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11,700 | 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). | https://en.wikipedia.org/wiki?curid=233488 |
11,701 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,702 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,703 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,704 | Some statisticians have adopted methods from machine learning, leading to a combined field that they call "statistical learning". | https://en.wikipedia.org/wiki?curid=233488 |
11,705 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,706 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,707 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,708 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,709 | 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: | https://en.wikipedia.org/wiki?curid=233488 |
11,710 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,711 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,712 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,713 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,714 | 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". | https://en.wikipedia.org/wiki?curid=233488 |
11,715 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,716 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,717 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,718 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,719 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,720 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,721 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,722 | As of 2022, deep learning is the dominant approach for much ongoing work in the field of machine learning. | https://en.wikipedia.org/wiki?curid=233488 |
11,723 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,724 | The self-learning algorithm updates a memory matrix W =||w(a,s)|| such that in each iteration executes the following machine learning routine: | https://en.wikipedia.org/wiki?curid=233488 |
11,725 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,726 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,727 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,728 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,729 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,730 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,731 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,732 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,733 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,734 | Robot learning is inspired by a multitude of machine learning methods, starting from supervised learning, reinforcement learning, and finally meta-learning (e.g. MAML). | https://en.wikipedia.org/wiki?curid=233488 |
11,735 | 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". | https://en.wikipedia.org/wiki?curid=233488 |
11,736 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,737 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,738 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,739 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,740 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,741 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,742 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,743 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,744 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,745 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,746 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,747 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,748 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,749 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,750 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,751 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,752 | Gaussian processes are popular surrogate models in Bayesian optimization used to do hyperparameter optimization. | https://en.wikipedia.org/wiki?curid=233488 |
11,753 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,754 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,755 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,756 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,757 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,758 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,759 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,760 | 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." | https://en.wikipedia.org/wiki?curid=233488 |
11,761 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,762 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,763 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,764 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,765 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,766 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,767 | 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). | https://en.wikipedia.org/wiki?curid=233488 |
11,768 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,769 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,770 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,771 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,772 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,773 | 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. | https://en.wikipedia.org/wiki?curid=233488 |
11,774 | Apollo 13 (April 1117, 1970) was the seventh crewed mission in the Apollo space program and the third meant to land on the Moon. The craft was launched from Kennedy Space Center on April 11, 1970, but the lunar landing was aborted after an oxygen tank in the service module (SM) failed two days into the mission. The crew instead looped around the Moon and returned safely to Earth on April 17. The mission was commanded by Jim Lovell, with Jack Swigert as command module (CM) pilot and Fred Haise as Lunar Module (LM) pilot. Swigert was a late replacement for Ken Mattingly, who was grounded after exposure to rubella. | https://en.wikipedia.org/wiki?curid=1770 |
11,775 | A routine stir of an oxygen tank ignited damaged wire insulation inside it, causing an explosion that vented the contents of both of the SM's oxygen tanks to space. Without oxygen, needed for breathing and for generating electric power, the SM's propulsion and life support systems could not operate. The CM's systems had to be shut down to conserve its remaining resources for reentry, forcing the crew to transfer to the LM as a lifeboat. With the lunar landing canceled, mission controllers worked to bring the crew home alive. | https://en.wikipedia.org/wiki?curid=1770 |
11,776 | Although the LM was designed to support two men on the lunar surface for two days, Mission Control in Houston improvised new procedures so it could support three men for four days. The crew experienced great hardship, caused by limited power, a chilly and wet cabin and a shortage of potable water. There was a critical need to adapt the CM's cartridges for the carbon dioxide scrubber system to work in the LM; the crew and mission controllers were successful in improvising a solution. The astronauts' peril briefly renewed public interest in the Apollo program; tens of millions watched the splashdown in the South Pacific Ocean on television. | https://en.wikipedia.org/wiki?curid=1770 |
11,777 | An investigative review board found fault with preflight testing of the oxygen tank and Teflon being placed inside it. The board recommended changes, including minimizing the use of potentially combustible items inside the tank; this was done for Apollo 14. The story of Apollo 13 has been dramatized several times, most notably in the 1995 film "Apollo 13" – based on "Lost Moon", the 1994 memoir co-authored by Lovell – and an episode of the 1998 miniseries "From the Earth to the Moon". | https://en.wikipedia.org/wiki?curid=1770 |
11,778 | In 1961, U.S. President John F. Kennedy challenged his nation to land an astronaut on the Moon by the end of the decade, with a safe return to Earth. NASA worked towards this goal incrementally, sending astronauts into space during Project Mercury and Project Gemini, leading up to the Apollo program. The goal was achieved with Apollo 11, which landed on the Moon on July 20, 1969. Neil Armstrong and Buzz Aldrin walked on the lunar surface while Michael Collins orbited the Moon in Command Module "Columbia". The mission returned to Earth on July 24, 1969, fulfilling Kennedy's challenge. | https://en.wikipedia.org/wiki?curid=1770 |
11,779 | NASA had contracted for fifteen Saturn V rockets to achieve the goal; at the time no one knew how many missions this would require. Since success was obtained in 1969 with the sixth SaturnV on Apollo 11, nine rockets remained available for a hoped-for total of ten landings. After the excitement of Apollo 11, the general public grew apathetic towards the space program and Congress continued to cut NASA's budget; Apollo 20 was canceled. Despite the successful lunar landing, the missions were considered so risky that astronauts could not afford life insurance to provide for their families if they died in space. | https://en.wikipedia.org/wiki?curid=1770 |
11,780 | Even before the first U.S. astronaut entered space in 1961, planning for a centralized facility to communicate with the spacecraft and monitor its performance had begun, for the most part the brainchild of Christopher C. Kraft Jr., who became NASA's first flight director. During John Glenn's Mercury "Friendship 7" flight in February 1962 (the first crewed orbital flight by the U.S.), one of Kraft's decisions was overruled by NASA managers. He was vindicated by post-mission analysis and implemented a rule that, during the mission, the flight director's word was absolute – to overrule him, NASA would have to fire him on the spot. Flight directors during Apollo had a one-sentence job description, "The flight director may take any actions necessary for crew safety and mission success." | https://en.wikipedia.org/wiki?curid=1770 |
11,781 | In 1965, Houston's Mission Control Center opened, in part designed by Kraft and now named for him. In Mission Control, each flight controller, in addition to monitoring telemetry from the spacecraft, was in communication via voice loop to specialists in a Staff Support Room (or "back room"), who focused on specific spacecraft systems. | https://en.wikipedia.org/wiki?curid=1770 |
11,782 | Apollo 13 was to be the second H mission, meant to demonstrate precision lunar landings and explore specific sites on the Moon. With Kennedy's goal accomplished by Apollo 11, and Apollo 12 demonstrating that the astronauts could perform a precision landing, mission planners were able to focus on more than just landing safely and having astronauts minimally trained in geology gather lunar samples to take home to Earth. There was a greater role for science on Apollo 13, especially for geology, something emphasized by the mission's motto, "Ex luna, scientia" (From the Moon, knowledge). | https://en.wikipedia.org/wiki?curid=1770 |
11,783 | Apollo 13's mission commander, Jim Lovell, was 42 years old at the time of the spaceflight. He was a graduate of the United States Naval Academy and had been a naval aviator and test pilot before being selected for the second group of astronauts in 1962; he flew with Frank Borman in Gemini 7 in 1965 and Buzz Aldrin in Gemini 12 the following year before flying in Apollo 8 in 1968, the first spacecraft to orbit the Moon. At the time of Apollo 13, Lovell was the NASA astronaut with the most time in space, with 572 hours over the three missions. | https://en.wikipedia.org/wiki?curid=1770 |
11,784 | Jack Swigert, the command module pilot (CMP), was 38 years old and held a B.S. in mechanical engineering and an M.S. in aerospace science; he had served in the Air Force and in state Air National Guards and was an engineering test pilot before being selected for the fifth group of astronauts in 1966. Fred Haise, the lunar module pilot (LMP), was 35 years old. He held a B.S. in aeronautical engineering, had been a Marine Corps fighter pilot, and was a civilian research pilot for NASA when he was selected as a Group5 astronaut. | https://en.wikipedia.org/wiki?curid=1770 |
11,785 | According to the standard Apollo crew rotation, the prime crew for Apollo 13 would have been the backup crew for Apollo 10, with Mercury and Gemini veteran Gordon Cooper in command, Donn F. Eisele as CMP and Edgar Mitchell as LMP. Deke Slayton, NASA's Director of Flight Crew Operations, never intended to rotate Cooper and Eisele to a prime crew assignment, as both were out of favorCooper for his lax attitude towards training, and Eisele for incidents aboard Apollo7 and an extramarital affair. He assigned them to the backup crew because no other veteran astronauts were available. Slayton's original choices for Apollo 13 were Alan Shepard as commander, Stuart Roosa as CMP, and Mitchell as LMP. However, management felt Shepard needed more training time, as he had only recently resumed active status after surgery for an inner ear disorder and had not flown since 1961. Thus, Lovell's crew (himself, Haise and Ken Mattingly), having all backed up Apollo 11 and being slated for Apollo 14, was swapped with Shepard's. | https://en.wikipedia.org/wiki?curid=1770 |
11,786 | Swigert was originally CMP of Apollo 13's backup crew, with John Young as commander and Charles Duke as lunar module pilot. Seven days before launch, Duke contracted rubella from a friend of his son. This exposed both the prime and backup crews, who trained together. Of the five, only Mattingly was not immune through prior exposure. Normally, if any member of the prime crew had to be grounded, the remaining crew would be replaced as well, and the backup crew substituted, but Duke's illness ruled this out, so two days before launch, Mattingly was replaced by Swigert. Mattingly never developed rubella and later flew on Apollo 16. | https://en.wikipedia.org/wiki?curid=1770 |
11,787 | For Apollo, a third crew of astronauts, known as the support crew, was designated in addition to the prime and backup crews used on projects Mercury and Gemini. Slayton created the support crews because James McDivitt, who would command Apollo 9, believed that, with preparation going on in facilities across the US, meetings that needed a member of the flight crew would be missed. Support crew members were to assist as directed by the mission commander. Usually low in seniority, they assembled the mission's rules, flight plan, and checklists, and kept them updated; for Apollo 13, they were Vance D. Brand, Jack Lousma and either William Pogue or Joseph Kerwin. | https://en.wikipedia.org/wiki?curid=1770 |
11,788 | For Apollo 13, flight directors were Gene Kranz, White team (the lead flight director); Glynn Lunney, Black team; Milton Windler, Maroon team and Gerry Griffin, Gold team. The CAPCOMs (the person in Mission Control, during the Apollo program an astronaut, who was responsible for voice communications with the crew) for Apollo 13 were Kerwin, Brand, Lousma, Young and Mattingly. | https://en.wikipedia.org/wiki?curid=1770 |
11,789 | The Apollo 13 mission insignia depicts the Greek god of the Sun, Apollo, with three horses pulling his chariot across the face of the Moon, and the Earth seen in the distance. This is meant to symbolize the Apollo flights bringing the light of knowledge to all people. The mission motto, "Ex luna, scientia" ("From the Moon, knowledge"), appears. In choosing it, Lovell adapted the motto of his alma mater, the Naval Academy, "Ex scientia, tridens" ("From knowledge, sea power"). | https://en.wikipedia.org/wiki?curid=1770 |
11,790 | On the patch, the mission number appeared in Roman numerals as Apollo XIII. It did not have to be modified after Swigert replaced Mattingly, as it is one of only two Apollo mission insigniathe other being Apollo 11not to include the names of the crew. It was designed by artist Lumen Martin Winter, who based it on a mural he had painted for the St. Regis Hotel in New York City. The mural was later purchased by actor Tom Hanks, who portrayed Lovell in the movie "Apollo 13", and is now in the Captain James A. Lovell Federal Health Care Center in Illinois. | https://en.wikipedia.org/wiki?curid=1770 |
11,791 | The mission's motto was in Lovell's mind when he chose the call sign "Aquarius" for the lunar module, taken from Aquarius, the bringer of water. Some in the media erroneously reported that the call sign was taken from a song by that name from the musical "Hair". The command module's call sign, "Odyssey", was chosen not only for its Homeric association but to refer to the recent movie, "", based on a short story by science fiction author Arthur C. Clarke. In his book, Lovell indicated he chose the name "Odyssey" because he liked the word and its definition: a long voyage with many changes of fortune. | https://en.wikipedia.org/wiki?curid=1770 |
11,792 | The Saturn V rocket used to carry Apollo 13 to the Moon was numbered SA-508, and was almost identical to those used on Apollo8 through 12. Including the spacecraft, the rocket weighed in at . The S-IC first stage's engines were rated to generate less total thrust than Apollo 12's, though they remained within specifications. To keep its liquid hydrogen propellent cold, the S-II second stage's cryogenic tanks were insulated; on earlier Apollo missions this came in the form of panels that were affixed, but beginning with Apollo 13, insulation was sprayed onto the exterior of the tanks. Extra propellant was carried as a test, since future J missions to the Moon would require more propellant for their heavier payloads. This made the vehicle the heaviest yet flown by NASA, and Apollo 13 was visibly slower to clear the launch tower than earlier missions. | https://en.wikipedia.org/wiki?curid=1770 |
11,793 | The Apollo 13 spacecraft consisted of Command Module 109 and Service Module 109 (together CSM-109), called "Odyssey", and Lunar Module7 (LM-7), called "Aquarius". Also considered part of the spacecraft was the launch escape system, which would propel the command module (CM) to safety in the event of a problem during liftoff, and the Spacecraft–LM Adapter, numbered as SLA-16, which housed the lunar module (LM) during the first hours of the mission. | https://en.wikipedia.org/wiki?curid=1770 |
11,794 | The LM stages, CM and service module (SM) were received at Kennedy Space Center (KSC) in June 1969; the portions of the Saturn V were received in June and July. Thereafter, testing and assembly proceeded, culminating with the rollout of the launch vehicle, with the spacecraft atop it, on December 15, 1969. Apollo 13 was originally scheduled for launch on March 12, 1970; in January of that year, NASA announced the mission would be postponed until April 11, both to allow more time for planning and to spread the Apollo missions over a longer period of time. The plan was to have two Apollo flights per year and was in response to budgetary constraints that had recently seen the cancellation of Apollo 20. | https://en.wikipedia.org/wiki?curid=1770 |
11,795 | The Apollo 13 prime crew undertook over 1,000 hours of mission-specific training, more than five hours for every hour of the mission's ten-day planned duration. Each member of the prime crew spent over 400 hours in simulators of the CM and (for Lovell and Haise) of the LM at KSC and at Houston, some of which involved the flight controllers at Mission Control. Flight controllers participated in many simulations of problems with the spacecraft in flight, which taught them how to react in an emergency. Specialized simulators at other locations were also used by the crew members. | https://en.wikipedia.org/wiki?curid=1770 |
11,796 | The astronauts of Apollo 11 had minimal time for geology training, with only six months between crew assignment and launch; higher priorities took much of their time. Apollo 12 saw more such training, including practice in the field, using a CAPCOM and a simulated backroom of scientists, to whom the astronauts had to describe what they saw. Scientist-astronaut Harrison Schmitt saw that there was limited enthusiasm for geology field trips. Believing an inspirational teacher was needed, Schmitt arranged for Lovell and Haise to meet his old professor, Caltech's Lee Silver. The two astronauts, and backups Young and Duke, went on a field trip with Silver at their own time and expense. At the end of their week together, Lovell made Silver their geology mentor, who would be extensively involved in the geology planning for Apollo 13. Farouk El-Baz oversaw the training of Mattingly and his backup, Swigert, which involved describing and photographing simulated lunar landmarks from airplanes. El-Baz had all three prime crew astronauts describe geologic features they saw during their flights between Houston and KSC; Mattingly's enthusiasm caused other astronauts, such as Apollo 14's CMP, Roosa, to seek out El-Baz as a teacher. | https://en.wikipedia.org/wiki?curid=1770 |
11,797 | Concerned about how close Apollo 11's LM, "Eagle", had come to running out of propellant during its lunar descent, mission planners decided that beginning with Apollo 13, the CSM would bring the LM to the low orbit from which the landing attempt would commence. This was a change from Apollo 11 and 12, on which the LM made the burn to bring it to the lower orbit. The change was part of an effort to increase the amount of hover time available to the astronauts as the missions headed into rougher terrain. | https://en.wikipedia.org/wiki?curid=1770 |
11,798 | The plan was to devote the first of the two four-hour lunar surface extravehicular activities (EVAs) to setting up the Apollo Lunar Surface Experiments Package (ALSEP) group of scientific instruments; during the second, Lovell and Haise would investigate Cone crater, near the planned landing site. The two astronauts wore their spacesuits for some 20 walk-throughs of EVA procedures, including sample gathering and use of tools and other equipment. They flew in the "Vomit Comet" in simulated microgravity or lunar gravity, including practice in donning and doffing spacesuits. To prepare for the descent to the Moon's surface, Lovell flew the Lunar Landing Training Vehicle (LLTV). Despite four of the five LLTVs and similar Lunar Landing Research Vehicles having crashed during the Apollo program, mission commanders considered flying them invaluable experience. | https://en.wikipedia.org/wiki?curid=1770 |
11,799 | Apollo 13's designated landing site was near Fra Mauro crater; the Fra Mauro formation was believed to contain much material spattered by the impact that had filled the Imbrium basin early in the Moon's history. Dating it would provide information not only about the Moon, but about the Earth's early history. Such material was likely to be available at Cone crater, a site where an impact was believed to have drilled deep into the lunar regolith. | https://en.wikipedia.org/wiki?curid=1770 |
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