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AI washing : AI washing is a deceptive marketing tactic that consists of promoting a product or a service by overstating the role of artificial intelligence (AI) integration in it. It raises concerns regarding transparency, consumer trust in the AI industry, and compliance with security regulations, potentially hamperi... |
AI washing : In September 2023, Coca-Cola released a new product called Coca‑Cola® Y3000 Zero Sugar. The company stated that the Y3000 flavor had been "co-created with human and artificial intelligence", yet gave no real explanation of how AI was involved in the process. The company was accused of AI washing due to no ... |
AI washing : Some companies have been accused and/or shuttered of trying to capitalize on this trend by exaggerating the role of AI in their offerings. In March 2024, the SEC imposed the first civil penalties on two companies, Delphia Inc and Global Predictions Inc, for misleading statements about their use of AI. And ... |
Siamese neural network : A Siamese neural network (sometimes called a twin neural network) is an artificial neural network that uses the same weights while working in tandem on two different input vectors to compute comparable output vectors. Often one of the output vectors is precomputed, thus forming a baseline again... |
Siamese neural network : Learning in twin networks can be done with triplet loss or contrastive loss. For learning by triplet loss a baseline vector (anchor image) is compared against a positive vector (truthy image) and a negative vector (falsy image). The negative vector will force learning in the network, while the ... |
Siamese neural network : Twin networks have been used in object tracking because of its unique two tandem inputs and similarity measurement. In object tracking, one input of the twin network is user pre-selected exemplar image, the other input is a larger search image, which twin network's job is to locate exemplar ins... |
Siamese neural network : Artificial neural network Triplet loss |
Siamese neural network : Chicco, Davide (2020), "Siamese neural networks: an overview", Artificial Neural Networks, Methods in Molecular Biology, vol. 2190 (3rd ed.), New York City, New York, USA: Springer Protocols, Humana Press, pp. 73–94, doi:10.1007/978-1-0716-0826-5_3, ISBN 978-1-0716-0826-5, PMID 32804361, S2CID ... |
Grok (chatbot) : Grok is a generative artificial intelligence chatbot developed by xAI. Based on the large language model (LLM) of the same name, it was launched in 2023 as an initiative by Elon Musk. The chatbot is advertised as having a "sense of humor" and direct access to sister platform X, formerly known as Twitte... |
Grok (chatbot) : The following table lists the versions of Grok, describing the innovations and improvements in each version: |
Grok (chatbot) : Grok is currently available on X, as well as on its standalone website and iOS and Android apps, with the latter currently being limited to Australia, Canada, India, Saudi Arabia, the Philippines and Italy. |
Grok (chatbot) : Grokking (machine learning) – Phase transition in machine learning |
Grok (chatbot) : Official website Grok on X |
Quickprop : Quickprop is an iterative method for determining the minimum of the loss function of an artificial neural network, following an algorithm inspired by the Newton's method. Sometimes, the algorithm is classified to the group of the second order learning methods. It follows a quadratic approximation of the pre... |
Quickprop : Scott E. Fahlman: An Empirical Study of Learning Speed in Back-Propagation Networks, September 1988 |
Natural Language Toolkit : The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for English written in the Python programming language. It supports classification, tokenization, stemming, tagging, parsing, and semantic r... |
Natural Language Toolkit : Discourse representation Lexical analysis: Word and text tokenizer n-gram and collocations Part-of-speech tagger Tree model and Text chunker for capturing Named-entity recognition |
String kernel : In machine learning and data mining, a string kernel is a kernel function that operates on strings, i.e. finite sequences of symbols that need not be of the same length. String kernels can be intuitively understood as functions measuring the similarity of pairs of strings: the more similar two strings a... |
String kernel : Suppose one wants to compare some text passages automatically and indicate their relative similarity. For many applications, it might be sufficient to find some keywords which match exactly. One example where exact matching is not always enough is found in spam detection. Another would be in computation... |
String kernel : Since several well-proven data clustering, classification and information retrieval methods (for example support vector machines) are designed to work on vectors (i.e. data are elements of a vector space), using a string kernel allows the extension of these methods to handle sequence data. The string ke... |
String kernel : A kernel on a domain D is a function K : D × D → R satisfying some conditions (being symmetric in the arguments, continuous and positive semidefinite in a certain sense). Mercer's theorem asserts that K can then be expressed as K ( x , y ) = φ ( x ) ⋅ φ ( y ) with φ mapping the arguments into an i... |
Isotropic position : In the fields of machine learning, the theory of computation, and random matrix theory, a probability distribution over vectors is said to be in isotropic position if its covariance matrix is equal to the identity matrix. |
Isotropic position : Let D be a distribution over vectors in the vector space R n ^ . Then D is in isotropic position if, for vector v sampled from the distribution, E v v T = I d . \,vv^=\mathrm . A set of vectors is said to be in isotropic position if the uniform distribution over that set is in isotropic posit... |
Isotropic position : Whitening transformation |
Isotropic position : Rudelson, M. (1999). "Random Vectors in the Isotropic Position". Journal of Functional Analysis. 164 (1): 60–72. arXiv:math/9608208. doi:10.1006/jfan.1998.3384. S2CID 7652247. |
NETtalk (artificial neural network) : NETtalk is an artificial neural network that learns to pronounce written English text by being shown text as input and matching phonetic transcriptions for comparison. It is the result of research carried out in the mid-1980s by Terrence Sejnowski and Charles Rosenberg. The intent ... |
NETtalk (artificial neural network) : The training dataset was a 20,008-word subset of the Brown Corpus, with manually annotated phoneme and stress for each letter. The development process was described in a 1993 interview. It took three months -- 250 person-hours -- to create the training dataset, but only a few days ... |
NETtalk (artificial neural network) : The network had three layers and 18,629 adjustable weights, large by the standards of 1986. There were worries that it would overfit the dataset, but it was trained successfully. The input of the network has 203 units, divided into 7 groups of 29 units each. Each group is a one-hot... |
NETtalk (artificial neural network) : NETtalk was created to explore the mechanisms of learning to correctly pronounce English text. The authors note that learning to read involves a complex mechanism involving many parts of the human brain. NETtalk does not specifically model the image processing stages and letter rec... |
NETtalk (artificial neural network) : Original NETtalk training set New York Times article about NETtalk |
Cost-sensitive machine learning : Cost-sensitive machine learning is an approach within machine learning that considers varying costs associated with different types of errors. This method diverges from traditional approaches by introducing a cost matrix, explicitly specifying the penalties or benefits for each type of... |
Cost-sensitive machine learning : Cost-sensitive machine learning optimizes models based on the specific consequences of misclassifications, making it a valuable tool in various applications. It is especially useful in problems with a high imbalance in class distribution and a high imbalance in associated costs Cost-se... |
Cost-sensitive machine learning : The cost matrix is a crucial element within cost-sensitive modeling, explicitly defining the costs or benefits associated with different prediction errors in classification tasks. Represented as a table, the matrix aligns true and predicted classes, assigning a cost value to each combi... |
Cost-sensitive machine learning : A typical challenge in cost-sensitive machine learning is the reliable determination of the cost matrix which may evolve over time. |
Cost-sensitive machine learning : Cost-Sensitive Machine Learning. USA, CRC Press, 2011. ISBN 9781439839287 Abhishek, K., Abdelaziz, D. M. (2023). Machine Learning for Imbalanced Data: Tackle Imbalanced Datasets Using Machine Learning and Deep Learning Techniques. (n.p.): Packt Publishing. ISBN 9781801070881 == Referen... |
Digital signal processing and machine learning : Digital signal processing and machine learning are two technologies that are often combined. |
Digital signal processing and machine learning : Digital Signal Processing (DSP) plays a crucial role across a wide range of applications: Audio Processing: DSP is integral to modern audio technology, facilitating tasks such as music compression, equalization, noise suppression, echo cancellation, sound spatialization,... |
Digital signal processing and machine learning : The integration of machine learning (ML) with digital signal processing (DSP) has significantly advanced various fields, enhancing the ability to process and analyze complex data. In image and video processing, ML-DSP systems enable more accurate object detection, facial... |
Digital signal processing and machine learning : In the field of digital signal processing (DSP), various challenges arise when analyzing and manipulating signals. One approach to addressing these challenges involves leveraging machine learning (ML). In this context, machine learning refers to the use of algorithms and... |
Digital signal processing and machine learning : The integration of machine learning and signal processing has become increasingly prevalent, significantly impacting various industries by enabling more accurate, efficient, and intelligent data analysis. This convergence provides numerous benefits that are reshaping the... |
Digital signal processing and machine learning : The integration of machine learning (ML) with digital signal processing (DSP) offers numerous opportunities for enhancing signal processing capabilities across various fields. However, this convergence also introduces several challenges, including the need for large trai... |
Grammar checker : A grammar checker, in computing terms, is a program, or part of a program, that attempts to verify written text for grammatical correctness. Grammar checkers are most often implemented as a feature of a larger program, such as a word processor, but are also available as a stand-alone application that ... |
Grammar checker : The earliest "grammar checkers" were programs that checked for punctuation and style inconsistencies, rather than a complete range of possible grammatical errors. The first system was called Writer's Workbench, and was a set of writing tools included with Unix systems as far back as the 1970s. The who... |
Grammar checker : The earliest writing style programs checked for wordy, trite, clichéd, or misused phrases in a text. This process was based on simple pattern matching. The heart of the program was a list of many hundreds or thousands of phrases that are considered poor writing by many experts. The list of questionabl... |
Grammar checker : Grammar checkers are considered as a type of foreign language writing aid which non-native speakers can use to proofread their writings as such programs endeavor to identify syntactical errors. However, as with other computerized writing aids such as spell checkers, popular grammar checkers are often ... |
Grammar checker : Spell checker Link grammar == References == |
Adaptive resonance theory : Adaptive resonance theory (ART) is a theory developed by Stephen Grossberg and Gail Carpenter on aspects of how the brain processes information. It describes a number of artificial neural network models which use supervised and unsupervised learning methods, and address problems such as patt... |
Adaptive resonance theory : The basic ART system is an unsupervised learning model. It typically consists of a comparison field and a recognition field composed of neurons, a vigilance parameter (threshold of recognition), and a reset module. The comparison field takes an input vector (a one-dimensional array of values... |
Adaptive resonance theory : There are two basic methods of training ART-based neural networks: slow and fast. In the slow learning method, the degree of training of the recognition neuron's weights towards the input vector is calculated to continuous values with differential equations and is thus dependent on the lengt... |
Adaptive resonance theory : ART 1 is the simplest variety of ART networks, accepting only binary inputs. ART 2 extends network capabilities to support continuous inputs. ART 2-A is a streamlined form of ART-2 with a drastically accelerated runtime, and with qualitative results being only rarely inferior to the full ART... |
Adaptive resonance theory : It has been noted that results of Fuzzy ART and ART 1 (i.e., the learnt categories) depend critically upon the order in which the training data are processed. The effect can be reduced to some extent by using a slower learning rate, but is present regardless of the size of the input data set... |
Adaptive resonance theory : Wasserman, Philip D. (1989), Neural computing: theory and practice, New York: Van Nostrand Reinhold, ISBN 0-442-20743-3 |
Adaptive resonance theory : Stephen Grossberg's website ART's implementation for unsupervised learning (ART 1, ART 2A, ART 2A-C and ART distance) Summary of the ART algorithm LibTopoART — TopoART implementations for supervised and unsupervised learning (TopoART, TopoART-AM, TopoART-C, TopoART-R, Episodic TopoART, Hyper... |
AUTINDEX : AUTINDEX is a commercial text mining software package based on sophisticated linguistics. AUTINDEX, resulting from research in information extraction, is a product of the Institute of Applied Information Sciences (IAI) which is a non-profit institute that has been researching and developing language technolo... |
AUTINDEX : Information retrieval Linguistics Knowledge Management Natural Language Processing Semantics |
AUTINDEX : Ripplinger, Bärbel 2001: Das Indexierungssystem AUTINDEX, in GLDV Tagung, Giessen. Paul Schmidt, Thomas Bähr & Dr.-Ing. Jens Biesterfeld &Thomas Risse & Kerstin Denecke & Claudiu Firan, 2008: LINSearch. Aufbereitung von Fachwissen für die gezielte Informationsversorgung. In: Proceedings of Knowtech, Frankfur... |
AUTINDEX : Institute for Applied Information Sciences |
DexNet : Dex-net is a robotic. It uses a Grasp Quality Convolutional Neural Network to learn how to grasp unusually shaped objects. |
DexNet : Dex-net was developed by University of California, Berkeley professor Ken Goldberg and graduate student Jeff Mahler. |
DexNet : Dex-net includes a high-resolution 3-D sensor and two arms, each controlled by a different neural network. One arm is equipped with a conventional robot gripper and another with a suction system. The robot’s software scans an object and then asks both neural networks to decide, on the fly, whether to grab or s... |
DexNet : A metric called mean picks per hour (MPPH) is calculated by multiplying the average time per pick and the average probability of success for a specific set of objects. The new metric allows labs working on picking robots to compare their results. Humans are capable of between 400 and 600 MPPH. In a contest org... |
Algorithmic inference : Algorithmic inference gathers new developments in the statistical inference methods made feasible by the powerful computing devices widely available to any data analyst. Cornerstones in this field are computational learning theory, granular computing, bioinformatics, and, long ago, structural pr... |
Algorithmic inference : Concerning the identification of the parameters of a distribution law, the mature reader may recall lengthy disputes in the mid 20th century about the interpretation of their variability in terms of fiducial distribution (Fisher 1956), structural probabilities (Fraser 1966), priors/posteriors (R... |
Algorithmic inference : Fisher fought hard to defend the difference and superiority of his notion of parameter distribution in comparison to analogous notions, such as Bayes' posterior distribution, Fraser's constructive probability and Neyman's confidence intervals. For half a century, Neyman's confidence intervals wo... |
Algorithmic inference : From a modeling perspective the entire dispute looks like a chicken-egg dilemma: either fixed data by first and probability distribution of their properties as a consequence, or fixed properties by first and probability distribution of the observed data as a corollary. The classic solution has o... |
Algorithmic inference : With insufficiently large samples, the approach: fixed sample – random properties suggests inference procedures in three steps: |
Algorithmic inference : Fraser, D. A. S. (1966), "Structural probability and generalization", Biometrika, 53 (1/2): 1–9, doi:10.2307/2334048, JSTOR 2334048. Fisher, M. A. (1956), Statistical Methods and Scientific Inference, Edinburgh and London: Oliver and Boyd Apolloni, B.; Malchiodi, D.; Gaito, S. (2006), Algorithmi... |
Manifold hypothesis : The manifold hypothesis posits that many high-dimensional data sets that occur in the real world actually lie along low-dimensional latent manifolds inside that high-dimensional space. As a consequence of the manifold hypothesis, many data sets that appear to initially require many variables to de... |
Manifold hypothesis : An empirically-motivated approach to the manifold hypothesis focuses on its correspondence with an effective theory for manifold learning under the assumption that robust machine learning requires encoding the dataset of interest using methods for data compression. This perspective gradually emerg... |
Manifold hypothesis : == Further reading == |
Delta rule : In machine learning, the delta rule is a gradient descent learning rule for updating the weights of the inputs to artificial neurons in a single-layer neural network. It can be derived as the backpropagation algorithm for a single-layer neural network with mean-square error loss function. For a neuron j w... |
Delta rule : The delta rule is derived by attempting to minimize the error in the output of the neural network through gradient descent. The error for a neural network with j outputs can be measured as E = ∑ j 1 2 ( t j − y j ) 2 . \left(t_-y_\right)^. In this case, we wish to move through "weight space" of the neuron... |
Delta rule : Stochastic gradient descent Backpropagation Rescorla–Wagner model – the origin of delta rule == References == |
Dehaene–Changeux model : The Dehaene–Changeux model (DCM), also known as the global neuronal workspace, or global cognitive workspace model, is a part of Bernard Baars's global workspace model for consciousness. It is a computer model of the neural correlates of consciousness programmed as a neural network. It attempts... |
Dehaene–Changeux model : The Dehaene–Changeux model was initially established as a spin glass neural network attempting to represent learning and to then provide a stepping stone towards artificial learning among other objectives. It would later be used to predict observable reaction times within the priming paradigm a... |
Dehaene–Changeux model : The DCM exhibits several surcritical emergent behaviors such as multistability and a Hopf bifurcation between two very different regimes which may represent either sleep or arousal with a various all-or-none behaviors which Dehaene et al. use to determine a testable taxonomy between different s... |
Dehaene–Changeux model : Rialle, V and Stip, E. (May 1994). "Cognitive modeling in psychiatry: from symbolic models to parallel and distributed models". J Psychiatry Neurosci. 19(3): 178–192. Zigmond, Michael J. (1999). Fundamental neuroscience. Academic Press, p1551. Dehaene, Stanislas (2001). The cognitive neuroscien... |
Dehaene–Changeux model : Artificial consciousness Complex system Neuroscience |
Dehaene–Changeux model : "Selected publications of Stanislas Dehaene" INSERM-CEA Cognitive Neuroimaging Unit. |
Fuzzy agent : In computer science a fuzzy agent is a software agent that implements fuzzy logic. This software entity interacts with its environment through an adaptive rule-base and can therefore be considered a type of intelligent agent. == References == |
Leabra : Leabra stands for local, error-driven and associative, biologically realistic algorithm. It is a model of learning which is a balance between Hebbian and error-driven learning with other network-derived characteristics. This model is used to mathematically predict outcomes based on inputs and previous learning... |
Leabra : It is the default algorithm in emergent (successor of PDP++) when making a new project, and is extensively used in various simulations. Hebbian learning is performed using conditional principal components analysis (CPCA) algorithm with correction factor for sparse expected activity levels. Error-driven learnin... |
Leabra : The pseudocode for Leabra is given here, showing exactly how the pieces of the algorithm described in more detail in the subsequent sections fit together. Iterate over minus and plus phases of settling for each event. o At start of settling, for all units: - Initialize all state variables (activation, v_m, etc... |
Leabra : Emergent is the original implementation of Leabra; its most recent implementation is written in Go. It was written chiefly by Dr. O'Reilly, but professional software engineers were recently hired to improve the existing codebase. This is the fastest implementation, suitable for constructing large networks. Alt... |
Leabra : Temporal differences and general dopamine modulation. Temporal differences (TD) is widely used as a model of midbrain dopaminergic firing. Primary value learned value (PVLV). PVLV simulates behavioral and neural data on Pavlovian conditioning and the midbrain dopaminergic neurons that fire in proportion to une... |
Leabra : Emergent about Leabra PDP++ about Leabra O'Reilly, R.C. (1996). The Leabra Model of Neural Interactions and Learning in the Neocortex. Phd Thesis, Carnegie Mellon University, Pittsburgh, PA PDF R version of Leabra Vignette for R version of Leabra |
Rectifier (neural networks) : In the context of artificial neural networks, the rectifier or ReLU (rectified linear unit) activation function is an activation function defined as the non-negative part of its argument, i.e., the ramp function: ReLU ( x ) = x + = max ( 0 , x ) = x + | x | 2 = (x)=x^=\max(0,x)==x&x>0,\... |
Rectifier (neural networks) : Advantages of ReLU include: Sparse activation: for example, in a randomly initialized network, only about 50% of hidden units are activated (i.e. have a non-zero output). Better gradient propagation: fewer vanishing gradient problems compared to sigmoidal activation functions that saturate... |
Rectifier (neural networks) : Possible downsides can include: Non-differentiability at zero (however, it is differentiable anywhere else, and the value of the derivative at zero can be chosen to be 0 or 1 arbitrarily). Not zero-centered: ReLU outputs are always non-negative. This can make it harder for the network to l... |
Rectifier (neural networks) : Softmax function Sigmoid function Tobit model Layer (deep learning) == References == |
Predictive text : Predictive text is an input technology used where one key or button represents many letters, such as on the physical numeric keypads of mobile phones and in accessibility technologies. Each key press results in a prediction rather than repeatedly sequencing through the same group of "letters" it repre... |
Predictive text : Short message service (SMS) permits a mobile phone user to send text messages (also called messages, SMSes, texts, and txts) as a short message. The most common system of SMS text input is referred to as "multi-tap". Using multi-tap, a key is pressed multiple times to access the list of letters on tha... |
Predictive text : Traditional disambiguation works by referencing a dictionary of commonly used words, though Eatoni offers a dictionaryless disambiguation system. In dictionary-based systems, as the user presses the number buttons, an algorithm searches the dictionary for a list of possible words that match the keypre... |
Predictive text : The predictive text and autocomplete technology was invented out of necessities by Chinese scientists and linguists in the 1950s to solve the input inefficiency of the Chinese typewriter, as the typing process involved finding and selecting thousands of logographic characters on a tray, drastically sl... |
Predictive text : On a typical phone keypad, if users wished to type the in a "multi-tap" keypad entry system, they would need to: Press 8 (tuv) once to select t. Press 4 (ghi) twice to select h. Press 3 (def) twice to select e. Meanwhile, in a phone with predictive text, they need only: Press 8 once to select the (tuv... |
Predictive text : Predictive text is developed and marketed in a variety of competing products, such as Nuance Communications's T9. Other products include Motorola's iTap; Eatoni Ergonomic's LetterWise (character, rather than word-based prediction); WordWise (word-based prediction without a dictionary); EQ3 (a QWERTY-l... |
Predictive text : Words produced by the same combination of keypresses have been called "textonyms"; also "txtonyms"; or "T9onyms" (pronounced "tynonyms" ), though they are not specific to T9. Selecting the wrong textonym can occur with no misspelling or typo, if the wrong textonym is selected by default or user error.... |
Predictive text : Textonyms are not the only issue limiting the effectiveness of predictive text implementations. Another significant problem are words for which the disambiguation produces a single, incorrect response. The system may, for example, respond with Blairf upon input of 252473, when the intended word was Bl... |
Predictive text : Smith, Sidney L.; Goodwin, Nancy C. (1971). "Alphabetic Data Entry Via the Touch-Tone Pad: A Comment". Human Factors. 13 (2): 189–190. doi:10.1177/001872087101300212. S2CID 61164630. |
Predictive text : New Scientist article on textonyms An Australian newspaper article on textonyms Technical notes on iTap (including lists of textonyms) |
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