The Mother, Sri Aurobindo Ashram

This is a habit with us, not only in this particular case but in all cases. It is rather remarkable that when we have a weaknessfor example a ridiculous habit, a defect or an imperfectionsince it is more or less part of our nature, we consider it to be very natural, it does not shock us. But as soon as we see this same weakness, this same imperfection, this same ridiculous habit in someone else, it seems quite shocking to us and we say, What! Hes like that?without noticing that we ourselves are like that. And so to the weakness and imperfection we add the absurdity of not even noticing them.

There is a lesson to be drawn from this. When something in a person seems to you completely unacceptable or ridiculous What! He is like that, he behaves like that, he says things like that, he does things like thatyou should say to yourself, Well, well, but perhaps I do the same thing without being aware of it. I would do better to look into myself first before criticizing him, so as to make sure that I am not doing the very same thing in a slightly different way.

If you have the good sense and intelligence to do this each time you are shocked by another persons behaviour, you will realise that in life your relations with others are like a mirror which is presented to you so that you can see more easily and clearly the weaknesses you carry within you. In general and almost absolute way, anything that shocks you in other people is the very thing you carry in yourself in a more or less veiled, more or less hidden form, though perhaps in a slightly different guise which allows you to delude yourself. And what in yourself seems inoffensive enough, becomes monstrous as soon as you see it in others.

Try to experience this; it will greatly help you to change yourselves. At the same time, it will bring a sunny tolerance to your relationships with others, the goodwill which comes from understanding, and it will very often put an end to these completely useless quarrels.

Link:

Guiding Light: Others are a mirror - Free Press Journal

New Delhi: All World Gayatri Pariwar, headquartered in Haridwar, has urged all its followers across the world to organise Gayatri Puja at home to fight against the outbreak of COVID-19 pandemic. The rituals will be performed from 9 AM to 4 PM. According to reports, at least 100 countries in the world has over one crore of followers who will take part in the rituals simultaneously. Gayatri Jayanti will be celebrated on June 1 or June 2. Also Read - Coronavirus in Kerala: Unable to Attend Online Classes, Class 9 Students Commits Suicide

There will be no gathering, crowding anywhere as all devotees have been asked to perform the rituals at their places with whatever thing available. Also Read - F1 Announces Dates For Opening Eight Races of Revised 2020 Calendar After COVID-19 Break

May 31 also marks the last day of lockdown 4.0. From June 8, all religious places will be opened with a limited number of worshippers. Also Read - 'Chances of Unwanted Pregnancies': Bihar Health Dept Distributes Condoms Among Migrant Workers

Shantikunj, the world-renowned ashram and the headquarters of All World Gayatri Pariwar, is situated at Haridwar. Established in 1971 by Pandit Sriram Sharma Acharya, Shantikunj is a major attraction in Haridwar among visitors in search of spirituality.

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Uttarakhand News: Gayatri Puja With Social Distancing From 9 AM Today in All Households to Fight COVID-19 - India.com

Media Release

Mangaluru, Jun 1: Msgr Maxim Noronha, the vicar general of the diocese, here, hoisted the flag of St Anthony and inaugurated the devotion of the thirteen-day novena in preparation for the annual feast of St Anthony to be celebrated on June 13.

The vicar general then offered a holy mass at 7 am and preached a homily on the descent of the Holy Spirit as the Church celebrates the feast of Pentecost. In his homily, the Vicar General said that the Church was born on the day the Holy Spirit descended on the apostles. With the descent of the Holy Spirit, the apostles were filled with courage and enthusiasm. They went about preaching the Good News.

The theme of the first day of the novena was Go and preach the Gospel. St Anthony, true to his calling preached the Word of God and performed several miracles during his lifetime in the name of Jesus. The vicar general called on the devotees of St Anthony to be witnesses to Jesus in whichever way possible.

Fr Onil DSouza, the director of the ashram, thanked the vicar general for offering the holy mass. He also added that every day on novena days, there will be a holy mass and novena in Konkani at 7 am and in English at 7 pm and the devotees can send petitions/prayer requests to sabjeppu@gmail.com or by whatsApp to 99449825580.

Fr Roshan DSouza, the assistant director joined for the concelebration. Fr Roshan DSouza conducted the first day of the novena.

Excerpt from:

Mangaluru: Thirteen-day novena begins in preparation for annual feast of St Anthony - Daijiworld.com

New Delhi: Global Social Connect a non-profit organisation planned and executed a free to attend online love webinar on 30thMay 2020. The topic for the evening was Social priority issues- during and after COVID-19. Global Goodwill Ambassador, Humanitarian for Singapore, India and Malaysia, Ganesh Somwanshi was a guest speaker along with other dignitaries like K.G.Suresh, Dean, School of Mass Media, University of Petroleum & Energy Studies (UPES), Dehradun and Samiksha Amte, daughter in law of social activist Baba Amte and Head Lok Biradari Ashram School. There was active participation from other countries like the US, Singapore and Canada and the webinar lasted for 90 minutes.

The top discussion for the evening was about how Carona can be fought by standing together and listening to Indian Governments discussion. Some of the other issues discussed were about economic downturn, migrant worker issues, unemployment, journalism and medias role in reporting the stories during the pandemic. The positive aspect of the pandemic too was discussed like lessened impact of global warming, excellent air quality and humanness.

Ganesh Somwanshi, Global Goodwill Ambassador, shares, The whole World has united to fight against Corona, it feels like a war. The webinar helped me to reach out to more people across borders, exchange ideas and learn. Hoping the world heals from the pandemic. Let us pledge to stay safe and connected with each other.

We are glad that our platform created an opportunity for people to voice their thoughts. We focus on result-oriented development programs that promote and seek to build stronger communities in which individuals are engaged . We are impartial , efficient , collaborative , global and local and focus on empowerment, added Richa Singh, Chairperson, Global Social Connect.

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Global Social Connect webinar on COVID reaches out to Indians across the borders to US, Malaysia, Singapore & Canada - India Education Diary

Scientific study of algorithms and statistical models that computer systems use to perform tasks without explicit instructions

Machine learning (ML) is the study of computer algorithms that improve automatically through experience.[1] It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so.[2] Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks.

Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning.[4][5] In its application across business problems, machine learning is also referred to as predictive analytics.

Machine learning involves computers discovering how they can perform tasks without being explicitly programmed to do so. For simple tasks assigned to computers, it is possible to program algorithms telling the machine how to execute all steps required to solve the problem at hand; on the computer's part, no learning is needed. For more advanced tasks, it can be challenging for a human to manually create the needed algorithms. In practice, it can turn out to be more effective to help the machine develop its own algorithm, rather than have human programmers specify every needed step.[6][7]

The discipline of machine learning employs various approaches to help computers learn to accomplish tasks where no fully satisfactory algorithm is available. In cases where vast numbers of potential answers exist, one approach is to label some of the correct answers as valid. This can then be used as training data for the computer to improve the algorithm(s) it uses to determine correct answers. For example, to train a system for the task of digital character recognition, the MNIST dataset has often been used. [6][7]

Early classifications for machine learning approaches sometimes divided them into three broad categories, depending on the nature of the "signal" or "feedback" available to the learning system. These were: Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs. Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning). Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent) As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximise. [3]

Other approaches or processes have since developed that don't fit neatly into this three-fold categorisation, and sometimes more than one is used by the same machine learning system. For example topic modeling, dimensionality reduction or meta learning. [8] As of 2020, deep learning has become the dominant approach for much ongoing work in the field of machine learning . [6]

The term machine learning was coined in 1959 by Arthur Samuel, an American IBMer and pioneer in the field of computer gaming and artificial intelligence. [9][10] A representative book of the machine learning research during the 1960s was the Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification.[11] Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973. [12] In 1981 a report was given on using teaching strategies so that a neural network learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal. [13]

Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E."[14] This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?".[15]

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 were 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.[16]Probabilistic reasoning was also employed, especially in automated medical diagnosis.[17]:488

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.[17]:488 By 1980, expert systems had come to dominate AI, and statistics was out of favor.[18] 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.[17]:708710; 755 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.[17]:25

Machine learning, 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 and probability theory.[18] As of 2019, many sources continue to assert that machine learning remains a sub field of AI. Yet some practitioners, for example Dr Daniel Hulme, who both teaches AI and runs a company operating in the field, argues that machine learning and AI are separate. [7][19][6]

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.

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). The difference between the two fields 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.[20]

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.[21] According to Michael I. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics.[22] He also suggested the term data science as a placeholder to call the overall field.[22]

Leo Breiman distinguished two statistical modeling paradigms: data model and algorithmic model,[23] wherein "algorithmic model" means more or less the machine learning algorithms like Random forest.

Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning.[24]

A core objective of a learner is to generalize from its experience.[3][25] 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.

The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory. 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 biasvariance decomposition is one way to quantify generalization error.

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.[26]

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.

The types of machine learning algorithms differ in their approach, the type of data they input and output, and the type of task or problem that they are intended to solve.

Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs.[27] 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.[28] 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.[14]

Types of supervised learning algorithms include Active learning , classification and regression.[29] 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.

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.

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.[30] Though unsupervised learning encompasses other domains involving summarizing and explaining data features.

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.

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.

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.[31]

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.[32] 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.

Self-learning as machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning named Crossbar Adaptive Array (CAA). [33] It is a learning with no external rewards and no external teacher advices. 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. [34] The self-learning algorithm updates a memory matrix W =||w(a,s)|| such that in each iteration executes the following machine learning routine:

It is a system with only one input, situation s, 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 behavioral environment where it behaves, and the other is 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. [35]

Several learning algorithms aim at discovering better representations of the inputs provided during training.[36] 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.

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[37] and various forms of clustering.[38][39][40]

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.[41]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.[42]

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 to attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms.

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.[43] 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.[44]

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.[45] 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.[46]

In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts in activity. This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods (in particular, unsupervised algorithms) will fail on such data, unless it has been aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro-clusters formed by these patterns.[47]

Three broad categories of anomaly detection techniques exist.[48] 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 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.

In developmental robotics, robot learning algorithms generate their own sequences of learning experiences, also known as a curriculum, to cumulatively acquire new skills through self-guided exploration and social interaction with humans. These robots use guidance mechanisms such as active learning, maturation, motor synergies and imitation.

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".[49]

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.[50] Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems.

Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliski 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.[51] For example, the rule { o n i o n s , p o t a t o e s } { b u r g e r } {displaystyle {mathrm {onions,potatoes} }Rightarrow {mathrm {burger} }} 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.

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.[52]

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 languages for representing hypotheses (and not only logic programming), such as functional programs.

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.[53][54][55] Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples.[56] 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.

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.

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.

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.

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.

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.[57]

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.

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 or the other.[58] 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.

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 (mathematics) 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 [59]), 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.

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.

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.[60][61] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[62]

Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model.

Federated learning is a new approach 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.[63]

There are many applications for machine learning, including:

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 on 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 $1million.[65] 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.[66] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[67] 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.[68] 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.[69] In 2019 Springer Nature published the first research book created using machine learning.[70]

Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[71][72][73] 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.[74]

In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[75] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of investment.[76][77]

Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only 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 same constitutional and unconscious biases already present in society.[78] Language models learned from data have been shown to contain human-like biases.[79][80] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[81][82] In 2015, Google photos would often tag black people as gorillas,[83] 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.[84] Similar issues with recognizing non-white people have been found in many other systems.[85] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[86] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[87] 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 "Theres nothing artificial about AI...Its inspired by people, its created by people, andmost importantlyit impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.[88]

Classification 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.[89]

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).[90]

Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[91] For example, 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 against similarity to previous successful applicants.[92][93] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning.

Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[94][95]

Other forms of ethical challenges, not related to personal biases, are more 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 increasing 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 huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[96]

Software suites containing a variety of machine learning algorithms include the following:

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Machine learning - Wikipedia

PHILADELPHIA, June 1, 2020 /PRNewswire/ -- Today, DataGroomr announced the release of its new Data Quality Management platform for Salesforce. A first of its kind, the platform utilizes Machine Learning algorithms to circumvent the need for any human intervention when it comes to identifying duplicates in Salesforce. Conveniently, the algorithms analyzeevery record in Salesforce to return a list of duplicates for review, saving admins the headache of designing and managing custom rules and filters. Similarly, by importing new records to Salesforce via Datagroomr, users can prevent new duplicates from being created.

Delivered as a Software-as-a-Service, the solution provides an intuitive interface for administrators to review duplicates, append record data, and merge faster than ever before. To ensure that Salesforce stays free of duplication, the platform includes robust automation capabilities for admins to schedule duplication analyses and mass merge tasks.

Co-Founder of DataGroomr, Steve Pogrebivsky explained that "the platform simplifies the approach to deduplication by harnessing the power of Machine Learning. Grappling with duplicate rules, filters, and cumbersome Excel analyses are a thing of the past this is truly a new era for the data quality focussed Salesforce Administrator."

Steve went on to say that "we're thrilled at the response we have received from our user community. It proves that our algorithms can greatly reduce the time and money spent on managing data quality in Salesforce."

A free 14-day trial of the platform is available directly from the DataGroomr website: http://www.datagroomr.com and from the Salesforce AppExchange

ABOUT DATAGROOMR

DataGroomr is the first Data Quality Management Platform for Salesforce to harness the power of Machine Learning to find and prevent duplicate records automatically. Delivered as a Software-as-a-Solution (SaaS), the platform equips users with everything that they need to keep Salesforce clean.

MEDIA CONTACT Steve Pogrebivsky Tel: +1 215 253 5600 [emailprotected] https://datagroomr.com/

SOURCE DataGroomr

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Announcing DataGroomr, the App that Utilizes Machine Learning to Find Duplicates in Salesforce Automatically - PRNewswire

The Experiences Per Mile Advisory Council, which unifies experts from the car, automotive and tech industry, has recently publisheda forecaston vehicle connectivity and the surrounding customer experience. According to the report, today 48% of all new cars globally include built-in connectivity, but by 2030 that figure will rise to 96%. Similarly, by 2030, 79% of vehicles shipped around the world will have an L2 autonomy or higher.

The report also says that customer expectations are shifting from just smart technologies to a connected experience, including vehicle maintenance. As such, 57% of European and 80% of North American respondents are interested in early detection of necessary maintenance and repairs; 80% of respondents were willing to share anonymous or personal connected car data to gain access to such capabilities. Big data allows automakers to predict the maintenance and repair needs of their vehicles, in turn enabling dealerships to be optimized and downtime to be minimized.

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Expert Reaction On Forecast That Machine Learning Will Seriously Change The Automotive Industry And Its Security - ISBuzz News

Mukesh Ambani-controlled Reliance Industries and its subsidiaries have invested over $2 billion in its four-pronged strategy to become a technology powerhouse.

The strategy includes spending over $1.6 billion on buying stakes in 24 tech firms across the US, UK, and India; winning 30 US patents out of the 53 it applied for, mostly in telecom and radio communications; and developing in-house tech in artificial intelligence, machine learning, block chain, virtual reality, big data, and 5G. Also, the Gennext programme is providing venture capital support and mentoring to ...

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AI to machine learning: RILs $2-billion bet to be a tech tornado - Business Standard

Kounts advanced fraud prevention uses adaptive AI and machine learning combined with the Identity Trust Global Network to protect businesses from chargebacks and false positives

Barclaycard Payments, which processes almost 40 per cent of card transactions in the UK, has announced a new partnership with leading fraud prevention provider Kount to give Barclays Transact customers access to award-winning fraud detection software.

Barclays Transact is a suite of tools designed to help merchants make their online transactions both simpler and safer.

Transacts new fraud module, powered by Kount, uses complex data linking within the Identity Trust Global Network and artificial intelligence algorithms to detect fraudulent transactions in real-time, at the point of check-out, helping protect the business from false positives and chargebacks.

Kount is the only fraud prevention system with the Identity Trust Global Network, the largest network of trust and risk signals, which is comprised of 32 billion annual interactions from more than 6,500 customers across 75+ industries. Kounts AI utilizes both supervised and unsupervised machine learning to detect emerging and existing complex fraud. Kounts customers have reported results such as a 99 percent reduction in chargebacks, 70 percent reduction in false positives, and an 83 percent reduction in manual reviews.

Recommended AI News: Proof Of Impact Launches B2B Platform So Companies Can Track Their Impact

The fraud module can also help businesses better prepare for the introduction of the EUs Strong Customer Authentication (SCA)regulation,which aims to tackle growing rates of fraud and cybercrime. SCA requires that all EEA transactions go through a two-factor authentication process, unless they qualify for an exemption. One consequence of this regulation is that the authentication process will introduce a degree of friction into the shopper journey, which may result in an increase in cart abandonment, and ultimately in lost revenue for retailers.

The Kount and Barclaycard Payments partnership is a mutually beneficial relationship. Barclaycard Payments gains access to Kounts Identity Trust Global Network to bring insights and protection to its global merchants, especially those in Europe requiring PSD2 and SCA support. Kount broadens its global visibility with a significant increase in UK card transactions, said David Mattei, Senior Analyst at Aite Group. The real winners are merchants using Barclays Transact for advanced AI-driven fraud mitigation and prevention.

Transacts fraud module can help businesses overcome this friction by taking advantage of SCA-approved Transaction Risk Assessment (TRA) exemptions which is where transactions are judged to be sufficiently genuine, and therefore allowed to skip the two-factor authentication process up to pre-agreed thresholds.

Recommended AI News: AgUnity And Etherisc Sign MOU To Kick-Start Their Cooperation

With Kounts state-of-the-art fraud analysis, all transactions are analyzed in real time and scored on a spectrum of low to high risk. The merchants gateway then uses this score to identify the transactions which qualify for TRA exemptions. This results in a more frictionless payment journey and a faster checkout experience for customers, ultimately resulting in lower levels of basket abandonment and increased sales. Higher-risk transactions requiring further inspection will still go through two-factor authentication, or be immediately declined, in accordance with the regulation and customer risk appetite.

Brad Wiskirchen, CEO, Kount said: The eCommerce environment is rapidly growing and changing. This partnership between Barclaycard and Kount brings together both stability and innovation to provide merchants with an innovative solution to combat emerging fraud strategies while providing a seamless payment experience for customers. Barclays Transact and Kounts Identity Trust Global network operate together to deliver a top of the line customer experience. By leaving fraud prevention and regulation management up to the experts, businesses can focus on what they do best.

David Jeffrey, Director of Product, Barclaycard Payments said: We are really excited to be partnering with Kount, because they share our goal of collaborative innovation, and a drive to deliver best-in-class shopper experiences. Thanks to Kounts award-winning fraud detection software, the new module will not only help customers to fight fraud and prevent unwanted chargebacks, it will also help them to maximize sales, improve customer experience, and better prepare for the introduction of SCA.

Recommended AI News: Topgolf and Golf Scope Partner to Launch New Virtual Reality Game

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Barclaycard Payments Partners With Kount to Deliver Industry-leading Fraud Prevention and Prepare Businesses for SCA - AiThority

A report Machine Learning has been recently published by Market Industry Reports (MIR). As per the report, the global machine learning market was estimated to be over ~US$ 2.7 billion in 2019. It is anticipated to grow at a CAGR of 43.5% from 2019 to 2030.

Major Key Players of the Machine Learning Market are: Intel, H2O.ai, Amazon Web Services, Hewlett Packard Enterprise Development LP, IBM, Google LLC, Microsoft, SAS Institute Inc., SAP SE, and BigML, Inc., among others.

Download PDF to Know the Impact of COVID-19 on Machine Learning Market at: https://www.marketindustryreports.com/pdf/133

There are various factors attributing to growth of the machine learning market including the availability of robust data sets and the adoption of machine learning techniques in modern applications such as self-driving cars, traffic alerts (Google Maps), product recommendations (Amazon), and transportation & commuting (Uber). Also, the adoption of machine learning across various industries, such as the finance industry, to minimize identity theft and detect fraud is adding to growth of the machine learning market.

Technologies powered by machine learning, capture and analyse data to improve marketing operations and enhance the customer experience. Moreover, the proliferation of large datasets, technological advancements, and techniques to provide a competitive edge in business operations are among major factors that will drive the machine learning market. Rapid urbanization, acceptance of machine learning in developed countries, rapid adoption of new technologies to minimize work and the presence of a large talent pool will push the machine learning market.

Major Applications of Machine Learning Market covered are: Healthcare & Life Sciences Manufacturing, Retail Telecommunications Government and Defense BFSI (Banking, financial services, and insurance) Energy and Utilities and Others

Research objectives:-

To study and analyze the global Machine Learning consumption (value & volume) by key regions/countries, product type and application, history data. To understand the structure of the Machine Learning market by identifying its various sub-segments. Focuses on the key global Machine Learning manufacturers, to define, describe and analyze the sales volume, value, market share, market competitive landscape, SWOT analysis, and development plans in the next few years. To analyze the Machine Learning with respect to individual growth trends, future prospects, and their contribution to the total market. To share detailed information about the key factors influencing the growth of the market (growth potential, opportunities, drivers, industry-specific challenges and risks).

Go For Interesting Discount Here:https://www.marketindustryreports.com/discount/133

Table of Content

1 Report Overview 1.1 Study Scope 1.2 Key Market Segments 1.3 Players Covered 1.4 Market Analysis by Type 1.5 Market by Application 1.6 Study Objectives 1.7 Years Considered

2 Global Growth Trends 2.1 Machine Learning Market Size 2.2 Machine Learning Growth Trends by Regions 2.3 Industry Trends

3 Market Share by Key Players 3.1 Machine Learning Market Size by Manufacturers 3.2 Machine Learning Key Players Head office and Area Served 3.3 Key Players Machine Learning Product/Solution/Service 3.4 Date of Enter into Machine Learning Market 3.5 Mergers & Acquisitions, Expansion Plans

4 Breakdown Data by Product 4.1 Global Machine Learning Sales by Product 4.2 Global Machine Learning Revenue by Product 4.3 Machine Learning Price by Product

5 Breakdown Data by End User 5.1 Overview 5.2 Global Machine Learning Breakdown Data by End User

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In the end, Machine Learning industry report specifics the major regions, market scenarios with the product price, volume, supply, revenue, production, and market growth rate, demand, forecast and so on. This report also presents SWOT analysis, investment feasibility analysis, and investment return analysis.

About Market Industry Reports

Market Industry Reports is a global leader in market measurement & advisory services, Market Industry Reports is at the forefront of innovation to address the worldwide industry trends and opportunities. We identified the caliber of market dynamics & hence we excel in the areas of innovation and optimization, integrity, curiosity, customer and brand experience, and strategic business intelligence through our research.

We continue to pioneer state-of-the-art approach in research & analysis that makes complex world simpler to stay ahead of the curve. By nurturing the perception of genius and optimized market intelligence we bring proficient contingency to our clients in the evolving world of technologies, mega trends and industry convergence. We empower and inspire Vanguards to fuel and shape their business to build and grow world-class consumer products.

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Machine Learning Market Projected to Register 43.5% CAGR to 2030 Intel, H2Oai - Cole of Duty