Benjamin Hilton, AI safety technical research career reviewer, discusses various roles and career paths within AI safety technical research, tips for becoming an ML engineer and learning safety techniques, self-study recommendations for machine learning and AI safety, considering a PhD for AI safety research, and various organizations and research labs focusing on AI safety.
Developing AI systems that are safe and beneficial is crucial for mitigating risks and ensuring ethical AI.
Technical AI safety research offers high social impact potential in reducing the risks and challenges posed by misaligned AI systems.
Approaches to AI safety include learning from human feedback, interpretability research, and building cooperative AI, requiring ongoing research and collaboration.
Deep dives
Opportunity in AI Safety Technical Research
The podcast episode discusses the progress in AI and the significant risks associated with it. It highlights the need to develop AI systems that are safe, ethical, and beneficial for everyone. Pursuing technical solutions to prevent AI systems from engaging in dangerous behavior is seen as a promising approach. The field of technical AI safety research is relatively new and highly neglected, but it offers high social impact potential. The episode emphasizes the importance of a quantitative background and programming skills for a successful career in this field.
Addressing AI Safety Risks
The episode explores the risks posed by misaligned AI systems and their potential to become an existential threat to civilization. It emphasizes the need to find technical solutions to prevent AI systems from executing plans that could jeopardize humanity's ability to influence the world. The discussion highlights the importance of reducing risks from AI through empirical research, theoretical analysis, and building cooperative AI systems. The field of AI safety technical research is still young, making it a highly challenging and impactful career path.
Technical Approaches to AI Safety
The episode presents various technical approaches to AI safety, including learning from human feedback, threat modeling, interpretability research, anti-misuse research, robustness of neural networks, and building cooperative AI. These approaches aim to reduce the risk of AI-related catastrophes by addressing issues such as safety, reliability, interpretation, and cooperation. The podcast highlights the need for ongoing research and discusses different organizations and academic institutions actively working in this field.
Challenges and Downsides
The episode mentions the challenges and downsides of pursuing a career in AI safety technical research. It acknowledges that entering the field can be competitive and requires strong quantitative and programming skills. It may also involve some delay in having a social impact due to the need to build career capital and expertise. Furthermore, there is a risk of doing harm and uncertainties surrounding the effectiveness of different technical approaches. The episode emphasizes the importance of personal fit and highlights alternative paths to address AI risks, such as AI governance and policy.
Entry and Career Development
The episode provides guidance on entering the field of AI safety technical research. It suggests acquiring a strong quantitative background and programming skills, along with gaining experience in software or ML engineering. It discusses the options of pursuing a PhD and highlights the importance of research experience and publications. The podcast also mentions several recommended organizations and institutions that focus on AI safety research, along with potential roles as an empirical or theoretical researcher. Additionally, it offers resources and advice for individuals interested in this career path.
