Surely a system smart enough to contribute to The New Yorker would have no trouble completing the sentence with the obvious word, fire. In another attempt, it suggested that dropping matches on logs in a fireplace would start an "irc channel full of people." Commonsense reasoning--the ability to make mundane inferences using basic knowledge about the world, like the fact that "matches" plus "logs" usually equals "fire"--has resisted AI researchers' efforts for decades. Marcus posted the exchanges to his Twitter account with his own added commentary: "LMAO," internet slang for a derisive chortle. Neural networks might be impressive linguistic mimics, but they clearly lack basic common sense.

In 2011, America's favorite quiz show, Jeopardy!, exposed viewers to what was for many their first brush with artificial intelligence when IBM's Watson faced off against two of the show's greatest champions, Ken Jennings and Brad Rutter. Watson stumbled a bit on the first day but dominated day two and ended the three-round competition with more than three times its opponents' winnings. "I, for one, welcome our new computer overlords," Jennings wrote on his video screen after Watson's win. While artificial intelligence has developed tremendously since then and AI assistants like Siri and Alexa have proliferated in our pockets and homes, there are still plenty of scenarios where these assistants don't seem so smart. A team of researchers at the University of Maryland capitalized on this in an article published in the 2019 issue of the journal Transactions of the Association for Computational Linguistics, creating a set of questions that stumped computers in order to learn more about how they think -- and perhaps teach them to think even better.