JavaScript

const { tavily } = require("@tavily/core");

// Step 1. Instantiating your Tavily client
const tvly = tavily({ apiKey: "tvly-YOUR_API_KEY" });

// Step 2. Executing a simple search query
const response = await tvly.search("Who is Leo Messi?");

// Step 3. That's it! You've done a Tavily Search!
console.log(response);

{
    "query": "who is Leo messi?",
    "follow_up_questions": null,
    "answer": null,
    "images": [],
    "results": [
        {
            "title": "Lionel Messi Facts | Britannica",
            "url": "https://www.britannica.com/facts/Lionel-Messi",
            "content": "Lionel Messi, an Argentine footballer, is widely regarded as one of the greatest football players of his generation. Born in 1987, Messi spent the majority of his career playing for Barcelona, where he won numerous domestic league titles and UEFA Champions League titles. Messi is known for his exceptional dribbling skills, vision, and goal",
            "score": 0.8091708,
            "raw_content": null
        },
        {
            "title": "Lionel Messi Biography - Facts, Childhood, Family Life & Achievements",
            "url": "https://www.thefamouspeople.com/profiles/lionel-messi-5242.php",
            "content": "Lionel Messi has won multiple FIFA Ballon d'Or awards, numerous La Liga titles with FC Barcelona, and holds the record for most goals scored in a calendar year. In the finals too Messi scored the winning goal to give Barcelona their third title in six years and fourth overall. In the 2018 Football World Cup Messi scored a goal in the Argentina's final group match against Nigeria and helped his team to a 2-1 victory. Messi has in his kitty 20 Player of the Year awards including FIFA World Player of the Year (1), World Soccer Player of the Year (3), Goal.com Player of the Year (2), UEFA Best Player in Europe Award (1), UEFA Club Footballer of the Year (1), FIFA U-20 World Cup Player of the Tournament (1), La Liga Player of the Year (3), La Liga Foreign Player of the Year (3) and La Liga Ibero-American Player of the Year (5).",
            "score": 0.57780564,
            "raw_content": null
        },
        {
            "title": "Lionel Messi | Biography, Trophies, Records, Ballon d'Or, Inter Miami ...",
            "url": "https://www.britannica.com/biography/Lionel-Messi",
            "content": "In early 2009 Messi capped off a spectacular 2008–09 season by helping FC Barcelona capture the club’s first “treble” (winning three major European club titles in one season): the team won the La Liga championship, the Copa del Rey (Spain’s major domestic cup), and the Champions League title. Messi’s play continued to rapidly improve over the years, and by 2008 he was one of the most dominant players in the world, finishing second to Manchester United’s Cristiano Ronaldo in the voting for the 2008 Ballon d’Or. At the 2014 World Cup, Messi put on a dazzling display, scoring four goals and almost single-handedly propelling an offense-deficient Argentina team through the group stage and into the knockout rounds, where Argentina then advanced to the World Cup final for the first time in 24 years. After Argentina was defeated in the Copa final—the team’s third consecutive finals loss in a major tournament—Messi said that he was quitting the national team, but his short-lived “retirement” lasted less than two months before he announced his return to the Argentine team. Messi helped Barcelona capture another treble during the 2014–15 season, leading the team with 43 goals scored over the course of the campaign, which resulted in his fifth world player of the year honour.",
            "score": 0.502564,
            "raw_content": null
        }
    ],
    "response_time": 2.6
}

from tavily import TavilyClient

# Step 1. Instantiating your TavilyClient
tavily_client = TavilyClient(api_key="tvly-YOUR_API_KEY")

# Step 2. Defining the list of URLs to extract content from
urls = [
    "https://en.wikipedia.org/wiki/Artificial_intelligence",
    "https://en.wikipedia.org/wiki/Machine_learning",
    "https://en.wikipedia.org/wiki/Data_science",
]

# Step 3. Executing the extract request
response = tavily_client.extract(urls=urls, include_images=True)

# Step 4. Printing the extracted raw content
print(response)

{
    "results": [
        {
            "url": "https://en.wikipedia.org/wiki/Artificial_intelligence",
            "raw_content": "Contents\nArtificial intelligence\n\n\nArtificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems. It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals.[1] Such machines may be called AIs.\n\nHigh-profile applications of AI include advanced web search engines (e.g., Google Search); recommendation systems (used by YouTube, Amazon, and Netflix); virtual assistants (e.g., Google Assistant, Siri, and Alexa); autonomous vehicles (e.g., Waymo); generative and creative tools (e.g., ChatGPT and AI art); and superhuman play and analysis in strategy games (e.g., chess and Go). However, many AI applications are not perceived as AI: \"A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore.\"[2][3]\n\nVarious subfields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and support for robotics.[a] General intelligence—the ability to complete any task performed by a human on an at least equal level—is among the field's long-term goals.[4] To reach these goals, AI researchers have adapted and integrated a wide range of techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, operations research, and economics.[b] AI also draws upon psychology, linguistics, philosophy, neuroscience, and other fields.[5]\n\nArtificial intelligence was founded as an academic discipline in 1956,[6] and the field went through multiple cycles of optimism throughout its history,[7][8] followed by periods of disappointment and loss of funding, known as AI winters.[9][10] Funding and interest vastly increased after 2012 when deep learning outperformed previous AI techniques.[11] This growth accelerated further after 2017 with the transformer architecture,[12] and by the early 2020s many billions of dollars were being invested in AI and the field experienced rapid ongoing progress in what has become known as the AI boom. The emergence of advanced generative AI in the midst of the AI boom and its ability to create and modify content exposed several unintended consequences and harms in the present and raised concerns about the risks of AI and its long-term effects in the future, prompting discussions about regulatory policies to ensure the safety and benefits of the technology.\n\nGoals\nThe general problem of simulating (or creating) intelligence has been broken into subproblems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention and cover the scope of AI research.[a]\n\nReasoning and problem-solving\nEarly researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[13] By the late 1980s and 1990s, methods were developed for dealing with uncertain or incomplete information, employing concepts from probability and economics.[14]\n\nMany of these algorithms are insufficient for solving large reasoning problems because they experience a \"combinatorial explosion\": They become exponentially slower as the problems grow.[15] Even humans rarely use the step-by-step deduction that early AI research could model. They solve most of their problems using fast, intuitive judgments.[16] Accurate and efficient reasoning is an unsolved problem.\n\nKnowledge representation\nKnowledge representation and knowledge engineering[17] allow AI programs to answer questions intelligently and make deductions about real-world facts. Formal knowledge representations are used in content-based indexing and retrieval,[18] scene interpretation,[19] clinical decision support,[20] knowledge discovery (mining \"interesting\" and actionable inferences from large databases),[21] and other areas.[22]\n\nA knowledge base is a body of knowledge represented in a form that can be used by a program. An ontology is the set of objects, relations, concepts, and properties used by a particular domain of knowledge.[23] Knowledge bases need to represent things such as objects, properties, categories, and relations between objects;[24] situations, events, states, and time;[25] causes and effects;[26] knowledge about knowledge (what we know about what other people know);[27] default reasoning (things that humans assume are true until they are told differently and will remain true even when other facts are changing);[28] and many other aspects and domains of knowledge.\n\nAmong the most difficult problems in knowledge representation are the breadth of commonsense knowledge (the set of atomic facts that the average person knows is enormous);[29] and the sub-symbolic form of most commonsense knowledge (much of what people know is not represented as \"facts\" or \"statements\" that they could express verbally).[16] There is also the difficulty of knowledge acquisition, the problem of obtaining knowledge for AI applications.[c]\n\nPlanning and decision-making\nAn \"agent\" is anything that perceives and takes actions in the world. A rational agent has goals or preferences and takes actions to make them happen.[d][32] In automated planning, the agent has a specific goal.[33] In automated decision-making, the agent has preferences—there are some situations it would prefer to be in, and some situations it is trying to avoid. The decision-making agent assigns a number to each situation (called the \"utility\") that measures how much the agent prefers it. For each possible action, it can calculate the \"expected utility\": the utility of all possible outcomes of the action, weighted by the probability that the outcome will occur. It can then choose the action with the maximum expected utility.[34]\n\nIn classical planning, the agent knows exactly what the effect of any action will be.[35] In most real-world problems, however, the agent may not be certain about the situation they are in (it is \"unknown\" or \"unobservable\") and it may not know for certain what will happen after each possible action (it is not \"deterministic\"). It must choose an action by making a probabilistic guess and then reassess the situation to see if the action worked.[36]\n\nIn some problems, the agent's preferences may be uncertain, especially if there are other agents or humans involved. These can be learned (e.g., with inverse reinforcement learning), or the agent can seek information to improve its preferences.[37] Information value theory can be used to weigh the value of exploratory or experimental actions.[38] The space of possible future actions and situations is typically intractably large, so the agents must take actions and evaluate situations while being uncertain of what the outcome will be.\n\nA Markov decision process has a transition model that describes the probability that a particular action will change the state in a particular way and a reward function that supplies the utility of each state and the cost of each action. A policy associates a decision with each possible state. The policy could be calculated (e.g., by iteration), be heuristic, or it can be learned.[39]\n\nGame theory describes the rational behavior of multiple interacting agents and is used in AI programs that make decisions that involve other agents.[40]\n\nLearning\nMachine learning is the study of programs that can improve their performance on a given task automatically.[41] It has been a part of AI from the beginning.[e]\n\nThere are several kinds of machine learning. Unsupervised learning analyzes a stream of data and finds patterns and makes predictions without any other guidance.[44] Supervised learning requires labeling the training data with the expected answers, and comes in two main varieties: classification (where the program must learn to predict what category the input belongs in) and regression (where the program must deduce a numeric function based on numeric input).[45]\n\nIn reinforcement learning, the agent is rewarded for good responses and punished for bad ones. The agent learns to choose responses that are classified as \"good\".[46] Transfer learning is when the knowledge gained from one problem is applied to a new problem.[47] Deep learning is a type of machine learning that runs inputs through biologically inspired artificial neural networks for all of these types of learning.[48]\n\nComputational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization.[49]\n\nNatural language processing\nNatural language processing (NLP)[50] allows programs to read, write and communicate in human languages such as English. Specific problems include speech recognition, speech synthesis, machine translation, information extraction, information retrieval and question answering.[51]\n\nEarly work, based on Noam Chomsky's generative grammar and semantic networks, had difficulty with word-sense disambiguation[f] unless restricted to small domains called \"micro-worlds\" (due to the common sense knowledge problem[29]). Margaret Masterman believed that it was meaning and not grammar that was the key to understanding languages, and that thesauri and not dictionaries should be the basis of computational language structure.\n\nModern deep learning techniques for NLP include word embedding (representing words, typically as vectors encoding their meaning),[52] transformers (a deep learning architecture using an attention mechanism),[53] and others.[54] In 2019, generative pre-trained transformer (or \"GPT\") language models began to generate coherent text,[55][56] and by 2023, these models were able to get human-level scores on the bar exam, SAT test, GRE test, and many other real-world applications.[57]\n\nPerception\nMachine perception is the ability to use input from sensors (such as cameras, microphones, wireless signals, active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Computer vision is the ability to analyze visual input.[58]\n\nThe field includes speech recognition,[59] image classification,[60] facial recognition, object recognition,[61]object tracking,[62] and robotic perception.[63]\n\nSocial intelligence\nAffective computing is a field that comprises systems that recognize, interpret, process, or simulate human feeling, emotion, and mood.[65] For example, some virtual assistants are programmed to speak conversationally or even to banter humorously; it makes them appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human–computer interaction.\n\nHowever, this tends to give naïve users an unrealistic conception of the intelligence of existing computer agents.[66] Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal sentiment analysis, wherein AI classifies the effects displayed by a videotaped subject.[67]\n\nGeneral intelligence\nA machine with artificial general intelligence should be able to solve a wide variety of problems with breadth and versatility similar to human intelligence.[4]\n\nTechniques\nAI research uses a wide variety of techniques to accomplish the goals above.[b]\n\nSearch and optimization\nAI can solve many problems by intelligently searching through many possible solutions.[68] There are two very different kinds of search used in AI: state space search and local search.\n\nState space search searches through a tree of possible states to try to find a goal state.[69] For example, planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.[70]\n\nSimple exhaustive searches[71] are rarely sufficient for most real-world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes.[15] \"Heuristics\" or \"rules of thumb\" can help prioritize choices that are more likely to reach a goal.[72]\n\nAdversarial search is used for game-playing programs, such as chess or Go. It searches through a tree of possible moves and countermoves, looking for a winning position.[73]\n\nLocal search uses mathematical optimization to find a solution to a problem. It begins with some form of guess and refines it incrementally.[74]\n\nGradient descent is a type of local search that optimizes a set of numerical parameters by incrementally adjusting them to minimize a loss function. Variants of gradient descent are commonly used to train neural networks,[75] through the backpropagation algorithm.\n\nAnother type of local search is evolutionary computation, which aims to iteratively improve a set of candidate solutions by \"mutating\" and \"recombining\" them, selecting only the fittest to survive each generation.[76]\n\nDistributed search processes can coordinate via swarm intelligence algorithms. Two popular swarm algorithms used in search are particle swarm optimization (inspired by bird flocking) and ant colony optimization (inspired by ant trails).[77]\n\nLogic\nFormal logic is used for reasoning and knowledge representation.[78]\nFormal logic comes in two main forms: propositional logic (which operates on statements that are true or false and uses logical connectives such as \"and\", \"or\", \"not\" and \"implies\")[79] and predicate logic (which also operates on objects, predicates and relations and uses quantifiers such as \"Every X is a Y\" and \"There are some Xs that are Ys\").[80]\n\nDeductive reasoning in logic is the process of proving a new statement (conclusion) from other statements that are given and assumed to be true (the premises).[81] Proofs can be structured as proof trees, in which nodes are labelled by sentences, and children nodes are connected to parent nodes by inference rules.\n\nGiven a problem and a set of premises, problem-solving reduces to searching for a proof tree whose root node is labelled by a solution of the problem and whose leaf nodes are labelled by premises or axioms. In the case of Horn clauses, problem-solving search can be performed by reasoning forwards from the premises or backwards from the problem.[82] In the more general case of the clausal form of first-order logic, resolution is a single, axiom-free rule of inference, in which a problem is solved by proving a contradiction from premises that include the negation of the problem to be solved.[83]\n\nInference in both Horn clause logic and first-order logic is undecidable, and therefore intractable. However, backward reasoning with Horn clauses, which underpins computation in the logic programming language Prolog, is Turing complete. Moreover, its efficiency is competitive with computation in other symbolic programming languages.[84]\n\nFuzzy logic assigns a \"degree of truth\" between 0 and 1. It can therefore handle propositions that are vague and partially true.[85]\n\nNon-monotonic logics, including logic programming with negation as failure, are designed to handle default reasoning.[28] Other specialized versions of logic have been developed to describe many complex domains.\n\nProbabilistic methods for uncertain reasoning\nMany problems in AI (including in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of tools to solve these problems using methods from probability theory and economics.[86] Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis,[87] and information value theory.[88] These tools include models such as Markov decision processes,[89] dynamic decision networks,[90] game theory and mechanism design.[91]\n\nBayesian networks[92] are a tool that can be used for reasoning (using the Bayesian inference algorithm),[g][94] learning (using the expectation–maximization algorithm),[h][96] planning (using decision networks)[97] and perception (using dynamic Bayesian networks).[90]\n\nProbabilistic algorithms can also be used for filtering, prediction, smoothing, and finding explanations for streams of data, thus helping perception systems analyze processes that occur over time (e.g., hidden Markov models or Kalman filters).[90]\n\nClassifiers and statistical learning methods\nThe simplest AI applications can be divided into two types: classifiers (e.g., \"if shiny then diamond\"), on one hand, and controllers (e.g., \"if diamond then pick up\"), on the other hand. Classifiers[98] are functions that use pattern matching to determine the closest match. They can be fine-tuned based on chosen examples using supervised learning. Each pattern (also called an \"observation\") is labeled with a certain predefined class. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.[45]\n\nThere are many kinds of classifiers in use.[99] The decision tree is the simplest and most widely used symbolic machine learning algorithm.[100] K-nearest neighbor algorithm was the most widely used analogical AI until the mid-1990s, and Kernel methods such as the support vector machine (SVM) displaced k-nearest neighbor in the 1990s.[101]\nThe naive Bayes classifier is reportedly the \"most widely used learner\"[102] at Google, due in part to its scalability.[103]\nNeural networks are also used as classifiers.[104]\n\nArtificial neural networks\nAn artificial neural network is based on a collection of nodes also known as artificial neurons, which loosely model the neurons in a biological brain. It is trained to recognise patterns; once trained, it can recognise those patterns in fresh data. There is an input, at least one hidden layer of nodes and an output. Each node applies a function and once the weight crosses its specified threshold, the data is transmitted to the next layer. A network is typically called a deep neural network if it has at least 2 hidden layers.[104]\n\nLearning algorithms for neural networks use local search to choose the weights that will get the right output for each input during training. The most common training technique is the backpropagation algorithm.[105] Neural networks learn to model complex relationships between inputs and outputs and find patterns in data. In theory, a neural network can learn any function.[106]\n\nIn feedforward neural networks the signal passes in only one direction.[107] Recurrent neural networks feed the output signal back into the input, which allows short-term memories of previous input events. Long short term memory is the most successful network architecture for recurrent networks.[108] Perceptrons[109] use only a single layer of neurons; deep learning[110] uses multiple layers. Convolutional neural networks strengthen the connection between neurons that are \"close\" to each other—this is especially important in image processing, where a local set of neurons must identify an \"edge\" before the network can identify an object.[111]\n\nDeep learning\nDeep learning[110] uses several layers of neurons between the network's inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input.",
            "images":[
                "https://en.wikipedia.org/static/images/mobile/copyright/wikipedia-wordmark-en.svg"
            ]
        },
        {
            "url": "https://en.wikipedia.org/wiki/Machine_learning",
            "raw_content": "Contents\nMachine learning\nMachine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions.[1] Within a subdiscipline in machine learning, advances in the field of deep learning have allowed neural networks, a class of statistical algorithms, to surpass many previous machine learning approaches in performance.[2]\n\nML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine.[3][4] The application of ML to business problems is known as predictive analytics.\n\nStatistics and mathematical optimization (mathematical programming) methods comprise the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysis (EDA) via unsupervised learning.[6][7]\n\nFrom a theoretical viewpoint, probably approximately correct learning provides a framework for describing machine learning.\n\nHistory\nThe term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence.[8][9] The synonym self-teaching computers was also used in this time period.[10][11]\n\nAlthough the earliest machine learning model was introduced in the 1950s when Arthur Samuel invented a program that calculated the winning chance in checkers for each side, the history of machine learning roots back to decades of human desire and effort to study human cognitive processes.[12] In 1949, Canadian psychologist Donald Hebb published the book The Organization of Behavior, in which he introduced a theoretical neural structure formed by certain interactions among nerve cells.[13] Hebb's model of neurons interacting with one another set a groundwork for how AIs and machine learning algorithms work under nodes, or artificial neurons used by computers to communicate data.[12] Other researchers who have studied human cognitive systems contributed to the modern machine learning technologies as well, including logician Walter Pitts and Warren McCulloch, who proposed the early mathematical models of neural networks to come up with algorithms that mirror human thought processes.[12]\n\nBy the early 1960s, an experimental \"learning machine\" with punched tape memory, called Cybertron, had been developed by Raytheon Company to analyse sonar signals, electrocardiograms, and speech patterns using rudimentary reinforcement learning. It was repetitively \"trained\" by a human operator/teacher to recognize patterns and equipped with a \"goof\" button to cause it to reevaluate incorrect decisions.[14] A representative book on research into machine learning during the 1960s was Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification.[15] Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973.[16] In 1981 a report was given on using teaching strategies so that an artificial neural network learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal.[17]\n\nTom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: \"A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P,  improves with experience E.\"[18] This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper \"Computing Machinery and Intelligence\", in which the question \"Can machines think?\" is replaced with the question \"Can machines do what we (as thinking entities) can do?\".[19]\n\nModern-day machine learning has two objectives.  One is to classify data based on models which have been developed; the other purpose is to make predictions for future outcomes based on these models. A hypothetical algorithm specific to classifying data may use computer vision of moles coupled with supervised learning in order to train it to classify the cancerous moles. A machine learning algorithm for stock trading may inform the trader of future potential predictions.[20]\n\nRelationships to other fields\nArtificial intelligence\nAs a scientific endeavor, machine learning grew out of the quest for artificial intelligence (AI). In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed \"neural networks\"; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics.[22] Probabilistic reasoning was also employed, especially in automated medical diagnosis.[23]: 488 \n\nHowever, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation.[23]: 488  By 1980, expert systems had come to dominate AI, and statistics was out of favor.[24] Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming(ILP), but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval.[23]: 708–710, 755  Neural networks research had been abandoned by AI and computer science around the same time. This line, too, was continued outside the AI/CS field, as \"connectionism\", by researchers from other disciplines including John Hopfield, David Rumelhart, and Geoffrey Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation.[23]: 25 \n\nMachine learning (ML), reorganized and recognized as its own field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics, fuzzy logic, and probability theory.[24]\n\nData compression\nThere is a close connection between machine learning and compression. A system that predicts the posterior probabilities of a sequence given its entire history can be used for optimal data compression (by using arithmetic coding on the output distribution). Conversely, an optimal compressor can be used for prediction (by finding the symbol that compresses best, given the previous history). This equivalence has been used as a justification for using data compression as a benchmark for \"general intelligence\".[25][26][27]\n\nAn alternative view can show compression algorithms implicitly map strings into implicit feature space vectors, and compression-based similarity measures compute similarity within these feature spaces. For each compressor C(.) we define an associated vector space ℵ, such that C(.) maps an input string x, corresponding to the vector norm ||~x||. An exhaustive examination of the feature spaces underlying all compression algorithms is precluded by space; instead, feature vectors chooses to examine three representative lossless compression methods, LZW, LZ77, and PPM.[28]\n\nAccording to AIXI theory, a connection more directly explained in Hutter Prize, the best possible compression of x is the smallest possible software that generates x. For example, in that model, a zip file's compressed size includes both the zip file and the unzipping software, since you can not unzip it without both, but there may be an even smaller combined form.\n\nExamples of AI-powered audio/video compression software include NVIDIA Maxine, AIVC.[29] Examples of software that can perform AI-powered image compression include OpenCV, TensorFlow, MATLAB's Image Processing Toolbox (IPT) and High-Fidelity Generative Image Compression.[30]\n\nIn unsupervised machine learning, k-means clustering can be utilized to compress data by grouping similar data points into clusters. This technique simplifies handling extensive datasets that lack predefined labels and finds widespread use in fields such as image compression.[31]\n\nData compression aims to reduce the size of data files, enhancing storage efficiency and speeding up data transmission. K-means clustering, an unsupervised machine learning algorithm, is employed to partition a dataset into a specified number of clusters, k, each represented by the centroid of its points. This process condenses extensive datasets into a more compact set of representative points. Particularly beneficial in image and signal processing, k-means clustering aids in data reduction by replacing groups of data points with their centroids, thereby preserving the core information of the original data while significantly decreasing the required storage space.[32]\n\nData mining\nMachine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as \"unsupervised learning\" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.\n\nMachine 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 preassigned labels of a set of examples).[35]\n\nGeneralization\nCharacterizing the generalization of various learning algorithms is an active topic of current research, especially for deep learning algorithms.\n\nStatistics\nMachine 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.[36] According to Michael I. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics.[37] He also suggested the term data science as a placeholder to call the overall field.[37]\n\nConventional statistical analyses require the a priori selection of a model most suitable for the study data set. In addition, only significant or theoretically relevant variables based on previous experience are included for analysis. In contrast, machine learning is not built on a pre-structured model; rather, the data shape the model by detecting underlying patterns. The more variables (input) used to train the model, the more accurate the ultimate model will be.[38]\n\nLeo Breiman distinguished two statistical modeling paradigms: data model and algorithmic model,[39] wherein \"algorithmic model\" means more or less the machine learning algorithms like Random Forest.\n\nSome statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning.[40]\n\nStatistical physics\nAnalytical and computational techniques derived from deep-rooted physics of disordered systems can be extended to large-scale problems, including machine learning, e.g., to analyse the weight space of deep neural networks.[41] Statistical physics is thus finding applications in the area of medical diagnostics.[42]\n\n Theory\nA core objective of a learner is to generalize from its experience.[5][43] 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.\n\nThe 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  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.\n\nFor 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.[44]\n\nIn 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.\n\nApproaches\n\n\nMachine 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:\n\nAlthough each algorithm has advantages and limitations, no single algorithm works for all problems.[45][46][47]\n\nSupervised learning\nSupervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs.[48] The data, known as training data, 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.[49] An optimal function allows 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.[18]\n\nTypes of supervised-learning algorithms include active learning, classification and regression.[50] 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. Examples of regression would be predicting the height of a person, or the future temperature.[51]\n\nSimilarity 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.\n\nUnsupervised learning\nUnsupervised learning algorithms find structures in 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. Central applications of unsupervised machine learning include clustering, dimensionality reduction,[7] and density estimation.[52]\n\nCluster 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.\n\nA special type of unsupervised learning called, self-supervised learning involves training a model by generating the supervisory signal from the data itself.[53][54]\n\nSemi-supervised learning\nSemi-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.\n\nIn 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.[55]\n\nReinforcement learning\nReinforcement 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 reinforcement learning, the environment is typically represented as a Markov decision process (MDP). Many reinforcements learning algorithms use dynamic programming techniques.[56] 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.\n\nDimensionality reduction\n",
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            "raw_content": "Contents\nData science\n\n\nData science is an interdisciplinary academic field[1] that uses statistics, scientific computing, scientific methods, processing, scientific visualization, algorithms and systems to extract or extrapolate knowledge from potentially noisy, structured, or unstructured data.[2]\n\nData science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine).[3] Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession.[4]\n\nData science is \"a concept to unify statistics, data analysis, informatics, and their related methods\" to \"understand and analyze actual phenomena\" with data.[5] It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge.[6] However, data science is different from computer science and information science. Turing Award winner Jim Gray imagined data science as a \"fourth paradigm\" of science (empirical, theoretical, computational, and now data-driven) and asserted that \"everything about science is changing because of the impact of information technology\" and the data deluge.[7][8]\n\nA data scientist is a professional who creates programming code and combines it with statistical knowledge to summarize data.[9]\n\nFoundations\nData science is an interdisciplinary field[10] focused on extracting knowledge from typically large data sets and applying the knowledge from that data to solve problems in other application domains. The field encompasses preparing data for analysis, formulating data science problems, analyzing data, and summarizing these findings. As such, it incorporates skills from computer science, mathematics, data visualization, graphic design, communication, and business.[11]\n\nVasant Dhar writes that statistics emphasizes quantitative data and description. In contrast, data science deals with quantitative and qualitative data (e.g., from images, text, sensors, transactions, customer information, etc.) and emphasizes prediction and action.[12] Andrew Gelman of Columbia University has described statistics as a non-essential part of data science.[13] Stanford professor David Donoho writes that data science is not distinguished from statistics by the size of datasets or use of computing and that many graduate programs misleadingly advertise their analytics and statistics training as the essence of a data-science program. He describes data science as an applied field growing out of traditional statistics.[14]\n\nEtymology\nEarly usage\nIn 1962, John Tukey described a field he called \"data analysis\", which resembles modern data science.[14] In 1985, in a lecture given to the Chinese Academy of Sciences in Beijing, C. F. Jeff Wu used the term \"data science\" for the first time as an alternative name for statistics.[15] Later, attendees at a 1992 statistics symposium at the University of Montpellier  II acknowledged the emergence of a new discipline focused on data of various origins and forms, combining established concepts and principles of statistics and data analysis with computing.[16][17]\n\nThe term \"data science\" has been traced back to 1974, when Peter Naur proposed it as an alternative name to computer science.[6] In 1996, the International Federation of Classification Societies became the first conference to specifically feature data science as a topic.[6] However, the definition was still in flux. After the 1985 lecture at the Chinese Academy of Sciences in Beijing, in 1997 C. F. Jeff Wu again suggested that statistics should be renamed data science. He reasoned that a new name would help statistics shed inaccurate stereotypes, such as being synonymous with accounting or limited to describing data.[18] In 1998, Hayashi Chikio argued for data science as a new, interdisciplinary concept, with three aspects: data design, collection, and analysis.[17]\n\nModern usage\nIn 2012, technologists Thomas H. Davenport and DJ Patil declared \"Data Scientist: The Sexiest Job of the 21st Century\",[19] a catchphrase that was picked up even by major-city newspapers like the New York Times[20] and the Boston Globe.[21] A decade later, they reaffirmed it, stating that \"the job is more in demand than ever with employers\".[22]\n\nThe modern conception of data science as an independent discipline is sometimes attributed to William S. Cleveland.[23] In 2014, the American Statistical Association's Section on Statistical Learning and Data Mining changed its name to the Section on Statistical Learning and Data Science, reflecting the ascendant popularity of data science.[24]\n\nThe professional title of \"data scientist\" has been attributed to DJ Patil and Jeff Hammerbacher in 2008.[25] Though it was used by the National Science Board in their 2005 report \"Long-Lived Digital Data Collections: Enabling Research and Education in the 21st Century\", it referred broadly to any key role in managing a digital data collection.[26]\n\nData science and data analysis\nData analysis typically involves working with structured datasets to answer specific questions or solve specific problems. This can involve tasks such as data cleaning and data visualization to summarize data and develop hypotheses about relationships between variables. Data analysts typically use statistical methods to test these hypotheses and draw conclusions from the data.[27]\n\nData science involves working with larger datasets that often require advanced computational and statistical methods to analyze. Data scientists often work with unstructured data such as text or images and use machine learning algorithms to build predictive models. Data science often uses statistical analysis, data preprocessing, and supervised learning.[28][29]\n\nCloud computing for data science\nCloud computing can offer access to large amounts of computational power and storage.[30] In big data, where volumes of information are continually generated and processed, these platforms can be used to handle complex and resource-intensive analytical tasks.[31]\n\nSome distributed computing frameworks are designed to handle big data workloads. These frameworks can enable data scientists to process and analyze large datasets in parallel, which can reduce processing times.[32]\n\nEthical consideration in data science\nData science involves collecting, processing, and analyzing data which often includes personal and sensitive information. Ethical concerns include potential privacy violations, bias perpetuation, and negative societal impacts [33][34]\n\nMachine learning models can amplify existing biases present in training data, leading to discriminatory or unfair outcomes.[35][36]\n\nSee also\nReferences\n",
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