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Paul Eremenko

05/27/25

@ Sequoia Capital

To create synthetic data sets for complex systems, we need a comprehensive catalog of components that is significantly larger than the typical system design, potentially requiring hundreds of millions of parts.

Video

From Data Centers to Dyson Spheres: P-1 AI's Path to Hardware Engineering AGI

@ Sequoia Capital

From Data Centers to Dyson Spheres: P-1 AI's Path to Hardware Engineering AGI

05/27/25

Related Takeaways

Paul Eremenko

05/27/25

@ Sequoia Capital

Currently, we manually build the component catalog for synthetic data generation, but we are developing AI tools to automate this process and intelligently assemble components into systems.

Max Jaderberg

04/29/25

@ Sequoia Capital

Synthetic data plays a crucial role in drug discovery, especially in chemistry, where we can use theoretical models to create scalable molecular simulations and enhance the information content of our datasets.

Paul Eremenko

05/27/25

@ Sequoia Capital

To create effective AI models for engineering, we need to generate synthetic training data that is physics-based and informed by supply chain considerations for various physical product domains.

Neri Oxman

09/02/23

@ Lex Fridman

Every new project must incorporate simultaneous complexity and novelty across synthetic biology, material science, robotics, engineering, and computational design to create something truly innovative.

Ethan Perez

02/28/25

@ Anthropic

Synthetic data is generated from broad categories of user requests, allowing the model to explore specific harmful examples and improve its classifiers.

Paul Eremenko

05/27/25

@ Sequoia Capital

The design space for physical products is vast, requiring careful sampling strategies to ensure that AI models learn effectively from both dominant designs and edge cases.

Demis Hassabis

05/23/25

@ Google for Developers

Synthetic data can be used in training models, but it must be created from the same distribution to maintain quality.

Jim Fan

05/07/25

@ Sequoia Capital

As we scale up classical simulations, we will encounter limitations due to the handcrafted nature of the system, which is why neural world models will exponentially enhance our capabilities.

Max Jaderberg

04/29/25

@ Sequoia Capital

Generative models are essential for navigating the vast molecular space, allowing us to explore potential designs that could lead to effective therapeutics.