Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by humans or blogger.comg AI textbooks define the field as the study of "intelligent agents": any system that perceives its environment and takes actions that maximize its chance of achieving its blogger.com popular accounts use the term "artificial intelligence" to The latest Lifestyle | Daily Life news, tips, opinion and advice from The Sydney Morning Herald covering life and relationships, beauty, fashion, health & wellbeing Jun 10, · If your book was part of an anthology, you do not need to put it in italics. Simply place quotation marks around the title. For example, The Lord of the Rings trilogy is sometimes published in one volume. In this case, you could write the name of the first novel as "The Fellowship of the Ring" when citing it in an essay
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Artificial intelligence AI is intelligence demonstrated by machinesas opposed to the natural intelligence displayed by humans or animals. Leading AI textbooks define the field as the study of " intelligent agents ": any system that perceives its environment and takes actions that maximize its chance of achieving its goals. AI applications include advanced web search engines, recommendation systems used by YouTubeAmazon and Netflixunderstanding human speech such as Siri or Alexaself-driving cars e.
Teslaand competing at the highest level in strategic game systems such as chess and Go[2] 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. Artificial intelligence was founded as an academic discipline inand in the years since has experienced several waves of optimism, [6] [7] followed by disappointment and the loss of funding known as an " AI winter "[8] [9] followed by new approaches, success and renewed funding.
In the first decades of the 21st century, highly mathematical statistical machine learning has dominated the field, and this technique has proved highly successful, helping to solve many challenging problems throughout industry and academia.
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 reasoningknowledge representationdo androids dream of electric sheep analytical essay, planninglearningnatural language processingperception and the ability to move and manipulate objects.
AI also draws upon computer sciencepsychologylinguisticsphilosophyand many other fields. The field was founded on the assumption that human intelligence "can be so precisely described that a machine can be made to simulate it". These issues have been explored by mythfiction and philosophy since antiquity. Thought-capable artificial beings appeared as storytelling devices in antiquity, [23] and have been common in fiction, as in Mary Shelley 's Frankenstein or Karel Čapek 's R.
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 computationwhich suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction, do androids dream of electric sheep analytical essay.
This insight, that digital computers can simulate any process of formal reasoning, is known as the Church—Turing thesis.
Turing proposed changing the question from whether a machine was intelligent, to "whether or not it is possible for machinery to show intelligent behaviour". The field of AI research was born at a workshop at Dartmouth College in[28] where the term "Artificial Intelligence" was coined by John McCarthy to distinguish the field from cybernetics and escape the influence of the cyberneticist Norbert Wiener.
was heavily funded by the Department of Defense [35] and laboratories had been established around the world. Marvin Minsky agreed, writing, "within a generation the problem of creating 'artificial intelligence' will substantially be solved".
They failed to recognize the difficulty of some of the remaining tasks. Progress slowed do androids dream of electric sheep analytical essay inin response to the criticism of Sir James Lighthill [38] and ongoing pressure from the US Congress to fund more productive projects, both the U. and British governments cut off exploratory research in AI. The next few years would later be called an " AI winter ", [8] a period when obtaining funding for AI projects was difficult.
In the early s, AI research was revived by the commercial success of expert systemsdo androids dream of electric sheep analytical essay, [39] a form of AI program that simulated the knowledge and analytical skills of human experts. Bythe 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. AI gradually restored its reputation in the late s and early 21st century by finding specific solutions to specific problems, such as logistics, data mining or medical diagnosis. ByAI solutions were being widely used behind the scenes. Faster computersalgorithmic 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 Clark also presents factual data indicating the improvements of AI since supported by lower error rates in image processing tasks.
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. Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions. 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.
They solve most of their problems using fast, intuitive judgments. Knowledge representation [49] and knowledge engineering [50] are central to classical AI research. Some "expert systems" attempt to gather explicit knowledge possessed by experts in some narrow domain. In addition, some projects attempt to gather the "commonsense knowledge" known to the average person into a database containing extensive knowledge about the world.
Among the things a comprehensive commonsense knowledge base would contain are: objects, properties, categories and relations between objects; [51] situations, events, states and time; [52] causes and effects; [53] knowledge about knowledge what we know about what other people know ; [54] and many other, less well researched domains. 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, do androids dream of electric sheep analytical essay.
The semantics of these are captured as description logic concepts, roles, and individuals, and typically implemented as classes, properties, and individuals in the Web Ontology Language. Such formal knowledge representations can be used in content-based indexing and retrieval, [57] scene interpretation, [58] clinical decision support, [59] knowledge discovery mining "interesting" and actionable inferences from large databases[60] and other areas.
Intelligent agents must be able to set goals and achieve them. In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions. This calls for an agent that can not only assess its environment and make predictions but also evaluate its predictions and adapt based on its assessment. Multi-agent planning uses the cooperation and competition of many agents to achieve a given goal. Emergent behavior such as this is used by evolutionary algorithms and swarm intelligence.
Machine learning MLa fundamental concept of AI research since the field's inception, [d] is the study of computer algorithms that improve automatically through experience. Unsupervised learning is the ability to find patterns in a stream of input, without requiring a human to label the inputs first.
Supervised learning includes both classification and numerical regressionwhich requires a human to label the input data first. Classification is used to determine what category something belongs in, and occurs after a program sees a number of examples of things from several categories.
Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change.
Computational learning theory can assess learners by computational complexityby sample complexity how much data is requiredor by other notions of optimization. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space. Natural language processing [78] NLP 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 natural language processing include information retrievaltext miningquestion answering and machine translation, do androids dream of electric sheep analytical essay.
Modern statistical NLP approaches can combine all these strategies as well as others, and often achieve acceptable accuracy at the page or paragraph level. Beyond semantic NLP, the ultimate goal of "narrative" NLP is to embody a full understanding of commonsense reasoning.
Machine perception [82] is the ability to use input from sensors such as cameras visible spectrum or infraredmicrophones, wireless signals, and active lidarsonar, radar, and tactile sensors to deduce aspects of the world. Applications include speech recognition[83] facial recognitionand object recognition. Such input is usually ambiguous; a giant, do androids dream of electric sheep analytical essay, fifty-meter-tall pedestrian far away may produce the same pixels as a nearby normal-sized pedestrian, requiring the AI to judge the relative likelihood and reasonableness of different interpretations, for example by using its "object model" to assess that fifty-meter pedestrians do not exist.
Do androids dream of electric sheep analytical essay is heavily used in robotics. Motion planning is the process of breaking down a movement task into "primitives" such as individual joint movements. Such movement often involves compliant motion, a process where movement requires maintaining physical do androids dream of electric sheep analytical essay with an object.
Affective computing is an interdisciplinary umbrella that comprises systems which recognize, interpret, process, or simulate human affects. General intelligence is the ability to take on any arbitrary problem. Current AI research has, for the most part, only produced programs that can solve exactly one problem.
Many researchers predict that such "narrow AI" work in different individual domains will eventually be incorporated into a machine with general intelligence, combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas.
For most of its history, no established unifying theory or paradigm has guided AI research. particular institutions or researchers but they also came from deep philosophical differences that led to very different approaches to AI. The unprecedented success of statistical machine learning in the s eclipsed all other approaches, so much so that some sources especially in the business world use the term "artificial intelligence" to mean "machine learning with neural networks".
However, do androids dream of electric sheep analytical essay questions that have divided AI research historically have remained unanswered and may have to be revisited by future research.
In the s and s, a number of researchers explored the connection between neurobiologyinformation theoryand cybernetics. When access to digital computers became possible in the mids, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation.
The research was centered in three institutions: Carnegie Mellon UniversityStanfordand MITand as described below, each one developed its own style of research. John Haugeland named these symbolic approaches to AI "good old fashioned AI" or " GOFAI ". Researchers in the s and the s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the goal of their field.
By the s, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially perceptionrobotics, learning and pattern recognition.
A number of researchers began do androids dream of electric sheep analytical essay look into "sub-symbolic" approaches to specific AI problems. Researchers from the related field of robotics, such as Rodney Brooksrejected symbolic AI and focused on the basic engineering problems that would allow robots to move, survive, do androids dream of electric sheep analytical essay, and learn their environment.
The called their work by several names: e. embodiedsituateddo androids dream of electric sheep analytical essay, behavior-based or developmental. This coincided with the development of the embodied mind thesis in the related field of cognitive science : the idea that aspects of the body such as movement, perception and visualization are required for higher intelligence.
Soft computing finds solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient. Soft computing approaches to AI include neural networksfuzzy systemsGrey system theoryevolutionary computation and many tools drawn from statistics do androids dream of electric sheep analytical essay mathematical optimization.
Interest in neural networks and " connectionism " was revived by Geoffrey HintonDavid Rumelhart and others in the middle of the s. In the s, AI researchers adopted sophisticated mathematical tools, such as hidden Markov models HMMinformation theoryand normative Bayesian decision theory to compare or to unify competing architectures.
The shared mathematical language permitted a high level of collaboration with more established fields like mathematicseconomics or operations research. The increased successes with real-world data led to increasing emphasis on comparing different approaches against shared test data to see which approach performed best in a broader context than that provided by idiosyncratic toy models; AI research was becoming more scientific.
Nowadays results of experiments are often rigorously measurable, and are sometimes with difficulty reproducible. Critics such as Noam Chomsky note that the shift from GOFAI to statistical learning is often also a shift away from explainable AI. In AGI research, do androids dream of electric sheep analytical essay, some scholars caution against over-reliance on statistical learning, and argue that continuing research into GOFAI will still be necessary to attain general intelligence.
Bernard Goetz and others became concerned that AI was no longer pursuing the original goal of creating versatile, fully intelligent machines. Statistical AI is overwhelmingly used to solve specific problems, even highly successful techniques such as deep learning.
They founded the subfield artificial general intelligence or "AGI"which had several well-funded institutions by the s.
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At our cheap essay writing service, you can be sure to get credible academic aid for a reasonable price, as the name of our website suggests. For years, we have been providing online custom writing assistance to students from countries all over the world, including the US, the UK, Australia, Canada, Italy, New Zealand, China, and Japan Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by humans or blogger.comg AI textbooks define the field as the study of "intelligent agents": any system that perceives its environment and takes actions that maximize its chance of achieving its blogger.com popular accounts use the term "artificial intelligence" to Jun 10, · If your book was part of an anthology, you do not need to put it in italics. Simply place quotation marks around the title. For example, The Lord of the Rings trilogy is sometimes published in one volume. In this case, you could write the name of the first novel as "The Fellowship of the Ring" when citing it in an essay
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