Today we’re joined by Luke Zettlemoyer, professor at University of Washington and a research manager at Meta. In our conversation with Luke, we cover multimodal generative AI, the effect of data on models, and the significance of open source and open science. We explore the grounding problem, the need for visual grounding and embodiment in text-based models, the advantages of discretization tokenization in image generation, and his paper Scaling Laws for Generative Mixed-Modal Language Models, which focuses on simultaneously training LLMs on various modalities. Additionally, we cover his papers on Self-Alignment with Instruction Backtranslation, and LIMA: Less Is More for Alignment.
The complete show notes for this episode can be found at twimlai.com/go/650.
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Modeling Human Cognition with RNNs and Curriculum Learning, w/ Kanaka Rajan - #524
Do You Dare Run Your ML Experiments in Production? with Ville Tuulos - #523
Delivering Neural Speech Services at Scale with Li Jiang - #522
AI’s Legal and Ethical Implications with Sandra Wachter - #521
Compositional ML and the Future of Software Development with Dillon Erb - #520
Generating SQL Database Queries from Natural Language with Yanshuai Cao - #519
Social Commonsense Reasoning with Yejin Choi - #518
Deep Reinforcement Learning for Game Testing at EA with Konrad Tollmar - #517
Exploring AI 2041 with Kai-Fu Lee - #516
Advancing Robotic Brains and Bodies with Daniela Rus - #515
Neural Synthesis of Binaural Speech From Mono Audio with Alexander Richard - #514
Using Brain Imaging to Improve Neural Networks with Alona Fyshe - #513
Adaptivity in Machine Learning with Samory Kpotufe - #512
A Social Scientist’s Perspective on AI with Eric Rice - #511
Applications of Variational Autoencoders and Bayesian Optimization with José Miguel Hernández Lobato - #510
Codex, OpenAI’s Automated Code Generation API with Greg Brockman - #509
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