“In a new paper titled ‘Why AI is Harder Than We Think,’ Mitchell lays out four common fallacies about AI that cause misunderstandings not only among the public and the media, but also among experts.
1) Narrow AI and general AI are not on the same scale. Designing systems that can solve single problems does not necessarily get us closer to solving more complicated problems. Mitchell describes the first fallacy as “Narrow intelligence is on a continuum with general intelligence.”
2) The easy things are hard to automate. ‘The things that we humans do without much thought—looking out in the world and making sense of what we see, carrying on a conversation, walking down a crowded sidewalk without bumping into anyone—turn out to be the hardest challenges for machines,’
3) Anthropomorphizing AI doesn’t help. We use terms such as “learn,” “understand,” “read,” and “think” to describe how AI algorithms work. While such anthropomorphic terms often serve as shorthand to help convey complex software mechanisms, they can mislead us to think that current AI systems work like the human mind.
4) AI without a body. ‘Instead, what we’ve learned from research in embodied cognition is that human intelligence seems to be a strongly integrated system with closely interconnected attributes, including emotions, desires, a strong sense of selfhood and autonomy, and a commonsense understanding of the world. It’s not at all clear that these attributes can be separated.’”