Digital garments can be worn and presented in virtual environments, social media, online gaming, virtual reality (VR), and augmented reality (AR) platforms. The field aims to contribute to the development of a more sustainable future for the fashion industry. It has been praised as a possible answer to ethical and creative concerns of traditional fashion by promoting innovation, reducing waste, and encouraging conscious consumption. However, empirical research has questioned whether digital fashion communities embody the radical and anti-consumerist values they claim. A 2025 study presented by YeSeung Lee at the FACTUM international conference on fashion communication analysed 88,141 posts across nine platforms over eight months using Pulsar. It found that only 4.8% of author biographies indicated any sociopolitical focus, and that discourse predominantly relied on generic slogans and trending buzzwords, primarily reinforcing existing fashion hierarchies and consumerist frameworks rather than challenging them. Digital fashion is also the interplay between digital technology and couture. Human AI is an intersection of technology and human representation, in which human value is emphasized and enhanced by technology and the possibilities of discovering design. Information and communication technologies (ICTs) have been deeply integrated both into the fashion industry, as well as within the experience of clients and prospects. Such interplay has happened at three main levels. ICTs are used to design and produce fashion products, while the industry organization also leverages digital technologies. ICTs impact marketing, distribution and sales. ICTs are extensively used in communication activities with all relevant stakeholders and contribute to co-create the fashion world. The fashion industry in general has paved the way for digital fashion to be introduced with more technology being in the industry, like virtual dressing rooms and the gamification of the fashion industry. Digital fashion is also seen on many different online fashion retail websites. This evolution in the fashion industry has called for more education and research of digital fashion. == Design, production, and organization == Among the many applications available to fashion designers to model the fusion of creativity with digital avenues, the Digital Textile Printing can be mentioned here. === Digital textile printing === Digital textile printing has brought together the worlds of fashion, technology, art, chemistry, and printing to produce a new process for printing textiles on clothing. Digital printing is a process in which prints are directly applied to fabrics with a printer, reducing 95% of the use of water, 75% of the use of energy and minimizing textile waste. The main advantage of digital printing is the ability to do very small runs of each design (even less than 1 yard). Digital Textile printing also offers other benefits, such as fast printing speeds that help the time and space needed to print different patterns on garments of choice. == Marketing, distribution, and sales == While all digital channels can be used in order to market and sell fashion completely online (eCommerce), they usually are implemented in connection with offline channels (so-called "omni-channel"). Here, virtual and augmented reality play a crucial role. The fashion industry has faced its own problems including pollution and fabric waste, which has resulted in a shift to more sustainable methods like digital fashion. The industry is also constantly being intertwined with digital media and has allowed for the use of digital tools within the business itself and with consumers. Two of the ways digital fashion is utilized with consumers is through virtual dressing rooms and virtual cosmetic counters. Prospects and clients can use ICTs - own computers, tablets and smartphones - to virtually simulate fitting rooms and cosmetics counters and see how they look in specific outfits and makeup. Customers can give any look and decide on what suits them and buy products. Oftentimes, beauty retailers will feature virtual fitting rooms to allow users to experience the look of their product before committing to a purchase. Some examples are color contact retailers Freshlook, which allows users to simulate contact lens wear in their color contacts studio before purchase. Colorful Eyes also offers a virtual color contact lens try-on room. === Virtual dressing room === A virtual dressing room (also often referred to as virtual fitting room and virtual changing room although they do perform different functions) is the online equivalent of the near-ubiquitous in-store changing room – that is, it enables shoppers to try on clothes to check one or more of size, fit or style, but virtually rather than physically. Fashion retailer Topshop installed a Kinect-powered virtual fitting room at its Moscow store. Created by AR Door, the Augmented Fitting Room system overlays 3D augmented reality clothes on the customer. Simple gestures and on-screen buttons let users "try on" different outfits. However, the high variability of virtual fit platforms to predict consumer clothes sizes called into question the accuracy of these systems in their current form. AI-powered Wardrobe and Outfit Planning Beyond virtual fitting rooms, the integration of artificial intelligence has enabled the rise of digital wardrobe management. These platforms use computer vision and machine learning to catalog a user’s physical or digital garments, providing automated outfit recommendations based on weather, occasion, and personal style trends. Fashion-tech startups utilize AI-driven garment simulation to help users plan outfits virtually, bridging the gap between digital-only fashion and physical wardrobe utility. This "smart closet" approach aims to reduce "wardrobe fatigue" and decrease unnecessary consumption by maximizing the use of existing items through digital visualization. === Communication and experience co-creation === Fashion is also a matter of socially negotiating what is "in" or "out", fashionable or not. In other words, fashion items do not only play on the economic market of physical goods but also - and sometimes even more importantly - on the semiotic market of the production of social tastes and customs. Thanks to social media, and to all services offered by the so-called web2.0, laypeople can contribute to co-create the fashion world, shaping tastes, customs, and fashion-related values. Social media, in general, has catapulted the impact fashion has on our everyday lives and values. Fashion has taken a central role in mass production and is constantly evolving due to the ever-lasting digital transformation. Social media has also helped evolve to a point where not only can brands reach consumers, but consumers can reach brands as well. TikTok for example started a trend in 2020 with #GucciModelChallenge. This creates a space where the brand is gaining awareness from their consumers in the ever-changing digital age. === Gamification === Gaming has played an important role in fostering digital aspects of the fashion world, first beginning with dress-up games that used avatars and allowed players to select garments. Nevertheless, it seems it will now move on to the real world and start using avatars of real people. Garments from luxurious brands have been copied and adapted into the aesthetics of games such as Animal Crossing: New Horizons and The Sims. As to the former, during COVID-19 lock-downs players recreated outfits from a variety of fashion brands, including Chanel, Gucci and Versace. It became a platform for users to showcase their costume designs. In April 2019, Moschino collaborated with simulation game The Sims in a capsule collection that featured signature Jeremy Scott garments. The collection was made available to shop and the campaign was set against the backdrop of a Sims-like atmosphere. Furthermore, in May 2019, Nike partnered up with Fortnite to include their iconic Jordan sneakers. In similar fashion, in May 2020, Marc Jacobs designed 6 of the brand's favorite looks for Nintendo's Animal Crossing: New Horizons in a partnership with Instagram user @AnimalCrossingFashionArchive. They were made available to download. Similarly, the other luxury brands mentioned, Louis Vuitton partnered with game League of Legends to create skins for characters within the game. Digital fashion in different video games allows users to express themselves beyond their avatars and combine the self-expression of fashion into the digital gaming realm. == Digital fashion education and research == Nowadays, the fashion industry needs experts in digital fashion, equipped with the above-ske

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  • Smart speaker

    Smart speaker

    A smart speaker is a type of loudspeaker and voice command device with an integrated virtual assistant that offers interactive actions and hands-free activation with the help of one "wake word" (or several "wake words"). Some smart speakers also act as smart home hubs by using Wi-Fi, Bluetooth, Thread, and other protocol standards to extend usage beyond audio playback and control home automation devices connected through a local area network. == History == Early voice-activated devices began in 2013 with MIT's Jasper project, which used multiple microphones and cloud software to power hands-free interactions from across a room. The first commercial smart speaker was the Amazon Echo, which was released in 2014 powered by Alexa and a ring of far-field microphones. Google followed in 2016 with Home, powered by Google Assistant. By 2017, devices like the Echo Show and Home Hub (later called Nest Hub) added touchscreens and video, creating the "smart display" subcategory. In 2018, Apple joined the smart speaker trend by launching the HomePod, which focused on high-quality audio alongside their built-in assistant Siri. ASUS release its own smart Speaker Xiao-Bu in 2019 with Artificial Intelligence, it terminates the Cloud Service on June 1st, 2025, which means all real-time service such as weather, news, currency conversion is affected. Sonos's 1st smart speaker Sonos One released in 2017, powered by Alexa. Invoke by Harman Kardon was powered by Microsoft's intelligent personal assistant, Cortana. In the early 2020s, smart speakers gained on-device voice processing for faster responses and improved privacy. New standards such as Matter and Thread allowed multitudes of smart-home devices (even from completely different brands) to work together. == Features == === Audio and Voice === Smart speakers use multiple microphones along with noise-cancelling software to pick up your voice from across the room, even when music is playing or the assistant is already talking. Noise suppression and echo cancellation is also used by the speaker so it can focus in on who is talking and ignore any background noises. Most smart speaker models can recognize who is speaking by voiceprint, which allows the speaker to grab information from that person's calendar, preferences, or music playlists. Listening to music on a speaker is when importance for good audio quality becomes apparent. Entry-level (cheaper) speakers such as the Home Mini or the Echo Dot have a single full-range driver. These lower-end speakers typically aren't great for listening to music as the audio quality is pretty poor. More advanced units such as the Home Max or Echo Studio have separate tweeters and woofers meant for listening to music in high quality. === Connectivity and smart-home control === Most connect over Wi-Fi or Bluetooth and support hub protocols like Thread and Matter. That lets them not only stream and play music but also allows you to control various brands of smart lights, thermostats, door locks, cameras, and much more-all from one point of control. Each can have its own designated interface and features in-house, usually launched or controlled via application or home automation software. These devices are able to communicate with each other via peer-to-peer connection through mesh networking. These speakers and related smart devices are typically controlled with one smartphone application. === Assistant services and skills === The built-in assistants handle timers, alarms, reminders, news briefings, weather updates, send messages to other smart devices, send texts, make calls, and simple questions. You can combine actions together in what are typically known as routines (for example saying "good morning" turns on lights, starts the coffee, says the weather, and reads the news) and add extra functions known as skills or actions (for things like ordering food or playing trivia games). This hands-free use of smart speakers can help assist those with disabilities. Most other technologies need the user to be able to physically interact with the device. Smart speakers are not bound by these limitations and can serve as an excellent tool for those who are unable to use their arms or legs or have vision issues. Although these tasks can be completed by a phone or computer, consumers tend to lean towards smart speakers due to factors such as their range being much greater than that of a phone and the need to not have to physically interact with the speaker to get the voice assistant as with most smartphones, certain parts of a phone may need to be interacted with to activate the speaking assistant. === Smart displays === Some smart speakers also include a screen to show the user a visual response. A smart speaker with a touchscreen is known as a smart display; these integrate a conversational user interface with display screens to augment voice interaction with images and video. They are powered by one of the common voice assistants and offer additional controls for smart home devices, feature streaming apps, and web browsers with touch controls for selecting content. The first smart displays were introduced in 2017 by Amazon (Amazon Echo Show) and Google (Google/Nest Home Hub). Hotel chain Marriott International partnered with Amazon to install Echo devices in select hotels since 2018. A Taiwanese startup, Aiello, launched the Aiello Voice Assistant (AVA) in the Asian hotel market in 2019, claiming it is powered by a multi-AI model system. Angie by Nomadix, which is similar to the Amazon Echo, launched its first product in 2017, specifically targeting hotel properties in the North America. In May 2019, Angie Hospitality acquired the assets of Roxy, a competitor that also built its own speech-enabled virtual assistant technology for hotels. This acquisition merged two proprietary NLP stacks into the current Nomadix product. === Artificial intelligence === The newest speakers can use on-device AI or cloud-based generative models to allow the smart speaker to carry on much more natural conversations, draft emails or recipes, suggest ideas based on context, or even create short pieces of music or art. This AI evolution allows these speakers to do far more than what they could do before. == Accuracy == According to a study by Proceedings of the National Academy of Sciences of the United States of America released In March 2020, the six biggest tech development companies, Amazon, Apple, Google, Yandex, IBM and Microsoft, have misidentified more words spoken by "black people" than "white people". The systems tested errors and unreadability, with a 19 and 35 percent discrepancy for the former and a 2 and 20 percent discrepancy for the latter. The North American Chapter of the Association for Computational Linguistics (NAACL) also identified a discrepancy between male and female voices. According to their research, Google's speech recognition software is 13 percent more accurate for men than women. It performs better than the systems used by Bing, AT&T, and IBM. == Privacy concerns == The built-in microphone in smart speakers is continuously listening for wake words followed by a command. However, these continuously listening microphones also raise privacy concerns among users. According to a survey taken by 1,007 people in Western Europe, it is clear that privacy is the biggest concern holding consumers back from buying "smart" products. these concerns include what is being recorded, how the data will be used, how it will be protected, and whether it will be used for invasive advertising. Furthermore, an analysis of Amazon Echo Dots showed that 30–38% of "spurious audio recordings were human conversations", suggesting that these devices capture audio other than strictly detection of the wake word. === As a wiretap === There are strong concerns that the ever-listening microphone of smart speakers presents a perfect candidate for wiretapping. In 2017, British security researcher Mark Barnes showed that pre-2017 Echos have exposed pins which allow for a compromised OS to be booted. According to Umar Iqbal, an assistant professor at Washington University in St. Louis, research indicates that data from consumer interactions with Alexa was used to targeted advertisements and products to consumer with over 40% of transmitted data lacking proper encryption raising privacy concerns. Further data indicates that due to the Smart Speakers ability to always capture audio, it begins to pick up on external conversations from consumers not related to commands given to the smart speaker. Things such as other members in the household, consumers on the phone and even TV audio can be picked up by these speakers and stored for future use by companies. === Voice assistance vs privacy === While voice assistants provide a valuable service, there can be some hesitation towards using them in various social contexts, such as in public or around other users. However, only more recently have users begun interac

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  • WS-SecurityPolicy

    WS-SecurityPolicy

    WS-Security Policy is a web services specification, created by IBM and 12 co-authors, that has become an OASIS standard as of version 1.2. It extends the fundamental security protocols specified by the WS-Security, WS-Trust and WS-Secure Conversation by offering mechanisms to represent the capabilities and requirements of web services as policies. Security policy assertions are based on the WS-Policy framework. Policy assertions can be used to require more generic security attributes like transport layer security , message level security or timestamps, and specific attributes like token types. Most policy assertion can be found in following categories: Protection assertions identify the elements of a message that are required to be signed, encrypted or existent. Token assertions specify allowed token formats (SAML, X509, Username etc.). Security binding assertions control basic security safeguards like transport and message level security, cryptographic algorithm suite and required timestamps. Supporting token assertions add functions like user sign-on using a username token. Policies can be used to drive development tools to generate code with certain capabilities, or may be used at runtime to negotiate the security aspects of web service communication. Policies may be attached to WSDL elements such as service, port, operation and message, as defined in WS Policy Attachment. == Sample Policies == Namespaces used by the following XML-snippets: ... Include a timestamp: Use either transport layer security (https) or message level security (XML Dsig/XML Enc): ... ... To define a SAML assertion as security token: ...#SAMLV2.0 Issued token assertion of providers with reference to the STS and required token format: http://sampleorg.com/sts http://docs.oasis-open.org/wss/oasis-wss-saml-token-profile-1.0#SAMLAssertionID ... ... Specify that message header and body need to be signed, and attachments are left unsigned: ? ... specify that message open source license need to be signed, and hydra security are left unsigned: ? ... == Other WS policy languages == The term Web Services Security Policy Language is used for two different XML-based languages: As described above, based on the WS-Policy framework, as defined in, published as version 1.3 in Feb. 2009 WSPL, based on XACML profile for Web-services, but that was not finalized.

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  • The Eye of Mexico

    The Eye of Mexico

    The Eye of Mexico (Spanish: El Ojo de México) is an outdoor sculpture in Mexico City. It is located in Ampliación Granada, Miguel Hidalgo, at the mixed-use development Neuchâtel Polanco, developed by the Canadian real estate company Ivanhoé Cambridge. The artwork was created by the Turkish artist Ferdi Alıcı and it was selected from among 350 proposals from artists from 35 countries. The project for The Eye of Mexico was developed by MIRA, a real estate investment and development company, and MASSIVart, a creative consulting agency. According to MIRA, upon its inauguration it became the first artwork in Latin America to use artificial intelligence (AI). The sculpture can read environmental and urban data using AI algorithms and transform the results into videos related to arts, science and technology. The ring was inaugurated on 20 May 2022 and it is 10 meters (33 ft) high and 3 meters (9.8 ft) wide.

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  • Gundam Build Divers Re:Rise

    Gundam Build Divers Re:Rise

    Gundam Build Divers Re:Rise (Japanese: ガンダムビルドダイバーズRe:RISE, Hepburn: Gandamu Birudo Daibāzu Re:Raizu) is a Japanese original net animation anime series produced by Sunrise Beyond, and the fourth series within the Gundam Build Series sub-series. A sequel to the 2018 anime Gundam Build Divers, it is the first Gundam anime series to be released in the Reiwa period, released to celebrate the franchise's 40th anniversary. The series is directed by Shinya Watada and written by Yasuyuki Muto. Initially announced at the Gundam 40th anniversary video, the series aired on its Gundam Channel YouTube channel from October 10 to December 26, 2019. A TV airing of the ONA began on BS11 on October 12, 2019, and on January 28, 2020, on Tokyo MX. A second season aired from April 9 to August 27, 2020. Two spinoffs of the series were later serialized in Kadokawa's Gundam Ace magazine and Hobby Japan. == Plot == Two years have passed since the EL-Diver Incident, an event that almost destroyed the Gunpla Battle Nexus Online (GBN) game until it was resolved by the force group known as "Build Divers", and soon after more EL-Divers were discovered. In order to make the game more secure, a newer version of the game was rolled out in order to prevent the same incident from happening again and with newer experiences that would make the gameplay more immersive to players. The story focuses on Hiroto Kuga, a high schooler who is a rogue mercenary Gunpla Diver in GBN, who goes in the game and wanders throughout its countless dimensions while helping out other Divers whether it is on insistence or by hire. Despite his selfless act, he chooses to remain unaffiliated with anyone and refuses rewards and Force (Diver parties) group invites, isolating himself from other people even in real life. His primary goal as a Diver is to be reunited with a mysterious girl from his past named Eve, who was in fact the very first EL-Diver to appear in the game. But after a special request mission, Hiroto is united with three other active Divers in a strange world named "Eldora" and forms the Force group "BUILD DiVERS" in what appears to be just another GBN gamespace event, until they learn the truth about Eldora and its consequences not only for GBN, but for the entire world. == Characters == === BUILD DiVERS === Hiroto Kuga (クガ・ヒロト, Kuga Hiroto) / Hiroto (ヒロト, Hiroto) Voiced by: Chiaki Kobayashi (Japanese); Billy Kametz (English) The main protagonist of the series and a high-school builder, veteran diver, and a former ace member of the Force group Avalon, who lives in Yokohama. He was one of the first minors to make it to the deep end of GBN, due to his conviction of being a person who does his best to help others. He was active prior and during the events of the previous series. Now working as a rogue diver for hire after leaving Avalon, he wanders the GBN gamespace alone, harboring regrets, resentments, and suffering from trauma after the death of his close friend and lover, the EL-Diver Eve. He is very calm and a man of few words, usually refusing others' reward and help, especially on joining other forces, but this stoic persona is a mental mask to hide his condition from everyone, including his parents. But when a special mission done by Freddie united him with Kazami, May and Parviz, they accidentally formed the force team named "BUILD DiVERS" to protect the Eldorans from the One-Eyes army. Currently he is the ace of his unit and the leader of the overall force. Hiroto uses the PFF-X7 Core Gundam as his main Gunpla, based on the RX-78-2 Gundam from the original Mobile Suit Gundam series. Its special armament system called the "core-change" gimmick and his first theme invented from that gimmick is the "Planets System". This allows the Core Gundam to be equipped with various types of armor and weapons, each for a different situation named after the eight planets. Hiroto later upgrades his Gunpla into the PFF-X7II Core Gundam II. This new Core Gundam can transform into the "Core Flyer", in a similar fashion to the original Gundam's FF-X7 Core Fighter for increased mobility and like its predecessor, it can also use the Planets System: Earth Armor (PFF-X7/E3 Earthree Gundam): Core Gundam's default blue armor, focused on traditional all-around combat. Mars Armor (PFF-X7/M4 Marsfour Gundam): A red armor whose focus is on fragments of four styles of close combat, hence "Cross-Combat". Venus Armor (PFF-X7/V2 Veetwo Gundam): A green armor whose focus is commando style ranged and bombardment combat, additionally with option works. Mercury Armor (PFF-X7/M1 Mercuone Gundam): A navy armor whose focus is underwater combat. Jupiter Armor (PFF-X7/J5 Jupitive Gundam): A white armor whose focus is fast orbital combat. Uranus Armor (PFF-X7II/U7 Uraven Gundam): An indigo armor focused on reconnaissance and high powered sniping. Saturn Armor (PFF-X7II/S6 Saturnix Gundam): An orange armor focused in demolition style close combat without beam weapons, originally developed to counter Gundam Frames. Neptune Armor (PFF-X7II/N8 Nepteight Gundam): An aqua-green armor equipped with a customized Volture Lumiere system similar to the one from Mobile Suit Gundam SEED C.E. 73: Stargazer, intended to be used for traveling through GBN's space in a short amount of time, but was used for launching into orbit instead of maneuvering in deep space. It is ultimately discarded in Eldora's orbit due to the strain of leaving Eldora's gravitational field. Pluto Armor (PFF-X7II+/P9 Plutine Gundam): Appearing only on Gundam Build Metaverse, the black colored armor is used for close combat and dueling purposes with its color scheme reminiscent of that of EcoPla. PFF-X7II/BUILD DiVERS Re:Rising Gundam: A special combination of the Core Gundam II with the WoDom Pod + and parts from the Gundam Aegis Knight and the EX Valkylander, armed with two giant beam sabers, eight miracle wings born from Eve's blessings, and the "Grand Cross Cannon", Hiroto's first special move, made with the help of his team. In one occasion, Hiroto changes his avatar to a Haro to pilot the Mobile Builder Haro Loader to help with the repairs on Cuadorn by making a prosthetic wing out of gunpla parts. During the Gunpla Battle Royal, he pilots an unmodified ASW-G-08 Gundam Barbatos Lupus Rex from Mobile Suit Gundam: Iron-Blooded Orphans. In Battlelogue, it is revealed that he has made a second Core Gundam II that he leaves on Eldora with the colors of the Gundam MK-II Titan. Another variant of this Gunpla sports the old "Gundam G3" colors with his team's personal crest, which is most likely to represent Sarah since the color of her hair, eyes, and dress embody Hiroto's time with Eve before they joined Avalon and to symbolize how he has officially befriended the original Build Divers. Each of the two units have unique advancements, the Titan color specializes in ground and underwater combat and the G3 color specializes in aerial and space combat. May (メイ, Mei) Voiced by: Mai Fuchigami (Japanese); Lauren Landa (English) A seemingly late teens female diver who prefers to play solo, she is a very calm and no-nonsense girl whose interest is in battles alone. However, she is not a fan of those who engage their opponents head on and prefers to implement a strategic approach. She is mature and has a strong sense of justice, and can be impulsive rushing into situations, especially for those in danger. Later in the series, she is revealed to be one of the 87 EL-Divers, however she was not one of those who were saved after the EL-Diver incident two years ago, she was born shortly after. After she was born she was given her own Mobile Doll body similar to Sarah, that is when she first met her, Koichi, Tsukasa, and Nanami. During the Lotus Challenge Eldoran style rehearsal battle it is revealed that she, as a new sister of Sarah, addresses the latter as the older since Sarah is chronologically older, regardless of her maturity. In the final episode, she is revealed to have been born with the remnant data originating from Eve, the first born EL-Diver who Hiroto befriended and fell in love with several years ago, and carries Eve's earring on her armband. In Battlelogue, it's implied that she is currently living with Hiroto IRL and in GBN is his attendant. May uses the JMA0530-MAY WoDom Pod as her main Gunpla, which is a customized JMA-0530 Walking Dome from Turn A Gundam. In the later episodes, the mobile suit is revealed to be a disguise for its true form, the HER-SELF Mobile Doll May. May later upgrades her WoDom Pod into the JMA0530-MAYBD WoDom Pod +. During the Gunpla Battle Royal, she uses her Mobile Doll (albeit with a new color scheme and the Gundam Base logo) along with an unmodified NZ-999 II Neo Zeong mobile armor from Mobile Suit Gundam Narrative. Kazami Torimachi (トリマチ・カザミ, Torimachi Kazami) / Kazami (カザミ, Kazami) Voiced by: Masaaki Mizunaka (Japanese); Ray Chase (English) A diver who was a former member of the diver group "Mu Dish". He is a very energet

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  • AI Action Summit 2025

    AI Action Summit 2025

    The Artificial Intelligence (AI) Action Summit (French: Sommet pour l'action sur l'intelligence artificielle or Sommet pour l'action sur l'IA, SAIA) was held at the Grand Palais in Paris, France, from 10 to 11 February 2025. The summit was co-chaired by French President Emmanuel Macron and Indian Prime Minister Narendra Modi. The 2025 AI Action Summit followed the 2023 AI Safety Summit hosted at Bletchley Park in the UK, and the 2024 AI Seoul Summit in South Korea. This series of AI summits continued with the AI Impact Summit in Delhi, which was hosted by India in February 2026. Whereas the 2023 AI Safety Summit was attended by representatives from 29 governments and executives from only a handful of AI companies, over 1,000 participants from more than 100 countries attended the 2025 Paris AI Summit, representing government leaders, international organisations, the academic and research community, the private sector, and civil society. == Background == The First International AI Safety Report was published on 29 January 2025. Commissioned after the Bletchley Park AI Safety Summit, the report focused on the risks and threats posed by general-purpose AI, and was slated for discussion at the Paris summit as part of the "Trust in AI" pillar. Whereas the first summit was focused on the catastrophic risks of AI and their mitigation, the Paris meeting was recast as an "AI Action Summit" emphasising innovation, practical implementation, and potential economic opportunities of AI, while also exploring a broader range of risks including its environmental impact and disruptions to the labour market. In the weeks leading up to the Paris summit, government leaders had also started to rally around "national champions" in AI, partly in response to Chinese AI startup DeepSeek, which had released a new model rivalling OpenAI o1. On Sunday 9 February, French President Emmanuel Macron posted a compilation of AI-generated deepfake video clips of himself on Instagram to help publicise the start of the 2025 AI Action Summit the following day. While acknowledging the humour of the deepfakes, the real Macron states in the video that using artificial intelligence, "we can do some very big things: change healthcare, energy, life in our society". == Proceedings == === Day 1 === In her opening address, French special envoy Anne Bouverot discussed the environmental impact of AI, acknowledging the technology's "current trajectory is unsustainable". General secretary Christy Hoffman of the UNI Global Union said that "AI-driven productivity gains risk turning the technology into yet another engine of inequality, further straining our democracies". Chinese Vice Premier Zhang Guoqing made a speech expressing China's willingness "to work with other countries to promote development, safeguard security, and share achievements in the field of artificial intelligence". Google CEO Sundar Pichai said in his speech that while the rise of AI brings many risks, "The biggest risk is missing out". He discussed Google's long track record of AI research and said that the company is investing further into "deep research" agents that can autonomously search the Internet and compile a full analysis for users. A new coalition, the Robust Open Online Safety Tools (ROOST) initiative, debuted at the summit. Supported by Google, Discord, OpenAI, and Roblox, and incubated at the Institute of Global Politics at Columbia University, the organisation is developing free, open-source tools to detect and report child sexual abuse material (CSAM). In his speech closing the first day, President Emmanuel Macron emphasized that France has the capability to deliver the power required by AI companies, thanks to its production of nuclear energy. While declaring that Europe was "back in the race" for AI, Macron said that the region was "too slow" for investors, and called on the EU to "simplify regulation" and "resynchronize with the rest of the world". === Day 2 === On 11 February 2025, the French government announced its $400 million endowment of Current AI, a new foundation to support the creation of AI "public goods" including high-quality datasets and open-source tools and infrastructure. Launched by President Macron, Current AI is backed by nine governments – Finland, France, Germany, Chile, India, Kenya, Morocco, Nigeria, Slovenia, and Switzerland – plus various philanthropic organisations such as the Omidyar Group and the McGovern Foundation, and private companies such as Google and Salesforce. Another initiative launched at the summit was the Coalition for Sustainable AI. Led by France, the UN Environment Programme (UNEP), and the International Telecommunication Union (ITU), the coalition has the support of 11 countries, five international organisations, and 37 tech companies including EDF, IBM, Nvidia, and SAP. The Summit of Heads of State and Government took place with a plenary session in the Grand Palais. Prime Minister Narendra Modi of India stressed the need to "democratise technology" and "[ensure] access to all, especially in the Global South". Vice President JD Vance of the United States used his speech to warn against "excessive regulation of the AI" which "could kill a transformative sector just as it's taking off". Vance also warned other leaders against cooperating with "authoritarian regimes" on AI, a comment widely interpreted as a reference to China. == Investments == At the summit, the European Union made several announcements related to planned investments supporting AI development. President Ursula von der Leyen of the European Commission launched InvestAI, a €200 billion initiative, including €20 billion to build four AI gigafactories to train highly complex, very large models. In addition, a coalition of more than 60 European companies launched the EU AI Champions Initiative. Led by venture capital firm General Catalyst, the coalition plans to invest €150 billion in AI-related businesses and infrastructure in Europe over five years. President Emmanuel Macron announced that private investors had pledged to invest nearly €110 billion in the AI sector in France. Financing of between €30 and €50 billion is expected from the United Arab Emirates to build a very large data centre campus, with another €20 billion from the Canadian investment firm Brookfield Corporation. French startup Mistral AI and Helsing, a German-British company, announced their partnership in developing vision-language-action models helping soldiers use AI on the battlefield. == Reactions == The Financial Times editorial board noted that the Paris summit "highlighted a shift in the dynamics towards geopolitical competition", which it characterised as "a new AI arms race" between the US and China, with Europe "trying to carve out its role". Fortune.com AI editor Jeremy Kahn described the 2025 Paris Summit as an "AI festival, complete with glitzy corporate side events and even a late night dance party", contrasting it with the "decidedly sober" mood of the inaugural AI Safety Summit at Bletchley Park. Many experts of the AI Safety Community expressed disappointment that the Paris Summit did not do enough to address AI risks, with Anthropic CEO Dario Amodei calling it a "missed opportunity". Others voicing similar concerns included David Leslie of the Alan Turing Institute and Max Tegmark of the Future of Life Institute. Reporting from Paris, technology columnist Kevin Roose of The New York Times wrote, "The biggest surprise of the Paris summit, for me, has been that policymakers can't seem to grasp how soon powerful AI systems could arrive, or how disruptive they could be." == Statement on inclusive and sustainable AI == At the summit, 58 countries, including France, China, and India, signed a joint declaration, the Statement on Inclusive and Sustainable Artificial Intelligence for People and the Planet. The statement outlines general principles such as accessibility and overcoming the digital divide; developing AI that is open, transparent, ethical, safe, and trustworthy; avoiding market concentration of AI development to encourage innovation; positive outcomes for labour markets; making AI sustainable; and promoting international cooperation and governance. The US and UK refused to sign the declaration on inclusive and sustainable AI. The UK government said in a brief statement that the international agreement did not go far enough in defining global governance of AI and addressing concerns about its impact on national security. === Signatories === The list of signatory countries to the statement for inclusive and sustainable AI in alphabetical order: Additional signatories included the following international bodies and research institutes: ALAI (Latin American Association on Internet) African Union (AU) Commission BEUC The European Consumer Organisation Center for Democracy and Technology Council of Europe European Commission (and the 27 member states) Hugging Face INRIA Institute of Advanced Study OEC

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  • Dark data

    Dark data

    Dark data is data which is acquired through various computer network operations but not used in any manner to derive insights or for decision making. The ability of an organisation to collect data can exceed the throughput at which it can analyse the data. In some cases the organisation may not even be aware that the data is being collected. IBM estimate that roughly 90 percent of data generated by sensors and analog-to-digital conversions never get used. In an industrial context, dark data can include information gathered by sensors and telematics. Organizations retain dark data for a multitude of reasons, and it is estimated that most companies are only analyzing 1% of their data. Often it is stored for regulatory compliance and record keeping. Some organizations believe that dark data could be useful to them in the future, once they have acquired better analytic and business intelligence technology to process the information. Because storage is inexpensive, storing data is easy. However, storing and securing the data usually entails greater expenses (or even risk) than the potential return profit. In academic discourse, the term dark data was essentially coined by Bryan P. Heidorn. He uses it to describe research data, especially from the long tail of science (the many, small research projects), which are not or no longer available for research because they disappear in a drawer without adequate data management. Without this, the data become dark, and further reasons for this are e.g. missing metadata annotation, missing data management plans and data curators. == Analysis == The term "dark data" very often refers to data that is not amenable to computer processing. For example, a company might have a great deal of data that exists only as scanned page-images. Even the bare text in such documents is not available without something like Optical character recognition, which can vary greatly in accuracy. Even with OCR, the significance of each part of the data is unavailable. An obvious examples is whether a capitalized word is a name or not, and if so, whether it represents a person, place, organization, or even a work of art. Bibliographic and other references, data within tables (that may be labeled quite adequately for humans, but not for processing), and countless assertions represented with the full complexity and ambiguity of human language. A lot of unused data is very valuable, and would be used if it could be; but is blocked because it is in formats that are difficult to process, categorise, identify, and analyse. Often the reason that business does not use their dark data is because of the amount of resources it would take and the difficulty of having that data analysed. In other words, the data is "dark" not because it is not used, but because it cannot (feasibly or affordably) be used, given its poor representation. There are many data representations that can make data much more accessible for automation. However, a great deal of information lacks any such identification of information items or relationships; and much more loses it during "downhill" conversion such as saving to page-oriented representations, printing, scanning, or faxing. The journey back "uphill" can be costly. According to Computer Weekly, 60% of organisations believe that their own business intelligence reporting capability is "inadequate" and 65% say that they have "somewhat disorganised content management approaches". == Relevance == Useful data may become dark data after it becomes irrelevant, as it is not processed fast enough. This is called "perishable insights" in "live flowing data". For example, if the geolocation of a customer is known to a business, the business can make offer based on the location, however if this data is not processed immediately, it may be irrelevant in the future. According to IBM, about 60 percent of data loses its value immediately. == Storage == According to the New York Times, 90% of energy used by data centres is wasted. If data was not stored, energy costs could be saved. Furthermore, there are costs associated with the underutilisation of information and thus missed opportunities. According to Datamation, "the storage environments of EMEA organizations consist of 54 percent dark data, 32 percent redundant, obsolete and trivial data and 14 percent business-critical data. By 2020, this can add up to $891 billion in storage and management costs that can otherwise be avoided." The continuous storage of dark data can put an organisation at risk, especially if this data is sensitive. In the case of a breach, this can result in serious repercussions. These can be financial, legal and can seriously hurt an organisation's reputation. For example, a breach of private records of customers could result in the stealing of sensitive information, which could result in identity theft. Another example could be the breach of the company's own sensitive information, for example relating to research and development. These risks can be mitigated by assessing and auditing whether this data is useful to the organisation, employing strong encryption and security and finally, if it is determined to be discarded, then it should be discarded in a way that it becomes unretrievable. == Future == It is generally considered that as more advanced computing systems for analysis of data are built, the higher the value of dark data will be. It has been noted that "data and analytics will be the foundation of the modern industrial revolution". Of course, this includes data that is currently considered "dark data" since there are not enough resources to process it. All this data that is being collected can be used in the future to bring maximum productivity and an ability for organisations to meet consumers' demand. Technology advancements are helping to leverage this dark data affordably. Furthermore, many organisations do not realise the value of dark data right now, for example in healthcare and education organisations deal with large amounts of data that could create a significant "potential to service students and patients in the manner in which the consumer and financial services pursue their target population".

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  • Anytime algorithm

    Anytime algorithm

    In computer science, an anytime algorithm is an algorithm that can return a valid solution to a problem even if it is interrupted before it ends. The algorithm is expected to find better and better solutions the longer it keeps running. Most algorithms run to completion: they provide a single answer after performing some fixed amount of computation. In some cases, however, the user may wish to terminate the algorithm prior to completion. The amount of computation required may be substantial, for example, and computational resources might need to be reallocated. Most algorithms either run to completion or they provide no useful solution information. Anytime algorithms, however, are able to return a partial answer, whose quality depends on the amount of computation they were able to perform. The answer generated by anytime algorithms is an approximation of the correct answer. == Names == An anytime algorithm may be also called an "interruptible algorithm". They are different from contract algorithms, which must declare a time in advance; in an anytime algorithm, a process can just announce that it is terminating. == Goals == The goal of anytime algorithms are to give intelligent systems the ability to make results of better quality in return for turn-around time. They are also supposed to be flexible in time and resources. They are important because artificial intelligence or AI algorithms can take a long time to complete results. This algorithm is designed to complete in a shorter amount of time. Also, these are intended to have a better understanding that the system is dependent and restricted to its agents and how they work cooperatively. An example is the Newton–Raphson iteration applied to finding the square root of a number. Another example that uses anytime algorithms is trajectory problems when you're aiming for a target; the object is moving through space while waiting for the algorithm to finish and even an approximate answer can significantly improve its accuracy if given early. What makes anytime algorithms unique is their ability to return many possible outcomes for any given input. An anytime algorithm uses many well defined quality measures to monitor progress in problem solving and distributed computing resources. It keeps searching for the best possible answer with the amount of time that it is given. It may not run until completion and may improve the answer if it is allowed to run longer. This is often used for large decision set problems. This would generally not provide useful information unless it is allowed to finish. While this may sound similar to dynamic programming, the difference is that it is fine-tuned through random adjustments, rather than sequential. Anytime algorithms are designed so that it can be told to stop at any time and would return the best result it has found so far. This is why it is called an interruptible algorithm. Certain anytime algorithms also maintain the last result, so that if they are given more time, they can continue from where they left off to obtain an even better result. == Decision trees == When the decider has to act, there must be some ambiguity. Also, there must be some idea about how to solve this ambiguity. This idea must be translatable to a state to action diagram. == Performance profile == The performance profile estimates the quality of the results based on the input and the amount of time that is allotted to the algorithm. The better the estimate, the sooner the result would be found. Some systems have a larger database that gives the probability that the output is the expected output. One algorithm can have several performance profiles. Most of the time performance profiles are constructed using mathematical statistics using representative cases. For example, in the traveling salesman problem, the performance profile was generated using a user-defined special program to generate the necessary statistics. In this example, the performance profile is the mapping of time to the expected results. This quality can be measured in several ways: certainty: where probability of correctness determines quality accuracy: where error bound determines quality specificity: where the amount of particulars determine quality == Algorithm prerequisites == Initial behavior: While some algorithms start with immediate guesses, others take a more calculated approach and have a start up period before making any guesses. Growth direction: How the quality of the program's "output" or result, varies as a function of the amount of time ("run time") Growth rate: Amount of increase with each step. Does it change constantly, such as in a bubble sort or does it change unpredictably? End condition: The amount of runtime needed

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  • Vehicle infrastructure integration

    Vehicle infrastructure integration

    The Vehicle Infrastructure Integration (VII), also known as "Connected Roadways" or "vehicle-to-everything" (V2X) technology, is a United States Department of Transportation initiative that aims to improve road safety by developing technology that connects road vehicles with their environment. This development draws on several disciplines, including transport engineering, electrical engineering, automotive engineering, telematics, and computer science. Although VII specifically covers road transport, similar technologies are under development for other modes of transport. For example, airplanes may use ground-based beacons for automated guidance, allowing the autopilot to fly the plane without human intervention. == Goals == The goal of VII is to establish a communication link between vehicles (via On-Board Equipment, or OBE) and roadside infrastructure (via Roadside Equipment, or RSE) to enhance the safety, efficiency, and convenience of transportation systems. Two potential approaches are the widespread deployment of a dedicated short-range communications (DSRC) link on the 5.9GHz band, and cellular communication (C-V2X). Either of these methods would allow vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. The initiative has three priorities: Stakeholder evaluation and acceptance of the business model and its deployment schedule, Validation of the technology, with a focus on communications systems, in relation to deployment costs, and Creation of legal structures and policies, especially concerning digital privacy, to improve the system's long-term potential for success. === Safety === Current automotive safety technology relies primarily on vehicle-based radar, lidar, and sonar systems. This technology allows, for instance, a potential reduction in rear-end collisions by monitoring obstacles in front of or behind the vehicle and automatically applying the brakes when necessary. This technology, however, is limited by the sensing range of vehicle-based radar, particularly in angled and left-turn collisions, such as a motorist losing control of the vehicle during an impending head-on collision. The rear-end collisions addressed by current technology are generally less severe than angled, left-turn, or head-on collisions. VII promotes the development of a direct communication link between road vehicles and all other vehicles nearby, allowing for the exchange of information on vehicle speed and orientation or driver awareness and intent. This real-time exchange of information may enable more effective automated emergency maneuvers, such as steering, decelerating, or braking. In addition to nearby vehicle awareness, VII promotes a communication link between vehicles and roadway infrastructure. Such a link may allow for improved real-time traffic information, better queue management, and feedback to vehicles. Existing implementations of VII use vehicle-based sensors that can recognize and respond to roadway markings or signs, automatically adjusting vehicle parameters to follow the recognized instructions. However, this information may also be acquired via roadside beacons or stored in a centralized database accessible to all vehicles. === Efficiency === With a VII system in place, vehicles will be linked together. The headway between vehicles may therefore be reduced so that there is less empty space on the road, increasing the available capacity per lane. More capacity per lane will in turn imply fewer lanes in general, possibly satisfying the community's concerns about the impact of roadway widening. VII will enable precise traffic-signal coordination by tracking vehicle platoons and will benefit from accurate timing by drawing on real-time traffic data covering volume, density, and turning movements. Real-time traffic data can also be used in the design of new roadways or modification of existing systems as the data could be used to provide accurate origin-destination studies and turning-movement counts for uses in transportation forecasting and traffic operations. Such technology would also lead to improvements for transport engineers to address problems whilst reducing the cost of obtaining and compiling data. Tolling is another prospect for VII technology as it could enable roadways to be automatically tolled. Data could be collectively transmitted to road users for in-vehicle display, outlining the lowest cost, shortest distance, and/or fastest route to a destination on the basis of real-time conditions. === Existing applications === To some extent, results along these lines have been achieved in trials performed around the globe, making use of GPS, mobile phone signals, and vehicle registration plates. GPS is becoming standard in many new high-end vehicles and is an option on most new low- and mid-range vehicles. In addition, many users also have mobile phones that transmit trackable signals (and may also be GPS-enabled). Mobile phones can already be traced for purposes of emergency response. GPS and mobile phone tracking, however, do not provide fully reliable data. Furthermore, integrating mobile phones in vehicles may be prohibitively difficult. Data from mobile phones, though useful, might even increase risks to motorists as they tend to look at their phones rather than concentrate on their driving. Automatic registration plate recognition can provide large quantities of data, but continuously tracking a vehicle through a corridor is a difficult task with existing technology. Today's equipment is designed for data acquisition and functions such as enforcement and tolling, not for returning data to vehicles or motorists for response. GPS will nevertheless be one of the key components in VII systems. == Limitations == === Privacy === VII architecture is designed to prevent identification of individual vehicles, with all data exchange between the vehicle and the system occurring anonymously. Exchanges between the vehicles and third parties such as OEMs and toll collectors will occur, but the network traffic will be sent via encrypted tunnels and will therefore not be decipherable by the VII system. Data sharing with law enforcement or Homeland Security was not included in system design as of 2006. === Technical issues === ==== Coordination ==== A major issue facing the deployment of VII is the problem of how to set up the system initially. The costs associated with installing the technology in vehicles and providing communications and power at every intersection are significant. ==== Maintenance ==== Another factor for consideration in regard to the technology's distribution is how to update and maintain the units. Traffic systems are highly dynamic, with new traffic controls implemented every day and roadways constructed or repaired every year. The vehicle-based option could be updated via the internet (preferably wireless) but may subsequently require all users to have access to internet technology. Alternatively, if receivers were placed in all vehicles and the VII system was primarily located along the roadside, information could be stored in a centralized database. This would allow the agency responsible to issue updates at any time. These would then be disseminated to the roadside units for passing motorists. Operationally, this method is currently considered to provide the greatest effectiveness but at a high cost to the authorities. ==== Security ==== Security of the units is another concern, especially in light of the public acceptance issue. Criminals could tamper, remove, or destroy VII units regardless of whether they are installed inside vehicles or along the roadside. Magnets, electric shocks, and malicious software (viruses, hacking, or jamming) could be used to damage VII systems – regardless of whether units are located inside vehicle or along the roadside. == Recent developments == Much of the current research and experimentation is conducted in the United States where coordination is ensured through the Vehicle Infrastructure Integration Consortium; consisting of automobile manufacturers (Ford, General Motors, Daimler Chrysler, Toyota, Nissan, Honda, Volkswagen, BMW), IT suppliers, U.S. Federal and state transportation departments, and professional associations. Trialing is taking place in Michigan and California. The specific applications now being developed under the U.S. initiative are: Warning drivers of unsafe conditions or imminent collisions. Warning drivers if they are about to run off the road or speed around a curve too fast. Informing system operators of real-time congestion, weather conditions and incidents. Providing operators with information on corridor capacity for real-time management, planning and provision of corridor-wide advisories to drivers. In mid-2007, a VII environment covering some 20 square miles (52 km2) near Detroit was used to test 20 prototype VII applications. Several automobile manufacturers are also conducting their own VII research and triali

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