Artificial intelligence

Artificial intelligence (AI) is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.^[1] Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as learning and problem solving.^[2]

As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect.^[3] A quip in Tesler's Theorem says "AI is whatever hasn't been done yet."^[4] For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology.

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.^[5] AI research has tried many different approaches, including symbolic methods, connectionism, statistical methods, and evolutionary computation. In the first decades of the 21st century, highly mathematical statistical machine learning has dominated the field, and this technique has proved highly successful, helping to solve many challenging problems throughout industry and academia.

History

Early development

The intellectual roots of AI, and the concept of intelligent machines, may be found in Greek mythology. Intelligent artifacts appear in literature since then, with real mechanical devices actually demonstrating some degree of artificial intelligence. After modern computers became available, it has become possible to create programs that perform difficult intellectual tasks.

The field of AI research was born at a workshop at Dartmouth College in 1956, where the term "artificial intelligence" was coined by John McCarthy. The workshop was attended by many of the founders of AI, including Marvin Minsky, Nathaniel Rochester, and Claude Shannon.

Golden years (1956–1974)

The years after the Dartmouth workshop were an era of discovery, of sprinting across new ground. The programs that were developed during this period were, to most people, simply astounding: computers were solving word problems, proving theorems in geometry, and learning to speak English. Researchers expressed an intense optimism in private and in print, predicting that a fully intelligent machine would be built in less than 20 years.

First AI winter (1974–1980)

By the mid-1970s, it had become obvious that researchers had grossly underestimated the difficulty of the problems they were trying to solve. The funding agencies became skeptical of grandiose predictions and cut funding for AI research. This period became known as the first "AI winter".

Boom (1980–1987)

A new form of AI program called an expert system became popular around the world. The first commercial expert system was R1 (also known as XCON), which began operation at Digital Equipment Corporation in 1982. By 1985, they were earning over a billion dollars for the AI industry.

Approaches

Machine learning

Machine learning (ML), a fundamental concept of AI research since the field's inception, is the study of computer algorithms that improve automatically through experience. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.

Deep learning

Deep learning uses multiple layers of artificial neural networks to progressively extract higher-level features from raw input. Deep learning has dramatically improved the performance of programs in many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing, image recognition and others.

Natural language processing

Natural language processing (NLP) gives machines the ability to read and understand human language. Many current approaches use word embeddings or language models to capture semantic properties of language.

Computer vision

Computer vision is the ability of a computer to identify and analyze visual images. Computer vision tasks include methods for acquiring, processing, analyzing, and understanding digital images.

Applications

AI is relevant to any intellectual task; it is truly a universal field. Modern artificial intelligence techniques are pervasive and are too numerous to list here. Some high-profile applications include:

• Autonomous vehicles - self-driving cars and delivery drones
• Medical diagnosis - AI systems that can diagnose diseases from medical images
• Game playing - programs that play chess, Go, and video games at superhuman levels
• Search engines - algorithms that find relevant information from vast datasets
• Recommendation systems - suggesting products, movies, or content to users
• Virtual assistants - Siri, Alexa, Google Assistant, and similar systems
• Image recognition - identifying objects, people, and scenes in photographs
• Machine translation - automatically translating text from one language to another

Philosophy and ethics

The development of full artificial intelligence could spell the end of the human race, according to physicist Stephen Hawking. "It would take off on its own, and re-design itself at an ever-increasing rate," he said. "Humans, who are limited by slow biological evolution, couldn't compete, and would be superseded."

Other technology industry leaders believe that artificial intelligence is helpful in its current form and will continue to assist humans. These include Mark Zuckerberg, Eric Schmidt, and Andrew Ng. A 2017 survey found that one in two experts in AI believe there is a 50% chance of AI outperforming humans in all cognitive tasks in 45 years and of automating all human jobs in 120 years.

Future directions

Current AI systems excel at specific tasks but lack general intelligence. The long-term goal of some researchers is to create artificial general intelligence (AGI) that matches or exceeds human intelligence across all cognitive abilities. Other near-term developments include improvements in AI safety, explainable AI, and AI alignment with human values.