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Many other physical properties of the elements exhibit periodic variation in accordance with the periodic law, such as melting points, boiling points, heats of fusion, heats of vaporisation, atomisation energy, and so on. Similar periodic variations appear for the compounds of the elements, which can be observed by com...
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Many terms have been used in the literature to describe sets of elements that behave similarly. The group names "alkali metal", "alkaline earth metal", "pnictogen", "chalcogen", "halogen", and "noble gas" are acknowledged by IUPAC; the other groups can be referred to by their number, or by their first element (e.g., gr...
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The "lanthanides" are considered to be the elements La–Lu, which are all very similar to each other: historically they included only Ce–Lu, but lanthanum became included by common usage. The "rare earth elements" (or rare earth metals) add scandium and yttrium to the lanthanides. Analogously, the "actinides" are consid...
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Many more categorisations exist and are used according to certain disciplines. In astrophysics, a metal is defined as any element with atomic number greater than 2, i.e. anything except hydrogen and helium. The term "semimetal" has a different definition in physics than it does in chemistry: bismuth is a semimetal by p...
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The scope of terms varies significantly between authors. For example, according to IUPAC, the noble gases extend to include the whole group, including the very radioactive superheavy element oganesson. However, among those who specialise in the superheavy elements, this is not often done: in this case "noble gas" is ty...
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In 1817, German physicist Johann Wolfgang Döbereiner began to formulate one of the earliest attempts to classify the elements. In 1829, he found that he could form some of the elements into groups of three, with the members of each group having related properties. He termed these groups triads. Chlorine, bromine, and i...
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German chemist Lothar Meyer noted the sequences of similar chemical and physical properties repeated at periodic intervals. According to him, if the atomic weights were plotted as ordinates (i.e. vertically) and the atomic volumes as abscissas (i.e. horizontally)—the curve obtained a series of maximums and minimums—the...
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The definitive breakthrough came from the Russian chemist Dmitri Mendeleev. Although other chemists (including Meyer) had found some other versions of the periodic system at about the same time, Mendeleev was the most dedicated to developing and defending his system, and it was his system that most impacted the scienti...
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In 1875, the French chemist Paul-Émile Lecoq de Boisbaudran, working without knowledge of Mendeleev's prediction, discovered a new element in a sample of the mineral sphalerite, and named it gallium. He isolated the element and began determining its properties. Mendeleev, reading de Boisbaudran's publication, sent a le...
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Mendeleev nevertheless had some trouble fitting the known lanthanides into his scheme, as they did not exhibit the periodic change in valencies that the other elements did. After much investigation, the Czech chemist Bohuslav Brauner suggested in 1902 that the lanthanides could all be placed together in one group on th...
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After the internal structure of the atom was probed, amateur Dutch physicist Antonius van den Broek proposed in 1913 that the nuclear charge determined the placement of elements in the periodic table. The New Zealand physicist Ernest Rutherford coined the word "atomic number" for this nuclear charge. In van der Broek's...
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The same year, English physicist Henry Moseley using X-ray spectroscopy confirmed van den Broek's proposal experimentally. Moseley determined the value of the nuclear charge of each element from aluminium to gold and showed that Mendeleev's ordering actually places the elements in sequential order by nuclear charge. Nu...
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The Danish physicist Niels Bohr applied Max Planck's idea of quantisation to the atom. He concluded that the energy levels of electrons were quantised: only a discrete set of stable energy states were allowed. Bohr then attempted to understand periodicity through electron configurations, surmising in 1913 that the inne...
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Bohr called his electron shells "rings" in 1913: atomic orbitals within shells did not exist at the time of his planetary model. Bohr explains in Part 3 of his famous 1913 paper that the maximum electrons in a shell is eight, writing, "We see, further, that a ring of n electrons cannot rotate in a single ring round a n...
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The first one to systematically expand and correct the chemical potentials of Bohr's atomic theory was Walther Kossel in 1914 and in 1916. Kossel explained that in the periodic table new elements would be created as electrons were added to the outer shell. In Kossel's paper, he writes: "This leads to the conclusion tha...
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In a 1919 paper, Irving Langmuir postulated the existence of "cells" which we now call orbitals, which could each only contain two electrons each, and these were arranged in "equidistant layers" which we now call shells. He made an exception for the first shell to only contain two electrons. The chemist Charles Rugeley...
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Prompted by Bohr, Wolfgang Pauli took up the problem of electron configurations in 1923. Pauli extended Bohr's scheme to use four quantum numbers, and formulated his exclusion principle which stated that no two electrons could have the same four quantum numbers. This explained the lengths of the periods in the periodic...
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The quantum theory clarified the transition metals and lanthanides as forming their own separate groups, transitional between the main groups, although some chemists had already proposed tables showing them this way before then: the English chemist Henry Bassett did so in 1892, the Danish chemist Julius Thomsen in 1895...
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By 1936, the pool of missing elements from hydrogen to uranium had shrunk to four: elements 43, 61, 85, and 87 remained missing. Element 43 eventually became the first element to be synthesised artificially via nuclear reactions rather than discovered in nature. It was discovered in 1937 by Italian chemists Emilio Segr...
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A significant controversy arose with elements 102 through 106 in the 1960s and 1970s, as competition arose between the LBNL team (now led by Albert Ghiorso) and a team of Soviet scientists at the Joint Institute for Nuclear Research (JINR) led by Georgy Flyorov. Each team claimed discovery, and in some cases each propo...
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The TWG's criteria were used to arbitrate later element discovery claims from LBNL and JINR, as well as from research institutes in Germany (GSI) and Japan (Riken). Currently, consideration of discovery claims is performed by a IUPAC/IUPAP Joint Working Party. After priority was assigned, the elements were officially a...
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In celebration of the periodic table's 150th anniversary, the United Nations declared the year 2019 as the International Year of the Periodic Table, celebrating "one of the most significant achievements in science". The discovery criteria set down by the TWG were updated in 2020 in response to experimental and theoreti...
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Although the modern periodic table is standard today, some variation can be found in period 1 and group 3. Discussion is ongoing about the placements of the relevant elements.
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Some variation can be found on the placements of the period 1 elements hydrogen and helium. Following electron configurations, hydrogen would be placed in group 1, and helium would be placed in group 2. The group 1 placement of hydrogen is common, but helium is almost always placed in group 18 with the other noble gase...
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Like the group 1 metals, hydrogen has one electron in its outermost shell and typically loses its only electron in chemical reactions. It has some metal-like chemical properties, being able to displace some metals from their salts. But hydrogen forms a diatomic nonmetallic gas at standard conditions, unlike the alkali ...
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Helium is an unreactive noble gas at standard conditions, and has a full outer shell: these properties are like the noble gases in group 18, but not at all like the reactive alkaline earth metals of group 2. Therefore, helium is nearly universally placed in group 18 which its properties best match. However, helium only...
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Published periodic tables show variation regarding the heavier members of group 3, which begins with scandium and yttrium. They are commonly lanthanum and actinium, but many physicists and chemists have argued that they should be lutetium and lawrencium. The spaces below yttrium are sometimes left blank as a third opti...
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In 2015, a IUPAC project chaired by Eric Scerri was set up to decide the question, giving only Sc-Y-La-Ac and Sc-Y-Lu-Lr as possible resolutions. In 2021, it decided on Sc-Y-Lu-Lr on the basis of three "desiderata": displaying all elements in order of increasing atomic number, avoiding a split of the d-block into "two ...
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The most recently named elements – nihonium (113), moscovium (115), tennessine (117), and oganesson (118) – completed the seventh row of the periodic table. Future elements would have to begin an eighth row. These elements may be referred to either by their atomic numbers (e.g. "element 119"), or by the IUPAC systemati...
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Currently, discussion continues regarding whether this future eighth period should follow the pattern set by the earlier periods or not, as calculations predict that by this point relativistic effects should result in significant deviations from the Madelung rule. Various different models have been suggested. All agree...
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Nuclear stability will likely prove a decisive factor constraining the number of possible elements. It depends on the balance between the electric repulsion between protons and the strong force binding protons and neutrons together. Protons and neutrons are arranged in shells, just like electrons, and so a closed shell...
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Alternatively, quark matter may become stable at high mass numbers, in which the nucleus is composed of freely flowing up and down quarks instead of binding them into protons and neutrons; this would create a continent of stability instead of an island. Other effects may come into play: for example, in very heavy eleme...
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Even if eighth-row elements can exist, producing them is likely to be difficult, and it should become even more difficult as atomic number rises. Although the 8s elements are expected to be reachable with present means, the first few elements are expected to require new technology, if they can be produced at all. Exper...
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The periodic law may be represented in multiple ways, of which the standard periodic table is only one. Within 100 years of the appearance of Mendeleev's table in 1869, Edward G. Mazurs had collected an estimated 700 different published versions of the periodic table. Many forms retain the rectangular structure, includ...
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Alternative periodic tables are often developed to highlight or emphasize chemical or physical properties of the elements that are not as apparent in traditional periodic tables, with different ones skewed more towards emphasizing chemistry or physics at either end. The standard form, which remains by far the most comm...
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The many different forms of the periodic table have prompted the questions of whether there is an optimal or definitive form of the periodic table, and if so, what it might be. There are no current consensus answers to either question. Janet's left-step table is being increasingly discussed as a candidate for being the...
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Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by animals and humans. Example tasks in which this is done include speech recognition, computer vision, translation between (natural) languages, as well as othe...
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the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
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AI applications include advanced web search engines (e.g., Google), recommendation systems (used by YouTube, Amazon and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Tesla), automated decision-making and competing at the highest level in strategic game systems (such as chess an...
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As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect. For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology.
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Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "AI winter"), followed by new approaches, success and renewed funding. AI research has tried and discarded many differe...
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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 the ability to move and manipulate objects. General intelligence ...
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The field was founded on the assumption that human intelligence "can be so precisely described that a machine can be made to simulate it".
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This raised philosophical arguments about the mind and the ethical consequences of creating artificial beings endowed with human-like intelligence; these issues have previously been explored by myth, fiction and philosophy since antiquity. Computer scientists and philosophers have since suggested that AI may become an ...
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and have been common in fiction, as in Mary Shelley's "Frankenstein" or Karel Čapek's "R.U.R." These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence.
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The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of mathematical logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical de...
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The first work that is now generally recognized as AI was McCullouch and Pitts' 1943 formal design for Turing-complete "artificial neurons".
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By the 1950s, two visions for how to achieve machine intelligence emerged. One vision, known as Symbolic AI or GOFAI, was to use computers to create a symbolic representation of the world and systems that could reason about the world. Proponents included Allen Newell, Herbert A. Simon, and Marvin Minsky. Closely associ...
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computers were learning checkers strategies, solving word problems in algebra, proving logical theorems and speaking English.
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Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the goal of their field.
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Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do".
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Marvin Minsky agreed, writing, "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved". They had failed to recognize the difficulty of some of the remaining tasks. Progress slowed and in 1974, in response to the criticism of Sir James Lighthill
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and ongoing pressure from the US Congress to fund more productive projects, both the U.S. and British governments cut off exploratory research in AI. The next few years would later be called an "AI winter", a period when obtaining funding for AI projects was difficult.
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a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U.S. and British governments to restore funding for academic research.
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However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting winter began.
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Many researchers began to doubt that the symbolic approach would be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems. Robotics researchers, such as Rodney ...
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Interest in neural networks and "connectionism" was revived by Geoffrey Hinton, David Rumelhart and others in the middle of the 1980s.
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Soft computing tools were developed in the 1980s, such as neural networks, fuzzy systems, Grey system theory, evolutionary computation and many tools drawn from statistics or mathematical optimization.
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AI gradually restored its reputation in the late 1990s and early 21st century by finding specific solutions to specific problems. The narrow focus allowed researchers to produce verifiable results, exploit more mathematical methods, and collaborate with other fields (such as statistics, economics and mathematics).
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By 2000, solutions developed by AI researchers were being widely used, although in the 1990s they were rarely described as "artificial intelligence".
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Faster computers, algorithmic improvements, and access to large amounts of data enabled advances in machine learning and perception; data-hungry deep learning methods started to dominate accuracy benchmarks around 2012.
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According to Bloomberg's Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI within Google increased from a "sporadic usage" in 2012 to more than 2,700 projects. He attributes this to an increase in affordable neural networks, due to a rise in cloud computi...
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Numerous academic researchers became concerned that AI was no longer pursuing the original goal of creating versatile, fully intelligent machines. Much of current research involves statistical AI, which is overwhelmingly used to solve specific problems, even highly successful techniques such as deep learning. This conc...
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The general problem of simulating (or creating) intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention.
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Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.
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By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.
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Many of these algorithms proved to be insufficient for solving large reasoning problems because they experienced a "combinatorial explosion": they became exponentially slower as the problems grew larger.
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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.
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A representation of "what exists" is an ontology: the set of objects, relations, concepts, and properties formally described so that software agents can interpret them.
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The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge and act as mediators between domain ontologies that cover specific knowledge about a particular knowledge domain (field of interest or area of concern). A truly intelligent program would also need acce...
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AI research has developed tools to represent specific domains, such as objects, properties, categories and relations between objects;
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default reasoning (things that humans assume are true until they are told differently and will remain true even when other facts are changing);
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as well as other domains. Among the most difficult problems in AI are: the breadth of commonsense knowledge (the number of atomic facts that the average person knows is enormous);
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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).
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Unsupervised learning finds patterns in a stream of input. Supervised learning requires a human to label the input data first, and comes in two main varieties: classification and numerical regression. Classification is used to determine what category something belongs in – the program sees a number of examples of thing...
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In reinforcement learning the agent is rewarded for good responses and punished for bad ones. The agent classifies its responses to form a strategy for operating in its problem space.
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Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization.
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allows machines to read and understand human language. A sufficiently powerful natural language processing system would enable natural-language user interfaces and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of NLP include information retri...
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Symbolic AI used formal syntax to translate the deep structure of sentences into logic. This failed to produce useful applications, due to the intractability of logic and the breadth of commonsense knowledge. Modern statistical techniques include co-occurrence frequencies (how often one word appears near another), "Key...
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is the ability to use input from sensors (such as cameras, microphones, wireless signals, and active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Applications include speech recognition,
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Affective computing is an interdisciplinary umbrella that comprises systems that recognize, interpret, process or simulate human feeling, emotion and mood.
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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.
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However, this tends to give naïve users an unrealistic conception of how intelligent existing computer agents actually are. 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...
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A machine with general intelligence can solve a wide variety of problems with breadth and versatility similar to human intelligence. There are several competing ideas about how to develop artificial general intelligence. Hans Moravec and Marvin Minsky argue that work in different individual domains can be incorporated ...
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Pedro Domingos hopes that there is a conceptually straightforward, but mathematically difficult, "master algorithm" that could lead to AGI.
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AI can solve many problems by intelligently searching through many possible solutions. Reasoning can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from premises to conclusions, where each step is the application of an inference rule. Planning algorithms s...
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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. The solution, for many problems, is to use "heuristics" or "rules of thumb" that prioritize choices in favor of those m...
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A very different kind of search came to prominence in the 1990s, based on the mathematical theory of optimization. For many problems, it is possible to begin the search with some form of a guess and then refine the guess incrementally until no more refinements can be made. These algorithms can be visualized as blind hi...
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is used for knowledge representation and problem-solving, but it can be applied to other problems as well. For example, the satplan algorithm uses logic for planning
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Several different forms of logic are used in AI research. Propositional logic involves truth functions such as "or" and "not". First-order logic
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adds quantifiers and predicates and can express facts about objects, their properties, and their relations with each other. Fuzzy logic assigns a "degree of truth" (between 0 and 1) to vague statements such as "Alice is old" (or rich, or tall, or hungry), that are too linguistically imprecise to be completely true or f...
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Default logics, non-monotonic logics and circumscription are forms of logic designed to help with default reasoning and the qualification problem.
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Several extensions of logic have been designed to handle specific domains of knowledge, such as description logics;
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Logics to model contradictory or inconsistent statements arising in multi-agent systems have also been designed, such as paraconsistent logics.
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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.
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are a very general tool that can be used for various problems, including reasoning (using the Bayesian inference algorithm),
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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).
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A key concept from the science of economics is "utility", a measure of how valuable something is to an intelligent agent. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis,
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and information value theory. These tools include models such as Markov decision processes, dynamic decision networks, game theory and mechanism design.
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The simplest AI applications can be divided into two types: classifiers ("if shiny then diamond") and controllers ("if diamond then pick up"). Controllers do, however, also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems. Classifiers are functions that ...
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