Advertisement

AI & SOCIETY

, Volume 34, Issue 4, pp 931–938 | Cite as

Machine learning: A structuralist discipline?

  • Christophe BruchanskyEmail author
Open Forum

Abstract

Advances in machine learning and natural language processing are revolutionizing the way we live, work, and think. As for any science, they are based on assumptions about what the world is, and how humans interact with it. In this paper, I discuss what is potentially one of these assumptions: structuralism, which states that all cultures share a hidden structure. I illustrate this assumption with political footprints: a machine-learning technique using pre-trained word vectors for political discourse analysis. I introduce some of the benefits and limitations of structuralism when applied to machine learning, and the risks of exploiting a technology before establishing the validity of all its hypotheses. I consider how machine-learning techniques could evolve towards hybrid structuralism or post-structuralism, and how deeply these developments would impact cultural studies.

Keywords

Machine learning Structuralism Post-structuralism Natural language processing Culture Political discourse Linguistic Knowledge Semantics Human mind 

Notes

Acknowledgement

I would like to thank Niel Chah (Chah, n.d.) for his support during the political footprint development process, Araz Taeihagh (Taeihagh, n.d.) and the reviewers from AI and Society for their constructive feedback.

References

  1. Abdulkader A, Lakshmiratan A, Zhang J (2016) Introducing DeepText: Facebook’s text understanding engine. Facebook code. https://code.facebook.com/posts/181565595577955/introducing-deeptext-facebook-s-text-understanding-engine/
  2. Bachelard G (1986) The new scientific spirit. Beacon Press, BostonGoogle Scholar
  3. Barthes R (1972) Mythologies. Hill and Wang, New YorkGoogle Scholar
  4. Bojanowski P, Grave E, Joulin A, Mikolov T (2016) Enriching word vectors with subword information. https://github.com/facebookresearch/fastText
  5. Bremmer I (2016) These 5 facts explain Donald Trump and Hillary Clinton’s Foreign policy debate. Time. http://time.com/4510023/hillary-clinton-donald-trump-foreign-policy-debate/
  6. Chomsky N (1965) Aspects of the Theory of Syntax. MIT Press, CambridgeGoogle Scholar
  7. Clark J (2015) Google turning its lucrative web search over to AI machines. Bloomberg. https://www.bloomberg.com/news/articles/2015-10-26/google-turning-its-lucrative-web-search-over-to-ai-machines
  8. Deeplearning4j (consulted in 2017) Thought vectors, deep learning & the future of AI. https://deeplearning4j.org/thoughtvectors
  9. Devlin H (2015) Google a step closer to developing machines with human-like intelligence. Guardian. https://www.theguardian.com/science/2015/may/21/google-a-step-closer-to-developing-machines-with-human-like-intelligence
  10. Flores R (2016) Where does Hillary Clinton stand? CBS news. http://www.cbsnews.com/media/where-does-hillary-clinton-stand/4/
  11. Gandel S (2016) Clinton-Trump debate: Where the candidates stand on 3 key economic topics. Fortune. http://fortune.com/2016/09/26/presidential-debate-economic-issues/
  12. Grassegger H, Krogerus M (2017) The data that turned the world upside down. https://motherboard.vice.com/en_us/article/how-our-likes-helped-trump-win
  13. Heuer H (2015) Text comparison using word vector representations and dimensionality reduction. https://arxiv.org/abs/1607.00534
  14. Lévi-Strauss C (1955) Tristes tropiques. Penguin Books, LondonGoogle Scholar
  15. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. https://code.google.com/archive/p/word2vec/
  16. Newcomb A (2016) Facebook’s artificial intelligence understands you. ABC News. http://abcnews.go.com/Technology/facebooks-artificial-intelligence-understands/story?id=39554515
  17. Pennington J, Socher R, Manning C (2014) Glove: global vectors for word representation. https://nlp.stanford.edu/pubs/glove.pdf
  18. Powell V, Lehe L (2017) Principal component analysis. http://setosa.io/ev/principal-component-analysis/
  19. SAS (2017) Machine learning: What it is and why it matters. https://www.sas.com/en_ca/insights/analytics/machine-learning.html
  20. The Associated Press (2016) Hillary Clinton is pledging more tax relief for families with young kids. Fortune. http://fortune.com/2016/10/11/hillary-clinton-tax-families/
  21. Taeihagh A (n.d.) Assistant professor of public policy, singapore management university (smu), form. oxford and unsw. https://scholar.google.ca/citations?user=Bwt1iRUAAAAJ&hl=en

Copyright information

© Springer-Verlag London Ltd. 2017

Authors and Affiliations

  1. 1.Plural think tankTorontoCanada

Personalised recommendations