Artificial Intelligence Research Overview

• Artificial intelligence is the ability of systems to perceive, synthesize, and infer information.
• AI was founded as an academic discipline in 1956.
• The various sub-fields of AI research are centered around particular goals and the use of particular tools.
• The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects.
• General intelligence (the ability to solve an arbitrary problem) is among the field's long-term goals.
• To solve these problems, AI researchers have adapted and integrated a wide range of problem-solving techniques – including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, probability and economics.
• AI also draws upon computer science, psychology, linguistics, philosophy, and many other fields.
• AI research was born at a workshop at Dartmouth College in 1956.
• The attendees became the founders and leaders of AI research.
• They and their students produced programs that the press described as "astonishing".[f]
• AI research was heavily funded by the Department of Defense in the 1960s and 1970s.
• However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute.
• In the early 1980s, AI research was revived by the commercial success of expert systems.
• By 1985, the market for AI had reached over a billion dollars.
• However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute.
• AI research has focused on the problem of simulating intelligence, which has been broken down into sub-problems of particular traits or capabilities that researchers expect an intelligent system to display.
• Early AI research focused on the step-by-step reasoning that humans use when they solve puzzles or make logical deductions. However, this approach proved insufficient for solving large reasoning problems.
• Knowledge representation and knowledge engineering allow AI programs to answer questions intelligently and make deductions about real-world facts.
• A representation of "what exists" is an ontology: the set of objects, relations, and properties that make up the world.
• The problem of creating artificial general intelligence (AGI) is currently being pursued by several well-funded institutions.
• AI research has developed tools to represent specific domains, such as objects, properties, categories and relations between objects;
• Machine learning (ML), a fundamental concept of AI research, is the study of computer algorithms that improve automatically through experience
• Unsupervised learning finds patterns in a stream of input. Supervised learning requires a human to label the input data first, and comes in two main varieties: classification and numerical regression. Classification is used to determine what category something belongs in – the program sees a number of examples of things from several categories and will learn to classify new inputs. 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".
• In reinforcement learning the agent is rewarded for good responses and punished for bad ones. The agent classifies its responses to form a strategy for operating in its problem space.
• Symbolic AI used formal syntax to translate the deep structure of sentences into logic. This failed to produce useful applications, due to the intractability of logic and the breadth of commonsense knowledge.

Test your knowledge of the history, goals, sub-fields, and tools of artificial intelligence research with this quiz. Explore the development of AI from its inception in 1956 to the present day, and learn about the various approaches and challenges encountered by AI researchers.