# References for DeepMind Science Research Engineer Application

Q2. About ML applications in scientific domains

1. [Black-box artificial intelligence: an epistemological and critical analysis](https://link.springer.com/article/10.1007/s00146-019-00888-w)
2. [1702.08608 Towards A Rigorous Science of Interpretable Machine Learning](https://arxiv.org/abs/1702.08608)
3. [2002.08512 The Problem with Metrics is a Fundamental Problem for AI](https://arxiv.org/abs/2002.08512)
4. [GitHub - georgestein/ml-in-cosmology: a comprehensive list of published machine learning applications to cosmology](https://github.com/georgestein/ml-in-cosmology)
5. [ExaLearn for Surrogates: Making Realistic Simulations on the Cheap](https://cs.lbl.gov/news-media/news/2019/exalearn-for-surrogates-making-realistic-simulations-on-the-cheap/)
6. In the future (~2300s), they use an "expert system" to recreate images of stars obscured by dust clouds. It creates a "computational lens that couldn't exist in the physical world". In one instance, the main characters use this technology to recreate images from a hand-terminal (smartphone) whose screen has been shattered. [Google Books Exceprt](https://books.google.co.in/books?id=T3smBgAAQBAJ&lpg=PT209&vq=computational%20lens&dq=Nemesis%20Games%20expert%20system&pg=PT208#v=snippet&q=computational%20lens&f=false)
7. [1901.10822 Design and Analysis of Machine Learning Exchange-Correlation Functionals via Rotationally Invariant Convolutional Descriptors](https://arxiv.org/abs/1901.10822)
8. [A quantitative uncertainty metric controls error in neural network-driven chemical discovery](https://pubs.rsc.org/en/content/articlelanding/2019/sc/c9sc02298h#!divAbstract)
9. [Terminator-free template-independent enzymatic DNA synthesis for digital information storage](https://www.nature.com/articles/s41467-019-10258-1)
10. [1905.11481 AI Feynman: a Physics-Inspired Method for Symbolic Regression](https://arxiv.org/abs/1905.11481)