Abstract
AbstractWe investigate expert disagreement over the potential and limitations of deep learning. We conducted 25 expert interviews to reveal the reasons and arguments that underlie the disagreement about the limitations of deep learning, here evaluated in respect to high-level machine intelligence. Experts in our sample named 40 limitations of deep learning. Using interview data, we identify and explore five crucial, unresolved research subjects that underpin this scholarly disagreement: abstraction, generalisation, explanatory models, emergence of planning and intervention. We suggest that such origins of disagreement can be used to form a research road map to guide efforts towards overcoming the limitations of deep learning.
Funder
Berkeley Existential Risk Initiative
Publisher
Springer Science and Business Media LLC
Reference42 articles.
1. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015). https://doi.org/10.1038/nature14539
2. Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349, 255–260 (2015). https://doi.org/10.1126/science.aaa8415
3. McCarthy, J., Minsky, M.L., Rochester, N., Shannon, C.E.: A proposal for the Dartmouth summer research project on artificial intelligence, august 31, 1955. AI Mag. 27, 12–12 (2006). https://doi.org/10.1609/aimag.v27i4.1904
4. Sutskever, I. : Ilya Sutskever at AI Frontiers 2018: Recent Advances in Deep learning and AI from OpenAI.Progress towards the OpenAI mission LINK (2018). https://sqlandsiva.blogspot.com/2019/01/day-202-ilya-sutskever-at-ai-frontiers.html. Accessed 5 Apr 2021
5. Ord, T.: The Precipice: Existential Risk and the Future of Humanity. Bloomsbury, London (2020)
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