Man vs. Machine: The Battle for the Soul of Data Science

  • David Reid


David Reid asks if data science is just a trendy rebadging of statistics, or whether it is something fundamentally new. The chapter describes how two camps—data scientists and statisticians—are battling for the ‘soul’ of data science. The essence of this fight is the argument for and against the concept of ‘automated reasoning’. Just as the wheel allowed mankind to physically carry far greater loads, could another technology—automated reasoning—enable people to share intellectual burdens? Advances in Artificial Intelligence using Big Data could mean people are no longer the sole agents of genuine discovery and may soon share this special attribute with genuinely intelligent and inventive machines.


Big Data Algorithms Data science Statistics Artificial intelligence Machine learning Automated reasoning Human intelligence 


  1. Culkin, J. (1967). A schoolman’s guide to Marshall McLuhan. Saturday Review, pp. 51–53, 71–72Google Scholar
  2. Culley, A., Clune, J., Tarapore, D., & Mouret, J.-B. (2015). Robots that can adapt like animals. Nature, 521(7553), 503–507.CrossRefGoogle Scholar
  3. Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2011). Large vocabulary continuous speech recognition with context-dependant DBN-HMMS. International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE (pp. 4688–4691).Google Scholar
  4. Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64–73.CrossRefGoogle Scholar
  5. Enlitic. (2015). A modern machine learning company dedicated to revolutionising diagnostic healthcare. Retrieved July 14, 2015, from
  6. Frey, C. B., & Osborne, M. (2013). The future of employment: How susceptible are jobs to computerisation? Oxford University Programme on the Impact of Future Technologies Machine and Employment Workshop.Google Scholar
  7. Furber, S. (2015). The Human Brain Project. Retrieved July 14, 2015, from
  8. Graves, A., & Schmidhuber, J. (2009). Offline handwriting recognition with multidimensional recurrent neural networks. In Y. Bengio and D. Schuurmans and J.D. Lafferty and C.K.I. Williams(Eds.), Advances in Neural Information Processing Systems 22 (NIPS 2009) (pp. 545–552). Cambridge, MA: MIT Press.Google Scholar
  9. Hawking, S. (2014). Stephen Hawking warns AI could end mankind. BBC Report. Retrieved July 14, 2015, from
  10. Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507.CrossRefGoogle Scholar
  11. Jackson, G. (2014). Big Data: When N doesn’t equal all. Retrieved July 14, 2015, from
  12. Lari, A., & Douma, F. (2014). Self-driving vehicles: Current status of autonomous vehicle development and Minnesota policy implications. Preliminary White Paper, Minnesota Journal of Law, Science and Technology.Google Scholar
  13. Le, Q. V., Ranzato, M. A., Monga, R., Devin, M., Chen, K., Corrado, G. S., et al. (2013). Building high-level features using large scale unsupervised learning. 29th International Conference in Machine Learning, Edinburgh.Google Scholar
  14. Mayer-Schönberger, V., & Cukier, K. (2013). Big Data: A revolution that will transform how we live, work and think. Boston, MA: Houghton Mifflin Harcourt. ISBN 10: 1848547900.Google Scholar
  15. Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., et al. (2015a). Playing Atari with deep reinforcement learning. Technical Report: DeepMind Technologies, arXiv:1312.5602.Google Scholar
  16. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., et al. (2015b). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533.Google Scholar
  17. Modha, D. S. (2015). Introducing a brain-inspired computer: TrueNorth’s neurons to revolutionize system architecture. Retrieved July 14, 2015, from
  18. Naur, P. (1974). Concise survey of computer methods. New York: Petrocelli Books. ISBN 10: 0884053148.Google Scholar
  19. Sharkey, N. (2008). Computer science: The ethical frontiers of robotics. Science, 322(5909), 1800–1801.CrossRefGoogle Scholar
  20. Smith, M. (2014). One-third of jobs in the UK at risk from automation. Retrieved July 14, 2015, from
  21. Tukey, J. W. (1962). The future of data analysis. The Annals of Mathematical Statistics, 33(1).Google Scholar
  22. Tukey, J. W. (1980). We need both exploratory and confirmatory. The American Statistician, 34(1), 23–25.Google Scholar
  23. Wakefield, J. (2015). Driverless car review launched by UK government. BBC Report. Retrieved July 14, 2015, from
  24. Watson. (2015). Say hello to Watson. Retrieved July 14, 2015, from

Copyright information

© The Editor(s) (if applicable) and The Author(s) 2016

Authors and Affiliations

  • David Reid
    • 1
  1. 1.Department of Mathematics and Computer ScienceLiverpool Hope UniversityLiverpoolUK

Personalised recommendations