Advancements in the Field

  • Francesco CoreaEmail author
Part of the Studies in Big Data book series (SBD, volume 50)


This chapter is divided into three sections, i.e., machine learning, neuroscience, and technology. This distribution corresponds to the main driving factors of the new AI revolution, meaning algorithms and data, knowledge of the brain structure, and greater computational power. The goal of the chapter is to give an overview of the state of art of these three blocks in order to understand what AI is going toward.


  1. Ahmad, S., & Hawkins, J. (2015). Properties of sparse distributed representations and their application to hierarchical temporal memory. arXiv:1503.07469.Google Scholar
  2. Arthur, B. W. (1994). Inductive reasoning and bounded rationality. American Economic Review, 84(2), 406–411.Google Scholar
  3. Barlow, H. B., Kaushal, T. P., & Mitchison, G. J. (1989). Finding minimum entropy codes. Neural Computation, 1(3), 412–423.CrossRefGoogle Scholar
  4. Bengio, Y., Courville, A. C., & Vincent, P. (2012). Unsupervised feature learning and deep learning: A review and new perspectives. CoRR. arXiv:abs/1206.5538.Google Scholar
  5. Candès, E. J., Romberg, J. K., & Tao, T. (2006). Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics: A Journal Issued by the Courant Institute of Mathematical Sciences, 59(8), 1207–1223.MathSciNetCrossRefGoogle Scholar
  6. Cao, Y., & Yang, J. (2015). Towards making systems forget with machine unlearning. IEEE Symposium on Security and Privacy, 2015, 463–480.Google Scholar
  7. Chen, X., Duan, X., Houthooft, R., Schulman, J., Sutskever, I., & Abbeel, P. (2016). InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets. arXiv:1606.03657.Google Scholar
  8. Giustolisi, O., & Savic, D. A. (2006). A symbolic data-driven technique based on evolutionary polynomial regression. Journal of Hydroinformatics, 8(3), 207–222.CrossRefGoogle Scholar
  9. Hawkins, J., & Ahmad, S. (2016). Why neurons have thousands of synapses, a theory of sequence memory in neocortex. Frontiers in Neural Circuits, 10, 23.Google Scholar
  10. Hinton, G., & Sejnowski, T. (1999). Unsupervised learning: Foundations of neural computation. Cambridge: MIT Press.Google Scholar
  11. Holland, J. H. (1975). Adaptation in natural and artificial systems. Cambridge: MIT Press.Google Scholar
  12. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of IEEE International Conference on Neural Networks (pp. 1942–1948).Google Scholar
  13. Koza, J. R. (1992). Genetic programming: On the programming of computers by means of natural selection. Cambridge: MIT Press.zbMATHGoogle Scholar
  14. McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115–133.MathSciNetCrossRefGoogle Scholar
  15. Rocki, K. (2016). Towards machine intelligence (pp. 1–15). CoRR. arXiv:abs/1603.08262.Google Scholar
  16. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.CrossRefGoogle Scholar
  17. Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016). Improved techniques for training GANs. arXiv:1606.03498.Google Scholar
  18. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117.CrossRefGoogle Scholar
  19. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(379–423), 623–656.MathSciNetCrossRefGoogle Scholar
  20. Stanley, K. O., & Lehman, J. (2015). Why greatness cannot be planned—The myth of the objective. Berlin: Springer International Publishing.Google Scholar
  21. Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. Transactions on Evolutionary Computation, 1(1), 67–82.CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of ManagementCa’ Foscari UniversityVeniceItaly

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