The 30-Year Cycle In The AI Debate
It addresses the problem of over-interpretation and poor public reporting on AI's rapid advancements for analysts and the broader public, highlighting an incremental historical pattern.
This paper identifies a recurring 30-year cycle in AI and machine learning, characterized by early successes followed by critique and stagnation, based on a review of the field's track-record since 1958.
In the last couple of years, the rise of Artificial Intelligence and the successes of academic breakthroughs in the field have been inescapable. Vast sums of money have been thrown at AI start-ups. Many existing tech companies -- including the giants like Google, Amazon, Facebook, and Microsoft -- have opened new research labs. The rapid changes in these everyday work and entertainment tools have fueled a rising interest in the underlying technology itself; journalists write about AI tirelessly, and companies -- of tech nature or not -- brand themselves with AI, Machine Learning or Deep Learning whenever they get a chance. Confronting squarely this media coverage, several analysts are starting to voice concerns about over-interpretation of AI's blazing successes and the sometimes poor public reporting on the topic. This paper reviews briefly the track-record in AI and Machine Learning and finds this pattern of early dramatic successes, followed by philosophical critique and unexpected difficulties, if not downright stagnation, returning almost to the clock in 30-year cycles since 1958.