<?xml version="1.0" encoding="utf-8"?>
<oembed>
  <version>1</version>
  <type>rich</type>
  <provider_name>Libsyn</provider_name>
  <provider_url>https://www.libsyn.com</provider_url>
  <height>90</height>
  <width>600</width>
  <title>37 - Jaime Sevilla on AI Forecasting</title>
  <description>Epoch AI is the premier organization that tracks the trajectory of AI - how much compute is used, the role of algorithmic improvements, the growth in data used, and when the above trends might hit an end. In this episode, I speak with the director of Epoch AI, Jaime Sevilla, about how compute, data, and algorithmic improvements are impacting AI, and whether continuing to scale can get us AGI. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast The transcript:  https://axrp.net/episode/2024/10/04/episode-37-jaime-sevilla-forecasting-ai.html &amp;amp;nbsp; Topics we discuss, and timestamps: 0:00:38 - The pace of AI progress 0:07:49 - How Epoch AI tracks AI compute 0:11:44 - Why does AI compute grow so smoothly? 0:21:46 - When will we run out of computers? 0:38:56 - Algorithmic improvement 0:44:21 - Algorithmic improvement and scaling laws 0:56:56 - Training data 1:04:56 - Can scaling produce AGI? 1:16:55 - When will AGI arrive? 1:21:20 - Epoch AI 1:27:06 - Open questions in AI forecasting 1:35:21 - Epoch AI and x-risk 1:41:34 - Following Epoch AI's research &amp;amp;nbsp; Links for Jaime and Epoch AI: Epoch AI: https://epochai.org/ Machine Learning Trends dashboard: https://epochai.org/trends Epoch AI on X / Twitter: https://x.com/EpochAIResearch Jaime on X / Twitter: https://x.com/Jsevillamol &amp;amp;nbsp; Research we discuss: Training Compute of Frontier AI Models Grows by 4-5x per Year:  https://epochai.org/blog/training-compute-of-frontier-ai-models-grows-by-4-5x-per-year Optimally Allocating Compute Between Inference and Training:  https://epochai.org/blog/optimally-allocating-compute-between-inference-and-training Algorithmic Progress in Language Models [blog post]: https://epochai.org/blog/algorithmic-progress-in-language-models Algorithmic progress in language models [paper]: https://arxiv.org/abs/2403.05812 Training Compute-Optimal Large Language Models [aka the Chinchilla scaling law paper]: https://arxiv.org/abs/2203.15556 Will We Run Out of Data? Limits of LLM Scaling Based on Human-Generated Data [blog post]:  https://epochai.org/blog/will-we-run-out-of-data-limits-of-llm-scaling-based-on-human-generated-data Will we run out of data? Limits of LLM scaling based on human-generated data [paper]: https://arxiv.org/abs/2211.04325 The Direct Approach: https://epochai.org/blog/the-direct-approach &amp;amp;nbsp; Episode art by Hamish Doodles:&amp;amp;nbsp;hamishdoodles.com </description>
  <author_name>AXRP - the AI X-risk Research Podcast</author_name>
  <author_url>https://axrp.net</author_url>
  <html>&lt;iframe title="Libsyn Player" style="border: none" src="//html5-player.libsyn.com/embed/episode/id/33334332/height/90/theme/custom/thumbnail/yes/direction/forward/render-playlist/no/custom-color/88AA3C/" height="90" width="600" scrolling="no"  allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen&gt;&lt;/iframe&gt;</html>
  <thumbnail_url>https://assets.libsyn.com/secure/content/179181972</thumbnail_url>
</oembed>
