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Affective Computing
Affective computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects. It is an interdisciplinary field spanning computer science, psychology, and cognitive science. While some core ideas in the field may be traced as far back as to early philosophical inquiries into emotion, the more modern branch of computer science originated with Rosalind Picard's 1995 paper entitled "Affective Computing" and her 1997 book of the same name published by MIT Press. One of the motivations for the research is the ability to give machines emotional intelligence, including to simulate empathy. The machine should interpret the emotional state of humans and adapt its behavior to them, giving an appropriate response to those emotions. Areas Detecting and recognizing emotional information Detecting emotional information usually begins with passive sensors that capture data about the user's physical state or behavior without inte ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Rosalind Picard
Rosalind Wright Picard (born 1962) is an American scholar and inventor who is Grover M. Hermann Professor of Health Sciences and Technology at MIT, founder and director of the Affective Computing Research Group at the MIT Media Lab, and co-founder of the startups Affectiva and Empatica. She has received many recognitions for her research and inventions. In 2005, she was named a Fellow of the Institute of Electrical and Electronics Engineers for contributions to image and video analysis and affective computing. In 2019 she received one of the highest professional honors accorded an engineer, election to the National Academy of Engineering for her contributions on affective computing and wearable computing. In 2021 she was recognized as a Fellow of the ACM for contributions to physiological signal sensing for individual health and wellbeing. In 2021 she was elected to the National Academy of Inventors, which recognizes outstanding inventions that have made a tangible impact on qua ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sophia At The AI For Good Global Summit 2018 (27254369347) (cropped)
Sophia most commonly refers to: * Sophia (wisdom), a central idea in Hellenistic philosophy and religion * Sophia (Gnosticism), a feminine figure in Gnosticism * Sophia (given name), including a list of people and fictional characters named Sophia or Sofia Sophia or SOPHIA may also refer to: Arts and entertainment Music * Sophia (Japanese band) * Sophia (singer) or Sophia Abrahão, pop singer from Brazil * Sophia (The Crüxshadows EP), ''Sophia'' (The Crüxshadows EP) * Sophia (Sophia Abrahão EP), ''Sophia'' (Sophia Abrahão EP) * Sophia (Nerina Pallot song), "Sophia" (Nerina Pallot song) * Sophia (Laura Marling song), "Sophia" (Laura Marling song) * "Sophia", a song from ''Think Before You Speak (album), Think Before You Speak'' by Good Shoes * "Sophia", a song from ''Mother's Spiritual'' by Laura Nyro * "Sophia", a song from ''Dust and Chimes'' by Six Organs of Admittance Other uses in arts and entertainment * Sophia (TV series), ''Sophia'' (TV series), a Russian historical dr ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Natural Language Processing
Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence. It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics. Major tasks in natural language processing are speech recognition, text classification, natural-language understanding, natural language understanding, and natural language generation. History Natural language processing has its roots in the 1950s. Already in 1950, Alan Turing published an article titled "Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of intelligence, though at the time that was not articulated as a problem separate from artificial intelligence. The proposed test includes a task that involves the automated interpretation and generation of natural language ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
K-nearest Neighbor Algorithm
In statistics, the ''k''-nearest neighbors algorithm (''k''-NN) is a non-parametric supervised learning method. It was first developed by Evelyn Fix and Joseph Hodges in 1951, and later expanded by Thomas Cover. Most often, it is used for classification, as a ''k''-NN classifier, the output of which is a class membership. An object is classified by a plurality vote of its neighbors, with the object being assigned to the class most common among its ''k'' nearest neighbors (''k'' is a positive integer, typically small). If ''k'' = 1, then the object is simply assigned to the class of that single nearest neighbor. The ''k''-NN algorithm can also be generalized for regression. In ''-NN regression'', also known as '' nearest neighbor smoothing'', the output is the property value for the object. This value is the average of the values of ''k'' nearest neighbors. If ''k'' = 1, then the output is simply assigned to the value of that single nearest neighbor, also known as ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Linear Classifier
In machine learning, a linear classifier makes a classification decision for each object based on a linear combination of its features. Such classifiers work well for practical problems such as document classification, and more generally for problems with many variables ( features), reaching accuracy levels comparable to non-linear classifiers while taking less time to train and use. Definition If the input feature vector to the classifier is a real vector \vec x, then the output score is :y = f(\vec\cdot\vec) = f\left(\sum_j w_j x_j\right), where \vec w is a real vector of weights and ''f'' is a function that converts the dot product of the two vectors into the desired output. (In other words, \vec is a one-form or linear functional mapping \vec x onto R.) The weight vector \vec w is learned from a set of labeled training samples. Often ''f'' is a threshold function, which maps all values of \vec\cdot\vec above a certain threshold to the first class and all other value ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Vector Space Model
Vector space model or term vector model is an algebraic model for representing text documents (or more generally, items) as vector space, vectors such that the distance between vectors represents the relevance between the documents. It is used in information filtering, information retrieval, index (search engine), indexing and relevancy rankings. Its first use was in the SMART Information Retrieval System. Definitions In this section we consider a particular vector space model based on the Bag-of-words model, bag-of-words representation. Documents and queries are represented as vectors. :d_j = ( w_ ,w_ , \dotsc ,w_ ) :q = ( w_ ,w_ , \dotsc ,w_ ) Each Dimension (vector space), dimension corresponds to a separate term. If a term occurs in the document, its value in the vector is non-zero. Several different ways of computing these values, also known as (term) weights, have been developed. One of the best known schemes is tf-idf weighting (see the example below). The definition of ' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knowledge Base
In computer science, a knowledge base (KB) is a set of sentences, each sentence given in a knowledge representation language, with interfaces to tell new sentences and to ask questions about what is known, where either of these interfaces might use inference. It is a technology used to store complex structured data used by a computer system. The initial use of the term was in connection with expert systems, which were the first knowledge-based systems. Original usage of the term The original use of the term knowledge base was to describe one of the two sub-systems of an expert system. A knowledge-based system consists of a knowledge-base representing facts about the world and ways of reasoning about those facts to deduce new facts or highlight inconsistencies. Properties The term "knowledge-base" was coined to distinguish this form of knowledge store from the more common and widely used term ''database''. During the 1970s, virtually all large management information sy ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
