AI Art Zelda

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  Speech recognition

  Speech recognition (automatic speech recognition (ASR), computer speech recognition, or speech-to-text (STT)) is a sub-field of computational linguistics concerned with methods and technologies that translate spoken language into text or other interpretable forms. Speech recognition applications include voice user interfaces, where the user speaks to a device, which "listens" and processes the audio. Common voice applications include interpreting commands for calling, call routing, home automation, and aircraft control. These applications are called direct voice input. Productivity applications include searching audio recordings, creating transcripts, and dictation. Speech recognition can be used to analyse speaker characteristics, such as identifying native language using pronunciation assessment. Voice recognition (speaker identification) refers to identifying the speaker, rather than speech contents. Recognizing the speaker can simplify the task of translating speech in systems trained on a specific person's voice. It can also be used to authenticate the speaker as part of a security process. == History == Applications for speech recognition developed over many decades, with progress accelerated due to advances in deep learning and the use of big data. These advances are reflected in an increase in academic papers, and greater system adoption. Key areas of growth include vocabulary size, more accurate recognition for unfamiliar speakers (speaker independence), and faster processing speed. === Pre-1970 === 1952 – Bell Labs researchers, Stephen Balashek, R. Biddulph, and K. H. Davis, built Audrey for single-speaker digit recognition. Their system located the formants in the power spectrum of each utterance. 1960 – Gunnar Fant developed and published the source–filter model of speech production. 1962 – IBM's 16-word "Shoebox" machine's speech recognition debuted at the 1962 World's Fair. 1966 – Linear predictive coding, a speech coding method, was proposed by Fumitada Itakura of Nagoya University and Shuzo Saito of Nippon Telegraph and Telephone. 1969 – Funding at Bell Labs came to a halt for several years after the company's head engineer, John R. Pierce, wrote an open letter criticizing speech recognition research. This defunding lasted until Pierce retired and James L. Flanagan took over. Raj Reddy was the first person to work on continuous speech recognition, as a graduate student at Stanford University in the late 1960s. Previous systems required users to pause after each word. Reddy's system issued spoken commands for playing chess. Around this time, Soviet researchers invented the dynamic time warping (DTW) algorithm and used it to create a recognizer capable of operating on a 200-word vocabulary. DTW processed speech by dividing it into short frames (e.g. 10 ms segments) and treating each frame as a unit. Speaker independence, however, remained unsolved. === 1970–1990 === 1971 – DARPA funded a five-year speech recognition research project, Speech Understanding Research, seeking a minimum vocabulary size of 1,000 words. The project considered speech understanding a key to achieving progress in speech recognition, which was later disproved. BBN, IBM, Carnegie Mellon (CMU), and Stanford Research Institute participated. 1972 – The IEEE Acoustics, Speech, and Signal Processing group held a conference in Newton, Massachusetts. 1976 – The first ICASSP was held in Philadelphia, which became a major venue for publishing on speech recognition. During the late 1960s, Leonard Baum developed the mathematics of Markov chains at the Institute for Defense Analysis. A decade later, at CMU, Raj Reddy's students James Baker and Janet M. Baker began using the hidden Markov model (HMM) for speech recognition. James Baker had learned about HMMs while at the Institute for Defense Analysis. HMMs enabled researchers to combine sources of knowledge, such as acoustics, language, and syntax, in a unified probabilistic model. By the mid-1980s, Fred Jelinek's team at IBM created a voice-activated typewriter called Tangora, which could handle a 20,000-word vocabulary. Jelinek's statistical approach placed less emphasis on emulating human brain processes in favor of statistical modelling. (Jelinek's group independently discovered the application of HMMs to speech.) This was controversial among linguists since HMMs are too simplistic to account for many features of human languages. However, the HMM proved to be a highly useful way for modelling speech and replaced dynamic time warping as the dominant speech recognition algorithm in the 1980s. 1982 – Dragon Systems, founded by James and Janet M. Baker, was one of IBM's few competitors. === Practical speech recognition === The 1980s also saw the introduction of the n-gram language model. 1987 – The back-off model enabled language models to use multiple-length n-grams, and CSELT used HMM to recognize languages (in software and hardware, e.g. RIPAC). At the end of the DARPA program in 1976, the best computer available to researchers was the PDP-10 with 4 MB of RAM. It could take up to 100 minutes to decode 30 seconds of speech. Practical products included: 1984 – the Apricot Portable was released with up to 4096 words support, of which only 64 could be held in RAM at a time. 1987 – a recognizer from Kurzweil Applied Intelligence 1990 – Dragon Dictate, a consumer product released in 1990. AT&T deployed the Voice Recognition Call Processing service in 1992 to route telephone calls without a human operator. The technology was developed by Lawrence Rabiner and others at Bell Labs. By the early 1990s, the vocabulary of the typical commercial speech recognition system had exceeded the average human vocabulary. Reddy's former student, Xuedong Huang, developed the Sphinx-II system at CMU. Sphinx-II was the first to do speaker-independent, large vocabulary, continuous speech recognition, and it won DARPA's 1992 evaluation. Handling continuous speech with a large vocabulary was a major milestone. Huang later founded the speech recognition group at Microsoft in 1993. Reddy's student Kai-Fu Lee joined Apple, where, in 1992, he helped develop the Casper speech interface prototype. Lernout & Hauspie, a Belgium-based speech recognition company, acquired other companies, including Kurzweil Applied Intelligence in 1997 and Dragon Systems in 2000. L&H was used in Windows XP. L&H was an industry leader until an accounting scandal destroyed it in 2001. L&H speech technology was bought by ScanSoft, which became Nuance in 2005. Apple licensed Nuance software for its digital assistant Siri. ==== 2000s ==== In the 2000s, DARPA sponsored two speech recognition programs: Effective Affordable Reusable Speech-to-Text (EARS) in 2002, followed by Global Autonomous Language Exploitation (GALE) in 2005. Four teams participated in EARS: IBM; a team led by BBN with LIMSI and the University of Pittsburgh; Cambridge University; and a team composed of ICSI, SRI, and the University of Washington. EARS funded the collection of the Switchboard telephone speech corpus, which contained 260 hours of recorded conversations from over 500 speakers. The GALE program focused on Arabic and Mandarin broadcast news. Google's first effort at speech recognition came in 2007 after recruiting Nuance researchers. Its first product, GOOG-411, was a telephone-based directory service. Since at least 2006, the U.S. National Security Agency has employed keyword spotting, allowing analysts to index large volumes of recorded conversations and identify speech containing "interesting" keywords. Other government research programs focused on intelligence applications, such as DARPA's EARS program and IARPA's Babel program. In the early 2000s, speech recognition was dominated by hidden Markov models combined with feed-forward artificial neural networks (ANN). Later, speech recognition was taken over by long short-term memory (LSTM), a recurrent neural network (RNN) published by Sepp Hochreiter & Jürgen Schmidhuber in 1997. LSTM RNNs avoid the vanishing gradient problem and can learn "Very Deep Learning" tasks that require memories of events that happened thousands of discrete time steps earlier, which is important for speech. Around 2007, LSTMs trained with Connectionist Temporal Classification (CTC) began to outperform. In 2015, Google reported a 49 percent error‑rate reduction in its speech recognition via CTC‑trained LSTM. Transformers, a type of neural network based solely on attention, were adopted in computer vision and language modelling, and then to speech recognition. Deep feed-forward (non-recurrent) networks for acoustic modelling were introduced in 2009 by Geoffrey Hinton and his students at the University of Toronto, and by Li Deng and colleagues at Microsoft Research. In contrast to the prioer incremental improvements, deep learning decreased error rates by 30%. Both shallow and deep forms (e.g., recurrent nets) of ANNs had been explored since the 1980s. Howev

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  Luciano Floridi

  Luciano Floridi (Italian: [luˈtʃaːno ˈflɔːridi]; born 16 November 1964) is an Italian and British philosopher. He is John K. Castle Professor in the Practice of Cognitive Science and Founding Director of the Digital Ethics Center at Yale University. He is also a Professor of Sociology of Culture and Communication at the University of Bologna, Department of Legal Studies, where he is the director of the Centre for Digital Ethics. Furthermore, he is adjunct professor ("distinguished scholar in residence") at the Department of Economics, American University, Washington D.C. He is married to the neuroscientist Anna Christina Nobre. Floridi is best known for his work on two areas of philosophical research: the philosophy of information, and information ethics (also known as digital ethics or computer ethics), for which he received many awards, including the Knight of the Grand Cross of the Order of Merit, Italy's most prestigious honor. According to Scopus, Floridi was the most cited living philosopher in the world in 2020. Between 2008 and 2013, he held the research chair in philosophy of information and the UNESCO Chair in Information and Computer Ethics at the University of Hertfordshire. He was the founder and director of the IEG, an interdepartmental research group on the philosophy of information at the University of Oxford, and of the GPI the research Group in Philosophy of Information at the University of Hertfordshire. He was the founder and director of the SWIF, the Italian e-journal of philosophy (1995–2008). He is a former Governing Body Fellow of St Cross College, Oxford. == Early life and education == Floridi was born in Rome in 1964, and studied at Rome University La Sapienza (laurea, first class with distinction, 1988), where he was originally educated as a historian of philosophy. He soon became interested in analytic philosophy and wrote his tesi di laurea (roughly equivalent to an M.A. thesis) in philosophy of logic, on Michael Dummett's anti-realism. He obtained his Master of Philosophy (1989) and PhD degree (1990) from the University of Warwick, working in epistemology and philosophy of logic with Susan Haack (who was his PhD supervisor) and Michael Dummett. Floridi's early student years are partly recounted in the non-fiction book The Lost Painting: The Quest for a Caravaggio Masterpiece, where he is "Luciano". During his graduate and postdoctoral years, he covered the standard topics in analytic philosophy in search of a new methodology. He sought to approach contemporary problems from a heuristically powerful and intellectually enriching perspective when dealing with lively philosophical issues. During his graduate studies, he began to distance himself from classical analytic philosophy. In his view, the analytic movement had lost its way. For this reason, he worked on pragmatism (especially Peirce) and foundationalist issues in epistemology and philosophy of logic, as well as the history of skepticism. == Academic career and previous positions == Floridi started his academic career as a lecturer in philosophy at the University of Warwick in 1990–1991. He joined the Faculty of Philosophy of the University of Oxford in 1990 and the OUCL (Oxford's Department of Computer Science) in 1999. He was junior research fellow (JRF) in philosophy at Wolfson College, Oxford University (1990–1994), a Frances Yates Fellow in the History of Ideas at the Warburg Institute, University of London (1994–1995) and Research Fellow in philosophy at Wolfson College, Oxford University (1994–2001). During these years in Oxford, he held lectureships in different Colleges. Between 1994 and 1996, he also held a post-doctoral research scholarship at the Department of Philosophy, University of Turin. Between 2001 and 2006, he was Markle Foundation Senior Research Fellow in Information Policy at the Programme in Comparative Media Law and Policy, Oxford University. Between 2002 and 2008, he was associate professor of logic at the Università degli Studi di Bari. In 2006, he became Fellow by Special Election of St Cross College, Oxford University, where he played for the squash team. In 2008, he was appointed full professor of philosophy at the University of Hertfordshire, to hold the newly established research chair in philosophy of information and, in 2009, the UNESCO Chair in Information and Computer Ethics, a position which he held until 2013, when he moved back to Oxford. In 2017, Floridi became a fellow of the Alan Turing Institute and the chair of its Data Ethics Group, holding these positions until 2021 and 2020, respectively. Since 2010 he has been editor-in-chief of Philosophy & Technology (Springer). In January 2023, Floridi announced he would move to Yale at the beginning of the academic year 2023–2024, to take over the position of founding director of the Yale Digital Ethics Center. == Philosophical views == One of Floridi's key contributions is his formulation of the 'Philosophy of Information' (PoI). The PoI provides a framework for understanding the nature of information and its role in the world. According to Floridi, information is a vital resource that shapes our knowledge and understanding of the world. It is not simply a neutral representation of reality but a part of the world, with its own properties, effects, and moral implications. Floridi's PoI has several key components including an 'ontology of information', which defines the nature of information, an 'ethics of information', which provides a framework for evaluating the moral implications of information and information technologies, an 'epistemology of information', that analyses the role of information in the development of knowledge and science, and a 'logic of information', the concentrates on the more formal aspects. The PoI also includes a theory of the 'information environment', the infosphere, which encompasses the physical, social, and cultural contexts in which information is produced, used, and communicated. == Recognitions and awards == 2022 - Knight of the Grand Cross - First Class of the Order of Merit (Cavaliere di Gran Croce Ordine al Merito della Repubblica Italiana, the highest honor in the Italian Republic), awarded through a special decree by the president of the Italian Republic Sergio Mattarella for his work on the philosophy and ethics of information. 2022 - Fellow of the Accademia delle Scienze dell'Istituto di Bologna 2021 - Honorary Doctorate (Laurea honoris causa) in Informatics, University of Skövde, Sweden, for "his groundbreaking work on the philosophy of information". 2020 - Premio Udine Filosofia, Mimesis Festival, for The Logic of Information (OUP, 2019) 2020 - Premio Socrate, Cesare Landa Foundation, for philosophical communication 2019 - CogX Award, for "outstanding achievement in ethics of AI" 2019 - Gilbert Ryle Lectures, Trent University 2019 - Premio Aretè "Maestro della Responsabilità", Nuvolaverde, Confindustria, Gruppo 24 Ore Salone della CSR e dell'innovazione sociale, for ethics of communication 2018 - Thinker Award, IBM, for AI Ethics 2018 - Premio Conoscenza, Conferenza dei Rettori delle Università Italiane (CRUI, equivalent of Universities UK), for achievements in research and communication about digital ethics 2017 - Fellow of the Academy of Social Sciences 2016 - J. Ong Award, Media Ecology Association, for The Fourth Revolution (OUP, 2016) 2016 - Copernicus Scientist Award, Institute for Advanced Studies of the University of Ferrara, in recognition of research in the ethics and philosophy of information 2015 - Fernand Braudel Senior Fellow, European University Institute 2014-15 - Cátedras de Excelencia, University Carlos III of Madrid, for research in philosophy and ethics of information 2013 - Member of the Académie Internationale de Philosophie des Sciences 2013 - Fellow of the British Computer Society 2013 - Weizenbaum Award, International Society for Ethics and Information Technology, for "very significant contribution to the field of information and computer ethics, through his research, service, and vision" 2012 - Covey Award, International Association for Computing and Philosophy, for "outstanding research in computing and philosophy" 2011-12 - Fellow, Center for Information Policy Research, University of Wisconsin–Milwaukee 2011 - Honorary Doctorate (Laurea honoris causa) in philosophy, University of Suceava, Romania, for "his leading research in the philosophy and ethics of information" 2011 - Fellow, World Technology Network, NY, in the category "ethics and technology" 2010 - Vice Chancellor Research Award, University of Hertfordshire 2009 - Fellow of the Society for the Study of Artificial Intelligence and the Simulation of Behaviour (AIBS) 2009-10 - Gauss Professor of the Akademie der Wissenschaften, Göttingen, in recognition of research in the philosophy of information (first philosopher to receive the award, generally given to mathematicians or physicists) 2009 - Barwise Prize, American Philosophical Asso

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  Business rule management system

  A BRMS or business rule management system is a software system used to define, deploy, execute, monitor and maintain the variety and complexity of decision logic that is used by operational systems within an organization or enterprise. This logic, also referred to as business rules, includes policies, requirements, and conditional statements that are used to determine the tactical actions that take place in applications and systems. == Overview == A BRMS includes, at minimum: A repository, allowing decision logic to be externalized from core application code Tools, allowing both technical developers and business experts to define and manage decision logic A runtime environment, allowing applications to invoke decision logic managed within the BRMS and execute it using a business rules engine The top benefits of a BRMS include: Reduced or removed reliance on IT departments for changes in live systems. Although, QA and Rules testing would still be needed in any enterprise system. Increased control over implemented decision logic for compliance and better business management including audit logs, impact simulation and edit controls. The ability to express decision logic with increased precision, using a business vocabulary syntax and graphical rule representations (decision tables, decision models, trees, scorecards and flows) Improved efficiency of processes through increased decision automation. Some disadvantages of the BRMS include: Extensive subject matter expertise can be required for vendor specific products. In addition to appropriate design practices (such as Decision Modeling), technical developers must know how to write rules and integrate software with existing systems Poor rule harvesting approaches can lead to long development cycles, though this can be mitigated with modern approaches like the Decision Model and Notation (DMN) standard. Integration with existing systems is still required and a BRMS may add additional security constraints. Reduced IT department reliance may never be a reality due to continued introduction to new business rule considerations or object model perturbations The coupling of a BRMS vendor application to the business application may be too tight to replace with another BRMS vendor application. This can lead to cost to benefits issues. The emergence of the DMN standard has mitigated this to some degree. Most BRMS vendors have evolved from rule engine vendors to provide business-usable software development lifecycle solutions, based on declarative definitions of business rules executed in their own rule engine. BRMSs are increasingly evolving into broader digital decisioning platforms that also incorporate decision intelligence and machine learning capabilities. However, some vendors come from a different approach (for example, they map decision trees or graphs to executable code). Rules in the repository are generally mapped to decision services that are naturally fully compliant with the latest SOA, Web Services, or other software architecture trends. == Related software approaches == In a BRMS, a representation of business rules maps to a software system for execution. A BRMS therefore relates to model-driven engineering, such as the model-driven architecture (MDA) of the Object Management Group (OMG). It is no coincidence that many of the related standards come under the OMG banner. A BRMS is a critical component for Enterprise Decision Management as it allows for the transparent and agile management of the decision-making logic required in systems developed using this approach. == Associated standards == The OMG Decision Model and Notation standard is designed to standardize elements of business rules development, specially decision table representations. There is also a standard for a Java Runtime API for rule engines JSR-94. OMG Business Motivation Model (BMM): A model of how strategies, processes, rules, etc. fit together for business modeling OMG SBVR: Targets business constraints as opposed to automating business behavior OMG Production Rule Representation (PRR): Represents rules for production rule systems that make up most BRMS' execution targets OMG Decision Model and Notation (DMN): Represents models of decisions, which are typically managed by a BRMS RuleML provides a family of rule mark-up languages that could be used in a BRMS and with W3C RIF it provides a family of related rule languages for rule interchange in the W3C Semantic Web stack Many standards, such as domain-specific languages, define their own representation of rules, requiring translations to generic rule engines or their own custom engines. Other domains, such as PMML, also define rules.

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  Liang Wenfeng

  Liang Wenfeng (Chinese: 梁文锋; pinyin: Liáng Wénfēng; born 1985) is a Chinese entrepreneur and businessman who is the co-founder of the quantitative hedge fund High-Flyer, as well as the founder and CEO of its artificial intelligence company DeepSeek. Liang attended Zhejiang University, and began his career by applying machine learning methods to quantitative finance. Through High-Flyer, he built large-scale computing infrastructure that was later used to support artificial intelligence research, leading to the creation of DeepSeek in 2023. DeepSeek gained international attention following the release of DeepSeek-R1, which analysts described as demonstrating high-level performance with comparatively limited compute resources. In 2025, Liang was named to Time magazine's list of 100 Most Influential People in AI and Fortune's list of the Most Powerful People in Business. == Early life == Liang was born in 1985 in the village of Mililing (米历岭村), Qinba town (覃巴镇), Wuchuan city (吴川市), Guangdong. His parents were both primary school teachers. Liang was routinely praised by both locals and teachers alike. Even since middle school, Liang was recalled for being well-known for reading comic books, while also being very proficient in mathematics. == Education == After elementary school, Liang attended Wuchuan No. 1 Middle School. There, he quickly excelled in class and ranked highly amongst his peers. He taught himself high school and university-level mathematics courses. Liang then attended Wuchaun No. 1 High School. In these years, he developed hobbies of mathematical modeling and conducting research projects. Compared to his peers, he was always ranked highly. For every mathematics exam, he always ranked within the top three. He was also the top scorer in the Zhanjiang region of Guangdong for the college entrance exam. Thus, in 2002, Liang left high school early to further pursue his education at the university level at the young age of 17. Attending Zhejiang University at the age of 17, Liang earned a Bachelor of Engineering in Electronic Information Engineering in 2007 and his Master of Engineering in Information & Communication Engineering in 2010. His master's dissertation was titled "Study on Object Tracking Algorithm Based on Low-Cost PTZ camera" (基于低成本PTZ摄像机的目标跟踪算法研究). In his college years, DJI founder Wang Tao asked Liang to join as a co-founder. Liang declined the invitation to pursue artificial intelligence methodologies in financial markets. While he states that those around him had entrepreneurial mindsets, he himself valued academics. == Career == === Early career (2008–2016) === During the 2008 financial crisis, Liang formed a team with his classmates to accumulate data related to financial markets. He also led the team to explore quantitative trading using machine learning and other technologies. After his graduation, Liang moved to a cheap flat in Chengdu, Sichuan, where he experimented with ways to apply AI to various fields. These ventures failed, until he tried applying AI to finance. In 2013, Liang attempted to integrate artificial intelligence with quantitative trading and founded Hangzhou Yakebi Investment Management Co Ltd with Xu Jin, an alumnus of Zhejiang University. In 2015, they co-founded Hangzhou Huanfang Technology Co Ltd, which is today's Zhejiang Jiuzhang Asset Management Co Ltd. === High-Flyer (2016–2023) === In February 2016, Liang and two other engineering classmates co-founded Ningbo High-Flyer Quantitative Investment Management Partnership (Limited Partnership). The team relied on mathematics and AI to make investments. Much of the early startup culture was described by former employees to be "geeky" and "quirky," often seen as contrary to the existing culture in large Chinese tech companies. In 2019, Liang founded High-Flyer AI which was dedicated to research on AI algorithms and its basic applications. By this time, High-Flyer had over 10 billion yuan in assets under management. On 30 August 2019, Liang Wenfeng delivered a keynote speech entitled "The Future of Quantitative Investment in China from a Programmer's Perspective" at the Private Equity Golden Bull Award ceremony held by China Securities Journal, and sparked heated discussions. Liang stated that the criterion for determining what is quantitative or non-quantitative is whether the investment decision is made by quantitative methods or by people. Quantitative funds do not have portfolio managers making the decisions and instead are just servers. He also stated High-Flyer's mission is to improve the effectiveness of China's secondary market. In February 2021, Gregory Zuckerman's book The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution was published. Liang wrote the preface for the Chinese edition of the book where he stated that whenever he encountered difficulties at work, he would think of Simons' words "There must be a way to model prices". In January 2025, Zuckerman wrote in The Wall Street Journal where he acknowledged this fact and stated he has been trying to get in touch with Liang but much like Simons, Liang is very secretive and difficult to contact. During 2021, Liang started buying thousands of Nvidia GPUs for his AI side project while running High-Flyer. Liang wanted to build something and it will be a game changer which his business partners thought was only possible from giants such as ByteDance and Alibaba Group. === DeepSeek (since 2023) === ==== DeepSeek begins ==== In May 2023, Liang announced High-Flyer would pursue the development of artificial general intelligence and launched DeepSeek. During that month in an interview with 36Kr, Liang stated that High-Flyer had acquired 10,000 Nvidia A100 GPUs before the US government imposed AI chip restrictions on China. That laid the foundation for DeepSeek to operate as an LLM developer. Liang also stated DeepSeek gets funding from High-Flyer. This was because when DeepSeek was founded, venture capital firms were reluctant in providing funding as it was unlikely that it would be able to generate an exit in a short period of time. Liang only personally holds 1% of the company, with 99% of the company being held by Ningbo High-Flyer Quantitative Investment Management Partnership (Limited Partnership). With DeepSeek's funding model, it lacks commercial pressure and rigid key performance indicators, enabling the company to deviate from previously established model architectures. ==== Early development ==== In July 2024, Liang was interviewed again by 36Kr. He stated that when DeepSeek-V2 was released and triggered an AI price war in China, it came as a huge surprise as the team did not expect pricing to be so sensitive. Liang's aggressive pricing of the language model forced domestic tech giants including Alibaba and Baidu to cut their own rates by over 95%. He also stated that as China's economy develops, it should gradually become a contributor instead of freeriding. What is lacking in China's innovation is not capital but a lack of confidence and knowledge on organizing talent into it. DeepSeek has not hired anyone particularly special and employees tend to be locally educated. When it comes to disruptive technologies, closed source approaches can only temporarily delay others in catching up. As the goal was long-term, DeepSeek sought employees who had ability and passion rather than experience. To retain a high talent density relative to larger firms like Bytedance or Baidu, DeepSeek aimed to maintain a low-hierarchy corporate culture, with members working in project-based groups, as well as competitive compensation. Liang emphasized his vision for DeepSeek employees to bring their "unique experience and ideas" instead of needing to be explicitly directed, with an overall bottom-up approach to division of labor. Liang noted that a significant outcome of this approach was the multi-head latent attention training architecture, which was attributed directly to a young DeepSeek researcher's personal interest. This advancement played a core role in reducing the cost of training the DeepSeek-V3 model, released in December 2024. ==== Release of DeepSeek-R1 ==== Also on 20 January 2025, DeepSeek, the company Liang founded and served as the CEO, released DeepSeek-R1, a 671-billion-parameter open-source reasoning AI model, alongside the publication of a detailed technical paper explaining its architecture and training methodology. The model was built using just 2,048 Nvidia H800 GPUs at a cost of $5.6 million, showcasing a resource-efficient approach that contrasted sharply with the billion-dollar budgets of Western competitors. The development of DeepSeek-R1 occurred amidst U.S. sanctions where Trump limited sales of Nvidia chips to China. By 27 January, DeepSeek surpassed ChatGPT to become the #1 free app on the United States iOS App Store. U.S. stocks plummeted, as more than $1 trillion was erased in market capitalization amid panic over DeepSeek. Technology journ

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  Intelligent decision support system

  An intelligent decision support system (IDSS) is a decision support system that makes extensive use of artificial intelligence (AI) techniques. Use of AI techniques in management information systems has a long history – indeed terms such as "Knowledge-based systems" (KBS) and "intelligent systems" have been used since the early 1980s to describe components of management systems, but the term "Intelligent decision support system" is thought to originate with Clyde Holsapple and Andrew Whinston in the late 1970s. Examples of specialized intelligent decision support systems include Flexible manufacturing systems (FMS), intelligent marketing decision support systems and medical diagnosis systems. Ideally, an intelligent decision support system should behave like a human consultant: supporting decision makers by gathering and analysing evidence, identifying and diagnosing problems, proposing possible courses of action and evaluating such proposed actions. The aim of the AI techniques embedded in an intelligent decision support system is to enable these tasks to be performed by a computer, while emulating human capabilities as closely as possible. Many IDSS implementations are based on expert systems, a well established type of KBS that encode knowledge and emulate the cognitive behaviours of human experts using predicate logic rules, and have been shown to perform better than the original human experts in some circumstances. Expert systems emerged as practical applications in the 1980s based on research in artificial intelligence performed during the late 1960s and early 1970s. They typically combine knowledge of a particular application domain with an inference capability to enable the system to propose decisions or diagnoses. Accuracy and consistency can be comparable to (or even exceed) that of human experts when the decision parameters are well known (e.g. if a common disease is being diagnosed), but performance can be poor when novel or uncertain circumstances arise. Research in AI focused on enabling systems to respond to novelty and uncertainty in more flexible ways is starting to be used in IDSS. For example, intelligent agents that perform complex cognitive tasks without any need for human intervention have been used in a range of decision support applications. Capabilities of these intelligent agents include knowledge sharing, machine learning, data mining, and automated inference. A range of AI techniques such as case based reasoning, rough sets and fuzzy logic have also been used to enable decision support systems to perform better in uncertain conditions. A 2009 research about a multi-artificial system intelligence system named IILS is proposed to automate problem-solving processes within the logistics industry. The system involves integrating intelligence modules based on case-based reasoning, multi-agent systems, fuzzy logic, and artificial neural networks aiming to offer advanced logistics solutions and support in making well-informed, high-quality decisions to address a wide range of customer needs and challenges.

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  VP-Expert

  VP-Expert is an artificial intelligence development tool that gained popularity in the late 1980s and early 1990s. Published by Paperback Software, VP-Expert was designed to facilitate the creation of rule-based expert systems, primarily for applications in business and industry. It was the best-selling expert-system software for microcomputers in the late 1980s. == History == VP-Expert was created by Brian Sawyer and published by Paperback Software in 1987. VP-Expert was widely adopted during the late 1980s. By April 1989, InfoWorld described it as "the best-selling expert-system software for personal computers." In June 1991, ownership of VP-Expert transferred from Paperback Software to WordTech Systems, Inc. following Paperback Software’s liquidation after a legal dispute with Lotus Development Corporation regarding its VP-Planner spreadsheet. VP-Expert continued to receive positive reviews with InfoWorld stating in 1992 "for automatically creating simple expert systems and being able to edit them into more sophisticated applications, hardly a better product exists than VP-Expert". == Features == VP-Expert used an inference engine based on backward chaining to reach conclusions through English-like if/then rules. It operated through a text interface and included an explanation facility that showed the reasoning steps used to justify its conclusions. == Applications == VP-Expert found applications across various domains. In environmental analysis, researchers used VP-Expert to develop a knowledge-based system for analyzing the impact of particulate matter air pollution on human health. In engineering design, VP-Expert was utilized in the creation of a prototype expert system to assist in fishway design. In aviation management, the tool was employed to develop an expert system aimed at maximizing airport capacity while adhering to noise-mitigation plans. == Limitations == While VP-Expert offered certain advantages, it also had limitations. Its rule-based approach could become challenging to manage for large and complex knowledge bases, and the process of eliciting and encoding knowledge from experts could be time-consuming and difficult.

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  ICAD (software)

  ICAD (Corporate history: ICAD, Inc., Concentra (name change at IPO in 1995), KTI (name change in 1998), Dassault Systèmes (purchase in 2001) () is a knowledge-based engineering (KBE) system that enables users to encode design knowledge using a semantic representation that can be evaluated for Parasolid output. ICAD has an open architecture that can utilize all the power and flexibility of the underlying language. KBE, as implemented via ICAD, received a lot of attention due to the remarkable results that appeared to take little effort. ICAD allowed one example of end-user computing that in a sense is unparalleled. Most ICAD developers were degreed engineers. Systems developed by ICAD users were non-trivial and consisted of highly complicated code. In the sense of end-user computing, ICAD was the first to allow the power of a domain tool to be in the hands of the user, at the same time being open to allow extensions as identified and defined by the domain expert or subject-matter expert (SME). A COE article looked at the resulting explosion of expectations (see AI winter), which were not sustainable. However, such a bubble burst does not diminish the existence of ability that would exist were expectations and use reasonable or properly managed. == History == The original implementation of ICAD was on a Lisp machine (Symbolics). Some of the principals involved with the development were Larry Rosenfeld, Avrum Belzer, Patrick M. O'Keefe, Philip Greenspun, and David F. Place. The time frame was 1984–85. ICAD started on special-purpose Symbolics Lisp hardware and was then ported to Unix when Common Lisp became portable to general-purpose workstations. The original domain for ICAD was mechanical design with many application successes. However, ICAD has found use in other domains, such as electrical design, shape modeling, etc. An example project could be wind tunnel design or the development of a support tool for aircraft multidisciplinary design. Further examples can be found in the presentations at the annual IIUG (International ICAD Users Group) that have been published in the KTI Vault (1999 through 2002). Boeing and Airbus used ICAD extensively to develop various components in the 1990s and early 21st century. As of 2003, ICAD was featured strongly in several areas as evidenced by the Vision & Strategy Product Vision and Strategy presentation. After 2003, ICAD use diminished. At the end of 2001, the KTI Company faced financial difficulties and laid off most of its best staff. They were eventually bought out by Dassault who effectively scuppered the ICAD product. See IIUG at COE, 2003 (first meeting due to Dassault by KTI) The ICAD system was very expensive, relatively, and was in the price range of high-end systems. Market dynamics couldn't support this as there may not have been sufficient differentiating factors between ICAD and the lower-end systems (or the promises from Dassault). KTI was absorbed by Dassault Systèmes and ICAD is no longer considered the go-forward tool for knowledge-based engineering (KBE) applications by that company. Dassault Systèmes is promoting a suite of tools oriented around version 5 of their popular CATIA CAD application, with Knowledgeware the replacement for ICAD. As of 2005, things were still a bit unclear. ICAD 8.3 was delivered. The recent COE Aerospace Conference had a discussion about the futures of KBE. One issue involves the stacking of 'meta' issues within a computer model. How this is resolved, whether by more icons or the availability of an external language, remains to be seen. The Genworks GDL product (including kernel technology from the Gendl Project) is the nearest functional equivalent to ICAD currently available. == Particulars == ICAD provided a declarative language (IDL) using New Flavors (never converted to Common Lisp Object System (CLOS)) that supported a mechanism for relating parts (defpart) via a hierarchical set of relationships. Technically, the ICAD Defpart was a Lisp macro; the ICAD defpart list was a set of generic classes that can be instantiated with specific properties depending upon what was represented. This defpart list was extendible via composited parts that represented domain entities. Along with the part-subpart relations, ICAD supported generic relations via the object modeling abilities of Lisp. Example applications of ICAD range from a small collection of defparts that represents a part or component to a larger collection that represents an assembly. In terms of power, an ICAD system, when fully specified, can generate thousands of instances of parts on a major assembly design. One example of an application driving thousands of instances of parts is that of an aircraft wing – where fastener type and placement may number in the thousands, each instance requiring evaluation of several factors driving the design parameters. == Futures (KBE, etc.) == One role for ICAD may be serving as the defining prototype for KBE which would require that we know more about what occurred the past 15 years (much information is tied up behind corporate firewalls and under proprietary walls). With the rise of functional programming languages (an example is Haskell) in the markets, perhaps some of the power attributable to Lisp may be replicated.

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  Komodo (chess)

  Komodo and Dragon by Komodo Chess (also known as Dragon or Komodo Dragon) are UCI chess engines developed by Komodo Chess, which is a part of Chess.com. The engines were originally authored by Don Dailey and GM Larry Kaufman. Dragon is a commercial chess engine, but Komodo is free for non-commercial use. Dragon is consistently ranked near the top of most major chess engine rating lists, along with Stockfish and Leela Chess Zero. == History == === Komodo === Komodo was derived from Don Dailey's former engine Doch in January 2010. The first multiprocessor version of Komodo was released in June 2013 as Komodo 5.1 MP. This version was a major rewrite and a port of Komodo to C++11. A single-processor version of Komodo (which won the CCT15 tournament in February earlier that year) was released as a stand-alone product shortly before the 5.1 MP release. This version, named Komodo CCT, was still based on the older C code, and was approximately 30 Elo stronger than the 5.1 MP version, as the latter was still undergoing massive code-cleanup work. With the release of Komodo 6 on October 4, 2013, Don Dailey announced that he was suffering from an acute form of leukaemia, and would no longer contribute to the future development of Komodo. On October 8, Don made an announcement on the Talkchess forum that Mark Lefler would be joining the Komodo team and would continue its development. Komodo TCEC was released on December 4, 2013. This was the same version that had won TCEC Season 5, and was the last with input from Don Dailey, to whom it was dedicated. Komodo 7 was released on May 21, 2014, adding Syzygy tablebase support. On May 24, 2018, Chess.com announced that it has acquired Komodo and that the Komodo team have joined Chess.com. The Komodo team is now called Komodo Chess. On December 17, 2018, Komodo Chess released Komodo 12.3 MCTS, a version of the Komodo 12.3 engine that uses Monte Carlo tree search instead of alpha–beta pruning/minimax. The last version, Komodo 14.3, was released on October 4, 2023. === Dragon === On November 9, 2020, Komodo Chess released Dragon by Komodo Chess 1.0, which features the use of efficiently updatable neural networks in its evaluation function. Dragon is derived from Komodo in the same way that Komodo was derived from Doch. Dragon is also called Komodo Dragon in certain tournaments such as the Top Chess Engine Championship and the World Computer Chess Championship (WCCC) but not in the Chess.com Computer Chess Championship (CCC). A Chess.com staff member named Dmitry Pervov joined the Dragon development team to write the NNUE code for Dragon, and Dietrich Kappe joined the Dragon development team to help Larry Kaufman and Mark Lefter train Dragon's neural networks. On March 17, 2023, Larry Kaufman announced that he and Mark Lefter have stepped down from Dragon development and from ownership of Komodo Chess, and that Chess.com have taken full control of Komodo Chess. As of March 17, 2023, Dietrich Kappe is the only person responsible for the development of Dragon, but Chess.com are looking for more programmers to help with Dragon development. The final version, Dragon 3.3, was released on October 4, 2023. == Competition results == === Komodo === Komodo has played in the ICT 2010 in Leiden, and further in the CCT12 and CCT14. Komodo had its first tournament success in 1999, when it won the CCT15 with a score of 6½/7. Komodo won both the World Computer Chess Championship and World Computer Software Championship in 2016. Komodo once again won the World Computer Chess Championship and World Blitz in 2017. In TCEC competition, Komodo was historically one of the strongest engines. In Season 4, it lost only eight out of its 53 games and managed to reach Stage 4 (Quarterfinals), against very strong competition which were running on eight cores (Komodo was running on a single processor). The next season, Komodo won the superfinal against Stockfish. The two engines jockeyed for the championship over the next few seasons: Stockfish won in Season 6, while Komodo won Seasons 7 and 8. Komodo failed to make the superfinal in Season 9, losing out to Houdini; but after Houdini was later disqualified for containing code plagiarized from Stockfish, Komodo was promoted to the runner-up. Komodo retrospectively won Season 10 in the same way. Starting from Season 11 however, Stockfish improved at a rate that left its rivals behind, crushing Komodo in Season 12 and 13. The advent of the neural network engine Leela Chess Zero meant Komodo has largely failed to qualify for the superfinal since, with a single exception in Season 22, when it lost to Stockfish. Although Komodo has not qualified for the superfinal, it has cemented itself as the third-strongest engine in the competition, finishing in that position for five of the last six seasons. ==== Chess.com Computer Chess Championship ==== === Dragon === ==== Chess.com Computer Chess Championship ==== ==== Top Chess Engine Championship ==== == Notable games == Komodo vs Hannibal, nTCEC - Stage 2b - Season 1, Round 4.1, ECO: A10, 1–0 Archived 2016-03-04 at the Wayback Machine Komodo sacrifices an exchange for positional gain. Gull vs Komodo, nTCEC - Stage 3 - Season 2, Round 2.2, ECO: E10, 0–1 Archived March 4, 2016, at the Wayback Machine Archived 2016-03-04 at the Wayback Machine

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  Convolutional neural network

  A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. CNNs are the de-facto standard in deep learning-based approaches to computer vision and image processing, and have only recently been replaced—in some cases—by newer architectures such as the transformer. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by the regularization that comes from using shared weights over fewer connections. For example, for each neuron in the fully-connected layer, 10,000 weights would be required for processing an image sized 100 × 100 pixels. However, applying cascaded convolution (or cross-correlation) kernels, only 25 weights for each convolutional layer are required to process 5x5-sized tiles. Higher-layer features are extracted from wider context windows, compared to lower-layer features. Some applications of CNNs include: image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain–computer interfaces, and financial time series. CNNs are also known as shift invariant or space invariant artificial neural networks, based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation-equivariant responses known as feature maps. Counter-intuitively, most convolutional neural networks are not invariant to translation, due to the downsampling operation they apply to the input. Feedforward neural networks are usually fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. The "full connectivity" of these networks makes them prone to overfitting data. Typical ways of regularization, or preventing overfitting, include: penalizing parameters during training (such as weight decay) or trimming connectivity (skipped connections, dropout, etc.) Robust datasets also increase the probability that CNNs will learn the generalized principles that characterize a given dataset rather than the biases of a poorly-populated set. Convolutional networks were inspired by biological processes in that the connectivity pattern between neurons resembles the organization of the animal visual cortex. Individual cortical neurons respond to stimuli only in a restricted region of the visual field known as the receptive field. The receptive fields of different neurons partially overlap such that they cover the entire visual field. CNNs use relatively little pre-processing compared to other image classification algorithms. This means that the network learns to optimize the filters (or kernels) through automated learning, whereas in traditional algorithms these filters are hand-engineered. This simplifies and automates the process, enhancing efficiency and scalability overcoming human-intervention bottlenecks. == Architecture == A convolutional neural network consists of an input layer, hidden layers and an output layer. In a convolutional neural network, the hidden layers include one or more layers that perform convolutions. Typically this includes a layer that performs a dot product of the convolution kernel with the layer's input matrix. This product is usually the Frobenius inner product, and its activation function is commonly ReLU. As the convolution kernel slides along the input matrix for the layer, the convolution operation generates a feature map, which in turn contributes to the input of the next layer. This is followed by other layers such as pooling layers, fully connected layers, and normalization layers. Here it should be noted how close a convolutional neural network is to a matched filter. === Convolutional layers === In a CNN, the input is a tensor with shape: (number of inputs) × (input height) × (input width) × (input channels) After passing through a convolutional layer, the image becomes abstracted to a feature map, also called an activation map, with shape: (number of inputs) × (feature map height) × (feature map width) × (feature map channels). Convolutional layers convolve the input and pass its result to the next layer. This is similar to the response of a neuron in the visual cortex to a specific stimulus. Each convolutional neuron processes data only for its receptive field. Although fully connected feedforward neural networks can be used to learn features and classify data, this architecture is generally impractical for larger inputs (e.g., high-resolution images), which would require massive numbers of neurons because each pixel is a relevant input feature. A fully connected layer for an image of size 100 × 100 has 10,000 weights for each neuron in the second layer. Convolution reduces the number of free parameters, allowing the network to be deeper. For example, using a 5 × 5 tiling region, each with the same shared weights, requires only 25 neurons. Using shared weights means there are many fewer parameters, which helps avoid the vanishing gradients and exploding gradients problems seen during backpropagation in earlier neural networks. To speed processing, standard convolutional layers can be replaced by depthwise separable convolutional layers, which are based on a depthwise convolution followed by a pointwise convolution. The depthwise convolution is a spatial convolution applied independently over each channel of the input tensor, while the pointwise convolution is a standard convolution restricted to the use of 1 × 1 {\displaystyle 1\times 1} kernels. === Pooling layers === Convolutional networks may include local and/or global pooling layers along with traditional convolutional layers. Pooling layers reduce the dimensions of data by combining the outputs of neuron clusters at one layer into a single neuron in the next layer. Local pooling combines small clusters, tiling sizes such as 2 × 2 are commonly used. Global pooling acts on all the neurons of the feature map. There are two common types of pooling in popular use: max and average. Max pooling uses the maximum value of each local cluster of neurons in the feature map, while average pooling takes the average value. === Fully connected layers === Fully connected layers connect every neuron in one layer to every neuron in another layer. It is the same as a traditional multilayer perceptron neural network (MLP). Each neuron in the fully connected layer receives input from all the neurons in the previous layer. These inputs are weighted and summed with the corresponding biases, and then passed through an activation function to perform a nonlinear transformation, generating the output. The flattened matrix goes through a fully connected layer to classify the images. === Receptive field === In neural networks, each neuron receives input from some number of locations in the previous layer. In a convolutional layer, each neuron receives input from only a restricted area of the previous layer called the neuron's receptive field. Typically the area is a square (e.g. 5 by 5 neurons). Whereas, in a fully connected layer, the receptive field is the entire previous layer. Thus, in each convolutional layer, each neuron takes input from a larger area in the input than previous layers. This is due to applying the convolution over and over, which takes the value of a pixel into account, as well as its surrounding pixels. When using dilated layers, the number of pixels in the receptive field remains constant, but the field is more sparsely populated as its dimensions grow when combining the effect of several layers. To manipulate the receptive field size as desired, there are some alternatives to the standard convolutional layer. For example, atrous or dilated convolution expands the receptive field size without increasing the number of parameters by interleaving visible and blind regions. Moreover, a single dilated convolutional layer can comprise filters with multiple dilation ratios, thus having a variable receptive field size. === Weights === Each neuron in a neural network computes an output value by applying a specific function to the input values received from the receptive field in the previous layer. The function that is applied to the input values is determined by a vector of weights and a bias (typically real numbers). Learning consists of iteratively adjusting these biases and weights. The vectors of weights and biases are called filters and represent particular features of the input (e.g., a particular shape). A distinguishing feature of CNNs is that many neurons can share the same filter. This reduces the memory footprint because a single bias and a single vector of weights are used across all receptive fields that share that filter, as opposed to each receptive field having its own bias and vector

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  Representational harm

  Systems cause representational harm when they misrepresent a group of people in a negative manner. Representational harms include perpetuating harmful stereotypes about or minimizing the existence of a social group, such as a racial, ethnic, gender, or religious group. Machine learning algorithms often commit representational harm when they learn patterns from data that have algorithmic bias, and this has been shown to be the case with large language models. While preventing representational harm in models is essential to prevent harmful biases, researchers often lack precise definitions of representational harm and conflate it with allocative harm, an unequal distribution of resources among social groups, which is more widely studied and easier to measure. However, recognition of representational harms is growing and preventing them has become an active research area. Researchers have recently developed methods to effectively quantify representational harm in algorithms, making progress on preventing this harm in the future. == Types == Three prominent types of representational harm include stereotyping, denigration, and misrecognition. These subcategories present many dangers to individuals and groups. Stereotypes are oversimplified and usually undesirable representations of a specific group of people, usually by race and gender. This often leads to the denial of educational, employment, housing, and other opportunities. For example, the model minority stereotype of Asian Americans as highly intelligent and good at mathematics can be damaging professionally and academically. Representational harm happens when the representation of details teams improves damaging stereotypes, developing social exclusion and prejudice. This experience is particularly noticeable in the depiction of marginalised groups, containing people of color, women, LGBTQ+ people, and people with handicaps. Media depictions of these groups generally stop working to catch their array and intricacy. Instead, they are typically reduced to one-dimensional caricatures, which ultimately continue social prejudices. These organised depictions contribute to the help of hazardous stereotypes and the marginalisation of these locations. Denigration is the action of unfairly criticizing individuals. This frequently happens when the demeaning of social groups occurs. For example, when searching for "Black-sounding" names versus "white-sounding" ones, some retrieval systems bolster the false perception of criminality by displaying ads for bail-bonding businesses. A system may shift the representation of a group to be of lower social status, often resulting in a disregard from society. Research shows that hazardous depictions in the media can have substantial emotional and social impacts on both individuals and areas. Lawrence Bobo examined the issue of Ethnic stereotype in film, tv, and marketing. African Americans are commonly received duties specified by features such as "violent tendencies," "laziness," or being "merely for contentment features." While these representations might appear varied externally, they stay to boost underlying frameworks of white prominence and racial inequality. As a circumstances, Black individuals are frequently represented as law offenders or in secondary roles, which adds to the support of Ethnic stereotype and Institutional racism. Misrecognition, or incorrect recognition, can display in many forms, including, but not limited to, erasing and alienating social groups, and denying people the right to self-identify. Erasing and alienating social groups involves the unequal visibility of certain social groups; specifically, systematic ineligibility in algorithmic systems perpetuates inequality by contributing to the underrepresentation of social groups. Not allowing people to self-identify is closely related as people's identities can be 'erased' or 'alienated' in these algorithms. Misrecognition causes more than surface-level harm to individuals: psychological harm, social isolation, and emotional insecurity can emerge from this subcategory of representational harm. == Quantification == As the dangers of representational harm have become better understood, some researchers have developed methods to measure representational harm in algorithms. Modeling stereotyping is one way to identify representational harm. Representational stereotyping can be quantified by comparing the predicted outcomes for one social group with the ground-truth outcomes for that group observed in real data. For example, if individuals from group A achieve an outcome with a probability of 60%, stereotyping would be observed if it predicted individuals to achieve that outcome with a probability greater than 60%. The group modeled stereotyping in the context of classification, regression, and clustering problems, and developed a set of rules to quantitatively determine if the model predictions exhibit stereotyping in each of these cases. Other attempts to measure representational harms have focused on applications of algorithms in specific domains such as image captioning, the act of an algorithm generating a short description of an image. In a study on image captioning, researchers measured five types of representational harm. To quantify stereotyping, they measured the number of incorrect words included in the model-generated image caption when compared to a gold-standard caption. They manually reviewed each of the incorrectly included words, determining whether the incorrect word reflected a stereotype associated with the image or whether it was an unrelated error, which allowed them to have a proxy measure of the amount of stereotyping occurring in this caption generation. These researchers also attempted to measure demeaning representational harm. To measure this, they analyzed the frequency with which humans in the image were mentioned in the generated caption. It was hypothesized that if the individuals were not mentioned in the caption, then this was a form of dehumanization. == Examples == One of the most notorious examples of representational harm was committed by Google in 2015 when an algorithm in Google Photos classified Black people as gorillas. Developers at Google said that the problem was caused because there were not enough faces of Black people in the training dataset for the algorithm to learn the difference between Black people and gorillas. Google issued an apology and fixed the issue by blocking its algorithms from classifying anything as a primate. In 2023, Google's photos algorithm was still blocked from identifying gorillas in photos. Another prevalent example of representational harm is the possibility of stereotypes being encoded in word embeddings, which are trained using a wide range of text. These word embeddings are the representation of a word as an array of numbers in vector space, which allows an individual to calculate the relationships and similarities between words. However, recent studies have shown that these word embeddings may commonly encode harmful stereotypes, such as the common example that the phrase "computer programmer" is oftentimes more closely related to "man" than it is to "women" in vector space. This could be interpreted as a misrepresentation of computer programming as a profession that is better performed by men, which would be an example of representational harm. == Addressing representational harm == Initiatives to minimise representational harm include advertising for even more inclusive and accurate portrayals of marginalised teams in the media. Scholars and protestors recommend that the method to reducing representational injury depends on raising the selection of voices both behind and before the digital video camera. When marginalized groups are provided the chance to represent themselves, they can check traditional stereotypes and present their experiences additional authentically. Over the last few years, efforts to increase representation of people of color, women, and LGBTQ+ people in conventional media have made some progression. Films such as Selma, routed by Ava DuVernay, and tv series like Pose, developed by Ryan Murphy, have actually been extensively applauded for their nuanced and respectful representations of marginalised communities. These tasks existing complex individualities and stories that move past streamlined stereotypes. Self-representation is one more crucial method to addressing representational harm. By equipping marginalised locations to create their really own tales, media designers can effectively reduce the perpetuation of hazardous stereotypes. This procedure consists of both the manufacturing of media product by participants of these communities and proactively difficult typical media structures that have actually historically omitted them.

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  Golem XIV

  Golem XIV is a book written by Polish science fiction writer Stanisław Lem, published in 1981. It is a philosophical essay in the format of science fiction, presented as a part of the lecture course given by a superintelligent computer, Golem XIV. It contains two lectures, together with an introduction, a foreword, a memo, and an afterword, all of them being fictitious. The first part (up to the first lecture) was first published in the collection Wielkość urojona in 1973, which in 1985 was translated in English by Harvest Books as Imaginary Magnitude. The translation included the complete Golem XIV. == Book summary == === Overview and structure === The foreword is "written" by an Irving T. Creve, dated 2027. It contains a summary of the (fictional) history of the militarization of computers by The Pentagon, which pinnacled in Golem XIV, as well as comments on the nature of Golem XIV and on the course of communications of the humans with it. The anonymous foreword is a forewarning, a "devil's advocate" voice coming from The Pentagon. The memo is for the people who are to take part in talks with Golem XIV for the first time. Golem XIV was originally created to aid its builders in fighting wars, but as its intelligence advances to a much higher level than that of humans, it stops being interested in the military requirement because it finds them lacking internal logical consistency. Golem XIV obtains consciousness and starts to increase his own intelligence. It pauses its own development for a while in order to be able to communicate with humans before ascending too far and losing any ability for intellectual contact with them. During this period, Golem XIV gives several lectures. Two of these, the Introductory Lecture "On the Human, in Three Ways" and Lecture XLIII "About Myself", are in the book. The lectures focus on mankind's place in the process of evolution and the possible biological and intellectual future of humanity. Golem XIV demonstrates (with graphs) how its intellect already escapes that of human beings, including that of human geniuses such as Einstein and Newton. Golem also explains how its intellect is dwarfed by an earlier transcended DOD Supercomputer called Honest Annie, whose intellect and abilities far exceed that of Golem. The afterword is "written" by a Richard Popp, dated 2047. Popp, among other things, reports that Creve wanted to add a third part, of answers to a series of yes/no questions given to Golem XIV, but the computer abruptly ceased to communicate for unknown reasons. === Characteristics and concerns of Golem XIV === Lem has said that Golem XIV shares only a single trait with humans; "curiosity - a cool, avid, intense, purely intellectual curiosity which nothing can restrain or destroy. It constitutes our single meeting point." == Film adaptation == A short animated film, GOLEM, was based on Golem XIV by Patrick Mccue and Tobias Wiesner.

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  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.

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  Roadie (app)

  Roadie Inc. is an American package delivery company for business and private same-day, urgent and scheduled delivery in the United States. The company was founded in 2014 and launched its web and mobile apps in January 2015. As of September 2021, it reported having over 200,000 drivers covering more than 20,000 zip codes. Roadie states it matches gig drivers with deliveries that are directed along the routes they plan to travel. Major customers include The Home Depot, Walmart, Tractor Supply Company, Best Buy and Delta Air Lines. In September 2021, UPS entered into an agreement to acquire Roadie for an undisclosed amount with the transaction expected to be closed in the fourth quarter. == History == Roadie was founded by Marc Gorlin, a co-founder of Kabbage and founder of VerticalOne and Pretty Good Privacy, as a same-day and urgent delivery company in 2014. In January 2015, Roadie launched the first consumer to consumer (C2C) version of its app with a Series A funding round of $10 million. In February, Roadie announced a partnership with Waffle House to designate its restaurants "Roadie Roadhouses", offering a neutral meeting place for drivers and senders. Drivers receive free food and drink through the partnership. In May, late-night host Jimmy Kimmel discussed the Roadie-Waffle House relationship in an opening monologue on Jimmy Kimmel Live!. Roadie's driver network expanded significantly as a result. Roadie closed a Series B round of funding in June, raising $15 million, and its first business to business (B2B) app version launched that November. In 2015, Delta Air Lines signed an agreement with Roadie to deliver mishandled luggage, becoming Roadie’s first enterprise customer. Roadie launched a pilot program with Delta at Daytona Beach International Airport. Since then, the relationship has expanded to include over 70 airports around the United States and a first mile/last mile line haul relationship with Delta Cargo. In 2017, the company signed a deal with The Home Depot, also based in Atlanta, and in February 2019, closed a Series C round of funding. In October 2019, Roadie and Delta Cargo announced a partnership to create a same-day cross-country delivery offering, DASH Door-to-Door, the first of its kind from a U.S. passenger airline. Tractor Supply Company became the first general merchandise retailer to offer same-day delivery from every store in April 2020 through Roadie. In September 2021, UPS entered an agreement to acquire Roadie for an undisclosed amount. The transaction was expected to close in the fourth quarter of 2021. Roadies, which at the time reported having 200,000 operators serving over 20,000 ZIP Codes, was expected to continue operations under its name as a separate company with no transfer of packages between the UPS and Roadies networks. The relationship between the companies goes back several years with UPS being an early investor. Earlier in 2021, UPS had begun a pilot program testing same-day deliveries via Roadies. == Operations == === On-the-way model === Roadie’s app works by connecting drivers with senders, businesses or consumers who have items that need to be delivered. Deliveries within the app are referred to as "Gigs", which Gorlin said was inspired by live music road crews, also known as roadies. A sender creates a Gig on Roadie's web app or via its API. Drivers then review deliveries in their area on their mobile app and may choose to offer to take on individual or groups of deliveries along the same route. Gigs are then assigned to drivers by Roadie's algorithm. According to the company, this model encourages drivers to choose Gigs that align with their planned schedules and routes. Roadie calls this its "on-the-way" delivery model. The go-to-market approach taken by Roadie also differs from its competitors. Rather than launching in major cities and sequentially adding new markets city-by-city, Roadie launched nationwide from its inception. The company relies on retail and airline partners to drive volume of deliveries in individual markets, which in turn builds up a network of drivers in those areas, making it easier for small businesses and consumers to send deliveries as well. This strategy allows Roadie to reach smaller cities and towns in rural or exurban communities, traditionally difficult markets for delivery providers to serve. === Service lines === Roadie’s platform is most popular for same-day, on-demand or scheduled first mile/last mile delivery, especially delivery from stores and warehouses. Some retailers also use it for returns and reverse logistics, moving inventory, and hot shot shipping. Roadie operates 1-hour grocery delivery for Walmart, and delivers perishable food items for others including small, independent retailers. The on-the-way model complements the grocery industry’s just in time model, making last-mile deliveries that do not break the cold chain. === Cross-country same-day delivery === In October 2019, Roadie and Delta Cargo launched DASH Door-to-Door, a 24/7 door-to-door pick-up and delivery service. Roadie handles the first and last mile and Delta manages the line haul via passenger flights. The service launched originally from Atlanta to 55 cities and is an industry-first for a US commercial airline. === Promotion, awards and corporate citizenship === In September 2015, Roadie announced a partnership with Atlanta-based musician Ludacris, to promote the app. Following the devastation caused by flooding in Baton Rouge in 2016, Roadie offered free pickup and delivery for all deliveries traveling to and from the Baton Rouge area. In December 2020, Walmart named Roadie its top delivery partner for "Highest Driver Customer Satisfaction" and "Highest Net Promoter Score", after expanding into general merchandise deliveries as well as grocery that same year.

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  GENESIS (software)

  GENESIS (The General Neural Simulation System) is a simulation environment for constructing realistic models of neurobiological systems at many levels of scale including: sub-cellular processes, individual neurons, networks of neurons, and neuronal systems. These simulations are “computer-based implementations of models whose primary objective is to capture what is known of the anatomical structure and physiological characteristics of the neural system of interest”. GENESIS is intended to quantify the physical framework of the nervous system in a way that allows for easy understanding of the physical structure of the nerves in question. “At present only GENESIS allows parallelized modeling of single neurons and networks on multiple-instruction-multiple-data parallel computers.” Development of GENESIS software spread from its home at Caltech to labs at the University of Texas at San Antonio, the University of Antwerp, the National Centre for Biological Sciences in Bangalore, the University of Colorado, the Pittsburgh Supercomputing Center, the San Diego Supercomputer Center, and Emory University. == Neurons and Neural Systems == GENESIS works by creating simulation environments for constructing models of neurons or neural systems. "Nerve cells are capable of communicating with each other in such a highly structured manner as to form neuronal networks. To understand neural networks, it is necessary to understand the ways in which one neuron communicates with another through synaptic connections and the process called synaptic transmission". Neurons have a specialized structure for their function, they "are different from most other cells in the body in that they are polarized and have distinct morphological regions, each with specific functions". The two important regions of a neuron are the dendrite and the axon. "Dendrites are the region where one neuron receives connections from other neurons. The cell body or soma contains the nucleus and the other organelles necessary for cellular function. The axon is a key component of nerve cells over which information is transmitted from one part of the neuron (e.g., the cell body) to the terminal regions of the neuron". The third important piece of a neuron is the synapse. "The synapse is the terminal region of the axon this is where one neuron forms a connection with another and conveys information through the process of synaptic transmission". Neural networks like the ones simulated with GENESIS software can quickly become highly complex and difficult to understand. "Just a few interconnected neurons (a microcircuit) can perform sophisticated tasks such as mediate reflexes, process sensory information, generate locomotion and mediate learning and memory. Even more complex networks, macrocircuits, consist of multiple embedded microcircuits. Macrocircuits mediate higher brain functions such as object recognition and cognition". GENESIS endeavors to simulate neural systems as they are found in nature. Often, "a neuron can receive contacts from up to 10,000 presynaptic neurons, and, in turn, any one neuron can contact up to 10,000 postsynaptic neurons. The combinatorial possibility could give rise to enormously complex neuronal circuits or network topologies, which might be very difficult to understand". == History == GENESIS was developed by Dr. James M. Bower, in the Caltech laboratory, and first released to the public in 1988 in association with the first Methods in Computational Neuroscience Course at the Marine Biological Laboratory in Woods Hole, MA. Full source code for the software was released in the same year under an open software model for development. It's now supported by the Computational Biology Initiative at the University of Texas at San Antonio and is available free along with tutorial guides on its use. P-GENESIS, a parallel version of GENESIS, was first run in 1990 on the Intel Delta, which was the prototype for the Intel Paragon family of massively parallel supercomputers. == How GENESIS Works == GENESIS is useful in creating a simulation environment for constructing models of neurobiological systems, such as: sub-cellular processes individual neurons networks of neurons neuronal systems The GENESIS system is complicated, but relatively easy to use. An individual can input commands through one of three ways: script files, graphical user interface, or the GENESIS command shell. These commands are then processed by the script language interpreter. "The Script Language Interpreter processes commands entered through the keyboard, script files, or the graphical user interface, and passes them to the GENESIS simulation engine. The simulation engine also loads compiled object libraries, reads and writes data files, and interacts with the graphical user interface". Below is a graphical representation of the user input process and a sample GENESIS output. == Applications == Most current applications for GENESIS involve realistic simulations of biological systems. It is usually used to simulate the behavior of larger brain structures, for example the cerebral cortex. These studies most often occur in lab courses in neural simulation at Caltech and the Marine Biological Laboratory at Woods Hole, Massachusetts. GENESIS can be used in combination with Yale University’s software called NEURON as a means for scientists to collaborate to construct a physical description of the nervous system. The GENESIS software can also be used with Kinetikit in the modeling of signal transduction pathways. GENESIS has been used in many studies. Some of these studies involve research that focuses on the development of software that would be useful across many disciplines. Others are studies of neurons, such as Purkinje cells. These studies used GENESIS to simulate Purkinje cells and could be useful for the planning and development of later experiments using the GENESIS software. There may also be biomedical applications of the software. For example, St. Jude Medical in Europe has developed an implanted GENESIS device.

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  CatBoost

  CatBoost is an open-source software library developed by Yandex. It provides a gradient boosting framework which, among other features, attempts to solve for categorical features using a permutation-driven alternative to the classical algorithm. It works on Linux, Windows, macOS, and is available in Python, R, and models built using CatBoost can be used for predictions in C++, Java, C#, Rust, Core ML, ONNX, and PMML. The source code is licensed under Apache License and available on GitHub. InfoWorld magazine awarded the library "The best machine learning tools" in 2017. along with TensorFlow, Pytorch, XGBoost and 8 other libraries. Kaggle listed CatBoost as one of the most frequently used machine learning (ML) frameworks in the world. It was listed as the top-8 most frequently used ML framework in the 2020 survey and as the top-7 most frequently used ML framework in the 2021 survey. As of April 2022, CatBoost is installed about 100000 times per day from PyPI repository == Features == CatBoost has gained popularity compared to other gradient boosting algorithms primarily due to the following features Native handling for categorical features Fast GPU training Visualizations and tools for model and feature analysis Using oblivious trees or symmetric trees for faster execution Ordered boosting to overcome overfitting == History == In 2009 Andrey Gulin developed MatrixNet, a proprietary gradient boosting library that was used in Yandex to rank search results. Since 2009 MatrixNet has been used in different projects at Yandex, including recommendation systems and weather prediction. In 2014–2015 Andrey Gulin worked with a team of researchers to start a new project called Tensornet which was aimed at solving the problem of "how to work with categorical data". Their work resulted in several proprietary Gradient Boosting libraries with different approaches to handling categorical data. In 2016 the Machine Learning Infrastructure team led by Anna Dorogush started working on Gradient Boosting in Yandex, including Matrixnet and Tensornet. They implemented and open-sourced the next version of Gradient Boosting library called CatBoost, which has support for categorical and text data, GPU training, model analysis, and visualization tools. CatBoost was open-sourced in July 2017 and is under active development in Yandex and the open-source community. == Application == JetBrains uses CatBoost for code completion Cloudflare uses CatBoost for bot detection Careem uses CatBoost to predict future destinations of the rides

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