Why AI Safety Research Matters

Current Challenges in AI Safety Research

1. Value Alignment: Ensuring AI systems act in accordance with human values and intentions.

2. Robustness: Developing AI systems that perform reliably in unexpected or adversarial situations.

3. Transparency and Interpretability: Creating AI systems whose decision-making processes can be understood and audited.

4. Scalable Oversight: Maintaining human control over increasingly complex AI systems.

5. AI Governance: Developing frameworks for the responsible development and deployment of AI.

6. Long-term Planning: Preparing for potential long-term consequences of advanced AI.

7. Reward Modeling: Accurately specifying the objectives we want AI systems to pursue.

Potential Research Directions

1. Technical AI Safety

• Developing advanced AI alignment techniques

• Investigating methods for safe exploration in reinforcement learning

• Creating frameworks for AI corrigibility and interruptibility

2. AI Governance and Policy

• Analyzing potential AI governance structures

• Developing international cooperation frameworks for AI development

• Investigating the societal impacts of different AI deployment scenarios

3. AI Ethics and Value Alignment

• Formalizing human values for AI systems

• Developing methods for aggregating diverse human preferences

• Investigating cultural differences in AI ethics

4. AI Transparency and Interpretability

• Developing techniques for explaining AI decision-making

• Creating methods for auditing AI systems

• Investigating the trade-offs between performance and interpretability

5. Robustness and Security in AI

• Developing adversarial training techniques

• Investigating AI systems' robustness to distribution shift

• Creating frameworks for AI system security

6. Long-term AI Development Scenarios

• Modeling potential development trajectories of AI

• Investigating the concept of artificial general intelligence (AGI)

• Analyzing potential economic and social impacts of advanced AI

7. AI Safety Measurements and Benchmarks

• Developing standardized tests for AI safety properties

• Creating benchmarks for AI alignment

• Investigating methods for measuring AI capabilities and risks

Sample Research Questions

1. How can we develop AI systems that can reliably identify and mitigate their own potential negative impacts?

2. What are the most promising approaches for aligning advanced AI systems with human values, and how can they be empirically evaluated?

3. How can we create AI governance structures that are robust to rapid technological advancements and potential power imbalances?

4. What are the potential long-term consequences of different AI development scenarios, and how can we prepare for them?

5. How can we develop AI systems that can accurately infer and respect implicit human preferences?

6. What are the most effective methods for ensuring the interpretability of deep learning models without significantly sacrificing performance?

7. How can we create AI systems that remain aligned with human values even as they potentially recursively self-improve?

Key Resources

• Machine Intelligence Research Institute

• Center for Human-Compatible AI

• Future of Humanity Institute - AI Safety Research

• OpenAI

• AI Alignment Forum

• Center for AI Safety

Get Started with Your AI Safety Thesis

Are you passionate about ensuring the beneficial development of AI? Here's how you can get started:

1. Explore the Topics: Review the research directions and sample questions above to find areas that align with your interests and skills.

2. Connect with Experts: Reach out to our network of AI Safety experts for guidance and mentorship.

3. Access Resources: Utilize our curated list of resources to deepen your understanding of the field.

4. Develop Your Proposal: Use our thesis proposal templates and guides to craft a strong research proposal.