Info about artificial intelligence

Artificial intelligence (AI) is the
intelligence exhibited by machines
or software. It is also an academic
field of study. Major AI researchers
and textbooks define this field as
"the study and design of
intelligent agents", [1] where an
intelligent agent is a system that
perceives its environment and
takes actions that maximize its
chances of success.[2] John
McCarthy, who coined the term in
1955, [3] defines it as "the science
and engineering of making
intelligent machines". [4]
AI research is highly technical and
specialised, and is deeply divided
into subfields that often fail to
communicate with each other. [5]
Some of the division is due to
social and cultural factors:
subfields have grown up around
particular institutions and the
work of individual researchers. AI
research is also divided by several
technical issues. Some subfields
focus on the solution of specific
problems. Others focus on one of
several possible approaches or on
the use of a particular tool or
towards the accomplishment of
particular applications.
The central problems (or goals) of
AI research include reasoning,
knowledge , planning, learning ,
natural language processing
(communication), perception and
the ability to move and
manipulate objects. [6] General
intelligence (or "strong AI") is still
among the field's long term goals.
[7] Currently popular approaches
include statistical methods ,
computational intelligence and
traditional symbolic AI. There are
a large number of tools used in
AI, including versions of search
and mathematical optimization ,
logic, methods based on
probability and economics, and
many others. The AI field is
interdisciplinary, in which a
number of sciences and
professions converge, including
computer science, psychology ,
linguistics, philosophy and
neuroscience, as well as other
specialized field such as artificial
psychology .
The field was founded on the
claim that a central property of
humans, intelligence—the
sapience of Homo sapiens—"can
be so precisely described that a
machine can be made to simulate
it." [8] This raises philosophical
issues about the nature of the
mind and the ethics of creating
artificial beings endowed with
human-like intelligence, issues
which have been addressed by
myth , fiction and philosophy since
antiquity.[9] Artificial intelligence
has been the subject of
tremendous optimism[10] but has
also suffered stunning setbacks.
[11] Today it has become an
essential part of the technology
industry, providing the heavy
lifting for many of the most
challenging problems in computer
science.[12]
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.[13] Human
likenesses believed to have
intelligence were built in every
major civilization: animated cult
images were worshiped in Egypt
and Greece [14] and humanoid
automatons were built by Yan Shi ,
Hero of Alexandria and Al-Jazari.
[15] It was also widely believed
that artificial beings had been
created by Jābir ibn Hayyān , Judah
Loew and Paracelsus . [16] By the
19th and 20th centuries, artificial
beings had become a common
feature in fiction, as in Mary
Shelley 's Frankenstein or Karel
Čapek's R.U.R. (Rossum's
Universal Robots) . [17] Pamela
McCorduck argues that all of these
are some examples of an ancient
urge, as she describes it, "to forge
the gods". [9] 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 the
programmable 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. [18][19]
This, along with concurrent
discoveries in neurology,
information theory and
cybernetics, inspired a small
group of researchers to begin to
seriously consider the possibility
of building an electronic brain.
[20]
The field of AI research was
founded at a conference on the
campus of Dartmouth College in
the summer of 1956. [21] The
attendees, including John
McCarthy, Marvin Minsky , Allen
Newell and Herbert Simon, became
the leaders of AI research for
many decades. [22] They and their
students wrote programs that
were, to most people, simply
astonishing: [23] computers were
solving word problems in algebra,
proving logical theorems and
speaking English.[24] By the
middle of the 1960s, research in
the U.S. was heavily funded by
the Department of Defense [25]
and laboratories had been
established around the world. [26]
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". [27]
They had failed to recognize the
difficulty of some of the problems
they faced. [28] In 1974, in
response to the criticism of Sir
James Lighthill and ongoing
pressure from the US Congress to
fund more productive projects,
both the U.S. and British
governments cut off all undirected
exploratory research in AI. The
next few years would later be
called an " AI winter ", [29] a period
when funding for AI projects was
hard to find.
In the early 1980s, AI research
was revived by the commercial
success of expert systems,[30] 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. [31]
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. [32]
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. [12] 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.
[33]
On 11 May 1997, Deep Blue
became the first computer chess-
playing system to beat a reigning
world chess champion, Garry
Kasparov .[34] In 2005, a Stanford
robot won the DARPA Grand
Challenge by driving
autonomously for 131 miles along
an unrehearsed desert trail. [35]
Two years later, a team from CMU
won the DARPA Urban Challenge
when their vehicle autonomously
navigated 55 miles in an urban
environment while adhering to
traffic hazards and all traffic laws.
[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] The Kinect ,
which provides a 3D body–motion
interface for the Xbox 360 and the
Xbox One, uses algorithms that
emerged from lengthy AI research
[38] as does the iPhone's Siri .
Goals
The general problem of simulating
(or creating) intelligence has been
broken down into a number of
specific sub-problems. These
consist of particular traits or
capabilities that researchers would
like an intelligent system to
display. The traits described
below have received the most
attention.[6]
Deduction, reasoning, problem
solving
Early AI researchers developed
algorithms that imitated the step-
by-step reasoning that humans
use when they solve puzzles or
make logical deductions. [39] By
the late 1980s and 1990s, AI
research had also developed
highly successful methods for
dealing with uncertain or
incomplete information, employing
concepts from probability and
economics. [40]
For difficult problems, most of
these algorithms can require
enormous computational resources
– most experience a
"combinatorial explosion": the
amount of memory or computer
time required becomes
astronomical when the problem
goes beyond a certain size. The
search for more efficient problem-
solving algorithms is a high
priority for AI research. [41]
Human beings solve most of their
problems using fast, intuitive
judgements rather than the
conscious, step-by-step deduction
that early AI research was able to
model. [42] AI has made some
progress at imitating this kind of
"sub-symbolic" problem solving:
embodied agent approaches
emphasize the importance of
sensorimotor skills to higher
reasoning; neural net research
attempts to simulate the
structures inside the brain that
give rise to this skill; statistical
approaches to AI mimic the
probabilistic nature of the human
ability to guess.
Knowledge representation
An ontology represents knowledge
as a set of concepts within a
domain and the relationships
between those concepts.
Main articles: Knowledge
representation and Commonsense
knowledge
Knowledge representation[43] and
knowledge engineering [44] are
central to AI research. Many of
the problems machines are
expected to solve will require
extensive knowledge about the
world. Among the things that AI
needs to represent are: objects,
properties, categories and
relations between objects; [45]
situations, events, states and
time; [46] causes and effects; [47]
knowledge about knowledge (what
we know about what other people
know); [48] and many other, less
well researched domains. A
representation of "what exists" is
an ontology: the set of objects,
relations, concepts and so on that
the machine knows about. The
most general are called upper
ontologies, which attempt to
provide a foundation for all other
knowledge. [49]
Among the most difficult problems
in knowledge representation are:
Default reasoning and the
qualification problem
Many of the things people
know take the form of "working
assumptions." For example, if a
bird comes up in conversation,
people typically picture an
animal that is fist sized, sings,
and flies. None of these things
are true about all birds. John
McCarthy identified this
problem in 1969 [50] as the
qualification problem: for any
commonsense rule that AI
researchers care to represent,
there tend to be a huge
number of exceptions. Almost
nothing is simply true or false
in the way that abstract logic
requires. AI research has
explored a number of solutions
to this problem. [51]