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Intelligent and Good Machines? The Role of Domain and Context Codification

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Abstract

There is a core problem with the modern Artificial Intelligence (AI) technologies, based on the current new wave of Artificial Neural Networks (ANNs). Whether they have been used in healthcare or for exploring Mars, we, the programmers who build them, do not know well why they make some decisions over others. Many are putting into question, hence, this aura of AI objectivity and infallibility; on our side, we, instead, identify a key issue around the problem of AI errors and bias into an insufficient human ability to determine the limits of the context, where the ANNs will have to operate. In fact, while it is of great amplitude the range of what the rational side of the human mind can master, machine intelligence has limited capacity to learn in completely unknown scenarios. Simply, an inaccurate or incomplete codification of the context may result into AI failures. We present here a simple cognification ANN-based case study, in an underwater scenario, where the difficulty of identifying and then codifying all the relevant contextual features has led to a situation of partial failure. This paper reports on our reflections, and subsequent technical actions taken to recover from this situation.

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References

  1. Lemley J, Bazrafkan S, Corcoran P (2017) Deep learning for consumer devices and services: pushing the limits for machine learning, artificial intelligence, and computer vision. IEEE Consum Electron Mag 6.2:48–56

    Article  Google Scholar 

  2. Wu Y, Schuster M, Chen Z, Le QV, Norouzi M, Macherey W, Krikun M, Cao Y, Gao Q, Macherey K, Klingner J, Shah A, Johnson M, Liu X, Kaiser L, Gouws S, Kato Y, Kudo T, Kazawa H, Stevens K, Kurian G, Patil N, Wang W, Young C, Smith J, Riesa J, Rudnick A, Vinyals O, Corrado G, Hughes M, Dean J (2016) Google’s neural machine translation system: bridging the gap between human and machine translation. CoRR abs/1609.08144 (2016). arXiv:1609.08144 http://arxiv.org/abs/1609.08144

  3. Van Den Oord A, Dieleman S, Zen H, Simonyi K, Vinyals O, Graves A, Kalchbrenner N, Senior AW, Kavukcuoglu K (2016) WaveNet: A generative model for raw audio. In SSW 125

  4. Son Chung J, Zisserman A (2018) Learning to lip read words by watching videos. Comput Vis Image Underst

  5. S. Suwajanakorn, S. M. Seitz, and I. Kemelmacher-Shlizerman. 2017. Synthesizing Obama: learning lip sync from audio. ACM Trans. Graph. 36, 4, Article 95 (July 2017), 13 pages. doi:https://doi.org/10.1145/3072959.3073640

  6. Chen F, Li B, Dong R, Zhao P (2018) High-performance OCR on packing boxes in industry based on deep learning. Pacific Rim International Conference on Artificial Intelligence Springer, 1018–1030

  7. Silver D, Huang A, Maddison CJ, Guez A, Sifre L, Van Den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M et al (2016) Mastering the game of go with deep neural networks and tree search. Nature 529 7587(2016):484

    Article  Google Scholar 

  8. Moravˇc’ık M, Schmid M, Burch N, Lis’y V, Morrill D, Bard N et al (2017) Deepstack: expert-level artificial intelligence in heads-up no-limit poker. Science 356(6337):508–513

    Article  MathSciNet  Google Scholar 

  9. AI O, (2018) The open ai dota 2 bot. [Accessed 20-August-2018]. [Online]. Available: https://blog.openai.com/more-on-dota-2/

  10. Ferretti S, Mirri S, Prandi C, Salomoni P (2016) Automatic web content personalization through reinforcement learning. J Syst Softw 121:157–169. https://doi.org/10.1016/j.jss.2016.02.008

    Article  Google Scholar 

  11. Roccetti M, Salomoni P, Prandi C, Marfia G, Montagnani M, Gningaye L (2016) Understanding Crohn’s disease patients reaction to infliximab from facebook: A medical perspective. Proceedings of the 2016 IEEE/ACM international conference on advances in social networks analysis and mining, ASONAM 2016. 1007 – 1010. doi:https://doi.org/10.1109/ASONAM.2016.7752364

  12. Roccetti M, Prandi C, Salomoni P, Marfia G (2016) Unleashing the true potential of social networks: confirming infliximab medical trials through Facebook posts. Netw Modeling Anal Health Inform Bioinform 5:1. https://doi.org/10.1007/s13721-016-0122-9

    Article  Google Scholar 

  13. Silvia Mirri, Catia Prandi, Marco Roccetti, and Paola Salomoni. (2017) Handmade narrations: handling digital narrations on food and gastronomic culture. ACM J Comput Cult Herit. doi:https://doi.org/10.1145/3097569

  14. Ferretti S, Mirri S, Prandi C, Salomoni P (2017) On personalizing web content through reinforcement learning. Univ Access Inf Soc 2017. https://doi.org/10.1007/s10209-016-0463-2

  15. Melis A, Mirri S, Prandi C, Prandini M, Salomoni P, Callegati F (2018) Integrating personalized and accessible itineraries in MaaS ecosystems through microservices. Mobile Networks and Applications 2018. https://doi.org/10.1007/s11036-017-0831-z

  16. Caruana R, Lou Y, Gehrke J, Koch P, Sturm M, Elhadad N (2015) Intelligible models for healthcare: predicting pneumonia risk and hospital 30-day readmission. Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. ACM: 1721–1730

  17. Melis A, Mirri S, Prandi C, Prandini M, Salomoni P, Callegati F (2016) Crowd sensing for smart mobility through a service-oriented architecture. IEEE 2nd Int Smart Cities Conf: Improving Citizens Qual Life, ISC2 2016. https://doi.org/10.1109/ISC2.2016.7580861

  18. Mirri S, Prandi C, Salomoni P, Monti L (2016) Social location awareness: A prototype of altruistic IoT. 2016 8th IFIP International Conference on New Technologies, Mobility and Security, NTMS 2016. https://doi.org/10.1109/NTMS.2016.7792477

  19. Zou J, Schiebinger L (2018) AI can be sexist and racist – it’s time to make it fair. Nature 559:324–326

    Article  Google Scholar 

  20. Buolamwini J, Gebru T (2018) Gender shades: intersectional accuracy disparities in commercial gender classification. Conf Fairness Account Transparency 77–91

  21. Delnevo G, Lipparini A, Roccetti M, Sobrero M (2018) Human intelligence, ambidexterity and ai: reflections on a data cognification case study in an underwater scenario. 2nd annual science fiction prototyping conference 2018: designing your future with science fiction. SciFi-It 2018(2018):50–55

    Google Scholar 

  22. Delnevo G, Roccetti M, Mirri S (2018) Intelligent machines for good?: more focus on the context. Goodtechs '18 international conference on smart objects and Technologies for Social Good Bologna, Italy

  23. Graham R, McCabe H, Sheridan S (2004) Neural networks for real-time pathfinding in computer games. ITB J 5(1):21

    Google Scholar 

  24. Kindermann T, Cruse H, Dautenhahn K (1996) A fast, three-layer neural network for path finding. Netw Comput Neural Syst 7(2):423–436

    Article  Google Scholar 

  25. Kim H, Son H, Roska T, Chua LO (2002) Optimal path finding with space- and time-variant metric weights via multi-layer CNN. Int J Circuit Theory Applic 30(2–3):247–270

    Article  Google Scholar 

  26. Benardos PG, Vosniakos G-C (2007) Optimizing feedforward artificial neural network architecture. Eng Appl Artif Intell 20.3:365–382

    Article  Google Scholar 

  27. Fails JA, Olsen DR Jr. (2003) Interactive machine learning. Proc 8th Int Conf Intell User Interf. ACM

  28. Arino A, De La Torre J (1998) Learning from failure: towards an evolutionary model of collaborative ventures. Organ Sci 9(3):306–325

    Article  Google Scholar 

  29. Coelho PRP, McClure JE (2005) Learning from failure. Am J Business 20(1):1

    Article  Google Scholar 

  30. Shepherd DA (2003) Learning from business failure: propositions of grief recovery for the self-employed. Acad Manag Rev 28(2):318–328

    Article  Google Scholar 

  31. Pan SJ, Yang O (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22.10:1345–1359

    Article  Google Scholar 

Download references

Acknowledgements

We are grateful to the company that has provided us with the experimental seabeds. Topologies and company name are kept secret for the sake of privacy and security. Our thanks also go to Maurizio Sobrero, Andrea Lipparini, Lorenzo Donatiello and Gustavo Marfia (University of Bologna) for many precious suggestions on a previous version of this manuscript.

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  1. Department of Computer Science and Engineering, University of Bologna, Bologna, Italy

    Giovanni Delnevo, Marco Roccetti & Silvia Mirri

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  1. Giovanni Delnevo
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  2. Marco Roccetti
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  3. Silvia Mirri
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Correspondence to Giovanni Delnevo.

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Delnevo, G., Roccetti, M. & Mirri, S. Intelligent and Good Machines? The Role of Domain and Context Codification. Mobile Netw Appl 25, 977–985 (2020). https://doi.org/10.1007/s11036-019-01233-7

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  • DOI: https://doi.org/10.1007/s11036-019-01233-7

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