Concrete AI safety problems
OpenAI, Berkeley, and Stanford just mapped the concrete problems keeping AI systems from doing what we actually want them to do.
Why it matters
A foundational research paper that codified AI safety as a rigorous academic discipline, establishing frameworks that would shape industry governance and investment for the next decade. Critical for understanding how modern safety debates evolved.
The key facts
10 to knowCo-authored by OpenAI, UC Berkeley, and Stanford researchers
Led by Google Brain team
Focuses on ensuring ML systems operate as intended
Published June 2016 — early foundational work in AI safety research
Explores concrete (not theoretical) safety research problems
Co-authored by OpenAI, Google Brain, UC Berkeley, and Stanford researchers
Titled 'Concrete Problems in AI Safety'
Published June 21, 2016
Explores research problems ensuring ML systems operate as intended
Foundational work that shaped industry safety discourse
Go to the source
OpenAI Blogopenai.com
Publisher excerpt: We (along with researchers from Berkeley and Stanford) are co-authors on today’s paper led by Google Brain researchers, Concrete Problems in AI Safety. The paper explores many research problems around ensuring that modern machine learning systems operate as intended.