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https://en.wikipedia.org/wiki/Machine_learning#51 | executes the following machine learning routine:
- in situation s perform action a
- receive a consequence situation s'
- compute emotion of being in the consequence situation v(s')
- update crossbar memory w'(a,s) = w(a,s) + v(s')
It is a system with only one input, situation, and only one output, action (or behaviour... |
https://en.wikipedia.org/wiki/Machine_learning#52 | y reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, one is the behavioural 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 behavioural enviro... |
https://en.wikipedia.org/wiki/Machine_learning#53 | vironment that contains both desirable and undesirable situations.[62]
Feature learning
[edit]Several learning algorithms aim at discovering better representations of the inputs provided during training.[63] Classic examples include principal component analysis and cluster analysis. Feature learning algorithms, also ca... |
https://en.wikipedia.org/wiki/Machine_learning#54 | hat 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... |
https://en.wikipedia.org/wiki/Machine_learning#55 | ning can be either supervised or unsupervised. In supervised feature learning, features are learned using labelled input data. Examples include artificial neural networks, multilayer perceptrons, and supervised dictionary learning. In unsupervised feature learning, features are learned with unlabelled input data. Examp... |
https://en.wikipedia.org/wiki/Machine_learning#56 | ering.[65][66][67]
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 lea... |
https://en.wikipedia.org/wiki/Machine_learning#57 | out reshaping them into higher-dimensional vectors.[68] 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... |
https://en.wikipedia.org/wiki/Machine_learning#58 | vated 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 ... |
https://en.wikipedia.org/wiki/Machine_learning#59 | rse dictionary learning is a feature learning method where a training example is represented as a linear combination of basis functions and assumed to be a sparse matrix. The method is strongly NP-hard and difficult to solve approximately.[70] A popular heuristic method for sparse dictionary learning is the k-SVD algor... |
https://en.wikipedia.org/wiki/Machine_learning#60 | 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 ... |
https://en.wikipedia.org/wiki/Machine_learning#61 | 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.[72] Typically, the anomalous items represent an issue such as bank fraud, a structural defect, medical problems or erro... |
https://en.wikipedia.org/wiki/Machine_learning#62 | xt 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 suc... |
https://en.wikipedia.org/wiki/Machine_learning#63 | hese patterns.[74]
Three broad categories of anomaly detection techniques exist.[75] Unsupervised anomaly detection techniques detect anomalies in an unlabelled 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 re... |
https://en.wikipedia.org/wiki/Machine_learning#64 | al" and involves training a classifier (the key difference from many other statistical classification problems is the inherently unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behaviour from a given normal training data set and then test the l... |
https://en.wikipedia.org/wiki/Machine_learning#65 | ne learning methods, starting from supervised learning, reinforcement learning,[76][77] and finally meta-learning (e.g. MAML).
Association rules
[edit]Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong r... |
https://en.wikipedia.org/wiki/Machine_learning#66 | chine 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 utilisation of a set of relational rules that collectively represent the knowledge captured by the system. This is i... |
https://en.wikipedia.org/wiki/Machine_learning#67 | tance in order to make a prediction.[79] 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 Imieliński and Arun Swami introduced association rules for discovering regularities ... |
https://en.wikipedia.org/wiki/Machine_learning#68 | e 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 a... |
https://en.wikipedia.org/wiki/Machine_learning#69 | duction, 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, typical... |
https://en.wikipedia.org/wiki/Machine_learning#70 | 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.[81]
Inductive logic programming (ILP) is an approach to rule learning using logic programming as a uniform representation for input examples, background knowle... |
https://en.wikipedia.org/wiki/Machine_learning#71 | f 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.
Inductive logic programm... |
https://en.wikipedia.org/wiki/Machine_learning#72 | oretical foundation for inductive machine learning in a logical setting.[82][83][84] Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples.[85] The term inductive here refers to philosophical induction, su... |
https://en.wikipedia.org/wiki/Machine_learning#73 | red set.
Models
[edit]A machine learning model is a type of mathematical model that, once "trained" on a given dataset, can be used to make predictions or classifications on new data. During training, a learning algorithm iteratively adjusts the model's internal parameters to minimise errors in its predictions.[86] By ... |
https://en.wikipedia.org/wiki/Machine_learning#74 | ng algorithms to a fully trained model with all its internal parameters tuned.[87]
Various types of models have been used and researched for machine learning systems, picking the best model for a task is called model selection.
Artificial neural networks
[edit]Artificial neural networks (ANNs), or connectionist systems... |
https://en.wikipedia.org/wiki/Machine_learning#75 | 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 tr... |
https://en.wikipedia.org/wiki/Machine_learning#76 | 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 ... |
https://en.wikipedia.org/wiki/Machine_learning#77 | creases 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... |
https://en.wikipedia.org/wiki/Machine_learning#78 | 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 recognitio... |
https://en.wikipedia.org/wiki/Machine_learning#79 | 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.[88]
Decision trees
[edit]Decision tree learning uses a decision tree a... |
https://en.wikipedia.org/wiki/Machine_learning#80 | lue (represented in the leaves). It is one of the predictive modelling 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 represen... |
https://en.wikipedia.org/wiki/Machine_learning#81 | ypically 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.
Random forest regression
[e... |
https://en.wikipedia.org/wiki/Machine_learning#82 | lds multiple decision trees and averages their predictions to improve accuracy and to avoid overfitting. To build decision trees, RFR uses bootstrapped sampling, for instance each decision tree is trained on random data of from training set. This random selection of RFR for training enables model to reduce bias predict... |
https://en.wikipedia.org/wiki/Machine_learning#83 | sor task. This makes RFR compatible to be used in various application.[89][90]
Support-vector machines
[edit]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 be... |
https://en.wikipedia.org/wiki/Machine_learning#84 | gory.[91] 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 ker... |
https://en.wikipedia.org/wiki/Machine_learning#85 | ompasses 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... |
https://en.wikipedia.org/wiki/Machine_learning#86 | s, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[92]), 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-dim... |
https://en.wikipedia.org/wiki/Machine_learning#87 | ultaneously. This approach estimates the relationships between a set of input variables and several output variables by fitting a multidimensional linear model. It is particularly useful in scenarios where outputs are interdependent or share underlying patterns, such as predicting multiple economic indicators or recons... |
https://en.wikipedia.org/wiki/Machine_learning#88 | ed 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 b... |
https://en.wikipedia.org/wiki/Machine_learning#89 | rning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalisations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams.
Gaussian processes
[edit]A Gaussian process is ... |
https://en.wikipedia.org/wiki/Machine_learning#90 | n, 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.
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... |
https://en.wikipedia.org/wiki/Machine_learning#91 | e popular surrogate models in Bayesian optimisation used to do hyperparameter optimisation.
Genetic algorithms
[edit]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 ... |
https://en.wikipedia.org/wiki/Machine_learning#92 | rsely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[97]
Belief functions
[edit]The theory of belief functions, also referred to as evidence theory or Dempster–Shafer theory, is a general framework for reasoning with uncertainty, with understood connection... |
https://en.wikipedia.org/wiki/Machine_learning#93 | ught of as a kind of learner and have some analogous properties of how evidence is combined (e.g., Dempster's rule of combination), just like how in a pmf-based Bayesian approach would combine probabilities.[98] However, there are many caveats to these beliefs functions when compared to Bayesian approaches in order to ... |
https://en.wikipedia.org/wiki/Machine_learning#94 | rning domain typically leverage a fusion approach of various ensemble methods to better handle the learner's decision boundary, low samples, and ambiguous class issues that standard machine learning approach tend to have difficulty resolving.[4][9] However, the computational complexity of these algorithms are dependent... |
https://en.wikipedia.org/wiki/Machine_learning#95 | pproaches.
Rule-based models
[edit]Rule-based machine learning (RBML) is a branch of machine learning that automatically discovers and learns 'rules' from data. It provides interpretable models, making it useful for decision-making in fields like healthcare, fraud detection, and cybersecurity. Key RBML techniques inclu... |
https://en.wikipedia.org/wiki/Machine_learning#96 | se methods extract patterns from data and evolve rules over time.
Training models
[edit]Typically, machine learning models require a high quantity of reliable data to perform accurate predictions. When training a machine learning model, machine learning engineers need to target and collect a large and representative sa... |
https://en.wikipedia.org/wiki/Machine_learning#97 | cted 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. Biased models may result in detrimental outcomes, thereby furthering the negative impacts on ... |
https://en.wikipedia.org/wiki/Machine_learning#98 | cs is becoming a field of study and notably, becoming integrated within machine learning engineering teams.
Federated learning
[edit]Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralises the training process, allowing for users' privacy to be... |
https://en.wikipedia.org/wiki/Machine_learning#99 | ng 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.[102]
Applications
[edit]There are many applications for machine learning, including:
- Agriculture
- Anatomy
- Adapti... |
https://en.wikipedia.org/wiki/Machine_learning#100 | interfaces
- Cheminformatics
- Citizen Science
- Climate Science
- Computer networks
- Computer vision
- Credit-card fraud detection
- Data quality
- DNA sequence classification
- Economics
- Financial market analysis[103]
- General game playing
- Handwriting recognition
- Healthcare
- Information retrieval
- Insurance... |
https://en.wikipedia.org/wiki/Machine_learning#101 | anslation
- Material Engineering
- Marketing
- Medical diagnosis
- Natural language processing
- Natural language understanding
- Online advertising
- Optimisation
- Recommender systems
- Robot locomotion
- Search engines
- Sentiment analysis
- Sequence mining
- Software engineering
- Speech recognition
- Structural he... |
https://en.wikipedia.org/wiki/Machine_learning#102 | onstruction[104]
- User behaviour analytics
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 ... |
https://en.wikipedia.org/wiki/Machine_learning#103 | ize in 2009 for $1 million.[105] Shortly after the prize was awarded, Netflix realised that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[106] In 2010 The Wall Street Journal wrote about the firm Rebel... |
https://en.wikipedia.org/wiki/Machine_learning#104 | od Khosla, predicted that 80% of medical doctors jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[108] 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 pr... |
https://en.wikipedia.org/wiki/Machine_learning#105 | ne learning.[110] In 2020, machine learning technology was used to help make diagnoses and aid researchers in developing a cure for COVID-19.[111] Machine learning was recently applied to predict the pro-environmental behaviour of travellers.[112] Recently, machine learning technology was also applied to optimise smart... |
https://en.wikipedia.org/wiki/Machine_learning#106 | achine learning algorithms (MLAs) can utilise a wide range of company characteristics to predict stock returns without overfitting. By employing effective feature engineering and combining forecasts, MLAs can generate results that far surpass those obtained from basic linear techniques like OLS.[116]
Recent advancement... |
https://en.wikipedia.org/wiki/Machine_learning#107 | nt effects on chemical reactions, thereby offering new tools for chemists to tailor experimental conditions for optimal outcomes.[117]
Machine Learning is becoming a useful tool to investigate and predict evacuation decision making in large scale and small scale disasters. Different solutions have been tested to predic... |
https://en.wikipedia.org/wiki/Machine_learning#108 | g on pre evacuation decisions in building fires.[121][122]
Machine learning is also emerging as a promising tool in geotechnical engineering, where it is used to support tasks such as ground classification, hazard prediction, and site characterization. Recent research emphasizes a move toward data-centric methods in th... |
https://en.wikipedia.org/wiki/Machine_learning#109 | nd patterns.[123]
Limitations
[edit]Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[124][125][126] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and a... |
https://en.wikipedia.org/wiki/Machine_learning#110 | ficant challenge. Black box refers to a situation where the algorithm or the process of producing an output is entirely opaque, meaning that even the coders of the algorithm cannot audit the pattern that the machine extracted out of the data.[128] The House of Lords Select Committee, which claimed that such an "intelli... |
https://en.wikipedia.org/wiki/Machine_learning#111 | "a full and satisfactory explanation for the decisions" it makes.[128]
In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[129] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars ... |
https://en.wikipedia.org/wiki/Machine_learning#112 | 132]
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 necessa... |
https://en.wikipedia.org/wiki/Machine_learning#113 | inable Machine Learning (XML), is artificial intelligence (AI) in which humans can understand the decisions or predictions made by the AI.[134] It contrasts with the "black box" concept in machine learning where even its designers cannot explain why an AI arrived at a specific decision.[135] By refining the mental mode... |
https://en.wikipedia.org/wiki/Machine_learning#114 | ay be an implementation of the social right to explanation.
Overfitting
[edit]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 penalising the... |
https://en.wikipedia.org/wiki/Machine_learning#115 | t 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.[137] A real-world example is that, unlike humans, current image classifiers often do not primarily make judgements from the s... |
https://en.wikipedia.org/wiki/Machine_learning#116 | ut 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.[138][139]
Adversarial vulnerabilities can also result in nonlinear systems, or from non-pattern perturbations. For some systems, it is po... |
https://en.wikipedia.org/wiki/Machine_learning#117 | e to manipulation or evasion via adversarial machine learning.[141]
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 that are often developed or trained by third parties. Parties can change ... |
https://en.wikipedia.org/wiki/Machine_learning#118 | white-box access.[142][143][144]
Model assessments
[edit]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 ... |
https://en.wikipedia.org/wiki/Machine_learning#119 | 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.[145]
In ... |
https://en.wikipedia.org/wiki/Machine_learning#120 | ue 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. Receiver operating characteristic (ROC) along with the accompanying Area Under the... |
https://en.wikipedia.org/wiki/Machine_learning#121 | el.[146]
Ethics
[edit]
The ethics of artificial intelligence covers a broad range of topics within AI that are considered to have particular ethical stakes.[147] This includes algorithmic biases, fairness,[148] automated decision-making,[149] accountability, privacy, and regulation. It also covers various emerging or p... |
https://en.wikipedia.org/wiki/Machine_learning#122 | arms race dynamics, AI safety and alignment, technological unemployment, AI-enabled misinformation, how to treat certain AI systems if they have a moral status (AI welfare and rights), artificial superintelligence and existential risks.[147]
Some application areas may also have particularly important ethical implicatio... |
https://en.wikipedia.org/wiki/Machine_learning#123 | 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 human-made data, machine learning is likely to pick up the constitutional and unconscious biases already p... |
https://en.wikipedia.org/wiki/Machine_learning#124 | bias), thus digitising cultural prejudices.[151] 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 that this program had denied nearly 60 candidates who were found to either be women o... |
https://en.wikipedia.org/wiki/Machine_learning#125 | ning system duplicating the bias by scoring job applicants by similarity to previous successful applicants.[152][153] Another example includes predictive policing company Geolitica's predictive algorithm that resulted in "disproportionately high levels of over-policing in low-income and minority communities" after bein... |
https://en.wikipedia.org/wiki/Machine_learning#126 | ystem is considered a critical part of machine learning, some researchers blame lack of participation and representation of minority population in the field of AI for machine learning's vulnerability to biases.[155] In fact, according to research carried out by the Computing Research Association (CRA) in 2021, "female ... |
https://en.wikipedia.org/wiki/Machine_learning#127 | , among the group of "new U.S. resident AI PhD graduates," 45% identified as white, 22.4% as Asian, 3.2% as Hispanic, and 2.4% as African American, which further demonstrates a lack of diversity in the field of AI.[156]
Language models learned from data have been shown to contain human-like biases.[157][158] Because hu... |
https://en.wikipedia.org/wiki/Machine_learning#128 | crosoft tested Tay, a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[161]
In an experiment carried out by ProPublica, an investigative journalism organisation, a machine learning algorithm's insight into the recidivism rates among prisoners falsely flagged "black defendants high... |
https://en.wikipedia.org/wiki/Machine_learning#129 | sed controversy. The gorilla label was subsequently removed, and in 2023, it still cannot recognise gorillas.[162] Similar issues with recognising non-white people have been found in many other systems.[163]
Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains... |
https://en.wikipedia.org/wiki/Machine_learning#130 | is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who said that "[t]here'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 res... |
https://en.wikipedia.org/wiki/Machine_learning#131 | 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 i... |
https://en.wikipedia.org/wiki/Machine_learning#132 | onals an additional tool to diagnose, medicate, and plan recovery paths for patients, but this requires these biases to be mitigated.[166]
Hardware
[edit]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... |
https://en.wikipedia.org/wiki/Machine_learning#133 | (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[168] OpenAI estimated the hardware compute 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 re... |
https://en.wikipedia.org/wiki/Machine_learning#134 | s) are specialised hardware accelerators developed by Google specifically for machine learning workloads. Unlike general-purpose GPUs and FPGAs, TPUs are optimised for tensor computations, making them particularly efficient for deep learning tasks such as training and inference. They are widely used in Google Cloud AI ... |
https://en.wikipedia.org/wiki/Machine_learning#135 | multiplication units and high-bandwidth memory to accelerate computations while maintaining energy efficiency.[171] Since their introduction in 2016, TPUs have become a key component of AI infrastructure, especially in cloud-based environments.
Neuromorphic computing
[edit]Neuromorphic computing refers to a class of co... |
https://en.wikipedia.org/wiki/Machine_learning#136 | d through software-based simulations on conventional hardware or through specialised hardware architectures.[172]
physical neural networks
[edit]A physical neural network is a specific type of neuromorphic hardware that relies on electrically adjustable materials, such as memristors, to emulate the function of neural s... |
https://en.wikipedia.org/wiki/Machine_learning#137 | implementations. It broadly refers to artificial neural networks that use materials with adjustable resistance to replicate neural synapses.[173][174]
Embedded machine learning
[edit]Embedded machine learning is a sub-field of machine learning where models are deployed on embedded systems with limited computing resourc... |
https://en.wikipedia.org/wiki/Machine_learning#138 | iminates the need to transfer and store data on cloud servers for further processing, thereby reducing the risk of data breaches, privacy leaks and theft of intellectual property, personal data and business secrets. Embedded machine learning can be achieved through various techniques, such as hardware acceleration,[179... |
https://en.wikipedia.org/wiki/Machine_learning#139 | knowledge distillation, low-rank factorisation, network architecture search, and parameter sharing.
Software
[edit]Software suites containing a variety of machine learning algorithms include the following:
Free and open-source software
[edit]Proprietary software with free and open-source editions
[edit]Proprietary soft... |
https://en.wikipedia.org/wiki/Machine_learning#140 | AI
- Google Prediction API
- IBM SPSS Modeller
- KXEN Modeller
- LIONsolver
- Mathematica
- MATLAB
- Neural Designer
- NeuroSolutions
- Oracle Data Mining
- Oracle AI Platform Cloud Service
- PolyAnalyst
- RCASE
- SAS Enterprise Miner
- SequenceL
- Splunk
- STATISTICA Data Miner
Journals
[edit]- Journal of Machine Lear... |
https://en.wikipedia.org/wiki/Machine_learning#141 | chine Intelligence
Conferences
[edit]- AAAI Conference on Artificial Intelligence
- Association for Computational Linguistics (ACL)
- European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)
- International Conference on Computational Intelligence Methods for B... |
https://en.wikipedia.org/wiki/Machine_learning#142 | g Representations (ICLR)
- International Conference on Intelligent Robots and Systems (IROS)
- Conference on Knowledge Discovery and Data Mining (KDD)
- Conference on Neural Information Processing Systems (NeurIPS)
See also
[edit]- Automated machine learning – Process of automating the application of machine learning
-... |
https://en.wikipedia.org/wiki/Machine_learning#143 | ntiable programming – Programming paradigm
- List of datasets for machine-learning research
- M-theory (learning framework)
- Machine unlearning
- Solomonoff's theory of inductive inference – A mathematical theory
References
[edit]- ^ The definition "without being explicitly programmed" is often attributed to Arthur Sa... |
https://en.wikipedia.org/wiki/Machine_learning#144 | hrase that appeared later. Confer "Paraphrasing Arthur Samuel (1959), the question is: How can computers learn to solve problems without being explicitly programmed?" in Koza, John R.; Bennett, Forrest H.; Andre, David; Keane, Martin A. (1996). "Automated Design of Both the Topology and Sizing of Analog Electrical Circ... |
https://en.wikipedia.org/wiki/Machine_learning#145 | nds: Springer Netherlands. pp. 151–170. doi:10.1007/978-94-009-0279-4_9. ISBN 978-94-010-6610-5.
- ^ "What is Machine Learning?". IBM. 22 September 2021. Archived from the original on 27 December 2023. Retrieved 27 June 2023.
- ^ Hu, Junyan; Niu, Hanlin; Carrasco, Joaquin; Lennox, Barry; Arvin, Farshad (2020). "Voronoi... |
https://en.wikipedia.org/wiki/Machine_learning#146 | icular Technology. 69 (12): 14413–14423. doi:10.1109/tvt.2020.3034800. ISSN 0018-9545. S2CID 228989788.
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