More Is Different for AI
Machine learning is touching increasingly many aspects of our society, and its effect will only continue to grow. Given this, I and many others care about risks from future ML systems and how to mitigate them. When thinking about safety risks from ML, there are two common approaches, which I'll
Machine learning is touching increasingly many aspects of our society, and its effect will only continue to grow. Given this, I and many others care about risks from future ML systems and how to mitigate them. When thinking about safety risks from ML, there are two common approaches, which I'll call the Engineering approach and the Philosophy approach: The Engineering approach tends to be empirically-driven, drawing experience from existing or past ML systems and looking at issues that either: (1) are already major problems, or (2) are minor problems, but can be expected to get worse in the fu
saved by
- Winnie Xu
- Enora ʕ ᄋ ᴥ ᄋʔ
- Micah Carroll
- Serena Ge
- Hangyul Lyna Kim
- Lydia Nottingham
- Natalie Ho
- Timothy Kostolansky
- Nibir Sankar
related reading
- Future ML Systems Will Be Qualitatively Differentbounded-regret.ghost.io
- “Alignment Engineering” vs. “Misalignment Science”lesswrong.com
- Recommendations for Technical AI Safety Research Directionsalignment.anthropic.com
- AI Safety Seems Hard to Measurecold-takes.com
- A Bird's Eye View of the ML Field [Pragmatic AI Safety #2] — AI Alignment Forumalignmentforum.org
- Taking LLMs Seriously (As Language Models) — LessWronglesswrong.com
- Predictable updating about AI riskjoecarlsmith.com
- Using Dangerous AI, But Safely?youtube.com
- Fields that I reference when thinking about AI takeover prevention — LessWronglesswrong.com
- Future ML Systems Will Be Qualitatively Different — LessWronglesswrong.com
- The Tragedies of Reality Are Coming for Youalexirpan.com
- The necessity of machine learning theory in mitigating AI riskmishabelkin.substack.com