The large language models (LLMs) that energy immediately’s chatbots have gotten so astoundingly capable, AI researchers are exhausting pressed to evaluate these capabilities—plainly no sooner is there a brand new take a look at than the AI techniques ace it. However what does that efficiency actually imply? Do these fashions genuinely perceive our world? Or are they merely a triumph of information and calculations that simulates true understanding?
To hash out these questions, IEEE Spectrum partnered with the Computer History Museum in Mountain View, Calif. to deliver two opinionated consultants to the stage. I used to be the moderator of the occasion, which occurred on 25 March. It was a fiery (however respectful) debate, effectively value watching in full.
Emily M. Bender is a College of Washington professor and director of its computational linguistics laboratory, and he or she has emerged over the previous decade as one of many fiercest critics of immediately’s main AI firms and their method to AI. She’s also called one of many coauthors of the seminal 2021 paper “On the Dangers of Stochastic Parrots,” a paper that laid out the attainable dangers of LLMs (and brought about Google to fireplace coauthor Timnit Gebru). Bender, unsurprisingly, took the “no” place.
Taking the “sure” place was Sébastien Bubeck, who not too long ago moved to OpenAI from Microsoft, the place he was VP of AI. Throughout his time at Microsoft he coauthored the influential preprint “Sparks of Artificial General Intelligence,” which described his early experiments with OpenAI’s GPT-4 whereas it was nonetheless beneath growth. In that paper, he described advances over prior LLMs that made him really feel that the mannequin had reached a brand new degree of comprehension.
With no additional ado, we deliver you the matchup that I name “Parrots vs. Sparks.”
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