"The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis

Automating Scientific Discovery, with Andrew White, Head of Science at Future House

Dec 5, 2024

In this engaging discussion, Andrew White, Professor of Chemical Engineering at the University of Rochester and Head of Science at Future House, shares his pioneering work in applying AI to scientific discovery. He highlights innovative projects like PaperQA and Aviary, showcasing how large language models are revolutionizing research methodologies. The conversation delves into the balance of human expertise and automation, while discussing challenges in computational biology and the immense potential for AI to streamline and enhance scientific inquiry.

01:58:32

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Episode guests

Andrew White

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Quick takeaways

AI systems like PaperQA are transforming scientific literature comprehension by achieving superhuman performance in question-answering tasks through innovations in context and citation usage.

Future House represents a paradigm shift in scientific discovery by employing semi-autonomous AI systems that augment rather than fully automate research processes.

Andrew White's transition to AI was influenced by his sabbatical at UCLA, highlighting the intersection of statistical mechanics, molecular simulation, and machine learning in modern science.

Aviary's structured training framework enhances language model agents' capabilities by modeling their interactions with environments as stochastic computation graphs.

Despite advancements in AI, the need for empirical experimentation remains vital in biology and chemistry, as computational models cannot fully capture complex biological systems.

Deep dives

Complexity of Biology and AI

Biology is inherently complex and cannot be simplified into basic diagrams or models. The discussion emphasizes that scientific discovery is driven more by empirical measurements and observations than by theoretical models, particularly in the context of artificial intelligence's understanding of biology. This perspective highlights the limitations of advanced AI systems, suggesting that a superintelligent model cannot simply deduce solutions to complex problems like cancer without substantial lab work. Ultimately, the importance of laboratory experimentation remains paramount, as no method has proven to be universally reliable.

Intro

5min

Navigating AI in Chemistry: Early Experiences and Insights

12min

Navigating the Rapid Rise of AI and Future Preparedness

2min

Exploring Biological Complexity

6min

Revolutionizing Science: The Future House Vision

9min

Exploring Automation in Scientific Discovery

4min

Challenges in Computational Biology and Drug Discovery

23min

Integrating AI in Scientific Discovery

22min

Navigating Scientific Discovery Tools

19min

10.

The Future of AI in Scientific Discovery

13min

In this episode of The Cognitive Revolution, Nathan interviews Andrew White, Professor of Chemical Engineering at the University of Rochester and Head of Science at Future House. We explore groundbreaking AI systems for scientific discovery, including PaperQA and Aviary, and discuss how large language models are transforming research. Join us for an insightful conversation about the intersection of AI and scientific advancement with this pioneering researcher in his first-ever podcast appearance.

Check out Future House: https://www.futurehouse.org

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SPONSORS:

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CHAPTERS:

(00:00:00) Teaser

(00:01:13) About the Episode

(00:04:37) Andrew White's Journey

(00:10:23) GPT-4 Red Team

(00:15:33) GPT-4 & Chemistry

(00:17:54) Sponsors: Oracle Cloud Infrastructure (OCI) | SelectQuote

(00:20:19) Biology vs Physics

(00:23:14) Conceptual Dark Matter

(00:26:27) Future House Intro

(00:30:42) Semi-Autonomous AI

(00:35:39) Sponsors: Shopify

(00:37:00) Lab Automation

(00:39:46) In Silico Experiments

(00:45:22) Cost of Experiments

(00:51:30) Multi-Omic Models

(00:54:54) Scale and Grokking

(01:00:53) Future House Projects

(01:10:42) Paper QA Insights

(01:16:28) Generalizing to Other Domains

(01:17:57) Using Figures Effectively

(01:22:01) Need for Specialized Tools

(01:24:23) Paper QA Cost & Latency

(01:27:37) Aviary: Agents & Environments

(01:31:42) Black Box Gradient Estimation

(01:36:14) Open vs Closed Models

(01:37:52) Improvement with Training

(01:40:00) Runtime Choice & Q-Learning

(01:43:43) Narrow vs General AI

(01:48:22) Future Directions & Needs

(01:53:22) Future House: What's Next?

(01:55:32) Outro

SOCIAL LINKS:

Website: https://www.cognitiverevolution.ai

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Twitter (Nathan): https://x.com/labenz

LinkedIn: https://www.linkedin.com/in/nathanlabenz/

Youtube: https://www.youtube.com/@CognitiveRevolutionPodcast

Apple: https://podcasts.apple.com/de/podcast/the-cognitive-revolution-ai-builders-researchers-and/id1669813431

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