The Paradigm Shifts in Artificial Intelligence

TL;DR

This paper analyzes the evolution of AI paradigms over 60 years using Kuhn's framework, highlighting the recent shift towards large pre-trained models like GPT-3 and ChatGPT, and discusses associated challenges.

Contribution

It applies Kuhn's scientific progress framework to AI, providing a new perspective on paradigm shifts and examining the implications of current large-scale models.

Findings

Identification of historical AI paradigm shifts

Analysis of the rise of large pre-trained models

Discussion of risks and future challenges

Abstract

Kuhn's framework of scientific progress (Kuhn, 1962) provides a useful framing of the paradigm shifts that have occurred in Artificial Intelligence over the last 60 years. The framework is also useful in understanding what is arguably a new paradigm shift in AI, signaled by the emergence of large pre-trained systems such as GPT-3, on which conversational agents such as ChatGPT are based. Such systems make intelligence a commoditized general purpose technology that is configurable to applications. In this paper, I summarize the forces that led to the rise and fall of each paradigm, and discuss the pressing issues and risks associated with the current paradigm shift in AI.