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Conceptual Foundations of Human-Centric AI

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Human-Centered Artificial Intelligence (ACAI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13500))

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Abstract

This tutorial does not focus on specific techniques or applications but on the conceptual foundations of human-centric AI. It discusses a number of fundamental questions: What is needed to make AI more human-centered or ‘humane’? Why do we need to combine reactive and deliberative intelligence for human-centric AI? What is the nature of meaning and understanding in human intelligence? Why is emulating understanding in artificial systems necessary but hard? What is the role of narratives in understanding? What are some of the open issues for realizing human-centric AI capable of narrative-based understanding?

The writing of this paper was funded by the EU Pathfinder Project MUHAI on ‘Meaning and Understanding in Human-centric AI’ (EU grant 951846 to the Venice International University) and by the ‘HumaneAI-Net’ EU Coordination Project (EU grant 952026 to the Universitat Pompeu Fabra in Barcelona).

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Notes

  1. 1.

    In december 2021 the chatbot ALEXA by Amazon recommended a 10 year old to ‘plug in a phone charger about halfway into a wall outlet, then touch a penny to the exposed prongs’.

  2. 2.

    In 2020 a scandal known as the ‘toeslagenaffaire’ (benefit scandal) hit the Dutch political world forcing a fall of the government. Due to excessive zeal of the tax agency controlling the allocation of child benefits and the use of machine learning on social data (which were supposed to be private) many families were pushed into poverty and experienced devastating legal difficulties.

  3. 3.

    For example, in Japan the green light looks more blueish because until a century ago Japanese did not have a basic color word for green, only for blue (“ao”) and green was considered a shade of ao. Contemporary Japanese has a word for green, “midori”, but the traffic light is still called “ao”. As a compromise to abide by international regulations but not deviate too much from language custom, traffic lights in Japan are a greenish shade of blue rather than prototypical green.

  4. 4.

    Werle, L. (2009) La Cucina della Mamma. Allegrio, Olen (BE), p. 22.

  5. 5.

    It is also common, particularly in earlier narratological research, to conflate fabula and plot, in which case the two terms become interchangable and there are only two levels.

  6. 6.

    Somewhat confusingly, a narration is also often called a narrative (cf. narrativo in Spanish), whereas here it refers to both the facts and the plot on the one hand and their narration on the other hand.

References

  1. Antoniou, G., Harmelen, F.V.: A Semantic Web Primer. The MIT Presss, Cambridge (2008)

    Google Scholar 

  2. Bal, M., Boheemen, C.V.: Narratology: Introduction to the Theory of Narrative. University of Toronto Press, Toronto (1997)

    Google Scholar 

  3. Barnes, J.: The Pedant in the Kitchen. Atlantic Books (2004)

    Google Scholar 

  4. Beetz, M., et al.: Cognition-enabled autonomous robot control for the realization of home chore task intelligence. Proc. IEEE 100(8), 2454–2471 (2012)

    Article  Google Scholar 

  5. Bender, E., Koller, A.: Climbing towards NLU: on meaning, form, and understanding in the age of data. In: Proceedings Annual Meeting of Association for Computational Linguistics Conference, pp. 5185–A5198. Association for Computational Linguistics (2020)

    Google Scholar 

  6. Bruner, J.: The narrative construction of reality. Crit. Inq. 18(1), 1–21 (1991)

    Article  Google Scholar 

  7. Charon, R., et al.: The Principles and Practice of Narrative Medicine. Oxford University Press, Oxford (2017)

    Google Scholar 

  8. De Haene, S., Changeux, J.P., Naccache, L.: The global neuronal workspace model of conscious access: from neuronal architectures to clinical applications. Res. Perspect. Neurosci. 18, 55–84 (2011)

    Google Scholar 

  9. Decuyper, J.C., Keymeulen, D., Steels, L.: A hybrid architecture for modeling liquid behavior. In: Glasgow, J., Narayanan, N., Chandrasekaran, B. (eds.) Diagrammatic Reasoning. AAAI Press, Menlo Park (1995)

    Google Scholar 

  10. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT (1), pp. 4171–4186. Association for Computational Linguistics (2019)

    Google Scholar 

  11. Eco, U.: Trattato di semiotica generale. La nave di Teseo, Milano (1975)

    Google Scholar 

  12. Englemore, R., Morgan, T.: Addison-Wesley, New York (1988)

    Google Scholar 

  13. Feigenbaum, E.: The art of artificial intelligence: themes and case studies of knowledge engineering. In: Proceedings of the Fifth International Joint Conference on Artificial Intelligence, pp. 1014–1029. IJCAI (1977)

    Google Scholar 

  14. Finlayson, M.: The story workbench: an extensible semi-automatic text annotation tool. In: AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, vol. WS-11-18 (2011)

    Google Scholar 

  15. Finlayson, M.: A survey of corpora in computational and cognitive narrative science. Sprache und Datenverarbeitung (Int. J. Lang. Data Process.) 37(1–2), 113–141 (2013)

    Google Scholar 

  16. Forbus, K.: Qualitative physics: past, present and future. In: Exploring Artificial Intelligence, pp. 239–296. Elsevier (1988)

    Google Scholar 

  17. Gervás, P., Concepción, E., León, C., Méndez, G., Delatorre, P.: The long path to narrative generation. IBM J. Res. Dev. 63(1), 1–8 (2019)

    Article  Google Scholar 

  18. Goffman, E.: Frame Analysis: An Essay on the Organization of Experience. Harper and Row, New York (1974)

    Google Scholar 

  19. van Harmelen, F., Lifschitz, V., Porter, B. (eds.): Handbook of Knowledge Representation. Elsevier, Amsterdam (2007)

    MATH  Google Scholar 

  20. Hovy, D., Fan, J., Gliozzo, A., Patwardhan, S., Welty, C.: When did that happen? - linking events and relations to timestamps. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pp. 185–193. Association for Computational Linguistics (2012)

    Google Scholar 

  21. Irvine, L., Pierce, J., Zussman, R. (eds.): Narrative Sociology. Vanderbilt University Press, Nashville (2019)

    Google Scholar 

  22. Kahneman, D.: Thinking Fast and Slow. Farrar, Strauss and Giroux, New York (2011)

    Google Scholar 

  23. Kolodner, J.: An introduction to case-based reasoning. Artif. Intell. Rev. 6, 3–34 (1992)

    Article  Google Scholar 

  24. Kroll, H., Nagel, D., Balke, W.-T.: Modeling narrative structures in logical overlays on top of knowledge repositories. In: Dobbie, G., Frank, U., Kappel, G., Liddle, S.W., Mayr, H.C. (eds.) ER 2020. LNCS, vol. 12400, pp. 250–260. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62522-1_18

    Chapter  Google Scholar 

  25. Lenat, D.: Cyc: A large-scale investment in knowledge infrastructure. Comm. of the ACMs 38(11), 33–38 (1995)

    Article  Google Scholar 

  26. Mateas, M., Sengers, P.: Narrative Intelligence. John Benjamins Publishing (2003)

    Google Scholar 

  27. McCarthy, J.: Programs with common sense. In: Symposium on Mechanization of Thought Processes. National Physical Laboratory, Teddington (1958)

    Google Scholar 

  28. McCarthy, J., Hayes, P.: Some philosophical problems from the standpoint of artificial intelligence. In: Michie, D., Meltzer, B. (eds.) Machine Intelligence, vol. 4, pp. 463–502. Edinburgh University Press (1969)

    Google Scholar 

  29. Meghini, C., Bartalesi, V., Metilli, D.: Representing narratives in digital libraries: the narrative ontology. Semant. Web 18, 1–24 (2021)

    Google Scholar 

  30. Minsky, M.: A framework of representing knowledge. In: Winston, P. (ed.) The Psychology of Computer Vision. McGraw-Hill, New York (1975)

    Google Scholar 

  31. Mitchell, M.: Artificial Intelligence. A Guide for Thinking Humans. Farrar, Strauss and Giroux, New York (2019)

    Google Scholar 

  32. Mohseni, S., Zarei, N., Ragan, E.: A multidisciplinary survey and framework for design and evaluation of explainable AI systems. ACM Trans. Interact. Intell. Syst. 1(1) (2021)

    Google Scholar 

  33. Moore, J., Swartout., W.: Explanation in expert systems: a survey. ISI Research Reports, ISI, Marina del Rey (1988)

    Google Scholar 

  34. Nowak, A., Lukowicz, P., Horodecki, P.: Assessing artificial intelligence for humanity: will AI be the our biggest ever advance? or the biggest threat. IEEE Technol. Soc. Mag. 37(4), 26–34 (2018)

    Article  Google Scholar 

  35. Pearl, J., Mackenzie, D.: The Book of Why. The New Science of Cause and Effect. Penguin Books, London (2019)

    MATH  Google Scholar 

  36. Porzel, R.: On formalizing narratives. In: CAOS 2021: 5th workshop on Cognition and Ontologies. CEUR Workshop Proceedings, Bolzano (2021)

    Google Scholar 

  37. Quillian, R.: Semantic memory. In: Minsky, M. (ed.) Semantic Information Processing, chap. 4, pp. 227–270. The MIT Press, Cambridge (1968)

    Google Scholar 

  38. Reddy, R., Erman, L., Fennell, R., Lowerre, B., Neely, R.: The hearsay speech understanding system. J. Acoust. Soc. Am. 55, 409 (1974)

    Article  Google Scholar 

  39. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection (2016)

    Google Scholar 

  40. Riedl, M.O.: Computational narrative intelligence: a human-centered goal for artificial intelligence. In: CHI 2016 Workshop on Human-Centered Machine Learning (2016)

    Google Scholar 

  41. Riedl, M.O.: What BERT is not: lessons from a new suite of psycholinguistic diagnostics for language models. Trans. Assoc. Comput. Linguist. 8, 34–49 (2020)

    Article  Google Scholar 

  42. Samuel, A.: Some studies in machine learning using the game of checkers. IBM J. Res. Dev. 3(3), 210–229 (1959)

    Article  MathSciNet  Google Scholar 

  43. Schank, R.: Dynamic Memory: A Theory of Reminding and Learning in Computers and People. Cambridge University Press, Cambridge (1990)

    Google Scholar 

  44. Schank, R.: Abelson: Scripts, Plans, Goals, and Understanding: An Inquiry into Human Knowledge Structures. L. Erlbaum, Hillsdale (1977)

    MATH  Google Scholar 

  45. Schiller, R.: Narrative Economics. How Stories Go Viral and Drive Major Economic Events. Princeton University Press, Princeton (2019)

    Book  Google Scholar 

  46. Shane, J.: You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It’s Making the World a Weirder Place. Little Brown and Company, New York (2021)

    Google Scholar 

  47. Simon, H.: The Sciences of the Artificial. The MIT Press, Cambridge (1969)

    Google Scholar 

  48. Solomonoff, R.: A formal theory of inductive inference. Inf. Control 7(1), 1–22 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  49. Sools, A., Tromp, T., Mooren, J.: Mapping letters from the future: exploring narrative processes of imagining the future. J. Health Psychol. 20(3), 350–364 (2015)

    Article  Google Scholar 

  50. Sperber, D., Wilson, D.: Relevance: Communication and Cognition. Wiley-Blackwell, London (1969)

    Google Scholar 

  51. Spranger, M., Loetzsch, M., Steels, L.: A perceptual system for language game experiments. In: Steels, L., Hild, M. (eds.) Language Grounding in Robots, pp. 89–110. Springer, Boston (2012). https://doi.org/10.1007/978-1-4614-3064-3_5

    Chapter  Google Scholar 

  52. Steels, L.: Constructivist development of grounded construction grammars. In: Proceedings Annual Meeting of Association for Computational Linguistics Conference, pp. 9–16. Association for Computational Linguistics (2004)

    Google Scholar 

  53. Steels, L.: Computational Issues in Fluid Construction Grammar. Lecture Notes in Computer Science, vol. 7249. Springer, Berlin (2012). https://doi.org/10.1007/978-3-642-34120-5

    Book  Google Scholar 

  54. Steels, L.: Experiments in Cultural Language Evolution. John Benjamins Pub., Amsterdam (2012)

    Book  Google Scholar 

  55. Steels, L.: Agent-based models for the emergence and evolution of grammar. Phil. Trans. R. Soc. B. 371, 20150447 (2016)

    Article  Google Scholar 

  56. Steels, L.: Personal dynamic memories are necessary to deal with meaning and understanding in human-centric AI. In: Saffiotti, A, L.S., Lukowicz, P. (eds.) Proceedings of the First International Workshop on New Foundations for Human-Centered AI (NeHuAI) Co-located with 24th European Conference on Artificial Intelligence, vol. Vol-2659. CEUR Workshop Proceedings (2020)

    Google Scholar 

  57. Steels, L., Brooks, R.: The ‘Artificial Life’ Route to ‘Artificial Intelligence’. Building Situated Embodied Agents, Lawrence Erlbaum Ass., New Haven (1994)

    Google Scholar 

  58. Steels, L., Hild, M.: Language Grounding in Robots. Springer, New York (2012). https://doi.org/10.1007/978-1-4614-3064-3

    Book  Google Scholar 

  59. Steels, L.: The origins of ontologies and communication conventions in multi-agent systems. Auton. Agent. Multi-Agent Syst. 1, 169–194 (1998)

    Article  Google Scholar 

  60. Szegedy, C., et al.: Intriguing properties of neural networks. In: International Conference on Learning Representations, pp. 159–166. Springer (2014)

    Google Scholar 

  61. Tegmark, M.: Life 3.0: Being Human in the Age of Artificial Intelligence. Knopff, New York (2017)

    Google Scholar 

  62. Vilarroya, O.: Somos lo que nos contamos. Cómo los relatos construyen el mundo en que vivimos. Editorial Planeta, Barcelona (2019)

    Google Scholar 

  63. Von der Leyen, U., et al.: White paper on artificial intelligence. EU Commission reports (2020)

    Google Scholar 

  64. Willems, R., Nastase, S., Milivojevic, B.: Narratives for neuroscience. Trends Neurosci. 44(5), 271–273 (2020)

    Article  Google Scholar 

  65. Winograd, T.: Understanding Natural Language. Academic Press, New York (1976)

    Google Scholar 

  66. Winston, P.: The strong story hypothesis and the directed perception hypothesis. In: 2011 AAAI Fall symposium, AAAI Press, Menlo Park (2011)

    Google Scholar 

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Acknowledgement

The author thanks Oscar Vilarroya (IMIM and UAB Barcelona) and Lise Stork (Vrije Universiteit Amsterdam) for valuable comments on the paper and Inès Blin (Sony CSL Paris) for introducing another didactic example of narrative-based understanding during the tutorial presentation at ACAI in Berlin (not included in this paper). The author is also indebted to many discussions with members of the MUHAI consortium: the team from the Free University of Brussels VUB AI Lab (Paul Van Eecke) and University of Namur (Katrien Beuls), the team of Frank van Harmelen at the Free University of Amsterdam (VUA), the team of Remi van Trijp and the Sony Computer Science Laboratory in Paris and the team of Robert Porzel at the University of Bremen, Computer Science Department.

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Authors and Affiliations

  1. Barcelona Supercomputing Center, Barcelona, Spain

    Luc Steels

  2. Venice International University, Venice, Italy

    Luc Steels

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  1. Luc Steels
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Correspondence to Luc Steels .

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  1. Sorbonne University, Paris, France

    Mohamed Chetouani

  2. Umeå University, Umeå, Sweden

    Virginia Dignum

  3. German Research Centre for Artificial Inteligence, Kaiserslautern, Germany

    Paul Lukowicz

  4. Artificial Intelligence Research Institute (IIIA-CSIC), Bellaterra, Spain

    Carles Sierra

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Steels, L. (2023). Conceptual Foundations of Human-Centric AI. In: Chetouani, M., Dignum, V., Lukowicz, P., Sierra, C. (eds) Human-Centered Artificial Intelligence. ACAI 2021. Lecture Notes in Computer Science(), vol 13500. Springer, Cham. https://doi.org/10.1007/978-3-031-24349-3_2

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