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Hierarchical navigable small world : HNSW is a key method for approximate nearest neighbor search in high-dimensional vector databases, for example in the context of embeddings from neural networks in large language models. Databases that use HNSW as search index include: Apache Lucene Vector Search Chroma Qdrant Vespa... |
AI/ML Development Platform : AI/ML development platforms, such as PyTorch and Hugging Face, are software ecosystems designed to facilitate the creation, training, deployment, and management of artificial intelligence (AI) and machine learning (ML) models. These platforms provide tools, frameworks, and infrastructure to... |
AI/ML Development Platform : AI/ML development platforms serve as comprehensive environments for building AI systems, ranging from simple predictive models to complex large language models (LLMs). They abstract technical complexities (e.g., distributed computing, hyperparameter tuning) while offering modular components... |
AI/ML Development Platform : Modern AI/ML platforms typically include: End-to-end workflow support: Data preparation: Tools for cleaning, labeling, and augmenting datasets. Model building: Libraries for designing neural networks (e.g., PyTorch, TensorFlow integrations). Training & Optimization: Distributed training, hy... |
AI/ML Development Platform : AI/ML development platforms underpin innovations in: Health care: Drug discovery, medical imaging analysis. Finance: Fraud detection, algorithmic trading. Natural language processing (NLP): Chatbots, translation systems. Autonomous systems: Self-driving cars, robotics. |
AI/ML Development Platform : Computational costs: Training LLMs requires massive GPU/TPU resources. Data privacy: Balancing model performance with GDPR/CCPA compliance. Skill gaps: High barrier to entry for non-experts. Bias and fairness: Mitigating skewed outcomes in sensitive applications. |
AI/ML Development Platform : Democratization: Low-code/no-code platforms (e.g., Google AutoML, DataRobot). Ethical AI integration: Tools for bias mitigation and transparency. Federated learning: Training models on decentralized data. Quantum machine learning: Hybrid platforms leveraging quantum computing. |
AI/ML Development Platform : Automated machine learning Large language model |
AI/ML Development Platform : MLflow Official Website – Open-source platform for the machine learning lifecycle. Hugging Face – Community and tools for NLP models. TensorFlow – Google's machine learning framework. Google AI Research – Publications on AI/ML advancements. |
Conversica : Conversica is a US-based cloud software technology company, headquartered in San Mateo, California, that provides two-way AI-driven conversational software and a suite of Intelligent Virtual Assistants for businesses to engage customers via email, chat, and SMS. |
Conversica : 2007: The company was founded by Ben Brigham in Bellingham, Washington, originally as AutoFerret.com. The company's initial product was a Customer Relationship Management (CRM) targeted at automotive dealerships. This soon expanded to lead generation, and then lead validation and qualification. The AI Conv... |
Conversica : Conversica's Revenue Digital Assistants™ are AI assistants who engage with leads, prospects, customers, employees, and other persons of interest (Contacts) in a two-way human-like manner, via email, SMS text, and website chat, in English, French, German, Spanish, Portuguese, and Japanese. The RDAs are buil... |
Conversica : Official website |
Smart object : A smart object is an object that enhances the interaction with not only people but also with other smart objects. Also known as smart connected products or smart connected things (SCoT), they are products, assets and other things embedded with processors, sensors, software and connectivity that allow dat... |
Smart object : In the early 1990s, Mark Weiser, from whom the term ubiquitous computing originated, referred to a vision "When almost every object either contains a computer or can have a tab attached to it, obtaining information will be trivial", Although Weiser did not specifically refer to an object as being smart, ... |
Smart object : Although we can view interaction with physical smart object in the physical world as distinct from interaction with virtual smart objects in a virtual simulated world, these can be related. Poslad considers the progression of: how humans use models of smart objects situated in the physical world to enhan... |
Smart object : Smart, connected products have three primary components:: 67 Physical – made up of the product's mechanical and electrical parts. Smart – made up of sensors, microprocessors, data storage, controls, software, and an embedded operating system with enhanced user interface. Connectivity – made up of ports, ... |
Smart object : AmbieSense Audiocubes Home network Intelligent maintenance system Nabaztag Smart speaker Wearable technology Ubiquitous computing |
Smart object : Donald A. Norman. Design of Future Things. Basic Books. 2007 Bruce Sterling. Cisco launches consortium for ‘Smart Objects'. Wired, September 25, 2008 2009 New Media Horizons Report Mike Isaac. home-google-io/ Google's Platform Extends Its Reach With Android@Home. Wired, May 11, 2011 |
Smart object : WorldCat publications about smart objects. The Internet of Things' Best-Kept Secret, Forbes A Very Short History Of The Internet Of Things, Forbes Three Steps to Combat the Impact of Digital Business Disruption on Value Creation, Gartner The Five SMART Technologies to Watch, Gartner Cisco White Paper: Th... |
ChatGPT Search : ChatGPT Search (originally SearchGPT) is a search engine developed by OpenAI. It combines traditional search engine features with generative pretrained transformers (GPT) to generate responses, including citations to external websites. |
ChatGPT Search : On July 25, 2024, SearchGPT was first introduced as a prototype in a limited release to 10,000 test users. This search feature positioned OpenAI as a direct competitor to major search engines, notably Google, Perplexity AI and Bing. OpenAI announced its partnership with publishers for SearchGPT, provid... |
ChatGPT Search : Comparison of search engines Google Search – Search engine from Google List of search engines Timeline of web search engines == References == |
Triplet loss : Triplet loss is a machine learning loss function widely used in one-shot learning, a setting where models are trained to generalize effectively from limited examples. It was conceived by Google researchers for their prominent FaceNet algorithm for face detection. Triplet loss is designed to support metri... |
Triplet loss : The loss function is defined using triplets of training points of the form ( A , P , N ) . In each triplet, A (called an "anchor point") denotes a reference point of a particular identity, P (called a "positive point") denotes another point of the same identity in point A , and N (called a "negative... |
Triplet loss : A baseline for understanding the effectiveness of triplet loss is the contrastive loss, which operates on pairs of samples (rather than triplets). Training with the contrastive loss pulls embeddings of similar pairs closer together, and pushes dissimilar pairs apart. Its pairwise approach is greedy, as i... |
Triplet loss : One crucial implementation detail when training with triplet loss is triplet "mining", which focuses on the smart selection of triplets for optimization. This process adds an additional layer of complexity compared to contrastive loss. A naive approach to preparing training data for the triplet loss invo... |
Triplet loss : Triplet loss has been extended to simultaneously maintain a series of distance orders by optimizing a continuous relevance degree with a chain (i.e., ladder) of distance inequalities. This leads to the Ladder Loss, which has been demonstrated to offer performance enhancements of visual-semantic embedding... |
Triplet loss : Siamese neural network t-distributed stochastic neighbor embedding Similarity learning == References == |
Bag-of-words model : The bag-of-words model (BoW) is a model of text which uses an unordered collection (a "bag") of words. It is used in natural language processing and information retrieval (IR). It disregards word order (and thus most of syntax or grammar) but captures multiplicity. The bag-of-words model is commonl... |
Bag-of-words model : The following models a text document using bag-of-words. Here are two simple text documents: Based on these two text documents, a list is constructed as follows for each document: Representing each bag-of-words as a JSON object, and attributing to the respective JavaScript variable: Each key is the... |
Bag-of-words model : Implementations of the bag-of-words model might involve using frequencies of words in a document to represent its contents. The frequencies can be "normalized" by the inverse of document frequency, or tf–idf. Additionally, for the specific purpose of classification, supervised alternatives have bee... |
Bag-of-words model : A common alternative to using dictionaries is the hashing trick, where words are mapped directly to indices with a hashing function. Thus, no memory is required to store a dictionary. Hash collisions are typically dealt via freed-up memory to increase the number of hash buckets. In practice, hashin... |
Bag-of-words model : Additive smoothing Feature extraction Machine learning MinHash Vector space model w-shingling |
Bag-of-words model : McTear, Michael (et al) (2016). The Conversational Interface. Springer International Publishing. |
Noisy text analytics : Noisy text analytics is a process of information extraction whose goal is to automatically extract structured or semistructured information from noisy unstructured text data. While Text analytics is a growing and mature field that has great value because of the huge amounts of data being produced... |
Noisy text analytics : Missing punctuation and the use of non-standard words can often hinder standard natural language processing tools such as part-of-speech tagging and parsing. Techniques to both learn from the noisy data and then to be able to process the noisy data are only now being developed. |
Noisy text analytics : World Wide Web: Poorly written text is found in web pages, online chat, blogs, wikis, discussion forums, newsgroups. Most of these data are unstructured and the style of writing is very different from, say, well-written news articles. Analysis for the web data is important because they are source... |
Noisy text analytics : Text analytics Information extraction Computational linguistics Natural language processing Named entity recognition Text mining Automatic summarization Statistical classification Data quality |
Noisy text analytics : "Wong, W., Liu, W. & Bennamoun, M. Enhanced Integrated Scoring for Cleaning Dirty Texts. In: IJCAI Workshop on Analytics for Noisy Unstructured Text Data (AND), 2007; Hyderabad, India.". "L. V. Subramaniam, S. Roy, T. A. Faruquie, S. Negi, A survey of types of text noise and techniques to handle ... |
Artificial psychology : Artificial psychology (AP) has had multiple meanings dating back to 19th century, with recent usage related to artificial intelligence (AI). In 1999, Zhiliang Wang and Lun Xie presented a theory of artificial psychology based on artificial intelligence. They analyze human psychology using inform... |
Artificial psychology : Dan Curtis (b. 1963) proposed AP is a theoretical discipline. The theory considers the situation when an artificial intelligence approaches the level of complexity where the intelligence meets two conditions: Condition I A: Makes all of its decisions autonomously B: Is capable of making decision... |
Artificial psychology : Holstein, Hans Jürgen; Stålberg, Lennart (1974). Homo Cyberneticus: Artificial psychology and generative micro-sociology. Sociografica. Lu, Quan; Chen, Jing; Meng, Bo (2006). "Web Personalization Based on Artificial Psychology". In Feng, Ling; Wang, Guoren; Zeng, Cheng; Huang, Ruhua (eds.). Web ... |
Autonomic networking : Autonomic networking follows the concept of Autonomic Computing, an initiative started by IBM in 2001. Its ultimate aim is to create self-managing networks to overcome the rapidly growing complexity of the Internet and other networks and to enable their further growth, far beyond the size of toda... |
Autonomic networking : The ever-growing management complexity of the Internet caused by its rapid growth is seen by some experts as a major problem that limits its usability in the future. What's more, increasingly popular smartphones, PDAs, networked audio and video equipment, and game consoles need to be interconnect... |
Autonomic networking : The autonomic nervous system (ANS) is the part of complex biological nervous systems that is not consciously controlled. It regulates bodily functions and the activity of specific organs. As proposed by IBM, future communication systems might be designed in a similar way to the ANS. |
Autonomic networking : As autonomics conceptually derives from biological entities such as the human autonomic nervous system, each of the areas can be metaphorically related to functional and structural aspects of a living being. In the human body, the autonomic system facilitates and regulates a variety of functions ... |
Autonomic networking : Consequently, it is currently under research by many research projects, how principles and paradigms of mother nature might be applied to networking. |
Autonomic networking : Autonomic Computing Autonomic system (computing) Cognitive networks Network Compartment Collaborative innovation network In-Network Management Generic Autonomic Networking Architecture (GANA) EFIPSANS Project http://www.efipsans.org/ |
Autonomic networking : IBM Autonomic Computing Website Intel White Paper: Towards an Autonomic Framework Ipanema Technologies: Autonomic Networking applied to application performance optimization Archived 2009-04-26 at the Wayback Machine |
Hyper basis function network : In machine learning, a Hyper basis function network, or HyperBF network, is a generalization of radial basis function (RBF) networks concept, where the Mahalanobis-like distance is used instead of Euclidean distance measure. Hyper basis function networks were first introduced by Poggio an... |
Hyper basis function network : The typical HyperBF network structure consists of a real input vector x ∈ R n ^ , a hidden layer of activation functions and a linear output layer. The output of the network is a scalar function of the input vector, ϕ : R n → R ^\to \mathbb , is given by where N is a number of neuron... |
Hyper basis function network : Training HyperBF networks involves estimation of weights a j , shape and centers of neurons R j and μ j . Poggio and Girosi (1990) describe the training method with moving centers and adaptable neuron shapes. The outline of the method is provided below. Consider the quadratic loss of t... |
Text nailing : Text Nailing (TN) is an information extraction method of semi-automatically extracting structured information from unstructured documents. The method allows a human to interactively review small blobs of text out of a large collection of documents, to identify potentially informative expressions. The ide... |
Text nailing : TN was developed at Massachusetts General Hospital and was tested in multiple scenarios including the extraction of smoking status, family history of coronary artery disease, identifying patients with sleep disorders, improve the accuracy of the Framingham risk score for patients with non-alcoholic fatty... |
Text nailing : A sample code for extracting smoking status from narrative notes using "nailed expressions" is available in GitHub. |
Text nailing : In July 2018 researchers from Virginia Tech and University of Illinois at Urbana–Champaign referred TN as an example for progressive cyber-human intelligence (PCHI). |
Text nailing : Chen & Asch 2017 wrote "With machine learning situated at the peak of inflated expectations, we can soften a subsequent crash into a “trough of disillusionment” by fostering a stronger appreciation of the technology’s capabilities and limitations." A letter published in Communications of the ACM, "Beyond... |
Text nailing : The human in-the-loop process is a way to generate features using domain experts. Using domain experts to come up with features is not a novel concept. However, the specific interfaces and method which helps the domain experts create the features are most likely novel. In this case the features the exper... |
Spike-and-slab regression : Spike-and-slab regression is a type of Bayesian linear regression in which a particular hierarchical prior distribution for the regression coefficients is chosen such that only a subset of the possible regressors is retained. The technique is particularly useful when the number of possible p... |
Spike-and-slab regression : Suppose we have P possible predictors in some model. Vector γ has a length equal to P and consists of zeros and ones. This vector indicates whether a particular variable is included in the regression or not. If no specific prior information on initial inclusion probabilities of particular va... |
Spike-and-slab regression : Bayesian model averaging Bayesian structural time series Lasso |
Spike-and-slab regression : Congdon, Peter D. (2020). "Regression Techniques using Hierarchical Priors". Bayesian Hierarchical Models (2nd ed.). Boca Raton: CRC Press. pp. 253–315. ISBN 978-1-03-217715-1. |
Timeline of machine learning : This page is a timeline of machine learning. Major discoveries, achievements, milestones and other major events in machine learning are included. |
Timeline of machine learning : History of artificial intelligence Timeline of artificial intelligence Timeline of machine translation |
Concept drift : In predictive analytics, data science, machine learning and related fields, concept drift or drift is an evolution of data that invalidates the data model. It happens when the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways. This c... |
Concept drift : In machine learning and predictive analytics this drift phenomenon is called concept drift. In machine learning, a common element of a data model are the statistical properties, such as probability distribution of the actual data. If they deviate from the statistical properties of the training data set,... |
Concept drift : Another important area is software engineering, where three types of data drift affecting data fidelity may be recognized. Changes in the software environment ("infrastructure drift") may invalidate software infrastructure configuration. "Structural drift" happens when the data schema changes, which may... |
Concept drift : "Data drift" may refer to the phenomenon when database records fail to match the real-world data due to the changes in the latter over time. This is a common problem with databases involving people, such as customers, employees, citizens, residents, etc. Human data drift may be caused by unrecorded chan... |
Concept drift : The behavior of the customers in an online shop may change over time. For example, if weekly merchandise sales are to be predicted, and a predictive model has been developed that works satisfactorily. The model may use inputs such as the amount of money spent on advertising, promotions being run, and ot... |
Concept drift : To prevent deterioration in prediction accuracy because of concept drift, reactive and tracking solutions can be adopted. Reactive solutions retrain the model in reaction to a triggering mechanism, such as a change-detection test, to explicitly detect concept drift as a change in the statistics of the d... |
Concept drift : Data stream mining Data mining Snyk, a company whose portfolio includes drift detection in software applications |
Concept drift : Many papers have been published describing algorithms for concept drift detection. Only reviews, surveys and overviews are here: |
Referring expression generation : Referring expression generation (REG) is the subtask of natural language generation (NLG) that received most scholarly attention. While NLG is concerned with the conversion of non-linguistic information into natural language, REG focuses only on the creation of referring expressions (n... |
Referring expression generation : A referring expression (RE), in linguistics, is any noun phrase, or surrogate for a noun phrase, whose function in discourse is to identify some individual object (thing, being, event...) The technical terminology for identify differs a great deal from one school of linguistics to anot... |
Referring expression generation : Dale and Reiter (1995) think about referring expressions as distinguishing descriptions. They define: The referent as the entity that should be described The context set as set of salient entities The contrast set or potential distractors as all elements of the context set except the r... |
Referring expression generation : Before 2000 evaluation of REG systems has been of theoretical nature like the one done by Dale and Reiter. More recently, empirical studies have become popular which are mostly based on the assumption that the generated expressions should be similar to human-produced ones. Corpus-based... |
Generative pre-trained transformer : A generative pre-trained transformer (GPT) is a type of large language model (LLM) and a prominent framework for generative artificial intelligence. It is an artificial neural network that is used in natural language processing by machines. It is based on the transformer deep learni... |
Generative pre-trained transformer : A foundation model is an AI model trained on broad data at scale such that it can be adapted to a wide range of downstream tasks. Thus far, the most notable GPT foundation models have been from OpenAI's GPT-n series. The most recent from that is GPT-4, for which OpenAI declined to p... |
Generative pre-trained transformer : A foundational GPT model can be further adapted to produce more targeted systems directed to specific tasks and/or subject-matter domains. Methods for such adaptation can include additional fine-tuning (beyond that done for the foundation model) as well as certain forms of prompt en... |
Generative pre-trained transformer : OpenAI, which created the first generative pre-trained transformer (GPT) in 2018, asserted in 2023 that "GPT" should be regarded as a brand of OpenAI. In April 2023, OpenAI revised the brand guidelines in its terms of service to indicate that other businesses using its API to run th... |
Generative pre-trained transformer : This section lists the main official publications from OpenAI and Microsoft on their GPT models. GPT-1: report, GitHub release. GPT-2: blog announcement, report on its decision of "staged release", GitHub release. GPT-3: report. No GitHub or any other form of code release thencefort... |
Military applications of artificial intelligence : Artificial intelligence (AI) has many applications in warfare, including in communications, intelligence, and munitions control. |
Military applications of artificial intelligence : AI can enhance command and control, communications, sensors, integration and interoperability. AI technologies enable coordination of sensors and effectors, threat detection and identification, marking of enemy positions, target acquisition, coordination and deconflict... |
Military applications of artificial intelligence : Various countries are researching and deploying AI military applications, in what has been termed the "artificial intelligence arms race". Ongoing research is focused on intelligence collection and analysis, logistics, cyber operations, information operations, and semi... |
Military applications of artificial intelligence : Military artificial intelligence systems have appeared in many works of fiction, often as antagonists. |
Activation function : The activation function of a node in an artificial neural network is a function that calculates the output of the node based on its individual inputs and their weights. Nontrivial problems can be solved using only a few nodes if the activation function is nonlinear. Modern activation functions inc... |
Activation function : Aside from their empirical performance, activation functions also have different mathematical properties: Nonlinear When the activation function is non-linear, then a two-layer neural network can be proven to be a universal function approximator. This is known as the Universal Approximation Theore... |
Activation function : The most common activation functions can be divided into three categories: ridge functions, radial functions and fold functions. An activation function f is saturating if lim | v | → ∞ | ∇ f ( v ) | = 0 |\nabla f(v)|=0 . It is nonsaturating if it is lim | v | → ∞ | ∇ f ( v ) | ≠ 0 |\nabla f(v)|\n... |
Activation function : Logistic function Rectifier (neural networks) Stability (learning theory) Softmax function |
Activation function : Kunc, Vladimír; Kléma, Jiří (2024-02-14), Three Decades of Activations: A Comprehensive Survey of 400 Activation Functions for Neural Networks, arXiv, doi:10.48550/arXiv.2402.09092, arXiv:2402.09092 Nwankpa, Chigozie; Ijomah, Winifred; Gachagan, Anthony; Marshall, Stephen (2018-11-08). "Activation... |
Abdul Majid Bhurgri Institute of Language Engineering : Abdul Majid Bhurgri Institute of Language Engineering (Sindhi: عبدالماجد ڀرڳڙي انسٽيٽيوٽ آف لئنگئيج انجنيئرنگ) is an autonomous body under the administrative control of the Culture, Tourism and Antiquities Department, Government of Sindh established for bringing S... |
Abdul Majid Bhurgri Institute of Language Engineering : In recognition to services of Abdul-Majid Bhurgri, who is the founder of Sindhi computing, Government of Sindh has established the institute after his name. The institute was primarily initiated on the concept given by a language engineer and linguist Amar Fayaz B... |
Abdul Majid Bhurgri Institute of Language Engineering : The Institute has developed several projects aimed at advancing the Sindhi language and promoting linguistic research. Notable initiatives include the AMBILE Hamiz Ali Sindhi Optical character recognition, which allows for the accurate digitization of Sindhi text,... |
Abdul Majid Bhurgri Institute of Language Engineering : The institute is established behind Sindh Museum and Sindhi Language Authority, N-5 National Highway, Qasimabad, Hyderabad, Sindh. == References == |
Moral outsourcing : Moral outsourcing refers to placing responsibility for ethical decision-making on to external entities, often algorithms. The term is often used in discussions of computer science and algorithmic fairness, but it can apply to any situation in which one appeals to outside agents in order to absolve t... |
Moral outsourcing : The term "moral outsourcing" was first coined by Dr. Rumman Chowdhury, a data scientist concerned with the overlap between artificial intelligence and social issues. Chowdhury used the term to describe looming fears of a so-called “Fourth Industrial Revolution” following the rise of artificial intel... |
Moral outsourcing : In the medical field, AI is increasingly involved in decision-making processes about which patients to treat, and how to treat them. The responsibility of the doctor to make informed decisions about what is best for their patients is outsourced to an algorithm. Sympathy is also noted to be an import... |
Moral outsourcing : Chowdhury has a prominent voice in the discussions about the intersection of ethics and AI. Her ideas have been included in The Atlantic, Forbes, MIT Technology Review, and the Harvard Business Review. === References === |
Proactive learning : Proactive learning is a generalization of active learning designed to relax unrealistic assumptions and thereby reach practical applications. "In real life, it is possible and more general to have multiple sources of information with differing reliabilities or areas of expertise. Active learning al... |
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