AXRP - the AI X-risk Research Podcast

37 - Jaime Sevilla on AI Forecasting

Oct 4, 2024

Jaime Sevilla, Director of Epoch AI, dives into the intricacies of AI forecasting and compute trends. He discusses the exponential growth in computational power and its implications for AI development. The conversation highlights the tight relationship between algorithmic improvements and scaling, considering whether scaling is the key to achieving AGI. Sevilla also tackles challenges in GPU production and the importance of transparent AI training processes. Get ready for some thought-provoking insights into the future of artificial intelligence!

01:44:25

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Jaime Sevilla

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Quick takeaways

The exponential growth of AI is driven by increasing compute power and faster algorithmic improvements, significantly enhancing output capabilities.

Hardware production limitations, especially in advanced GPUs, may restrict AI scaling and future technological advancements, emphasizing the need for careful resource management.

Forecasting AI's future necessitates understanding the balance between data saturation and algorithmic innovations, which ultimately determine the effectiveness and evolution of AI models.

Deep dives

Exponential Growth of AI Inputs and Outputs

The rapid progression of artificial intelligence (AI) is marked by exponential increases in both input and output capacities. The amount of computational power used for training modern machine learning systems is doubling roughly every year, with an annual increase of about four times in floating-point operations (FLOPs). Noteworthy advancements in output capabilities are evident, for instance, in image generation; AI now achieves photorealistic results compared to less recognizable outputs from just four years ago. Text generation has similarly advanced, with tools like ChatGPT becoming commonly used in daily tasks, exemplifying the significant strides made in AI's effectiveness and usability.

Scale Up for Performance Insight

00:57

Innovation Outpaces Longevity

00:54

Balance Supply and Demand Wisely

01:00

Longer Training, Uncertain Outcomes

01:02

Intro

2min

The Power of AI Training Compute

14min

Strategic Scaling in AI Model Training

2min

The Dynamics of Reverse Interest Rates in AI Training

2min

Unpacking Model Training Timelines and Public Releases in AI

2min

Navigating GPU Production and AI Training

15min

Optimizing Machine Learning Training and Inference

20min

Computational Limits and AI Growth

24min

Navigating AI Trends and Insights

18min

10.

The Art of Forecasting: Big Picture vs. Detail-Oriented Approaches

2min

11.

Exploring Epoch AI Resources and Social Media Engagement

3min

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.

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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?