Interperforming in AI: question of ‘natural’ in machine learning and recurrent neural networks
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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.
KeywordsPerformativity Machine learning Natural language processing Recurrent neural networks Derrida Facebook
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.
This research did not receive any specific grant from funding agencies in the public, commercial or non-profit sectors.
- Allen JF (2006) Natural language processing. Encyclopedia of cognitive scienceGoogle Scholar
- Austin JL (1962) How to do things with words. The William James lectures delivered at Harvard University in 1955. Clarendon Press, OxfordGoogle Scholar
- Bennett IM, Babu BR, Morkhandikar K, Gururaj P (2003) US Patent no. 6,665,640. US Patent and Trademark Office, Washington, DCGoogle Scholar
- Conneau A, Schwenk H, Barrault L, Lecun Y (2016) Very deep convolutional networks for natural language processing. arXiv preprintGoogle Scholar
- 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
- Derrida J (1988) Signature event context. Limited Inc. Northwestern University Press, EvanstonGoogle Scholar
- 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 272–278Google Scholar
- Mikolov T, Karafiát LM, Burget JC, Khudanpur S (2010) Recurrent neural network based language model. In: Proceedings of interspeech, vol 2, p 3Google Scholar
- 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
- Shannon CE, Weaver W (1949) The mathematical theory of communication. Urbana, ILGoogle Scholar
- 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 1422–1432Google Scholar
- 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