D/Vision Pro was one of the earliest marketed non-linear editing systems. It was released by TouchVision Systems, Inc. in the mid-1990s. The program was DOS-based and worked on either Intel's 386 or 486 processor. The system used AVI compression and worked with the Action Media II board. The system allowed users to digitize video, audio, and timecode, create an edit decision list (EDL), instantly play back the edited program, and output the finished EDL in a wide variety of formats. These cost-effective editing systems were used by numerous independent filmmakers and in low-budget productions during the mid-late 1990s. D/Vision Pro's low-quality compression led TouchVision (later renamed D/Vision Systems) to abandon it in favor of D/Vision Online, which was purchased by Discreet Logic and renamed edit. In June 2002, Discreet discontinued edit, as they did not want it to interfere with smoke sales which were more profitable. Discreet was later purchased by Autodesk.
IT baseline protection
The IT baseline protection (German: IT-Grundschutz) approach from the German Federal Office for Information Security (BSI) is a methodology to identify and implement computer security measures in an organization. The aim is the achievement of an adequate and appropriate level of security for IT systems. To reach this goal the BSI recommends "well-proven technical, organizational, personnel, and infrastructural safeguards". Organizations and federal agencies show their systematic approach to secure their IT systems (e.g. Information Security Management System) by obtaining an ISO/IEC 27001 Certificate on the basis of IT-Grundschutz. == Overview baseline security == The term baseline security signifies standard security measures for typical IT systems. It is used in various contexts with somewhat different meanings. For example: Microsoft Baseline Security Analyzer: Software tool focused on Microsoft operating system and services security Cisco security baseline: Vendor recommendation focused on network and network device security controls Nortel baseline security: Set of requirements and best practices with a focus on network operators ISO/IEC 13335-3 defines a baseline approach to risk management. This standard has been replaced by ISO/IEC 27005, but the baseline approach was not taken over yet into the 2700x series. There are numerous internal baseline security policies for organizations, The German BSI has a comprehensive baseline security standard, that is compliant with the ISO/IEC 27000-series == BSI IT baseline protection == The foundation of an IT baseline protection concept is initially not a detailed risk analysis. It proceeds from overall hazards. Consequently, sophisticated classification according to damage extent and probability of occurrence is ignored. Three protection needs categories are established. With their help, the protection needs of the object under investigation can be determined. Based on these, appropriate personnel, technical, organizational and infrastructural security measures are selected from the IT Baseline Protection Catalogs. The Federal Office for Security in Information Technology's IT Baseline Protection Catalogs offer a "cookbook recipe" for a normal level of protection. Besides probability of occurrence and potential damage extents, implementation costs are also considered. By using the Baseline Protection Catalogs, costly security analyses requiring expert knowledge are dispensed with, since overall hazards are worked with in the beginning. It is possible for the relative layman to identify measures to be taken and to implement them in cooperation with professionals. The BSI grants a baseline protection certificate as confirmation for the successful implementation of baseline protection. In stages 1 and 2, this is based on self declaration. In stage 3, an independent, BSI-licensed auditor completes an audit. Certification process internationalization has been possible since 2006. ISO/IEC 27001 certification can occur simultaneously with IT baseline protection certification. (The ISO/IEC 27001 standard is the successor of BS 7799-2). This process is based on the new BSI security standards. This process carries a development price which has prevailed for some time. Corporations having themselves certified under the BS 7799-2 standard are obliged to carry out a risk assessment. To make it more comfortable, most deviate from the protection needs analysis pursuant to the IT Baseline Protection Catalogs. The advantage is not only conformity with the strict BSI, but also attainment of BS 7799-2 certification. Beyond this, the BSI offers a few help aids like the policy template and the GSTOOL. One data protection component is available, which was produced in cooperation with the German Federal Commissioner for Data Protection and Freedom of Information and the state data protection authorities and integrated into the IT Baseline Protection Catalog. This component is not considered, however, in the certification process. == Baseline protection process == The following steps are taken pursuant to the baseline protection process during structure analysis and protection needs analysis: The IT network is defined. IT structure analysis is carried out. Protection needs determination is carried out. A baseline security check is carried out. IT baseline protection measures are implemented. Creation occurs in the following steps: IT structure analysis (survey) Assessment of protection needs Selection of actions Running comparison of nominal and actual. === IT structure analysis === An IT network includes the totality of infrastructural, organizational, personnel, and technical components serving the fulfillment of a task in a particular information processing application area. An IT network can thereby encompass the entire IT character of an institution or individual division, which is partitioned by organizational structures as, for example, a departmental network, or as shared IT applications, for example, a personnel information system. It is necessary to analyze and document the information technological structure in question to generate an IT security concept and especially to apply the IT Baseline Protection Catalogs. Due to today's usually heavily networked IT systems, a network topology plan offers a starting point for the analysis. The following aspects must be taken into consideration: The available infrastructure, The organizational and personnel framework for the IT network, Networked and non-networked IT systems employed in the IT network. The communications connections between IT systems and externally, IT applications run within the IT network. === Protection needs determination === The purpose of the protection needs determination is to investigate what protection is sufficient and appropriate for the information and information technology in use. In this connection, the damage to each application and the processed information, which could result from a breach of confidentiality, integrity or availability, is considered. Important in this context is a realistic assessment of the possible follow-on damages. A division into the three protection needs categories "low to medium", "high" and "very high" has proved itself of value. "Public", "internal" and "secret" are often used for confidentiality. === Modelling === Heavily networked IT systems typically characterize information technology in government and business these days. As a rule, therefore, it is advantageous to consider the entire IT system and not just individual systems within the scope of an IT security analysis and concept. To be able to manage this task, it makes sense to logically partition the entire IT system into parts and to separately consider each part or even an IT network. Detailed documentation about its structure is prerequisite for the use of the IT Baseline Protection Catalogs on an IT network. This can be achieved, for example, via the IT structure analysis described above. The IT Baseline Protection Catalogs' components must ultimately be mapped onto the components of the IT network in question in a modelling step. === Baseline security check === The baseline security check is an organisational instrument offering a quick overview of the prevailing IT security level. With the help of interviews, the status quo of an existing IT network (as modelled by IT baseline protection) relative to the number of security measures implemented from the IT Baseline Protection Catalogs are investigated. The result is a catalog in which the implementation status "dispensable", "yes", "partly", or "no" is entered for each relevant measure. By identifying not yet, or only partially, implemented measures, improvement options for the security of the information technology in question are highlighted. The baseline security check gives information about measures, which are still missing (nominal vs. actual comparison). From this follows what remains to be done to achieve baseline protection through security. Not all measures suggested by this baseline check need to be implemented. Peculiarities are to be taken into account! It could be that several more or less unimportant applications are running on a server, which have lesser protection needs. In their totality, however, these applications are to be provided with a higher level of protection. This is called the (cumulation effect). The applications running on a server determine its need for protection. Several IT applications can run on an IT system. When this occurs, the application with the greatest need for protection determines the IT systems protection category. Conversely, it is conceivable that an IT application with great protection needs does not automatically transfer this to the IT system. This may happen because the IT system is configured redundantly, or because only an inconsequential part is running on it. This is called the (distribution effect). This is the case, fo
Letter frequency
Letter frequency is the number of times letters of the alphabet appear on average in written language. Letter frequency analysis dates back to the Arab mathematician Al-Kindi (c. AD 801–873), who formally developed the method to break ciphers. Letter frequency analysis gained importance in Europe with the development of movable type in AD 1450, wherein one must estimate the amount of type required for each letterform. Linguists use letter frequency analysis as a rudimentary technique for language identification, where it is particularly effective as an indication of whether an unknown writing system is alphabetic, syllabic, or logographic. The use of letter frequencies and frequency analysis plays a fundamental role in cryptograms and several word puzzle games, including hangman, Scrabble, Wordle and the television game show Wheel of Fortune. One of the earliest descriptions in classical literature of applying the knowledge of English letter frequency to solving a cryptogram is found in Edgar Allan Poe's famous story "The Gold-Bug", where the method is successfully applied to decipher a message giving the location of a treasure hidden by Captain Kidd. Herbert S. Zim, in his classic introductory cryptography text Codes and Secret Writing, gives the English letter frequency sequence as "ETAON RISHD LFCMU GYPWB VKJXZQ", the most common letter pairs as "TH HE AN RE ER IN ON AT ND ST ES EN OF TE ED OR TI HI AS TO", and the most common doubled letters as "LL EE SS OO TT FF RR NN PP CC". Different ways of counting can produce somewhat different orders. Letter frequencies also have a strong effect on the design of some keyboard layouts. The most frequent letters are placed on the home row of the Blickensderfer typewriter, the Dvorak keyboard layout, Colemak and other optimized layouts, while the commonly used QWERTY layout places common letters apart from each other to prevent typewriter jamming. == Background == The frequency of letters in text has been studied for use in cryptanalysis, and frequency analysis in particular, dating back to the Arab mathematician al-Kindi (c. AD 801–873 ), who formally developed the method (the ciphers breakable by this technique go back at least to the Caesar cipher used by Julius Caesar, so this method could have been explored in classical times). Letter frequency analysis gained additional importance in Europe with the development of movable type in AD 1450, wherein one must estimate the amount of type required for each letterform, as evidenced by the variations in letter compartment size in typographer's type cases. No exact letter frequency distribution underlies a given language, since all writers write slightly differently. However, most languages have a characteristic distribution which is strongly apparent in longer texts. Even language changes as extreme as from Old English to modern English (regarded as mutually unintelligible) show strong trends in related letter frequencies: over a small sample of Biblical passages, from most frequent to least frequent, enaid sorhm tgþlwu æcfy ðbpxz of Old English compares to eotha sinrd luymw fgcbp kvjqxz of modern English, with the most extreme differences concerning letterforms not shared. Linotype machines for the English language assumed the letter order, from most to least common, to be etaoin shrdlu cmfwyp vbgkqj xz based on the experience and custom of manual compositors. The equivalent for the French language was elaoin sdrétu cmfhyp vbgwqj xz. Arranging the alphabet in Morse into groups of letters that require equal amounts of time to transmit, and then sorting these groups in increasing order, yields e it san hurdm wgvlfbk opxcz jyq. Letter frequency was used by other telegraph systems, such as the Murray Code. Similar ideas are used in modern data-compression techniques such as Huffman coding. Letter frequencies, like word frequencies, tend to vary, both by writer and by subject. For instance, ⟨d⟩ occurs with greater frequency in fiction, as most fiction is written in past tense and thus most verbs will end in the inflectional suffix -ed / -d. One cannot write an essay about x-rays without using ⟨x⟩ frequently, and the essay will have an idiosyncratic letter frequency if the essay is about, say, Queen Zelda of Zanzibar requesting X-rays from Qatar to examine hypoxia in zebras. Different authors have habits which can be reflected in their use of letters. Hemingway's writing style, for example, is visibly different from Faulkner's. Letter, bigram, trigram, word frequencies, word length, and sentence length can be calculated for specific authors and used to prove or disprove authorship of texts, even for authors whose styles are not so divergent. Accurate average letter frequencies can only be gleaned by analyzing a large amount of representative text. With the availability of modern computing and collections of large text corpora, such calculations are easily made. Examples can be drawn from a variety of sources (press reporting, religious texts, scientific texts and general fiction) and there are differences especially for general fiction with the position of ⟨h⟩ and ⟨i⟩, with ⟨h⟩ becoming more common. Different dialects of a language will also affect a letter's frequency. For example, an author in the United States would produce something in which ⟨z⟩ is more common than an author in the United Kingdom writing on the same topic: words like "analyze", "apologize", and "recognize" contain the letter in American English, whereas the same words are spelled "analyse", "apologise", and "recognise" in British English. This would highly affect the frequency of the letter ⟨z⟩, as it is rarely used by British writers in the English language. The "top twelve" letters constitute about 80% of the total usage. The "top eight" letters constitute about 65% of the total usage. Letter frequency as a function of rank can be fitted well by several rank functions, with the two-parameter Cocho/Beta rank function being the best. Another rank function with no adjustable free parameter also fits the letter frequency distribution reasonably well (the same function has been used to fit the amino acid frequency in protein sequences.) A spy using the VIC cipher or some other cipher based on a straddling checkerboard typically uses a mnemonic such as "a sin to err" (dropping the second "r") or "at one sir" to remember the top eight characters. == Relative frequencies of letters in the English language == There are three ways to count letter frequency that result in very different charts for common letters. The first method, used in the chart below, is to count letter frequency in lemmas of a dictionary. The lemma is the word in its canonical form. The second method is to include all word variants when counting, such as "abstracts", "abstracted" and "abstracting" and not just the lemma of "abstract". This second method results in letters like ⟨s⟩ appearing much more frequently, such as when counting letters from lists of the most used English words on the Internet. ⟨s⟩ is especially common in inflected words (non-lemma forms) because it is added to form plurals and third person singular present tense verbs. A final method is to count letters based on their frequency of use in actual texts, resulting in certain letter combinations like ⟨th⟩ becoming more common due to the frequent use of common words like "the", "then", "both", "this", etc. Absolute usage frequency measures like this are used when creating keyboard layouts or letter frequencies in old fashioned printing presses. An analysis of entries in the Concise Oxford dictionary, ignoring frequency of word use, gives an order of "EARIOTNSLCUDPMHGBFYWKVXZJQ". The letter-frequency table above is taken from Pavel Mička's website, which cites Robert Lewand's Cryptological Mathematics. According to Lewand, arranged from most to least common in appearance, the letters are: etaoinshrdlcumwfgypbvkjxqz. Lewand's ordering differs slightly from others, such as Cornell University Math Explorer's Project, which produced a table after measuring 40,000 words. In English, the space character occurs almost twice as frequently as the top letter (⟨e⟩) and the non-alphabetic characters (digits, punctuation, etc.) collectively occupy the fourth position (having already included the space) between ⟨t⟩ and ⟨a⟩. == Relative frequencies of the first letters of a word in the English language == The frequency of the first letters of words or names is helpful in pre-assigning space in physical files and indexes. Given 26 filing cabinet drawers, rather than a 1:1 assignment of one drawer to one letter of the alphabet, it is often useful to use a more equal-frequency-letter code by assigning several low-frequency letters to the same drawer (often one drawer is labeled VWXYZ), and to split up the most-frequent initial letters (⟨s, a, c⟩) into several drawers (often 6 drawers Aa-An, Ao-Az, Ca-Cj, Ck-Cz, Sa-Si, Sj-Sz). The same system is used in some mult
Serge Belamant
Serge Belamant (born 1953) is a French-born South African entrepreneur best known for designing the Universal Electronic Payment System (UEPS) and the Chip Offline Pre-authorised Card (COPAC). He founded the cash-payments company Net1 UEPS Technologies in 1989, led it through dual listings on the NASDAQ and the Johannesburg Stock Exchange, and oversaw the contentious welfare-payments contract with the South African Social Security Agency (SASSA) until his retirement in 2017. Since 2018 he has been non-executive chair of London-based buy-now-pay-later fintech Zilch. == Early life and education == Belamant moved from France to South Africa with his family in 1967 and matriculated from Highlands North Boys' High School, Johannesburg. In 1972 he entered the University of the Witwatersrand to study civil engineering but switched to computer science and applied mathematics in his second year. He left the university without a degree and later took short courses in information systems at the University of South Africa (UNISA). == Early career and SASWITCH (1981–1989) == Belamant worked for Control Data Corporation as a systems analyst for a decade before joining SASWITCH Ltd in 1985. Economic sanctions had left the consortium's national ATM network dependent on unsupported Christian Rovsing computers. Belamant led a rebuild on fault-tolerant Stratus hardware and wrote protocol-translation software that allowed fourteen banks to connect without altering their host systems. By 1988 SASWITCH was handling about three million ATM transactions a month, according to the Competition Commission. The switch—now run by BankservAfrica—remains the backbone of South Africa's shared ATM network. == Net1 UEPS Technologies (1989–2017) == === Founding and UEPS === In 1989, Serge Belamant developed the Universal Electronic Payment System (UEPS), enabling secure, real-time transactions even in areas with limited connectivity. In the same year, he founded NET1 UEPS Technologies Inc., serving as its CEO and Director. === COPAC for VISA === In 1995, VISA tasked Belamant with designing the Chip Offline Pre-authorized Card (COPAC), a technology still widely used in chip-enabled credit and debit cards. A year later, he listed his company APLITEC (Applied Technology Holdings Limited) on the Johannesburg Stock Exchange. === Listings and acquisitions === In 1999, Belamant acquired Cash Payment Services (CPS) from First National Bank of South Africa, modernizing its welfare payment system to serve millions in rural areas. In 2005, he led NET1 Technologies to an IPO, listing it as NET1 UEPS Technologies Inc. on the Nasdaq. A secondary listing on the Johannesburg Stock Exchange (JSE) followed in 2008. === SASSA contract === Under Belamant's leadership, NET1 managed welfare payments for the South African Social Security Agency (SASSA), handling payments for over 10 million beneficiaries monthly. Despite criticism over handling the SASSA contract, investigations by the U.S. Department of Justice and the South African Constitutional Court found no wrongdoing. == Zilch (2018–present) == Belamant co-founded London-based "buy-now-pay-later" firm Zilch Technology in 2018 and serves as non-executive chair. Zilch reported £145 million in annual-recurring revenue and 4.5 million customers in January 2025. == Patents == Belamant is listed as inventor on more than a dozen payment-security patents, including: "Funds transfer system" (US RE36,788, 2000) – the basis for UEPS. "Financial transactions with a varying PIN" (WO 2014/037869, 2014).
Chaotic cryptology
Chaotic cryptology is the application of mathematical chaos theory to the practice of cryptography, the study or techniques used to privately and securely transmit information with the presence of a third-party or adversary. Since first being investigated by Robert Matthews in 1989, the use of chaos in cryptography has attracted much interest. However, long-standing concerns about its security and implementation speed continue to limit its implementation. Chaotic cryptology consists of two opposite processes: Chaotic cryptography and Chaotic cryptanalysis. Cryptography refers to encrypting information for secure transmission, whereas cryptanalysis refers to decrypting and deciphering encoded encrypted messages. In order to use chaos theory efficiently in cryptography, the chaotic maps are implemented such that the entropy generated by the map can produce required Confusion and diffusion. Properties in chaotic systems and cryptographic primitives share unique characteristics that allow for the chaotic systems to be applied to cryptography. If chaotic parameters, as well as cryptographic keys, can be mapped symmetrically or mapped to produce acceptable and functional outputs, it will make it next to impossible for an adversary to find the outputs without any knowledge of the initial values. Since chaotic maps in a real life scenario require a set of numbers that are limited, they may, in fact, have no real purpose in a cryptosystem if the chaotic behavior can be predicted. One of the most important issues for any cryptographic primitive is the security of the system. However, in numerous cases, chaos-based cryptography algorithms are proved insecure. The main issue in many of the cryptanalyzed algorithms is the inadequacy of the chaotic maps implemented in the system. == Types == Chaos-based cryptography has been divided into two major groups: Symmetric chaos cryptography, where the same secret key is used by sender and receiver. Asymmetric chaos cryptography, where one key of the cryptosystem is public. Some of the few proposed systems have been broken. The majority of chaos-based cryptographic algorithms are symmetric. Many use discrete chaotic maps in their process. == Applications == === Image encryption === Bourbakis and Alexopoulos in 1991 proposed supposedly the earliest fully intended digital image encryption scheme which was based on SCAN language. Later on, with the emergence of chaos-based cryptography hundreds of new image encryption algorithms, all with the aim of improving the security of digital images were proposed. However, there were three main aspects of the design of an image encryption that was usually modified in different algorithms (chaotic map, application of the map and structure of algorithm). The initial and perhaps most crucial point was the chaotic map applied in the design of the algorithms. The speed of the cryptosystem is always an important parameter in the evaluation of the efficiency of a cryptography algorithm, therefore, the designers were initially interested in using simple chaotic maps such as tent map, and the logistic map. However, in 2006 and 2007, the new image encryption algorithms based on more sophisticated chaotic maps proved that application of chaotic map with higher dimension could improve the quality and security of the cryptosystems. === Hash function === Chaotic behavior can generate hash functions, such as applying the Chirikov/Julia 3D trajectory translation into a SHA-512 hash. === Random number generation === The unpredictable behavior of the chaotic maps can be used in the generation of random numbers. Some of the earliest chaos-based random number generators tried to directly generate random numbers from the logistic map. Many more recent works did so using the numerical solutions of hyperchaotic systems of differential equations, either at the integer-order, or the fractional-order.
WaveNet
WaveNet is a deep neural network for generating raw audio. It was created by researchers at London-based AI firm DeepMind. The technique, outlined in a paper in September 2016, is able to generate relatively realistic-sounding human-like voices by directly modelling waveforms using a neural network method trained with recordings of real speech. Tests with US English and Mandarin reportedly showed that the system outperforms Google's best existing text-to-speech (TTS) systems, although as of 2016 its text-to-speech synthesis still was less convincing than actual human speech. WaveNet's ability to generate raw waveforms means that it can model any kind of audio, including music. == History == Generating speech from text is an increasingly common task thanks to the popularity of software such as Apple's Siri, Microsoft's Cortana, Amazon Alexa and the Google Assistant. Most such systems use a variation of a technique that involves concatenated sound fragments together to form recognisable sounds and words. The most common of these is called concatenative TTS. It consists of large library of speech fragments, recorded from a single speaker that are then concatenated to produce complete words and sounds. The result sounds unnatural, with an odd cadence and tone. The reliance on a recorded library also makes it difficult to modify or change the voice. Another technique, known as parametric TTS, uses mathematical models to recreate sounds that are then assembled into words and sentences. The information required to generate the sounds is stored in the parameters of the model. The characteristics of the output speech are controlled via the inputs to the model, while the speech is typically created using a voice synthesiser known as a vocoder. This can also result in unnatural sounding audio. == Design and ongoing research == === Background === WaveNet is a type of feedforward neural network known as a deep convolutional neural network (CNN). In WaveNet, the CNN takes a raw signal as an input and synthesises an output one sample at a time. It does so by sampling from a softmax (i.e. categorical) distribution of a signal value that is encoded using μ-law companding transformation and quantized to 256 possible values. === Initial concept and results === According to the original September 2016 DeepMind research paper WaveNet: A Generative Model for Raw Audio, the network was fed real waveforms of speech in English and Mandarin. As these pass through the network, it learns a set of rules to describe how the audio waveform evolves over time. The trained network can then be used to create new speech-like waveforms at 16,000 samples per second. These waveforms include realistic breaths and lip smacks – but do not conform to any language. WaveNet is able to accurately model different voices, with the accent and tone of the input correlating with the output. For example, if it is trained with German, it produces German speech. The capability also means that if the WaveNet is fed other inputs – such as music – its output will be musical. At the time of its release, DeepMind showed that WaveNet could produce waveforms that sound like classical music. === Content (voice) swapping === According to the June 2018 paper Disentangled Sequential Autoencoder, DeepMind has successfully used WaveNet for audio and voice "content swapping": the network can swap the voice on an audio recording for another, pre-existing voice while maintaining the text and other features from the original recording. "We also experiment on audio sequence data. Our disentangled representation allows us to convert speaker identities into each other while conditioning on the content of the speech." (p. 5) "For audio, this allows us to convert a male speaker into a female speaker and vice versa [...]." (p. 1) According to the paper, a two-digit minimum amount of hours (c. 50 hours) of pre-existing speech recordings of both source and target voice are required to be fed into WaveNet for the program to learn their individual features before it is able to perform the conversion from one voice to another at a satisfying quality. The authors stress that "[a]n advantage of the model is that it separates dynamical from static features [...]." (p. 8), i. e. WaveNet is capable of distinguishing between the spoken text and modes of delivery (modulation, speed, pitch, mood, etc.) to maintain during the conversion from one voice to another on the one hand, and the basic features of both source and target voices that it is required to swap on the other. The January 2019 follow-up paper Unsupervised speech representation learning using WaveNet autoencoders details a method to successfully enhance the proper automatic recognition and discrimination between dynamical and static features for "content swapping", notably including swapping voices on existing audio recordings, in order to make it more reliable. Another follow-up paper, Sample Efficient Adaptive Text-to-Speech, dated September 2018 (latest revision January 2019), states that DeepMind has successfully reduced the minimum amount of real-life recordings required to sample an existing voice via WaveNet to "merely a few minutes of audio data" while maintaining high-quality results. Its ability to clone voices has raised ethical concerns about WaveNet's ability to mimic the voices of living and dead persons. According to a 2016 BBC article, companies working on similar voice-cloning technologies (such as Adobe Voco) intend to insert watermarking inaudible to humans to prevent counterfeiting, while maintaining that voice cloning satisfying, for instance, the needs of entertainment-industry purposes would be of a far lower complexity and use different methods than required to fool forensic evidencing methods and electronic ID devices, so that natural voices and voices cloned for entertainment-industry purposes could still be easily told apart by technological analysis. == Applications == At the time of its release, DeepMind said that WaveNet required too much computational processing power to be used in real world applications. As of October 2017, Google announced a 1,000-fold performance improvement along with better voice quality. WaveNet was then used to generate Google Assistant voices for US English and Japanese across all Google platforms. In November 2017, DeepMind researchers released a research paper detailing a proposed method of "generating high-fidelity speech samples at more than 20 times faster than real-time", called "Probability Density Distillation". At the annual I/O developer conference in May 2018, it was announced that new Google Assistant voices were available and made possible by WaveNet; WaveNet greatly reduced the number of audio recordings that were required to create a voice model by modeling the raw audio of the voice actor samples.
What I eat in a day video
"What I eat in a day" videos are a trend on several social media platforms where a person describes all the meals and snacks that they eat during a given day, often as part of a given diet. The videos, shared on platforms including Twitter, TikTok and YouTube, become increasingly popular in 2020, with some of them accumulating millions of views, and they are considered a profitable industry for the people making them. Some have raised concerns that the videos may promote an unrealistic standard for healthy eating and contribute to the development of eating disorders. == Format == These videos often feature a montage of the food that the creator eats over the course of the day, sometimes with the associated calorie count of the foods that they describe. Unlike related mukbang videos, however, in which participants eat large amounts of food, the diets described are often restrictive. However, other videos are labeled as "unhealthy" and depict large portion sizes and higher amounts of processed food. == Popularity == "What I eat in a day" videos have existed for a long time, especially on YouTube, but they have become much more widespread in recent years. This phenomenon is self-reinforcing because when social media users watch or like these videos they are likely to see more of them in the future. Indeed, some of the most successful videos have tens of millions of view each. == Criticism and controversy == Several dieticians and mental health professionals over the impacts that these videos can have, as they can advocate a restrictive style of eating and not "promote body diversity." They have also raised concerns that this trend could contribute to a rise in disordered eating, especially since use of social media is known to increase feelings of negative body image. This trend is particularly prevalent among young adults, which are also the group with the highest vulnerability to eating disorders. More recently, a portion of these videos have begun to challenge diets and depict more realistic ways of eating in order to reduce the potential consequences of the trend.