That assumes it can even be done. It's worth looking into. There have been some projects in those areas. Mixing probabilistic logic with deep learning: https:

That assumes it can even be done. It's worth looking into. There have been some projects in those areas. Mixing probabilistic logic with deep learning: https://arxiv.org/abs/1808.08485 https://arxiv.org/abs/1808.08485 https://github.com/ML-KULeuven/deepproblog https://github.com/ML-KULeuven/deepproblog Combining decision trees with neural nets for interpretability: https://arxiv.org/abs/2011.07553 https://arxiv.org/abs/2011.07553 https://arxiv.org/pdf/2106.02824v1 https://arxiv.org/pdf/2106.02824v1 https://arxiv.org/pdf/1806.06988 https://arxiv.org/pdf/1806.06988 https://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-65.pdf https://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-... It looks like model transfer from uninterpretable, pretrained models to interpretable models is the best strategy to keep using. That also justifies work like Ai2's OLMo model where all pretraining data is available to use other techniques, like those in search engines, to help explainable models connect facts back to source material.