Science is learning from start-ups (with Adam Marblestone)

May 15, 2023 1h 13m 14 insights Episode Page ↗
Spencer Greenberg speaks with Adam Marblestone about Focused Research Organizations (FROs) as a new model for scientific research, strategies for field building in science, and the beneficial applications of AI, particularly large language models, to accelerate scientific progress and address societal challenges.
Actionable Insights

1. Form Focused Research Organizations

Create special-purpose, finite-duration (5-7 years) non-profit organizations with a fixed mission to build a specific tool, system, or dataset for science. This addresses a gap where projects need startup-like capital and team structure but aim to create public goods, not just market-driven products.

2. Diversify Science Funding Models

Explore and advocate for varied funding and organizational structures beyond the traditional grant-based academic model, such as FROs or ARPA-like agencies. This can address systemic bottlenecks and enable a wider range of high-impact scientific projects.

3. Embrace Time-Limited Projects

Structure scientific initiatives with a finite duration and clear mission (e.g., 5-7 years) to maintain urgency and focus on delivering a specific capability or public good. This prevents mission creep and the dilution of purpose often seen in permanent institutions.

4. Adopt Startup Management Practices

Implement project management, deliberate structured meetings, and efficient hiring/people management techniques from startups within scientific endeavors. This helps FROs operate with agility and precision, focusing on execution and goal attainment.

5. Facilitate Scientist Entrepreneurship

Design scientific projects (like FROs) to develop professionalized teams and skills that can lead to spin-off companies, industry integration, or new ventures. This provides alternative career paths for scientists and leverages their expertise for broader impact beyond academia.

6. Counteract Sociological Barriers

Be aware of how premature commercialization, exaggerated claims, or association with fringe topics can prevent legitimate scientific inquiry. This helps avoid fields being rejected by the mainstream, leading to a weak evidence base due to lack of research.

7. Prioritize Data-Driven Research

Focus on generating fundamental data and measurements with less agenda-driven hypotheses, especially in politicized or controversial areas. This builds a robust scientific foundation without getting entangled in specific, potentially polarizing, applications.

8. Cultivate Critical Mass for Fields

Actively work to gather a sufficient number of researchers, resources, journals, peer review processes, standards, and benchmarks for emerging scientific areas. This is crucial for validating breakthroughs and enabling progress that individual researchers cannot achieve alone.

9. Adopt Empowered Program Manager Model

Implement a model where technically strong program managers actively define milestones and incentivize researchers to pursue specific directions, similar to the DARPA model. This can effectively “pull” researchers and bootstrap new fields.

10. Utilize Field Strategists

Employ individuals (field strategists) to identify systemic bottlenecks, problems, and obstacles within a scientific field and create roadmaps for its progress. This provides a structured approach for philanthropists or other funders to support field development.

11. Formalize Mathematics for AI Proofs

Convert mathematical proofs into formalized programming languages (e.g., Lean) to enable automated theorem proving. This creates a verifiable reward signal for AI training and allows LLMs to predict next steps, accelerating mathematical progress and secure software development.

12. Develop “System Two” Recommenders

Design recommender systems that optimize for users’ long-term goals, reflective preferences, and higher values, rather than immediate engagement or clickbait. This empowers users to customize their information flow to counteract biases and align with their desired future self.

13. Create Contextualization Engines

Develop AI systems (e.g., using LLMs) that, upon query, provide comprehensive, relatively unbiased, and multifaceted context on a topic, similar to a Wikipedia article. This aims to improve epistemic understanding by offering a global picture rather than optimizing for user relevance or clicks.

14. Apply ML to Societal Problems

Frame societal problems in a machine learning context by identifying how to generate training data and feedback loops for improvement. This allows for the application of powerful AI tools to address challenges like misinformation or aligning systems with human values.