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(opens in a new window). The paper explores many research problems around ensuring that modern machine learning systems operate as intended.
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(opens in a new window). The paper explores many research problems around ensuring that modern machine learning systems operate as intended. (The problems are very practical, and we’ve already seen some being integrated intoOpenAI Gym(opens in a new window).)
Advancing AI requires making AI systems smarter, but it also requires preventing accidents—that is, ensuring that AI systems do what people actually want them to do. There’s been an increasing focus onsafety research(opens in a new window)from the machine learning community, such as a recentpaper(opens in a new window)fromDeepMind(opens in a new window)andFHI(opens in a new window). Still, many machine learning researchers have wondered just how much safety research can be done today.
The authors discuss five areas:
Many of the problems are not new, but the paper explores them in the context of cutting-edge systems. We hope they’ll inspire more people to work on AI safety research, whetherat OpenAIor elsewhere.
We’re particularly excited to have participated in this paper as a cross-institutional collaboration. We think that broad AI safety collaborations will enable everyone to build better machine learning systems.Let us know(opens in a new window)if you have a future paper you’d like to collaborate on!
Paul Christiano, Greg Brockman
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