TY  - CONF
AU  - Rathkopf, Charles
TI  - Deep learning models in science: some risks and opportunities
M1  - FZJ-2024-05893
PY  - 2024
AB  - Deep neural networks offer striking improvements in predictive accuracy in many areas of science, and in biological sequence modeling in particular. But that predictive power comes at a steep price: we must give up on interpretability. In this talk, I argue - contrary to many voices in AI ethics calling for more interpretable models - that this is a price we should be willing to pay.
T2  - Helmholtz workshop on the ethics of AI in scientific practice
CY  - 11 Jun 2024, Jülich/Düsseldorf (Germany)
Y2  - 11 Jun 2024
M2  - Jülich/Düsseldorf, Germany
LB  - PUB:(DE-HGF)31
UR  - https://juser.fz-juelich.de/record/1031969
ER  -