Artificial Intelligence: History and Concepts

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

AI research is focused on solving problems in a variety of fields, including reasoning, knowledge representation, planning, learning, natural language processing, perception, and movement.

The goal of AI is to create machines that are as intelligent as or more intelligent than humans.

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.

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.

The first 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.

James Manyika and others have compared the two approaches to the mind (Symbolic AI) and the brain (connectionist). Manyika argues that symbolic approaches dominated the push for artificial intelligence in this period, due in part to its connection to intellectual traditions of Descarte, Boole, Gottlob Frege, Bertrand Russell, and others.

Connectionist approaches based on cybernetics or artificial neural networks were pushed to the background but have gained new prominence in recent decades.

In the early 1980s, AI research was revived by the commercial success of expert systems, and 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, and a second, longer-lasting winter began.

AI research has focused on the development of algorithms that imitate the steps that humans use when they solve problems.

These algorithms became known as symbolic AI.

However, later research developed methods for dealing with uncertain or incomplete information, which led to the development of probabilistic AI.

In the late 1990s and early 21st century, AI research became focused on the development of algorithms that could solve large reasoning problems.

However, these algorithms became slow and exponential as the problems grew larger, prompting researchers to focus on knowledge representation and knowledge engineering.

These methods allow AI programs to answer questions and make deductions about real-world facts.

Machine learning is the study of computer algorithms that improve automatically through experience.

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.