In the field of computer science, artificial
intelligence (AI), sometimes called machine
intelligence, is intelligence demonstrated
by machines, in contrast to the natural intelligence
displayed by humans and other animals. Computer
science defines AI research as the study of
"intelligent agents": any device that perceives
its environment and takes actions that maximize
its chance of successfully achieving its goals.
More specifically, Kaplan and Haenlein define
AI as “a system’s ability to correctly
interpret external data, to learn from such
data, and to use those learnings to achieve
specific goals and tasks through flexible
adaptation”. 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".The scope of AI is disputed:
as machines become increasingly capable, tasks
considered as requiring "intelligence" are
often removed from the definition, a phenomenon
known as the AI effect, leading to the quip
in Tesler's Theorem, "AI is whatever hasn't
been done yet." For instance, optical character
recognition is frequently excluded from "artificial
intelligence", having become a routine technology.
Modern machine capabilities generally classified
as AI include successfully understanding human
speech, competing at the highest level in
strategic game systems (such as chess and
Go), autonomously operating cars, and intelligent
routing in content delivery networks and military
simulations.
Borrowing from the management literature,
Kaplan and Haenlein classify artificial intelligence
into three different types of AI systems:
analytical, human-inspired, and humanized
artificial intelligence. Analytical AI has
only characteristics consistent with cognitive
intelligence generating cognitive representation
of the world and using learning based on past
experience to inform future decisions. Human-inspired
AI has elements from cognitive as well as
emotional intelligence, understanding, in
addition to cognitive elements, human emotions
and considering them in their decision making.
Humanized AI shows characteristics of all
types of competencies (i.e., cognitive, emotional,
and social intelligence), able to be self-conscious
and self-aware in interactions with others.
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.
For most of its history, AI research has been
divided into subfields that often fail to
communicate with each other. These sub-fields
are based on technical considerations, such
as particular goals (e.g. "robotics" or "machine
learning"), the use of particular tools ("logic"
or artificial neural networks), or deep philosophical
differences. Subfields have also been based
on social factors (particular institutions
or the work of particular researchers).The
traditional problems (or goals) of AI research
include reasoning, knowledge representation,
planning, learning, natural language processing,
perception and the ability to move and manipulate
objects. General intelligence is among the
field's long-term goals. Approaches include
statistical methods, computational intelligence,
and traditional symbolic AI. Many tools are
used in AI, including versions of search and
mathematical optimization, artificial neural
networks, and methods based on statistics,
probability and economics. The AI field draws
upon computer science, information engineering,
mathematics, psychology, linguistics, philosophy,
and many other fields.
The field was founded on the claim that human
intelligence "can be so precisely described
that a machine can be made to simulate it".
This raises philosophical arguments about
the nature of the mind and the ethics of creating
artificial beings endowed with human-like
intelligence which are issues that have been
explored by myth, fiction and philosophy since
antiquity. Some people also consider AI to
be a danger to humanity if it progresses unabated.
Others believe that AI, unlike previous technological
revolutions, will create a risk of mass unemployment.In
the twenty-first century, AI techniques have
experienced a resurgence following concurrent
advances in computer power, large amounts
of data, and theoretical understanding; and
AI techniques have become an essential part
of the technology industry, helping to solve
many challenging problems in computer science,
software engineering and operations research.
== History ==
Thought-capable artificial beings appeared
as storytelling devices in antiquity, and
have been common in fiction, as in Mary Shelley's
Frankenstein or Karel Čapek's R.U.R. (Rossum's
Universal Robots). These characters and their
fates raised many of the same issues now discussed
in the ethics of artificial intelligence.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 deduction.
This insight, that digital computers can simulate
any process of formal reasoning, is known
as the Church–Turing thesis. Along with
concurrent discoveries in neurobiology, information
theory and cybernetics, this led researchers
to consider the possibility of building an
electronic brain. Turing proposed that "if
a human could not distinguish between responses
from a machine and a human, the machine could
be considered "intelligent". The first work
that is now generally recognized as AI was
McCullouch and Pitts' 1943 formal design for
Turing-complete "artificial neurons".The field
of AI research was born at a workshop at Dartmouth
College in 1956. Attendees Allen Newell (CMU),
Herbert Simon (CMU), John McCarthy (MIT),
Marvin Minsky (MIT) and Arthur Samuel (IBM)
became the founders and leaders of AI research.
They and their students produced programs
that the press described as "astonishing":
computers were learning checkers strategies
(c. 1954) (and by 1959 were reportedly playing
better than the average human), solving word
problems in algebra, proving logical theorems
(Logic Theorist, first run c. 1956) and speaking
English. By the middle of the 1960s, research
in the U.S. was heavily funded by the Department
of Defense and laboratories had been established
around the world. AI's founders were optimistic
about the future: Herbert Simon predicted,
"machines will be capable, within twenty years,
of doing any work a man can do". 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 and 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 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.
In the early 1980s, AI research was revived
by the commercial success of expert systems,
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. However, beginning
with the collapse of the Lisp Machine market
in 1987, AI once again fell into disrepute,
and a second, longer-lasting hiatus began.In
the late 1990s and early 21st century, AI
began to be used for logistics, data mining,
medical diagnosis and other areas. The success
was due to increasing computational power
(see Moore's law), greater emphasis on solving
specific problems, new ties between AI and
other fields (such as statistics, economics
and mathematics), and a commitment by researchers
to mathematical methods and scientific standards.
Deep Blue became the first computer chess-playing
system to beat a reigning world chess champion,
Garry Kasparov, on 11 May 1997.In 2011, 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. 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.
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 as do intelligent personal assistants
in smartphones. In March 2016, AlphaGo won
4 out of 5 games of Go in a match with Go
champion Lee Sedol, becoming the first computer
Go-playing system to beat a professional Go
player without handicaps. In the 2017 Future
of Go Summit, AlphaGo won a three-game match
with Ke Jie, who at the time continuously
held the world No. 1 ranking for two years.
This marked the completion of a significant
milestone in the development of Artificial
Intelligence as Go is an extremely complex
game, more so than Chess.
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.
Clark also presents factual data indicating
that error rates in image processing tasks
have fallen significantly since 2011. He attributes
this to an increase in affordable neural networks,
due to a rise in cloud computing infrastructure
and to an increase in research tools and datasets.
Other cited examples include Microsoft's development
of a Skype system that can automatically translate
from one language to another and Facebook's
system that can describe images to blind people.
In a 2017 survey, one in five companies reported
they had "incorporated AI in some offerings
or processes". Around 2016, China greatly
accelerated its government funding; given
its large supply of data and its rapidly increasing
research output, some observers believe it
may be on track to becoming an "AI superpower".
== Basics ==
A typical AI perceives its environment and
takes actions that maximize its chance of
successfully achieving its goals. An AI's
intended goal function can be simple ("1 if
the AI wins a game of Go, 0 otherwise") or
complex ("Do actions mathematically similar
to the actions that got you rewards in the
past"). Goals can be explicitly defined, or
can be induced. If the AI is programmed for
"reinforcement learning", goals can be implicitly
induced by rewarding some types of behavior
and punishing others. Alternatively, an evolutionary
system can induce goals by using a "fitness
function" to mutate and preferentially replicate
high-scoring AI systems; this is similar to
how animals evolved to innately desire certain
goals such as finding food, or how dogs can
be bred via artificial selection to possess
desired traits. Some AI systems, such as nearest-neighbor,
instead reason by analogy; these systems are
not generally given goals, except to the degree
that goals are somehow implicit in their training
data. Such systems can still be benchmarked
if the non-goal system is framed as a system
whose "goal" is to successfully accomplish
its narrow classification task.AI often revolves
around the use of algorithms. An algorithm
is a set of unambiguous instructions that
a mechanical computer can execute. A complex
algorithm is often built on top of other,
simpler, algorithms. A simple example of an
algorithm is the following (optimal for first
player) recipe for play at tic-tac-toe:
If someone has a "threat" (that is, two in
a row), take the remaining square. Otherwise,
if a move "forks" to create two threats at
once, play that move. Otherwise,
take the center square if it is free. Otherwise,
if your opponent has played in a corner, take
the opposite corner. Otherwise,
take an empty corner if one exists. Otherwise,
take any empty square.Many AI algorithms are
capable of learning from data; they can enhance
themselves by learning new heuristics (strategies,
or "rules of thumb", that have worked well
in the past), or can themselves write other
algorithms. Some of the "learners" described
below, including Bayesian networks, decision
trees, and nearest-neighbor, could theoretically,
if given infinite data, time, and memory,
learn to approximate any function, including
whatever combination of mathematical functions
would best describe the entire world. These
learners could therefore, in theory, derive
all possible knowledge, by considering every
possible hypothesis and matching it against
the data. In practice, it is almost never
possible to consider every possibility, because
of the phenomenon of "combinatorial explosion",
where the amount of time needed to solve a
problem grows exponentially. Much of AI research
involves figuring out how to identify and
avoid considering broad swaths of possibilities
that are unlikely to be fruitful. For example,
when viewing a map and looking for the shortest
driving route from Denver to New York in the
East, one can in most cases skip looking at
any path through San Francisco or other areas
far to the West; thus, an AI wielding an pathfinding
algorithm like A* can avoid the combinatorial
explosion that would ensue if every possible
route had to be ponderously considered in
turn.The earliest (and easiest to understand)
approach to AI was symbolism (such as formal
logic): "If an otherwise healthy adult has
a fever, then they may have influenza". A
second, more general, approach is Bayesian
inference: "If the current patient has a fever,
adjust the probability they have influenza
in such-and-such way". The third major approach,
extremely popular in routine business AI applications,
are analogizers such as SVM and nearest-neighbor:
"After examining the records of known past
patients whose temperature, symptoms, age,
and other factors mostly match the current
patient, X% of those patients turned out to
have influenza". A fourth approach is harder
to intuitively understand, but is inspired
by how the brain's machinery works: the artificial
neural network approach uses artificial "neurons"
that can learn by comparing itself to the
desired output and altering the strengths
of the connections between its internal neurons
to "reinforce" connections that seemed to
be useful. These four main approaches can
overlap with each other and with evolutionary
systems; for example, neural nets can learn
to make inferences, to generalize, and to
make analogies. Some systems implicitly or
explicitly use multiple of these approaches,
alongside many other AI and non-AI algorithms;
the best approach is often different depending
on the problem.
Learning algorithms work on the basis that
strategies, algorithms, and inferences that
worked well in the past are likely to continue
working well in the future. These inferences
can be obvious, such as "since the sun rose
every morning for the last 10,000 days, it
will probably rise tomorrow morning as well".
They can be nuanced, such as "X% of families
have geographically separate species with
color variants, so there is an Y% chance that
undiscovered black swans exist". Learners
also work on the basis of "Occam's razor":
The simplest theory that explains the data
is the likeliest. Therefore, to be successful,
a learner must be designed such that it prefers
simpler theories to complex theories, except
in cases where the complex theory is proven
substantially better. Settling on a bad, overly
complex theory gerrymandered to fit all the
past training data is known as overfitting.
Many systems attempt to reduce overfitting
by rewarding a theory in accordance with how
well it fits the data, but penalizing the
theory in accordance with how complex the
theory is. Besides classic overfitting, learners
can also disappoint by "learning the wrong
lesson". A toy example is that an image classifier
trained only on pictures of brown horses and
black cats might conclude that all brown patches
are likely to be horses. A real-world example
is that, unlike humans, current image classifiers
don't determine the spatial relationship between
components of the picture; instead, they learn
abstract patterns of pixels that humans are
oblivious to, but that linearly correlate
with images of certain types of real objects.
Faintly superimposing such a pattern on a
legitimate image results in an "adversarial"
image that the system misclassifies.
Compared with humans, existing AI lacks several
features of human "commonsense reasoning";
most notably, humans have powerful mechanisms
for reasoning about "naïve physics" such
as space, time, and physical interactions.
This enables even young children to easily
make inferences like "If I roll this pen off
a table, it will fall on the floor". Humans
also have a powerful mechanism of "folk psychology"
that helps them to interpret natural-language
sentences such as "The city councilmen refused
the demonstrators a permit because they advocated
violence". (A generic AI has difficulty inferring
whether the councilmen or the demonstrators
are the ones alleged to be advocating violence.)
This lack of "common knowledge" means that
AI often makes different mistakes than humans
make, in ways that can seem incomprehensible.
For example, existing self-driving cars cannot
reason about the location nor the intentions
of pedestrians in the exact way that humans
do, and instead must use non-human modes of
reasoning to avoid accidents.
== Problems ==
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.
=== Reasoning, problem solving ===
Early researchers developed algorithms that
imitated step-by-step reasoning that humans
use when they solve puzzles or make logical
deductions. By the late 1980s and 1990s, AI
research had developed methods for dealing
with uncertain or incomplete information,
employing concepts from probability and economics.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. In fact,
even humans rarely use the step-by-step deduction
that early AI research was able to model.
They solve most of their problems using fast,
intuitive judgements.
=== Knowledge representation ===
Knowledge representation and knowledge engineering
are central to classical AI research. Some
"expert systems" attempt to gather together
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; situations, events, states and time;
causes and effects; knowledge about knowledge
(what we know about what other people know);
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. 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. The most general
ontologies are called upper ontologies, which
attempt to provide a foundation for all other
knowledge by acting as mediators between domain
ontologies that cover specific knowledge about
a particular knowledge domain (field of interest
or area of concern). Such formal knowledge
representations can be used in content-based
indexing and retrieval, scene interpretation,
clinical decision support, knowledge discovery
(mining "interesting" and actionable inferences
from large databases), and other areas.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 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.
The breadth of commonsense knowledge
The number of atomic facts that the average
person knows is very large. Research projects
that attempt to build a complete knowledge
base of commonsense knowledge (e.g., Cyc)
require enormous amounts of laborious ontological
engineering—they must be built, by hand,
one complicated concept at a time.
The subsymbolic form of some commonsense knowledge
Much of what people know is not represented
as "facts" or "statements" that they could
express verbally. For example, a chess master
will avoid a particular chess position because
it "feels too exposed" or an art critic can
take one look at a statue and realize that
it is a fake. These are non-conscious and
sub-symbolic intuitions or tendencies in the
human brain. Knowledge like this informs,
supports and provides a context for symbolic,
conscious knowledge. As with the related problem
of sub-symbolic reasoning, it is hoped that
situated AI, computational intelligence, or
statistical AI will provide ways to represent
this kind of knowledge.
=== Planning ===
Intelligent agents must be able to set goals
and achieve them. 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.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. 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.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.
=== Learning ===
Machine learning, a fundamental concept of
AI research since the field's inception, 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
regression, which requires a human to label
the input data first. 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. Both classifiers and
regression learners can be viewed as "function
approximators" trying to learn an unknown
(possibly implicit) function; for example,
a spam classifier can be viewed as learning
a function that maps from the text of an email
to one of two categories, "spam" or "not spam".
Computational learning theory can assess learners
by computational complexity, by sample complexity
(how much data is required), or by other notions
of optimization. In reinforcement learning
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.
=== Natural language processing ===
Natural language processing (NLP) 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
retrieval, text mining, question answering
and machine translation. Many current approaches
use word co-occurrence frequencies to construct
syntactic representations of text. "Keyword
spotting" strategies for search are popular
and scalable but dumb; a search query for
"dog" might only match documents with the
literal word "dog" and miss a document with
the word "poodle". "Lexical affinity" strategies
use the occurrence of words such as "accident"
to assess the sentiment of a document. Modern
statistical NLP approaches can combine all
these strategies as well as others, and often
achieve acceptable accuracy at the page or
paragraph level, but continue to lack the
semantic understanding required to classify
isolated sentences well. Besides the usual
difficulties with encoding semantic commonsense
knowledge, existing semantic NLP sometimes
scales too poorly to be viable in business
applications. Beyond semantic NLP, the ultimate
goal of "narrative" NLP is to embody a full
understanding of commonsense reasoning.
=== Perception ===
Machine perception is the ability to use input
from sensors (such as cameras (visible spectrum
or infrared), microphones, wireless signals,
and active lidar, sonar, radar, and tactile
sensors) to deduce aspects of the world. Applications
include speech recognition, facial recognition,
and object recognition. Computer vision is
the ability to analyze visual input. Such
input is usually ambiguous; a giant, fifty-meter-tall
pedestrian far away may produce exactly 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.
=== Motion and manipulation ===
AI is heavily used in robotics. Advanced robotic
arms and other industrial robots, widely used
in modern factories, can learn from experience
how to move efficiently despite the presence
of friction and gear slippage. A modern mobile
robot, when given a small, static, and visible
environment, can easily determine its location
and map its environment; however, dynamic
environments, such as (in endoscopy) the interior
of a patient's breathing body, pose a greater
challenge. 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
contact with an object. Moravec's paradox
generalizes that low-level sensorimotor skills
that humans take for granted are, counterintuitively,
difficult to program into a robot; the paradox
is named after Hans Moravec, who stated in
1988 that "it is comparatively easy to make
computers exhibit adult level performance
on intelligence tests or playing checkers,
and difficult or impossible to give them the
skills of a one-year-old when it comes to
perception and mobility". This is attributed
to the fact that, unlike checkers, physical
dexterity has been a direct target of natural
selection for millions of years.
=== Social intelligence ===
Moravec's paradox can be extended to many
forms of social intelligence. Distributed
multi-agent coordination of autonomous vehicles
remains a difficult problem. Affective computing
is an interdisciplinary umbrella that comprises
systems which recognize, interpret, process,
or simulate human affects. Moderate successes
related to affective computing include textual
sentiment analysis and, more recently, multimodal
affect analysis (see multimodal sentiment
analysis), wherein AI classifies the affects
displayed by a videotaped subject.In the long
run, social skills and an understanding of
human emotion and game theory would be valuable
to a social agent. Being able to predict the
actions of others by understanding their motives
and emotional states would allow an agent
to make better decisions. Some computer systems
mimic human emotion and expressions to appear
more sensitive to the emotional dynamics of
human interaction, or to otherwise facilitate
human–computer interaction. Similarly, some
virtual assistants are programmed to speak
conversationally or even to banter humorously;
this tends to give naïve users an unrealistic
conception of how intelligent existing computer
agents actually are.
=== General intelligence ===
Historically, projects such as the Cyc knowledge
base (1984–) and the massive Japanese Fifth
Generation Computer Systems initiative (1982–1992)
attempted to cover the breadth of human cognition.
These early projects failed to escape the
limitations of non-quantitative symbolic logic
models and, in retrospect, greatly underestimated
the difficulty of cross-domain AI. Nowadays,
the vast majority of current AI researchers
work instead on tractable "narrow AI" applications
(such as medical diagnosis or automobile navigation).
Many researchers predict that such "narrow
AI" work in different individual domains will
eventually be incorporated into a machine
with artificial general intelligence (AGI),
combining most of the narrow skills mentioned
in this article and at some point even exceeding
human ability in most or all these areas.
Many advances have general, cross-domain significance.
One high-profile example is that DeepMind
in the 2010s developed a "generalized artificial
intelligence" that could learn many diverse
Atari games on its own, and later developed
a variant of the system which succeeds at
sequential learning. Besides transfer learning,
hypothetical AGI breakthroughs could include
the development of reflective architectures
that can engage in decision-theoretic metareasoning,
and figuring out how to "slurp up" a comprehensive
knowledge base from the entire unstructured
Web. Some argue that some kind of (currently-undiscovered)
conceptually straightforward, but mathematically
difficult, "Master Algorithm" could lead to
AGI. Finally, a few "emergent" approaches
look to simulating human intelligence extremely
closely, and believe that anthropomorphic
features like an artificial brain or simulated
child development may someday reach a critical
point where general intelligence emerges.Many
of the problems in this article may also require
general intelligence, if machines are to solve
the problems as well as people do. For example,
even specific straightforward tasks, like
machine translation, require that a machine
read and write in both languages (NLP), follow
the author's argument (reason), know what
is being talked about (knowledge), and faithfully
reproduce the author's original intent (social
intelligence). A problem like machine translation
is considered "AI-complete", because all of
these problems need to be solved simultaneously
in order to reach human-level machine performance.
== Approaches ==
There is no established unifying theory or
paradigm that guides AI research. Researchers
disagree about many issues. A few of the most
long standing questions that have remained
unanswered are these: should artificial intelligence
simulate natural intelligence by studying
psychology or neurobiology? Or is human biology
as irrelevant to AI research as bird biology
is to aeronautical engineering?
Can intelligent behavior be described using
simple, elegant principles (such as logic
or optimization)? Or does it necessarily require
solving a large number of completely unrelated
problems?
=== Cybernetics and brain simulation ===
In the 1940s and 1950s, a number of researchers
explored the connection between neurobiology,
information theory, and cybernetics. Some
of them built machines that used electronic
networks to exhibit rudimentary intelligence,
such as W. Grey Walter's turtles and the Johns
Hopkins Beast. Many of these researchers gathered
for meetings of the Teleological Society at
Princeton University and the Ratio Club in
England. By 1960, this approach was largely
abandoned, although elements of it would be
revived in the 1980s.
=== Symbolic ===
When access to digital computers became possible
in the middle 1950s, 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 University, Stanford and MIT,
and 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". During the 1960s,
symbolic approaches had achieved great success
at simulating high-level thinking in small
demonstration programs. Approaches based on
cybernetics or artificial neural networks
were abandoned or pushed into the background.
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.
==== Cognitive simulation ====
Economist Herbert Simon and Allen Newell studied
human problem-solving skills and attempted
to formalize them, and their work laid the
foundations of the field of artificial intelligence,
as well as cognitive science, operations research
and management science. Their research team
used the results of psychological experiments
to develop programs that simulated the techniques
that people used to solve problems. This tradition,
centered at Carnegie Mellon University would
eventually culminate in the development of
the Soar architecture in the middle 1980s.
==== Logic-based ====
Unlike Simon and Newell, John McCarthy felt
that machines did not need to simulate human
thought, but should instead try to find the
essence of abstract reasoning and problem-solving,
regardless of whether people used the same
algorithms. His laboratory at Stanford (SAIL)
focused on using formal logic to solve a wide
variety of problems, including knowledge representation,
planning and learning. Logic was also the
focus of the work at the University of Edinburgh
and elsewhere in Europe which led to the development
of the programming language Prolog and the
science of logic programming.
==== Anti-logic or scruffy ====
Researchers at MIT (such as Marvin Minsky
and Seymour Papert) found that solving difficult
problems in vision and natural language processing
required ad-hoc solutions—they argued that
there was no simple and general principle
(like logic) that would capture all the aspects
of intelligent behavior. Roger Schank described
their "anti-logic" approaches as "scruffy"
(as opposed to the "neat" paradigms at CMU
and Stanford). Commonsense knowledge bases
(such as Doug Lenat's Cyc) are an example
of "scruffy" AI, since they must be built
by hand, one complicated concept at a time.
==== Knowledge-based ====
When computers with large memories became
available around 1970, researchers from all
three traditions began to build knowledge
into AI applications. This "knowledge revolution"
led to the development and deployment of expert
systems (introduced by Edward Feigenbaum),
the first truly successful form of AI software.
A key component of the system architecture
for all expert systems is the knowledge base,
which stores facts and rules that illustrate
AI. The knowledge revolution was also driven
by the realization that enormous amounts of
knowledge would be required by many simple
AI applications.
=== Sub-symbolic ===
By the 1980s, 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 perception,
robotics, learning and pattern recognition.
A number of researchers began to look into
"sub-symbolic" approaches to specific AI problems.
Sub-symbolic methods manage to approach intelligence
without specific representations of knowledge.
==== Embodied intelligence ====
This includes embodied, situated, behavior-based,
and nouvelle AI. Researchers from the related
field of robotics, such as Rodney Brooks,
rejected symbolic AI and focused on the basic
engineering problems that would allow robots
to move and survive. Their work revived the
non-symbolic viewpoint of the early cybernetics
researchers of the 1950s and reintroduced
the use of control theory in AI. 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.
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.).
==== Computational intelligence and soft computing
====
Interest in neural networks and "connectionism"
was revived by David Rumelhart and others
in the middle of the 1980s. Artificial neural
networks are an example of soft computing—they
are solutions to problems which cannot be
solved with complete logical certainty, and
where an approximate solution is often sufficient.
Other soft computing approaches to AI include
fuzzy systems, evolutionary computation and
many statistical tools. The application of
soft computing to AI is studied collectively
by the emerging discipline of computational
intelligence.
=== Statistical learning ===
Much of traditional GOFAI got bogged down
on ad hoc patches to symbolic computation
that worked on their own toy models but failed
to generalize to real-world results. However,
around the 1990s, AI researchers adopted sophisticated
mathematical tools, such as hidden Markov
models (HMM), information theory, and 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 mathematics, economics or operations
research). Compared with GOFAI, new "statistical
learning" techniques such as HMM and neural
networks were gaining higher levels of accuracy
in many practical domains such as data mining,
without necessarily acquiring semantic understanding
of the datasets. 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.
Different statistical learning techniques
have different limitations; for example, basic
HMM cannot model the infinite possible combinations
of natural language. Critics note that the
shift from GOFAI to statistical learning is
often also a shift away from Explainable AI.
In AGI research, some scholars caution against
over-reliance on statistical learning, and
argue that continuing research into GOFAI
will still be necessary to attain general
intelligence.
=== Integrating the approaches ===
Intelligent agent paradigm
An intelligent agent is a system that perceives
its environment and takes actions which maximize
its chances of success. The simplest intelligent
agents are programs that solve specific problems.
More complicated agents include human beings
and organizations of human beings (such as
firms). The paradigm allows researchers to
directly compare or even combine different
approaches to isolated problems, by asking
which agent is best at maximizing a given
"goal function". An agent that solves a specific
problem can use any approach that works—some
agents are symbolic and logical, some are
sub-symbolic artificial neural networks and
others may use new approaches. The paradigm
also gives researchers a common language to
communicate with other fields—such as decision
theory and economics—that also use concepts
of abstract agents. Building a complete agent
requires researchers to address realistic
problems of integration; for example, because
sensory systems give uncertain information
about the environment, planning systems must
be able to function in the presence of uncertainty.
The intelligent agent paradigm became widely
accepted during the 1990s.Agent architectures
and cognitive architectures
Researchers have designed systems to build
intelligent systems out of interacting intelligent
agents in a multi-agent system. A hierarchical
control system provides a bridge between sub-symbolic
AI at its lowest, reactive levels and traditional
symbolic AI at its highest levels, where relaxed
time constraints permit planning and world
modelling. Some cognitive architectures are
custom-built to solve a narrow problem; others,
such as Soar, are designed to mimic human
cognition and to provide insight into general
intelligence. Modern extensions of Soar are
hybrid intelligent systems that include both
symbolic and sub-symbolic components.
== Tools ==
AI has developed a large number of tools to
solve the most difficult problems in computer
science. A few of the most general of these
methods are discussed below.
=== Search and optimization ===
Many problems in AI can be solved in theory
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 search through trees of goals and
subgoals, attempting to find a path to a target
goal, a process called means-ends analysis.
Robotics algorithms for moving limbs and grasping
objects use local searches in configuration
space. Many learning algorithms use search
algorithms based on optimization.
Simple exhaustive searches 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 that are more likely to
reach a goal and to do so in a shorter number
of steps. In some search methodologies heuristics
can also serve to entirely eliminate some
choices that are unlikely to lead to a goal
(called "pruning the search tree"). Heuristics
supply the program with a "best guess" for
the path on which the solution lies. Heuristics
limit the search for solutions into a smaller
sample size.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 hill climbing:
we begin the search at a random point on the
landscape, and then, by jumps or steps, we
keep moving our guess uphill, until we reach
the top. Other optimization algorithms are
simulated annealing, beam search and random
optimization.
Evolutionary computation uses a form of optimization
search. For example, they may begin with a
population of organisms (the guesses) and
then allow them to mutate and recombine, selecting
only the fittest to survive each generation
(refining the guesses). Classic evolutionary
algorithms include genetic algorithms, gene
expression programming, and genetic programming.
Alternatively, distributed search processes
can coordinate via swarm intelligence algorithms.
Two popular swarm algorithms used in search
are particle swarm optimization (inspired
by bird flocking) and ant colony optimization
(inspired by ant trails).
=== Logic ===
Logic 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
and inductive logic programming is a method
for learning.Several different forms of logic
are used in AI research. Propositional logic
involves truth functions such as "or" and
"not". First-order logic adds quantifiers
and predicates, and can express facts about
objects, their properties, and their relations
with each other. Fuzzy set theory 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 false.
Fuzzy logic is successfully used in control
systems to allow experts to contribute vague
rules such as "if you are close to the destination
station and moving fast, increase the train's
brake pressure"; these vague rules can then
be numerically refined within the system.
Fuzzy logic fails to scale well in knowledge
bases; many AI researchers question the validity
of chaining fuzzy-logic inferences.Default
logics, non-monotonic logics and circumscription
are forms of logic designed to help with default
reasoning and the qualification problem. Several
extensions of logic have been designed to
handle specific domains of knowledge, such
as: description logics; situation calculus,
event calculus and fluent calculus (for representing
events and time); causal calculus; belief
calculus; and modal logics.Overall, qualitiative
symbolic logic is brittle and scales poorly
in the presence of noise or other uncertainty.
Exceptions to rules are numerous, and it is
difficult for logical systems to function
in the presence of contradictory rules.
=== Probabilistic methods for uncertain reasoning
===
Many problems in AI (in reasoning, planning,
learning, perception, and robotics) require
the agent to operate with incomplete or uncertain
information. AI researchers have devised a
number of powerful tools to solve these problems
using methods from probability theory and
economics.Bayesian networks are a very general
tool that can be used for a large number of
problems: reasoning (using the Bayesian inference
algorithm), learning (using the expectation-maximization
algorithm), planning (using decision networks)
and perception (using dynamic Bayesian networks).
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). Compared with symbolic
logic, formal Bayesian inference is computationally
expensive. For inference to be tractable,
most observations must be conditionally independent
of one another. Complicated graphs with diamonds
or other "loops" (undirected cycles) can require
a sophisticated method such as Markov Chain
Monte Carlo, which spreads an ensemble of
random walkers throughout the Bayesian network
and attempts to converge to an assessment
of the conditional probabilities. Bayesian
networks are used on Xbox Live to rate and
match players; wins and losses are "evidence"
of how good a player is. AdSense uses a Bayesian
network with over 300 million edges to learn
which ads to serve.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, and information value theory. These
tools include models such as Markov decision
processes, dynamic decision networks, game
theory and mechanism design.
=== Classifiers and statistical learning methods
===
The simplest AI applications can be divided
into two types: classifiers ("if shiny then
diamond") and controllers ("if shiny 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
use pattern matching to determine a closest
match. They can be tuned according to examples,
making them very attractive for use in AI.
These examples are known as observations or
patterns. In supervised learning, each pattern
belongs to a certain predefined class. A class
can be seen as a decision that has to be made.
All the observations combined with their class
labels are known as a data set. When a new
observation is received, that observation
is classified based on previous experience.A
classifier can be trained in various ways;
there are many statistical and machine learning
approaches. The decision tree is perhaps the
most widely used machine learning algorithm.
Other widely used classifiers are the neural
network,k-nearest neighbor algorithm,kernel
methods such as the support vector machine
(SVM),Gaussian mixture model, and the extremely
popular naive Bayes classifier. Classifier
performance depends greatly on the characteristics
of the data to be classified, such as the
dataset size, distribution of samples across
classes, the dimensionality, and the level
of noise. Model-based classifiers perform
well if the assumed model is an extremely
good fit for the actual data. Otherwise, if
no matching model is available, and if accuracy
(rather than speed or scalability) is the
sole concern, conventional wisdom is that
discriminative classifiers (especially SVM)
tend to be more accurate than model-based
classifiers such as "naive Bayes" on most
practical data sets.
=== Artificial neural networks ===
Neural networks, or neural nets, were inspired
by the architecture of neurons in the human
brain. A simple "neuron" N accepts input from
multiple other neurons, each of which, when
activated (or "fired"), cast a weighted "vote"
for or against whether neuron N should itself
activate. Learning requires an algorithm to
adjust these weights based on the training
data; one simple algorithm (dubbed "fire together,
wire together") is to increase the weight
between two connected neurons when the activation
of one triggers the successful activation
of another. The net forms "concepts" that
are distributed among a subnetwork of shared
neurons that tend to fire together; a concept
meaning "leg" might be coupled with a subnetwork
meaning "foot" that includes the sound for
"foot". Neurons have a continuous spectrum
of activation; in addition, neurons can process
inputs in a nonlinear way rather than weighing
straightforward votes. Modern neural nets
can learn both continuous functions and, surprisingly,
digital logical operations. Neural networks'
early successes included predicting the stock
market and (in 1995) a mostly self-driving
car. In the 2010s, advances in neural networks
using deep learning thrust AI into widespread
public consciousness and contributed to an
enormous upshift in corporate AI spending;
for example, AI-related M&A in 2017 was over
25 times as large as in 2015.The study of
non-learning artificial neural networks began
in the decade before the field of AI research
was founded, in the work of Walter Pitts and
Warren McCullouch. Frank Rosenblatt invented
the perceptron, a learning network with a
single layer, similar to the old concept of
linear regression. Early pioneers also include
Alexey Grigorevich Ivakhnenko, Teuvo Kohonen,
Stephen Grossberg, Kunihiko Fukushima, Christoph
von der Malsburg, David Willshaw, Shun-Ichi
Amari, Bernard Widrow, John Hopfield, Eduardo
R. Caianiello, and others.
The main categories of networks are acyclic
or feedforward neural networks (where the
signal passes in only one direction) and recurrent
neural networks (which allow feedback and
short-term memories of previous input events).
Among the most popular feedforward networks
are perceptrons, multi-layer perceptrons and
radial basis networks. Neural networks can
be applied to the problem of intelligent control
(for robotics) or learning, using such techniques
as Hebbian learning ("fire together, wire
together"), GMDH or competitive learning.Today,
neural networks are often trained by the backpropagation
algorithm, which had been around since 1970
as the reverse mode of automatic differentiation
published by Seppo Linnainmaa, and was introduced
to neural networks by Paul Werbos.Hierarchical
temporal memory is an approach that models
some of the structural and algorithmic properties
of the neocortex.To summarize, most neural
networks use some form of gradient descent
on a hand-created neural topology. However,
some research groups, such as Uber, argue
that simple neuroevolution to mutate new neural
network topologies and weights may be competitive
with sophisticated gradient descent approaches.
One advantage of neuroevolution is that it
may be less prone to get caught in "dead ends".
==== Deep feedforward neural networks ====
Deep learning is any artificial neural network
that can learn a long chain of causal links.
For example, a feedforward network with six
hidden layers can learn a seven-link causal
chain (six hidden layers + output layer) and
has a "credit assignment path" (CAP) depth
of seven. Many deep learning systems need
to be able to learn chains ten or more causal
links in length. Deep learning has transformed
many important subfields of artificial intelligence,
including computer vision, speech recognition,
natural language processing and others.According
to one overview, the expression "Deep Learning"
was introduced to the Machine Learning community
by Rina Dechter in 1986 and gained traction
after
Igor Aizenberg and colleagues introduced it
to Artificial Neural Networks in 2000. The
first functional Deep Learning networks were
published by Alexey Grigorevich Ivakhnenko
and V. G. Lapa in 1965. These networks are
trained one layer at a time. Ivakhnenko's
1971 paper describes the learning of a deep
feedforward multilayer perceptron with eight
layers, already much deeper than many later
networks. In 2006, a publication by Geoffrey
Hinton and Ruslan Salakhutdinov introduced
another way of pre-training many-layered feedforward
neural networks (FNNs) one layer at a time,
treating each layer in turn as an unsupervised
restricted Boltzmann machine, then using supervised
backpropagation for fine-tuning. Similar to
shallow artificial neural networks, deep neural
networks can model complex non-linear relationships.
Over the last few years, advances in both
machine learning algorithms and computer hardware
have led to more efficient methods for training
deep neural networks that contain many layers
of non-linear hidden units and a very large
output layer.Deep learning often uses convolutional
neural networks (CNNs), whose origins can
be traced back to the Neocognitron introduced
by Kunihiko Fukushima in 1980. In 1989, Yann
LeCun and colleagues applied backpropagation
to such an architecture. In the early 2000s,
in an industrial application CNNs already
processed an estimated 10% to 20% of all the
checks written in the US.
Since 2011, fast implementations of CNNs on
GPUs have
won many visual pattern recognition competitions.CNNs
with 12 convolutional layers were used in
conjunction with reinforcement learning by
Deepmind's "AlphaGo Lee", the program that
beat a top Go champion in 2016.
==== Deep recurrent neural networks ====
Early on, deep learning was also applied to
sequence learning with recurrent neural networks
(RNNs) which are in theory Turing complete
and can run arbitrary programs to process
arbitrary sequences of inputs. The depth of
an RNN is unlimited and depends on the length
of its input sequence; thus, an RNN is an
example of deep learning. RNNs can be trained
by gradient descent but suffer from the vanishing
gradient problem. In 1992, it was shown that
unsupervised pre-training of a stack of recurrent
neural networks can speed up subsequent supervised
learning of deep sequential problems.Numerous
researchers now use variants of a deep learning
recurrent NN called the long short-term memory
(LSTM) network published by Hochreiter & Schmidhuber
in 1997. LSTM is often trained by Connectionist
Temporal Classification (CTC). At Google,
Microsoft and Baidu this approach has revolutionised
speech recognition. For example, in 2015,
Google's speech recognition experienced a
dramatic performance jump of 49% through CTC-trained
LSTM, which is now available through Google
Voice to billions of smartphone users. Google
also used LSTM to improve machine translation,
Language Modeling and Multilingual Language
Processing. LSTM combined with CNNs also improved
automatic image captioning and a plethora
of other applications.
=== Evaluating progress ===
AI, like electricity or the steam engine,
is a general purpose technology. There is
no consensus on how to characterize which
tasks AI tends to excel at. While projects
such as AlphaZero have succeeded in generating
their own knowledge from scratch, many other
machine learning projects require large training
datasets. Researcher Andrew Ng has suggested,
as a "highly imperfect rule of thumb", that
"almost anything a typical human can do with
less than one second of mental thought, we
can probably now or in the near future automate
using AI." Moravec's paradox suggests that
AI lags humans at many tasks that the human
brain has specifically evolved to perform
well.Games provide a well-publicized benchmark
for assessing rates of progress. AlphaGo around
2016 brought the era of classical board-game
benchmarks to a close. Games of imperfect
knowledge provide new challenges to AI in
the area of game theory. E-sports such as
StarCraft continue to provide additional public
benchmarks. There are many competitions and
prizes, such as the Imagenet Challenge, to
promote research in artificial intelligence.
The most common areas of competition include
general machine intelligence, conversational
behavior, data-mining, robotic cars, and robot
soccer as well as conventional games.The "imitation
game" (an interpretation of the 1950 Turing
test that assesses whether a computer can
imitate a human) is nowadays considered too
exploitable to be a meaningful benchmark.
A derivative of the Turing test is the Completely
Automated Public Turing test to tell Computers
and Humans Apart (CAPTCHA). As the name implies,
this helps to determine that a user is an
actual person and not a computer posing as
a human. In contrast to the standard Turing
test, CAPTCHA is administered by a machine
and targeted to a human as opposed to being
administered by a human and targeted to a
machine. A computer asks a user to complete
a simple test then generates a grade for that
test. Computers are unable to solve the problem,
so correct solutions are deemed to be the
result of a person taking the test. A common
type of CAPTCHA is the test that requires
the typing of distorted letters, numbers or
symbols that appear in an image undecipherable
by a computer.Proposed "universal intelligence"
tests aim to compare how well machines, humans,
and even non-human animals perform on problem
sets that are generic as possible. At an extreme,
the test suite can contain every possible
problem, weighted by Kolmogorov complexity;
unfortunately, these problem sets tend to
be dominated by impoverished pattern-matching
exercises where a tuned AI can easily exceed
human performance levels.
== Applications ==
AI is relevant to any intellectual task. Modern
artificial intelligence techniques are pervasive
and are too numerous to list here. Frequently,
when a technique reaches mainstream use, it
is no longer considered artificial intelligence;
this phenomenon is described as the AI effect.High-profile
examples of AI include autonomous vehicles
(such as drones and self-driving cars), medical
diagnosis, creating art (such as poetry),
proving mathematical theorems, playing games
(such as Chess or Go), search engines (such
as Google search), online assistants (such
as Siri), image recognition in photographs,
spam filtering, predicting flight delays,
prediction of judicial decisions and targeting
online advertisements.With social media sites
overtaking TV as a source for news for young
people and news organisations increasingly
reliant on social media platforms for generating
distribution, major publishers now use artificial
intelligence (AI) technology to post stories
more effectively and generate higher volumes
of traffic.
=== Healthcare ===
AI is being applied to the high cost problem
of dosage issues—where findings suggested
that AI could save $16 billion. In 2016, a
ground breaking study in California found
that a mathematical formula developed with
the help of AI correctly determined the accurate
dose of immunosuppressant drugs to give to
organ patients.
Artificial intelligence is breaking into the
healthcare industry by assisting doctors.
According to Bloomberg Technology, Microsoft
has developed AI to help doctors find the
right treatments for cancer. There is a great
amount of research and drugs developed relating
to cancer. In detail, there are more than
800 medicines and vaccines to treat cancer.
This negatively affects the doctors, because
there are too many options to choose from,
making it more difficult to choose the right
drugs for the patients. Microsoft is working
on a project to develop a machine called "Hanover".
Its goal is to memorize all the papers necessary
to cancer and help predict which combinations
of drugs will be most effective for each patient.
One project that is being worked on at the
moment is fighting myeloid leukemia, a fatal
cancer where the treatment has not improved
in decades. Another study was reported to
have found that artificial intelligence was
as good as trained doctors in identifying
skin cancers. Another study is using artificial
intelligence to try and monitor multiple high-risk
patients, and this is done by asking each
patient numerous questions based on data acquired
from live doctor to patient interactions.
One study was done with transfer learning,
the machine performed a diagnosis similarly
to a well-trained ophthalmologist, and could
generate a decision within 30 seconds on whether
or not the patient should be referred for
treatment, with more than 95% percent accuracy.According
to CNN, a recent study by surgeons at the
Children's National Medical Center in Washington
successfully demonstrated surgery with an
autonomous robot. The team supervised the
robot while it performed soft-tissue surgery,
stitching together a pig's bowel during open
surgery, and doing so better than a human
surgeon, the team claimed. IBM has created
its own artificial intelligence computer,
the IBM Watson, which has beaten human intelligence
(at some levels). Watson not only won at the
game show Jeopardy! against former champions,
but was declared a hero after successfully
diagnosing a woman who was suffering from
leukemia.
=== Automotive ===
Advancements in AI have contributed to the
growth of the automotive industry through
the creation and evolution of self-driving
vehicles. As of 2016, there are over 30 companies
utilizing AI into the creation of driverless
cars. A few companies involved with AI include
Tesla, Google, and Apple.Many components contribute
to the functioning of self-driving cars. These
vehicles incorporate systems such as braking,
lane changing, collision prevention, navigation
and mapping. Together, these systems, as well
as high performance computers, are integrated
into one complex vehicle.Recent developments
in autonomous automobiles have made the innovation
of self-driving trucks possible, though they
are still in the testing phase. The UK government
has passed legislation to begin testing of
self-driving truck platoons in 2018. Self-driving
truck platoons are a fleet of self-driving
trucks following the lead of one non-self-driving
truck, so the truck platoons aren't entirely
autonomous yet. Meanwhile, the Daimler, a
German automobile corporation, is testing
the Freightliner Inspiration which is a semi-autonomous
truck that will only be used on the highway.One
main factor that influences the ability for
a driver-less automobile to function is mapping.
In general, the vehicle would be pre-programmed
with a map of the area being driven. This
map would include data on the approximations
of street light and curb heights in order
for the vehicle to be aware of its surroundings.
However, Google has been working on an algorithm
with the purpose of eliminating the need for
pre-programmed maps and instead, creating
a device that would be able to adjust to a
variety of new surroundings. Some self-driving
cars are not equipped with steering wheels
or brake pedals, so there has also been research
focused on creating an algorithm that is capable
of maintaining a safe environment for the
passengers in the vehicle through awareness
of speed and driving conditions.Another factor
that is influencing the ability for a driver-less
automobile is the safety of the passenger.
To make a driver-less automobile, engineers
must program it to handle high-risk situations.
These situations could include a head-on collision
with pedestrians. The car's main goal should
be to make a decision that would avoid hitting
the pedestrians and saving the passengers
in the car. But there is a possibility the
car would need to make a decision that would
put someone in danger. In other words, the
car would need to decide to save the pedestrians
or the passengers. The programming of the
car in these situations is crucial to a successful
driver-less automobile.
=== Finance and economics ===
Financial institutions have long used artificial
neural network systems to detect charges or
claims outside of the norm, flagging these
for human investigation. The use of AI in
banking can be traced back to 1987 when Security
Pacific National Bank in US set-up a Fraud
Prevention Task force to counter the unauthorised
use of debit cards. Programs like Kasisto
and Moneystream are using AI in financial
services.
Banks use artificial intelligence systems
today to organize operations, maintain book-keeping,
invest in stocks, and manage properties. AI
can react to changes overnight or when business
is not taking place. In August 2001, robots
beat humans in a simulated financial trading
competition. AI has also reduced fraud and
financial crimes by monitoring behavioral
patterns of users for any abnormal changes
or anomalies.The use of AI machines in the
market in applications such as online trading
and decision making has changed major economic
theories. For example, AI based buying and
selling platforms have changed the law of
supply and demand in that it is now possible
to easily estimate individualized demand and
supply curves and thus individualized pricing.
Furthermore, AI machines reduce information
asymmetry in the market and thus making markets
more efficient while reducing the volume of
trades. Furthermore, AI in the markets limits
the consequences of behavior in the markets
again making markets more efficient. Other
theories where AI has had impact include in
rational choice, rational expectations, game
theory, Lewis turning point, portfolio optimization
and counterfactual thinking.
=== Government ===
=== Video games ===
In video games, artificial intelligence is
routinely used to generate dynamic purposeful
behavior in non-player characters (NPCs).
In addition, well-understood AI techniques
are routinely used for pathfinding. Some researchers
consider NPC AI in games to be a "solved problem"
for most production tasks. Games with more
atypical AI include the AI director of Left
4 Dead (2008) and the neuroevolutionary training
of platoons in Supreme Commander 2 (2010).
=== Military ===
Worldwide annual military spending on robotics
rose from US$5.1 billion in 2010 to US$7.5
billion in 2015. Military drones capable of
autonomous action are widely considered a
useful asset. Many artificial intelligence
researchers seek to distance themselves from
military applications of AI.
=== Audit ===
For financial statements audit, AI makes continuous
audit possible. AI tools could analyze many
sets of different information immediately.
The potential benefit would be the overall
audit risk will be reduced, the level of assurance
will be increased and the time duration of
audit will be reduced.
=== Advertising ===
It is possible to use AI to predict or generalize
the behavior of customers from their digital
footprints in order to target them with personalized
promotions or build customer personas automatically.
A documented case reports that online gambling
companies were using AI to improve customer
targeting.Moreover, the application of Personality
computing AI models can help reducing the
cost of advertising campaigns by adding psychological
targeting to more traditional sociodemographic
or behavioral targeting.
=== Art ===
Artificial Intelligence has inspired numerous
creative applications including its usage
to produce visual art. The exhibition "Thinking
Machines: Art and Design in the Computer Age,
1959–1989" at MoMA provides a good overview
of the historical applications of AI for art,
architecture, and design. Recent exhibitions
showcasing the usage of AI to produce art
include the Google-sponsored benefit and auction
at the Gray Area Foundation in San Francisco,
where artists experimented with the deepdream
algorithm and the exhibition "Unhuman: Art
in the Age of AI," which took place in Los
Angeles and Frankfurt in the fall of 2017.
In the spring of 2018, the Association of
Computing Machinery dedicated a special magazine
issue to the subject of computers and art
highlighting the role of machine learning
in the arts.
== Philosophy and ethics ==
There are three philosophical questions related
to AI:
Is artificial general intelligence possible?
Can a machine solve any problem that a human
being can solve using intelligence? Or are
there hard limits to what a machine can accomplish?
Are intelligent machines dangerous? How can
we ensure that machines behave ethically and
that they are used ethically?
Can a machine have a mind, consciousness and
mental states in exactly the same sense that
human beings do? Can a machine be sentient,
and thus deserve certain rights? Can a machine
intentionally cause harm?
=== The limits of artificial general intelligence
===
Can a machine be intelligent? Can it "think"?
Alan Turing's "polite convention"
We need not decide if a machine can "think";
we need only decide if a machine can act as
intelligently as a human being. This approach
to the philosophical problems associated with
artificial intelligence forms the basis of
the Turing test.The Dartmouth proposal
"Every aspect of learning or any other feature
of intelligence can be so precisely described
that a machine can be made to simulate it."
This conjecture was printed in the proposal
for the Dartmouth Conference of 1956, and
represents the position of most working AI
researchers.Newell and Simon's physical symbol
system hypothesis
"A physical symbol system has the necessary
and sufficient means of general intelligent
action." Newell and Simon argue that intelligence
consists of formal operations on symbols.
Hubert Dreyfus argued that, on the contrary,
human expertise depends on unconscious instinct
rather than conscious symbol manipulation
and on having a "feel" for the situation rather
than explicit symbolic knowledge. (See Dreyfus'
critique of AI.)Gödelian arguments
Gödel himself, John Lucas (in 1961) and Roger
Penrose (in a more detailed argument from
1989 onwards) made highly technical arguments
that human mathematicians can consistently
see the truth of their own "Gödel statements"
and therefore have computational abilities
beyond that of mechanical Turing machines.
However, the modern consensus in the scientific
and mathematical community is that these "Gödelian
arguments" fail.The artificial brain argument
The brain can be simulated by machines and
because brains are intelligent, simulated
brains must also be intelligent; thus machines
can be intelligent. Hans Moravec, Ray Kurzweil
and others have argued that it is technologically
feasible to copy the brain directly into hardware
and software and that such a simulation will
be essentially identical to the original.The
AI effect
Machines are already intelligent, but observers
have failed to recognize it. When Deep Blue
beat Garry Kasparov in chess, the machine
was acting intelligently. However, onlookers
commonly discount the behavior of an artificial
intelligence program by arguing that it is
not "real" intelligence after all; thus "real"
intelligence is whatever intelligent behavior
people can do that machines still cannot.
This is known as the AI Effect: "AI is whatever
hasn't been done yet."
=== 
Potential harm ===
Widespread use of artificial intelligence
could have unintended consequences that are
dangerous or undesirable. Scientists from
the Future of Life Institute, among others,
described some short-term research goals to
see how AI influences the economy, the laws
and ethics that are involved with AI and how
to minimize AI security risks. In the long-term,
the scientists have proposed to continue optimizing
function while minimizing possible security
risks that come along with new technologies.
==== Existential risk ====
Physicist Stephen Hawking, Microsoft founder
Bill Gates, and SpaceX founder Elon Musk have
expressed concerns about the possibility that
AI could evolve to the point that humans could
not control it, with Hawking theorizing that
this could "spell the end of the human race".
The development of full artificial intelligence
could spell the end of the human race. Once
humans develop artificial intelligence, it
will take off on its own and redesign itself
at an ever-increasing rate. Humans, who are
limited by slow biological evolution, couldn't
compete and would be superseded.
In his book Superintelligence, Nick Bostrom
provides an argument that artificial intelligence
will pose a threat to mankind. He argues that
sufficiently intelligent AI, if it chooses
actions based on achieving some goal, will
exhibit convergent behavior such as acquiring
resources or protecting itself from being
shut down. If this AI's goals do not reflect
humanity's—one example is an AI told to
compute as many digits of pi as possible—it
might harm humanity in order to acquire more
resources or prevent itself from being shut
down, ultimately to better achieve its goal.
Concern over risk from artificial intelligence
has led to some high-profile donations and
investments. A group of prominent tech titans
including Peter Thiel, Amazon Web Services
and Musk have committed $1billion to OpenAI,
a nonprofit company aimed at championing responsible
AI development. The opinion of experts within
the field of artificial intelligence is mixed,
with sizable fractions both concerned and
unconcerned by risk from eventual superhumanly-capable
AI. In January 2015, Elon Musk donated ten
million dollars to the Future of Life Institute
to fund research on understanding AI decision
making. The goal of the institute is to "grow
wisdom with which we manage" the growing power
of technology. Musk also funds companies developing
artificial intelligence such as Google DeepMind
and Vicarious to "just keep an eye on what's
going on with artificial intelligence. I think
there is potentially a dangerous outcome there."For
this danger to be realized, the hypothetical
AI would have to overpower or out-think all
of humanity, which a minority of experts argue
is a possibility far enough in the future
to not be worth researching. Other counterarguments
revolve around humans being either intrinsically
or convergently valuable from the perspective
of an artificial intelligence.
==== Devaluation of humanity ====
Joseph Weizenbaum wrote that AI applications
cannot, by definition, successfully simulate
genuine human empathy and that the use of
AI technology in fields such as customer service
or psychotherapy was deeply misguided. Weizenbaum
was also bothered that AI researchers (and
some philosophers) were willing to view the
human mind as nothing more than a computer
program (a position is now known as computationalism).
To Weizenbaum these points suggest that AI
research devalues human life.
==== Social justice ====
One concern is that AI programs may be programmed
to be biased against certain groups, such
as women and minorities, because most of the
developers are wealthy Caucasian men. Support
for artificial intelligence is higher among
men (with 47% approving) than women (35% approving).
==== Decrease in demand for human labor ====
The relationship between automation and employment
is complicated. While automation eliminates
old jobs, it also creates new jobs through
micro-economic and macro-economic effects.
Unlike previous waves of automation, many
middle-class jobs may be eliminated by artificial
intelligence; The Economist states that "the
worry that AI could do to white-collar jobs
what steam power did to blue-collar ones during
the Industrial Revolution" is "worth taking
seriously". Subjective estimates of the risk
vary widely; for example, Michael Osborne
and Carl Benedikt Frey estimate 47% of U.S.
jobs are at "high risk" of potential automation,
while an OECD report classifies only 9% of
U.S. jobs as "high risk". Jobs at extreme
risk range from paralegals to fast food cooks,
while job demand is likely to increase for
care-related professions ranging from personal
healthcare to the clergy. Author Martin Ford
and others go further and argue that a large
number of jobs are routine, repetitive and
(to an AI) predictable; Ford warns that these
jobs may be automated in the next couple of
decades, and that many of the new jobs may
not be "accessible to people with average
capability", even with retraining. Economists
point out that in the past technology has
tended to increase rather than reduce total
employment, but acknowledge that "we're in
uncharted territory" with AI.
==== Autonomous weapons ====
Currently, 50+ countries are researching battlefield
robots, including the United States, China,
Russia, and the United Kingdom. Many people
concerned about risk from superintelligent
AI also want to limit the use of artificial
soldiers and drones.
=== Ethical machines ===
Machines with intelligence have the potential
to use their intelligence to prevent harm
and minimize the risks; they may have the
ability to use ethical reasoning to better
choose their actions in the world. Research
in this area includes machine ethics, artificial
moral agents, and friendly AI.
==== Artificial moral agents ====
Wendell Wallach introduced the concept of
artificial moral agents (AMA) in his book
Moral Machines For Wallach, AMAs have become
a part of the research landscape of artificial
intelligence as guided by its two central
questions which he identifies as "Does Humanity
Want Computers Making Moral Decisions" and
"Can (Ro)bots Really Be Moral". For Wallach
the question is not centered on the issue
of whether machines can demonstrate the equivalent
of moral behavior in contrast to the constraints
which society may place on the development
of AMAs.
==== Machine ethics ====
The field of machine ethics is concerned with
giving machines ethical principles, or a procedure
for discovering a way to resolve the ethical
dilemmas they might encounter, enabling them
to function in an ethically responsible manner
through their own ethical decision making.
The field was delineated in the AAAI Fall
2005 Symposium on Machine Ethics: "Past research
concerning the relationship between technology
and ethics has largely focused on responsible
and irresponsible use of technology by human
beings, with a few people being interested
in how human beings ought to treat machines.
In all cases, only human beings have engaged
in ethical reasoning. The time has come for
adding an ethical dimension to at least some
machines. Recognition of the ethical ramifications
of behavior involving machines, as well as
recent and potential developments in machine
autonomy, necessitate this. In contrast to
computer hacking, software property issues,
privacy issues and other topics normally ascribed
to computer ethics, machine ethics is concerned
with the behavior of machines towards human
users and other machines. Research in machine
ethics is key to alleviating concerns with
autonomous systems—it could be argued that
the notion of autonomous machines without
such a dimension is at the root of all fear
concerning machine intelligence. Further,
investigation of machine ethics could enable
the discovery of problems with current ethical
theories, advancing our thinking about Ethics."
Machine ethics is sometimes referred to as
machine morality, computational ethics or
computational morality. A variety of perspectives
of this nascent field can be found in the
collected edition "Machine Ethics" that stems
from the AAAI Fall 2005 Symposium on Machine
Ethics.
==== Malevolent and friendly AI ====
Political scientist Charles T. Rubin believes
that AI can be neither designed nor guaranteed
to be benevolent. He argues that "any sufficiently
advanced benevolence may be indistinguishable
from malevolence." Humans should not assume
machines or robots would treat us favorably
because there is no a priori reason to believe
that they would be sympathetic to our system
of morality, which has evolved along with
our particular biology (which AIs would not
share). Hyper-intelligent software may not
necessarily decide to support the continued
existence of humanity and would be extremely
difficult to stop. This topic has also recently
begun to be discussed in academic publications
as a real source of risks to civilization,
humans, and planet Earth.
One proposal to deal with this is to ensure
that the first generally intelligent AI is
'Friendly AI', and will then be able to control
subsequently developed AIs. Some question
whether this kind of check could really remain
in place.
Leading AI researcher Rodney Brooks writes,
"I think it is a mistake to be worrying about
us developing malevolent AI anytime in the
next few hundred years. I think the worry
stems from a fundamental error in not distinguishing
the difference between the very real recent
advances in a particular aspect of AI, and
the enormity and complexity of building sentient
volitional intelligence."
=== Machine consciousness, sentience and mind
===
If an AI system replicates all key aspects
of human intelligence, will that system also
be sentient—will it have a mind which has
conscious experiences? This question is closely
related to the philosophical problem as to
the nature of human consciousness, generally
referred to as the hard problem of consciousness.
==== Consciousness ====
David Chalmers identified two problems in
understanding the mind, which he named the
"hard" and "easy" problems of consciousness.
The easy problem is understanding how the
brain processes signals, makes plans and controls
behavior. The hard problem is explaining how
this feels or why it should feel like anything
at all. Human information processing is easy
to explain, however human subjective experience
is difficult to explain.
For example, consider what happens when a
person is shown a color swatch and identifies
it, saying "it's red". The easy problem only
requires understanding the machinery in the
brain that makes it possible for a person
to know that the color swatch is red. The
hard problem is that people also know something
else—they also know what red looks like.
(Consider that a person born blind can know
that something is red without knowing what
red looks like.) Everyone knows subjective
experience exists, because they do it every
day (e.g., all sighted people know what red
looks like). The hard problem is explaining
how the brain creates it, why it exists, and
how it is different than knowledge and other
aspects of the brain.
==== Computationalism and functionalism ====
Computationalism is the position in the philosophy
of mind that the human mind or the human brain
(or both) is an information processing system
and that thinking is a form of computing.
Computationalism argues that the relationship
between mind and body is similar or identical
to the relationship between software and hardware
and thus may be a solution to the mind-body
problem. This philosophical position was inspired
by the work of AI researchers and cognitive
scientists in the 1960s and was originally
proposed by philosophers Jerry Fodor and Hilary
Putnam.
==== Strong AI hypothesis ====
The philosophical position that John Searle
has named "strong AI" states: "The appropriately
programmed computer with the right inputs
and outputs would thereby have a mind in exactly
the same sense human beings have minds." Searle
counters this assertion with his Chinese room
argument, which asks us to look inside the
computer and try to find where the "mind"
might be.
==== Robot rights ====
If a machine can be created that has intelligence,
could it also feel? If it can feel, does it
have the same rights as a human? This issue,
now known as "robot rights", is currently
being considered by, for example, California's
Institute for the Future, although many critics
believe that the discussion is premature.
Some critics of transhumanism argue that any
hypothetical robot rights would lie on a spectrum
with animal rights and human rights. The subject
is profoundly discussed in the 2010 documentary
film Plug & Pray.
=== Superintelligence ===
Are there limits to how intelligent machines—or
human-machine hybrids—can be? A superintelligence,
hyperintelligence, or superhuman intelligence
is a hypothetical agent that would possess
intelligence far surpassing that of the brightest
and most gifted human mind. Superintelligence
may also refer to the form or degree of intelligence
possessed by such an agent.
==== Technological singularity ====
If research into Strong AI produced sufficiently
intelligent software, it might be able to
reprogram and improve itself. The improved
software would be even better at improving
itself, leading to recursive self-improvement.
The new intelligence could thus increase exponentially
and dramatically surpass humans. Science fiction
writer Vernor Vinge named this scenario "singularity".
Technological singularity is when accelerating
progress in technologies will cause a runaway
effect wherein artificial intelligence will
exceed human intellectual capacity and control,
thus radically changing or even ending civilization.
Because the capabilities of such an intelligence
may be impossible to comprehend, the technological
singularity is an occurrence beyond which
events are unpredictable or even unfathomable.Ray
Kurzweil has used Moore's law (which describes
the relentless exponential improvement in
digital technology) to calculate that desktop
computers will have the same processing power
as human brains by the year 2029, and predicts
that the singularity will occur in 2045.
==== Transhumanism ====
Robot designer Hans Moravec, cyberneticist
Kevin Warwick and inventor Ray Kurzweil have
predicted that humans and machines will merge
in the future into cyborgs that are more capable
and powerful than either. This idea, called
transhumanism, which has roots in Aldous Huxley
and Robert Ettinger.
Edward Fredkin argues that "artificial intelligence
is the next stage in evolution", an idea first
proposed by Samuel Butler's "Darwin among
the Machines" (1863), and expanded upon by
George Dyson in his book of the same name
in 1998.
== In fiction ==
Thought-capable artificial beings appeared
as storytelling devices since antiquity,
and have been a persistent theme in science
fiction.
A common trope in these works began with Mary
Shelley's Frankenstein, where a human creation
becomes a threat to its masters. This includes
such works as Arthur C. Clarke's and Stanley
Kubrick's 2001: A Space Odyssey (both 1968),
with HAL 9000, the murderous computer in charge
of the Discovery One spaceship, as well as
The Terminator (1984) and The Matrix (1999).
In contrast, the rare loyal robots such as
Gort from The Day the Earth Stood Still (1951)
and Bishop from Aliens (1986) are less prominent
in popular culture.Isaac Asimov introduce
the Three Laws of Robotics in many books and
stories, most notably the "Multivac" series
about a super-intelligent computer of the
same name. Asimov's laws are often brought
up during layman discussions of machine ethics;
while almost all artificial intelligence researchers
are familiar with Asimov's laws through popular
culture, they generally consider the laws
useless for many reasons, one of which is
their ambiguity.Transhumanism (the merging
of humans and machines) is explored in the
manga Ghost in the Shell and the science-fiction
series Dune. In the 1980s, artist Hajime Sorayama's
Sexy Robots series were painted and published
in Japan depicting the actual organic human
form with lifelike muscular metallic skins
and later "the Gynoids" book followed that
was used by or influenced movie makers including
George Lucas and other creatives. Sorayama
never considered these organic robots to be
real part of nature but always unnatural product
of the human mind, a fantasy existing in the
mind even when realized in actual form.
Several works use AI to force us to confront
the fundamental of question of what makes
us human, showing us artificial beings that
have the ability to feel, and thus to suffer.
This appears in Karel Čapek's "R.U.R.", the
films "A.I. Artificial Intelligence" and "Ex
Machina", as well as the novel Do Androids
Dream of Electric Sheep?, by Philip K. Dick.
Dick considers the idea that our understanding
of human subjectivity is altered by technology
created with artificial intelligence.
== See also ==
== Explanatory notes
