Science in the age of machine learning

The rise of machine learning is moving research away from tightly controlled, theory-guided experiments towards an approach based on data-driven searches. Abbas Ourmazd describes how this change might profoundly affect our understanding and practice of physics.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    Ollivier, Y. Riemannian metrics for neural networks I: feedforward networks. Inf. Inference 4, 108–153 (2015).

    MathSciNet  Article  Google Scholar 

  2. 2.

    Pascanu, R., Gulcehre, C., Cho, K. & Bengio, Y. How to construct deep recurrent neural networks. In Proceedings of the Second International Conference on Learning Representations (ICLR, 2014).

  3. 3.

    Alber, M. et al. Integrating machine learning and multiscale modeling — perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. NPJ Digit. Med. 2, 115 (2019).

    Article  Google Scholar 

  4. 4.

    Giannakis, D., Schwander, P. & Ourmazd, A. The symmetries of image formation by scattering. I. Theoretical framework. Opt. Express 20, 12799–12826 (2012).

    ADS  Article  Google Scholar 

  5. 5.

    Hutson, M. How researchers are teaching AI to learn like a child. Science https://doi.org/10.1126/science.aau2576 (2018).

    Article  Google Scholar 

  6. 6.

    Heaven, D. Why deep-learning AIs are so easy to fool. Nature 574, 163–166 (2019).

    ADS  Article  Google Scholar 

  7. 7.

    Lafon, S., Keller, Y. & Coifman, R. R. Data fusion and multicue data matching by diffusion maps. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1784–1797 (2006).

    Article  Google Scholar 

  8. 8.

    Fung, R. et al. Dynamics from noisy data with extreme timing uncertainty. Nature 532, 471–475 (2016).

    ADS  Article  Google Scholar 

  9. 9.

    Hosseinizadeh, A. et al. Conformational landscape of a virus by single-particle X-ray scattering. Nat. Methods 14, 877–881 (2017).

    Article  Google Scholar 

  10. 10.

    Dashti, A. et al. Trajectories of the ribosome as a Brownian nanomachine. Proc. Natl Acad. Sci. USA 111, 17492–17497 (2014).

    ADS  Article  Google Scholar 

Download references

Acknowledgements

The author acknowledges valuable discussions with Tony Hey, Larry Jackel, E. Lattman and many UWM colleagues. Any errors are the sole responsibility of the author. This work was supported by the US Department of Energy, Office of Science, Basic Energy Sciences under award DE-SC0002164 (algorithm design and development), and by the US National Science Foundation under awards STC 1231306 (numerical trial models and data analysis) and 1551489 (underlying analytical models).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Abbas Ourmazd.

Ethics declarations

Competing interests

The author declares no competing interests.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ourmazd, A. Science in the age of machine learning. Nat Rev Phys 2, 342–343 (2020). https://doi.org/10.1038/s42254-020-0191-7

Download citation