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No Priors Ep. 35 | With Sarah Guo and Elad Gil

What Does it Take to Improve by 10x or 100x? This week is another host-only episode. Sarah and Elad talk about the path to better model quality, the potential for fine tuning to different use cases, retrieval systems (RAG), feedback systems (RLHF, RLAIF) and Meta’s sponsorship of the open source model ecosystem. Plus Sarah and Elad ask if we’re finally at the beginning of a new set of consumer applications and social networks. 00:00 Introduction 03:00 - AI Models, Open AI Advances, and Fine Tuning 08:59 - Addressing Hallucinations in AI Models 13:22 - Open Source Models in Consumer Engagement 16:23 - New Trends in Social Content Creation 21:53 - Balancing Ambition With Realistic Customer Expectations

Sarah GuohostElad Gilhost
Oct 5, 202323mWatch on YouTube ↗

Episode Details

EPISODE INFO

Released
October 5, 2023
Duration
23m
Channel
No Priors
Watch on YouTube
▶ Open ↗

EPISODE DESCRIPTION

What Does it Take to Improve by 10x or 100x? This week is another host-only episode. Sarah and Elad talk about the path to better model quality, the potential for fine tuning to different use cases, retrieval systems (RAG), feedback systems (RLHF, RLAIF) and Meta’s sponsorship of the open source model ecosystem. Plus Sarah and Elad ask if we’re finally at the beginning of a new set of consumer applications and social networks. 00:00 Introduction 03:00 - AI Models, Open AI Advances, and Fine Tuning 08:59 - Addressing Hallucinations in AI Models 13:22 - Open Source Models in Consumer Engagement 16:23 - New Trends in Social Content Creation 21:53 - Balancing Ambition With Realistic Customer Expectations

SPEAKERS

  • Sarah Guo

    host
  • Elad Gil

    host

EPISODE SUMMARY

In this episode of No Priors, featuring Sarah Guo and Elad Gil, No Priors Ep. 35 | With Sarah Guo and Elad Gil explores how Fine-Tuning, RAG, and Open Source Will 10X AI’s Impact Hosts Sarah Guo and Elad Gil outline six levers—multimodality, long context, customization, memory, recursion, and model orchestration—that can make today’s AI systems 10–100x more useful without waiting for dramatically bigger base models.

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