Making machine learning robust, generalizable and collaborative by engaging humans and AI in the problem-solving loop. (IMAGE)
Caption
Real-world scientific research, for example, iteratively improves through learning in the design-build-test-learn loop (on the right). On the left, active re-learning from expert human knowledge in parallel with simulation in the design-build-test-loop. In combination, a solution for machine learning that can be robustly deployed ‘out-of-distribution’ i.e. outside of its learning context, which can lead to accelerated breakthroughs in R&D.
Credit
Matti Ahlgren/Aalto University
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Credit must be given to the creator. Adaptations must be shared under the same terms.
License
CC BY-SA