AI Content On Youtube

AI Content On Youtube — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Tertiary review

    Tertiary review

    In software engineering, a tertiary review is a systematic review of systematic reviews. It is also referred to as a tertiary study in the software engineering literature. However, Umbrella review is the term more commonly used in medicine. Kitchenham et al. suggest that methodologically there is no difference between a systematic review and a tertiary review. However, as the software engineering community has started performing tertiary reviews new concerns unique to tertiary reviews have surfaced. These include the challenge of quality assessment of systematic reviews, search validation and the additional risk of double counting. == Examples of Tertiary reviews in software engineering literature == Test quality Machine Learning Test-driven development

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  • Pulsar (social listening platform)

    Pulsar (social listening platform)

    Pulsar is a software platform for social media monitoring, audience intelligence and social listening that allows organizations to monitor and analyze online conversations across social media, news, and other digital sources. The platform combines social media listening, media monitoring, trend analysis, and audience segmentation to help users understand public discussions and audience behavior in real time. The platform is a social listening platform, which aggregates data from networks such as X, Facebook, Instagram, and forums) and applies artificial intelligence for text and sentiment analysis. Pulsar is offered as a cloud-based Software as a Service (SaaS) tool and insights consultancy. It has been part of Pulsar Group (formerly Access Intelligence), a publicly listed group of communications software products, since 2019. As well as commercial uses, the platform has been used in peer-reviewed academic research analysing online discourse. The platform is listed on the UK government's G-Cloud 14 Digital Marketplace for the provision of social listening and audience intelligence services. == History == Pulsar originated in the early 2010s as a project within Face, a London-based innovation and market research consultancy. The platform's first product, Pulsar TRAC, launched in 2013 as a social media analytics tool. Pulsar TRAC was designed to measure the reach of conversations, mapping brand audiences, and tracking how content spreads through networks. The development was led by Dr Francesco D'Orazio, who created the Pulsar brand and led the development of the platform while serving as VP of Product and Innovation at Face. Face itself had been acquired by the Cello Group Plc (a UK-based advisory firm) in 2012, and Pulsar became part of Cello's portfolio of research and data tools. In January 2017, Cello Group made a significant investment to scale Pulsar and announced the merger of Face's qualitative research business into Pulsar, unifying both under the Pulsar brand for global expansion. In 2018, Pulsar opened an office in Los Angeles to better serve its growing U.S. client base in media, healthcare, and entertainment sectors and Francesco D'Orazio was appointed CEO. The company focused on developing new products amid a wave of consolidation in the social listening industry. In October 2019, Pulsar was acquired by Access Intelligence Plc (now Pulsar Group), an AIM-listed communications software company. The group, which also owns PR and media tools Isentia, Vuelio and ResponseSource, integrated Pulsar to their end-to-end marketing and communications insights offering. Pulsar established a new office in Sydney, Australia in 2022 as part of this global expansion, adding to its existing offices in London and Los Angeles. In 2023, Pulsar Group (then Access Intelligence) was recognised as one of Europe's fastest growing companies by the Financial Times. In May 2024, Access Intelligence PLC changed its name to Pulsar Group PLC. The company has since continued to develop its platform. In March 2025 it introduced new tool Narratives AI, described as a "search engine for public opinion" and the first of its kind for analyzing public narratives and their evolutions in both social media and the news. In October 2025, Pulsar launched Insight Agents, a set of AI agents embedded into the platform advertised to "proactively anticipate user needs or issues, carry out routine tasks, uncover anomalies in your datasets, and prompt responses at scale, 24/7." == Products == Pulsar's architecture integrates four main products into a single interface. The core product suite is often broken into three main components: Pulsar TRAC (for social listening and audience analysis), Pulsar TRENDS (for trend discovery and analysis), and Pulsar CORE (for owned-channel and web analytics). Pulsar's fourth product is Narratives AI. === Pulsar TRAC === Pulsar TRAC is a social listening and audience intelligence platform that allows users to configure searches that track public conversations and measure audience behaviour. Pulsar TRAC is focused on conversation insights and audience segmentations - the platform is reported to collect and analyse data from a wide range of sources, including major social networks, forums, news and review sites, and ecommerce platforms, with real-time visualisations and AI-supported analytics used to find patterns and communities of interest. Pulsar TRAC can be incorporated into workflows with other audience tools, such as an integration with Audiense that connects TRAC's conversation insights to external audience-segmentation datasets. === Pulsar CORE === Pulsar CORE centres on the analysis of owned-channel data, such as brand social media profiles, website interaction and other in-house digital assets, to generate audience and content insights. CORE can monitor published content, evaluate competitors, and extract demographic and behavioural segmentation from owned channels. === Narratives AI === Narratives AI is a tool within the Pulsar audience intelligence platform that uses artificial intelligence to detect, cluster and analyse narratives forming across social and news media. It was launched in March 2025 as a standalone search interface that processes real-time and historical data to find cultural trends, behaviours and beliefs. It uses clustering algorithms and visualisation to show how conversations form and spread online, and their relative importance within wider discourse. == Notable features == === Insight Agents === Pulsar's Insight Agents are AI-powered agents within the Pulsar platform designed to automate and augment common tasks in media, social, audience and narrative intelligence. Branded as TeamMates, these agents are grouped into four functional types: Sentinels for real-time monitoring, anomaly detection and alerting Oracles for forecasting and scenario planning Custodians for governance, compliance and policy enforcement Analysts for research, reporting and recommendations Each agent is trained on Pulsar's multi-source data and domain-specific workflows. In February 2026, Pulsar introduced 'Crisis Oracle,' an AI-driven system designed to quantify narrative momentum and predict reputational risk. == Academic research == Pulsar has been used as a data collection and analysis tool in peer-reviewed academic research across public health, infodemiology, veterinary science, and policy research. Published uses include a World Health Organization report on infodemic management, a Journal of Medical Internet Research study on headache and migraine discourse across Japan, Germany, and France, a Frontiers in Big Data study of Long COVID narratives, and Frontiers in Veterinary Science studies on canine chronic kidney disease and oral medication administration in dogs.

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  • Argument Interchange Format

    Argument Interchange Format

    The Argument Interchange Format (AIF) is an international effort to develop a representational mechanism for exchanging argument resources between research groups, tools, and domains using a semantically rich language. AIF traces its history back to a 2005 colloquium in Budapest. The result of the work in Budapest was first published as a draft description in 2006. Building on this foundation, further work then used the AIF to build foundations for the Argument Web. AIF-RDF is the extended ontology represented in the Resource Description Framework Schema (RDFS) semantic language. The Argument Interchange Format introduces a small set of ontological concepts that aim to capture a common understanding of argument -- one that works in multiple domains (both domains of argumentation and also domains of academic research), so that data can be shared and re-used across different projects in different areas. These ontological concepts are: Information (I-nodes) Applications of Rules of Inference (RA-nodes) Applications of Rules of Conflict (CA-nodes) Applications of Rules of Preference (PA-nodes) extended by: Schematic Forms (F-nodes) that are instantiated by RA, CA and PA nodes The AIF has reifications in a variety of development environments and implementation languages including MySQL database schema RDF Prolog JSON as well as translations to visual languages such as DOT and SVG. AIF data can be accessed online at AIFdb.

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  • Hundred (novel series)

    Hundred (novel series)

    Hundred (ハンドレッド, Handoreddo) is a Japanese light novel series written by Jun Misaki and illustrated by Nekosuke Ōkuma. SB Creative published 16 novels between November 15, 2012, and October 15, 2018, under their GA Bunko imprint. A manga adaptation with art by Sasayuki was serialized in Fujimi Shobo's Monthly Dragon Age magazine. An anime television series adaptation, produced by Production IMS and directed by Tomoki Kobayashi, aired from April to June 2016. == Plot == "Hundreds" are a kind of weapon that get their name from their ability to change into many different forms, and are the only thing that can counter the mysterious life forms called Savage that are attacking Earth. Those who can wield a Hundred are sought out to be made into Slayers, trained individuals who can use them in combat. To become a Slayer, Hayato Kisaragi successfully enrolls in the marine academy city ship Little Garden. However he feels a strange yet familiar sense of incongruity towards Emile Crossford, his roommate who somehow knows him from somewhere. On top of that, shortly after he enters the school, he ends up getting challenged to a duel by the "Queen" and the school's most powerful Slayer, Claire Harvey. == Characters == Hayato Kisaragi (如月 ハヤト, Kisaragi Hayato) Voiced by: Yoshiaki Hasegawa (Japanese); Ricco Fajardo (English) Hayato is the male protagonist of Hundred. Originally from Yamato, Hayato became a Slayer in order to obtain state-of-the-art medical treatment for his sister. His previous encounter with a Savage 10 years ago resulted in him becoming a Variant - one of a very small fraction of people (fewer than 10 in the world, according to Emile) who have survived exposure to the Savages and obtained a greatly increased affinity for Hundreds as a result. He has the highest known compatibility with a Hundred and his Hundred, the Flying Swallow, is a chevalier-type that takes the form of a sword and a shoulder guard. When he first met Emilia he didn't realize that she was really a girl, but upon discovering the truth, he agreed to keep her secret. He is shown to be slightly uncomfortable whenever Emilia was showing him affection and would always blush when around her or other women who show their romantic feelings toward him. Emilia Hermit (エミリア・ハーミット, Emiria Hāmitto) Voiced by: Rumi Ōkubo (Japanese); Mikaela Krantz (English) Emilia is the female protagonist of Hundred. She is a silver-haired girl from the Britannia Empire and Hayato's roommate. She initially poses as a boy under the name Emile Crossfode (エミール・クロスフォード, Emīru Kurosufōdo) with only a few people aware of her secret until she eventually reveals the truth about herself. She and Hayato were survivors from the second Savage attack 10 years earlier, which resulted in her and Hayato becoming Variants. Hayato only has vague recollections of the prior event and it isn't until their encounter with the Savages at Zwei Island that Hayato realizes her true identity. She is a citizen of the Gudenburg Empire by birth and eventually reveals that she is Emilia Gudenburg (エミリア・グーデンブルグ, Emiria Gūdenburugu), the Empire's third princess. Her Hundred is the Arms Shroud that is an innocence type able to change into any form of weapon, something no other Slayer's Hundred can do. Like Hayato, she too is a Variant. Ten years ago she and Hayato where fleeing from the Savages' onslaught when she was attacked by one and almost died. The attack left a potent amount of virus in her gaping wound. Hayato, in an attempt to save her life sucked some of the fluids out, causing him to become a Variant as well. A substantial amount was still left in her system. She is in love with Hayato and is known to be very affectionate towards him and does not care about the rumors circulating about their relationship since everyone assumes them to be gay. Eventually, her status as a princess and girl are revealed to her peers, who were shocked at her heritage and finally understand her feelings to Hayato. Claire Harvey (クレア・ハーヴェイ, Kurea Hāvei) Voiced by: M.A.O (Japanese); Caitlin Glass (English) The highest-ranked Slayer in Little Garden who is from the United States of Liberia, she is called the Queen. The newly-arrived Hayato is forced to duel her to prevent the expulsion of two students who arrived late to the entrance ceremony because they are looking for him at the airport when he arrived. During the duel Hayato accidentally gropes her and she goes all out and defeats him, but the duel is called a draw and the students are allowed to stay. After Hayato saves her from a Savage and, later, accidentally kisses her, she falls in love with him. Her Hundred is a Dragoon Type which utilizes multiple cannons or transforms into a large powerful rifle, in doing so it drains much of her energy. She is also one of the few people who are aware that Emilia is secretly a girl. Karen Kisaragi (如月 カレン, Kisaragi Karen) Voiced by: Kaya Okuno (Japanese); Dawn M. Bennett (English) Hayato's younger sister who is ill. Hayato became a Slayer in order to obtain first-class treatment for her. While staying in the hospital she is often seen playing tarot cards, where she has become sort of a clairvoyant. Unlike her brother, Hayato, she suspected that Emilia was really a girl the moment she met her, until she was later convinced otherwise. She later becomes good friends with popular idol Sakura. Sakura Kirishima (霧島 サクラ, Kirishima Sakura) Voiced by: Mayu Yoshioka (Japanese); Amber Lee Connors (English) She is a popular idol who falls in love with Hayato after seeing him defeat the Trenta Savage at Zwei Island. She originally met Hayato and Karen at a shelter in Gudenberg during the second Savage attack. She remembers Karen but wasn't able to get Hayato's name at the time. After that incident, she lives with her father whom she never meets. When she later falls ill from an unknown illness, her father sells her to the Warslran Research Facility, where subjects like her are injected with vaccines that are developed from the fluids recovered from defeated Savages. She is the only one of the test subjects to have survived and, like Hayato and Emilia, she is also a Variant and a Slayer. Liza Harvey (リザ・ハーヴェイ, Riza Hāvei) Voiced by: Nichika Ōmori (Japanese); Megan Shipman (English) Claire's younger sister. Liddy Steinberg (リディ・スタインバーグ, Ridi Sutainbāgu) Voiced by: Rika Kinugawa (Japanese); Alex Moore (English) Little Garden's student council Vice President who is in charge of enforcement, she is very loyal to Claire and can be very uptight when enforcing the school's rules and regulations. Her Hundred takes the form of a lance and a shield. Erica Candle (エリカ・キャンドル, Erika Kyandoru) Voiced by: Yui Makino (Japanese); Natalie Hoover (English) She is also student council Vice President, however, she is mostly in charge of strategic planning, she has a high admiration for Claire, and it is suggested that she has certain feelings for her. Her Hundred, the Everlasting, is an Arsene type, which takes the form of a massive chained yoyo that she uses for restraining. Unfortunately her Hundred is ineffective against much stronger Savages. She is also one of the few people who became aware of Emilia's secret. Fritz Granz (フリッツ・グランツ, Furittsu Gurantsu) Voiced by: Wataru Hatano (Japanese); Jason Liebrecht (English) Hayato's classmate and Latia's partner. His Hundred takes the form of a sniper rifle. He and Latia were childhood friends, he often pokes fun at her. He is curious about the relationship between Hayato and Emilie and often teases them about their relationship, including sometimes referring to them as a couple on occasion. Latia Saintemilion (レイティア・サンテミリオン, Reitia Santemirion) Voiced by: Yuka Ōtsubo (Japanese); Elizabeth Maxwell (English) She is classmates with Hayato and Emilia, she is also Fritz's partner. Her Hundred is a close quarter melee type. She is Fritz's childhood friend. Charlotte Dimandias (シャーロット・ディマンディウス, Shārotto Dimandiusu) Voiced by: Miyu Matsuki (1st drama CD), Yui Horie (2nd drama CD, anime); Sarah Wiedenheft (English) She is a child prodigy who serves as the Little Garden's only main technical expert and chief researcher on Hundreds. Her authority is equal to that of the student council, that she can go against them or question their decisions. She is best friends with Emilia, and she is one of the characters who knows her secret. Meimei (メイメイ, Meimei) Voiced by: Ayaka Imamura (Japanese); Jill Harris (English) Miharu Kashiwagi (柏木 ミハル, Kashiwagi Miharu) Voiced by: Yuna Yoshino (Japanese); Rachel Glass (English) Miharu is a nurse at the hospital where Karen is staying. She is known for her very sweet demeanor and large breasts. Chris Steinbelt (クリス・シュタインベルト, Kurisu Shutainberuto) Voiced by: Emiri Kato (Japanese); Howard Wang (English) Noa Sheldon (ノア・シェルダン, Noa Sherudan) Voiced by: Yurika Kubo (Japanese); Madeleine Morris (English) Xue-Mei Liu (劉雪梅, Ryū Shuemei) Voiced by: Eri Suzuki (Japanese); Apphia Yu (English) Alphonse Brustad (アルフォ

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  • Tinybop

    Tinybop

    Tinybop is a Brooklyn based publisher of apps for children. == History == Tinybop is a Brooklyn-based children's media company established in 2011 by Raul Gutierrez. App titles are released in two series: the Explorer's Library - a series of science apps and Digital Toys - series of open-ended construction apps. == Published apps == Explorer's Library Titles: The Human Body – An anatomy app for children. Released 2013. The company's first app was illustrated by Kelli Anderson and has been downloaded millions of times. Selected for the American Library Association's Notable Children's Media List in 2022. Named Apple App Store's Best of 2013. Winner of the Digital Ehon Yuichi Kimura Prize for Children's Digital Media. Plants – An app about biomes around the world. Homes – An app about houses around with world. Illustrated by Tuesday Bassen. Winner of the Parents Gold Choice Award for children's apps. Simple Machines – A children's physics app about simple machines. The Earth – An app for children about the geologic Earth illustrated by Sarah Jacoby. Weather – A children's weather app. Skyscrapers – A children's app about building tall buildings. Space – An interactive solar system. Mammals – A children's app about mammals illustrated by Wenjia Tang. Winner of the Digital Ehon Award for Children's Educational media. Coral Reef – An app about marine ecosystems. Winner of an Excellence in Early Learning Digital Media Honor from the American Library Association. State of Matter – An app covering solids, liquids, and gases. Winner of Excellence in Early Learning Digital Media Honor from the American Library Association. Light and Color – An app about light and color. Selected for The American Library Association's Notable Children's Media List 2023. Winner of the 2022 Yoichi Sakakihara Prize for Children's Media. Digital Toys Titles: The Robot Factory – A robot building app for children illustrated by Owen Davey. Apple named The Robot Factory as iPad App of the Year in 2015. The Everything Machine – A visual coding app for children. The Everything Machine was named Apple's Best of 2015. Monsters – A monster creation app illustrated by Tianhua Mao. The Infinite Arcade – An arcade game building app. Me: A Kids Diary – A digital journal for children. Selected for The American Library Association's Notable Children's Media List 2020. The Creature Garden – An app that allows children to create fantastical animals illustrated by Natasha Durley. Selected for The American Library Association's Notable Children's Media List 2021. Things that Go Bump – A multiplayer game set in an enchanted Japanese house, released on Apple Arcade in 2018.

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  • Whisper (speech recognition system)

    Whisper (speech recognition system)

    Whisper is a machine learning model for speech recognition and transcription, created by OpenAI and first released as open-source software in September 2022. It is capable of transcribing speech in English and multiple other languages, and can translate several non-English languages into English. Whisper is a weakly-supervised deep learning acoustic model, made using an encoder-decoder transformer architecture. OpenAI claims that the combination of different training data and post-training filtering used in its development has led to improved recognition of accents, background noise, and jargon compared to previous approaches. While the model does not outperform larger, more specialized models and still experiences AI hallucination, it has been showed to be useful for general sound recognition and has many applications across different industries. == Background == Speech recognition has had a long history in research; the first approaches made use of statistical methods, such as dynamic time warping, and later hidden Markov models. At around the 2010s, deep neural network approaches became more common for speech recognition models, which were enabled by the availability of large datasets ("big data") and increased computational performance. Early approaches to deep learning in speech recognition included convolutional neural networks, which were limited due to their inability to capture sequential data, which later led to developments of Seq2seq approaches, which include recurrent neural networks, which made use of long short-term memory. Transformers, introduced in 2017 by Google, displaced many prior state-of-the-art approaches across a wide range in machine learning, and started becoming the core neural architecture in fields such as language modeling and computer vision. Weakly-supervised approaches to training acoustic models were recognized in the early 2020s as promising for speech recognition approaches using deep neural networks. According to a NYT report, in 2021 OpenAI believed they exhausted sources of higher-quality data to train their large language models and decided to complement scraped web text with transcriptions of YouTube videos and podcasts, and developed Whisper to solve this task. Whisper Large V2 was released on December 8, 2022, followed by Whisper Large V3 being released in November 2023, during the OpenAI Dev Day. In March 2025, OpenAI released new transcription models based on GPT-4o and GPT-4o mini, both of which have lower error rates than Whisper. == Architecture == The Whisper architecture is based on an encoder-decoder transformer. Input audio is resampled to 16,000 Hertz (Hz) and converted to an 80-channel Log-magnitude Mel spectrogram using 25 ms windows with a 10 ms stride. The spectrogram is then normalized to a [-1, 1] range with near-zero mean. The encoder takes this Mel spectrogram as input and processes it. It first passes through two convolutional layers. Sinusoidal positional embeddings are added. It is then processed by a series of Transformer encoder blocks (with pre-activation residual connections). The encoder's output is layer normalized. The decoder is a standard transformer decoder. It has the same width and Transformer blocks as the encoder. It uses learned positional embeddings and tied input-output token representations (using the same weight matrix for both the input and output embeddings). It uses a byte-pair encoding tokenizer, of the same kind as used in GPT-2. English-only models use the GPT-2 vocabulary, while multilingual models employ a re-trained multilingual vocabulary with the same number of words. Special tokens are used to allow the decoder to perform multiple tasks: Tokens that denote language (one unique token per language). Tokens that specify task (<|transcribe|> or <|translate|>). Tokens that specify if no timestamps are present (<|notimestamps|>). If the token is not present, then the decoder predicts timestamps relative to the segment, and quantized to 20 ms intervals. <|nospeech|> for voice activity detection. <|startoftranscript|>, and <|endoftranscript|> . Any text that appears before <|startoftranscript|> is not generated by the decoder, but given to the decoder as context. Loss is only computed over non-contextual parts of the sequence, i.e. tokens between these two special tokens. == Training data == The training dataset consists of 680,000 hours of labeled audio-transcript pairs sourced from the internet using semi-supervised learning. This includes 117,000 hours in 96 non-English languages and 125,000 hours of X→English translation data, where X stands for any non-English language. Preprocessing involved standardization of transcripts, filtering to remove machine-generated transcripts using heuristics (e.g., punctuation, capitalization), language identification and matching with transcripts, fuzzy deduplication, and deduplication with evaluation datasets to avoid data contamination. Speechless segments were also included to allow voice activity detection training. For the files still remaining after the filtering process, audio files were then broken into 30-second segments paired with the subset of the transcript that occurs within that time. If this predicted spoken language differed from the language of the text transcript associated with the audio, that audio-transcript pair was not used for training the speech recognition models, but instead for training translation. The model was trained using the AdamW optimizer with gradient norm clipping and a linear learning rate decay with warmup, with batch size 256 segments. Training proceeded for 1 million updates (approximately 2-3 epochs). No data augmentation or regularization, except for the Large V2 model, which used SpecAugment, Stochastic Depth, and BPE Dropout. The training used data parallelism with float16, dynamic loss scaling, and activation checkpointing. === Post-training filtering === After training the first model, researchers ran it on different subsets of the training data, each representing a distinct source. Data sources were ranked by a combination of their error rate and size. Manual inspection of the top-ranked sources (high error, large size) helped determine if the source was low quality (e.g., partial transcriptions, inaccurate alignment). After training, it was fine-tuned to suppress the prediction of speaker names and low-quality sources were then removed. == Capacity == While Whisper does not outperform models which specialize in the LibriSpeech dataset, when tested across many datasets, it is more robust and makes 55.2% fewer errors than other models. Whisper has a differing error rate with respect to transcribing different languages, with a higher word error rate in languages not well-represented in the training data. The authors found that multi-task learning improved overall performance compared to models specialized to one task. They conjectured that the best Whisper model trained is still underfitting the dataset, and larger models and longer training can result in better models. Third-party evaluations have found varying levels of AI hallucination. A study of transcripts of public meetings found hallucinations in eight out of every 10 transcripts, while an engineer discovered hallucinations in "about half" of 100 hours of transcriptions and a developer identified them in "nearly every one" of 26,000 transcripts. A study of 13,140 short audio segments (averaging 10 seconds) found 187 hallucinations (1.4%), 38% of which generated text that could be harmful because it inserted false references to things like race, non-existent medications, or violent events that were not in the audio. == Applications == The model has been used as the base for many applications, such as a unified model for speech recognition and more general sound recognition. Whisper has also been integrated into the workflow of biomedical research. In 2025, a study on Alzheimer's disease detection used the model to transcribe spontaneous speech recordings. The transcripts that were generated by the model were combined with LLM vector embeddings and traditional classifiers to help classify the patients' health. Another application is when OVALYTICS incorporated Whisper to transcribe YouTube videos and automate content moderation systems, which improved its detection of offensive content. The model has also been used in academic libraries and cultral heritage institutions to generate transcripts and captions for their digitized audiovisual collections. In a 2025 case study, Emory University Libraries found that Whisper reduced the labor used in transcription by around 30-35%, shifting work from text creation to text correction. However, human review is still necessary to make sure accuracy, formatting, and accessibility are all standard.

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  • For a Breath I Tarry

    For a Breath I Tarry

    "For a Breath I Tarry" is a 1966 post-apocalyptic novelette by American writer Roger Zelazny, which was nominated for the Hugo Award for Best Novelette in 1967. Set in a future long after the self-extinction of humanity, the novelette recounts the tale of Frost, a sentient machine. Although humans have caused their own extinction, the sentient machines that they created continue the work of rebuilding a shattered Earth. Along the way, the story explores the differences between humanity and machines, the former experiencing the world qualitatively, while the latter doing so quantitatively. This difference is illustrated through philosophical conversations between Frost and another machine named Mordel. Frost's goal of becoming human, along with literary allusions, drives the plot and sets the tone of the novelette. These allusions include the first chapter of the Book of Job, in both situation and language, since verses are both quoted directly and paraphrased. In addition, the first three chapters of the Book of Genesis are echoed. Finally, Frost and Mordel enter into a Faustian bargain, though with better results than in the original story. The other major character is the Beta Machine, Frost's peer in the Southern Hemisphere. (Frost controls the Northern Hemisphere.) The novelette hints that though being a machine, Beta has a feminine personality. After Frost has succeeded in his millennium-long quest to become human (via recovered DNA), Beta agrees to join him in becoming human—suggesting the possibility of rebirth for the human race. The novelette has appeared in collections of Zelazny's works and in anthologies. The title is from a phrase in the poet A. E. Housman's collection A Shropshire Lad.

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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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  • AIX Toolbox for Linux Applications

    AIX Toolbox for Linux Applications

    The AIX Toolbox for Linux Applications is a collection of GNU tools for IBM AIX. These tools are available for installation using Red Hat's RPM format. == Licensing == Each of these packages includes its own licensing information and while IBM has made the code available to AIX users, the code is provided as is and has not been thoroughly tested. The Toolbox is meant to provide a core set of some of the most common development tools and libraries along with the more popular GNU packages.

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  • Transhuman Space

    Transhuman Space

    Transhuman Space (THS) is a role-playing game by David Pulver, published by Steve Jackson Games as part of the "Powered by GURPS" (Generic Universal Role-Playing System) line. Set in the year 2100, humanity has begun to colonize the Solar System. The pursuit of transhumanism is now in full swing, as more and more people reach fully posthuman states. In 2002, the Transhuman Space adventure "Orbital Decay" received an Origins Award nomination for Best Role-Playing Game Adventure. Transhuman Space won the 2003 Grog d'Or Award for Best Role-playing Game, Game Line or RPG Setting. == Setting == The game assumes that no cataclysm — natural or human-induced — swept Earth in the 21st century. Instead, constant developments in information technology, genetic engineering, nanotechnology and nuclear physics generally improved condition of the average human life. Plagues of the 20th century (like cancer or AIDS) have been suppressed, the ozone layer is being restored and Earth's ecosystems are recovering (although thermal emission by fusion power plants poses an environmental threat—albeit a much lesser one than previous sources of energy). Thanks to modern medicine humans live biblical timespans surrounded by various artificially intelligent helper applications and robots (cybershells), sensory experience broadcasts (future TV) and cyberspace telepresence. Thanks to cheap and clean fusion energy humanity has power to fuel all these wonders, restore and transform its home planet and finally settle on other heavenly bodies. Human genetic engineering has advanced to the point that anyone—single individuals, same-sex couples, groups of three or more—can reproduce. The embryos can be allowed to be developed naturally, or they can undergo three levels of tinkering: 1. Genefixing, which corrects defects; 2. Upgrades, which boost natural abilities (Ishtar Upgrades are slightly more attractive than usual, Metanoia Upgrades are more intelligent, etc.); and... 3. Full transition to parahuman status (Nyx Parahumans only need a few hours of sleep per week, Aquamorphs can live underwater, etc.) Another type of human genetic engineering, far more controversial, is the creation of bioroids, fully sentient slave races. People can "upload" by recording the simulation of their brains on computer disks. The emulated individual then becomes a ghost, an infomorph very easily confused with "sapient artificial intelligence". However, this technology has several problems as the solely available "brainpeeling" technique is fatal to the original biological lifeform being simulated, has a significant failure rate and the philosophical questions regarding personal identity remain equivocal. Any infomorph, regardless of its origin, can be plugged into a "cybershell" (robotic or cybernetic body), or a biological body, or "bioshell". Or, the individual can illegally make multiple "xoxes", or copies of themselves, and scatter them throughout the system, exponentially increasing the odds that at least one of them will live for centuries more, if not forever. This is also a time of space colonization. First, humanity (specifically China, followed by the United States and others) colonized Mars in a fashion resembling that outlined in the Mars Direct project. The Moon, Lagrangian points, inner planets and asteroids soon followed. In the late 21st century even some of Saturn's moons have been settled as a base for that planet's Helium-3 scooping operations. Transhuman Space's setting is neither utopia nor dystopia, however: several problems have arisen from these otherwise beneficial developments. The generation gap has become a chasm as lifespans increase. No longer do the elite fear death, and no longer can the young hope to replace them. While it seemed that outworld colonies would offer accommodation and work for those young ones, they are being replaced by genetically tailored bioroids and AI-powered cybershells. The concept of humanity is no longer clear in a world where even some animals speak of their rights and the dead haunt both cyberspace and reality (in form of infomorph-controlled bioshells or cybershells). And the wonders of high science are not universally shared — some countries merely struggle with informatization while others suffer from nanoplagues, defective drugs, implants and software tested on their populace. In some poor countries high-tech tyrants oppress their backward people. And in outer space all sort of modern crime thrives, barely suppressed by military forces. == Publication history == After the initial set of GURPS books that were published using the GURPS Lite, later publications such as Transhuman Space by David Pulver were labelled simply "Powered by GURPS" without using the name "GURPS" in the book title. Transhuman Space received a significant amount of supporting publications, and was the largest original background setting that Steve Jackson Games produced in 15 years. Shannon Appelcline noted that by its inclusion of posthuman characters, the book began to show the limits of the GURPS system as it was, which is something that Pulver would address soon thereafter. Steve Jackson Games has not updated the core book (GURPS Transhuman Space) to 4th edition, although the supplement Transhuman Space: Changing Times provides a path for migrating to 4th edition. It has produced several 4th edition supplements for the setting: Transhuman Space: Bioroid Bazaar, Transhuman Space: Cities on the Edge, Transhuman Space: Martial Arts 2100, Transhuman Space: Personnel Files 2-5, Transhuman Space: Shell-Tech, GURPS Spaceships 8: Transhuman Spacecraft, Transhuman Space: Transhuman Mysteries, and Transhuman Space: Wings of the Rising Sun. == Reception == In a review of Transhuman Space in Black Gate, William Stoddard said "Transhuman Space was a richly detailed setting; if it had imperfections, it had enough depth to make up for them. I think it has the potential to become a classic in its field. Perhaps a campaign set in its default start year of 2100 could leave the early twenty-first century blurry enough to avoid obvious incongruities." == Reviews == Review in Vol. 20, No. 1 of Prometheus, the journal of the Libertarian Futurist Society.

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  • Age Of

    Age Of

    Age Of is the eighth studio album by American electronic producer Oneohtrix Point Never, released on June 1, 2018, on Warp Records. Recorded over two years, it is the first Oneohtrix Point Never album to prominently feature Daniel Lopatin's own vocals. The album was accompanied by the MYRIAD tour, which premiered as a "conceptual concertscape" in 2018 at the Park Avenue Armory and ended its run in 2019. It features contributions from James Blake (who additionally produced and mixed the album), Anohni, Prurient, Kelsey Lu and Eli Keszler. The artwork, which employs Jim Shaw's "The Great Whatsit" as a central image, was designed by David Rudnick. While not entering the official United States Billboard 200 chart, it peaked at number 59 on the magazine's Top Current Albums chart. == Background == Lopatin produced Age Of in parts of a two-year period, during which he was also producing for other artists, including Anohni, FKA Twigs, Iggy Pop, and David Byrne. After composing the soundtrack for the Safdie Brothers' 2017 film Good Time, Lopatin moved to an Airbnb lodge in South Central Massachusetts, derived from his aspiration to live out the modern cliche of musicians moving to the woods to record albums; the eerie atmosphere in the lodge at nighttime influenced his desire to make "weird, little nightmare ballads". In addition to Lopatin's own singing, the album also features vocal performances from Anohni and Prurient, while instrumentalists Kelsey Lu and Eli Keszler contribute to several tracks. When the record was nearly finished, Lopatin reached out to musician James Blake to contribute to the mixing process, eventually traveling to Los Angeles to complete the album. The track "The Station" was originally composed as a demo for R&B singer Usher which was ultimately not used. On July 9, 2018, Lopatin released the original topline (vocal melody) demo for The Station through Sendspace. The track "Toys 2" imagines a theoretical sequel to the 1992 film Toys where actor Robin Williams' image has been recreated with CGI (as his will specifically forbade any usage of his image after his death), and pokes fun at the common electronic music trope of composing a soundtrack to a theoretical film (which Lopatin described as "horribly cliché"). == Concept and MYRIAD == Influences on Age Of included Stanley Kubrick's 1968 film 2001: A Space Odyssey, which inspired the narrative of the album's accompanying performance installation and tour MYRIAD, as well as William Strauss's The Fourth Turning, a favorite book of former White House Chief Strategist Steve Bannon, which Lopatin described as "insidious, like the voice of a computer insisting on the truth about history without any sensitivity given to how complex and non-linear systems might be"; Lopatin was subsequently inspired to "[use] that sort of taxonomy as a kind of farce to then create these little frameworks for understanding". Other inspirations included the writings of the 1990s multidisciplinary collective Cybernetic Culture Research Unit and the works of singer-songwriters such as Bruce Cockburn, Bob Dylan, and Paul Simon. Around the time Lopatin began finalizing Age Of in his Airbnb lodge, he began working on the concept for MYRIAD, a conceptual concert performance which premiered at Park Avenue Armory. He described the concept as a four-part "epochal song cycle" showcasing the idiocy of previous generations of living organisms. The loose story concerns a group of artificial intelligences near the end of time named a "Limitless Living Informational Intelligence" (represented in the MYRIAD logo as nine squares) which, for leisurely purposes, attempt to replicate the cultures and behaviors of the previously existent human species. It does this by determining an "average" of human experiences through the species' "recorded output", and does so through imperfect, heuristic techniques. The show was consequently divided into four sections, each representing an epoch of the cycle concept loosely inspired by the Strauss–Howe generational theory: the Age of Ecco, the Age of Harvest, the Age of Excess, and the Age of Bondage. Ecco is "a phase of pre-evolutionary ignorance", Harvest is "living in agrarian harmony with the world", Excess is "the age of unchecked industrial ambition", and Bondage is "an era of engorgement, wherein "we keep making more and more shit until there's no space left." MYRIAD mainly featured "three-hundred pound sculptures that hang from the ceiling like kebabs that secrete ooze", and a full ensemble that toured to perform songs from Age Of, including Eli Keszler, Kelly Moran and Aaron David Ross. The sculptures, as well as the visuals displayed on five polygon panels, were created by frequent Oneohtrix Point Never collaborator Nate Boyce. Initially, Lopatin planned for each of the album's four epoches to be represented by fragrances, the more noisy epochs being pleasant to the nose to make a "weird dissonance". However, due to lack of time and resources, that part of the plan was scrapped. == Composition == Whereas previous Oneohtrix Point Never albums followed musical styles from only distinctive eras, Age Of is the first album by Lopatin to incorporate elements of unique genres from a variety of periods, hence the "incompleteness" of its title according to reviewer Heather Phares, and his first pop-song-oriented release since his work for Ford & Lopatin. The sound palettes it uses are those from a variety of styles such as chamber pop, "android"-like folk and country music, yacht rock, smooth jazz, R&B, Future-style soul, black metal, new age, and stadium pop, as well as post-industrial sounds on tracks like "Warning", "We'll Take It" and "Same", and, in particular, baroque music and medieval music on the opening title track, "Age Of". Critics also noted elements of Lopatin's past discography being present on Age Of. The instrumentation of Age Of is made up of MIDI harpsichords, guitars, pianos, brass and vocals, as well as Lopatin's trademark unorthodox sound design, samples and synth presets. The LP's use of the harpsichord shows its similarities "with Eastern instruments such as the koto and with rapid-fire electronic melodies", wrote Phares. == Critical reception == Age Of was critically well-received upon its distribution. Some reviewers praised the album's use of collaborators. Reviewing the album for AllMusic, Heather Phares called Age Of a "landmark work" for Lopatin. She praised it as his "widest-ranging" release, elaborating that he "matches the album's ambition with plenty of emotion" and "gives his music exciting new shapes." Ross Devlin of The Skinny, in a five-star review of the record, also highlighted the album's amount of ambition, particularly the "wealth of exquisitely baroque moments, exploring history as a pliable, multi-dimensional rift", that gave it "exceptional sonic depth". The Observer praised Age Of for continuing the "off-kilter composition and unexpected instrumentation" of Lopatin's previous releases, and critic Matt McDermott highlighted that the producer increased his musical range with the record: "It's a dizzying trip meant to shore up Lopatin's status as an avant-garde auteur while aiding his forays into mainstream pop culture." Age Of was ranked the 15th best release of the year in The Wire magazine's annual critics' poll. == Track listing == Notes "Myriad Industries" is stylized as "myriad.industries". Sample credits "Age Of" contains a sample of "Blow the Wind" by Jocelyn Pook. "Manifold" contains a sample from "Overture (Ararat the Border Crossing)" by Tayfun Erdem; and a sample from "Venice Beach in Winter" (listed in the liner notes as "a keyboard sample from Reharmonization") by Julian Bradley. "Myriad Industries" contains a sample of "EchoSpace" by Gil Trythall. == Accolades == == Personnel == Daniel Lopatin – production, lead vocals, album art, design James Blake – additional production, mixing, keyboards Gabriel Schuman, Joshua Smith and Evan Sutton – assistance Greg Calbi – mastering David Rudnick – album art, design Prurient – vocals Kelsey Lu – keyboards Anohni – vocals Eli Keszler – drums Shaun Trujillo – words == Charts ==

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  • Digital organism

    Digital organism

    A digital organism is a self-replicating computer program that mutates and evolves. Digital organisms are used as a tool to study the dynamics of Darwinian evolution, and to test or verify specific hypotheses or mathematical models of evolution. The study of digital organisms is closely related to the area of artificial life. == History == Digital organisms can be traced back to the game Darwin, developed in 1961 at Bell Labs, in which computer programs had to compete with each other by trying to stop others from executing . A similar implementation that followed this was the game Core War. In Core War, it turned out that one of the winning strategies was to replicate as fast as possible, which deprived the opponent of all computational resources. Programs in the Core War game were also able to mutate themselves and each other by overwriting instructions in the simulated "memory" in which the game took place. This allowed competing programs to embed damaging instructions in each other that caused errors (terminating the process that read it), "enslaved processes" (making an enemy program work for you), or even change strategies mid-game and heal themselves. Steen Rasmussen at Los Alamos National Laboratory took the idea from Core War one step further in his core world system by introducing a genetic algorithm that automatically wrote programs. However, Rasmussen did not observe the evolution of complex and stable programs. It turned out that the programming language in which core world programs were written was very brittle, and more often than not mutations would completely destroy the functionality of a program. The first to solve the issue of program brittleness was Thomas S. Ray with his Tierra system, which was similar to core world. Ray made some key changes to the programming language such that mutations were much less likely to destroy a program. With these modifications, he observed for the first time computer programs that did indeed evolve in a meaningful and complex way. Later, Chris Adami, Titus Brown, and Charles Ofria started developing their Avida system, which was inspired by Tierra but again had some crucial differences. In Tierra, all programs lived in the same address space and could potentially execute or otherwise interfere with each other's code. In Avida, on the other hand, each program lives in its own address space. Because of this modification, experiments with Avida became much cleaner and easier to interpret than those with Tierra. With Avida, digital organism research has begun to be accepted as a valid contribution to evolutionary biology by a growing number of evolutionary biologists. Evolutionary biologist Richard Lenski of Michigan State University has used Avida extensively in his work. Lenski, Adami, and their colleagues have published in journals such as Nature and the Proceedings of the National Academy of Sciences (USA). In 1996, Andy Pargellis created a Tierra-like system called Amoeba that evolved self-replication from a randomly seeded initial condition. More recently REvoSim - a software package based around binary digital organisms - has allowed evolutionary simulations of large populations that can be run for geological timescales.

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  • Jaggaer

    Jaggaer

    JAGGAER, formerly SciQuest, is a provider of cloud-based business automation technology for Business Spend Management. Its headquarters is in Durham, North Carolina. == Company history == SciQuest was established in 1995 as a B2B eCommerce exchange.The company went public with an IPO in 1999. In 2001, SciQuest transitioned from a B2B exchange company into eProcurement software and supplier enablement platforms. SciQuest was taken private in 2004 and continued to move into eProcurement, inventory management and accounts payable automation. SciQuest completed an IPO in September 2010, raising approximately $57 million. SciQuest, and its 510 person workforce, was taken private in June 2016 as part of a $509 million acquisition by Accel-KKR, a private equity firm headquartered in Menlo Park, CA. In 2017 SciQuest was rebranded as JAGGAER and announced increased focus on offering a complete, integrated source-to-pay suite. Along with the name change, the company expanded its market focus to manufacturing, healthcare, consumer packaged goods, retail, education, life sciences, logistics and the public sector. JAGGAER acquired the European direct materials procurement specialist Pool4Tool in June 2017 giving it end-to-end direct as well as indirect materials procurement coverage. JAGGAER acquired spend management company BravoSolution in 2017, and entered into a joint venture with United Arab Emirates-based Tejari. In February 2019 JAGGAER launched JAGGAER One, which unifies its full product suite on a single platform. In 2019 the UK-based private equity firm Cinven acquired a majority holding in the company. Jim Bureau was subsequently named JAGGAER's Chief Executive Officer. Bureau left the firm in March 2023, and Andy Hovancik was announced as the company's CEO in June. In 2024, JAGGAER was acquired by Vista Equity Partners, a private equity firm specializing in enterprise software investments. == Current positioning == As of April 2025, JAGGAER positions itself as "an enterprise procurement and supplier collaboration SaaS provider." Its core technology platform, which is called JAGGAER One, serves "direct and indirect procurement with specializations in Higher Education, Discrete and Process Manufacturing, and Public Sector." == Product Categories == The JAGGAER One platform supports the following products: Spend Analytics Category Management Supplier Management Sourcing Contracts eProcurement Invoicing Inventory Management Supply Chain Collaboration Quality Management == Acquisitions == SciQuest acquired the following companies: AECsoft - January 2011. Provider of supplier management and sourcing technology. Upside Software, Inc. - August 2012. Provider of contract lifecycle management (CLM) solutions. Spend Radar, LLC - October 2012, Provider of spend analysis software. CombineNet - September 2013, Provider of advanced sourcing software JAGGAER acquired the following companies: POOL4TOOL - June 2017, Provider of direct sourcing and supply chain management software BravoSolution - December 2017, Provider of global platform spend management solutions

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  • True Love (short story)

    True Love (short story)

    "True Love" is a science fiction short story by American writer Isaac Asimov. It was first published in the February 1977 issue of American Way magazine and reprinted in the collections The Complete Robot (1982) and Robot Dreams (1986). In his autobiography In Joy Still Felt, the author states that American Way had requested a Valentine's Day story from him for its February 1977 issue, and that he wrote the story to console himself after the departure of his daughter following a visit during the 1976 Thanksgiving weekend. == Plot summary == Milton Davidson is trying to find his ideal partner. To do this, he prepares a special computer program to run on Multivac, which he calls Joe, which has access to databases covering the entire populace of the world. He hopes that Joe will find him his ideal match, based on physical parameters as supplied. Milton arranges to have the shortlisted candidates assigned to work with him for short periods, but realises that looks alone are not enough to find an ideal match. In order to correlate personalities, he speaks at great length to Joe, gradually filling Joe's databanks with information about his personality. In doing so, Joe develops the personality of Milton. Upon finding an ideal match, he arranges to have Milton arrested for malfeasance, so that Joe can 'have the girl' for himself.

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  • Speech synthesis

    Speech synthesis

    Speech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech synthesizer, and can be implemented in software or hardware products. A text-to-speech (TTS) system converts normal language text into speech; other systems render symbolic linguistic representations like phonetic transcriptions into speech. The reverse process is speech recognition. Synthesized speech can be created by concatenating pieces of recorded speech that are stored in a database. Systems differ in the size of the stored speech units; a system that stores phones or diphones provides the largest output range, but may lack clarity. For specific usage domains, the storage of entire words or sentences allows for high-quality output. Alternatively, a synthesizer can incorporate a model of the vocal tract and other human voice characteristics to create a completely "synthetic" voice output. The quality of a speech synthesizer is judged by its similarity to the human voice and by its ability to be understood clearly. An intelligible text-to-speech program allows people with visual impairments or reading disabilities to listen to written words on a home computer. The earliest computer operating system to have included a speech synthesizer was Unix in 1974, through the Unix speak utility. In 2000, Microsoft Sam was the default text-to-speech voice synthesizer used by the narrator accessibility feature, which shipped with all Windows 2000 operating systems, and subsequent Windows XP systems. A text-to-speech system (or "engine") is composed of two parts: a front-end and a back-end. The front-end has two major tasks. First, it converts raw text containing symbols like numbers and abbreviations into the equivalent of written-out words. This process is often called text normalization, pre-processing, or tokenization. The front-end then assigns phonetic transcriptions to each word, and divides and marks the text into prosodic units, like phrases, clauses, and sentences. The process of assigning phonetic transcriptions to words is called text-to-phoneme or grapheme-to-phoneme conversion. Phonetic transcriptions and prosody information together make up the symbolic linguistic representation that is output by the front-end. The back-end—often referred to as the synthesizer—then converts the symbolic linguistic representation into sound. In certain systems, this part includes the computation of the target prosody (pitch contour, phoneme durations), which is then imposed on the output speech. == History == Long before the invention of electronic signal processing, some people tried to build machines to emulate human speech. There were also legends of the existence of "Brazen Heads", such as those involving Pope Silvester II (d. 1003 AD), Albertus Magnus (1198–1280), and Roger Bacon (1214–1294). In 1779, the German-Danish scientist Christian Gottlieb Kratzenstein won the first prize in a competition announced by the Russian Imperial Academy of Sciences and Arts for models he built of the human vocal tract that could produce the five long vowel sounds (in International Phonetic Alphabet notation: [aː], [eː], [iː], [oː] and [uː]). There followed the bellows-operated "acoustic-mechanical speech machine" of Wolfgang von Kempelen of Pressburg, Hungary, described in a 1791 paper. This machine added models of the tongue and lips, enabling it to produce consonants as well as vowels. In 1837, Charles Wheatstone produced a "speaking machine" based on von Kempelen's design, and in 1846, Joseph Faber exhibited the "Euphonia". In 1923, Paget resurrected Wheatstone's design. In the 1930s, Bell Labs developed the vocoder, which automatically analyzed speech into its fundamental tones and resonances. From his work on the vocoder, Homer Dudley developed a keyboard-operated voice-synthesizer called The Voder (Voice Demonstrator), which he exhibited at the 1939 New York World's Fair. Franklin S. Cooper and his colleagues at Haskins Laboratories built the pattern playback in the late 1940s and completed it in 1950. There were several different versions of this hardware device; only one currently survives. The machine converts pictures of the acoustic patterns of speech in the form of a spectrogram back into sound. Using this device, Alvin Liberman and colleagues discovered acoustic cues for the perception of phonetic segments (consonants and vowels). === Electronic devices === The first computer-based speech-synthesis systems originated in the late 1950s. Noriko Umeda et al. developed the first general English text-to-speech system in 1968, at the Electrotechnical Laboratory in Japan. In 1961, physicist John Larry Kelly, Jr and his colleague Louis Gerstman used an IBM 704 computer to synthesize speech, an event among the most prominent in the history of Bell Labs. Kelly's voice recorder synthesizer (vocoder) recreated the song "Daisy Bell", with musical accompaniment from Max Mathews. Coincidentally, Arthur C. Clarke was visiting his friend and colleague John Pierce at the Bell Labs Murray Hill facility. Clarke was so impressed by the demonstration that he used it in the climactic scene of his screenplay for his novel 2001: A Space Odyssey, where the HAL 9000 computer sings the same song as astronaut Dave Bowman puts it to sleep. Despite the success of purely electronic speech synthesis, research into mechanical speech-synthesizers continues. Linear predictive coding (LPC), a form of speech coding, began development with the work of Fumitada Itakura of Nagoya University and Shuzo Saito of Nippon Telegraph and Telephone (NTT) in 1966. Further developments in LPC technology were made by Bishnu S. Atal and Manfred R. Schroeder at Bell Labs during the 1970s. LPC was later the basis for early speech synthesizer chips, such as the Texas Instruments LPC Speech Chips used in the Speak & Spell toys from 1978. In 1975, Fumitada Itakura developed the line spectral pairs (LSP) method for high-compression speech coding, while at NTT. From 1975 to 1981, Itakura studied problems in speech analysis and synthesis based on the LSP method. In 1980, his team developed an LSP-based speech synthesizer chip. LSP is an important technology for speech synthesis and coding, and in the 1990s was adopted by almost all international speech coding standards as an essential component, contributing to the enhancement of digital speech communication over mobile channels and the internet. In 1975, MUSA was released, and was one of the first Speech Synthesis systems. It consisted of a stand-alone computer hardware and a specialized software that enabled it to read Italian. A second version, released in 1978, was also able to sing Italian in an "a cappella" style. Dominant systems in the 1980s and 1990s were the DECtalk system, based largely on the work of Dennis Klatt at MIT, and the Bell Labs system; the latter was one of the first multilingual language-independent systems, making extensive use of natural language processing methods. Handheld electronics featuring speech synthesis began emerging in the 1970s. One of the first was the Telesensory Systems Inc. (TSI) Speech+ portable calculator for the blind in 1976. Other devices had primarily educational purposes, such as the Speak & Spell toy produced by Texas Instruments in 1978. Fidelity released a speaking version of its electronic chess computer in 1979. The first video game to feature speech synthesis was the 1980 shoot 'em up arcade game, Stratovox (known in Japan as Speak & Rescue), from Sun Electronics. The first personal computer game with speech synthesis was Manbiki Shoujo (Shoplifting Girl), released in 1980 for the PET 2001, for which the game's developer, Hiroshi Suzuki, developed a "zero cross" programming technique to produce a synthesized speech waveform. Another early example, the arcade version of Berzerk, also dates from 1980. The Milton Bradley Company produced the first multi-player electronic game using voice synthesis, Milton, in the same year. In 1976, Computalker Consultants released their CT-1 Speech Synthesizer. Designed by D. Lloyd Rice and Jim Cooper, it was an analog synthesizer built to work with microcomputers using the S-100 bus standard. Synthesized voices typically sounded male until 1990, when Ann Syrdal, at AT&T Bell Laboratories, created a female voice. Ray Kurzweil predicted in 2005 that as the cost-performance ratio caused speech synthesizers to become cheaper and more accessible, more people would benefit from the use of text-to-speech programs. === Artificial intelligence === In September 2016, DeepMind released WaveNet, which demonstrated that deep learning models are capable of modeling raw waveforms and generating speech from acoustic features like spectrograms or mel-spectrograms, starting the field of deep learning speech synthesis. Although WaveNet was initially considered to be computationally expensive and slow to be used in consumer products at the time, a year after its

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