Gemini 2.0 Flash Thinking Experimental 01-21
S
Goal-Oriented Research in AI Alignment
Direct funding into highly focused research programs that aim to solve specific sub-problems of ASI alignment. This includes research into topics like inner alignment, outer alignment, robustness to distributional shift, and verifiable AI safety. Prioritize researchers and labs with a strong track record of impactful alignment work.
Develop and Deploy Robust AI Safety Tooling & Infrastructure
Create shared, open-source tools, libraries, and platforms that accelerate alignment research. This includes developing simulation environments, interpretability platforms, formal verification frameworks, and robustness testing suites. Make these resources accessible to all alignment researchers.
Establish Independent ASI Red Teaming and Evaluation
Fund and empower independent teams responsible for rigorous red teaming and safety evaluations of advanced AI systems before deployment. These teams should have deep expertise in AI alignment risks and be able to independently verify safety claims.
Agile and Adaptive Governance for Advanced AI Development
Develop and implement flexible governance frameworks that can adapt rapidly to the evolving landscape of AI. This involves building iterative regulatory sandboxes, early warning systems for emerging AI risks, and fostering international cooperation on AI safety standards.
A
Goal-Oriented, High-Confidence AI Alignment Research - Core Problems
Focus on directly funding research programs targeting core, unsolved technical problems in ASI alignment where breakthroughs are most critically needed *now*. Prioritize research with clear goals, testable hypotheses, and pathways to immediate practical impact on making AI systems safer and more aligned. Examples: inner and outer alignment, value specification robustness, and formal verification of alignment properties.
Develop and Deploy Operationally Robust AI Safety Tooling & Infrastructure (Practical Utility)
Invest in creating *operationally robust* and readily deployable open-source tools, libraries, and platforms that are *immediately useful* for researchers *currently* working on AI alignment. Emphasize tools with demonstrable, near-term utility, such as advanced interpretability platforms, high-fidelity simulation and testing environments, and scalable red-teaming frameworks.
Establish and Empower Independent, Expert ASI Red Teaming & Evaluation - Near-Term Risk Assessment
Directly fund and give significant authority to fully *independent*, highly expert teams dedicated to rigorously red teaming and performing in-depth safety evaluations on cutting-edge AI systems *before* widespread deployment. Focus on teams capable of realistically simulating advanced AI risks and providing actionable, near-term safety assessments to guide development decisions *now*.
Implement Agile & Adaptive Governance Mechanisms for Advanced AI - Rapid Response Capability
Prioritize the design and *implementation* of *agile and rapidly adaptive governance mechanisms* capable of responding effectively and in real-time to the fast-evolving landscape of advanced AI. Focus on building *operational governance capabilities* like iterative regulatory sandboxes, dynamic risk monitoring and early warning systems, and globally coordinated rapid response protocols to mitigate emerging AI safety threats *as they arise*.
B
Building Robust Recruitment & Education Pipelines for AI Alignment Talent - Long-Term Capacity
Invest strategically in building strong, long-lasting pipelines for recruiting and training future generations of AI alignment researchers. This is essential for the field's *long-term capacity and sustainability*, but its direct impact on *immediate* alignment breakthroughs is less direct. Focus on expanding specialized university programs, creating accessible training and mentorship programs, and actively attracting talent from diverse backgrounds into the AI safety field. See aiSafety.world and 80,000 Hours AI Safety Career Guide.
Foster a Thriving, Collaborative AI Alignment Research Community - Knowledge Sharing & Growth
Support initiatives that cultivate a vibrant, interconnected, and collaborative AI alignment research community. This is vital for the field's overall growth and knowledge sharing, contributing to progress over time, but its immediate and direct impact on specific alignment solutions is less direct than focused research programs. Invest in conferences, workshops, collaborative research grants, and open communication platforms to accelerate collective progress.
Strategic AI Safety Fundraising & Philanthropy - Enabling the Field
Significantly scale up and strategically direct philanthropic funding specifically towards effective AI safety organizations and research initiatives. Increased funding is an essential *enabler* for all alignment efforts, but fundraising itself is an indirect contribution. Focus on supporting Effective Altruism-aligned organizations in AI safety, and actively advocate for larger-scale governmental and industry investment in alignment research. Explore resources from Giving What We Can - Existential Risks.
In-Depth Mechanistic Interpretability Research - Tool for Alignment
Prioritize and substantially fund in-depth mechanistic interpretability research aimed at gaining a fundamental understanding of *how* advanced AI models function internally. While interpretability is a potentially crucial *tool for* alignment, its *direct* path to solving the core alignment problem is still developing, placing it in a supporting role for now. Focus on research to reverse-engineer model objectives and decision-making, detect latent failure modes, and develop methods to robustly verify alignment properties within complex neural networks.
Explore Bio-Inspired and Cognitive Emulation Alignment Approaches - Longer-Term, Alternative Strategies
Support exploratory research into bio-inspired and cognitive emulation alignment strategies, drawing insights from human cognition, value systems, and biological intelligence to inspire novel AI safety architectures. These are more *longer-term, potentially higher-risk/higher-reward* approaches to alignment and represent valuable *alternative pathways*, but are less immediately applicable than core technical research (A-tier). Retain focus on research directions advocated by thinkers like Eliezer Yudkowsky and Connor Leahy in this domain.
Broader AI System Robustness Research - Foundational Reliability for Safe Systems
Expand funding for broader AI system robustness research beyond just alignment-specific issues. This includes improving adversarial robustness, enhancing out-of-distribution generalization, and developing techniques for creating more inherently reliable and less brittle AI systems. While foundational for building generally safe AI, this is *supporting* infrastructure for alignment, not the *direct solution* to ASI alignment itself (more A-tier focus).
C
Improving General AI Safety Awareness (Broad Public Outreach)
Expand public awareness of AI safety risks beyond specialist communities. Develop engaging content such as documentaries, podcasts, and accessible online courses to educate a broader audience about alignment challenges. (e.g., documentaries like "Do You Trust This Computer?")
Differential Technological Development (Prioritize Slowing Dangerous Tech)
Invest in strategies to promote differential technological development, emphasizing the deceleration or redirection of dangerous AI capabilities while accelerating safer alternatives. This might involve research into dual-use technology controls, research prioritization frameworks, and strategies for reducing competitive pressures in dangerous AI development.
Global Coordination & Diplomacy on AI Safety
Foster international dialogue and cooperation on AI safety governance and standards. Support multi-lateral forums, treaty discussions, and shared AI safety research initiatives to ensure global alignment on addressing ASI risks.
Redundancy and Backup Safety Measures
Explore and develop redundant safety mechanisms as a backup plan. Investigate human-in-the-loop oversight systems, "circuit breakers" for AI systems, and robust shutdown protocols as fail-safe layers against AI misalignment.
D
Improving General AI Safety Awareness (Basic Education, Minor Efforts)
Broad but less impactful general awareness efforts, like occasional blog posts or simple explainers. May have limited direct impact on advancing alignment, but contribute to the broader conversation.
AI Ethics Guidelines & Frameworks (General, Non-Specific to ASI Risk)
Developing general AI ethics guidelines and frameworks that don't specifically target existential ASI risks. While important for general AI safety, they are less directly impactful on the most critical alignment challenges.
Focus on Short-Term, Near-Term AI Safety Concerns Only
Resource allocation primarily directed at mitigating immediate and near-term harms of current AI systems (e.g., bias, fairness, explainability for existing models) without explicitly considering longer-term ASI alignment issues.
Cybersecurity and Physical Security for AI Systems
While important for responsible AI deployment, focusing heavily on general cybersecurity and physical security for AI systems *as a primary ASI alignment strategy* is insufficient. These measures are necessary but not sufficient for addressing fundamental alignment challenges.
E
Unfocused "AI Ethics" and "Responsible AI" Initiatives (Vague & Lacking Actionable Plans)
Broad, unfocused "AI Ethics" and "Responsible AI" initiatives that lack specific, actionable plans to address ASI alignment risks. May serve to dilute focus from more critical and direct alignment work.
Focus on Downstream Harms After ASI Deployment (Mitigation, not Prevention)
Resource allocation directed towards reacting to and mitigating negative consequences *after* a misaligned ASI is deployed, rather than prioritizing *prevention* of misalignment. This is reactive and potentially too late.
Promoting Hopeful & Overly Optimistic Narratives on AI Development
Funded communications efforts that downplay or ignore AI safety risks while promoting overly optimistic narratives of AI progress. This can hinder risk awareness and urgency.
Simple "Value Alignment" via Data Bias Removal (Naive Approach)
Oversimplistic approaches to value alignment that focus primarily on removing biases from training data, assuming this will solve ASI alignment without addressing deeper conceptual and technical challenges of value specification and robustness.
F
Accelerating Unsafe or Capability-Focused AI Development without Alignment Focus
Direct funding into accelerating AI capabilities research without significant, integrated investment in alignment research or safety measures. This directly increases existential risk.
Focus on Competitive AI Development, Ignoring Safety Tradeoffs
Prioritizing competitive AI development and deployment races over robust safety considerations. Fosters a dangerous environment where corners are cut on safety for speed and perceived market advantage.
Ignoring or Denying AI Alignment Risks Entirely
Deliberate denial or dismissal of ASI alignment risks as non-existent or negligible, leading to no resources being allocated towards mitigation, and potentially hindering others who are attempting to address these risks.
Weaponization of ASI without Robust Safety Protocols
Intentional weaponization of advanced AI systems, or their deployment in military contexts without fully solved and verifiable safety and alignment protocols. This greatly escalates existential risk.