Artificial intelligence
Artificial Intelligence (AI) is the intelligence of machines and the branch ofcomputer science which aims to create it. Major AI textbooks define the field as "the study and design of intelligent agents,"[1] where an intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success.[2] John McCarthy, who coined the term in 1956,[3] defines it as "the science and engineering of making intelligent machines."[4]
The field was founded on the claim that a central property of human beings, intelligence—the sapience of Homo sapiens—can be so precisely described that it can be simulated by a machine.[5] This raises philosophical issues about the nature of the mind and limits of scientific hubris, issues which have been addressed by myth, fiction and philosophy since antiquity.[6] Artificial intelligence has been the subject of breathtaking optimism,[7] has suffered stunning setbacks[8] and, today, has become an essential part of the technology industry, providing the heavy lifting for many of the most difficult problems in computer science.[9]
AI research is highly technical and specialized, so much so that some critics decry the "fragmentation" of the field.[10] Subfields of AI are organized around particular problems, the application of particular tools and around longstanding theoretical differences of opinion. The central problems of AI include such traits as reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects.[11] General intelligence (or "strong AI") is still a long-term goal of (some) research.[12]
- 1 Perspectives on AI
- 2 AI research
- 3 Applications of artificial intelligence
- 4 See also
- 5 Notes
- 6 References
- 7 Further reading
- 8 External links
Perspectives on AI
[edit]AI in myth, fiction and speculation
Thinking machines and artificial beings appear in Greek myths, such as Talos of Crete, the golden robots of Hephaestus and Pygmalion'sGalatea.[13] Human likenesses believed to have intelligence were built in many ancient societies; some of the earliest being the sacred statues worshipped in Egypt and Greece,[14][15] and including the machines of Yan Shi,[16] Hero of Alexandria,[17] Al-Jazari[18] orWolfgang von Kempelen.[19] It was widely believed that artificial beings had been created by Geber,[20] Judah Loew[21] and Paracelsus.[22]Stories of these creatures and their fates discuss many of the same hopes, fears and ethical concerns that are presented by artificial intelligence.[6]
Mary Shelley's Frankenstein,[23] considers a key issue in the ethics of artificial intelligence: 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 being? The idea also appears in modern science fiction: the film Artificial Intelligence: A.I. considers a machine in the form of a small boy which has been given the ability to feel human emotions, including, tragically, the capacity to suffer. This issue, now known as "robot rights", is currently being considered by, for example, California's Institute for the Future,[24] although many critics believe that the discussion is premature.[25]
Another issue explored by both science fiction writers and futurists is the impact of artificial intelligence on society. In fiction, AI has appeared as a servant (R2D2 in Star Wars), a law enforcer (K.I.T.T. "Knight Rider"), a comrade (Lt. Commander Data in Star Trek), a conqueror (The Matrix), a dictator (With Folded Hands), an exterminator (Terminator, Battlestar Galactica), an extension to human abilities (Ghost in the Shell) and the saviour of the human race (R. Daneel Olivaw in the Foundation Series). Academic sources have considered such consequences as: a decreased demand for human labor,[26] the enhancement of human ability or experience,[27] and a need for redefinition of human identity and basic values.[28]
Several futurists argue that artificial intelligence will transcend the limits of progress and fundamentally transform humanity. Ray Kurzweilhas used Moore's law (which describes the relentless exponential improvement in digital technology with uncanny accuracy) to calculate that desktop computers will have the same processing power as human brains by the year 2029, and that by 2045 artificial intelligence will reach a point where it is able to improve itself at a rate that far exceeds anything conceivable in the past, a scenario that science fictionwriter Vernor Vinge named the "technological singularity".[27] Edward Fredkin argues that "artificial intelligence is the next stage in evolution,"[29] 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. Several futurists and science fiction writers have predicted that human beings 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 Huxleyand Robert Ettinger, is now associated with robot designer Hans Moravec, cyberneticist Kevin Warwick and inventor Ray Kurzweil.[27]Transhumanism has been illustrated in fiction as well, for example in the manga Ghost in the Shell and the science fiction series Dune.Pamela McCorduck writes that these scenarios are expressions of an ancient human desire to, as she calls it, "forge the gods."[6]
[edit]History of AI research
In the middle of the 20th century, a handful of scientists began a new approach to building intelligent machines, based on recent discoveries in neurology, a new mathematical theory of information, an understanding of control and stability called cybernetics, and above all, by the invention of the digital computer, a machine based on the abstract essence of mathematical reasoning.[30]
The field of modern AI research was founded at a conference on the campus of Dartmouth College in the summer of 1956.[31] Those who attended would become the leaders of AI research for many decades, especially John McCarthy, Marvin Minsky, Allen Newell and Herbert Simon, who founded AI laboratories at MIT, CMU and Stanford. They and their students wrote programs that were, to most people, simply astonishing:[32] computers were solving word problems in algebra, proving logical theorems and speaking English.[33] By the middle 60s their research was heavily funded by the U.S. Department of Defense,[34] and they were optimistic about the future of the new field:
- 1965, H. A. Simon: "[M]achines will be capable, within twenty years, of doing any work a man can do"[35]
- 1967, Marvin Minsky: "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved."[36]
These predictions, and many like them, would not come true. They had failed to recognize the difficulty of some of the problems they faced.[37] In 1974, in response to the criticism of England's Sir James Lighthill and ongoing pressure from Congress to fund more productive projects, the U.S. and British governments cut off all undirected, exploratory research in AI. This was the first AI winter.[38]
In the early 80s, AI research was revived by the commercial success of expert systems,[39] 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 more than a billion dollars, and governments around the world poured money back into the field.[40] However, just a few years later, 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.[41]
In the 90s 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.[9] The success was due to several factors: the incredible power of computers today (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 above all a new commitment by researchers to solid mathematical methods and rigorous scientific standards.[42]
Philosophy of AI
Artificial intelligence, by claiming to be able to recreate the capabilities of the human mind, is both a challenge and an inspiration for philosophy. Are there limits to how intelligent machines can be? Is there an essential difference between human intelligence and artificial intelligence? Can a machine have a mind and consciousness? A few of the most influential answers to these questions are given below.[43]
- Turing's "polite convention"
- If a machine acts as intelligently as a human being, then it is as intelligent as a human being. Alan Turing theorized that, ultimately, we can only judge the intelligence of a machine based on its behavior. This theory forms the basis of the Turing test.[44]
- 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 assertion was printed in the proposal for the Dartmouth Conference of 1956, and represents the position of most working AI researchers.[5]
- Newell and Simon's physical symbol system hypothesis
- "A physical symbol system has the necessary and sufficient means of general intelligent action." This statement claims that the essence of intelligence is symbol manipulation.[45] 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.[46][47]
- Gödel's incompleteness theorem
- A formal system (such as a computer program) can not prove all true statements. Roger Penrose is among those who claim that Gödel's theorem limits what machines can do.[48][49]
- Searle's strong AI hypothesis
- "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds."[50] 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.[51]
- The artificial brain argument
- The brain can be simulated. 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.[52]
[edit]AI research
In the 21st century, AI research has become highly specialized and technical. It is deeply divided into subfields that often fail to communicate with each other.[10] Subfields have grown up around particular institutions, the work of particular researchers, particular problems (listed below), long standing differences of opinion about how AI should be done (listed as "approaches" below) and the application of widely differing tools (see tools of AI, below).
[edit]Problems of AI
The 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.[11]
[edit]Deduction, reasoning, problem solving
Early AI researchers developed algorithms that imitated the step-by-step reasoning that human beings use when they solve puzzles, play board games or make logical deductions.[53] By the late 80s and 90s, AI research had also developed highly successful methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.[54]
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.[55]
Human beings solve most of their problems using fast, intuitive judgments rather than the conscious, step-by-step deduction that early AI research was able to model.[56] AI has made some progress at imitating this kind of "sub-symbolic" problem solving: embodiedapproaches emphasize the importance of sensorimotor skills to higher reasoning; neural net research attempts to simulate the structures inside human and animal brains that gives rise to this skill.
[edit]Knowledge representation
Knowledge representation[57] and knowledge engineering[58] 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;[59] situations, events, states and time;[60] causes and effects;[61] knowledge about knowledge (what we know about what other people know);[62] and many other, less well researched domains. A complete representation of "what exists" is anontology[63] (borrowing a word from traditional philosophy), of which the most general are called upper ontologies.
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[64] 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.[65]
- The breadth of commonsense knowledge
- The number of atomic facts that the average person knows is astronomical. 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.[66]
- The subsymbolic form of some commonsense knowledge
- Much of what people know isn't represented as "facts" or "statements" that they could actually say out loud. For example, a chess master will avoid a particular chess position because it "feels too exposed"[67] or an art critic can take one look at a statue and instantly realize that it is a fake.[68] These are intuitions or tendencies that are represented in the brain non-consciously and sub-symbolically.[69] 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 or computational intelligence will provide ways to represent this kind of knowledge.[69]
[edit]Planning
Intelligent agents must be able to set goals and achieve them.[70] They need a way to visualize the future (they must have 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 the available choices.[71]
In some planning problems, the agent can assume that it is the only thing acting on the world and it can be certain what the consequences of its actions may be.[72] However, if this is not true, it must periodically check if the world matches its predictions and it must change its plan as this becomes necessary, requiring the agent to reason under uncertainty.[73]
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.[74]
[edit]Learning
Machine learning[75] has been central to AI research from the beginning.[76] Unsupervised learning is the ability to find patterns in a stream of input. Supervised learning includes both classification (be able to determine what category something belongs in, after seeing a number of examples of things from several categories) and regression (given a set of numerical input/output examples, discover a continuous function that would generate the outputs from the inputs). In reinforcement learning[77] the agent is rewarded for good responses and punished for bad ones. These can be analyzed in terms of decision theory, using concepts like utility. The mathematical analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory.
[edit]Natural language processing
Natural language processing[78] gives machines the ability to read and understand the languages that the human beings speak. Many researchers hope that a sufficiently powerful natural language processing system would be able to acquire knowledge on its own, by reading the existing text available over the internet. Some straightforward applications of natural language processing include information retrieval (or text mining) and machine translation.[79]
Motion and manipulation
The field of robotics[80] is closely related to AI. Intelligence is required for robots to be able to handle such tasks as object manipulation[81] and navigation, with sub-problems of localization(knowing where you are), mapping (learning what is around you) and motion planning (figuring out how to get there).[82]
Perception
Machine perception[83] is the ability to use input from sensors (such as cameras, microphones, sonar and others more exotic) to deduce aspects of the world. Computer vision[84] is the ability to analyze visual input. A few selected subproblems are speech recognition,[85] facial recognition andobject recognition.[86]
Social intelligence
Emotion and social skills play two roles for an intelligent agent:[87]
- It must be able to predict the actions of others, by understanding their motives and emotional states. (This involves elements of game theory, decision theory, as well as the ability to model human emotions and the perceptual skills to detect emotions.)
- For good human-computer interaction, an intelligent machine also needs to display emotions — at the very least it must appear polite and sensitive to the humans it interacts with. At best, it should have normal emotions itself.
