Advertisement

Synthese

pp 1–21 | Cite as

Making AI meaningful again

  • Jobst LandgrebeEmail author
  • Barry Smith
Article

Abstract

Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s, but this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial intelligence encouraged by these successes, especially in the domain of language processing. We then show an alternative approach to language-centric AI, in which we identify a role for philosophy.

Keywords

Artificial intelligence Deep neural networks Semantics Logic Basic formal ontology (BFO) 

Notes

Acknowledgements

We would like to thank Prodromos Kolyvakis, Kevin Keane, James Llinas and Kirsten Gather for helpful comments.

References

  1. Arp, R., Smith, B., & Spear, A. (2015). Building ontologies with basic formal ontology. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
  2. Ashburner, M. (2000). Gene ontology: Tool for the unification of biology. Nature Genetics, 25, 25–29.CrossRefGoogle Scholar
  3. Boolos, G. S., Burgess, J. P., & Jeffrey, R. C. (2007). Computability and logic. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  4. Carey, S., & Xu, F. (2001). Infants’ knowledge of objects: Beyond object files and object tracking. Cognition, 80, 179–213.CrossRefGoogle Scholar
  5. Chen, Y., Gilroy, S., Knight, K., & Jonathan. (2017). Recurrent neural networks as weighted language recognizers. CoRR, arXiv:1711.05408.
  6. Chomsky, N. (1956). Three models for the description of language. IRE Transactions on Information Theory, 2, 113–124.CrossRefGoogle Scholar
  7. Cooper, S. B. (2004). Computability theory. London: Chapman & Hall/CRC.Google Scholar
  8. Dummett, M. (1996). Origins of analytical philosophy. Boston, MA: Harvard University Press.Google Scholar
  9. Feng, S., Wallace, E., Iyyer, M., Rodriguez, P., Grissom II, A., & Boyd-Graber, J. L.(2018). Right answer for the wrong reason: Discovery and mitigation. CoRR, arXiv:1804.07781.
  10. Finkel, J. R., Kleeman, A., & Manning, C. D. (2008). Efficient, feature-based, conditional random field parsing. In Proceedings of ACL-08: HLT (pp. 959–967). Association for Computational Linguistics.Google Scholar
  11. Gamut, L. T. F. (1991). Logic, language and meaning (Vol. 2). Chicago, London: The University of Chicago Press.Google Scholar
  12. Gelman, S. A. (2003). The essential child: Origins of essentialism in everyday thought. London: Oxford Series in Cognitive Development.CrossRefGoogle Scholar
  13. Gelman, S. A., & Byrnes, J. P. (1991). Perspectives on language and thought. Cambridge, MA: Cambridge University Press.CrossRefGoogle Scholar
  14. Gelman, S. A., & Wellman, H. M. (1991). Insides and essences: Early understandings of the non-obvious. Cognition, 38(3), 213–244.CrossRefGoogle Scholar
  15. Gibson, J. J. (1979). An ecological theory of perception. Boston, MA: Houghton Miflin.Google Scholar
  16. Gopnik, A. (2000). Explanation as orgasm and the drive for causal understanding. In F. Keil & R. Wilson (Eds.), Cognition and explanation. Cambridge, MA: MIT Press.Google Scholar
  17. Gutierrez-Basulto, V., & Schockaert, S. (2018). From knowledge graph embedding to ontology embedding? An analysis of the compatibility between vector space representations and rules. In Principles of knowledge representation and reasoning: Proceedings of the sixteenth international conference, KR 2018, Tempe, Arizona, 30 October–2 November 2018, pp. 379–388.Google Scholar
  18. Hastie, T., Tishirani, T., & Friedman, J. (2008). The elements of statistical learning (2nd ed.). Berlin: Springer.Google Scholar
  19. Hayes, P. J. (1985). The second naive physics manifesto. In J. R. Hobbs & R. C. Moore (Eds.), Formal theories of the common-sense world. Norwoord: Ablex Publishing Corporation.Google Scholar
  20. Honnibal, M., & Montani, I. (2018). spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing (in press).Google Scholar
  21. Jaderberg, M., & Czarnecki, W. M. (2018). Human-level performance in first-person multiplayer games with population-based deep reinforcement learning.Google Scholar
  22. Jo, J., & Bengio, Y. (2017). Measuring the tendency of CNNs to learn surface statistical regularities. CoRR, arXiv:1711.11561.
  23. Keil, F. (1989). Concepts. Kinds and Cognitive Development. Cambridge, MA: MIT Press.Google Scholar
  24. Keil, F. (1995). The growth of causal understanding of natural kinds. In D. Premack & J. Premack (Eds.), Causal cognition. London: Oxford University Press.Google Scholar
  25. Kim, I. K., & Spelke, E. S. (1999). Perception and understanding of effects of gravity and inertia on object motion. Developmental Science, 2(3), 339–362.CrossRefGoogle Scholar
  26. Koller, D., & Friedman, N. (2009). Probabilistic graphical models: Principles and techniques. Cambridge, MA: MIT.Google Scholar
  27. Kowsari, K., Brown, D. E., Heidarysafa, M., Meimandi, K. J., Gerber, M. S., & Barnes, L. E. (2017). HDLTex: Hierarchical deep learning for text classification. CoRR, arXiv:1709.08267.
  28. Leslie, A. (1979). The representation of perceived causal connection in infancy. Oxford: University of Oxford.Google Scholar
  29. Marcus, G. (2018). Deep learning: A critical appraisal.Google Scholar
  30. McCarthy, J., & Hayes, P. J. (1969). Some philosophical problems from the standpoint of artificial intelligence. Machine Intelligence, 4, 463–502.Google Scholar
  31. Medin, D., & Ross, B. H. (1989). The specific character of abstract thought: Categorization, problem solving, and induction. In Advances in the psychology of human intelligence (Vol. 5).Google Scholar
  32. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, & K. Q. Weinberger (Eds.), Advances in neural information processing systems (Vol. 26, pp. 3111–3119). Red Hook: Curran Associates Inc.Google Scholar
  33. Millikan, R. (2001). On clear and confused ideas. Cambridge Studies in Philosophy. Cambridge, MA: Cambridge University Press.Google Scholar
  34. Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., & Frossard, P. (2016). Universal adversarial perturbations. CoRR, arXiv:1610.08401.
  35. Nienhuys-Cheng, S.-H., & de Wolf, R. (2008). Foundations of inductive logic programming. Berlin: Springer.Google Scholar
  36. Papineni, K., Roukos, S., Ward, T., & Zhu, W. J. (2002). BLEU: A method for automatic evaluation of machine translation. In ACL (pp. 311–318). ACL.Google Scholar
  37. Poplin, R., Varadarajan, A. V., & Blumer, K. (2018). Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering, 2, 158–164.CrossRefGoogle Scholar
  38. Povinelli, D. J. (2000). Folk physics for apes: The chimpanzee’s theory of how the world works. London: Oxford University Press.Google Scholar
  39. Rehder, B. (1999). A causal model theory of categorization. In Proceedings of the 21st annual meeting of the cognitive science society (pp. 595–600).Google Scholar
  40. Robinson, A., & Voronkov, A. (2001). Handbook of automated reasoning. Cambridge, MA: Elsevier Science.Google Scholar
  41. Russell, S., & Norvig, P. (2014). Artificial intelligence: A modern approach. Harlow, Essex: Pearson Education.Google Scholar
  42. Silver, David, Huang, Aja, Maddison, Chris J., Guez, Arthur, Sifre, Laurent, van den Driessche, George, et al. (2016). Mastering the game of go with deep neural networks and tree search. Nature, 529(7587), 484–489.CrossRefGoogle Scholar
  43. Smith, B. (2003). Ontology. In Blackwell guide to the philosophy of computing and information (pp. 155–166). Blackwell.Google Scholar
  44. Solomon, K. O., Medin, D., & Lynch, E. (1999). Concepts do more than categorize. Trends in Cognitive Sciences, 3, 99–105.CrossRefGoogle Scholar
  45. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. Cambridge, MA: The MIT Press.Google Scholar
  46. Tenenbaum, J. B. (1999). A Bayesian framework for concept learning. Cambridge, MA: Massachusetts Institute of Technology.Google Scholar
  47. Tenenbaum, J. B., & Griffiths, T. L. (2001). Generalization, similarity, and Bayesian inference. Behavioral and brain sciences, 24(4), 629–640.Google Scholar
  48. Vaswani, A., Shazeeri, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. CoRR, arXiv:1706.03762.
  49. Zheng, S., Wang, F., Bao, H., Hao, Y., Zhou, P., & Xu, B. (2017). Joint extraction of entities and relations based on a novel tagging scheme. CoRR, arXiv:1706.05075.

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Cognotekt GmbHCologneGermany
  2. 2.University at BuffaloBuffaloUSA

Personalised recommendations