Abstract
This article offers a critical inquiry of contemporary neural network models as an instance of machine learning, from an interdisciplinary perspective of AI studies and performativity. It shows the limits on the architecture of these network systems due to the misemployment of ‘natural’ performance, and it offers ‘context’ as a variable from a performative approach, instead of a constant. The article begins with a brief review of machine learning-based natural language processing systems and continues with a concentration on the relevant model of recurrent neural networks, which is applied in most commercial research such as Facebook AI Research. It demonstrates that the logic of performativity is not brought into account in all recurrent nets, which is an integral part of human performance and languaging, and it argues that recurrent network models, in particular, fail to grasp human performativity. This logic works similarly to the theory of performativity articulated by Jacques Derrida in his critique of John L. Austin’s concept of the performative. Applying Jacques Derrida’s work on performativity, and linguistic traces as spatially organized entities that allow for this notion of performance, the article argues that recurrent nets fall into the trap of taking ‘context’ as a constant, of treating human performance as a ‘natural’ fix to be encoded, instead of performative. Lastly, the article applies its proposal more concretely to the case of Facebook AI Research’s Alice and Bob.
Similar content being viewed by others
Change history
18 October 2019
The article Interperforming in AI:
Notes
FAIR published their early MTD research findings on the Facebook Code Blog—available at https://code.fb.com/ml-applications/deal-or-no-deal-training-ai-bots-to-negotiate/.
References
Allen JF (2006) Natural language processing. Encyclopedia of cognitive science
Austin JL (1962) How to do things with words. The William James lectures delivered at Harvard University in 1955. Clarendon Press, Oxford
Bennett IM, Babu BR, Morkhandikar K, Gururaj P (2003) US Patent no. 6,665,640. US Patent and Trademark Office, Washington, DC
Chowdhury GG (2003) Natural language processing. Ann Rev Inf Sci Technol 37(1):51–89
Conneau A, Schwenk H, Barrault L, Lecun Y (2016) Very deep convolutional networks for natural language processing. arXiv preprint
Conneau A, Kiela D, Schwenk H, Barrault L, Bordes A (2017) Supervised learning of universal sentence representations from natural language inference data. arXiv preprint. arXiv:1705.02364
Danaher J (2018) Toward an ethics of ai assistants: an initial framework. Philos Technol 31(4):629–653
Derrida J (1988) Signature event context. Limited Inc. Northwestern University Press, Evanston
Gao M, Shi G, Li S (2018) Online prediction of ship behavior with automatic identification system sensor data using bidirectional long short-term memory recurrent neural network. Sensors 18:4211. https://doi.org/10.3390/s18124211
IBM (2018) The new AI innovation equation. IBM Blog. https://ibm.com/watson/advantage-reports/future-of-artificial-intelligence/ai-innovation-equation.html
Kelly K, IBM (2018) What’s next for AI? Q&A with the co-founder of Wired Kevin Kelly. IBM Blog. https://ibm.com/watson/advantage-reports/future-of-artificial-intelligence/kevin-kelly.html
Leviathan Y, Matias Y (2018) Google duplex: An ai system for accomplishing real-world tasks over the phone. Google AI Blog. https://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.html
Lewis M, Yarats D, Dauphin YN, Parikh D, Batra D (2017) Deal or no deal? Training AI bots to negotiate. Facebook Code. https://code.fb.com/ml-applications/deal-or-no-deal-training-ai-bots-to-negotiate/
Michaely AH, Zhang X, Simko G, Parada C, Aleksic P (2017) Keyword spotting for Google assistant using contextual speech recognition. In: Automatic speech recognition and understanding workshop (ASRU), 2017 IEEE. IEEE, pp 272–278
Mikolov T, Karafiát LM, Burget JC, Khudanpur S (2010) Recurrent neural network based language model. In: Proceedings of interspeech, vol 2, p 3
Oord AVD, Li Y, Babuschkin I, Simonyan K, Vinyals O, Kavukcuoglu K, Casagrande N (2017) Parallel WaveNet: fast high-fidelity speech synthesis. arXiv preprint. arXiv:1711.10433
Russell S, Norvig P (2016) Artificial intelligence: a modern approach (global 3rd edition). Pearson, Essex
Shannon CE, Weaver W (1949) The mathematical theory of communication. Urbana, IL
Tang D, Qin B, Liu T (2015) Document modeling with gated recurrent neural network for sentiment classification. In: Proceedings of conference of empirical methods natural language processing, pp 1422–1432
Turing AM (1950) Mind. Mind 59(236):433–460
Yang Z, Zhang S, Urbanek J, Feng W, Miller AH, Szlam A, Weston J (2017) Mastering the Dungeon: grounded language learning by mechanical Turker Descent. arXiv preprint. arXiv:1711.07950
Acknowledgements
I would like to express my gratitude to my beloved one, who both visibly and invisibly interperformed with me in numerous spaces before, during and after the development of this manuscript. I should also thank my professor Denise Albanese (George Mason University) for her invaluable help in the process of initial revisions of the manuscript.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial or non-profit sectors.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The original version of this article was revised due to a retrospective Open Access Cancellation.
Rights and permissions
About this article
Cite this article
Yalur, T. Interperforming in AI: question of ‘natural’ in machine learning and recurrent neural networks. AI & Soc 35, 737–745 (2020). https://doi.org/10.1007/s00146-019-00910-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00146-019-00910-1