Porr B. (2021) Why Is Current AI Divinatory?. Constructivist Foundations 16(3): 362–363

Volume 16 · Number 3 · Pages 362–363

< Previous Paper · Next Paper >

Why Is Current AI Divinatory?

Log in download the full text in PDF

> Citation > Similar > References > Add Comment

Abstract

Open peer commentary on the article “Systems Theory and Algorithmic Futures: Interview with Elena Esposito” by Elena Esposito, Katrin Sold & Bénédicte Zimmermann. Abstract: Esposito claims that current algorithms are divinatory. I agree with her, but for different reasons. Her point is that they are divinatory because they only use correlations, but current algorithms do use predictions and models. Instead, I argue that algorithms are divinatory because they are mostly unexplainable black boxes.

Citation

Porr B. (2021) Why is current ai divinatory? Constructivist Foundations 16(3): 362–363. https://constructivist.info/16/3/362

Export article citation data: Plain Text · BibTex · EndNote · Reference Manager (RIS)

References

Ashby W. R. (1956) An introduction to cybernetics. Chapman and Hall, London. ▸︎ Google︎ Scholar

D’Azzo J. J. (1988) Linear control system analysis and design. McGraw, New York. ▸︎ Google︎ Scholar

Foerster H. von (1984) Technology, what will it mean to librarians? A Response. In: Observing systems. Second edition. Intersystems Publicatons, Seaside CA: 211–230. ▸︎ Google︎ Scholar

Guo X., Singh S., Lee H., Lewis R. L. & Wang X. (2014) Deep learning for real-time Atari game play using offline Monte-Carlo tree search planning. Advances in Neural Information Processing Systems 27: 3338–3346. ▸︎ Google︎ Scholar

Hochreiter S. & Schmidhuber J. (1997) Long short-term memory. Neural Computation 9(8): 1735–1780. ▸︎ Google︎ Scholar

Kalman R. E. (1963) Mathematical description of linear dynamical systems. Journal of the Society for Industrial and Applied Mathematics, Series A: Control 1(2): 152–192. ▸︎ Google︎ Scholar

Kroll J. A., Huey J., Barocas S., Felton E. W., Reidenberg J. R., Robinson D. G. & Yu H. (2017) Accountable algorithms. University of Pennsylvania Law Review 848: 633–705. ▸︎ Google︎ Scholar

Luhmann N. (1984) Soziale Systeme. Suhrkamp, Frankfurt am Main. English translation: Luhmann N. (1995) Social systems. Stanford University Press, Stanford CA. ▸︎ Google︎ Scholar

Luhmann N. (1990) Die Wissenschaft der Gesellschaft [The science of society]. Suhrkamp, Frankfurt am Main. ▸︎ Google︎ Scholar

Maturana H. R. & Varela F. J. (1980) Autopoiesis and cognition: The realization of the living. Reidel, Dordrecht. ▸︎ Google︎ Scholar

Porr B. & Wörgötter F. (2005) Inside embodiment: What means embodiment to radical constructivists? Kybernetes 34(1/2): 105–117. https://cepa.info/5170

Porr B., von Ferber C. & Wörgötter F. (2003) ISO-learning approximates a solution to the inverse-controller problem in an unsupervised behavioural paradigm. Neural Computation 15: 865–884. ▸︎ Google︎ Scholar

Roberts M., Driggs D., Thorpe M. et al. (2021) Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nature Machine Intelligence 3(3): 199–217. https://doi.org/10.1038/s42256–021–00307–0

Rudin C. (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence 1(5): 206–215. ▸︎ Google︎ Scholar

Wu Y., Schuster M., Chen Z., Le Q. V., Norouzi M., Macherey W., Krikun M., Cao Y., Gao Q., Macherey K. & Klingner J. (2016) Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv:1609. 08144. ▸︎ Google︎ Scholar

Comments: 0

To stay informed about comments to this publication and post comments yourself, please log in first.