Why are birth rates plummeting? And how much does it matter? (with Malcolm & Simone Collins)

May 18, 2023 1h 12m 14 insights Episode Page ↗
Spencer Greenberg speaks with Adam Marblestone about focused research organizations (FROs) as a new model for scientific progress, the importance of field building to overcome sociological barriers in science, and beneficial applications of AI, including large language models, for research and societal challenges.
Actionable Insights

1. Create Focused Research Organizations

Establish special-purpose, non-profit organizations with a fixed, 5-7 year mission to build scientific tools or datasets, acting like startups to address gaps not served by traditional grants or venture capital.

2. Diversify Science Funding Models

Advocate for and implement a more diversified approach to funding and organizing scientific research, moving beyond the single-professor grant model to include structures like FROs and ARPA-like programs.

3. Counteract Sociological Barriers in Science

Actively work to counteract non-scientific factors (e.g., premature commercialization, bad reputations) that can stall scientific progress, ensuring legitimate research areas are not rejected for sociological reasons.

4. Leverage DARPA Program Management

Implement an empowered program manager model, similar to DARPA, where technically strong managers actively guide research programs with clear milestones to bootstrap and develop new scientific fields.

5. Employ Field Strategists

Utilize field strategists to identify bottlenecks and systemic obstacles within scientific fields, creating roadmaps for how philanthropists or other entities can effectively support progress.

6. Pursue Hypothesis-Free Data Generation

Prioritize and fund the development of technologies for generating fundamental measurements and data sets in a hypothesis-free manner to broaden understanding and avoid getting stuck on single hypotheses.

7. Formalize Math for AI Proofs

Formalize mathematical proofs into programming languages that allow for automated verification, creating a structured environment for AI systems to generate and validate new mathematical insights efficiently.

8. Build System Two Recommenders

Develop recommender systems that optimize for users’ reflective, long-term preferences and higher values, rather than immediate engagement, to promote epistemically positive AI applications.

9. Build AI Contextualization Engines

Create AI systems that provide comprehensive, unbiased, and multifaceted context for queried topics, optimizing for a global understanding rather than personalized relevance or clicks.

10. Enable Customizable Recommendations

Design recommender systems with user-adjustable controls, allowing individuals to actively reduce biases or tailor recommendations to better align with their evolving interests and thinking.

11. Implement Startup Project Management

Apply startup-inspired techniques for project management, hiring, meeting structures, and people management to scientific endeavors to enhance efficiency and execution.

12. Set Finite Project Durations

Implement time-limited missions (e.g., 5-7 years) for scientific projects to create a forcing function for urgency, maintain clarity of purpose, and prevent mission dilution.

13. Plan Career Transitions for Scientists

Deliberately design personnel strategies and transition plans for non-traditional scientific roles, such as FROs, to support diverse career paths including entrepreneurship, industry, or a return to academia.

14. Empower Entrepreneurial Scientists

Create structures and provide support (e.g., coaching on operational aspects) to enable entrepreneurial-minded scientists to lead and execute projects effectively, potentially leading to spin-off companies.