Towards artificial intelligence: Advances, challenges, and risks
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.
En aquest article es fa la distinció entre IA forta i feble i entre IA general i específica. Després es descriuen els principals models existents insistint en la importància de la corporeïtat, aspecte clau per assolir IA de tipus general. També es discuteix la necessitat de poder dotar de coneixements de sentit comú a les màquines per avançar cap a l’objectiu de construir IA general. Després parlem de les tendències en IA basada en l’anàlisi de grans quantitats de dades que han permès assolir espectaculars progressos molt recentment, esmentant també les dificultats actuals d’aquesta aproximació a la IA. Finalment parlem d’altres temes que són clau en IA i tanquem amb una breu reflexió sobre els riscos de la IA.
Bibliographic data
| Translated title: | Progressos, reptes i riscos de la Intel·ligència Artificial |
|---|---|
| Journal Title: | Mètode Science Studies Journal |
| Author: | Ramon López de Mántaras |
| Palabras clave: | |
| Traslated Keywords: | |
| Language: | English |
| Get full text: | https://ojs.uv.es/index.php/Metode/article/view/11145 |
| Resource type: | Journal Article |
| Source: | Mètode Science Studies Journal; No 9 (Year 2019). |
| DOI: | http://dx.doi.org/10.7203/metode.9.11145 |
| Publisher: | Universitat de València |
| Usage rights: | Reconocimiento - NoComercial - SinObraDerivada (by-nc-nd) |
| Knowledge areas / Categories: | Social Sciences/Humanities --> History --AMP-- Philosophy Of Science |
Statistical data
- Views
- Consultations
- Citation style
- Share
- Export record
- Favourites
Bibliometric data
WOS
| Bibliography: |
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. |
|---|