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Thinking like animals or thinking like colleagues?

Published online by Cambridge University Press:  10 November 2017

Daniel C. Dennett
Affiliation:
Center for Cognitive Studies, Tufts University, Medford, MA 02155. [email protected]@gmail.comhttp://ase.tufts.edu/cogstud/dennett/http://ase.tufts.edu/cogstud/faculty.html
Enoch Lambert
Affiliation:
Center for Cognitive Studies, Tufts University, Medford, MA 02155. [email protected]@gmail.comhttp://ase.tufts.edu/cogstud/dennett/http://ase.tufts.edu/cogstud/faculty.html

Abstract

We comment on ways in which Lake et al. advance our understanding of the machinery of intelligence and offer suggestions. The first set concerns animal-level versus human-level intelligence. The second concerns the urgent need to address ethical issues when evaluating the state of artificial intelligence.

Type
Open Peer Commentary
Copyright
Copyright © Cambridge University Press 2017 

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References

Deacon, T. (2012) Incomplete nature: How mind emerged from matter. W.W. Norton.Google Scholar
Dennett, D. C. (2013) Aching voids and making voids [Review of the book Incomplete nature: How mind emerged from matter by T. Deacon]. The Quarterly Review of Biology 88(4):321–24.Google Scholar
Dennett, D. C. (2017) From bacteria to Bach and back: The evolution of minds. W.W. Norton.Google Scholar
Dietvorst, B. J., Simmons, J. P. & Massey, C. (2015) Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General 144(1):114–26.Google Scholar
Dietvorst, B. J., Simmons, J. P. & Massey, C. (2016) Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2616787.Google Scholar
Gibson, J. J. (1979) The ecological approach to visual perception. Houghton Mifflin.Google Scholar
Herrmann, E., Call, J., Hernandez-Lloreda, M. V., Hare, B. & Tomasello, M. (2007) Humans have evolved specialized skills of social cognition: The cultural intelligence hypothesis. Science 317(5843):1360–66.Google Scholar
Herrmann, E., Hernandez-Lloreda, M. V., Call, J., Hare, B. & Tomasello, M. (2010) The structure of individual differences in the cognitive abilities of children and chimpanzees. Psychological Science 21(1):102–10.CrossRefGoogle ScholarPubMed
Hurley, M., Dennett, D. C. & Adams, R., (2011) Inside jokes: Using humor to reverse-engineer the mind. MIT Press.CrossRefGoogle Scholar
Hutson, M. (2017) In bots we distrust. Boston Globe, p. K4.Google Scholar
Kiraly, I., Csibra, G. & Gergely, G. (2013) Beyond rational imitation: Learning arbitrary means actions from communicative demonstrations. Journal of Experimental Child Psychology 116(2):471–86.CrossRefGoogle ScholarPubMed
Pratt, G. (2016, December 6). Presentation to Professor Deb Roy's class on machine learning and society at the MIT Media Lab. Class presentation that was videotaped but has not been made public.Google Scholar
Sterelny, K. (2012) The evolved apprentice. MIT Press.Google Scholar
Sterelny, K. (2013) The informational commonwealth. In: Arguing about human nature: Contemporary debates, ed. Downes, L S. M. & Machery, E., pp. 274–88. Routledge, Taylor & Francis.Google Scholar