Can Language Models Be Too Big? 🦜 with Emily...

Voice AI has gotten remarkably good, but natural conversation remains a high bar. Small delays, awkward interruptions, or the wrong tone can quickly break the illusion—and adding vision and visual presence only raises the stakes. In this episode, Alex Smola, co-founder and CEO of ...  Show more

As reasoning models consume more tokens and AI systems become more expensive to run, understanding what those tokens actually buy is becoming increasingly important. In this episode, Stanford professor and Big Spin co-founder Chris Potts joins us to discuss AI tokenomics and his ...  Show more

In this episode, Emily and Lukas dive into the problems with bigger and bigger language models, the difference between form and meaning, the limits of benchmarks, and why it's important to name the languages we study.Show notes (links to papers and transcript): http://wandb.me/gd ...  Show more

Peoples & Things host, Lee Vinsel, talks to Emily Bender, Professor of Linguistics, Director of the Masters of Science in Computational Linguistics program, and Director of the Computational Linguistics Laboratory at University of Washington, about her work on artificial intellig ...  Show more

Are generative AI systems such as OpenAI’s ChatGPT really intelligent? Large language models such as GPT 4 appear to use human-level cognitive abilities when they engage in legal reasoning, write essays or solve complex problems. Hosts John Thornhill and Madhumita Murgia speak ...

Host Jennifer Strong and MIT Technology Review’s editors explore what it means to entrust AI with our most sensitive decisions.