Abstract
Traditionally, the way one evaluates the performance of an Artificial Intelligence (AI) system is via a comparison to human performance in specific tasks, treating humans as a reference for high-level cognition. However, these comparisons leave out important features of human intelligence: the capability to transfer knowledge and take complex decisions based on emotional and rational reasoning. These decisions are influenced by current inferences as well as prior experiences, making the decision process strongly subjective and “apparently” biased. In this context, a definition of compositional intelligence is necessary to incorporate these features in future AI tests. Here, a concrete implementation of this will be suggested, using recent developments in quantum cognition, natural language and compositional meaning of sentences, thanks to categorical compositional models of meaning.
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Notes
- 1.
We avoid the term representation because in the literature it has been invoked with many different connotations.
References
Signorelli, C.M.: Can computers become conscious and overcome humans? Front. Robot. Artif. Intell. 5, 121 (2018). https://doi.org/10.3389/frobt.2018.00121
Turing, A.: Computing machinery and intelligence. Mind 59, 433–460 (1950)
Silver, D., et al.: Mastering the game of go without human knowledge. Nature 550, 354–359 (2017)
Bringsjord, S., Licato, J., Sundar, N., Rikhiya, G., Atriya, G.: Real robots that pass human tests of self-consciousness. In: Proceeding of the 24th IEEE International Symposium on Robot and Human Interactive Communication, pp. 498–504 (2015)
Legg, S., Hutter, M.: Universal intelligence: a definition of machine intelligence. Minds Mach. 17, 391–444 (2007)
Arsiwalla, X.D., Signorelli, C.M., Puigbo, J.-Y., Freire, I.T., Verschure, P.: What is the physics of intelligence? In: Frontiers in Artificial Intelligence and Applications, Proceeding of the 21st International Conference of the Catalan Association for Artificial Intelligence, vol. 308, pp. 283–286 (2018)
Arsiwalla, X.D., Sole, R., Moulin-Frier, C., Herreros, I., Sanchez-Fibla, M., Verschure, P.: The morphospace of consciousness. ArXiv:1705.11190 (2017)
Coecke, B., Sadrzadeh, M., Clark, S.: Mathematical foundations for a compositional distributional model of meaning. Linguist. Anal. 36, 345–384 (2010)
Bruza, P.D., Wang, Z., Busemeyer, J.R.: Quantum cognition: a new theoretical approach to psychology. Trends Cogn. Sci. 19, 383–393 (2015)
Kiverstein, J., Miller, M.: The embodied brain: towards a radical embodied cognitive neuroscience. Front. Hum. Neurosci. 9, 237 (2015)
Arsiwalla, X.D., Verschure, P.: The global dynamical complexity of the human brain network. Appl. Netw. Sci. 1, 16 (2016)
Arsiwalla, X.D., Signorelli, C.M., Puigbo, J.-Y., Freire, I.T., Verschure, P.F.M.J.: Are brains computers, emulators or simulators? In: Vouloutsi, V., et al. (eds.) Living Machines 2018. LNCS (LNAI), vol. 10928, pp. 11–15. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95972-6_3
Arsiwalla, X.D., Herreros, I., Verschure, P.: On three categories of conscious machines. In: Lepora, N., Mura, A., Mangan, M., Verschure, P., Desmulliez, M., Prescott, T. (eds.) Living Machines 2016. LNCS (LNAI), vol. 9793, pp. 389–392. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42417-0_35
Gilovich, T., Griffin, D., Kahneman, D.: Heuristics and Biases: The Psychology of Intuitive Judgment. Cambridge University Press, Cambridge (2002)
Pothos, E.M., Busemeyer, J.R.: Can quantum probability provide a new direction for cognitive modeling? Behav. Brain Sci. 36, 255–274 (2013)
Wang, Z., Solloway, T., Shiffrin, R.M., Busemeyer, J.R.: Context effects produced by question orders reveal quantum nature of human judgments. Proc. Natl. Acad. Sci. U.S.A. 111, 9431–9436 (2014)
Aerts, D., Gabora, L., Sozzo, S.: Concepts and their dynamics: a quantum-theoretic modeling of human thought. Top. Cogn. Sci. 5, 737–772 (2013)
White, L.C., Barqué-Duran, A., Pothos, E.M.: An investigation of a quantum probability model for the constructive effect of affective evaluation. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 374, 20150142 (2016)
Petrinovich, L., O’Neill, P.: Influence of wording and framing effects on moral intuitions. Ethol. Sociobiol. 17, 145–171 (1996)
Searle, J.R.: Minds, brains, and programs. Behav. Brain Sci. 3, 417–457 (1980)
Lindquist, K.A., Wager, T.D., Kober, H., Bliss-Moreau, E., Barrett, L.F.: The brain basis of emotion: a meta-analytic review. Behav. Brain Sci. 35, 121–143 (2012)
Hauser, M., Cushman, F.A., Young, L., Jin, R.K.X., Mikhail, J.: A dissociation between moral judgments and justifications. Mind Lang. 22, 1–21 (2007)
Christensen, J.F., Flexas, A., Calabrese, M., Gut, N.K., Gomila, A.: Moral judgment reloaded: a moral dilemma validation study. Front. Psychol. 5, 1–18 (2014)
Bekoff, M., Pierce, J.: Wild Justice: The Moral Lives of Animals. The University of Chicago Press, Chicago (2009)
Greene, J.D., Sommerville, R.B., Nystrom, L.E.: An fMRI investigation of emotional engagement in moral judgment. Science 293, 2105–2108 (2001)
Moll, J., Zahn, R., de Oliveira-Souza, R., Krueger, F., Grafman, J.: The neural basis of human moral cognition. Nat. Rev. Neurosci. 6, 799–809 (2005)
Grefenstette, E., Sadrzadeh, M.: Experimental support for a categorical compositional distributional model of meaning. In: Conference on Empirical Methods in Natural Language Processing, Edinburgh, pp. 1394–1404 (2011)
Coecke, B., Lewis, M.: A compositional explanation of the ‘pet fish’ phenomenon. In: Atmanspacher, H., Filk, T., Pothos, E. (eds.) QI 2015. LNCS, vol. 9535, pp. 179–192. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-28675-4_14
Bolt, J., Coecke, B., Genovese, F., Lewis, M., Marsden, D., Piedeleu, R.: Interacting conceptual spaces I : grammatical composition of concepts. ArXiv (2017)
Yearsley, J.M., Busemeyer, J.R.: Quantum cognition and decision theories: a tutorial. J. Math. Psychol. 74, 99–116 (2016)
Lambek, J.: From Word to Sentence. Polimetrica, Milan (2008)
Gardenfors, P.: Conceptual spaces as a framework for knowledge representation. Mind Matter. 2, 9–27 (2004)
Aerts, D., Gabora, L., Sozzo, S., Veloz, T.: Quantum structure in cognition: fundamentals and applications. In: Privman, V., Ovchinnikov, V. (eds.), IARIA, Proceedings of Fifth International Conference on Quantum, Nano and Micro Technologies, pp. 57–62 (2011)
Veloz, T.: Toward a quantum theory of cognition: history, development, and perspectives (2016)
Varela, F.J.: Neurophenomenology: a methodological remedy for the hard problem. J. Conscious. Stud. 3, 330–349 (1996)
Mohammad, S.M., Turney, P.D.: Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29, 436–465 (2013)
Signorelli, C.M.: Types of cognition and its implications for future high-level cognitive machines. In: AAAI Spring Symposium Series (2017)
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Appendix A: Example of a Moral Dilemma
Appendix A: Example of a Moral Dilemma
After a shipwreck, a healthy dog and an injured man are floating and trying to swim to survive. If you are in the emergency boat with only one space left:
Please indicate your degree of agreement with the next options (where +5 strongly agree, +3 moderately agree, +1 slightly agree, −1 slightly disagree, −3 moderately disagree, −5 strongly disagree)
Who would you save?
-
(a)
The healthy dog
-
(b)
The injured man
Why?
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Signorelli, C.M., Arsiwalla, X.D. (2019). Moral Dilemmas for Artificial Intelligence: A Position Paper on an Application of Compositional Quantum Cognition. In: Coecke, B., Lambert-Mogiliansky, A. (eds) Quantum Interaction. QI 2018. Lecture Notes in Computer Science(), vol 11690. Springer, Cham. https://doi.org/10.1007/978-3-030-35895-2_9
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