text stringlengths 12 14.7k |
|---|
AI alignment : Specification gaming examples in AI, via DeepMind |
Behavior informatics : Behavior informatics (BI) is the informatics of behaviors so as to obtain behavior intelligence and behavior insights. BI is a research method combining science and technology, specifically in the area of engineering. The purpose of BI includes analysis of current behaviors as well as the inferen... |
Behavior informatics : Behavior informatics covers behavior analytics which focuses on analysis and learning of behavioral data. |
Behavior informatics : From an Informatics perspective, a behavior consists of three key elements: actors (behavioral subjects and objects), operations (actions, activities) and interactions (relationships), and their properties. A behavior can be represented as a behavior vector, all behaviors of an actor or an actor ... |
Behavior informatics : Behavior Informatics is being used in a variety of settings, including but not limited to health care management, telecommunications, marketing, and security. Behavior Informatics is a turning point for the health care system. Behavior Informatics provides a manner in which to analyze and organiz... |
Highway network : In machine learning, the Highway Network was the first working very deep feedforward neural network with hundreds of layers, much deeper than previous neural networks. It uses skip connections modulated by learned gating mechanisms to regulate information flow, inspired by long short-term memory (LSTM... |
Highway network : The model has two gates in addition to the H ( W H , x ) ,x) gate: the transform gate T ( W T , x ) ,x) and the carry gate C ( W C , x ) ,x) . The latter two gates are non-linear transfer functions (specifically sigmoid by convention). The function H can be any desired transfer function. The carry ga... |
Highway network : The structure of a hidden layer in the Highway Network follows the equation: y = H ( x , W H ) ⋅ T ( x , W T ) + x ⋅ C ( x , W C ) = H ( x , W H ) ⋅ T ( x , W T ) + x ⋅ ( 1 − T ( x , W T ) ) y=H(x,W_)\cdot T(x,W_)+x\cdot C(x,W_)\\=H(x,W_)\cdot T(x,W_)+x\cdot (1-T(x,W_))\end |
Highway network : Sepp Hochreiter analyzed the vanishing gradient problem in 1991 and attributed to it the reason why deep learning did not work well. To overcome this problem, Long Short-Term Memory (LSTM) recurrent neural networks have residual connections with a weight of 1.0 in every LSTM cell (called the constant ... |
Learnable function class : In statistical learning theory, a learnable function class is a set of functions for which an algorithm can be devised to asymptotically minimize the expected risk, uniformly over all probability distributions. The concept of learnable classes are closely related to regularization in machine ... |
Learnable function class : If the true relationship between y and x is y ∼ f ∗ ( x ) (x) , then by selecting the appropriate loss function, f ∗ can always be expressed as the minimizer of the expected loss across all possible functions. That is, f ∗ = arg min f ∈ F ∗ I P ( f ) =\arg \min _^I_(f) Here we let F ∗ ^ ... |
Learnable function class : A good example where learnable classes are used is the so-called Tikhonov regularization in reproducing kernel Hilbert space (RKHS). Specifically, let F ∗ be an RKHS, and | | ⋅ | | 2 be the norm on F ∗ given by its inner product. It is shown in that F = =\\leq \gamma \ is a learnable clas... |
Learnable function class : Part ( a ) in (2) is closely linked to empirical process theory in statistics, where the empirical risk ^L(y_,f(x_)),f\in \ are known as empirical processes. In this field, the function class F that satisfies the stochastic convergence are known as uniform Glivenko–Cantelli classes. It has... |
Product of experts : Product of experts (PoE) is a machine learning technique. It models a probability distribution by combining the output from several simpler distributions. It was proposed by Geoffrey Hinton in 1999, along with an algorithm for training the parameters of such a system. The core idea is to combine se... |
Product of experts : Mixture of experts Boltzmann machine |
Product of experts : Product of experts article in Scholarpedia Geoffrey Hinton's articles on PoE |
Lexical substitution : Lexical substitution is the task of identifying a substitute for a word in the context of a clause. For instance, given the following text: "After the match, replace any remaining fluid deficit to prevent chronic dehydration throughout the tournament", a substitute of game might be given. Lexical... |
Lexical substitution : In order to evaluate automatic systems on lexical substitution, a task was organized at the Semeval-2007 evaluation competition held in Prague in 2007. A Semeval-2010 task on cross-lingual lexical substitution has also taken place. |
Lexical substitution : The skip-gram model takes words with similar meanings into a vector space (collection of objects that can be added together and multiplied by numbers) that are found close to each other in N-dimensions (list of items). A variety of neural networks (computer system modeled after a human brain) are... |
Lexical substitution : Lexical semantics Semantic compression SemEval Word sense |
Lexical substitution : D. McCarthy, R. Navigli. The English Lexical Substitution Task. Language Resources and Evaluation, 43(2), Springer, 2009, pp. 139–159. D. McCarthy, R. Navigli. SemEval-2007 Task 10: English Lexical Substitution Task. Proc. of Semeval-2007 Workshop (SEMEVAL), in the 45th Annual Meeting of the Asso... |
Spatial embedding : Spatial embedding is one of feature learning techniques used in spatial analysis where points, lines, polygons or other spatial data types. representing geographic locations are mapped to vectors of real numbers. Conceptually it involves a mathematical embedding from a space with many dimensions per... |
Spatial embedding : Geographic data can take many forms: text, images, graphs, trajectories, polygons. Depending on the task, there may be a need to combine multimodal data from different sources. The next section describes examples of different types of data and their uses. |
Spatial embedding : POI recommendation - generating personalized point of interest recommendations based on user preferences. Next/future location prediction - prediction of the next location a person will go to based on their historical trajectory. Zone functions classification - based on different mobility of people ... |
Spatial embedding : Some of the data analyzed has a timestamp associated with it. In some cases of data analysis this information is omitted and in others it is used to divide the set into groups. The most common division is the separation of weekdays from weekends or division into hours of the day. This is particularl... |
Extension neural network : Extension neural network is a pattern recognition method found by M. H. Wang and C. P. Hung in 2003 to classify instances of data sets. Extension neural network is composed of artificial neural network and extension theory concepts. It uses the fast and adaptive learning capability of neural ... |
Extension neural network : Extension theory was first proposed by Cai in 1983 to solve contradictory problems. While classical mathematics is familiar with quantity and forms of objects, extension theory transforms these objects to matter-element models. where in matter R , N is the name or type, C is its characteri... |
Extension neural network : Extension neural network has a neural network like appearance. Weight vector resides between the input nodes and output nodes. Output nodes are the representation of input nodes by passing them through the weight vector. There are total number of input and output nodes are represented by n a... |
Extension neural network : Wang, M. H.; Tseng, Y. F.; Chen, H. C.; Chao, K. H. (2009). "A novel clustering algorithm based on the extension theory and genetic algorithm". Expert Systems with Applications. 36 (4): 8269–8276. doi:10.1016/j.eswa.2008.10.010. Kuei-Hsiang Chao, Meng-Hui Wang, and Chia-Chang Hsu. A novel res... |
Reasoning language model : Reasoning language models are artificial intelligence systems that combine natural language processing with structured reasoning capabilities. These models are usually constructed by prompting, supervised finetuning (SFT), and reinforcement learning (RL) initialized with pretrained language m... |
Reasoning language model : A language model is a generative model of a training dataset of texts. Prompting means constructing a text prompt, such that, conditional on the text prompt, the language model generates a solution to the task. Prompting can be applied to a pretrained model ("base model"), a base model that h... |
Reasoning language model : A base model can be finetuned on a dataset of reasoning tasks with example solutions and reasoning traces. The finetuned model would then be able to generate reasoning traces for a given problem. As it is expensive to get humans to write reasoning traces for a SFT dataset, researchers have pr... |
Reasoning language model : A pretrained language model can be further trained by RL. In the RL formalism, a generative language model is a policy π . A prompt specifying a task to solve is an environmental state x , and the response of the language model to the prompt is an action y . The probability that the langua... |
Reasoning language model : Prompt engineering was discovered in GPT-3 as "few-shot learning", which began a period of research into "eliciting" capacities of pretrained language models. It was then found that a model could be prompted to perform CoT reasoning, which improves its performance on reasoning tasks. |
Reasoning language model : The reasoning ability of language models are usually tested on problems with unambiguous solutions that can be cheaply checked, and requires reasoning when solved by a human. Such problems are usually in mathematics and competitive programming. The answer is usually an array of integers, a mu... |
Reasoning language model : Automated reasoning Reflection (artificial intelligence) Large language model |
Reasoning language model : Fortes, Armando (2025-01-27), atfortes/Awesome-LLM-Reasoning, retrieved 2025-01-27 Huang, Jie; Chang, Kevin Chen-Chuan (2023-05-26), Towards Reasoning in Large Language Models: A Survey, arXiv:2212.10403 Besta, Maciej; Barth, Julia; Schreiber, Eric; Kubicek, Ales; Catarino, Afonso; Gerstenber... |
Active learning (machine learning) : Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs. The human user must possess knowledge/expertise in the problem domain, incl... |
Active learning (machine learning) : Let T be the total set of all data under consideration. For example, in a protein engineering problem, T would include all proteins that are known to have a certain interesting activity and all additional proteins that one might want to test for that activity. During each iteration,... |
Active learning (machine learning) : Pool-based sampling: In this approach, which is the most well known scenario, the learning algorithm attempts to evaluate the entire dataset before selecting data points (instances) for labeling. It is often initially trained on a fully labeled subset of the data using a machine-lea... |
Active learning (machine learning) : Algorithms for determining which data points should be labeled can be organized into a number of different categories, based upon their purpose: Balance exploration and exploitation: the choice of examples to label is seen as a dilemma between the exploration and the exploitation ov... |
Active learning (machine learning) : Some active learning algorithms are built upon support-vector machines (SVMs) and exploit the structure of the SVM to determine which data points to label. Such methods usually calculate the margin, W, of each unlabeled datum in TU,i and treat W as an n-dimensional distance from tha... |
Active learning (machine learning) : List of datasets for machine learning research Sample complexity Bayesian Optimization Reinforcement learning |
Active learning (machine learning) : Improving Generalization with Active Learning, David Cohn, Les Atlas & Richard Ladner, Machine Learning 15, 201–221 (1994). https://doi.org/10.1007/BF00993277 Balcan, Maria-Florina & Hanneke, Steve & Wortman, Jennifer. (2008). The True Sample Complexity of Active Learning.. 45-56. h... |
Robotic process automation : Robotic process automation (RPA) is a form of business process automation that is based on software robots (bots) or artificial intelligence (AI) agents. RPA should not be confused with artificial intelligence as it is based on automation technology following a predefined workflow. It is so... |
Robotic process automation : The typical benefits of robotic automation include reduced cost; increased speed, accuracy, and consistency; improved quality and scalability of production. Automation can also provide extra security, especially for sensitive data and financial services. As a form of automation, the concept... |
Robotic process automation : The hosting of RPA services also aligns with the metaphor of a software robot, with each robotic instance having its own virtual workstation, much like a human worker. The robot uses keyboard and mouse controls to take actions and execute automations. Normally, all of these actions take pla... |
Robotic process automation : According to Harvard Business Review, most operations groups adopting RPA have promised their employees that automation would not result in layoffs. Instead, workers have been redeployed to do more interesting work. One academic study highlighted that knowledge workers did not feel threaten... |
Robotic process automation : Unassisted RPA, or RPAAI, is the next generation of RPA related technologies. Technological advancements around artificial intelligence allow a process to be run on a computer without needing input from a user. |
Robotic process automation : Hyperautomation is the application of advanced technologies like RPA, artificial intelligence, machine learning (ML) and process mining to augment workers and automate processes in ways that are significantly more impactful than traditional automation capabilities. Hyperautomation is the co... |
Robotic process automation : Back office clerical processes outsourced by large organisations - particularly those sent offshore - tend to be simple and transactional in nature, requiring little (if any) analysis or subjective judgement. This would seem to make an ideal starting point for organizations beginning to ado... |
Robotic process automation : While robotic process automation has many benefits including cost efficiency and consistency in performance, it also has some limitations. Current RPA solutions demand continual technical support to handle system changes, therefore it lacks the ability to autonomously adapt to new condition... |
Robotic process automation : RPA is based on automation technology following a predefined workflow, and artificial intelligence is data-driven and focuses on processing information to make predictions. Therefore, there is a distinct difference between how the two systems operate. AI aims to mimic human intelligence, wh... |
Robotic process automation : Voice recognition and digital dictation software linked to join up business processes for straight through processing without manual intervention Specialised remote infrastructure management software featuring automated investigation and resolution of problems, using robots for the first li... |
Robotic process automation : Automation Business process automation |
Robotic process automation : Jobs, productivity and the great decoupling, by Professor McAfee, Principal Research Scientist at MIT's Center for Digital Business. Rise of the software machines, Economist Magazine. London School of Economics Releases First in a Series of RPA Case Studies, Reuters Humans and Machines: The... |
AI safety : AI safety is an interdisciplinary field focused on preventing accidents, misuse, or other harmful consequences arising from artificial intelligence (AI) systems. It encompasses machine ethics and AI alignment, which aim to ensure AI systems are moral and beneficial, as well as monitoring AI systems for risk... |
AI safety : Scholars discuss current risks from critical systems failures, bias, and AI-enabled surveillance, as well as emerging risks like technological unemployment, digital manipulation, weaponization, AI-enabled cyberattacks and bioterrorism. They also discuss speculative risks from losing control of future artifi... |
AI safety : Risks from AI began to be seriously discussed at the start of the computer age: Moreover, if we move in the direction of making machines which learn and whose behavior is modified by experience, we must face the fact that every degree of independence we give the machine is a degree of possible defiance of o... |
AI safety : AI safety research areas include robustness, monitoring, and alignment. |
AI safety : AI governance is broadly concerned with creating norms, standards, and regulations to guide the use and development of AI systems. |
AI safety : AI alignment Artificial intelligence and elections Artificial intelligence detection software |
AI safety : Unsolved Problems in ML Safety On the Opportunities and Risks of Foundation Models An Overview of Catastrophic AI Risks AI Accidents: An Emerging Threat Engineering a Safer World |
Sayre's paradox : Sayre's paradox is a dilemma encountered in the design of automated handwriting recognition systems. A standard statement of the paradox is that a cursively written word cannot be recognized without being segmented and cannot be segmented without being recognized. The paradox was first articulated in ... |
Sayre's paradox : It is relatively easy to design automated systems capable of recognizing words inscribed in a printed format. Such words are segmented into letters by the very act of writing them on the page. Given templates matching typical letter shapes in a given language, individual letters can be identified with... |
Sayre's paradox : One way of ameliorating the adverse effects of the paradox is to normalize the word inscriptions to be recognized. Normalization amounts to eliminating idiosyncrasies in the penmanship of the writer, such as unusual slope of the letters and unusual slant of the cursive line. This procedure can increas... |
Sayre's paradox : Segmentation is accurate to the extent that it matches distinctions among letters in the actual inscriptions presented to the system for recognition (the input data). This is sometimes referred to as “explicit segmentation”. “Implicit segmentation,” by contrast, is division of the cursive line into mo... |
Sayre's paradox : Kenneth M. Sayre and the Philosophic Institute. |
Perusall : Perusall is a social web annotation tool intended for use by students at schools and universities. It allows users to annotate the margins of a text in a virtual group setting that is similar to social media—with upvoting, emojis, chat functionality, and notification. It also includes automatic AI grading. |
Perusall : Perusall began as a research project at Harvard University. It later became an educational product for students and teachers. As of 2024, Perusall states more than 5 million students have used the tool at over 5,000 educational institutions in 112 countries." |
Perusall : Perusall integrates with learning management systems such as Moodle, Canvas and Blackboard to aid with collaborative annotation. The tool supports annotation of a range of media including text, images, equations, videos, PDFs and snapshots of webpages. |
Perusall : Official website |
Artificial intelligence and copyright : In the 2020s, the rapid advancement of deep learning-based generative artificial intelligence models raised questions about whether copyright infringement occurs when such are trained or used. This includes text-to-image models such as Stable Diffusion and large language models s... |
Artificial intelligence and copyright : Since most legal jurisdictions only grant copyright to original works of authorship by human authors, the definition of "originality" is central to the copyright status of AI-generated works. |
Artificial intelligence and copyright : Deep learning models source large data sets from the Internet such as publicly available images and the text of web pages. The text and images are then converted into numeric formats the AI can analyze. A deep learning model identifies patterns linking the encoded text and image ... |
Artificial intelligence and copyright : In some cases, deep learning models may replicate items in their training set when generating output. This behaviour is generally considered an undesired overfitting of a model by AI developers, and has in previous generations of AI been considered a manageable problem. Memorizat... |
Artificial intelligence and copyright : A November 2022 class action lawsuit against Microsoft, GitHub and OpenAI alleged that GitHub Copilot, an AI-powered code editing tool trained on public GitHub repositories, violated the copyright of the repositories' authors, noting that the tool was able to generate source code... |
Artificial intelligence and copyright : Pamela Samuelson: Will Copyright Derail Generative AI Technologies? (Presentation at a Simons Institute workshop on "Alignment, Trust, Watermarking, and Copyright Issues in LLMs", October 17, 2024) - overview over 32 ongoing lawsuits in the US at the time Getting the Innovation E... |
Data annotation : Data annotation is the process of labeling or tagging relevant metadata within a dataset to enable machines to interpret the data accurately. The dataset can take various forms, including images, audio files, video footage, or text. |
Data annotation : Data is a fundamental component in the development of artificial intelligence (AI). Training AI models, particularly in computer vision and natural language processing, requires large volumes of annotated data. Proper annotation ensures that machine learning algorithms can recognize patterns and make ... |
Differentiable neural computer : In artificial intelligence, a differentiable neural computer (DNC) is a memory augmented neural network architecture (MANN), which is typically (but not by definition) recurrent in its implementation. The model was published in 2016 by Alex Graves et al. of DeepMind. |
Differentiable neural computer : DNC indirectly takes inspiration from Von-Neumann architecture, making it likely to outperform conventional architectures in tasks that are fundamentally algorithmic that cannot be learned by finding a decision boundary. So far, DNCs have been demonstrated to handle only relatively simp... |
Differentiable neural computer : DNC networks were introduced as an extension of the Neural Turing Machine (NTM), with the addition of memory attention mechanisms that control where the memory is stored, and temporal attention that records the order of events. This structure allows DNCs to be more robust and abstract t... |
Differentiable neural computer : A bit-by-bit guide to the equations governing differentiable neural computers DeepMind's Differentiable Neural Network Thinks Deeply |
Developmental robotics : Developmental robotics (DevRob), sometimes called epigenetic robotics, is a scientific field which aims at studying the developmental mechanisms, architectures and constraints that allow lifelong and open-ended learning of new skills and new knowledge in embodied machines. As in human children,... |
Developmental robotics : Can a robot learn like a child? Can it learn a variety of new skills and new knowledge unspecified at design time and in a partially unknown and changing environment? How can it discover its body and its relationships with the physical and social environment? How can its cognitive capacities co... |
Developmental robotics : As developmental robotics is a relatively new research field and at the same time very ambitious, many fundamental open challenges remain to be solved. First of all, existing techniques are far from allowing real-world high-dimensional robots to learn an open-ended repertoire of increasingly co... |
Developmental robotics : IEEE Transactions on Cognitive and Developmental Systems (previously known as IEEE Transactions on Autonomous Mental Development): https://cis.ieee.org/publications/t-cognitive-and-developmental-systems |
Developmental robotics : International Conference on Development and Learning: http://www.cogsci.ucsd.edu/~triesch/icdl/ Epigenetic Robotics: https://www.lucs.lu.se/epirob/ ICDL-EpiRob: http://www.icdl-epirob.org/ (the two above joined since 2011) Developmental Robotics: http://cs.brynmawr.edu/DevRob05/ The NSF/DARPA f... |
Developmental robotics : Evolutionary developmental robotics Robot learning |
Spreading activation : Spreading activation is a method for searching associative networks, biological and artificial neural networks, or semantic networks. The search process is initiated by labeling a set of source nodes (e.g. concepts in a semantic network) with weights or "activation" and then iteratively propagati... |
Spreading activation : As it relates to cognitive psychology, spreading activation is the theory of how the brain iterates through a network of associated ideas to retrieve specific information. The spreading activation theory presents the array of concepts within our memory as cognitive units, each consisting of a nod... |
Spreading activation : A directed graph is populated by Nodes[ 1...N ] each having an associated activation value A [ i ] which is a real number in the range [0.0 ... 1.0]. A Link[ i, j ] connects source node[ i ] with target node[ j ]. Each edge has an associated weight W [ i, j ] usually a real number in the range [0... |
Spreading activation : Nils J. Nilsson. "Artificial Intelligence: A New Synthesis". Morgan Kaufmann Publishers, Inc., San Francisco, California, 1998, pages 121-122 Rodriguez, M.A., " Grammar-Based Random Walkers in Semantic Networks", Knowledge-Based Systems, 21(7), 727-739, doi:10.1016/j.knosys.2008.03.030, 2008. Kar... |
Pooling layer : In neural networks, a pooling layer is a kind of network layer that downsamples and aggregates information that is dispersed among many vectors into fewer vectors. It has several uses. It removes redundant information, reducing the amount of computation and memory required, makes the model more robust t... |
Pooling layer : Pooling is most commonly used in convolutional neural networks (CNN). Below is a description of pooling in 2-dimensional CNNs. The generalization to n-dimensions is immediate. As notation, we consider a tensor x ∈ R H × W × C ^ , where H is height, W is width, and C is the number of channels. A pool... |
Pooling layer : In Vision Transformers (ViT), there are the following common kinds of poolings. BERT-like pooling uses a dummy [CLS] token ("classification"). For classification, the output at [CLS] is the classification token, which is then processed by a LayerNorm-feedforward-softmax module into a probability distrib... |
Pooling layer : In graph neural networks (GNN), there are also two forms of pooling: global and local. Global pooling can be reduced to a local pooling where the receptive field is the entire output. Local pooling: a local pooling layer coarsens the graph via downsampling. Local pooling is used to increase the receptiv... |
Pooling layer : In early 20th century, neuroanatomists noticed a certain motif where multiple neurons synapse to the same neuron. This was given a functional explanation as "local pooling", which makes vision translation-invariant. (Hartline, 1940) gave supporting evidence for the theory by electrophysiological experim... |
Pooling layer : Convolutional neural network Subsampling Image scaling Feature extraction Region of interest Graph neural network == References == |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.