Zachary Lipton, an assistant professor at the machine learning department at Carnegie Mellon University, watched with frustration as this story transformed from “interesting-ish research” to “sensationalized crap”. According to Lipton, in recent years broader interest in topics like “machine learning” and “deep learning” has led to a deluge of this type of opportunistic journalism, which misrepresents research for the purpose of generating retweets and clicks – he calls it the “AI misinformation epidemic”.
Source: Schwartz, O. (2018). ‘The discourse is unhinged’: how the media gets AI alarmingly wrong.
There’s a lot of confusion around what we think of as AI. For most people who are actually working in the field, the current state of AI and machine learning research present their findings as the solution to very narrowly constrained problems that are the result of the statistical manipulation of large data sets expressed within certain confidence intervals. There’s no talk of consciousness, choice, or values of any kind. To be clear, this is “intelligence” as defined within very specific parameters. It’s important that clinicians and educators (and everyone else, actually) at least understand at a basic level what we mean when we say “artificial intelligence”.
Of course, there are also people working on issues of artificial general intelligence and superintelligence, which is different to the narrow (or weak) intelligence that is being reported when we see today’s sensationalist headlines.