When AI research is evolving at warp speed and takes significant capital and compute power, what is the role of academia? Dr. Percy Liang – Stanford computer science professor and director of the Stanford Center for Research on Foundation Models talks about training costs, distributed infrastructure, model evaluation, alignment, and societal impact.Sarah Guo and Elad Gil join Percy at his office to discuss the evolution of research in NLP, why AI developers should aim for superhuman levels of performance, the goals of the Center for Research on Foundation Models, and Together, a decentralized cloud for artificial intelligence.No Priors is now on YouTube! Subscribe to the channel on YouTube and like this episode.Show Links: See Percy’s Research on Google Scholar See Percy’s bio on Stanford’s website Percy on Stanford’s Blog: What to Expect in 2023 in AI Together, a decentralized cloud for artificial intelligence Foundation AI models GPT-3 and DALL-E need release standards - Protocol The Time Is Now to Develop Community Norms for the Release of Foundation Models - Stanford Sign up for new podcasts every week. Email feedback to show@no-priors.comFollow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @PercyLiangShow Notes: [1:44] - How Percy got into machine learning research and started the Center for Research and Foundation Models at Stanford[7:23] - The role of academia and academia’s competitive advantages[13:30] - Research on natural language processing and computational semantics[27:20] - Smaller scale architectures that are competitive with transformers[35:08] - Helm, holistic evaluation of language models, a project with the the goal is to evaluate language models[42:13] - Together, a decentralized cloud for artificial intelligence

Percy Liang, a Stanford professor and co-founder of Together AI, dives into the fascinating evolution of AI from simple token prediction to complex autonomous agents. He discusses the challenges of AI safety and interpretability, emphasizing a holistic approach to mitigating risks. The conversation also touches on generative agents in simulations, their potential in social dynamics, and the importance of evaluating AI systems with new methodologies. Plus, Liang shares insights into AI's future role in fields ranging from education to music, showcasing the technology's transformative power.

Greylock general partner Saam Motamedi interviews David Luan, who is the co-founder of ML research and product lab Adept, and Percy Liang, who is the associate professor of computer science and statistics at Stanford. In this conversation, they discuss the major developments in working with large scale language models. This interview took place during Greylock’s Intelligent Future event, a day-long summit featuring some of the leading voices in artificial intelligence. You can watch the video from this interview on our YouTube channel here: https://youtu.be/_ydBm3tADvA You can read a transcript of the conversation on our website here: https://greylock.com/greymatter/ai-language-words-into-action/ ‎ Learn more about your ad choices. Visit megaphone.fm/adchoices

A re-broadcast of Greylock general partner Saam Motamedi's interview with Adept CEO and co-founder David Luan and Stanford computer science and statistics professor Percy Liang. In this conversation (recorded in mid-2022), David and Percy discuss how large language models are paving the way for the next wave of AI. Adept is developing an AI "teammate" tool that is trained to use every software tool and API for knowledge workers. The company just raised $350 million in Series B funding to further its mission. Greylock has been partnered with Adept since co-leading the Series A in 2022. You can watch the video from this interview on our YouTube channel here: https://youtu.be/_ydBm3tADvA You can read a transcript of the conversation on our website here: https://greylock.com/greymatter/ai-language-words-into-action/ ‎ Learn more about your ad choices. Visit megaphone.fm/adchoices

Percy Liang, Stanford professor, discusses foundation models, reproducible research, and societal impacts of AI. Topics include paradigm shifts in AI, generative agents for social dynamics, academia's role in model development, aligning language models with human values, and dissent in science and society.

Together AI co-founder Percy Liang discusses advancements in AI benchmarking, HELM framework for language models, open-source's role in AI development, challenges of English language bias, and shaping future AI with equitable technologies. Chapters also cover deploying language models, optimizing AI agents, open-source vs closed-source models, and the intersection of music and AI.

Percy Liang, an authority on AI, discusses the paradigm shift in AI powered by foundation models. He explains how they are built, evaluated, and the concerns with lack of openness. Topics include training on trillions of words, context length's impact on predictions, hallucination in models, and alignment in training.