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POWER ENGLISH: Artificial intelligence  

2017-07-06 08:23:51|  分类: POWER ENGLISH |  标签: |举报 |字号 订阅

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"AI" redirects here. For other uses, see AI and Artificial intelligence (disambiguation).
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Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, the field of AI research defines itself as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of success at some goal.[1] Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".[2]

As machines become increasingly capable, mental facilities once thought to require intelligence are removed from the definition. For instance, optical character recognition is no longer perceived as an example of "artificial intelligence", having become a routine technology.[3] Capabilities currently classified as AI include successfully understanding human speech,[4] competing at a high level in strategic game systems (such as chess and Go[5]), autonomous cars, intelligent routing in content delivery networks, military simulations, and interpreting complex data.

AI research is divided into subfields[6] that focus on specific problemsapproaches, the use of a particular tool, or towards satisfying particular applications.

The central problems (or goals) of AI research include reasoningknowledgeplanninglearningnatural language processing (communication), perception and the ability to move and manipulate objects.[7] General intelligenceis among the field's long-term goals.[8] Approaches include statistical methodscomputational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimizationlogicmethods based on probability and economics. The AI field draws upon computer sciencemathematicspsychologylinguisticsphilosophyneuroscienceartificial psychology and many others.

The field was founded on the claim that human intelligence "can be so precisely described that a machine can be made to simulate it".[9] This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been explored by mythfiction and philosophy since antiquity.[10] Some people also consider AI a danger to humanity if it progresses unabatedly.[11] Attempts to create artificial intelligence have experienced many setbacks, including the ALPAC report of 1966, the abandonment of perceptrons in 1970, the Lighthill Report of 1973, the second AI winter 1987–1993 and the collapse of the Lisp machine market in 1987.

In the twenty-first century, AI techniques, both hard (using a symbolic approach) and soft (sub-symbolic), have experienced a resurgence following concurrent advances in computer power, sizes of training sets, and theoretical understanding, and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science.[12] Recent advancements in AI, and specifically in machine learning, have contributed to the growth of Autonomous Things such as drones and self-driving cars, becoming the main driver of innovation in the automotive industry.


Planning[edit]

hierarchical control system is a form of control system in which a set of devices and governing software is arranged in a hierarchy.

Intelligent agents must be able to set goals and achieve them.[60] They need a way to visualize the future—a representation of the state of the world and be able to make predictions about how their actions will change it—and be able to make choices that maximize the utility (or "value") of available choices.[61]

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.[62] However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty. 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.[63]

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.[64]

Learning[edit]

Main article: Machine learning

Machine learning, a fundamental concept of AI research since the field's inception,[65] is the study of computer algorithms that improve automatically through experience.[66][67]

Unsupervised learning is the ability to find patterns in a stream of input. Supervised learning includes both classification and numerical regression. Classification is used to determine what category something belongs in, after seeing 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. In reinforcement learning[68] the agent is rewarded for good responses and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space. These three types of learning can be analyzed in terms of decision theory, using concepts like utility. The mathematical analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory.[69]

Within developmental robotics, developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous self-exploration, social interaction with human teachers, and the use of guidance mechanisms (active learning, maturation, motor synergies, etc.).[70][71][72][73]

Natural language processing[edit]

parse tree represents the syntacticstructure of a sentence according to some formal grammar.

Natural language processing[74] gives machines the ability 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[75] and machine translation.[76]

A common method of processing and extracting meaning from natural language is through semantic indexing. Although these indexes require a large volume of user input, it is expected that increases in processor speeds and decreases in data storage costs will result in greater efficiency.

Perception[edit]

Machine perception[77] is the ability to use input from sensors (such as cameras, microphones, tactile sensors, sonar and others) to deduce aspects of the world. Computer vision[78] is the ability to analyze visual input. A few selected subproblems are speech recognition,[79] facial recognition and object recognition.[80]

Motion and manipulation[edit]

Main article: Robotics

The field of robotics[81] is closely related to AI. Intelligence is required for robots to handle tasks such as object manipulation[82] and navigation, with sub-problems such as localizationmapping, and motion planning. These systems require that an agent is able to: Be spatially cognizant of its surroundings, learn from and build a map of its environment, figure out how to get from one point in space to another, and execute that movement (which often involves compliant motion, a process where movement requires maintaining physical contact with an object).[83][84]


Goals[edit]

The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. 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.[7]

Erik Sandwell emphasizes planning and learning that is relevant and applicable to the given situation.[42]


CULTURE IS THE ROOT, EDUCATION SHOULD BE REGARDED AS THE FOUNDATION.


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