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Adversarial machine learning : Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. A survey from May 2020 revealed practitioners' common feeling for better protection of machine learning systems in industrial applications. Machine learning t... |
Adversarial machine learning : At the MIT Spam Conference in January 2004, John Graham-Cumming showed that a machine-learning spam filter could be used to defeat another machine-learning spam filter by automatically learning which words to add to a spam email to get the email classified as not spam. In 2004, Nilesh Dal... |
Adversarial machine learning : There are a large variety of different adversarial attacks that can be used against machine learning systems. Many of these work on both deep learning systems as well as traditional machine learning models such as SVMs and linear regression. A high level sample of these attack types inclu... |
Adversarial machine learning : Researchers have proposed a multi-step approach to protecting machine learning. Threat modeling – Formalize the attackers goals and capabilities with respect to the target system. Attack simulation – Formalize the optimization problem the attacker tries to solve according to possible atta... |
Adversarial machine learning : Pattern recognition Fawkes (image cloaking software) Generative adversarial network |
Adversarial machine learning : MITRE ATLAS: Adversarial Threat Landscape for Artificial-Intelligence Systems NIST 8269 Draft: A Taxonomy and Terminology of Adversarial Machine Learning NIPS 2007 Workshop on Machine Learning in Adversarial Environments for Computer Security AlfaSVMLib Archived 2020-09-24 at the Wayback ... |
Echo state network : An echo state network (ESN) is a type of reservoir computer that uses a recurrent neural network with a sparsely connected hidden layer (with typically 1% connectivity). The connectivity and weights of hidden neurons are fixed and randomly assigned. The weights of output neurons can be learned so t... |
Echo state network : The Echo State Network (ESN) belongs to the Recurrent Neural Network (RNN) family and provide their architecture and supervised learning principle. Unlike Feedforward Neural Networks, Recurrent Neural Networks are dynamic systems and not functions. Recurrent Neural Networks are typically used for: ... |
Echo state network : Echo state networks can be built in different ways. They can be set up with or without directly trainable input-to-output connections, with or without output reservation feedback, with different neurotypes, different reservoir internal connectivity patterns etc. The output weight can be calculated ... |
Echo state network : RNNs were rarely used in practice before the introduction of the ESN, because of the complexity involved in adjusting their connections (e.g., lack of autodifferentiation, susceptibility to vanishing/exploding gradients, etc.). RNN training algorithms were slow and often vulnerable to issues, such ... |
Intrinsic motivation (artificial intelligence) : Intrinsic motivation in the study of artificial intelligence and any robotics is a mechanism for enabling artificial agents (including robots) to exhibit inherently rewarding behaviours such as exploration and curiosity, grouped under the same term in the study of psycho... |
Intrinsic motivation (artificial intelligence) : An intelligent agent is intrinsically motivated to act if the information content alone, or the experience resulting from the action, is the motivating factor. Information content in this context is measured in the information-theoretic sense of quantifying uncertainty. ... |
Intrinsic motivation (artificial intelligence) : The study of intrinsic motivation in psychology and neuroscience began in the 1950s with some psychologists explaining exploration through drives to manipulate and explore, however, this homeostatic view was criticised by White. An alternative explanation from Berlyne in... |
Intrinsic motivation (artificial intelligence) : An influential early computational approach to implement artificial curiosity in the early 1990s by Schmidhuber, has since been developed into a "Formal theory of creativity, fun, and intrinsic motivation”. Intrinsic motivation is often studied in the framework of comput... |
Intrinsic motivation (artificial intelligence) : Oudeyer and Kaplan have made a substantial contribution to the study of intrinsic motivation. They define intrinsic motivation based on Berlyne's theory, and divide approaches to the implementation of intrinsic motivation into three categories that broadly follow the roo... |
Intrinsic motivation (artificial intelligence) : Intrinsically motivated (or curiosity-driven) learning is an emerging research topic in artificial intelligence and developmental robotics that aims to develop agents that can learn general skills or behaviours, that can be deployed to improve performance in extrinsic ta... |
Intrinsic motivation (artificial intelligence) : Reinforcement Learning Markov decision process Motivation Predictive coding Perceptual control theory == References == |
Gemini (language model) : Gemini is a family of multimodal large language models developed by Google DeepMind, and the successor to LaMDA and PaLM 2. Comprising Gemini Ultra, Gemini Pro, Gemini Flash, and Gemini Nano, it was announced on December 6, 2023, positioned as a competitor to OpenAI's GPT-4. It powers the chat... |
Gemini (language model) : The first generation of Gemini ("Gemini 1") has three models, with the same architecture. They are decoder-only transformers, with modifications to allow efficient training and inference on TPUs. They have a context length of 32,768 tokens, with multi-query attention. Two versions of Gemini Na... |
Gemini (language model) : Gemini's launch was preluded by months of intense speculation and anticipation, which MIT Technology Review described as "peak AI hype". In August 2023, Dylan Patel and Daniel Nishball of research firm SemiAnalysis penned a blog post declaring that the release of Gemini would "eat the world" a... |
Gemini (language model) : Gato, a multimodal neural network developed by DeepMind Gemini Robotics |
Gemini (language model) : Official website Press release via The Keyword White paper for 1.0 and 1.5 |
GPT-4.5 : GPT-4.5 (codenamed Orion) is a large language model within OpenAI's GPT series. It was released on February 27, 2025. GPT-4.5 can be accessed by Plus and Pro users through the model picker on web, mobile, and desktop, with plans to expand to other tiers. It can also be accessed via the OpenAI API or the OpenA... |
GPT-4.5 : It was primarily trained using unsupervised learning, which improves its ability to recognize patterns, draw connections, and generate creative insights without reasoning. This method was combined with supervised fine-tuning and reinforcement learning from human feedback. It was trained using Microsoft Azure.... |
GPT-4.5 : Cade Metz, writing for New York Times, stated that the model "signifies the end of an era" and was "unlikely to generate as much excitement as GPT-4". Many other outlets, such as The Verge and Axios, also covered the model's release. == References == |
Undetectable.ai : Undetectable AI (or Undetectable.ai) is an artificial intelligence content detection and modification software designed to identify and alter artificially generated text, such as that produced by large language models. |
Undetectable.ai : Undetectable AI was developed by Bars Juhasz, a PhD student from Loughborough University, along with Christian Perry and Devan Leos. It was publicly released in May 2023. |
Undetectable.ai : Undetectable AI has been discussed in technology and news outlets such as TechTudo and The Inquirer, and others such as Hollywood Life and OK! Magazine. |
Undetectable.ai : GPTZero Turnitin Content similarity detection |
Undetectable.ai : The software says my student cheated using AI. They say they're innocent. Who do I believe? |
FaceNet : FaceNet is a facial recognition system developed by Florian Schroff, Dmitry Kalenichenko and James Philbina, a group of researchers affiliated with Google. The system was first presented at the 2015 IEEE Conference on Computer Vision and Pattern Recognition. The system uses a deep convolutional neural network... |
FaceNet : On the widely used Labeled Faces in the Wild (LFW) dataset, the FaceNet system achieved an accuracy of 99.63% which is the highest score on LFW in the unrestricted with labeled outside data protocol. On YouTube Faces DB the system achieved an accuracy of 95.12%. |
FaceNet : DeepFace FindFace |
FaceNet : Rajesh Gopakumar; Karunagar A; Kotegar, M.; Vishal Anand (September 2023). "A Quantitative Study on the FaceNet System": in Proceedings of ICACCP 2023. Singapore: Springer Nature. pp. 211–222. ISBN 9789819942848. Ivan William; De Rosal Ignatius Moses Setiadi; Eko Hari Rachmawanto; Heru Agus Santoso; Christy A... |
Cognitive robotics : Cognitive Robotics or Cognitive Technology is a subfield of robotics concerned with endowing a robot with intelligent behavior by providing it with a processing architecture that will allow it to learn and reason about how to behave in response to complex goals in a complex world. Cognitive robotic... |
Cognitive robotics : While traditional cognitive modeling approaches have assumed symbolic coding schemes as a means for depicting the world, translating the world into these kinds of symbolic representations has proven to be problematic if not untenable. Perception and action and the notion of symbolic representation ... |
Cognitive robotics : Cognitive robotics views human or animal cognition as a starting point for the development of robotic information processing, as opposed to more traditional Artificial Intelligence techniques. Target robotic cognitive capabilities include perception processing, attention allocation, anticipation, p... |
Cognitive robotics : Some researchers in cognitive robotics have tried using architectures such as (ACT-R and Soar (cognitive architecture)) as a basis of their cognitive robotics programs. These highly modular symbol-processing architectures have been used to simulate operator performance and human performance when mo... |
Cognitive robotics : Some of the fundamental questions to still be answered in cognitive robotics are: How much human programming should or can be involved to support the learning processes? How can one quantify progress? Some of the adopted ways is the reward and punishment. But what kind of reward and what kind of pu... |
Cognitive robotics : Cognitive Robotics book by Hooman Samani, takes a multidisciplinary approach to cover various aspects of cognitive robotics such as artificial intelligence, physical, chemical, philosophical, psychological, social, cultural, and ethical aspects. |
Cognitive robotics : Artificial intelligence Intelligent agent Cognitive architecture Cognitive science Cybernetics Developmental robotics Embodied cognitive science Epigenetic robotics Evolutionary robotics Hybrid intelligent system iCub Intelligent control |
Cognitive robotics : The Symbolic and Subsymbolic Robotic Intelligence Control System (SS-RICS) Intelligent Systems Group - University of Utrecht The Cognitive Robotics Group - University of Toronto The IDSIA Robotics Lab and Cognitive Robotics Lab of Juergen Schmidhuber What Does the Future Hold for Cognitive Robots? ... |
Cognitive robotics : RoboBusiness: Robots that Dream of Being Better www.Conscious-Robots.com The Cognitive Robotics Association |
Multivariate adaptive regression spline : In statistics, multivariate adaptive regression splines (MARS) is a form of regression analysis introduced by Jerome H. Friedman in 1991. It is a non-parametric regression technique and can be seen as an extension of linear models that automatically models nonlinearities and in... |
Multivariate adaptive regression spline : This section introduces MARS using a few examples. We start with a set of data: a matrix of input variables x, and a vector of the observed responses y, with a response for each row in x. For example, the data could be: Here there is only one independent variable, so the x matr... |
Multivariate adaptive regression spline : MARS builds models of the form f ^ ( x ) = ∑ i = 1 k c i B i ( x ) . (x)=\sum _^c_B_(x). The model is a weighted sum of basis functions B i ( x ) (x) . Each c i is a constant coefficient. For example, each line in the formula for ozone above is one basis function multiplied by... |
Multivariate adaptive regression spline : A key part of MARS models are hinge functions taking the form max ( 0 , x − c ) or max ( 0 , c − x ) where c is a constant, called the knot. The figure on the right shows a mirrored pair of hinge functions with a knot at 3.1. A hinge function is zero for part of its range, s... |
Multivariate adaptive regression spline : MARS builds a model in two phases: the forward and the backward pass. This two-stage approach is the same as that used by recursive partitioning trees. |
Multivariate adaptive regression spline : No regression modeling technique is best for all situations. The guidelines below are intended to give an idea of the pros and cons of MARS, but there will be exceptions to the guidelines. It is useful to compare MARS to recursive partitioning and this is done below. (Recursive... |
Multivariate adaptive regression spline : Generalized linear models (GLMs) can be incorporated into MARS models by applying a link function after the MARS model is built. Thus, for example, MARS models can incorporate logistic regression to predict probabilities. Non-linear regression is used when the underlying form o... |
Multivariate adaptive regression spline : Linear regression Local regression Rational function modeling Segmented regression Spline interpolation Spline regression |
Multivariate adaptive regression spline : Hastie T., Tibshirani R., and Friedman J.H. (2009) The Elements of Statistical Learning, 2nd edition. Springer, ISBN 978-0-387-84857-0 (has a section on MARS) Faraway J. (2005) Extending the Linear Model with R, CRC, ISBN 978-1-58488-424-8 (has an example using MARS with R) Hep... |
Multivariate adaptive regression spline : Several free and commercial software packages are available for fitting MARS-type models. Free software R packages: earth function in the earth package mars function in the mda package polymars function in the polspline package. Not Friedman's MARS. bass function in the BASS pa... |
Legal information retrieval : Legal information retrieval is the science of information retrieval applied to legal text, including legislation, case law, and scholarly works. Accurate legal information retrieval is important to provide access to the law to laymen and legal professionals. Its importance has increased be... |
Legal information retrieval : Application of standard information retrieval techniques to legal text can be more difficult than application in other subjects. One key problem is that the law rarely has an inherent taxonomy. Instead, the law is generally filled with open-ended terms, which may change over time. This can... |
Legal information retrieval : Computer-assisted legal research |
Pronunciation assessment : Automatic pronunciation assessment is the use of speech recognition to verify the correctness of pronounced speech, as distinguished from manual assessment by an instructor or proctor. Also called speech verification, pronunciation evaluation, and pronunciation scoring, the main application o... |
Pronunciation assessment : The earliest work on pronunciation assessment avoided measuring genuine listener intelligibility, a shortcoming corrected in 2011 at the Toyohashi University of Technology, and included in the Versant high-stakes English fluency assessment from Pearson and mobile apps from 17zuoye Education &... |
Pronunciation assessment : Although there are as yet no industry-standard benchmarks for evaluating pronunciation assessment accuracy, researchers occasionally release evaluation speech corpuses for others to use for improving assessment quality. Such evaluation databases often emphasize formally unaccented pronunciati... |
Pronunciation assessment : Some promising areas for improvement being developed in 2024 include articulatory feature extraction and transfer learning to suppress unnecessary corrections. Other interesting advances under development include "augmented reality" interfaces for mobile devices using optical character recogn... |
Pronunciation assessment : Phonetics Speech segmentation — often called "forced alignment" (of audio to its expected phonemes) in this context Statistical classification |
Pronunciation assessment : International Speech Communication Association (ISCA) Special Interest Group on Speech and Language Technologies in Education (SLaTE) |
Artificial intelligence content detection : Artificial intelligence detection software aims to determine whether some content (text, image, video or audio) was generated using artificial intelligence (AI). However, the reliability of such software is a topic of debate, and there are concerns about the potential misappl... |
Artificial intelligence content detection : Multiple AI detection tools have been demonstrated to be unreliable in terms of accurately and comprehensively detecting AI-generated text. In a study conducted by Weber-Wulff et al., and published in 2023, researchers evaluated 14 detection tools including Turnitin and GPT Z... |
Artificial intelligence content detection : For text, this is usually done to prevent alleged plagiarism, often by detecting repetition of words as telltale signs that a text was AI-generated (including AI hallucinations). They are often used by teachers marking their students, usually on an ad hoc basis. Following the... |
Artificial intelligence content detection : There is software available designed to bypass AI text detection. A study published in August 2023 analyzed 20 abstracts from papers published in the Eye Journal, which were then paraphrased using GPT-4.0. The AI-paraphrased abstracts were examined for plagiarism using QueTex... |
Artificial intelligence content detection : One shortcoming of most AI content detection software is their inability to identify AI-generated text in any language. Large language models (LLMs) like ChatGPT, Claude, and Gemini can write in different languages, but traditional AI text detection tools have primarily been ... |
Artificial intelligence content detection : Several purported AI image detection software exist, to detect AI-generated images (for example, those originating from Midjourney or DALL-E). They are not completely reliable. Others claim to identify video and audio deepfakes, but this technology is also not fully reliable ... |
Artificial intelligence content detection : AI alignment Artificial intelligence and elections Comparison of anti-plagiarism software Content similarity detection Hallucination (artificial intelligence) Natural language processing == References == |
Decision list : Decision lists are a representation for Boolean functions which can be easily learnable from examples. Single term decision lists are more expressive than disjunctions and conjunctions; however, 1-term decision lists are less expressive than the general disjunctive normal form and the conjunctive normal... |
Decision list : A decision list (DL) of length r is of the form: if f1 then output b1 else if f2 then output b2 ... else if fr then output br where fi is the ith formula and bi is the ith boolean for i ∈ . The last if-then-else is the default case, which means formula fr is always equal to true. A k-DL is a decision ... |
Decision list : Decision stump == References == |
Machine learning control : Machine learning control (MLC) is a subfield of machine learning, intelligent control, and control theory which aims to solve optimal control problems with machine learning methods. Key applications are complex nonlinear systems for which linear control theory methods are not applicable. |
Machine learning control : Four types of problems are commonly encountered: Control parameter identification: MLC translates to a parameter identification if the structure of the control law is given but the parameters are unknown. One example is the genetic algorithm for optimizing coefficients of a PID controller or ... |
Machine learning control : MLC has been successfully applied to many nonlinear control problems, exploring unknown and often unexpected actuation mechanisms. Example applications include: spacecraft attitude control, thermal control of buildings, feedback control of turbulence, and remotely operated underwater vehicles... |
Radial basis function network : In the field of mathematical modeling, a radial basis function network is an artificial neural network that uses radial basis functions as activation functions. The output of the network is a linear combination of radial basis functions of the inputs and neuron parameters. Radial basis f... |
Radial basis function network : Radial basis function (RBF) networks typically have three layers: an input layer, a hidden layer with a non-linear RBF activation function and a linear output layer. The input can be modeled as a vector of real numbers x ∈ R n \in \mathbb ^ . The output of the network is then a scalar ... |
Radial basis function network : RBF networks are typically trained from pairs of input and target values x ( t ) , y ( t ) (t),y(t) , t = 1 , … , T by a two-step algorithm. In the first step, the center vectors c i _ of the RBF functions in the hidden layer are chosen. This step can be performed in several ways; cen... |
Radial basis function network : Radial basis function kernel instance-based learning In Situ Adaptive Tabulation Predictive analytics Chaos theory Hierarchical RBF Cerebellar model articulation controller Instantaneously trained neural networks |
Radial basis function network : J. Moody and C. J. Darken, "Fast learning in networks of locally tuned processing units," Neural Computation, 1, 281-294 (1989). Also see Radial basis function networks according to Moody and Darken T. Poggio and F. Girosi, "Networks for approximation and learning," Proc. IEEE 78(9), 148... |
Argumentation framework : In artificial intelligence and related fields, an argumentation framework is a way to deal with contentious information and draw conclusions from it using formalized arguments. In an abstract argumentation framework, entry-level information is a set of abstract arguments that, for instance, re... |
Argumentation framework : The abstract framework of Dung has been instantiated to several particular cases. |
Jais (language model) : Jais is an open-source large language model developed in the United Arab Emirates and launched in August 2023. It was trained on both English- and Arabic-language data. |
Jais (language model) : Jais is named after Jebel Jais, the highest mountain in the United Arab Emirates. It was created in collaboration between Inception, a subsidiary of G42, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) in Abu Dhabi and California-based Cerebras Systems. |
Jais (language model) : Jais has 13 billion parameters, with an update for 30 billion in the works as of October 2023. It was trained for over 21 days by a team in Abu Dhabi on a subset of Cerebras's Condor Galaxy 1 supercomputer. Its training dataset consisted of Arabic and English, some containing computer code. Acco... |
Jais (language model) : Jais focuses exclusively on English and Arabic translations. Additional functionality for working with images, graphs and tabular data is planned for future releases. |
Llama.cpp : llama.cpp is an open source software library that performs inference on various large language models such as Llama. It is co-developed alongside the GGML project, a general-purpose tensor library. Command-line tools are included with the library, alongside a server with a simple web interface. |
Llama.cpp : Towards the end of September 2022, Georgi Gerganov started work on the GGML library, a C library implementing tensor algebra. Gerganov developed the library with the intention of strict memory management and multi-threading. The creation of GGML was inspired by Fabrice Bellard's work on LibNC. Before llama.... |
Llama.cpp : llama.cpp began development in March 2023 by Georgi Gerganov as an implementation of the Llama inference code in pure C/C++ with no dependencies. This improved performance on computers without GPU or other dedicated hardware, which was a goal of the project. llama.cpp gained traction with users who lacked s... |
Llama.cpp : llama.cpp supports multiple hardware targets including x86, ARM, CUDA, Metal, Vulkan (version 1.2 or greater) and SYCL. These back-ends make up the GGML tensor library which is used by the front-end model-specific llama.cpp code. llama.cpp supports ahead of time model quantization as opposed to on-the-fly q... |
Llama.cpp : The GGUF (GGML Universal File) file format is a binary format that stores both tensors and metadata in a single file, and is designed for fast saving, and loading of model data. It was introduced in August 2023 by the llama.cpp project to better maintain backwards compatibility as support was added for othe... |
Llama.cpp : == References == |
Computational neurogenetic modeling : Computational neurogenetic modeling (CNGM) is concerned with the study and development of dynamic neuronal models for modeling brain functions with respect to genes and dynamic interactions between genes. These include neural network models and their integration with gene network m... |
Computational neurogenetic modeling : While the term artificial neural network is usually used in computational neurogenetic modeling to refer to models of the central nervous system meant to possess biological accuracy, the general use of the term can be applied to many gene regulatory networks as well. |
Computational neurogenetic modeling : Both gene regulatory networks and artificial neural networks have two main strategies for improving their accuracy. In both cases the output of the network is measured against known biological data using some function, and subsequent improvements are made by altering the structure ... |
Computational neurogenetic modeling : A variety of potential applications have been suggested for accurate computational neurogenetic models, such as simulating genetic diseases, examining the impact of potential treatments, better understanding of learning and cognition, and development of hardware able to interface w... |
Computational neurogenetic modeling : http://ecos.watts.net.nz/Algorithms/ |
Production (computer science) : A production or production rule in computer science is a rewrite rule specifying a symbol substitution that can be recursively performed to generate new symbol sequences. A finite set of productions P is the main component in the specification of a formal grammar (specifically a generat... |
Production (computer science) : To generate a string in the language, one begins with a string consisting of only a single start symbol, and then successively applies the rules (any number of times, in any order) to rewrite this string. This stops when a string containing only terminals is obtained. The language consis... |
Production (computer science) : Formal grammar Finite automata Generative grammar L-system Rewrite rule Backus–Naur form (A compact form for writing the productions of a context-free grammar.) Phrase structure rule Post canonical system (Emil Post's production systems- a model of computation.) == References == |
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