Sokoban, and Thanks for All the Fish!

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In COVID-related AI news, Andy and Dave discuss a machine learning transformer model from Facebook AI and the NYU School of Medicine that uses x-rays to determine whether a COVID patient might need more intensive care. A for-pay report from Synced provides a survey of China's AI efforts in response to COVID-19. In regular AI news, European Parliament members adopt guidelines for military and non-military uses of AI. Meanwhile, the UK Competition and Markets Authority cautions that algorithms can damage online competition and should face regulatory scrutiny. Researchers at NOIRLab use machine learning to identify just over 1200 potential new gravitational lenses. Researchers at Harvard use fish-inspired robots to demonstrate coordinated swarm movements without any outside control. Nature provides reflections from various authors on AI. And the AI Newsletter compiles a list of the 100 most influential people in AI. In research topics, researchers at Cornell demonstrate a curriculum strategy to solve hard Sokoban (the "warehouse man" game) problems, and builds on a pool of sub-tasks. And in a similar, but unrelated effort, researchers at Berkeley and Google Research create a trio of agents to create challenging but feasible environments for the primary agent, the protagonist to navigate; they use an antagonist agent, which tries to create hard environments, while a third agent maximizes the differential between the other two agents, which keeps the tasks just at the edge of the protagonist's ability to solve. An article in the Journal of AI Research demonstrates that containment of a superintelligence is impossible, due to fundamental limits inherent in computing itself. And finally, Chitta Ranjan provides the book of the week, in Understanding Deep Learning: Application to Rare Event Prediction.