Source ::: Wikipedia.org
"AI" redirects here. For other uses, see Ai (disambiguation).
For other uses, see Artificial intelligence (disambiguation).
TOPIO, a humanoid robot, played ping pong at Tokyo International Robot Exhibition (IREX)
2009.[1]
Artificial intelligence (AI) is the intelligence of machines and the branch of
computer sciencethat aims to create it. AI textbooks define the field as "the
study and design of intelligent agents"[2] where an intelligent agent is a system
that perceives its environment and takes actions that maximize its chances of
success.[3] John McCarthy, who coined the term in 1956,[4] defines it as "the
science and engineering of making intelligent machines."[5]
The field was founded on the claim that a central property of humans,
intelligence—the sapienceof Homo sapiens—can be so precisely described
that it can be simulated by a machine.[6] This raises philosophical issues
about the nature of the mind and the ethics of creating artificial beings, issues
which have been addressed by myth, fiction and philosophy since antiquity.[7]
Artificial intelligence has been the subject of optimism,[8] but has also suffered
setbacks[9] and, today, has become an essential part of the technology industry,
providing the heavy lifting for many of the most difficult problems in computer
science.[10]
AI research is highly technical and specialized, deeply divided into subfields that
often fail to communicate with each other.[11] Subfields have grown up around
particular institutions, the work of individual researchers, the solution of specific
problems, longstanding differences of opinion about how AI should be done and
the application of widely differing tools. The central problems of AI include such
traits as reasoning, knowledge, planning, learning, communication, perception
and the ability to move and manipulate objects.[12] General intelligence
(or "strong AI") is still among the field's long term goals.[13]
History
Main articles: History of artificial intelligence and Timeline of artificial intelligence
Thinking machines and artificial beings appear in Greek myths, such as
Talos of Crete, the bronze robot of Hephaestus and Pygmalion's Galatea.[14]
Human likenesses believed to have intelligence were built in every major
civilization: animated cult images were worshipped in Egypt and Greece[15]
and humanoidautomatons were built by Yan Shi, Hero of Alexandria and Al-
Jazari.[16] It was also widely believed that artificial beings had been created
by Jābir ibn Hayyān,Judah Loew and Paracelsus.[17] By the 19th and 20th
centuries, artificial beings had become a common feature in fiction, as in Mary
Shelley's Frankenstein orKarel Čapek's R.U.R. (Rossum's Universal Robots).[18]
Pamela McCorduck argues that all of these are examples of an ancient urge, as
she describes it, "to forge the gods".[7] Stories of these creatures and their fates
discuss many of the same hopes, fears and ethical concerns that are presented
by artificial intelligence.
Mechanical or "formal" reasoning has been developed by philosophers
and mathematicians since antiquity. The study of logic led directly to the
invention of theprogrammable digital electronic computer, based on the work of
mathematician Alan Turing and others. Turing's theory of computation suggested
that a machine, by shuffling symbols as simple as "0" and "1", could simulate
any conceivable act of mathematical deduction.[19][20] This, along with recent
discoveries inneurology, information theory and cybernetics, inspired a small
group of researchers to begin to seriously consider the possibility of building an
electronic brain.[21]
The field of AI research was founded at a conference on the campus of
Dartmouth College in the summer of 1956.[22] The attendees, including John
McCarthy,Marvin Minsky, Allen Newell and Herbert Simon, became the leaders
of AI research for many decades.[23] They and their students wrote programs
that were, to most people, simply astonishing:[24] computers were solving word
problems in algebra, proving logical theorems and speaking English.[25] By the
middle of the 1960s, research in the U.S. was heavily funded by the Department
of Defense[26] and laboratories had been established around the world.[27]
AI's founders were profoundly optimistic about the future of the new field:
Herbert Simon predicted that "machines will be capable, within twenty years, of
doing any work a man can do" and Marvin Minsky agreed, writing that "within a
generation ... the problem of creating 'artificial intelligence' will substantially be
solved".[28]
They had failed to recognize the difficulty of some of the problems they faced.[29]
In 1974, in response to the criticism of England's Sir James Lighthill and ongoing
pressure from Congress to fund more productive projects, the U.S. and British
governments cut off all undirected, exploratory research in AI. The next few
years, when funding for projects was hard to find, would later be called an "AI
winter".[30]
In the early 1980s, AI research was revived by the commercial success of expert
systems,[31] a form of AI program that simulated the knowledge and analytical
skills of one or more 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 in the field.[32] However, beginning with the collapse of the Lisp
Machine market in 1987, AI once again fell into disrepute, and a second, longer
lasting AI winter began.[33]
In the 1990s and early 21st century, AI achieved its greatest successes,
albeit somewhat behind the scenes. Artificial intelligence is used for logistics,
data mining, medical diagnosis and many other areas throughout the
technology industry.[10] The success was due to several factors: the increasing
computational power of computers (see Moore's law), a greater emphasis on
solving specific subproblems, the creation of new ties between AI and other fields
working on similar problems, and a new commitment by researchers to solid
mathematical methods and rigorous scientific standards.[34]
The artificial intelligence quiz show contestant "Watson", appearing on the US quiz show
Jeopardy! in 2011.
On 11 May 1997, Deep Blue became the first computer chess-playing system to
beat a reigning world chess champion,Garry Kasparov.[35] In 2005, a Stanford
robot won the DARPA Grand Challenge by driving autonomously for 131 miles
along an unrehearsed desert trail.[36] In February 2011, in a Jeopardy! quiz
show exhibition match, IBM's question answering system, Watson, defeated
the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a
significant margin.[37]
Learning
Main article: Machine learning
Machine learning[60] has been central to AI research from the beginning.[61]
In 1956, at the original Dartmouth AI summer conference, Ray Solomonoff
wrote a report on unsupervised probabilistic machine learning: "An Inductive
Inference Machine".[62] 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 takes a set of numerical input/output examples and attempts to
discover a continuous function that would generate the outputs from the inputs.
In reinforcement learning[63] the agent is rewarded for good responses and
punished for bad ones. These 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 oftheoretical computer science known as
computational learning theory.[64]
Natural language processing
ASIMO uses sensors and intelligent algorithms to avoid obstacles and navigate stairs.
Main article: Natural language processing
Natural language processing[65] gives machines the ability to read and
understand the languages that humans speak. Many researchers hope that
a sufficiently powerful natural language processing system would be able to
acquire knowledge on its own, by reading the existing text available over the
internet. Some straightforward applications of natural language processing
include information retrieval (or text mining) and machine translation.[66]
Motion and manipulation
Main article: Robotics
The field of robotics[67] is closely related to AI. Intelligence is required for robots
to be able to handle such tasks as object manipulation[68] and navigation, with
sub-problems of localization (knowing where you are), mapping (learning what is
around you) and motion planning (figuring out how to get there).[69]
Perception
Main articles: Machine perception, Computer vision, and Speech recognition
Machine perception[70] is the ability to use input from sensors (such as
cameras, microphones, sonar and others more exotic) to deduce aspects
of the world.Computer vision[71] is the ability to analyze visual input. A few
selected subproblems are speech recognition,[72] facial recognition and object
recognition.[73]
Social intelligence
Main article: Affective computing
Kismet, a robot with rudimentary social skills
Emotion and social skills[74] play two roles for an intelligent agent. First, it must
be able to predict the actions of others, by understanding their motives and
emotional states. (This involves elements of game theory, decision theory, as
well as the ability to model human emotions and the perceptual skills to detect
emotions.) Also, for good human-computer interaction, an intelligent machine
also needs to display emotions. At the very least it must appear polite and
sensitive to the humans it interacts with. At best, it should have normal emotions
itself.
***Muhammad Ishaque Memon***
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