Interview
summary
Survey of AI that contrasts perceptual gains from deep learning with long-range decision work, citing AlphaGo as offline machine learning plus online search and AI roots back to the 1950s. Describes concrete research: open universe probability models (Turing-equivalent probabilistic languages) to combine prior knowledge and data, algorithms for long-horizon planning under uncertainty, and a formal goal of provably beneficial systems that remain explicitly uncertain about human objectives and learn preferences cautiously.
AI-written summary · about
related talks
46mAI vs Humans Rewriting the Rules of Success
12mAugmenting Human Capabilities to New Dimensions
54mAI vs Humans: Rewriting the Rules of Success
12mThe History of AI - what can we learn from the past?
21mLearning from History: The Past as a Compass for Systems Engineers in the Age of AI
15mMore than 70 years of AI history in 15 minutes!
21mWhen the machines out-smarts us
41mAI: Q&A