This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Twenty years ago, the people interested in artificial intelligence research were mostly confined in universities and non-profit AI labs. AI research projects were mostly long-term engagements that spanned across several years--or even decades-- and the goal was to serve science and expand human knowledge. But in the past decade, thanks to advances in deep learning and artificial neural networks, the AI industry has undergone a dramatic change. Today, AI has found its way into many practical applications.

Twenty years ago, the people interested in artificial intelligence research were mostly confined in universities and non-profit AI labs. AI research projects were mostly long-term engagements that spanned across several years--or even decades-- and the goal was to serve science and expand human knowledge. But in the past decade, thanks to advances in deep learning and artificial neural networks, the AI industry has undergone a dramatic change. Today, AI has found its way into many practical applications. Scientists, tech executives and world leaders have all touted AI in general and machine learning in particular as one of the most influential technologies of the next decade.

This article is part of a special report on artificial intelligence, The AI Issue. Digital personal assistants, software that can trounce board game champions, algorithms serving up customized online advertising -- wherever you turn, artificial intelligence appears to be taking over the world. But look past the self-driving cars and facial-recognition cameras, and you'll see that the technology is a lot less intelligent than it may at first appear. It's likely to be decades, at best, before even the smartest forms of AI can outdo humans in the complex tasks that make up daily life. "The real world is a complicated, messy place," said Michael Wooldridge, program co-director at the Alan Turing Institute, the United Kingdom's national center of excellence for data science and artificial intelligence.

A study by researchers from the University of Rochester found an exodus of artificial intelligence (AI) professors from North American universities to the private sector has reduced the prospect that graduate students will found new AI companies. Those graduates who did start a company usually attracted less venture capital, with the field of deep learning especially affected, according to "Artificial Intelligence, Human Capital, and Innovation," by Michael Gofman and Zhao Jin. This academic attrition could hinder innovation and economic expansion over time, the researchers suggest. The technology industry mostly ignored deep learning's potential until 2010, but interest grew as the Internet produced more data and new computer chips reduced the analytical burden. Large tech companies have hired many academic specialists, including two recent recipients of the ACM A.M. Turing Award honored for their work on neural networks.

Eat too much and there won't be grass for anyone. In an essay written in 1833, the British economist William Forster Lloyd made a profound observation using the example of cattle grazing. Lloyd described a hypothetical scenario involving herders who share a pasture, and individually decide how many of their animals would graze there. If few herders exercised restraint, overgrazing would occur, reducing the pasture's future usefulness and eventually hurting everybody. The sinister beauty of this example is that the rational course of action is to behave selfishly.