Towards artificial intelligence: Advances, challenges, and risks
DOI:
https://doi.org/10.7203/metode.9.11145Keywords:
strong artificial intelligence, weak artificial intelligence, common-sense knowledge, deep learningAbstract
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.
Downloads
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.
Downloads
Additional Files
Published
How to Cite
-
Abstract9500
-
(Español)9
-
PDF1285
Issue
Section
License
Copyright (c) 2023 CC BY SA
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All the documents in the OJS platform are open access and property of their respective authors.
Authors publishing in the journal agree to the following terms:
- Authors keep the rights and guarantee Metode Science Studies Journal the right to be the first publication of the document, licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License that allows others to share the work with an acknowledgement of authorship and publication in the journal.
- Authors are allowed and encouraged to spread their work through electronic means using personal or institutional websites (institutional open archives, personal websites or professional and academic networks profiles) once the text has been published.