From Machine Learning to Machine Teaching – Making Machines AND Humans Smarter (ML2MT)

Inspired by the success of machine learning, like in the board game Go (AlphaGo Zero) as a prime example, the project aims at a better understanding how humans and machines in collaborative human-AI systems can unlock new knowledge from symbiotic interaction with each other. To this end, the consortium explores the analytical and technical foundations that account for success in transferring new knowledge from intelligent machines to humans and vice versa. This will be investigated in case studies from medical diagnostics, economic decision-making and financial market forecasting using hybrid human-machine systems. The team wants to derive generalizable socio-technological and psychological findings and make recommendations in order to further improve the interaction between humans and machines.

Partner institutions: Goethe University Frankfurt, University Clinic Frankfurt, TU Darmstadt, Leibniz Institute for Research and Information in Education (DIPF)
Project leaders: Hendrik Drachsler, Oliver Hinz (Coordinator), Kristian Kersting, Yee Lee Shing, Loriana Pelizzon, Gernot Rohde, Tobias Tröger
Project duration: 4 years
More information