Joel Lehman is a Scientist at OpenAI. Prior to that, he was a Senior Research Scientist at Uber AI where he led efforts into AI safety research. Before his stint at Uber AI, he was the first employee of Geometric Intelligence (acquired by Uber) and a tenure-track professor at the IT University of Copenhagen, where his research focused on evolutionary computation, neural networks, artificial life, and computational creativity.
He was co-inventor of the popular novelty search evolutionary algorithm, and co-wrote a popular science book called Why Greatness Cannot be Planned, on what AI search algorithms imply for individual and societal accomplishment.
It is well-known among practitioners of machine learning that algorithms often find unexpected ways to satisfy an objective you supply to them. For reinforcement learning algorithms, where agents are trained to perform complicated tasks through rewards and punishments, specifying the correct rewards is in particular surprisingly challenging.
I first review many examples of (often entertaining) machine creativity resulting from unanticipated flaws in reward functions and simulators for reinforcement learning. I will then introduce the field of AI safety, which aims to alleviate such unexpected machine behavior, by posing and solving the technical challenges that cause learning algorithms’ behavior to deviate from our intentions for them.
I will highlight current research and future challenges, including the challenge of making machines with ethical sensibilities, concluding by arguing that progress in AI safety is important for both commercial endeavours and society at large.
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