The DOI matches its Crossref record; 36 of the 39 references it cites were found in a registry or an index.
What the Near Future of Artificial Intelligence Could Be
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“we will not develop AI systems in such a way that they will gradually adjust to difficult situations; rather, we will gradually turn difficult tasks into complex tasks.”
From ChatGPT, Education, and Understanding (2025) · cites this paper as Floridi 2019
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“Now, AI systems thrive in the complexity, but they fail miserably in the difficulty.”
From ChatGPT, Education, and Understanding (2025) · cites this paper as Floridi 2019
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Citations
Cites
Artificial intelligence: a modern approach
Kumar, D. A., Balasubramanian, S., Byeon, D. H., & Manerkar, P. G. V. (1995) · Choice Reviews Online · doi:10.5860/choice.33-1577 · Cited by 22,191
GAN(Generative Adversarial Nets)
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
I.—COMPUTING MACHINERY AND INTELLIGENCE
TURING, A. M. (1950) · Mind · doi:10.1093/mind/lix.236.433 · Cited by 10,290 · cited 1×
“…same counterfactual understanding of AI underpins the Turing test (Floridi et al. 2009), which, in this case, checks the ability of a machine to perform a task in such a way that the outcome would be indistinguishable from the outcome of a human agent working to achieve the same task (Turing 1950).”
ResultComputing Machinery and Intelligence (1950)
Turing, A. M. (2004) · doi:10.1093/oso/9780198250791.003.0017 · Cited by 6,120
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al. (2018) · Science · doi:10.1126/science.aar6404 · Cited by 3,562
Introduction to the Theory of Computation
Sipser, M. (1996) · ACM SIGACT News · doi:10.1145/230514.571645 · Cited by 2,886
Clinically applicable deep learning for diagnosis and referral in retinal disease
De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0107-6 · Cited by 2,639
Computational Complexity: A Modern Approach
Arora, S., & Barak, B. (2009) · Cited by 2,374
Computational Complexity: A Modern Approach
Arora, S., & Barak, B. (2009) · doi:10.1017/cbo9780511804090 · Cited by 1,690
Campbell, M., Hoane, A. J., & Hsu, F.-H. (2002) · Artificial Intelligence · doi:10.1016/s0004-3702(01)00129-1 · Cited by 1,226
On the Morality of Artificial Agents
Floridi, L., & Sanders, J. W. (2004) · Minds and Machines · doi:10.1023/b:mind.0000035461.63578.9d · Cited by 1,218
Self-citationThe Fourth Revolution: How the infosphere is reshaping human reality
Floridi, L. (2014) · International Linear Collider · Cited by 1,216
Self-citationFloridi, L. (2010) · Oxford University Press eBooks · doi:10.1093/actrade/9780199551378.001.0001 · Cited by 876
Self-citationFloridi, L. (2010) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199641321.001.0001 · Cited by 727
Self-citationEvaluation and accurate diagnoses of pediatric diseases using artificial intelligence
Liang, H., Tsui, B., Ni, H., Valentim, C. C. S., Baxter, S. L., Liu, G., Cai, W., Kermany, D., Sun, X., Chen, J., et al. (2019) · Nature Medicine · doi:10.1038/s41591-018-0335-9 · Cited by 682
Computational Complexity: ADVANCED TOPICS
Arora, S., & Barak, B. (2009) · Cited by 578
Information: A Very Short Introduction
Floridi, L. (2010) · Cited by 558
Self-citationIntroduction to the Theory of Computation
Ina, I., Rachel, & Aarón (2013) · Cited by 523
Clinical applications of machine learning algorithms: beyond the black box
Watson, D. S., Krutzinna, J., Bruce, I. N., Griffiths, C. E., McInnes, I. B., Barnes, M. R., & Floridi, L. (2019) · BMJ · doi:10.1136/bmj.l886 · Cited by 450
Self-citationFloridi, L. (2019) · doi:10.1093/oso/9780198833635.001.0001 · Cited by 401
Self-citationThe Method of Levels of Abstraction
Floridi, L. (2008) · Minds and Machines · doi:10.1007/s11023-008-9113-7 · Cited by 377
Self-citationRosenblueth, A., & Wiener, N. (1945) · Philosophy of Science · doi:10.1086/286874 · Cited by 377
(2013) · eYLS (Yale Law School) · Cited by 376
Big Data and Their Epistemological Challenge
Floridi, L. (2012) · Philosophy & Technology · doi:10.1007/s13347-012-0093-4 · Cited by 217
Self-citationOpen Data, Data Protection, and Group Privacy
Floridi, L. (2014) · Philosophy & Technology · doi:10.1007/s13347-014-0157-8 · Cited by 178
Self-citationFloridi, L., Taddeo, M., & Turilli, M. (2008) · Minds and Machines · doi:10.1007/s11023-008-9130-6 · Cited by 117
Self-citationUnderstanding Epistemic Relevance
Floridi, L. (2007) · Erkenntnis · doi:10.1007/s10670-007-9087-5 · Cited by 102
Self-citationAI Knowledge Map: How to Classify AI Technologies
Corea, F. (2018) · Studies in big data · doi:10.1007/978-3-030-04468-8_4 · Cited by 94
Synthetic Data for Social GoodPreprint
Howe, B., Stoyanovich, J., Ping, H., Herman, B., & Gee, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.08874 · Cited by 68
Digital’s Cleaving Power and Its Consequences
Floridi, L. (2017) · Philosophy & Technology · doi:10.1007/s13347-017-0259-1 · Cited by 66
Self-citationWhat a Maker’s Knowledge Could Be
Floridi, L. (2016) · Synthese · doi:10.1007/s11229-016-1232-8 · Cited by 62
Self-citationCorea, F. (2018) · Studies in big data · doi:10.1007/978-3-030-04468-8 · Cited by 44
Robotic ironing with 3D perception and force/torque feedback in household environments
Estevez, D., Victores, J. G., Fernandez-Fernandez, R., & Balaguer, C. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206556 · Cited by 37
Technoscience and Ethics Foresight
Floridi, L. (2014) · Philosophy & Technology · doi:10.1007/s13347-014-0180-9 · Cited by 35
Self-citationAllo, P. (2016) · VUBIR (Vrije Universiteit Brussel) · doi:10.4324/9781315757544.ch08 · Cited by 6
Kulas, J., Roji, R. G. P. P., & Smith, A. (2021) · doi:10.1002/9781119417453.ch3 · Cited by 5
Research works printed but not linked: 2
- Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems.
- McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955. AI Magazine, 27(4), 12.
Cited by
Sorted by citations
GPT-3: Its Nature, Scope, Limits, and Consequences
Floridi, L., & Chiriatti, M. (2020) · Minds and Machines · doi:10.1007/s11023-020-09548-1 · Cited by 2,519
Self-citationA Unified Framework of Five Principles for AI in Society
Floridi, L., & Cowls, J. (2019) · Harvard Data Science Review · doi:10.1162/99608f92.8cd550d1 · Cited by 1,631
Self-citationThe ethics of AI in health care: A mapping review
Morley, J., Machado, C. C. V., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2020) · Social Science & Medicine · doi:10.1016/j.socscimed.2020.113172 · Cited by 1,105
Self-citationAI revolutionizing industries worldwide: A comprehensive overview of its diverse applications
Rashid, A. B., & Kausik, M. A. K. (2024) · Hybrid Advances · doi:10.1016/j.hybadv.2024.100277 · Cited by 764
The Ethics of Artificial Intelligence
Floridi, L. (2023) · doi:10.1093/oso/9780198883098.001.0001 · Cited by 682
Self-citation
Metadata
- Authors
- Floridi, L.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Floridi, Luciano
- ORCID
- Floridi, Luciano
- Venue
- Philosophy & Technology
- Published
- 2019-03
- Volume
- 32
- Issue
- 1
- Pages
- 1-15
- ISSN
- 2210-5433 (ISSN-L)
- 2210-5441
- OpenAlex
- W2925023100
- Cited by
- 191
- References
- 39
- Citations by year
- 2026: 17 · 2025: 19 · 2024: 22 · 2023: 43 · 2022: 15 · 2021: 24 · 2020: 37 · 2019: 13
- FWCI
- 38.90
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Philosophy & Technology (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Cybernetics and Technology in Society
- Keywords
- artificial intelligence, future of AI, artificial intelligence forecasting, AI regulation, EU AI policy, dystopian scenarios, AI risks, AI ethics
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