![]() ![]() They’re great at mimicry and bad at facts. They work by looking for patterns in huge troves of text and then using those patterns to guess what the next word in a string of words should be. The paper’s official title is “Climbing Towards NLU: On Meaning, Form, and Understanding in the Age of Data.” NLU stands for “natural-language understanding.” How should we interpret the natural-sounding (i.e., humanlike) words that come out of LLMs? The models are built on statistics. No way to give relevant instructions, like to go grab some coconuts and rope and build a catapult. How could it succeed? The octopus has no referents, no idea what bears or sticks are. I’ve got some sticks.” The octopus, impersonating B, fails to help. Then one day A calls out: “I’m being attacked by an angry bear. ![]() This ruse works for a while, and A believes that O communicates as both she and B do - with meaning and intent. Soon, the octopus enters the conversation and starts impersonating B and replying to A. Over time, O learns to predict with great accuracy how B will respond to each of A’s utterances. O knows nothing about English initially but is very good at detecting statistical patterns. Meanwhile, O, a hyperintelligent deep-sea octopus who is unable to visit or observe the two islands, discovers a way to tap into the underwater cable and listen in on A and B’s conversations. A and B start happily typing messages to each other. They soon discover that previous visitors to these islands have left behind telegraphs and that they can communicate with each other via an underwater cable. Say that A and B, both fluent speakers of English, are independently stranded on two uninhabited islands. The goal was to illustrate what large language models, or LLMs - the technology behind chatbots like ChatGPT - can and cannot do. She published the paper in 2020 with fellow computational linguist Alexander Koller. Bender co-wrote the octopus paper.īender is a computational linguist at the University of Washington. But before Microsoft’s Bing started cranking out creepy love letters before Meta’s Galactica spewed racist rants before ChatGPT began writing such perfectly decent college essays that some professors said, “Screw it, I’ll just stop grading” and before tech reporters sprinted to claw back claims that AI was the future of search, maybe the future of everything else, too, Emily M. ![]()
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