Brief History of Artificial Intelligence

  • Nikesh Muthukrishnan
    Affiliations
    Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology & Research Institute of the McGill University Health Centre, 5252 Boulevard de Maisonneuve Ouest, Montreal, Quebec H4A 3S5, Canada
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  • Farhad Maleki
    Affiliations
    Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology & Research Institute of the McGill University Health Centre, 5252 Boulevard de Maisonneuve Ouest, Montreal, Quebec H4A 3S5, Canada
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  • Katie Ovens
    Affiliations
    Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology & Research Institute of the McGill University Health Centre, 5252 Boulevard de Maisonneuve Ouest, Montreal, Quebec H4A 3S5, Canada

    University of Saskatchewan, 116 - 110 Science Place, Saskatoon, SK S7N 5C9, Canada
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  • Caroline Reinhold
    Affiliations
    Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology & Research Institute of the McGill University Health Centre, 5252 Boulevard de Maisonneuve Ouest, Montreal, Quebec H4A 3S5, Canada

    Department of Radiology, McGill University, 1650 Cedar Avenue, Montreal, Quebec H3G 1A4, Canada
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  • Behzad Forghani
    Affiliations
    Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology & Research Institute of the McGill University Health Centre, 5252 Boulevard de Maisonneuve Ouest, Montreal, Quebec H4A 3S5, Canada

    Gerald Bronfman Department of Oncology, McGill University, Suite 720, 5100 Maisonneuve Boulevard West, Montreal, Quebec H4A3T2, Canada
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  • Reza Forghani
    Correspondence
    Corresponding author. Room C02.5821, 1001 Decarie Boulevard, Montreal, Quebec H4A 3J1, Canada.
    Affiliations
    Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology & Research Institute of the McGill University Health Centre, 5252 Boulevard de Maisonneuve Ouest, Montreal, Quebec H4A 3S5, Canada

    Department of Radiology, McGill University, 1650 Cedar Avenue, Montreal, Quebec H3G 1A4, Canada

    Gerald Bronfman Department of Oncology, McGill University, Suite 720, 5100 Maisonneuve Boulevard West, Montreal, Quebec H4A3T2, Canada

    Segal Cancer Centre, Lady Davis Institute for Medical Research, Jewish General Hospital, 3755 Cote Ste-Catherine Road, Montreal, Quebec H3T 1E2, Canada

    Department of Otolaryngology - Head and Neck Surgery, Royal Victoria Hospital, McGill University Health Centre, 1001 boul. Decarie Boulevard, Montreal, Quebec H3A 3J1, Canada
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Published:September 18, 2020DOI:https://doi.org/10.1016/j.nic.2020.07.004

      Keywords

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      References

        • Devlin J.
        • Chang M.-W.
        • Lee K.
        • et al.
        BERT: pre-training of deep bidirectional transformers for language understanding.
        arXiv. 2018; (1810.04805. Available at:) (Accessed October 1, 2018)
      1. Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks. Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1; Palais des Congres de Montreal, Montreal, Canada, December 7-12, 2015.

        • Pierson H.A.
        • Gashler M.S.
        Deep learning in robotics: a review of recent research.
        Adv Robot. 2017; 31: 821-835
        • Miotto R.
        • Wang F.
        • Wang S.
        • et al.
        Deep learning for healthcare: review, opportunities and challenges.
        Brief Bioinformatics. 2018; 19: 1236-1246
        • McCulloch W.S.
        • Pitts W.
        A logical calculus of the ideas immanent in nervous activity.
        The Bulletin of Mathematical Biophysics. 1943; 5: 115-133
        • Rosenblatt F.
        The perceptron: a probabilistic model for information storage and organization in the brain.
        Psychol Rev. 1958; 65: 386-408
        • Turing A.M.
        I.—Computing machinery and intelligence.
        Mind. 1950; LIX: 433-460
        • Pinar Saygin A.
        • Cicekli I.
        • Akman V.
        Turing test: 50 years later.
        Minds Mach (Dordr). 2000; 10: 463-518
        • McCorduck P.
        Machines who think.
        2nd edition. A K Peters, Ltd., Natick (MA)2004
        • Stone P.
        • Brooks R.
        • Brynjolfsson E.
        • et al.
        Artificial intelligence and life in 2030.
        in: One hundred year study on artificial intelligence: report of the 2015-2016 study panel, Stanford University. Stanford University, Stanford (CA)2016 (Available at: https://ai100.sites.stanford.edu/sites/g/files/sbiybj9861/f/ai100report10032016fnl_singles.pdf. Accessed September 6, 2016)
        • Minsky M.
        • Papert S.
        Perceptrons; an introduction to computational geometry.
        MIT Press, Cambridge (MA)1969
        • Lighthill J.
        Artificial intelligence: a general survey.
        Science Research Council, London1973
        • Hendler J.
        Avoiding Another AI Winter.
        IEEE Intell Syst. 2008; 23: 2-4
        • McCarthy J.
        Some expert systems need common sense.
        Ann N Y Acad Sci. 1984; 426: 129-137
        • Rumelhart D.E.
        • Hinton G.E.
        • Williams R.J.
        Learning internal representations by error propagation.
        in: Parallel distributed processing: explorations in the microstructure of cognition. vol. 1. MIT Press, Cambridge (MA)1985: 318-362
        • Rumelhart D.E.
        • Hinton G.E.
        • Williams R.J.
        Learning representations by back-propagating errors.
        Nature. 1986; 323: 533-536
        • Werbos P.
        • John P.
        Beyond regression: new tools for prediction and analysis in the behavioral sciences.
        PhD thesis, Harvard University, 1974
        • Fukushima K.
        Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position.
        Biol Cybern. 1980; 36: 193-202
        • Parker D.B.
        Learning-logic: casting the cortex of the human brain in silicon. Technical report Tr-47.
        Center for Computational Research in Economics and Management Science. MIT, Cambridge (MA)1985
      2. Boser BE, Guyon IM, Vapnik VN. A training algorithm for optimal margin classifiers. Proceedings of the fifth annual workshop on Computational learning theory. Pittsburgh, PA, July 27-29, 1992.

        • Schaeffer J.
        • Plaat A.
        Kasparov versus deep blue: the rematch.
        J Int Comput Games Assoc. 1997; 20: 95-101
        • Campbell M.
        • Hoane A.J.
        • Hsu F-h
        Deep Blue.
        Artif Intelligence. 2002; 134: 57-83
        • Lecun Y.
        • Bottou L.
        • Bengio Y.
        • et al.
        Gradient-based learning applied to document recognition.
        Proc IEEE. 1998; 86: 2278-2324
        • Hinton G.E.
        • Osindero S.
        • Teh Y.-W.
        A fast learning algorithm for deep belief nets.
        Neural Comput. 2006; 18: 1527-1554
        • Cireşan D.C.
        • Meier U.
        • Gambardella L.M.
        • et al.
        Deep, big, simple neural nets for handwritten digit recognition.
        Neural Comput. 2010; 22: 3207-3220
      3. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1. Lake Tahoe (NV), December 3-6, 2012.

        • Strickland E.
        IBM Watson, heal thyself: how IBM overpromised and underdelivered on AI health care.
        IEEE Spectr. 2019; 56: 24-31