Search results

17 items matching your search terms.
Filter the results.
Select tags
Selecting tags searches for items matching some or all of these tags.
Item type












New items since



Sort by relevance · date (newest first) · alphabetically
Levermann
Located in PIK Members
Kuhla
Located in PIK Members
Kitzmann
Located in PIK Members
Kubiczek
Located in PIK Members
Lüdeke
Located in PIK Members Matthias Lüdeke
Numerical analysis of global economic impacts
Located in Institute Complexity Science Research
New method to better understand much-employed self-learning Artificial Intelligence
11/04/2019 - Recent advancements in Artificial Intelligence (AI) research result from the combination of deep neuronal networks and reinforcement learning. In the latter, agents are able to learn rewarding behaviours in unknown environments by an iterative trial-and-error behaviour update process. But this process is not yet fully understood. Reinforcement learning agents are a specific area of AI. As AI can have a big impact on society, a better understanding AI systems is crucial to assess potential challenges and risks. Already today, AI is employed to steer cars, manage production lines, or even draft texts. A team of scientists from the Potsdam Institute for Climate Impact Research has developed a new method to investigate those algorithms using insights from statistical physics. Published in the journal Physical Reviews E, their insights can help to improve the design of large-scale AI reinforcement learning systems.
Located in News Latest News
Willner
Located in PIK Members
Quante
Located in PIK Members
Schuster
Located in PIK Members