Vocabulary choices shape public debates. Imagine if the vague term "AI" didn’t exist and machine learning were known by the more honest term applied function approximation. That would expose the absurdity of "strong AI" predictions—it’s as likely as building a ladder to the moon.
My point isn't that strong AI is impossible. My point is that while the current spurt of progress in ML is really cool, we have absolutely no reason to think that it constitutes meaningful progress towards Artificial General Intelligence.
Ha, it looks like others have made the same analogy:
This is not just an academic debate. Because of the media hype around AI, and the failure to explain the chasm between narrow and strong AI, the public thinks that AGI—including automation of all jobs—is just around the corner! governanceai.github.io/US-Public-Opin… 
AI/ML researchers are far more cautious in their predictions of when (if ever) AGI might arrive. So you'd think experts would be keen to correct the public's misperception. Imagine how it would warp our priorities if people thought alien contact was only 10 years out!
However, most researchers just want to focus on their (interesting and useful) work on machine learning in narrow domains and ignore the AGI ruckus. So a gang of pundits and self-styled experts has stepped in to pander to (and stoke) the public's AGI hopes and fears.
The AGI crowd is now getting the attention of policy makers, often using a deeply flawed Pascal's wager-type argument: the costs of action are finite, but if there is a nonzero chance of AGI, however small, the costs of inaction are potentially infinite! en.wikipedia.org/wiki/Pascal%27…
The reason we should care about all this is that it's drawing attention and resources away from real and immediate problems, including the harms arising from the irresponsible use of ML in e.g. hiring (which is often just regression sold using the aura of AI).
(RANT ENDS)
(RANT ENDS)
