Making AI meaningful again
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.
KeywordsArtificial intelligence Deep neural networks Semantics Logic Basic formal ontology (BFO)
We would like to thank Prodromos Kolyvakis, Kevin Keane, James Llinas and Kirsten Gather for helpful comments.
- Chen, Y., Gilroy, S., Knight, K., & Jonathan. (2017). Recurrent neural networks as weighted language recognizers. CoRR, arXiv:1711.05408.
- Cooper, S. B. (2004). Computability theory. London: Chapman & Hall/CRC.Google Scholar
- Dummett, M. (1996). Origins of analytical philosophy. Boston, MA: Harvard University Press.Google Scholar
- 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.
- 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
- Gamut, L. T. F. (1991). Logic, language and meaning (Vol. 2). Chicago, London: The University of Chicago Press.Google Scholar
- Gibson, J. J. (1979). An ecological theory of perception. Boston, MA: Houghton Miflin.Google Scholar
- 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
- 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
- Hastie, T., Tishirani, T., & Friedman, J. (2008). The elements of statistical learning (2nd ed.). Berlin: Springer.Google Scholar
- 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
- Honnibal, M., & Montani, I. (2018). spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing (in press).Google Scholar
- Jaderberg, M., & Czarnecki, W. M. (2018). Human-level performance in first-person multiplayer games with population-based deep reinforcement learning.Google Scholar
- Jo, J., & Bengio, Y. (2017). Measuring the tendency of CNNs to learn surface statistical regularities. CoRR, arXiv:1711.11561.
- Keil, F. (1989). Concepts. Kinds and Cognitive Development. Cambridge, MA: MIT Press.Google Scholar
- 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
- Koller, D., & Friedman, N. (2009). Probabilistic graphical models: Principles and techniques. Cambridge, MA: MIT.Google Scholar
- 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.
- Leslie, A. (1979). The representation of perceived causal connection in infancy. Oxford: University of Oxford.Google Scholar
- Marcus, G. (2018). Deep learning: A critical appraisal.Google Scholar
- McCarthy, J., & Hayes, P. J. (1969). Some philosophical problems from the standpoint of artificial intelligence. Machine Intelligence, 4, 463–502.Google Scholar
- 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
- 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
- Millikan, R. (2001). On clear and confused ideas. Cambridge Studies in Philosophy. Cambridge, MA: Cambridge University Press.Google Scholar
- Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., & Frossard, P. (2016). Universal adversarial perturbations. CoRR, arXiv:1610.08401.
- Nienhuys-Cheng, S.-H., & de Wolf, R. (2008). Foundations of inductive logic programming. Berlin: Springer.Google Scholar
- 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
- Povinelli, D. J. (2000). Folk physics for apes: The chimpanzee’s theory of how the world works. London: Oxford University Press.Google Scholar
- 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
- Robinson, A., & Voronkov, A. (2001). Handbook of automated reasoning. Cambridge, MA: Elsevier Science.Google Scholar
- Russell, S., & Norvig, P. (2014). Artificial intelligence: A modern approach. Harlow, Essex: Pearson Education.Google Scholar
- Smith, B. (2003). Ontology. In Blackwell guide to the philosophy of computing and information (pp. 155–166). Blackwell.Google Scholar
- Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. Cambridge, MA: The MIT Press.Google Scholar
- Tenenbaum, J. B. (1999). A Bayesian framework for concept learning. Cambridge, MA: Massachusetts Institute of Technology.Google Scholar
- Tenenbaum, J. B., & Griffiths, T. L. (2001). Generalization, similarity, and Bayesian inference. Behavioral and brain sciences, 24(4), 629–640.Google Scholar
- 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.
- 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.