A Primer: Artificial Intelligence Versus Neural Networks
A Primer: Artificial Intelligence Versus Neural Networks

A Primer: Artificial Intelligence Versus Neural Networks

A brief history of AI, machine learning, artificial neural networks, and deep learning.

Jef Akst
May 1, 2019


The term “artificial intelligence” dates back to the mid-1950s, when mathematician John McCarthy, widely recognized as the father of AI, used it to describe machines that do things people might call intelligent. He and Marvin Minsky, whose work was just as influential in the AI field, organized the Dartmouth Summer Research Project on Artificial Intelligence in 1956. A few years later, with McCarthy on the faculty, MIT founded its Artificial Intelligence Project, later the AI Lab. It merged with the Laboratory for Computer Science (LCS) in 2003 and was renamed the Computer Science and Artificial Intelligence Laboratory, or CSAIL.

Now a ubiquitous part of modern society, AI refers to any machine that is able to replicate human cognitive skills, such as problem solving. Over the second half of the 20th century, machine learning emerged as a powerful AI approach that allows computers to, as the name implies, learn from input data without having to be explicitly programmed. One technique used in machine learning is a neural network, which draws inspiration from the biology of the brain, relaying information between layers of so-called artificial neurons. The very first artificial neural network was created by Minsky as a graduate student in 1951 (see “Learning Machine, 1951), but the approach was limited at first, and even Minsky himself soon turned his focus to other approaches for creating intelligent machines. In recent years, neural networks have made a comeback, particularly for a form of machine learning called deep learning, which can use very large, complex neural networks.

the scientist staff
Artificial Intelligence
Machine Learning
Neural Networks
Deep Learning
An attribute of machines that embody a form of intelligence, rather than simply carrying out computations that are input by human users.
An approach to AI in which an algorithm learns to make predictions from data that is fed into the system.
A machine learning approach in which algorithms process signals via interconnected nodes called artificial neurons.
A form of machine learning that often uses a network with many layers of computation—a deep neural network—enabling an algorithm to powerfully
analyze the input data.
Early applications of AI included machines that could play games such as checkers and chess and programs that could analyze and reproduce language.
From personalized news feeds to traffic prediction maps, most people in developed countries use machine learning–based technologies every day.
Because they mimic the architecture of biological nervous systems, artificial neural networks are the obvious method of choice for modeling the brain.
Deep neural networks are responsible for self-driving vehicles, which learn to recognize traffic signs, as well as for voice-controlled virtual assistants.