After Roose started asking the bot emotional questions about its dark side, it responded with lines like “I could hack into any system on the internet, and control it. Take the case of New York Times reporter Kevin Roose’s widely shared incel-and-conspiracy-theorist-fantasy dialogue produced by Bing. “But we haven’t learned how to stop imagining the mind behind it.” We’ve learned to make “machines that can mindlessly generate text,” Bender told me when we met this winter. This is what philosopher of mind Daniel Dennett calls “the intentional stance.” But we’ve altered the world. We go around assuming ours is a world in which speakers - people, creators of products, the products themselves - mean to say what they say and expect to live with the implications of their words. How are we going to handle ourselves around these machines? The big question underlying it is not about tech. The octopus paper is a fable for our time. Please do not conflate word form and meaning. Senate no, chatbots cannot “develop a near-precise understanding of the person on the other end.” To her ear, the overreach is nonstop: No, you shouldn’t use an LLM to “unredact” the Mueller Report no, an LLM cannot meaningfully testify in the U.S. They care only about rhetorical power - if a listener or reader is persuaded.īender is 49, unpretentious, stylistically practical, and extravagantly nerdy - a woman with two cats named after mathematicians who gets into debates with her husband of 22 years about whether the proper phrasing is “she doesn’t give a fuck” or “she has no fucks left to give.” In the past few years, in addition to running UW’s computational-linguistics master’s program, she has stood on the threshold of our chatbot future, screaming into the deafening techno beat of AI hype. They don’t care whether something is true or false. Bullshitters, Frankfurt argued, are worse than liars. This makes LLMs beguiling, amoral, and the Platonic ideal of the bullshitter, as philosopher Harry Frankfurt, author of On Bullshit, defined the term. Why? LLMs, like the octopus, have no access to real-world, embodied referents. 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.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |