History and Concepts of Artificial Intelligence

Artificial intelligence is the ability of systems to perceive, synthesize, and infer information.

The field of artificial intelligence was founded in the 1950s and 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.

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.

The field was founded on the assumption that human intelligence "can be so precisely described that a machine can be made to simulate it".

This raised philosophical arguments about the mind and the ethical consequences of creating artificial beings endowed with human-like intelligence; these issues have previously been explored by myth, fiction and philosophy since antiquity.

Computer scientists and philosophers have since suggested that AI may become an existential risk to humanity if its rational capacities are not steered towards beneficial goals.

In the 1950s, two visions for how to achieve machine intelligence emerged: one vision, known as Symbolic AI or GOFAI, was to use computers to create a symbolic representation of the world and systems that could reason about the world. Proponents included Allen Newell, Herbert A. Simon, and Marvin Minsky. Closely associated with this approach was the "heuristic search" approach, which likened intelligence to a problem of exploring a space of possibilities for answers. The second vision, known as the connectionist approach, sought to achieve intelligence through learning. Proponents of this approach, most prominently Frank Rosenblatt, sought to connect Perceptron in ways inspired by connections of neurons.

The field of 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": computers were learning checkers strategies, solving word problems in algebra, proving logical theorems 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.

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.

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 had 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 winter began.

Artificial intelligence research has focused on mimicking the processes of human cognition, especially perception, robotics, learning, and pattern recognition.

By 2000, AI research had restored its reputation by finding specific solutions to specific problems.

Faster computers, algorithmic improvements, and access to large amounts of dataenabled advances in machine learning and perception; data-hungry deep learning methods started to dominate accuracy benchmarks around 2012.

In 2015, artificial intelligence was declared to be a "major milestone" by Google's CEO.

The goal of artificial intelligence is to create intelligent machines. However, much of current research involves statistical AI, which is overwhelmingly used to solve specific problems, even highly successful techniques such as deep learning.

There is a concern that AI is no longer pursuing the original goal of creating versatile, fully intelligent machines. This concern has led to the subfield of artificial general intelligence (or "AGI"), which had several well-funded institutions by the 2010s.

AI is the study of computer programs that can improve automatically through experience.

Machine learning 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.

In reinforcement learning the agent is rewarded for good responses and punished for bad ones.

Transfer learning is when the knowledge gained from one problem is applied to a new problem.

Natural language processing allows machines to read and understand human language.

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.