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.
Drop shadow
In graphic design and computer graphics, a drop shadow is a visual effect consisting of a drawing element which looks like the shadow of an object, giving the impression that the object is raised above the objects behind it. The drop shadow is often used for elements of a graphical user interface such as windows or menus, and for simple text. The text label for icons on desktops in many desktop environments has a drop shadow, as this effect effectively distinguishes the text from any colored background it may be in front of. A simple way of drawing a drop shadow of a rectangular object is to draw a gray or black area underneath and offset from the object. In general, a drop shadow is a copy in black or gray of the object, drawn in a slightly different position. Realism may be increased by: Darkening the colors of the pixels where the shadow casts instead of making them gray. This can be done with alpha blending the shadow with the area it is cast on. Softening the edges of the shadow. This can be done by adding Gaussian blur to the shadow's alpha channel before blending. Inset drop shadows are a type which draws the shadows inside the element. This allows the interface element to appear as if it is sunken into the interface. == Photo editing == In photo editing or photography post-production, a drop shadow may be added right beneath a model or product in the image. It is used to create contrast between the background and the subject. To add a drop shadow, retouchers use graphic editing tools like Adobe Photoshop. Drop shadows are often used as a visual effect in e-commerce. This is done to improve the presentation of product images and create depth in the image. == Use == Generally, window managers which are capable of compositing allow drop shadow effects, whereas incapable window managers do not. In some operating systems like macOS, drop shadow is used to differentiate between active and inactive windows. Websites are able to use drop shadow effects through the CSS properties box-shadow, text-shadow, and drop-shadow() filter function in filter. The first two are used for elements and text respectively, while the filter applies to the element's content, letting it support oddly shaped elements or transparent images.
Library classification
A library classification is a system used within a library to organize materials, including books, sound and video recordings, electronic materials, etc., both on shelves and in catalogs and indexes. Each item is typically assigned a call number, which identifies the location of the item within the system. Materials can be arranged by many different factors, typically in either a hierarchical tree structure based on the subject or using a faceted classification system, which allows the assignment of multiple classifications to an object, enabling the classifications to be ordered in many ways. == Description == Library classification is an important and crucial aspect in library and information science. It is distinct from scientific classification in that it has as its goal to provide a useful ordering of documents rather than a theoretical organization of knowledge. Although it has the practical purpose of creating a physical ordering of documents, it does generally attempt to adhere to accepted scientific knowledge. Library classification helps to accommodate all the newly published literature in an already created order of arrangement in a filial sequence. Library classification can be defined as the arrangement of books on shelves, or description of them, in the manner which is most useful to those who read with the ultimate aim of grouping similar things together. Library classification is meant to achieve these four purposes: ordering the fields of knowledge in a systematic way, bring related items together in the most helpful sequence, provide orderly access on the shelf, and provide a location for an item on the shelf. Library classification is distinct from the application of subject headings in that classification organizes knowledge into a systematic order, while subject headings provide access to intellectual materials through vocabulary terms that may or may not be organized as a knowledge system. The characteristics that a bibliographic classification demands for the sake of reaching these purposes are: a useful sequence of subjects at all levels, a concise memorable notation, and a host of techniques and devices of number synthesis. == History == Library classifications were preceded by classifications used by bibliographers such as Conrad Gessner. The earliest library classification schemes organized books in broad subject categories. The earliest known library classification scheme is the Pinakes by Callimachus, a scholar at the Library of Alexandria during the third century BC. During the Renaissance and Reformation era, "Libraries were organized according to the whims or knowledge of individuals in charge." This changed the format in which various materials were classified. Some collections were classified by language and others by how they were printed. After the printing revolution in the sixteenth century, the increase in available printed materials made such broad classification unworkable, and more granular classifications for library materials had to be developed in the nineteenth century. In 1627 Gabriel Naudé published a book called Advice on Establishing a Library. At the time, he was working in the private library of Président à mortier Henri de Mesmes II. Mesmes had around 8,000 printed books and many more Greek, Latin and French written manuscripts. Although it was a private library, scholars with references could access it. The purpose of Advice on Establishing a Library was to identify rules for private book collectors to organize their collections in a more orderly way to increase the collection's usefulness and beauty. Naudé developed a classification system based on seven different classes: theology, medicine, jurisprudence, history, philosophy, mathematics, and the humanities. These seven classes would later be increased to twelve. Advice on Establishing a Library was about a private library, but within the same book, Naudé encouraged the idea of public libraries open to all people regardless of their ability to pay for access to the collection. One of the most famous libraries that Naudé helped improve was the Bibliothèque Mazarine in Paris. Naudé spent ten years there as a librarian. Because of Naudé's strong belief in free access to libraries to all people, the Bibliothèque Mazarine became the first public library in France around 1644. Although libraries created order within their collections from as early as the fifth century BC, the Paris Bookseller's classification, developed in 1842 by Jacques Charles Brunet, is generally seen as the first of the modern book classifications. Brunet provided five major classes: theology, jurisprudence, sciences and arts, belles-lettres, and history. Classification can now be seen as a provider of subject access to information in a networked environment. == Types == There are many standard systems of library classification in use, and many more have been proposed over the years. However, in general, classification systems can be divided into three types depending on how they are used: === Universal schemes === Covers all subjects, e.g. the Dewey Decimal Classification (DDC), Universal Decimal Classification (UDC), and Colon Classification (CC). === Specific classification schemes === Covers particular subjects or types of materials, e.g. Iconclass (art), British Catalogue of Music Classification, and Dickinson classification (music), or the NLM Classification (medicine). === National schemes === Specially created for certain countries, e.g. Swedish library classification system, SAB (Sveriges Allmänna Biblioteksförening). The Library of Congress Classification was designed around the collection of the US Library of Congress and has an American, European, and Christian bias. Nevertheless, it is used widely in large academic and research libraries. In terms of functionality, classification systems are often described as: === Enumerative === Subject headings are listed alphabetically, with numbers assigned to each heading in alphabetical order. === Hierarchical === Subjects are divided hierarchically, from most general to most specific. === Faceted/analytico-synthetic === Subjects are divided into mutually exclusive orthogonal facets. There are few completely enumerative systems or faceted systems; most systems are a blend but favouring one type or the other. The most common classification systems, LCC and DDC, are essentially enumerative, though with some hierarchical and faceted elements (more so for DDC), especially at the broadest and most general level. The first true faceted system was the colon classification of S. R. Ranganathan. == Methods or systems == Classification types denote the classification or categorization according to the form or characteristics or qualities of a classification scheme or schemes. Method and system has similar meaning. Method or methods or system means the classification schemes like Dewey Decimal Classification or Universal Decimal Classification. The types of classification is for identifying and understanding or education or research purposes while classification method means those classification schemes like DDC, UDC. === English language universal classification systems === The most common systems in English-speaking countries are: Dewey Decimal Classification (DDC) Library of Congress Classification (LCC) Universal Decimal Classification (UDC) Other systems include: Book Industry Standards and Communications (BISAC), originally developed for use by U.S. booksellers, has become increasingly popular in libraries. Bliss bibliographic classification used in some British libraries Colon classification (CC) Garside classification used in most libraries of University College London Gladstone Library Classification, devised by W.E. Gladstone and used exclusively at Gladstone's Library Harvard-Yenching Classification, an English classification system for Chinese language materials === Non-English universal classification systems === German Regensburger Verbundklassifikation (RVK) A system of book classification for Chinese libraries (Liu's Classification) library classification for user New Classification Scheme for Chinese Libraries Nippon Decimal Classification (NDC) Chinese Library Classification (CLC) Korean Decimal Classification (KDC) Russian Library-Bibliographical Classification (BBK) Swedish library classification system (SAB) === Universal classification systems that rely on synthesis (faceted systems) === Bliss bibliographic classification Colon classification Cutter Expansive Classification Universal Decimal Classification Newer classification systems tend to use the principle of synthesis (combining codes from different lists to represent the different attributes of a work) heavily, which is comparatively lacking in LC or DDC. == Practice == Library classification is associated with library (descriptive) cataloging under the rubric of cataloging and classification, sometimes grouped together as technical serv
Copyright and artificial intelligence in the United Kingdom
The interaction of artificial intelligence and copyright law has become one of the most contentious tech policy debates in the United Kingdom, centering on whether AI developers should be permitted to train their models on copyrighted material without explicit consent or remuneration. This debate has exposed a deep fracture between the creative industries, which seek to protect their intellectual property from unauthorised commercial exploitation, and tech companies. The academic and library sectors are also impacted, and argue that overly restrictive copyright laws hinder scientific research and the UK's sovereign AI capabilities. In 2024, the UK government proposed a broad text and data mining (TDM) exception to copyright that would have allowed AI companies to use publicly available copyrighted material for training, offering creators only an "opt-out" mechanism, similar to the exception introduced in Europe. This proposal faced intense opposition from across the creative sector. Trade unions representing writers, musicians, performers, and journalists argued that such an exception would effectively expropriate their members' work for the commercial benefit of tech giants. A report from the House of Lords Communications and Digital Committee, warned that generative AI posed a "clear and present danger" to the £124 billion creative economy. The government abandoned the opt-out model in March 2026, opting instead to build a stronger evidence base before pursuing any copyright reform. Conversely, the academic and library sectors have raised significant concerns that the UK's current TDM exception, which is strictly limited to non-commercial research, is too narrow. Universities and research libraries occupy a dual role as both creators of vast datasets and beneficiaries of TDM exceptions. They argue that the current legal framework restricts their ability to computationally analyse the very research they produce, thereby hobbling the UK's "AI for Science" strategy. Advocacy groups have highlighted a "triple payment" problem, wherein publicly funded research is handed over to publishers, who then charge universities substantial subscription fees and demand additional payments for specific TDM licences. This tension is further complicated by the commercial practices of major academic publishers. While publishers often restrict universities from using subscribed databases for AI training, they have simultaneously entered into lucrative, multi-million-dollar licensing agreements to sell access to this academic content to commercial AI developers. Furthermore, academics have accused publishers of actively steering authors away from permissive open-access licences towards more restrictive variants. By doing so, publishers retain the exclusive commercial rights necessary to strike these AI training deals, often without consulting the original authors or offering them any additional remuneration. This dynamic has not only reopened debates within the Open Access movement but has also created complex legal scenarios where publishers, rather than authors, control the terms of copyright litigation against major tech companies. == Training on copyrighted material == The question of whether AI developers should be permitted to train their models on copyrighted material without payment or consent has been one of the most contentious policy debates in the UK AI landscape. In 2024, the then-Conservative government proposed a broad text and data mining (TDM) exception that would have allowed AI companies to use any publicly available copyrighted material for training purposes, with creators able only to "opt out" of having their work used. This proposal provoked intense opposition from writers, musicians, visual artists, publishers, and broadcasters, who argued it would effectively expropriate their intellectual property for the commercial benefit of AI companies. The debate over text and data mining exceptions extends significantly beyond generative AI and the creative industries, implicating a wide range of scientific, industrial, and academic research applications. TDM is a foundational process for analysing large datasets to identify patterns, trends, and correlations, which is heavily utilised in fields such as medical research, climate modelling, and financial services. In the scientific and academic sectors, researchers rely on TDM to process vast amounts of published literature. For example, in biomedical research, TDM is used to accelerate drug discovery, identify new uses for existing medicines, and extract insights from clinical notes and genomic datasets. However, the application of traditional copyright frameworks to scientific literature has been criticised by academics. Researchers argue that scientific writing is intended to convey factual, verifiable information rather than creative originality, and that copyright restrictions on TDM hinder reproducibility, validation, and the advancement of science. The current UK copyright exception for TDM (Section 29A of the Copyright, Designs and Patents Act 1988) is limited strictly to non-commercial research, which creates barriers for public-private research partnerships and commercial scientific development. Beyond academia, non-generative AI and TDM are critical to various industrial and commercial operations. In the financial services sector, TDM is employed to monitor transactions, detect fraud, and analyse market feeds. Other non-generative applications include search engine indexing, plagiarism detection software, and media monitoring. A 2026 report by Public First estimated that 19% of UK businesses use specialised TDM tools, and that a restrictive copyright regime requiring licenses for all copyrighted content could cost the UK economy £220 billion in lost AI-driven GDP growth by 2035 compared to a broad commercial TDM exemption. Industry advocates argue that the lack of a commercial TDM exception in the UK creates legal uncertainty that stifles innovation across these broader, non-generative applications of data analysis. === Tech and AI industry positions === The technology and artificial intelligence industries lobbied for a broad text and data mining (TDM) exception to UK copyright law, arguing that such an exception is essential for the UK to remain globally competitive in AI development. Industry bodies such as techUK have argued that without a TDM exception, the UK risks becoming an "AI taker rather than an AI maker," as developers will relocate training operations to jurisdictions with more permissive copyright regimes, such as the United States, Japan, Singapore, and the European Union. During the UK government's 2024–2025 consultation on copyright and AI, major AI developers and trade associations strongly supported "Option 2" (a broad TDM exception) or "Option 3" (a TDM exception with an opt-out mechanism). OpenAI stated in its consultation response that a broad TDM exception is "necessary to drive AI innovation and investment in the UK," arguing that developers should be permitted to train models on lawfully accessed copies without further distribution. The Computer and Communications Industry Association (CCIA) similarly argued that restricting TDM to non-commercial development would undermine the government's ambitions for the UK tech sector and frustrate partnerships between commercial entities and research institutions. Tech industry advocates have also highlighted the economic implications of copyright policy. According to analysis by the think tank UK Day One, adopting an overly restrictive licensing-only approach could result in the UK economy losing up to £182 billion over 20 years, whereas a broad TDM exception could generate a positive impact of £131.61 billion over the same period. Following the government's March 2026 decision to drop plans for a TDM exception in favour of a market-led licensing approach, techUK's Deputy CEO Antony Walker criticised the move, stating that "copyright material cannot be used for AI development and training without permission" under the current framework, which he argued would push AI model training to the US. === Creative sector and political opposition to text and data mining === In March 2026, the House of Lords Communications and Digital Committee published a report, AI, Copyright and the Creative Industries, which concluded that the creative industries face "a clear and present danger from generative AI" and that it would be "a very poor bet" for the government to weaken copyright protections to attract AI investment. The Committee noted that the creative industries contributed £124 billion to the UK economy in 2023 and employed 2.4 million people, compared to the AI sector's £12 billion GVA and 86,000 employees in 2024. The Committee called on the government to develop a "licensing-first" regime underpinned by mandatory transparency requirements, and to rule out any new commercial TDM exception with an opt-out model. Tra
IPO underpricing algorithm
IPO underpricing is the increase in stock value from the initial offering price to the first-day closing price. Many believe that underpriced IPOs leave money on the table for corporations, but some believe that underpricing is inevitable. Investors state that underpricing signals high interest to the market which increases the demand. On the other hand, overpriced stocks will drop long-term as the price stabilizes so underpricing may keep the issuers safe from investor litigation. == IPO underpricing algorithms == Underwriters and investors and corporations going for an initial public offering (IPO), issuers, are interested in their market value. There is always tension that results since the underwriters want to keep the price low while the companies want a high IPO price. Underpricing may also be caused by investor over-reaction causing spikes on the initial days of trading. The IPO pricing process is similar to pricing new and unique products where there is sparse data on market demand, product acceptance, or competitive response. Thus it is difficult to determine a clear price which is compounded by the different goals issuers and investors have. The problem with developing algorithms to determine underpricing is dealing with noisy, complex, and unordered data sets. Additionally, people, environment, and various environmental conditions introduce irregularities in the data. To resolve these issues, researchers have found various techniques from artificial intelligence that normalizes the data. == Evolutionary models == Evolutionary programming is often paired with other algorithms e.g. artificial neural networks to improve the robustness, reliability, and adaptability. Evolutionary models reduce error rates by allowing the numerical values to change within the fixed structure of the program. Designers provide their algorithms the variables, they then provide training data to help the program generate rules defined in the input space that make a prediction in the output variable space. In this approach, the solution is made an individual and the population is made of alternatives. However, the outliers cause the individuals to act unexpectedly as they try to create rules to explain the whole set. === Rule-based system === For example, Quintana first abstracts a model with 7 major variables. The rules evolved from the Evolutionary Computation system developed at Michigan and Pittsburgh: Underwriter prestige – Is the underwriter prestigious in role of lead manager? 1 for true, 0 otherwise. Price range width – The width of the non-binding reference price range offered to potential customers during the roadshow. This width can be interpreted as a sign of uncertainty regarding the real value of the company and a therefore, as a factor that could influence the initial return. Price adjustment – The difference between the final offer price and the price range width. It can be viewed as uncertainty if the adjustment is outside the previous price range. Offering price – The final offer price of the IPO Retained stock – Ratio of number of shares sold at the IPO divided by post-offering number of shares minus the number of shares sold at the IPO. Offering size – Logarithm of the offering size in millions of dollars excluding the over-allotment option Technology – Is this a technology company? 1 for true, 0 otherwise. Quintana uses these factors as signals that investors focus on. The algorithm his team explains shows how a prediction with a high-degree of confidence is possible with just a subset of the data. === Two-layered evolutionary forecasting === Luque approaches the problem with outliers by performing linear regressions over the set of data points (input, output). The algorithm deals with the data by allocating regions for noisy data. The scheme has the advantage of isolating noisy patterns which reduces the effect outliers have on the rule-generation system. The algorithm can come back later to understand if the isolated data sets influence the general data. Finally, the worst results from the algorithm outperformed all other algorithms' predictive abilities. == Agent-based modelling == Currently, many of the algorithms assume homogeneous and rational behavior among investors. However, there's an approach alternative to financial modeling, and it's called agent-based modelling (ABM). ABM uses different autonomous agents whose behavior evolves endogenously which lead to complicated system dynamics that are sometimes impossible to predict from the properties of individual agents. ABM is starting to be applied to computational finance. Though, for ABM to be more accurate, better models for rule-generation need to be developed.
Peanut App
Peanut, a product of Peanut App Ltd. is an online community for women who are planning to become pregnant, women who are pregnant, women who have had children, and women who are experiencing menopause. Profiles of potential friends are displayed to users who can swipe up to show intent to connect. Users can also connect via discussion threads, groups, and live audio conversations. The app allows users to select their stage of life (trying to conceive, pregnancy, motherhood, or menopause), so as to meet women at a similar life stage, and to discover relevant content. Peanut was founded by Michelle Kennedy shortly after she left Bumble, a female-first dating app. She has described Peanut as, "the app she wishes she had when she first became a mother". == History == Peanut was initially launched in 2017 for mothers and pregnant women. The app focuses on helping users find others with shared interests, such as spoken languages, occupations, and hobbies. It also displays a woman's life stage, such as the age of her children, or the stage of pregnancy. In 2018, it launched a community discussion feature that intended to give women an "alternative to other social platforms". In 2019, it started to serve women who are trying to conceive. In April 2021, it integrated live audio, in response to the COVID-19 pandemic, and the restrictions around in-person socializing. in September 2021, it started to include women who are navigating perimenopause, menopause, and postmenopausal. Although it had initially catered for younger women navigating into new families, a large number of users had undergone surgically or chemically induced menopause due to medical conditions. In July 2021, Peanut launched an investment micro fund, Peanut StartHER, focused on investing in women-owned businesses, as well as other historically excluded founders. == Operation == The Peanut app is a social network exclusively for women, focusing on topics of pregnancy, motherhood, fertility, and menopause. It is available on iOS and Android devices. Users must prove their identity, in keeping with the primary function of in-app safety, and then they can create a profile to interact with other users. For pregnant users, the “Bump Buddies” feature helps connect them with other Peanut users who have a similar due date, which aimed to help expecting mothers combat loneliness during the COVID-19 pandemic. Peanut users also have the option to join “Groups” ‒ sub-sections of users focused on specific topics, including (but not limited to) location, life stage, pregnancy due date, and interests or hobbies. The live voice chat feature “Pods”, enables Peanut users to socialize without the pressure of photos or video chat. It offers features such as a muted audience of listeners who need to virtually raise their hand to speak, emoji reactions, and hosts who can moderate the conversations and invite people to speak.
Shakey the robot
Shakey the Robot was the first general-purpose mobile robot able to reason about its own actions. While other robots would have to be instructed on each individual step of completing a larger task, Shakey could analyze commands and break them down into basic chunks by itself. Due to its nature, the project combined research in robotics, computer vision, and natural language processing. Because of this, it was the first project that melded logical reasoning and physical action. Shakey was developed at the Artificial Intelligence Center of Stanford Research Institute (now called SRI International). Some of the most notable results of the project include the A search algorithm, the Hough transform, and the visibility graph method. == History == Shakey was developed from approximately 1966 through 1972 with Charles Rosen, Nils Nilsson and Peter Hart as project managers. Other major contributors included Alfred Brain, Sven Wahlstrom, Bertram Raphael, Richard Duda, Richard Fikes, Thomas Garvey, Helen Chan Wolf and Michael Wilber. The project was funded by the Defense Advanced Research Projects Agency (DARPA) based on a SRI proposal submitted in April 1964 for research in "Intelligent Automata", later "Intelligent Automata to Reconnaissance". It was originally designed to have two retractable arms. Now retired from active duty, Shakey is currently on view in a glass display case at the Computer History Museum in Mountain View, California. The project inspired numerous other robotics projects, most notably the Centibots. == Software == The robot's programming was primarily done in LISP. The Stanford Research Institute Problem Solver (STRIPS) planner it used was conceived as the main planning component for the software it utilized. As the first robot that was a logical, goal-based agent, Shakey experienced a limited world. A version of Shakey's world could contain a number of rooms connected by corridors, with doors and light switches available for the robot to interact with. Shakey had a short list of available actions within its planner. These actions involved traveling from one location to another, turning the light switches on and off, opening and closing the doors, climbing up and down from rigid objects, and pushing movable objects around. The STRIPS automated planner could devise a plan to enact all the available actions, even though Shakey himself did not have the capability to execute all the actions within the plan personally. An example mission for Shakey might be something like, an operator types the command "push the block off the platform" at a computer console. Shakey looks around, identifies a platform with a block on it, and locates a ramp in order to reach the platform. Shakey then pushes the ramp over to the platform, rolls up the ramp onto the platform, and pushes the block off the platform. == Hardware == Physically, the robot was particularly tall, and had an antenna for a radio link, sonar range finders, a television camera, on-board processors, and collision detection sensors ("bump detectors"). The robot's tall stature and tendency to shake resulted in its name: We worked for a month trying to find a good name for it, ranging from Greek names to whatnot, and then one of us said, 'Hey, it shakes like hell and moves around, let’s just call it Shakey.' == Research results == The development of Shakey provided far-reaching impact on the fields of robotics and artificial intelligence, as well as computer science in general. Some of the more notable results include the development of the A search algorithm, which is widely used in pathfinding and graph traversal, the process of plotting an efficiently traversable path between points; the Hough transform, which is a feature extraction technique used in image analysis, computer vision, and digital image processing; and the visibility graph method for finding Euclidean shortest paths among obstacles in the plane. == Media and awards == In 1969 the SRI published "SHAKEY: Experimentation in Robot Learning and Planning", a 24-minute video. The project then received media attention. This included an article in the New York Times on April 10, 1969. In 1970, Life referred to Shakey as the "first electronic person"; and in November 1970 National Geographic Magazine covered Shakey and the future of computers. The Association for the Advancement of Artificial Intelligence's AI Video Competition's awards are named "Shakeys" because of the significant impact of the 1969 video. Shakey was inducted into Carnegie Mellon University's Robot Hall of Fame in 2004 alongside such notables as ASIMO and C-3PO. Shakey has been honored with an IEEE Milestone in Electrical Engineering and Computing. Shakey was showcased in the BBC's Towards Tomorrow: Robot (1967) documentary.