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Artificial intelligence industry in Italy : The roots of AI research in Italy extend back to the 1970s, when Italian scholars began exploring automated reasoning, programming language semantics, and pattern recognition. Researchers such as those involved in early projects at the National Research Council and various un... |
Artificial intelligence industry in Italy : Consiglio Nazionale delle Ricerche (CNR) CINECA Minerva AI (Sapienza University of Rome) Velvet AI (Almawave) Vitruvian-1 (ASC27) Istituto Italiano per l'Intelligenza Artificiale nell'Industria (AI4I) Istituto Italiano di Tecnologia (IIT) Agenzia per la Cybersicurezza Naziona... |
Stochastic semantic analysis : Stochastic semantic analysis is an approach used in computer science as a semantic component of natural language understanding. Stochastic models generally use the definition of segments of words as basic semantic units for the semantic models, and in some cases involve a two layered appr... |
Stochastic semantic analysis : Stochastically-based semantic analysis by Wolfgang Minker, Alex Waibel, Joseph Mariani 1999 ISBN 0-7923-8571-3 == Notes == |
Cloem : Cloem is a company based in Cannes, France, which applies natural language processing (NLP) technologies to assist patent applicants in creating variants of patent claims, called "cloems". According to the company, these "computer-generated claims can be published to keep potential competitors from attempting t... |
Cloem : According to Cloem, dictionaries, ontologies and proprietary claim-drafting algorithms are used to draft alternative claims based on a client's original set of claims. In particular, the original set of claims is subject to various permutations and linguistic manipulations "by considering alternative definition... |
Cloem : Cloem can optionally publish one or more created texts, as electronic publications or as paper-printed publications. These can potentially serve – through a defensive publication – as prior art to prevent another party for obtaining a patent on the subject-matter at stake. In other words, after an initial paten... |
Outline of machine learning : The following outline is provided as an overview of, and topical guide to, machine learning: Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. In 1959, Arthur Samuel d... |
Outline of machine learning : An academic discipline A branch of science An applied science A subfield of computer science A branch of artificial intelligence A subfield of soft computing Application of statistics |
Outline of machine learning : Applications of machine learning Bioinformatics Biomedical informatics Computer vision Customer relationship management Data mining Earth sciences Email filtering Inverted pendulum (balance and equilibrium system) Natural language processing Named Entity Recognition Automatic summarization... |
Outline of machine learning : Graphics processing unit Tensor processing unit Vision processing unit |
Outline of machine learning : Comparison of deep learning software |
Outline of machine learning : List of artificial intelligence projects List of datasets for machine learning research |
Outline of machine learning : History of machine learning Timeline of machine learning |
Outline of machine learning : Machine learning projects: DeepMind Google Brain OpenAI Meta AI Hugging Face |
Outline of machine learning : Alberto Broggi Andrei Knyazev Andrew McCallum Andrew Ng Anuraag Jain Armin B. Cremers Ayanna Howard Barney Pell Ben Goertzel Ben Taskar Bernhard Schölkopf Brian D. Ripley Christopher G. Atkeson Corinna Cortes Demis Hassabis Douglas Lenat Eric Xing Ernst Dickmanns Geoffrey Hinton Hans-Peter... |
Outline of machine learning : Outline of artificial intelligence Outline of computer vision Outline of robotics Accuracy paradox Action model learning Activation function Activity recognition ADALINE Adaptive neuro fuzzy inference system Adaptive resonance theory Additive smoothing Adjusted mutual information AIVA AIXI... |
Outline of machine learning : Trevor Hastie, Robert Tibshirani and Jerome H. Friedman (2001). The Elements of Statistical Learning, Springer. ISBN 0-387-95284-5. Pedro Domingos (September 2015), The Master Algorithm, Basic Books, ISBN 978-0-465-06570-7 Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar (2012). Foundat... |
Outline of machine learning : Data Science: Data to Insights from MIT (machine learning) Popular online course by Andrew Ng, at Coursera. It uses GNU Octave. The course is a free version of Stanford University's actual course taught by Ng, see.stanford.edu/Course/CS229 available for free]. mloss is an academic database... |
Equalized odds : Equalized odds, also referred to as conditional procedure accuracy equality and disparate mistreatment, is a measure of fairness in machine learning. A classifier satisfies this definition if the subjects in the protected and unprotected groups have equal true positive rate and equal false positive rat... |
Equalized odds : Fairness (machine learning) Color blindness (racial classification) == References == |
Multi-document summarization : Multi-document summarization is an automatic procedure aimed at extraction of information from multiple texts written about the same topic. The resulting summary report allows individual users, such as professional information consumers, to quickly familiarize themselves with information ... |
Multi-document summarization : Multi-document summarization creates information reports that are both concise and comprehensive. With different opinions being put together & outlined, every topic is described from multiple perspectives within a single document. While the goal of a brief summary is to simplify informati... |
Multi-document summarization : The multi-document summarization task is more complex than summarizing a single document, even a long one. The difficulty arises from thematic diversity within a large set of documents. A good summarization technology aims to combine the main themes with completeness, readability, and con... |
Multi-document summarization : The multi-document summarization technology is now coming of age - a view supported by a choice of advanced web-based systems that are currently available. ReviewChomp presents summaries of customer reviews for any given product or service. Some products have thousands of online reviews w... |
Multi-document summarization : Günes Erkan; Dragomir R. Radev (1 December 2004). "LexRank: Graph-based Lexical Centrality as Salience in Text Summarization". Journal of Artificial Intelligence Research. 22: 457–479. arXiv:1109.2128. doi:10.1613/JAIR.1523. ISSN 1076-9757. Wikidata Q81312697. Dragomir R. Radev, Hongyan J... |
Multi-document summarization : Automatic summarization Text mining News aggregators |
Multi-document summarization : Document Understanding Conferences Columbia NLP Projects NewsInEssence: Web-based News Summarization ReviewChomp |
Overfitting : In mathematical modeling, overfitting is "the production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit to additional data or predict future observations reliably". An overfitted model is a mathematical model that contains more parameters ... |
Overfitting : In statistics, an inference is drawn from a statistical model, which has been selected via some procedure. Burnham & Anderson, in their much-cited text on model selection, argue that to avoid overfitting, we should adhere to the "Principle of Parsimony". The authors also state the following.: 32–33 Overfi... |
Overfitting : Usually, a learning algorithm is trained using some set of "training data": exemplary situations for which the desired output is known. The goal is that the algorithm will also perform well on predicting the output when fed "validation data" that was not encountered during its training. Overfitting is the... |
Overfitting : Underfitting is the inverse of overfitting, meaning that the statistical model or machine learning algorithm is too simplistic to accurately capture the patterns in the data. A sign of underfitting is that there is a high bias and low variance detected in the current model or algorithm used (the inverse o... |
Overfitting : Benign overfitting describes the phenomenon of a statistical model that seems to generalize well to unseen data, even when it has been fit perfectly on noisy training data (i.e., obtains perfect predictive accuracy on the training set). The phenomenon is of particular interest in deep neural networks, but... |
Overfitting : Leinweber, D. J. (2007). "Stupid data miner tricks". The Journal of Investing. 16: 15–22. doi:10.3905/joi.2007.681820. S2CID 108627390. Tetko, I. V.; Livingstone, D. J.; Luik, A. I. (1995). "Neural network studies. 1. Comparison of Overfitting and Overtraining" (PDF). Journal of Chemical Information and M... |
Overfitting : Christian, Brian; Griffiths, Tom (April 2017), "Chapter 7: Overfitting", Algorithms To Live By: The computer science of human decisions, William Collins, pp. 149–168, ISBN 978-0-00-754799-9 |
Overfitting : The Problem of Overfitting Data – Stony Brook University What is "overfitting," exactly? – Andrew Gelman blog CSE546: Linear Regression Bias / Variance Tradeoff – University of Washington What is Underfitting – IBM |
Voice computing : Voice computing is the discipline that develops hardware or software to process voice inputs. It spans many other fields including human-computer interaction, conversational computing, linguistics, natural language processing, automatic speech recognition, speech synthesis, audio engineering, digital ... |
Voice computing : Voice computing has a rich history. First, scientists like Wolfgang Kempelen started to build speech machines to produce the earliest synthetic speech sounds. This led to further work by Thomas Edison to record audio with dictation machines and play it back in corporate settings. In the 1950s-1960s th... |
Voice computing : A voice computer is assembled hardware and software to process voice inputs. Note that voice computers do not necessarily need a screen, such as in the traditional Amazon Echo. In other embodiments, traditional laptop computers or mobile phones could be used as voice computers. Moreover, there has bec... |
Voice computing : Voice computing software can read/write, record, clean, encrypt/decrypt, playback, transcode, transcribe, compress, publish, featurize, model, and visualize voice files. Here are some popular software packages related to voice computing: |
Voice computing : Voice computing applications span many industries including voice assistants, healthcare, e-Commerce, finance, supply chain, agriculture, text-to-speech, security, marketing, customer support, recruiting, cloud computing, microphones, speakers, and podcasting. Voice technology is projected to grow at ... |
Voice computing : In the United States, the states have varying telephone call recording laws. In some states, it is legal to record a conversation with the consent of only one party, in others the consent of all parties is required. Moreover, COPPA is a significant law to protect minors using the Internet. With an inc... |
Voice computing : There are many research conferences that relate to voice computing. Some of these include: International Conference on Acoustics, Speech, and Signal Processing Interspeech AVEC IEEE Int'l Conf. on Automatic Face and Gesture Recognition ACII2019 The 8th Int'l Conf. on Affective Computing and Intelligen... |
Voice computing : Google Assistant has roughly 2,000 actions as of January 2018. There are over 50,000 Alexa skills worldwide as of September 2018. In June 2017, Google released AudioSet, a large-scale collection of human-labeled 10-second sound clips drawn from YouTube videos. It contains 1,010,480 videos of human spe... |
Voice computing : Speech recognition Natural language processing Voice user interface Audio codec Ubiquitous computing Hands-free computing == References == |
Qwen : Qwen (also called Tongyi Qianwen, Chinese: 通义千问) is a family of large language models developed by Alibaba Cloud. In July 2024, it was ranked as the top Chinese language model in some benchmarks and third globally behind the top models of Anthropic and OpenAI. |
Qwen : Alibaba first launched a beta of Qwen in April 2023 under the name Tongyi Qianwen. The model's architecture was based on the LLM Llama developed by Meta AI. It was publicly released in September 2023 after receiving approval from the Chinese government. In December 2023 it released its 72B and 1.8B models as ope... |
Qwen : Official website Qwen on GitHub Qwen on Hugging Face |
Prompt engineering : Prompt engineering is the process of structuring or crafting an instruction in order to produce the best possible output from a generative artificial intelligence (AI) model. A prompt is natural language text describing the task that an AI should perform. A prompt for a text-to-text language model ... |
Prompt engineering : In 2018, researchers first proposed that all previously separate tasks in natural language processing (NLP) could be cast as a question-answering problem over a context. In addition, they trained a first single, joint, multi-task model that would answer any task-related question like "What is the s... |
Prompt engineering : Multiple distinct prompt engineering techniques have been published. |
Prompt engineering : In 2022, text-to-image models like DALL-E 2, Stable Diffusion, and Midjourney were released to the public. These models take text prompts as input and use them to generate AI-generated images. Text-to-image models typically do not understand grammar and sentence structure in the same way as large l... |
Prompt engineering : Some approaches augment or replace natural language text prompts with non-text input. |
Prompt engineering : Prompt injection is a cybersecurity exploit in which adversaries craft inputs that appear legitimate but are designed to cause unintended behavior in machine learning models, particularly large language models (LLMs). This attack takes advantage of the model's inability to distinguish between devel... |
Prompt engineering : Social engineering (security) == References == |
List of publications in data science : This is a list of publications in data science, generally organized by order of use in a data analysis workflow. See the list of publications in statistics for more research-based and fundamental publications; while this list is more applied, business oriented, and cross-disciplin... |
List of publications in data science : Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) Author: Leo Breiman Publication data: Online version: https://projecteuclid.org/journals/statistical-science/volume-16/issue-3/Statistical-Modeling--The-Two-Cultures-with-comments-and-a/10.1214/ss... |
List of publications in data science : Tidy Data Author: Hadley Wickham Publication data: Online version: https://www.jstatsoft.org/article/view/v059i10/ https://vita.had.co.nz/papers/tidy-data.pdf Description: Describes a framework for data cleaning that is summarized in the quote, "each variable is a column, each obs... |
List of publications in data science : Quantitative Graphics in Statistics: A Brief History Author: James R. Beniger and Dorothy L. Robyn Publication data: Online version: https://www.jstor.org/stable/2683467 Description: Outlines history and evolution of quantitative graphics in statistics, going through spatial organ... |
List of publications in data science : Hidden Technical Debt in Machine Learning Systems Author: D. Sculley, Gary Holy, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, Dan Dennison Publication data: Online version: https://proceedings.neurips.cc/paper_... |
List of publications in data science : The Introductory Statistics Course: A Ptolemaic Curriculum Author: George W. Cobb Publication data: Online version: https://escholarship.org/uc/item/6hb3k0nz Description: This paper argues for a rethinking of how teachers of statistics should structure their introductory statistic... |
List of publications in data science : Papers and tech blogs by companies sharing their work on data science and machine learning in production. |
Ray-Ban Meta : Ray-Ban Meta is a range of smartglasses created by Meta Platforms and EssilorLuxottica. They include two cameras, open-ear speakers, a microphone, and touchpad built into the frame. They are the latest in a line of smartglasses released by major companies including Snap Inc and Google and are designed as... |
Ray-Ban Meta : The partnership between EssilorLuxottica, Ray-Ban's parent company, and Facebook to create the first generation of Ray-Ban Stories was publicly announced on September 20, 2020, by CEO Mark Zuckerberg during the seventh annual Facebook Connect conference. During the keynote video, Zuckerberg described sev... |
Ray-Ban Meta : According to Facebook, the Luxottica team re-engineered the components of the glasses to fit technology such as: a set of micro-speakers, a three-microphone audio array, an optimized Snapdragon processor, a capacitive touchpad, and a battery. As the glasses are very small, their size caused the engineers... |
Ray-Ban Meta : On October 17, 2023, Meta and Ray-ban released the second generation smart glasses referred to as Meta Smart Glasses. "Meta Stories" refers to the first generation of smart glasses produced by Meta (formerly Facebook), called Ray-Ban Stories, which primarily focused on capturing photos and videos for soc... |
Ray-Ban Meta : Ray-Ban Meta smart glasses have been released amid much debate about privacy and ethics. The glasses are designed to look like conventional Ray-Ban sunglasses, so critics fear users will be able to record or photograph those around them without their consent, raising fears about surveillance in public an... |
Ray-Ban Meta : Smartglasses Google Glass – Smartglasses developed by Google Spectacles (product) – AR smart glasses by Snapchat EyeTap – eye-mounted camera and head-up display (HUD) Golden-i – head-mounted computer Microsoft HoloLens – Windows 10 based AR unit, with high-definition 3D optical head-mounted display and s... |
Ray-Ban Meta : Meta Ray Ban Smart Glasses – official site Discover Ray-Ban Stories Ray-Ban Stories – official site == References == |
Astroinformatics : Astroinformatics is an interdisciplinary field of study involving the combination of astronomy, data science, machine learning, informatics, and information/communications technologies. The field is closely related to astrostatistics. Data-driven astronomy (DDA) refers to the use of data science in a... |
Astroinformatics : Astroinformatics is primarily focused on developing the tools, methods, and applications of computational science, data science, machine learning, and statistics for research and education in data-oriented astronomy. Early efforts in this direction included data discovery, metadata standards developm... |
Astroinformatics : The data retrieved from the sky surveys are first brought for data preprocessing. In this, redundancies are removed and filtrated. Further, feature extraction is performed on this filtered data set, which is further taken for processes. Some of the renowned sky surveys are listed below: The Palomar D... |
Astroinformatics : Additional conferences and conference lists: |
Astroinformatics : Astronomy and Computing Astrophysics Data System Astrophysics Source Code Library Astrostatistics Committee on Data for Science and Technology Data-driven astronomy Galaxy Zoo International Astrostatistics Association International Virtual Observatory Alliance (IVOA) MilkyWay@home Virtual Observatory... |
Astroinformatics : International AstroInformatics Association (IAIA) Astronomical Data Analysis Software and Systems (ADASS) Astrostatistics and Astroinformatics Portal Cosmostatistics Initiative (COIN) Astroinformatics and Astrostatistics Commission of the International Astronomical Union |
Lernmatrix : Lernmatrix (German for "learning matrix") is a special type of artificial neural network (ANN) architecture, similar to associative memory, invented around 1960 by Karl Steinbuch, a pioneer in computer science and ANNs. This model for learning systems could establish complex associations between certain se... |
Lernmatrix : The Lernmatrix generally consists of n "characteristic lines" and m "meaning lines," where each characteristic line is connected to each meaning line, similar to how neurons in the brain are connected by synapses. (This can be realized in various ways – according to Steinbuch, this could be done by hardwar... |
Lernmatrix : Artificial neural networks |
Lernmatrix : A new theoretical framework for the Steinbuch's Lernmatrix Pattern recognition and classification using weightless neural networks (WNN) and Steinbuch Lernmatrix DARPA project will study neural network processes Discussion in the newsgroup de.sci.informatik.ki (in German) Wolfgang Hilberg: "Karl Steinbuch,... |
Lernmatrix : Karl Steinbuch: Automat und Mensch. 1st ed. Springer 1961. (in German) == References == |
Averbis : Averbis has a focus on healthcare, pharma, automotive and intellectual property analytics. Averbis is involved in various research projects of the German Federal Ministry of Economics and Energy and the European Union such as DebugIT, EUCases, Mantra and SEMCARE. In addition to these projects, Averbis was als... |
Averbis : Enterprise Search Information retrieval Linguistics Knowledge Management Natural Language Processing Semantics |
Averbis : Official website Technology Competition Federal Ministry of Economy and Energy BMWi Research Project Trusted Cloud BMWi Research Project cloud4health |
Stemming : In linguistic morphology and information retrieval, stemming is the process of reducing inflected (or sometimes derived) words to their word stem, base or root form—generally a written word form. The stem need not be identical to the morphological root of the word; it is usually sufficient that related words... |
Stemming : A stemmer for English operating on the stem cat should identify such strings as cats, catlike, and catty. A stemming algorithm might also reduce the words fishing, fished, and fisher to the stem fish. The stem need not be a word, for example the Porter algorithm reduces argue, argued, argues, arguing, and ar... |
Stemming : The first published stemmer was written by Julie Beth Lovins in 1968. This paper was remarkable for its early date and had great influence on later work in this area. Her paper refers to three earlier major attempts at stemming algorithms, by Professor John W. Tukey of Princeton University, the algorithm dev... |
Stemming : There are several types of stemming algorithms which differ in respect to performance and accuracy and how certain stemming obstacles are overcome. A simple stemmer looks up the inflected form in a lookup table. The advantages of this approach are that it is simple, fast, and easily handles exceptions. The d... |
Stemming : While much of the early academic work in this area was focused on the English language (with significant use of the Porter Stemmer algorithm), many other languages have been investigated. Hebrew and Arabic are still considered difficult research languages for stemming. English stemmers are fairly trivial (wi... |
Stemming : There are two error measurements in stemming algorithms, overstemming and understemming. Overstemming is an error where two separate inflected words are stemmed to the same root, but should not have been—a false positive. Understemming is an error where two separate inflected words should be stemmed to the s... |
Stemming : Stemming is used as an approximate method for grouping words with a similar basic meaning together. For example, a text mentioning "daffodils" is probably closely related to a text mentioning "daffodil" (without the s). But in some cases, words with the same morphological stem have idiomatic meanings which a... |
Stemming : Computational linguistics – Use of computational tools for the study of linguistics Derivation – In linguistics, the process of forming a new word on the basis of an existing onePages displaying short descriptions of redirect targets — stemming is a form of reverse derivation Inflection – Process of word for... |
Stemming : Apache OpenNLP—includes Porter and Snowball stemmers SMILE Stemmer—free online service, includes Porter and Paice/Husk' Lancaster stemmers (Java API) Themis—open source IR framework, includes Porter stemmer implementation (PostgreSQL, Java API) Snowball—free stemming algorithms for many languages, includes s... |
.ai : .ai is the Internet country code top-level domain (ccTLD) for Anguilla, a British Overseas Territory in the Caribbean. It is administered by the government of Anguilla. It is a popular domain hack with companies and projects related to the artificial intelligence industry (AI). Google's ad targeting treats .ai as... |
.ai : Registrations within off.ai, com.ai, net.ai, and org.ai are available worldwide without restriction. From 15 September 2009, second level registrations within .ai are available to everyone worldwide. |
.ai : The minimum registration term allowed for a .ai domain is 2 years for registration and 2 years for renewal. The authority in charge of managing this extension is “WHOIS.AI”. Registrations began on 1995-02-16. The minimum length is 2 and the maximum is 63 characters. There are no requirements for registering a dom... |
.ai : Domains are $180 for each two-year period. As of December 2017, the ".ai" registry supports Extensible Provisioning Protocol. Consequently, many registrars are allowed to sell ".ai" domains. Since then, the .ai ccTLD has also been popular with artificial intelligence companies and organizations. Though such trend... |
.ai : The registration fees earned from the .ai domains go to the treasury of Government of Anguilla. As per a New York Times report, in 2018, the total revenue generated out of selling .ai domains was $2.9 million. In 2023, Anguilla’s government made about US$32 million from fees collected for registering .ai domains.... |
.ai : Internet in Anguilla Internet in the United Kingdom |
.ai : IANA .ai whois information old .ai NIC page .ai domain registration page .ai whois page |
Backpropagation through time : Backpropagation through time (BPTT) is a gradient-based technique for training certain types of recurrent neural networks, such as Elman networks. The algorithm was independently derived by numerous researchers. |
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