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37 - Jaime Sevilla on AI Forecasting

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Manage episode 443577239 series 2844728
Content provided by Daniel Filan. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Daniel Filan or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

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

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

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

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

Episode art by Hamish Doodles: hamishdoodles.com

  continue reading

54 episodes

Artwork
iconShare
 
Manage episode 443577239 series 2844728
Content provided by Daniel Filan. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Daniel Filan or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

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

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

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

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

Episode art by Hamish Doodles: hamishdoodles.com

  continue reading

54 episodes

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