title: Machine learning

text: As a scientific endeavor, machine learning grew out of the quest for artificial intelligence. In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what was then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics. Probabilistic reasoning was also employed, especially in automated medical diagnosis.


title: Machine learning

text: However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation. By 1980, expert systems had come to dominate AI, and statistics was out of favor. Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval. Neural networks research had been abandoned by AI and computer science around the same time. This line, too, was continued outside the AI/CS field, as "connectionism", by researchers from other disciplines including Hopfield, Rumelhart, and Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation.


title: Machine learning

text: Machine learning (ML),is reorganized as a separate field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics, fuzzy logic, and probability theory.


title: Machine learning

text: The difference between ML and AI is frequently misunderstood. ML learns and predicts based on passive observations, whereas AI implies an agent interacting with the environment to learn and take actions that maximize its chance of successfully achieving its goals.


title: Machine learning

text: As of 2020, many sources continue to assert that ML remains a subfield of AI. Others have the view that not all ML is part of AI, but only an 'intelligent subset' of ML should be considered AI.


title: Machine learning

text: Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on "known" properties learned from the training data, data mining focuses on the discovery of (previously) "unknown" properties in the data (this is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to "reproduce known" knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously "unknown" knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.


title: Machine learning

text: Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples).


title: Machine learning

text: The difference between optimization and machine learning arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples. Characterizing the generalization of various learning algorithms is an active topic of current research, especially for deep learning algorithms.


title: Machine learning

text: Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalizable predictive patterns. According to Michael I. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics. He also suggested the term data science as a placeholder to call the overall field.


title: Machine learning

text: Leo Breiman distinguished two statistical modeling paradigms: data model and algorithmic model, wherein "algorithmic model" means more or less the machine learning algorithms like Random forest.


title: Machine learning

text: Some statisticians have adopted methods from machine learning, leading to a combined field that they call "statistical learning".


title: Machine learning

text: A core objective of a learner is to generalize from its experience. Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases.


title: Machine learning

text: The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory via the Probably Approximately Correct Learning (PAC) model. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. The bias–variance decomposition is one way to quantify generalization error.


title: Machine learning

text: For the best performance in the context of generalization, the complexity of the hypothesis should match the complexity of the function underlying the data. If the hypothesis is less complex than the function, then the model has under fitted the data. If the complexity of the model is increased in response, then the training error decreases. But if the hypothesis is too complex, then the model is subject to overfitting and generalization will be poorer.


title: Machine learning

text: In addition to performance bounds, learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results: Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time.


title: Machine learning

text: Machine learning approaches are traditionally divided into three broad categories, which correspond to learning paradigms, depending on the nature of the "signal" or "feedback" available to the learning system:


title: Machine learning

text: Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. The data is known as training data, and consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal. In the mathematical model, each training example is represented by an array or vector, sometimes called a feature vector, and the training data is represented by a matrix. Through iterative optimization of an objective function, supervised learning algorithms learn a function that can be used to predict the output associated with new inputs. An optimal function will allow the algorithm to correctly determine the output for inputs that were not a part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task.


title: Machine learning

text: Types of supervised-learning algorithms include active learning, classification and regression. Classification algorithms are used when the outputs are restricted to a limited set of values, and regression algorithms are used when the outputs may have any numerical value within a range. As an example, for a classification algorithm that filters emails, the input would be an incoming email, and the output would be the name of the folder in which to file the email.


title: Machine learning

text: Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are. It has applications in ranking, recommendation systems, visual identity tracking, face verification, and speaker verification.


title: Machine learning

text: Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms, therefore, learn from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. A central application of unsupervised learning is in the field of density estimation in statistics, such as finding the probability density function. Though unsupervised learning encompasses other domains involving summarizing and explaining data features.


title: Machine learning

text: Cluster analysis is the assignment of a set of observations into subsets (called "clusters") so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some "similarity metric" and evaluated, for example, by "internal compactness", or the similarity between members of the same cluster, and "separation", the difference between clusters. Other methods are based on "estimated density" and "graph connectivity".


title: Machine learning

text: Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce a considerable improvement in learning accuracy.


title: Machine learning

text: In weakly supervised learning, the training labels are noisy, limited, or imprecise; however, these labels are often cheaper to obtain, resulting in larger effective training sets.


title: Machine learning

text: Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Due to its generality, the field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In machine learning, the environment is typically represented as a Markov decision process (MDP). Many reinforcement learning algorithms use dynamic programming techniques. Reinforcement learning algorithms do not assume knowledge of an exact mathematical model of the MDP, and are used when exact models are infeasible. Reinforcement learning algorithms are used in autonomous vehicles or in learning to play a game against a human opponent.


title: Machine learning

text: Dimensionality reduction is a process of reducing the number of random variables under consideration by obtaining a set of principal variables. In other words, it is a process of reducing the dimension of the feature set, also called the "number of features". Most of the dimensionality reduction techniques can be considered as either feature elimination or extraction.


title: Machine learning

text: One of the popular methods of dimensionality reduction is principal component analysis (PCA). PCA involves changing higher-dimensional data (e.g., 3D) to a smaller space (e.g., 2D). This results in a smaller dimension of data (2D instead of 3D), while keeping all original variables in the model without changing the data.


title: Machine learning

text: The manifold hypothesis proposes that high-dimensional data sets lie along low-dimensional manifolds, and many dimensionality reduction techniques make this assumption, leading to the area of manifold learning and manifold regularization.


title: Machine learning

text: Other approaches have been developed which don't fit neatly into this three-fold categorization, and sometimes more than one is used by the same machine learning system. For example topic modeling, meta-learning.


title: Machine learning

text: As of 2022, deep learning is the dominant approach for much ongoing work in the field of machine learning.


title: Machine learning

text: Self-learning, as a machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning, named "crossbar adaptive array" (CAA). It is learning with no external rewards and no external teacher advice. The CAA self-learning algorithm computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about consequence situations. The system is driven by the interaction between cognition and emotion.


title: Machine learning

text: The self-learning algorithm updates a memory matrix W =||w(a,s)|| such that in each iteration executes the following machine learning routine:


title: Machine learning

text: It is a system with only one input, situation, and only one output, action (or behavior) a. There is neither a separate reinforcement input nor an advice input from the environment. The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, one is the behavioral environment where it behaves, and the other is the genetic environment, wherefrom it initially and only once receives initial emotions about situations to be encountered in the behavioral environment. After receiving the genome (species) vector from the genetic environment, the CAA learns a goal-seeking behavior, in an environment that contains both desirable and undesirable situations.


title: Machine learning

text: Several learning algorithms aim at discovering better representations of the inputs provided during training. Classic examples include principal components analysis and cluster analysis. Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input but also transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions. This technique allows reconstruction of the inputs coming from the unknown data-generating distribution, while not being necessarily faithful to configurations that are implausible under that distribution. This replaces manual feature engineering, and allows a machine to both learn the features and use them to perform a specific task.


title: Machine learning

text: Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labeled input data. Examples include artificial neural networks, multilayer perceptrons, and supervised dictionary learning. In unsupervised feature learning, features are learned with unlabeled input data. Examples include dictionary learning, independent component analysis, autoencoders, matrix factorization and various forms of clustering.


title: Machine learning

text: Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional. Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse, meaning that the mathematical model has many zeros. Multilinear subspace learning algorithms aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into higher-dimensional vectors. Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features. It has been argued that an intelligent machine is one that learns a representation that disentangles the underlying factors of variation that explain the observed data.


title: Machine learning

text: Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensory data has not yielded attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms.


title: Machine learning

text: Sparse dictionary learning is a feature learning method where a training example is represented as a linear combination of basis functions, and is assumed to be a sparse matrix. The method is strongly NP-hard and difficult to solve approximately. A popular heuristic method for sparse dictionary learning is the K-SVD algorithm. Sparse dictionary learning has been applied in several contexts. In classification, the problem is to determine the class to which a previously unseen training example belongs. For a dictionary where each class has already been built, a new training example is associated with the class that is best sparsely represented by the corresponding dictionary. Sparse dictionary learning has also been applied in image de-noising. The key idea is that a clean image patch can be sparsely represented by an image dictionary, but the noise cannot.


title: Machine learning

text: In data mining, anomaly detection, also known as outlier detection, is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically, the anomalous items represent an issue such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are referred to as outliers, novelties, noise, deviations and exceptions.


title: Machine learning

text: In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts of inactivity. This pattern does not adhere to the common statistical definition of an outlier as a rare object. Many outlier detection methods (in particular, unsupervised algorithms) will fail on such data unless aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro-clusters formed by these patterns.


title: Machine learning

text: Three broad categories of anomaly detection techniques exist. Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal, by looking for instances that seem to fit the least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherently unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set and then test the likelihood of a test instance to be generated by the model.


title: Machine learning

text: Robot learning is inspired by a multitude of machine learning methods, starting from supervised learning, reinforcement learning, and finally meta-learning (e.g. MAML).


title: Machine learning

text: Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".


title: Machine learning

text: Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction. Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems.


title: Machine learning

text: Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets. For example, the rule formula_1 found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions.


title: Machine learning

text: Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.


title: Machine learning

text: Inductive logic programming (ILP) is an approach to rule learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming language for representing hypotheses (and not only logic programming), such as functional programs.


title: Machine learning

text: Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting. Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples. The term "inductive" here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set.


title: Machine learning

text: Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems.


title: Machine learning

text: Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules.


title: Machine learning

text: An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times.


title: Machine learning

text: The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis.


title: Machine learning

text: Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.


title: Machine learning

text: Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels, and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision-making.


title: Machine learning

text: Support-vector machines (SVMs), also known as support-vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category. An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.


title: Machine learning

text: Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space.


title: Machine learning

text: A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams.


title: Machine learning

text: A Gaussian process is a stochastic process in which every finite collection of the random variables in the process has a multivariate normal distribution, and it relies on a pre-defined covariance function, or kernel, that models how pairs of points relate to each other depending on their locations.


title: Machine learning

text: Given a set of observed points, or input–output examples, the distribution of the (unobserved) output of a new point as function of its input data, can be directly computed by looking as the observed points and the covariances between those points and the new, unobserved point.


title: Machine learning

text: Gaussian processes are popular surrogate models in Bayesian optimization used to do hyperparameter optimization.


title: Machine learning

text: A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s. Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.


title: Machine learning

text: Typically, machine learning models require a high quantity of reliable data in order for the models to perform accurate predictions. When training a machine learning model, machine learning engineers need to target and collect a large and representative sample of data. Data from the training set can be as varied as a corpus of text, a collection of images, sensor data, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Trained models derived from biased or non-evaluated data can result in skewed or undesired predictions. Bias models may result in detrimental outcomes thereby furthering the negative impacts on society or objectives. Algorithmic bias is a potential result of data not being fully prepared for training. Machine learning ethics is becoming a field of study and notably be integrated within machine learning engineering teams.


title: Machine learning

text: Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.


title: Machine learning

text: In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1  million. Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly. In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis. In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors jobs would be lost in the next two decades to automated machine learning medical diagnostic software. In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists. In 2019 Springer Nature published the first research book created using machine learning. In 2020, machine learning technology was used to help make diagnoses and aid researchers in developing a cure for COVID-19. Machine learning is recently applied to predict the green behavior of human-being. Recently, machine learning technology is also applied to optimise smartphone's performance and thermal behaviour based on the user's interaction with the phone.


title: Machine learning

text: Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results. Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.


title: Machine learning

text: In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision. Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.


title: Machine learning

text: Machine learning has been used as a strategy to update the evidence related to a systematic review and increased reviewer burden related to the growth of biomedical literature. While it has improved with training sets, it has not yet developed sufficiently to reduce the workload burden without limiting the necessary sensitivity for the findings research themselves.


title: Machine learning

text: Machine learning approaches in particular can suffer from different data biases. A machine learning system trained specifically on current customers may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the constitutional and unconscious biases already present in society. Language models learned from data have been shown to contain human-like biases. Machine learning systems used for criminal risk assessment have been found to be biased against black people. In 2015, Google photos would often tag black people as gorillas, and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all. Similar issues with recognizing non-white people have been found in many other systems. In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language. Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains. Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There's nothing artificial about AI...It's inspired by people, it's created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility."


title: Machine learning

text: Explainable AI (XAI), or Interpretable AI, or Explainable Machine Learning (XML), is artificial intelligence (AI) in which humans can understand the decisions or predictions made by the AI. It contrasts with the "black box" concept in machine learning where even its designers cannot explain why an AI arrived at a specific decision. By refining the mental models of users of AI-powered systems and dismantling their misconceptions, XAI promises to help users perform more effectively. XAI may be an implementation of the social right to explanation.


title: Machine learning

text: Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data, but penalizing the theory in accordance with how complex the theory is.


title: Machine learning

text: Learners can also disappoint by "learning the wrong lesson". A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses. A real-world example is that, unlike humans, current image classifiers often don't primarily make judgments from the spatial relationship between components of the picture, and they learn relationships between pixels that humans are oblivious to, but that still correlate with images of certain types of real objects. Modifying these patterns on a legitimate image can result in "adversarial" images that the system misclassifies.


title: Machine learning

text: Adversarial vulnerabilities can also result in nonlinear systems, or from non-pattern perturbations. Some systems are so brittle that changing a single adversarial pixel predictably induces misclassification. Machine learning models are often vulnerable to manipulation and/or evasion via adversarial machine learning.


title: Machine learning

text: Researchers have demonstrated how backdoors can be placed undetectably into classifying (e.g. for categories "spam" and well-visible "not spam" of posts) machine learning models which are often developed and/or trained by third parties. Parties can change the classification of any input, including in cases for which a type of data/software transparency is provided, possibly including white-box access.


title: Machine learning

text: Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.


title: Machine learning

text: In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).


title: Machine learning

text: Machine learning poses a host of ethical questions. Systems that are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices. For example, in 1988, the UK's Commission for Racial Equality found that St. George's Medical School had been using a computer program trained from data of previous admissions staff and this program had denied nearly 60 candidates who were found to be either women or had non-European sounding names. Using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants by similarity to previous successful applicants. Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning.


title: Machine learning

text: AI can be well-equipped to make decisions in technical fields, which rely heavily on data and historical information. These decisions rely on the objectivity and logical reasoning. Because human languages contain biases, machines trained on language "corpora" will necessarily also learn these biases.


title: Machine learning

text: Other forms of ethical challenges, not related to personal biases, are seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increase profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is potential for machine learning in health care to provide professionals an additional tool to diagnose, medicate, and plan recovery paths for patients, but this requires these biases to be mitigated.


title: Machine learning

text: Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units. By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI. OpenAI estimated the hardware computing used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.


title: Machine learning

text: A physical neural network or Neuromorphic computer is a type of artificial neural network in which an electrically adjustable material is used to emulate the function of a neural synapse. "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate neurons as opposed to software-based approaches. More generally the term is applicable to other artificial neural networks in which a memristor or other electrically adjustable resistance material is used to emulate a neural synapse.


title: Machine learning

text: Embedded Machine Learning is a sub-field of machine learning, where the machine learning model is run on embedded systems with limited computing resources such as wearable computers, edge devices and microcontrollers. Running machine learning model in embedded devices removes the need for transferring and storing data on cloud servers for further processing, henceforth, reducing data breaches and privacy leaks happening because of transferring data, and also minimizes theft of intellectual properties, personal data and business secrets. Embedded Machine Learning could be applied through several techniques including hardware acceleration, using approximate computing, optimization of machine learning models and many more.


title: Ron Perlman

text: Ronald Perlman (born April 13, 1950) is an American actor. His credits include the roles of Amoukar in "Quest for Fire" (1981), Salvatore in "The Name of the Rose" (1986), Vincent in the television series "Beauty and the Beast" (1987–1990), for which he won a Golden Globe Award, One in "The City of Lost Children" (1995), Johner in "Alien Resurrection" (1997), Hellboy in both "Hellboy" (2004) and its sequel "" (2008), Clay Morrow on the television series "Sons of Anarchy" (2008–2013), Nino in "Drive" (2011) and Benedict Drask in "Don't Look Up" (2021).


title: Ron Perlman

text: Perlman is also known as a collaborator of "Hellboy" director Guillermo del Toro, having roles in the del Toro films "Cronos" (1993), "Blade II" (2002), "Pacific Rim" (2013) and "Nightmare Alley" (2021). His voice-over work includes the narrator of the post-apocalyptic game series "Fallout" (1997–present), Clayface in the DC Animated Universe, Slade in "Teen Titans" (2003–2006), Mr. Lancer in "Danny Phantom" (2004–2007), Lord Hood in the video games "Halo 2" (2004) and "Halo 3" (2007), the Stabbington brothers in "Tangled" (2010), The Lich in "Adventure Time" (2011–2017), Xibalba in "The Book of Life" (2014) and Optimus Primal in both the "" (2018) animated series, and the forthcoming film "" (2023).


title: Ron Perlman

text: Ronald Perlman was born on April 13, 1950 in Washington Heights, New York City. His mother, Dorothy (née Rosen; 1921–2018), was a municipal employee, and his father, Bertram "Bert" Perlman (1919–1969), was a jazz drummer and television repairman. His family is Jewish, originally from Hungary and Poland, and Perlman had a Bar Mitzvah ceremony.


title: Ron Perlman

text: Perlman said in 1988, "It was not a bad childhood but I had a perception of myself that was... I was terribly overweight as a young kid, and it was sort of a low self image." He stated this experience is something that attracts him to roles where he portrays "these sorts of deformed people who are very endearing." Perlman had a "very close" relationship with his father, who convinced his son he "had to" pursue a career as an actor after seeing him perform in a college production of "Guys and Dolls". He graduated from George Washington High School in 1967 and Lehman College in 1971. He later attended the University of Minnesota where he graduated with a master's degree in theater arts in 1973.


title: Ron Perlman

text: Perlman started his career as a stage actor, appearing in various productions, and made his feature film debut in Jean-Jacques Annaud's film "Quest for Fire" (1981). Annaud later revealed that when he contacted Perlman to ask him about playing Salvatore in "The Name of the Rose" (1986), Perlman was thinking of abandoning his career. After various minor and supporting roles in films and television series, his breakthrough role came when he played Vincent on the television series "Beauty and the Beast", opposite Linda Hamilton, from 1987 to 1990. This earned him a Golden Globe Award for Best Performance by an Actor in a Television Series in 1989.


title: Ron Perlman

text: He went on to play roles in many films and television series throughout the 1980s and 1990s as well as the 2000s. His most notable film appearances were in films such as "The Name of the Rose" (1986), "Romeo is Bleeding" (1993), "The Adventures of Huck Finn" (1993), "" (1994), "The Last Supper" (1995), "The Island of Dr. Moreau" (1996), "Alien Resurrection" (1997), "Enemy at the Gates" (2001), "Blade II" and "" (both 2002) and two Stephen King adaptations, "Sleepwalkers" and "Desperation". His appearances on television series include "", "The Outer Limits", "The Magnificent Seven", and the Amazon series "Hand of God".


title: Ron Perlman

text: He played his first leading film role in 1995, when he played "One" in Jean-Pierre Jeunet and Marc Caro's French-language "The City of Lost Children". In 2003, Perlman starred in a commercial for Stella Artois beer. This commercial, which was called "Devil's Island", won a Silver Award at the 2003 British Advertising Awards. He got another leading film role in 2004 when he played the title role in the comic book adaptation "Hellboy". Perlman reprised his role as Hellboy in the straight to DVD animated features "Hellboy: Sword of Storms" (2006) and "Hellboy: Blood and Iron" (2007) as well as "", released on July 11, 2008.


title: Ron Perlman

text: In 2008, Perlman joined the cast of the television series "Sons of Anarchy" on FX playing Clay Morrow, the national president of the Sons of Anarchy Motorcycle Club. He also played Wes Chandler in the second and third seasons of "StartUp" in 2017–18.


title: Ron Perlman

text: In 2022 Perlman starred in Steven Brand's noir thriller "Joe Baby" alongside Dichen Lachman, Willa Fitzgerald and Harvey Keitel.


title: Ron Perlman

text: Perlman also has a successful career as a voice actor. He has portrayed characters in numerous video games and animated series, and done voice-over work for television commercials. These include Casper High English teacher and vice-principal Mr. Lancer in "Danny Phantom", "Mickey Kaline" in "Hey Arnold", The Lich in "Adventure Time", Kurtis Stryker in "", Justice in "Afro Samurai" and various characters in DC Comics based series such as the villainous Slade, a version of DC character Deathstroke in the "Teen Titans" animated series and again in "", Clayface in "" and "The New Batman Adventures", Jax-Ur in "", Orion in "Justice League" and "Justice League Unlimited" (the former in which he reprised his role as Clayface as well as voice Orion), Sozin in "", several villains (Killer Croc, Rumor and Bane) in "The Batman", Doctor Double X in "", and Sinestro in ". "In addition, he served as the narrator for "1000 Ways To Die."


title: Ron Perlman

text: His video game credits include Terrence Hood in the games "Halo 2" and "Halo 3", Jagger Valance in "", and Batman in "Justice League Heroes". He is well known by "Fallout" fans for narrating the introductory movies in the series, including uttering the famous phrase "War. War never changes." He also voices "Slade" in the 2008 "Turok" game, and Emil Blonsky / Abomination in "", "Conan" for the PS3 and Xbox 360, and voices the fast-talking Mayor Hoodoo Brown in the Neversoft game "Gun". He also made an appearance in "Payday 2" as "Rust", part of the "Biker Pack" DLC. In "" (2016), Perlman offered his likeness and voice to one of the lead playable characters in the game's popular Zombies mode.


title: Ron Perlman

text: They separated in May 2019 and Perlman later filed for divorce. On June 28, 2022 it was announced that Perlman married Allison Dunbar in Italy after three years of dating. His son produces electronic music under the stage name Delroy Edwards.


title: Ron Perlman

text: Perlman described himself as a lifelong Democrat in November 2016. He has been a vocal critic of former U.S. President Donald Trump.


title: Ron Perlman

text: On June 25, 2018, Perlman claimed that he intentionally urinated on his hand prior to shaking Harvey Weinstein's hand followed by the message "I think about that every time lil Donnie opens up his KFC." The story gained notoriety and gave birth to the "Lil Donnie" internet meme format, a series of fake tweets where Perlman would account subjecting himself to bizarre and disgusting acts for the purpose of infuriating Trump.


title: Ron Perlman

text: On November 9, 2016, Perlman announced via Facebook his intention to run for U.S. President in the 2020 election. In January 2019, he endorsed the candidacy of Kamala Harris.


title: David

text: David (; , "beloved one") was, according to the Hebrew Bible, the third king of the United Kingdom of Israel. In the Books of Samuel, he is described as a young shepherd and harpist who gains fame by slaying Goliath, a champion of the Philistines, in southern Canaan. David becomes a favourite of Saul, the first king of Israel; he also forges a notably close friendship with Jonathan, a son of Saul. However, under the paranoia that David is seeking to usurp the throne, Saul attempts to kill David, forcing the latter to go into hiding and effectively operate as a fugitive for several years. After Saul and Jonathan are both killed in battle against the Philistines, a 30-year-old David is anointed king over all of Israel and Judah. Following his rise to power, David conquers the city of Jerusalem and establishes it as Israel's capital, subsequently taking the Ark of the Covenant into the city to be the central point of worship in the Israelite religion.


title: David

text: According to the biblical narrative, David commits adultery with Bathsheba, leading him to arrange the death of her husband, Uriah the Hittite. David's son Absalom later schemes to overthrow him and, during the ensuing rebellion, David flees from Jerusalem, but returns after Absalom's death to continue his reign over Israel and Judah. He desires to construct a temple to Yahweh in which to house the Ark but, because he shed much blood, Yahweh denies David the opportunity to do so. David rules as king of the Israelites until his death at age 70, prior to which he chooses Solomon—a son born to him and Bathsheba—as his successor instead of Adonijah, his eldest surviving son with Haggith. He is honoured in Jewish prophetic literature as an ideal king and the forefather of the future Hebrew Messiah, and many psalms are ascribed to him.


title: David

text: Historians of the Ancient Near East agree that David probably lived around 1000 BCE, but there is little else that is agreed on about him as a historical figure. According to Jewish tradition, as defined in the works "Seder Olam Rabbah", "Seder Olam Zutta", and "Sefer ha-Qabbalah", David ascended the throne as the king of Judah in 885 BCE. The Tel Dan stele, a Canaanite-inscribed stone that was erected by a king of Aram-Damascus in the late-9th/early-8th centuries BCE to commemorate his victory over two enemy kings, contains the Hebrew-language phrase (), which is translated to "House of David" by most scholars. The Mesha stele, erected by king Mesha of Moab in the 9th century BCE, may also refer to the "House of David" although this is disputed. Apart from this, all that is known of David comes from biblical literature, the historicity of which has been extensively challenged, and there is little detail about David that is concrete and undisputed.


title: David

text: David is richly represented in post-biblical Jewish written and oral tradition, and is also discussed in the New Testament. The early Christians interpreted the life of Jesus of Nazareth in light of references to the Hebrew Messiah and to David; Jesus is described as being directly descended from David in the Gospel of Matthew and the Gospel of Luke. In the Quran and hadith, David is described as an Israelite king as well as a prophet of Allah. The biblical David has inspired many interpretations in art and literature over the centuries.


title: David

text: The First Book of Samuel and the First Book of Chronicles both identify David as the son of Jesse, the Bethlehemite, the youngest of eight sons. He also had at least two sisters, Zeruiah, whose sons all went on to serve in David's army, and Abigail, whose son Amasa went on to serve in Absalom's army, Absalom being one of David's younger sons. While the Bible does not name his mother, the Talmud identifies her as Nitzevet, a daughter of a man named Adael, and the Book of Ruth claims him as the great-grandson of Ruth, the Moabite, by Boaz.


title: David

text: David is described as cementing his relations with various political and national groups through marriage. In 1 Samuel 17:25, it states that King Saul had said that he would make whoever killed Goliath a very wealthy man, give his daughter to him and declare his father's family exempt from taxes in Israel. Saul offered David his oldest daughter, Merab, a marriage which David respectfully declined. Saul then gave Merab in marriage to Adriel the Meholathite. Having been told that his younger daughter Michal was in love with David, Saul gave her in marriage to David upon David's payment in Philistine foreskins (ancient Jewish historian Josephus lists the dowry as 100 Philistine heads). Saul became jealous of David and tried to have him killed. David escaped. Then Saul sent Michal to Galim to marry Palti, son of Laish. David then took wives in Hebron, according to 2 Samuel 3; they were Ahinoam the Yizre'elite; Abigail, the wife of Nabal the Carmelite; Maacah, the daughter of Talmay, king of Geshur; Haggith; Abital; and Eglah. Later, David wanted Michal back and Abner, Ish-bosheth's army commander, delivered her to David, causing her husband (Palti) great grief.


title: David

text: The Book of Chronicles lists his sons with his various wives and concubines. In Hebron, David had six sons: Amnon, by Ahinoam; Daniel, by Abigail; Absalom, by Maachah; Adonijah, by Haggith; Shephatiah, by Abital; and Ithream, by Eglah. By Bathsheba, his sons were Shammua, Shobab, Nathan, and Solomon. David's sons born in Jerusalem of his other wives included Ibhar, Elishua, Eliphelet, Nogah, Nepheg, Japhia, Elishama and Eliada. Jerimoth, who is not mentioned in any of the genealogies, is mentioned as another of his sons in 2 Chronicles 11:18. His daughter Tamar, by Maachah, is raped by her half-brother Amnon. David fails to bring Amnon to justice for his violation of Tamar, because he is his firstborn and he loves him, and so, Absalom (her full brother) murders Amnon to avenge Tamar. Despite the great sins they had committed, David showed grief at the deaths of his sons, weeping twice for Amnon [2 Samuel 13:31–26] and weeping seven times for Absalom.


title: David

text: God is angered when Saul, Israel's king, unlawfully offers a sacrifice and later disobeys a divine command both to kill all of the Amalekites and to destroy their confiscated property. Consequently, God sends the prophet Samuel to anoint a shepherd, David, the youngest son of Jesse of Bethlehem, to be king instead.


title: David

text: After God sends an evil spirit to torment Saul, his servants recommend that he send for a man skilled in playing the lyre. A servant proposes David, whom the servant describes as "skillful in playing, a man of valor, a warrior, prudent in speech, and a man of good presence; and the Lord is with him." David enters Saul's service as one of the royal armour-bearers and plays the lyre to soothe the king.


title: David

text: War comes between Israel and the Philistines, and the giant Goliath challenges the Israelites to send out a champion to face him in single combat. David, sent by his father to bring provisions to his brothers serving in Saul's army, declares that he can defeat Goliath. Refusing the king's offer of the royal armour, he kills Goliath with his sling. Saul inquires the name of the young hero's father.


title: David

text: Saul sets David over his army. All Israel loves David, but his popularity causes Saul to fear him ("What else can he wish but the kingdom?"). Saul plots his death, but Saul's son Jonathan, one of those who loves David, warns him of his father's schemes and David flees. He goes first to Nob, where he is fed by the priest Ahimelech and given Goliath's sword, and then to Gath, the Philistine city of Goliath, intending to seek refuge with King Achish there. Achish's servants or officials question his loyalty, and David sees that he is in danger there. He goes next to the cave of Adullam, where his family joins him. From there he goes to seek refuge with the king of Moab, but the prophet Gad advises him to leave and he goes to the Forest of Hereth, and then to Keilah, where he is involved in a further battle with the Philistines. Saul plans to besiege Keilah so that he can capture David, so David leaves the city in order to protect its inhabitants. From there he takes refuge in the mountainous Wilderness of Ziph.


title: David

text: Jonathan meets with David again and confirms his loyalty to David as the future king. After the people of Ziph notify Saul that David is taking refuge in their territory, Saul seeks confirmation and plans to capture David in the Wilderness of Maon, but his attention is diverted by a renewed Philistine invasion and David is able to secure some respite at Ein Gedi. Returning from battle with the Philistines, Saul heads to Ein Gedi in pursuit of David and enters the cave where, as it happens, David and his supporters are hiding, "to attend to his needs". David realises he has an opportunity to kill Saul, but this is not his intention: he secretly cuts off a corner of Saul's robe, and when Saul has left the cave he comes out to pay homage to Saul as the king and to demonstrate, using the piece of robe, that he holds no malice towards Saul. The two are thus reconciled and Saul recognises David as his successor.


title: David

text: A similar passage occurs in 1 Samuel 26, when David is able to infiltrate Saul's camp on the hill of Hachilah and remove his spear and a jug of water from his side while he and his guards lie asleep. In this account, David is advised by Abishai that this is his opportunity to kill Saul, but David declines, saying he will not "stretch out [his] hand against the Lord's anointed". Saul confesses that he has been wrong to pursue David and blesses him.


title: David

text: In 1 Samuel 27:1–4, Saul ceases to pursue David because David took refuge a second time with Achish, the Philistine king of Gath. Achish permits David to reside in Ziklag, close to the border between Gath and Judea, from where he leads raids against the Geshurites, the Girzites and the Amalekites, but leads Achish to believe he is attacking the Israelites in Judah, the Jerahmeelites and the Kenites. Achish believes that David had become a loyal vassal, but he never wins the trust of the princes or lords of Gath, and at their request Achish instructs David to remain behind to guard the camp when the Philistines march against Saul. David returns to Ziklag and saves his wives and the citizens from the Amalekites. Jonathan and Saul are killed in battle, and David is anointed king over Judah. In the north, Saul's son Ish-Bosheth is anointed king of Israel, and war ensues until Ish-Bosheth is murdered.


title: David

text: With the death of Saul's son, the elders of Israel come to Hebron and David is anointed king over all of Israel. He conquers Jerusalem, previously a Jebusite stronghold, and makes it his capital. He brings the Ark of the Covenant to the city, intending to build a temple for God, but the prophet Nathan forbids it, prophesying that the temple would be built by one of David's sons. Nathan also prophesies that God has made a covenant with the house of David stating, "your throne shall be established forever". David wins additional victories over the Philistines, Moabites, Edomites, Amalekites, Ammonites and king Hadadezer of Aram-Zobah, after which they become tributaries. His fame increase as a result, earning the praise of figures like king Toi of Hamath, Hadadezer's rival.


title: David

text: During a siege of the Ammonite capital of Rabbah, David remains in Jerusalem. He spies a woman, Bathsheba, bathing and summons her; she becomes pregnant. The text in the Bible does not explicitly state whether Bathsheba consented to sex. David calls her husband, Uriah the Hittite, back from the battle to rest, hoping that he will go home to his wife and the child will be presumed to be his. Uriah does not visit his wife, however, so David conspires to have him killed in the heat of battle. David then marries the widowed Bathsheba. In response, Nathan, after trapping the king in his guilt with a parable that actually described his sin in analogy, prophesies the punishment that will fall upon him, stating "the sword shall never depart from your house." When David acknowledges that he has sinned, Nathan advises him that his sin is forgiven and he will not die, but the child will.


title: David

text: In fulfillment of Nathan's words, the child born of the union between David and Bathsheba dies, and another of David's sons, Absalom, fueled by vengeance and lust for power, rebels. Thanks to Hushai, a friend of David who was ordered to infiltrate Absalom's court to successfully sabotage his plans, Absalom's forces are routed at the battle of the Wood of Ephraim, and he is caught by his long hair in the branches of a tree where, contrary to David's order, he is killed by Joab, the commander of David's army. David laments the death of his favourite son: "O my son Absalom, my son, my son Absalom! Would I had died instead of you, O Absalom, my son, my son!" until Joab persuades him to recover from "the extravagance of his grief" and to fulfill his duty to his people. David returns to Gilgal and is escorted across the River Jordan and back to Jerusalem by the tribes of Judah and Benjamin.


title: David

text: When David is old and bedridden, Adonijah, his eldest surviving son and natural heir, declares himself king. Bathsheba and Nathan go to David and obtain his agreement to crown Bathsheba's son Solomon as king, according to David's earlier promise, and the revolt of Adonijah is put down. David dies at the age of 70 after reigning for 40 years, and on his deathbed counsels Solomon to walk in the ways of God and to take revenge on his enemies.


title: David

text: The Book of Samuel calls David a skillful harp (lyre) player and "the sweet psalmist of Israel." Yet, while almost half of the Psalms are headed "A Psalm of David" (also translated as "to David" or "for David") and tradition identifies several with specific events in David's life (e.g., Psalms 3, 7, 18, 34, 51, 52, 54, 56, 57, 59, 60, 63 and 142), the headings are late additions and no psalm can be attributed to David with certainty.


title: David

text: Psalm 34 is attributed to David on the occasion of his escape from Abimelech (or King Achish) by pretending to be insane. According to the parallel narrative in 1 Samuel 21, instead of killing the man who had exacted so many casualties from him, Abimelech allows David to leave, exclaiming, "Am I so short of madmen that you have to bring this fellow here to carry on like this in front of me? Must this man come into my house?"


title: David

text: David is an important figure in Rabbinic Judaism, with many legends around him. According to one tradition, David was raised as the son of his father Jesse and spent his early years herding his father's sheep in the wilderness while his brothers were in school.


title: David

text: David's adultery with Bathsheba is interpreted as an opportunity to demonstrate the power of repentance, and the Talmud states that it was not adultery at all, quoting a Jewish practice of divorce on the eve of battle. Furthermore, according to Talmudic sources, the death of Uriah was not to be considered murder, on the basis that Uriah had committed a capital offense by refusing to obey a direct command from the King. However, in tractate Sanhedrin, David expressed remorse over his transgressions and sought forgiveness. God ultimately forgave David and Bathsheba but would not remove their sins from Scripture.


title: David

text: In Jewish legend, David's sin with Bathsheba is the punishment for David's excessive self-consciousness who had besought God to lead him into temptation so that he might give proof of his constancy as Abraham, Isaac, and Jacob (who successfully passed the test) whose names later were united with God's, while David eventually failed through the temptation of a woman.


title: David

text: According to midrashim, Adam gave up 70 years of his life for the life of David. Also, according to the Talmud Yerushalmi, David was born and died on the Jewish holiday of Shavuot (Feast of Weeks). His piety was said to be so great that his prayers could bring down things from Heaven.


title: David

text: The Messiah concept is fundamental in Christianity. Originally an earthly king ruling by divine appointment ("the anointed one", as the title Messiah had it), the "son of David" became in the last two centuries BCE the apocalyptic and heavenly one who would deliver Israel and usher in a new kingdom. This was the background to the concept of Messiahship in early Christianity, which interpreted the career of Jesus "by means of the titles and functions assigned to David in the mysticism of the Zion cult, in which he served as priest-king and in which he was the mediator between God and man".


title: David

text: The early Church believed that "the life of David foreshadowed the life of Christ; Bethlehem is the birthplace of both; the shepherd life of David points out Christ, the Good Shepherd; the five stones chosen to slay Goliath are typical of the five wounds; the betrayal by his trusted counsellor, Ahitophel, and the passage over the Cedron remind us of Christ's Sacred Passion. Many of the Davidic Psalms, as we learn from the New Testament, are clearly typical of the future Messiah." In the Middle Ages, "Charlemagne thought of himself, and was viewed by his court scholars, as a 'new David'. [This was] not in itself a new idea, but [one whose] content and significance were greatly enlarged by him".


title: David

text: Western Rite churches (Lutheran, Roman Catholic) celebrate his feast day on 29 December or on 6 October, Eastern-rite on 19 December. The Eastern Orthodox Church and Eastern Catholic Churches celebrate the feast day of the "Holy Righteous Prophet and King David" on the Sunday of the Holy Forefathers (two Sundays before the Great Feast of the Nativity of the Lord), when he is commemorated together with other ancestors of Jesus. He is also commemorated on the Sunday after the Nativity, together with Joseph and James, the Brother of the Lord.


title: David

text: In European Christian culture of the Middle Ages, David was made a member of the Nine Worthies, a group of heroes encapsulating all the ideal qualities of chivalry. His life was thus proposed as a valuable subject for study by those aspiring to chivalric status. This aspect of David in the Nine Worthies was popularised firstly through literature, and was thereafter adopted as a frequent subject for painters and sculptors.


title: David

text: David was considered as a model ruler and a symbol of divinely-ordained monarchy throughout medieval Western Europe and Eastern Christendom. David was perceived as the biblical predecessor to Christian Roman and Byzantine emperors and the name "New David" was used as an honorific reference to these rulers. The Georgian Bagratids and the Solomonic dynasty of Ethiopia claimed a direct biological descent from him. Likewise, kings of the Frankish Carolingian dynasty frequently connected themselves to David; Charlemagne himself occasionally used the name of David as his pseudonym.


title: David

text: David (Arabic: داوود "Dā'ūd" or "Dāwūd") is an important figure in Islam as one of the major prophets sent by God to guide the Israelites. David is mentioned several times in the Quran with the Arabic name داود, "Dāwūd" or "Dā'ūd", often with his son Solomon. In the Quran David killed Goliath (), a giant soldier in the Philistine army. When David killed Goliath, God granted him kingship and wisdom and enforced it (). David was made God's "vicegerent on earth" () and God further gave David sound judgment (; , Q26) as well as the Psalms, regarded as books of divine wisdom (; ). The birds and mountains united with David in uttering praise to God (; ; ), while God made iron soft for David (), God also instructed David in the art of fashioning chain mail out of iron (); this knowledge gave David a major advantage over his bronze and cast iron-armed opponents, not to mention the cultural and economic impact. Together with Solomon, David gave judgment in a case of damage to the fields () and David judged the matter between two disputants in his prayer chamber (). Since there is no mention in the Quran of the wrong David did to Uriah nor any reference to Bathsheba, Muslims reject this narrative.


title: David

text: Muslim tradition and the "hadith" stress David's zeal in daily prayer as well as in fasting. Quran commentators, historians and compilers of the numerous "Stories of the Prophets" elaborate upon David's concise quranic narratives and specifically mention David's gift in singing his Psalms as well as his beautiful recitation and vocal talents. His voice is described as having had a captivating power, weaving its influence not only over man but over all beasts and nature, who would unite with him to praise God.


title: David

text: Biblical literature and archaeological finds are the only sources that attest to David's life. Some scholars have concluded that this was likely compiled from contemporary records of the 11th and 10th centuries BCE, but that there is no clear historical basis for determining the exact date of compilation. Other scholars believe that the Books of Samuel were substantially composed during the time of King Josiah at the end of the 7th century BCE, extended during the Babylonian exile (6th century BCE), and substantially complete by about 550 BCE. Old Testament scholar Graeme Auld contends that further editing was done even after then—the silver quarter-shekel which Saul's servant offers to Samuel in 1 Samuel 9 "almost certainly fixes the date of the story in the Persian or Hellenistic period" because a quarter-shekel was known to exist in Hasmonean times. The authors and editors of Samuel drew on many earlier sources, including, for their history of David, the "history of David's rise" and the "succession narrative". The Book of Chronicles, which tells the story from a different point of view, was probably composed in the period 350–300 BCE, and uses Samuel and Kings as its source.


title: David

text: Biblical evidence indicates that David's Judah was something less than a full-fledged monarchy: it often calls him "negid", meaning "prince" or "chief", rather than "melek", meaning "king"; the biblical David sets up none of the complex bureaucracy that a kingdom needs (even his army is made up of volunteers), and his followers are largely related to him and from his small home-area around Hebron.


title: David

text: Beyond this, the full range of possible interpretations is available. A number of scholars consider the David story to be a heroic tale similar to King Arthur's legend or Homer's epics, whereas others think that such comparisons are questionable. One theme that has been paralleled with other Near Eastern literature is the homoerotic nature of the relationship between David and Jonathan. The instance in the Book of Jashar, excerpted in Samuel 2 (1:26), where David "proclaims that Jonathan's love was sweeter to him than the love of a women", has been compared to Achilles' comparison of Patroclus to a girl and Gilgamesh's love for Enkidu "as a woman". Others hold that the David story is a political apology—an answer to contemporary charges against him, of his involvement in murders and regicide. The authors and editors of Samuel and Chronicles did not aim to record history, but to promote David's reign as inevitable and desirable, and for this reason there is little about David that is concrete and undisputed.


title: David

text: Some other studies of David have been written: Baruch Halpern has pictured David as a brutal tyrant, a murderer and a lifelong vassal of Achish, the Philistine king of Gath; Steven McKenzie argues that David came from a wealthy family, was "ambitious and ruthless" and a tyrant who murdered his opponents, including his own sons. Joel S. Baden has described him as "an ambitious, ruthless, flesh-and-blood man who achieved power by any means necessary, including murder, theft, bribery, sex, deceit, and treason. William G. Dever described him as "a serial killer".


title: David

text: Jacob L. Wright has written that the most popular legends about David, including his killing of Goliath, his affair with Bathsheba, and his ruling of a United Kingdom of Israel rather than just Judah, are the creation of those who lived generations after him, in particular those living in the late Persian or Hellenistic periods.


title: David

text: Isaac Kalimi wrote about the tenth century BCE that: "Almost all that one can say about King Solomon and his time is unavoidably based on the biblical texts. Nevertheless, here also one cannot always offer conclusive proof that a certain biblical passage reflects the actual historical situation in the tenth century BCE, beyond arguing that it is plausible to this or that degree."


title: David

text: The Tel Dan Stele, discovered in 1993, is an inscribed stone erected by Hazael, a king of Damascus in the late 9th/early 8th centuries BCE. It commemorates the king's victory over two enemy kings, and contains the phrase , "bytdwd", which most scholars translate as "House of David". Other scholars have challenged this reading, but it is likely that this is a reference to a dynasty of the Kingdom of Judah which traced its ancestry to a founder named David.


title: David

text: Two epigraphers, André Lemaire and Émile Puech, hypothesised in 1994 that the Mesha Stele from Moab, dating from the 9th century, also contain the words "House of David" at the end of Line 31, although this was considered as less certain than the mention in the Tel Dan inscription. In May 2019, Israel Finkelstein, Nadav Na'aman, and Thomas Römer concluded from the new images that the ruler's name contained three consonants and started with a "bet", which excludes the reading "House of David" and, in conjunction with the monarch's city of residence "Horonaim" in Moab, makes it likely that the one mentioned is King Balak, a name also known from the Hebrew Bible. Later that year, Michael Langlois used high-resolution photographs of both the inscription itself, and the 19th-century original squeeze of the then still intact stele to reaffirm Lemaire's view that line 31 contains the phrase "House of David". Replying to Langlois, Na'aman argued that the "House of David" reading is unacceptable because the resulting sentence structure is extremely rare in West Semitic royal inscriptions.


title: David

text: Besides the two steles, Bible scholar and Egyptologist Kenneth Kitchen suggests that David's name also appears in a relief of Pharaoh Shoshenq, who is usually identified with Shishak in the Bible. The relief claims that Shoshenq raided places in Palestine in 925 BCE, and Kitchen interprets one place as "Heights of David", which was in Southern Judah and the Negev where the Bible says David took refuge from Saul. The relief is damaged and interpretation is uncertain.


title: David

text: Of the evidence in question, John Haralson Hayes and James Maxwell Miller wrote in 2006: "If one is not convinced in advance by the biblical profile, then there is nothing in the archaeological evidence itself to suggest that much of consequence was going on in Palestine during the tenth century BCE, and certainly nothing to suggest that Jerusalem was a great political and cultural center." This echoed the 1995 conclusion of Amélie Kuhrt, who noted that "there are no royal inscriptions from the time of the united monarchy (indeed very little written material altogether), and not a single contemporary reference to either David or Solomon," while noting, "against this must be set the evidence for substantial development and growth at several sites, which is plausibly related to the tenth century."


title: David

text: In 2007, Israel Finkelstein and Neil Asher Silberman stated that the archaeological evidence shows that Judah was sparsely inhabited and Jerusalem no more than a small village. The evidence suggested that David ruled only as a chieftain over an area which cannot be described as a state or as a kingdom, but more as a chiefdom, much smaller and always overshadowed by the older and more powerful kingdom of Israel to the north. They posited that Israel and Judah were not monotheistic at the time, and that later seventh-century redactors sought to portray a past golden age of a united, monotheistic monarchy in order to serve contemporary needs. They noted a lack of archeological evidence for David's military campaigns and a relative underdevelopment of Jerusalem, the capital of Judah, compared to a more developed and urbanized Samaria, capital of Israel during the 9th century BCE.


title: David

text: In 2014, Amihai Mazar wrote that the United Monarchy of the 10th century BCE can be described as a "state in development". He has also compared David to Labaya, a Caananite warlord living during the time of Pharaoh Akhenaten. While Mazar believes that David reigned over Israel during the 11th century BCE, he argues that much of the Biblical text is “literary-legendary nature”. According to William G. Dever, the reigns of Saul, David and Solomon are reasonably well attested, but "most archeologists today would argue that the United Monarchy was not much more than a kind of hill-country chiefdom".


title: David

text: Lester L. Grabbe wrote in 2017 that: "The main question is what kind of settlement Jerusalem was in Iron IIA: was it a minor settlement, perhaps a large village or possibly a citadel but not a city, or was it the capital of a flourishing – or at least an emerging – state? Assessments differ considerably …" Isaac Kalimi wrote in 2018 that: "No contemporaneous extra-biblical source offers any account of the political situation in Israel and Judah during the tenth century BCE, and as we have seen, the archaeological remains themselves cannot provide any unambiguous evidence of events."


title: David

text: The view of Davidic Jerusalem as a village has been challenged by Eilat Mazar's excavation of the Large Stone Structure and the Stepped Stone Structure in 2005. Eilat Mazar proposed that these two structures may have been architecturally linked as one unit, and that they date back to the time of King David. Mazar supports this dating with a number of artifacts; including pottery, two Phoenician-style ivory inlays, a black-and-red jug, and a radiocarbon dated bone, estimated to be from the 10th century. Amihai Mazar, Avraham Faust, Nadav Na'aman and William G. Dever have also argued in favour of the 10th century BCE dating and have responded to challenges against it. In 2010, archaeologist Eilat Mazar announced the discovery of part of the ancient city walls around the City of David which she believes date to the tenth century BCE. According to Mazar, this would prove that an organized state did exist in the 10th century. In 2006, Kenneth Kitchen also came to a similar conclusion, arguing that "the physical archaeology of tenth-century Canaan is consistent with the former existence of a unified state on its terrain."


title: David

text: Scholars such as Israel Finkelstein, Lily Singer-Avitz, Ze'ev Herzog and David Ussishkin do not accept these conclusions. Finkelstein does not accept the dating of these structures to the 10th century BCE, based in part on the fact that later structures on the site penetrated deep into underlying layers, that the entire area had been excavated in the early 20th century and then backfilled, that pottery from later periods was found below earlier strata, and that consequently the finds collected by E. Mazar cannot necessarily be considered as retrieved in situ. Aren Maeir said in 2010 that he has seen no evidence that these structures are from the 10th century BCE, and that proof of the existence of a strong, centralized kingdom at that time remains "tenuous."


title: David

text: Excavations at Khirbet Qeiyafa by archaeologists Yosef Garfinkel and Saar Ganor found an urbanized settlement radiocarbon dated dating to the 10th century, which supports the existence of an urbanised kingdom. Following such discovery, the Israel Antiquities Authority stated, "The excavations at Khirbat Qeiyafa clearly reveal an urban society that existed in Judah already in the late eleventh century BCE. It can no longer be argued that the Kingdom of Judah developed only in the late eighth century BCE or at some other later date." However, the techniques and interpretations to reach some conclusions related to Khirbet Qeiyafa have been criticized by other scholars, such as Israel Finkelstein and Alexander Fantalkin of Tel Aviv University, who have, instead, proposed that the city is to be identified as part of a northern Israelite polity.


title: David

text: In 2018, Avraham Faust and Yair Sapir stated that a Canaanite site at Tel Eton, about 30 miles from Jerusalem, was taken over by a Judahite community by peaceful assimilation, and transformed from a village into a central town at some point in the late 11th or early 10th century BCE. This transformation used some ashlar blocks in construction, which they argued supports the United Monarchy theory.


title: David

text: For a considerable period, starting in the 15th century and continuing until the 19th, French playing card manufacturers assigned to each of the court cards names taken from history or mythology. In this context, the King of spades was often known as "David".


title: State Bank of India

text: State Bank of India (SBI) is an Indian multinational public sector bank and financial services statutory body headquartered in Mumbai, Maharashtra. SBI is the 49th largest bank in the world by total assets and ranked 221st in the "Fortune Global 500" list of the world's biggest corporations of 2020, being the only Indian bank on the list. It is a public sector bank and the largest bank in India with a 23% market share by assets and a 25% share of the total loan and deposits market. It is also the fifth largest employer in India with nearly 250,000 employees. On 14 September 2022, State Bank of India became the third lender (after HDFC Bank and ICICI Bank) and seventh Indian company to cross the 5-trillion market capitalisation on the Indian stock exchanges for the first time.


title: State Bank of India

text: The bank descends from the Bank of Calcutta, founded in 1806 via the Imperial Bank of India, making it the oldest commercial bank in the Indian subcontinent. The Bank of Madras merged into the other two presidency banks in British India, the Bank of Calcutta and the Bank of Bombay, to form the Imperial Bank of India, which in turn became the State Bank of India in 1955. Overall the bank has been formed from the merger and acquisition of nearly twenty banks over the course of its 200 year history. The Government of India took control of the Imperial Bank of India in 1955, with Reserve Bank of India (India's central bank) taking a 60% stake, renaming it State Bank of India.


title: State Bank of India

text: On 16th Aug 2022 an attempt to facilitate and support start-ups in the country, the State Bank of India (SBI) announced the launch of its first "state-of-the-art" dedicated branch for start-ups in the country in Bengaluru.


title: State Bank of India

text: The roots of State Bank of India lie in the first decade of the 19th century when the Bank of Calcutta later renamed the Bank of Bengal, was established on 2 June 1806. The Bank of Bengal was one of three Presidency banks, the other two being the Bank of Bombay (incorporated on 15 April 1840) and the Bank of Madras (incorporated on 1 July 1843). All three Presidency banks were incorporated as joint stock companies and were the result of royal charters. These three banks received the exclusive right to issue paper currency till 1861 when, with the Paper Currency Act, the right was taken over by the Government of India. The Presidency banks amalgamated on 27 January 1921, and the re-organised banking entity took as its name Imperial Bank of India. The Imperial Bank of India remained a joint-stock company but without Government participation.


title: State Bank of India

text: Pursuant to the provisions of the State Bank of India Act of 1955, the Reserve Bank of India, which is India's central bank, acquired a controlling interest in the Imperial Bank of India. On 1 July 1955, the Imperial Bank of India became the State Bank of India. In 2008, the Government of India acquired the Reserve Bank of India's stake in SBI so as to remove any conflict of interest because the RBI is the country's banking regulatory authority.


title: State Bank of India

text: In 1959, the government passed the State Bank of India (Subsidiary Banks) Act. This made eight banks that had belonged to princely states into subsidiaries of SBI. This was at the time of the First Five Year Plan, which prioritised the development of rural India. The government integrated these banks into the State Bank of India system to expand its rural outreach. In 1963 SBI merged State Bank of Jaipur (est. 1943) and State Bank of Bikaner (est.1944).


title: State Bank of India

text: SBI has acquired local banks in rescues. The first was the Bank of Bihar (est. 1911), which SBI acquired in 1969, together with its 28 branches. The next year SBI acquired National Bank of Lahore (est. 1942), which had 24 branches. Five years later, in 1975, SBI acquired Krishnaram Baldeo Bank, which had been established in 1916 in Gwalior State, under the patronage of Maharaja Madho Rao Scindia. The bank had been the "Dukan Pichadi", a small moneylender, owned by the Maharaja. The new bank's first manager was Jall N. Broacha, a Parsi. In 1985, SBI acquired the Bank of Cochin in Kerala, which had 120 branches. SBI was the acquirer as its affiliate, the State Bank of Travancore, already had an extensive network in Kerala.


title: State Bank of India

text: There was, even before it actually happened, a proposal to merge all the associate banks into SBI to create a single very large bank and streamline operations.


title: State Bank of India

text: The first step towards unification occurred on 13 August 2008 when State Bank of Saurashtra merged with SBI, reducing the number of associate state banks from seven to six. On 19 June 2009, the SBI board approved the absorption of State Bank of Indore, in which SBI held 98.3%. (Individuals who held the shares prior to its takeover by the government held the balance of 1.7%.)


title: State Bank of India

text: The acquisition of State Bank of Indore added 470 branches to SBI's existing network of branches. Also, following the acquisition, SBI's total assets approached 10 trillion. The total assets of SBI and the State Bank of Indore were 9,981,190 million as of March 2009. The process of merging of State Bank of Indore was completed by April 2010, and the SBIndore branches started functioning as SBI branches on 26 August 2010.


title: State Bank of India

text: On 7 October 2013, Arundhati Bhattacharya became the first woman to be appointed Chairperson of the bank. Mrs. Bhattacharya received an extension of two years of service to merge into SBI the five remaining associate banks.


title: State Bank of India

text: SBI provides a range of banking products through its network of branches in India and overseas, including products aimed at non-resident Indians (NRIs). SBI has 16 regional hubs and 57 zonal offices that are located at important cities throughout India.


title: State Bank of India

text: SBI has over 24000 branches in India. In the financial year 2012–13, its revenue was , out of which domestic operations contributed to 95.35% of revenue. Similarly, domestic operations contributed to 88.37% of total profits for the same financial year.


title: State Bank of India

text: Under the Pradhan Mantri Jan Dhan Yojana of financial inclusion launched by Government in August 2014, SBI held 11,300 camps and opened over 3 million accounts by September, which included 2.1 million accounts in rural areas and 1.57 million accounts in urban areas.


title: State Bank of India

text: As of 2014–15, the bank had 191 overseas offices spread over 36 countries having the largest presence in foreign markets among Indian banks.


title: State Bank of India

text: The SBI Botswana subsidiary was registered on the 27th January 2006 and was issued a banking licence by the Bank of Botswana on the 29th July 2013. The subsidiary handed over its banking licence and closed its operations in the country.


title: State Bank of India

text: In Nepal, SBI owns 55% of share. (The state-owned Employees Provident Fund of Nepal owns 15% and the general public owns the remaining 30%.) Nepal SBI Bank Limited has branches throughout the country.


title: State Bank of India

text: In Nigeria, SBI operates as INMB Bank. This bank began in 1981 as the Indo–Nigerian Merchant Bank and received permission in 2002 to commence retail banking. It now has five branches in Nigeria.


title: State Bank of India

text: In Moscow, SBI owns 60% of Commercial Bank of India, with Canara Bank owning the rest. In Indonesia, it owns 76% of PT Bank Indo Monex. State Bank of India already has a branch in Shanghai and plans to open one in Tianjin.


title: State Bank of India

text: In Kenya, State Bank of India owns 76% of Giro Commercial Bank, which it acquired for 8 million in October 2005.


title: State Bank of India

text: In 1982, the bank established a subsidiary, State Bank of India, which now has ten branches—nine branches in the state of California and one in Washington, D.C. The 10th branch was opened in Fremont, California on 28 March 2011. The other eight branches in California are located in Los Angeles, Artesia, San Jose, Canoga Park, Fresno, San Diego, Tustin and Bakersfield.


title: State Bank of India

text: SBI acquired the control of seven banks in 1960. They were the seven regional banks of former Indian princely states. They were renamed, prefixing them with 'State Bank of'. These seven banks were State Bank of Bikaner and Jaipur (SBBJ), State Bank of Hyderabad (SBH), State Bank of Indore (SBN), State Bank of Mysore (SBM), State Bank of Patiala (SBP), State Bank of Saurashtra (SBS) and State Bank of Travancore (SBT). All these banks were given the same logo as the parent bank, SBI. State Bank of India and all its associate banks used the same blue "Keyhole" logo said to have been inspired by Ahmedabad's Kankaria Lake. The State Bank of India wordmark usually had one standard typeface, but also utilised other typefaces. The wordmark now has the keyhole logo followed by "SBI".


title: State Bank of India

text: The plans for making SBI a single very large bank by merging the associate banks started in 2008, and in September the same year, SBS merged with SBI. The very next year, State Bank of Indore (SBN) also merged.


title: State Bank of India

text: Following a merger process, the merger of the 5 remaining associate banks, (viz. State Bank of Bikaner and Jaipur, State Bank of Hyderabad, State Bank of Mysore, State Bank of Patiala, State Bank of Travancore); and the Bharatiya Mahila Bank) with the SBI was given an in-principle approval by the Union Cabinet on 15 June 2016. This came a month after the SBI board had, on 17 May 2016, cleared a proposal to merge its five associate banks and Bharatiya Mahila Bank with itself.


title: State Bank of India

text: On 15 February 2017, the Union Cabinet approved the merger of five associate banks with SBI. An analyst foresaw an initial negative impact as a result of different pension liability provisions and accounting policies for bad loans. The merger went into effect from 1 April 2017.


title: State Bank of India

text: Apart from five of its associate banks (merged with SBI since 1 April 2017), SBI's non-banking subsidiaries include:


title: State Bank of India

text: In March 2001, SBI (with 74% of the total capital), joined with BNP Paribas (with 26% of the remaining capital), to form a joint venture life insurance company named SBI Life Insurance company Ltd.


title: State Bank of India

text: As of 31 March 2017, the SBI group had 59,291 ATMs. Since November 2017, SBI also offers an integrated digital banking platform named YONO.


title: State Bank of India

text: State Bank of India acquired 48.2% of the shares of Yes Bank as part of RBI directed rescue deal in March 2020.


title: State Bank of India

text: As on 31 March 2017, Government of India held around 61.23% equity shares in SBI. The Life Insurance Corporation of India, itself state-owned, is the largest non-promoter shareholder in the company with 8.82% shareholding.


title: State Bank of India

text: The equity shares of SBI are listed on the Bombay Stock Exchange, where it is a constituent of the BSE SENSEX index, and the National Stock Exchange of India, where it is a constituent of the CNX Nifty. Its Global Depository Receipts (GDRs) are listed on the London Stock Exchange.


title: State Bank of India

text: SBI is one of the largest employers in the world with 245,652 employees as on 31 March 2021. Out of the total workforce, the representation of women employees is nearly 26%. The percentage of Officers, Associates and Subordinate staffs was 44.28%, 41.03% and 14.69% respectively on the same date. Each employee contributed a net profit of during FY 2020–21.


title: Chloë Sevigny

text: Chloë Stevens Sevigny (, born November 18, 1974) is an American actress, model, filmmaker and fashion designer. Known for her work in independent films, often appearing in controversial or experimental features, Sevigny is the recipient of several accolades, including a Golden Globe Award, a Satellite Award, an Independent Spirit Award, as well as nominations for an Academy Award and three Screen Actors Guild Awards. She also has a career in fashion design concurrent with her acting work. Over the years, her alternative fashion sense has earned her a reputation as a "style icon".