We are living through an AI renaissance thought wholly unimaginable just a few decades ago — automobiles are becoming increasingly autonomous, machine learning systems can craft prose nearly as well as human poets, and almost every smartphone on the market now comes equipped with an AI assistant. Oxford professor Michael Woolridge has spent the past quarter decade studying technology. In his new book, A Brief History of Artificial Intelligence, Woolridge leads readers on an exciting tour of the history of AI, its present capabilities, and where the field is heading into the future.
It was excerpted from A Brief History of Artificial Intelligence. Copyright © 2021 by Michael Woolridge. It was excerpted by permission of Flatiron Books, a division of Macmillan Publishers. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.
Robots and Rationality
In his 1962 book, The Structure of Scientific Revolutions, the philosopher Thomas Kuhn argued that, as scientific understanding advances, there will be times when established scientific orthodoxy can no longer hold up under the strain of manifest failures. At such times of crisis, he argued, a new orthodoxy will emerge and replace the established order: the scientific paradigm will change. By the late 1980s, the boom days of expert systems were over, and another AI crisis was looming. Once again, the AI community was criticized for overselling ideas, promising too much, and delivering too little. This time, the paradigm being questioned was not just the “Knowledge is power” doctrine that had driven the expert systems boom but the fundamental assumptions that had underpinned AI since the 1950s, symbolic AI in particular. The fiercest critics of AI in the late 1980s were not outsiders but came from within the field itself.
The most eloquent and influential critic of the prevailing AI paradigm was the roboticist Rodney Brooks, who was born in Australia in 1954. Brooks’s primary interest was in building robots that could carry out practical tasks in the real world. Throughout the early 1980s, he became frustrated with the then-prevailing idea that the key to making such robots was to encode knowledge about the world in a form that the robot could use as the basis for reasoning and decision-making. He took up a faculty position at MIT in the mid-1980s and began his campaign to rethink AI at its most fundamental level.