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Guest IP-штамп: fr0ypB6hbdFeM гость |
вчера, 23:21
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Guest IP-штамп: fr0ypB6hbdFeM гость |
вчера, 23:22
Technological advancements have led to significant changes in society. The earliest known technology is the stone tool, used during prehistoric times, followed by the control of fire, which contributed to the growth of the human brain and the development of language during the Ice Age. The invention of the wheel in the Bronze Age allowed greater travel and the creation of more complex machines. More recent technological inventions, including the printing press, telephone, and the Internet, have lowered barriers to communication and ushered in the knowledge economy. While technology contributes to economic development and improves human prosperity, it can also have negative impacts like pollution and resource depletion, and can cause social harms like technological unemployment resulting from automation. As a result, there are ongoing philosophical and political debates about the role and use of technology, the ethics of technology, and ways to mitigate its downsides. Etymology Technology is a term dating back to the early 17th century that meant 'systematic treatment' (from Greek Τεχνολογία, from the Greek: τέχνη, romanized: tékhnē, lit. 'craft, art' and -λογία, 'study, knowledge').[4][5] It is predated in use by the Ancient Greek word tékhnē, used to mean 'knowledge of how to make things', which encompassed activities like architecture.[6] Starting in the 19th century, continental Europeans started using the terms Technik (German) or technique (French) to refer to a 'way of doing', which included all technical arts, such as dancing, navigation, or printing, whether or not they required tools or instruments.[7] At the time, Technologie (German and French) referred either to the academic discipline studying the "methods of arts and crafts", or to the political discipline "intended to legislate on the functions of the arts and crafts."[8] Since the distinction between Technik and Technologie is absent in English, both were translated as technology. The term was previously uncommon in English and mostly referred to the academic discipline, as in the Massachusetts Institute of Technology.[9] In the 20th century, as a result of scientific progress and the Second Industrial Revolution, technology stopped being considered a distinct academic discipline and took on its current-day meaning: the systemic use of knowledge to practical ends.[10] |
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Guest IP-штамп: fr0ypB6hbdFeM гость |
вчера, 23:23
The subject of technology is treated in a number of articles. For general treatment, see technology, history of; hand tool. For description of the materials that are both the object and means of manipulating the environment, see elastomers; industrial ceramics; industrial glass; metallurgy; mineral deposit; mineral processing; mining; plastic. For the generation of energy, see energy conversion; coal mining; coal utilization; petroleum production; petroleum refining. For treatment of food production, see agriculture, history of; agricultural economics; beekeeping; beer; cereal farming; coffee; commercial fishing; dairy farming; distilled spirit; food preservation; fruit farming; livestock farming; poultry farming; soft drink; tea; vegetable farming; wine. For the techniques of construction technology, see bridge; building construction; canals and inland waterways; dam; harbours and sea works; lighthouse; roads and highways; tunnels and underground excavations; environmental works. For the manufacture and design of the means of transportation, see aerospace industry; automotive industry; ship construction. For communications technology, see broadcasting; computer science; information processing; photography; printing; photoengraving; typography; telecommunication. For the processes and products of other manufacturing industries, see adhesive; clothing and footwear industry; dye; explosive; floor covering; forestry; chemical industry; man-made fibre; surface coating; papermaking; soap and detergent; textile. For medical applications of technology, see diagnosis; therapeutics; drug; medicine, history of; pharmaceutical industry. For military applications, see military technology. For treatment of the organization of technological systems, see automation; engineering; production system; systems engineering; work, history of the organization of. |
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Guest IP-штамп: fr0ypB6hbdFeM гость |
вчера, 23:23
People can use technology to: Produce goods or services Carry out goals, such as scientific investigation or sending a spaceship to the moon Solve problems, such as disease or famine Do things we already do, but more easily. Technology can be knowledge of how to do things. Some machiness have automatic controls. This lets others use the machines without knowing how they work. Technological systems use technology by taking something, changing it, then producing a result. They are also known as technology systems. The most simple form of technology is the development and use of basic tools. The discovery of fire and the Neolithic Revolution made food easier to get. Other inventions, such as the wheel and the ship, helped people to transport goods and themselves. Information technology, such as the printing press, the telephone, and the Internet, has led to globalization. People have used technology for millions of years. Without food technology, most people would die. |
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Guest IP-штамп: fr0ypB6hbdFeM гость |
вчера, 23:24
technology noun [ C or U ] UK /tekˈnɒl.ə.dʒi/ US /tekˈnɑː.lə.dʒi/ Add to word list B1 (the study and knowledge of) the practical, especially industrial, use of scientific discoveries: computer technology Modern technology is amazing, isn't it? What this country needs is a long-term policy for investment in science and technology. See also biotechnology Fewer examples There were huge advances in aviation technology during the Second World War. Satellite technology offers the opportunity, as never before, for continuous television coverage of major international events. With computer technology, even people working on their own can produce professional-looking documents. Although the technology originated in the UK, it has been developed in the US. Company profits have doubled since the introduction of new technology. |
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Guest IP-штамп: fr0ypB6hbdFeM гость |
вчера, 23:25
AI technology is widely used throughout industry, government, and science. Some high-profile applications include advanced web search engines (e.g., Google Search); recommendation systems (used by YouTube, Amazon, and Netflix); interacting via human speech (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).[2] 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."[3][4] Alan Turing was the first person to conduct substantial research in the field that he called machine intelligence.[5] Artificial intelligence was founded as an academic discipline in 1956.[6] The field went through multiple cycles of optimism,[7][8] followed by periods of disappointment and loss of funding, known as AI winter.[9][10] Funding and interest vastly increased after 2012 when deep learning surpassed all previous AI techniques,[11] and after 2017 with the transformer architecture.[12] This led to the AI boom of the early 2020s, with companies, universities, and laboratories overwhelmingly based in the United States pioneering significant advances in artificial intelligence.[13] The growing use of artificial intelligence in the 21st century is influencing a societal and economic shift towards increased automation, data-driven decision-making, and the integration of AI systems into various economic sectors and areas of life, impacting job markets, healthcare, government, industry, and education. This raises questions about the long-term effects, ethical implications, and risks of AI, prompting discussions about regulatory policies to ensure the safety and benefits of the technology. The various sub-fields 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 performable by a human on an at least equal level—is among the field's long-term goals.[14] 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.[15] |
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Guest IP-штамп: fr0ypB6hbdFeM гость |
вчера, 23:25
Reasoning and problem solving Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[16] By the late 1980s and 1990s, methods were developed for dealing with uncertain or incomplete information, employing concepts from probability and economics.[17] Many of these algorithms are insufficient for solving large reasoning problems because they experience a "combinatorial explosion": they became exponentially slower as the problems grew larger.[18] 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.[19] Accurate and efficient reasoning is an unsolved problem. Knowledge representation An ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts. Knowledge representation and knowledge engineering[20] 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,[21] scene interpretation,[22] clinical decision support,[23] knowledge discovery (mining "interesting" and actionable inferences from large databases),[24] and other areas.[25] A 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.[26] Knowledge bases need to represent things such as: objects, properties, categories and relations between objects;[27] situations, events, states and time;[28] causes and effects;[29] knowledge about knowledge (what we know about what other people know);[30] default reasoning (things that humans assume are true until they are told differently and will remain true even when other facts are changing);[31] and many other aspects and domains of knowledge. Among 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);[32] 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).[19] There is also the difficulty of knowledge acquisition, the problem of obtaining knowledge for AI applications.[c] Planning and decision making An "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][35] In automated planning, the agent has a specific goal.[36] 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.[37] In classical planning, the agent knows exactly what the effect of any action will be.[38] 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.[39] In 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.[40] Information value theory can be used to weigh the value of exploratory or experimental actions.[41] The space of possible future actions and situations is typically intractably large, so the agents must take actions and evaluate situations while being uncertain what the outcome will be. A 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.[42] Game theory describes rational behavior of multiple interacting agents, and is used in AI programs that make decisions that involve other agents.[43] |
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Guest IP-штамп: fr0ypB6hbdFeM гость |
вчера, 23:25
Machine learning is the study of programs that can improve their performance on a given task automatically.[44] It has been a part of AI from the beginning.[e] There are several kinds of machine learning. Unsupervised learning analyzes a stream of data and finds patterns and makes predictions without any other guidance.[47] Supervised learning requires a human to label the input data first, 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).[48] In 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".[49] Transfer learning is when the knowledge gained from one problem is applied to a new problem.[50] Deep learning is a type of machine learning that runs inputs through biologically inspired artificial neural networks for all of these types of learning.[51] Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization.[52] Natural language processing Natural language processing (NLP)[53] 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.[54] Early 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[32]). 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. Modern deep learning techniques for NLP include word embedding (representing words, typically as vectors encoding their meaning),[55] transformers (a deep learning architecture using an attention mechanism),[56] and others.[57] In 2019, generative pre-trained transformer (or "GPT") language models began to generate coherent text,[58][59] 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.[60] Perception Machine 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.[61] The field includes speech recognition,[62] image classification,[63] facial recognition, object recognition,[64] and robotic perception.[65] |
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Guest IP-штамп: fr0ypB6hbdFeM гость |
вчера, 23:26
Kismet, a robot head which was made in the 1990s; a machine that can recognize and simulate emotions.[66] Affective computing is an interdisciplinary umbrella that comprises systems that recognize, interpret, process or simulate human feeling, emotion and mood.[67] 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. However, this tends to give naïve users an unrealistic conception of the intelligence of existing computer agents.[68] Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal sentiment analysis, wherein AI classifies the affects displayed by a videotaped subject.[69] General intelligence A machine with artificial general intelligence should be able to solve a wide variety of problems with breadth and versatility similar to human intelligence.[14] Techniques AI research uses a wide variety of techniques to accomplish the goals above.[b] Search and optimization AI can solve many problems by intelligently searching through many possible solutions.[70] There are two very different kinds of search used in AI: state space search and local search. State space search State space search searches through a tree of possible states to try to find a goal state.[71] 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.[72] Simple exhaustive searches[73] 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.[18] "Heuristics" or "rules of thumb" can help to prioritize choices that are more likely to reach a goal.[74] Adversarial search is used for game-playing programs, such as chess or Go. It searches through a tree of possible moves and counter-moves, looking for a winning position.[75] Local search Illustration of gradient descent for 3 different starting points. Two parameters (represented by the plan coordinates) are adjusted in order to minimize the loss function (the height). Local search uses mathematical optimization to find a solution to a problem. It begins with some form of guess and refines it incrementally.[76] Gradient 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.[77] Another 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.[78] Distributed 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).[79] |
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Guest IP-штамп: fr0ypB6hbdFeM гость |
вчера, 23:26
Formal logic is used for reasoning and knowledge representation.[80] Formal 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")[81] 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").[82] Deductive 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).[83] 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. Given 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.[84] 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.[85] Inference 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.[86] Fuzzy logic assigns a "degree of truth" between 0 and 1. It can therefore handle propositions that are vague and partially true.[87] Non-monotonic logics, including logic programming with negation as failure, are designed to handle default reasoning.[31] Other specialized versions of logic have been developed to describe many complex domains. Probabilistic methods for uncertain reasoning A simple Bayesian network, with the associated conditional probability tables Many 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.[88] Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis,[89] and information value theory.[90] These tools include models such as Markov decision processes,[91] dynamic decision networks,[92] game theory and mechanism design.[93] Bayesian networks[94] are a tool that can be used for reasoning (using the Bayesian inference algorithm),[g][96] learning (using the expectation-maximization algorithm),[h][98] planning (using decision networks)[99] and perception (using dynamic Bayesian networks).[92] Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes that occur over time (e.g., hidden Markov models or Kalman filters).[92] |
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Guest IP-штамп: fr0ypB6hbdFeM гость |
вчера, 23:26
The 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[100] 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.[48] There are many kinds of classifiers in use. The decision tree is the simplest and most widely used symbolic machine learning algorithm.[101] 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.[102] The naive Bayes classifier is reportedly the "most widely used learner"[103] at Google, due in part to its scalability.[104] Neural networks are also used as classifiers.[105] Artificial neural networks A neural network is an interconnected group of nodes, akin to the vast network of neurons in the human brain. An 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.[105] Learning 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.[106] 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.[107] In feedforward neural networks the signal passes in only one direction.[108] 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.[109] Perceptrons[110] use only a single layer of neurons, deep learning[111] 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.[112] |
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Guest IP-штамп: fr0ypB6hbdFeM гость |
вчера, 23:26
Deep learning[111] 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. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.[113] Deep learning has profoundly improved the performance of programs in many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing, image classification[114] and others. The reason that deep learning performs so well in so many applications is not known as of 2023.[115] The sudden success of deep learning in 2012–2015 did not occur because of some new discovery or theoretical breakthrough (deep neural networks and backpropagation had been described by many people, as far back as the 1950s)[i] but because of two factors: the incredible increase in computer power (including the hundred-fold increase in speed by switching to GPUs) and the availability of vast amounts of training data, especially the giant curated datasets used for benchmark testing, such as ImageNet.[j] GPT Generative pre-trained transformers (GPT) are large language models that are based on the semantic relationships between words in sentences (natural language processing). Text-based GPT models are pre-trained on a large corpus of text which can be from the internet. The pre-training consists in predicting the next token (a token being usually a word, subword, or punctuation). Throughout this pre-training, GPT models accumulate knowledge about the world, and can then generate human-like text by repeatedly predicting the next token. Typically, a subsequent training phase makes the model more truthful, useful and harmless, usually with a technique called reinforcement learning from human feedback (RLHF). Current GPT models are still prone to generating falsehoods called "hallucinations", although this can be reduced with RLHF and quality data. They are used in chatbots, which allow you to ask a question or request a task in simple text.[124][125] Current models and services include: Gemini (formerly Bard), ChatGPT, Grok, Claude, Copilot and LLaMA.[126] Multimodal GPT models can process different types of data (modalities) such as images, videos, sound and text.[127] Specialized hardware and software Main articles: Programming languages for artificial intelligence and Hardware for artificial intelligence In the late 2010s, graphics processing units (GPUs) that were increasingly designed with AI-specific enhancements and used with specialized TensorFlow software, had replaced previously used central processing unit (CPUs) as the dominant means for large-scale (commercial and academic) machine learning models' training.[128] Historically, specialized languages, such as Lisp, Prolog, Python and others, had been used. |
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Guest IP-штамп: fr0ypB6hbdFeM гость |
вчера, 23:31
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