DOI: https://doi.org/10.7203/metode.9.11145

Towards artificial intelligence: Advances, challenges, and risks


Abstract


This text contains some reflections on artificial intelligence (AI). First, we distinguish between strong and weak AI, as well as the concepts related to general and specific AI. Following this, we briefly describe the main current AI models and discuss the need to provide common-sense knowledge to machines in order to advance towards the goal of a general AI. Next, we talk about the current trends in AI based on the analysis of large amounts of data, which has recently allowed experts to make spectacular progress. Finally, we discuss other topics which, now and in the future, will continue to be key in AI, before closing with a brief reflection on the risks of AI.


Keywords


strong artificial intelligence; weak artificial intelligence; common-sense knowledge; deep learning

Full Text:

PDF

References


  • Bengio., Y. (2009). Learning deep architectures for AI. Foundations and Trends in Machine Learning, 2(1), 1–127. doi: 10.1561/2200000006

  • Colton, S., Halskov, J., Ventura, D., Gouldstone, I., Cook, M., & Pérez-Ferrer, B. (2015). The Painting Fool sees! New projects with the automated painter. In International Conference on Computational Creativity (ICCC 2015) (pp. 189–196). Utah, UT: Brighma Young University. 

  • Colton, S., López de Mántaras, R., & Stock, O. (2009). Computational creativity: Coming of age. AI Magazine, 30(3), 11–14. doi: 10.1609/aimag.v30i3.2257

  • Dreyfus, H. L. (1965). Alchemy and artificial intelligence. Santa Monica, CA: RAND Corporation.

  • Dreyfus, H. L. (1992). What computers still can’t do: A critique of artificial reason. Cambridge, MA: MIT Press.

  • Ferrucci, D. A., Levas, A., Bagchi, S., Gondek, D., & Mueller, E. T. (2013). Watson: Beyond Jeopardy! Artificial Intelligence, 199, 93–105. doi: 10.1016/j.artint.2012.06.009

  • López de Mántaras, R. (2016). Artificial intelligence and the arts: Toward computational creativity. In The next step: Exponential life (pp. 100–125). Madrid: BBVA.

  • McCulloch, W. S., & Pitts, W. (1943). A logical calculus of ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115–133. doi: 10.1007/BF02478259

  • Newell, A., & Simon, H. (1976). Computer science as empirical inquiry: Symbols and search. Communications of the ACM, 19(3), 113–126. doi: 10.1145/360018.360022

  • Searle, J. R. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3(3), 417–424. doi: 10.1017/S0140525X00005756

  • Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van den Driessche, ... Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489. doi: 10.1038/nature16961

  • Weizenbaum, J. (1976). Computer power and human reasoning: From judgment to calculation. San Francisco, CA: W. H. Freeman and Co.




Bookmark and Share



Creative Commons LicenseTexts in the journal are –unless otherwise indicated– published under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

____________________________________________________________________________________________________________________