I found this article published recently in Harvard Data Science Review by Michael Jordan (the academic) a joyful read. Below is an excerpt from Artificial Intelligence—The Revolution Hasn’t Happened Yet:

Most of what is labeled AI today, particularly in the public sphere, is actually machine learning (ML), a term in use for the past several decades. ML is an algorithmic field that blends ideas from statistics, computer science and many other disciplines to design algorithms that process data, make predictions, and help make decisions. In terms of impact on the real world, ML is the real thing, and not just recently. Indeed, that ML would grow into massive industrial relevance was already clear in the early 1990s, and by the turn of the century forward-looking companies such as Amazon were already using ML throughout their business, solving mission-critical, back-end problems in fraud detection and supply-chain prediction, and building innovative consumer-facing services such as recommendation systems. As datasets and computing resources grew rapidly over the ensuing two decades, it became clear that ML would soon power not only Amazon but essentially any company in which decisions could be tied to large-scale data. New business models would emerge. The phrase ‘data science’ emerged to refer to this phenomenon, reflecting both the need of ML algorithms experts to partner with database and distributed-systems experts to build scalable, robust ML systems, as well as reflecting the larger social and environmental scope of the resulting systems.This confluence of ideas and technology trends has been rebranded as ‘AI’ over the past few years. This rebranding deserves some scrutiny.

https://hdsr.mitpress.mit.edu/pub/wot7mkc1/