Beyond artificial intelligence: exploring artificial wisdom
Open accessAbstract
BACKGROUND: The ultimate goal of artificial intelligence (AI) is to develop technologies that are best able to serve humanity. This will require advancements that go beyond the basic components of general intelligence. The term "intelligence" does not best represent the technological needs of advancing society, because it is "wisdom", rather than intelligence, that is associated with greater well-being, happiness, health, and perhaps even longevity of the individual and the society. Thus, the future need in technology is for artificial wisdom (AW). METHODS: We examine the constructs of human intelligence and human wisdom in terms of their basic components, neurobiology, and relationship to aging, based on published empirical literature. We review the development of AI as inspired and driven by the model of human intelligence, and consider possible governing principles for AW that would enable humans to develop computers which can operationally utilize wise principles and result in wise acts. We review relevant examples of current efforts to develop such wise technologies. RESULTS: AW systems will be based on developmental models of the neurobiology of human wisdom. These AW systems need to be able to a) learn from experience and self-correct; b) exhibit compassionate, unbiased, and ethical behaviors; and c) discern human emotions and help the human users to regulate their emotions and make wise decisions. CONCLUSIONS: A close collaboration among computer scientists, neuroscientists, mental health experts, and ethicists is necessary for developing AW technologies, which will emulate the qualities of wise humans and thus serve the greatest benefit to humanity. Just as human intelligence and AI have helped further the understanding and usefulness of each other, human wisdom and AW can aid in promoting each other's growth.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The development of AW necessitates close collaborations by computer scientists, neuroscientists, mental health experts, and ethicists.
“The development of AW necessitates the close and prudent collaborations by computer scientists, neuroscientists, mental health experts, and ethicists that collectively provide the greatest benefits to humanity (Jeste et al., 2020).”
From SHIFTing artificial intelligence to be responsible in healthcare: a systematic review (2022) · cites this paper as Jeste et al., 2020
The future need in AI technology is for artificial wisdom (AW) not artificial intelligence.
“Jeste et al. (2020) contend that the future need in AI technology is for artificial wisdom (AW) not artificial intelligence”
From SHIFTing artificial intelligence to be responsible in healthcare: a systematic review (2022) · cites this paper as Jeste et al., 2020
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
I.—COMPUTING MACHINERY AND INTELLIGENCE
TURING, A. M. (1950) · Mind · doi:10.1093/mind/lix.236.433 · Cited by 10,290 · cited 2× · see citing works
“Since 1950, the Turing Test has been considered a test of a machine’s (computer’s) ability to exhibit intelligent behavior equivalent to or indistinguishable from a human’s (Turing, 1950).”
BackgroundMethodComputing Machinery and Intelligence (1950)
Turing, A. M. (2004) · doi:10.1093/oso/9780198250791.003.0017 · Cited by 6,120 · see citing works
Pretense and representation: The origins of "theory of mind."
Leslie, A. M. (1987) · Psychological Review · doi:10.1037/0033-295x.94.4.412 · Cited by 3,379 · see citing works
Theory of fluid and crystallized intelligence: A critical experiment.
Cattell, R. B. (1963) · Journal of Educational Psychology · doi:10.1037/h0046743 · Cited by 2,556 · see citing works
Superintelligence: paths, dangers, strategies
(2015) · Choice Reviews Online · doi:10.5860/choice.187536 · Cited by 1,999 · see citing works
(2021) · Encyclopedia of the UN sustainable development goals · doi:10.1007/978-3-319-95963-4_300069 · Cited by 1,585 · see citing works
An Introduction to Deep Reinforcement Learning
François-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G., & Pineau, J. (2018) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000071 · Cited by 1,353 · see citing works
Killing, Letting Die, and the Trolley Problem
Thomson, J. J. (1976) · The Monist · doi:10.5840/monist197659224 · Cited by 1,349 · see citing works
Mainstream science on intelligence: An editorial with 52 signatories, history, and bibliography
Gottfredson, L. S. (1997) · Intelligence · doi:10.1016/s0160-2896(97)90011-8 · Cited by 1,137 · see citing works
The neuroscience of human intelligence differences
Deary, I. J., Penke, L., & Johnson, W. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2793 · Cited by 1,038 · see citing works
Artificial intelligence for mental health and mental illnesses: an overview
Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H.-C., & Jeste, D. V. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1094-0 · Cited by 970 · see citing works
Self-citationRebok, G. W., Ball, K., Guey, L. T., Jones, R. N., Kim, H., King, J. W., Marsiske, M., Morris, J. N., Tennstedt, S. L., Unverzagt, F. W., et al. (2014) · Journal of the American Geriatrics Society · doi:10.1111/jgs.12607 · Cited by 965 · see citing works
Self-citationSuperintelligence: Paths, Dangers, Strategies
Mulgan, T. (2015) · The Philosophical Quarterly · doi:10.1093/pq/pqv034 · Cited by 878 · see citing works
Guerra, A. G., & de Gómez, L. S. (2018) · Advances in higher education and professional development book series · doi:10.4018/978-1-5225-5837-8.ch009 · Cited by 818 · see citing works
Stanford-Binet intelligence scale : manual for the third revision form L-M
Terman, L. M., & Merrill, M. A. (1973) · Houghton Mifflin eBooks · Cited by 740 · see citing works
Future Progress in Artificial Intelligence: A Survey of Expert Opinion
Müller, V. C., & Bostrom, N. (2016) · Synthese Library · doi:10.1007/978-3-319-26485-1_33 · Cited by 655 · see citing works
Artificial intelligence-enabled healthcare delivery
Reddy, S., Fox, J., & Purohit, M. P. (2019) · Journal of the Royal Society of Medicine · doi:10.1177/0141076818815510 · Cited by 635 · see citing works
An introduction to deep reinforcement learning
François-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G., & Pineau, J. (2018) · doi:10.1561/9781680835397 · Cited by 544 · see citing works
Exploring the neurological substrate of emotional and social intelligence
Bar-On, R. (2003) · Brain · doi:10.1093/brain/awg177 · Cited by 537 · see citing works
Battling the Modern Behavioral Epidemic of Loneliness
Jeste, D. V., Lee, E., & Cacioppo, S. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.0027 · Cited by 367 · see citing works
Self-citationHuman intelligence and brain networks
Colom, R., Karama, S., Jung, R. E., & Haier, R. J. (2010) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2010.12.4/rcolom · Cited by 348 · see citing works
Wisdom and Life Satisfaction in Old Age
Ardelt, M. (1997) · The Journals of Gerontology Series B · doi:10.1093/geronb/52b.1.p15 · Cited by 343 · see citing works
Cognitive Training Does Not Enhance General Cognition
Sala, G., & Gobet, F. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.10.004 · Cited by 303 · cited 2× · see citing works
“…are malleable to training (Rebok et al., 2014); yet, such interventions do not always generalize across domains (e.g. techniques to improvememory do not help spatial orientation or inductive reasoning) and may not improve overall intellectual functioning (Butler et al., 2018; Sala and Gobet, 2019).”
Lee, E. E., Depp, C., Palmer, B. W., Glorioso, D., Daly, R., Liu, J., Tu, X. M., Kim, H.-C., Tarr, P., Yamada, Y., et al. (2019) · International Psychogeriatrics · doi:10.1017/s1041610218002120 · Cited by 286 · see citing works
Self-citationPrediction of School Failure From Third-Grade Data
Lloyd, D. N. (1978) · Educational and Psychological Measurement · doi:10.1177/001316447803800442 · Cited by 257 · see citing works
Critique of Cattell's "Theory of fluid and crystallized intelligence: A critical experiment."
Humphreys, L. G. (1967) · Journal of Educational Psychology · doi:10.1037/h0024654 · Cited by 254 · see citing works
Does Cognitive Training Prevent Cognitive Decline?: A Systematic Review
Butler, M., McCreedy, E., Nelson, V. A., Desai, P., Ratner, E., Fink, H. A., Hemmy, L. S., McCarten, J. R., Barclay, T. R., Brasure, M., et al. (2018) · Annals of Internal Medicine · doi:10.7326/m17-1531 · Cited by 236 · see citing works
Meeks, T. W., & Jeste, D. V. (2009) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2009.8 · Cited by 233 · see citing works
Self-citationOlder and Wiser? Integrating Results on the Relationship between Age and Wisdom-related Performance
Staudinger, U. M. (1999) · International Journal of Behavioral Development · doi:10.1080/016502599383739 · Cited by 227 · see citing works
Graham, S. A., Lee, E., Jeste, D. V., Patten, R. V., Twamley, E. W., Nebeker, C., Yamada, Y., Kim, H., & Depp, C. A. (2019) · Psychiatry Research · doi:10.1016/j.psychres.2019.112732 · Cited by 223 · see citing works
Self-citationVoss, C., Schwartz, J., Daniels, J., Kline, A., Haber, N., Washington, P., Tariq, Q., Robinson, T. N., Desai, M., Phillips, J. M., et al. (2019) · JAMA Pediatrics · doi:10.1001/jamapediatrics.2019.0285 · Cited by 206 · see citing works
Sex Differences in the Heritability of Resilience
Boardman, J. D., Blalock, C. L., & Button, T. M. M. (2008) · Twin Research and Human Genetics · doi:10.1375/twin.11.1.12 · Cited by 185 · see citing works
Advanced Applications of Neural Networks and Artificial Intelligence: A Review
Kumar, K., & Thakur, G. S. M. (2012) · International Journal of Information Technology and Computer Science · doi:10.5815/ijitcs.2012.06.08 · Cited by 180 · see citing works
Moral judgment reloaded: a moral dilemma validation study
Christensen, J. F., Flexas, A., Calabrese, M., Gut, N. K., & Gomila, A. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.00607 · Cited by 162 · see citing works
Worthy, D. A., Gorlick, M. A., Pacheco, J. L., Schnyer, D. M., & Maddox, W. T. (2011) · Psychological Science · doi:10.1177/0956797611420301 · Cited by 159 · see citing works
Spengler, M., Brunner, M., Damian, R. I., Lüdtke, O., Martin, R., & Roberts, B. W. (2015) · Developmental Psychology · doi:10.1037/dev0000025 · Cited by 149 · see citing works
Thomas, M. L., Bangen, K. J., Palmer, B. W., Martin, A. S., Avanzino, J. A., Depp, C. A., Glorioso, D., Daly, R. E., & Jeste, D. V. (2017) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2017.09.005 · Cited by 131 · see citing works
Self-citationSelf-Reported Wisdom and Happiness: An Empirical Investigation
Bergsma, A., & Ardelt, M. (2011) · Journal of Happiness Studies · doi:10.1007/s10902-011-9275-5 · Cited by 126 · see citing works
A Survey of Behavioral Models for Social Robots
Nocentini, O., Fiorini, L., Acerbi, G., Sorrentino, A., Mancioppi, G., & Cavallo, F. (2019) · Robotics · doi:10.3390/robotics8030054 · Cited by 120 · see citing works
Jeste, D. V., & Lee, E. (2019) · Harvard Review of Psychiatry · doi:10.1097/hrp.0000000000000205 · Cited by 113 · see citing works
Self-citationGoldstein, S., Princiotta, D. K., & Naglieri, J. A. (2014) · Springer eBooks · doi:10.1007/978-1-4939-1562-0 · Cited by 112 · see citing works
Ali, A., Ambler, G., Strydom, A., Rai, D., Cooper, C., McManus, S., Weich, S., Meltzer, H., Dein, S. L., & Hassiotis, A. (2012) · Psychological Medicine · doi:10.1017/s0033291712002139 · Cited by 103 · see citing works
Development of a 12-Item Abbreviated Three-Dimensional Wisdom Scale (3D-WS-12)
Thomas, M. L., Bangen, K. J., Ardelt, M., & Jeste, D. V. (2015) · Assessment · doi:10.1177/1073191115595714 · Cited by 102 · see citing works
Self-citationArtificial consciousness: Utopia or real possibility?
Buttazzo, G. (2001) · Computer · doi:10.1109/2.933500 · Cited by 90 · see citing works
Lee, E. E., Bangen, K. J., Avanzino, J. A., Hou, B., Ramsey, M., Eglit, G., Liu, J., Tu, X. M., Paulus, M., & Jeste, D. V. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.0821 · Cited by 85 · see citing works
Self-citationEmotional and Social Intelligence and Behavior
Boyatzis, R. E., Gaskin, J. E., & Wei, H. (2014) · Springer eBooks · doi:10.1007/978-1-4939-1562-0_17 · Cited by 77 · see citing works
A pragmatic trial of a group intervention in senior housing communities to increase resilience
Treichler, E. B. H., Glorioso, D., Lee, E. E., Wu, T.-C., Tu, X. M., Daly, R., O’Brien, C., Smith, J. L., & Jeste, D. V. (2020) · International Psychogeriatrics · doi:10.1017/s1041610219002096 · Cited by 69 · see citing works
Self-citationIntrinsically motivated reinforcement learning for human–robot interaction in the real-world
Qureshi, A. H., Nakamura, Y., Yoshikawa, Y., & Ishiguro, H. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.03.014 · Cited by 66 · see citing works
A 20-Year Community Roadmap for Artificial Intelligence Research in the USPreprint
Gil, Y., & Selman, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.02624 · Cited by 63 · see citing works
The New Science of Practical Wisdom
Jeste, D. V., Lee, E., Cassidy, C., Caspari, R., Gagneux, P., Glorioso, D., Miller, B. L., Semendeferi, K., Vogler, C., Nusbaum, H. C., et al. (2019) · Perspectives in biology and medicine · doi:10.1353/pbm.2019.0011 · Cited by 60 · see citing works
Self-citationPersonality, cognitive/psychological traits and psychiatric resilience: a multivariate twin study
Amstadter, A. B., Moscati, A., Maes, H. H., Myers, J. M., & Kendler, K. S. (2016) · Personality and Individual Differences · doi:10.1016/j.paid.2015.11.041 · Cited by 58 · see citing works
Modarresi, S., Divine, A., Grahn, J. A., Overend, T. J., & Hunter, S. W. (2018) · International Psychogeriatrics · doi:10.1017/s1041610218001783 · Cited by 47 · see citing works
Grassi, M., Loewenstein, D., Caldirola, D., Schruers, K. R. J., Duara, R., & Perna, G. (2018) · International Psychogeriatrics · doi:10.1017/s1041610218001618 · Cited by 44 · see citing works
Empowerment As Replacement for the Three Laws of Robotics
Salge, C., & Polani, D. (2017) · Frontiers in Robotics and AI · doi:10.3389/frobt.2017.00025 · Cited by 40 · see citing works
A Survey of Behavioural Models for Social RobotsPreprint
Nocentini, O., Fiorini, L., Acerbi, G., et al. (2019) · Preprints.org · doi:10.20944/preprints201905.0251.v1 · Cited by 36 · see citing works
The quest for artificial wisdom
Casacuberta, D. (2012) · AI & Society · doi:10.1007/s00146-012-0390-6 · Cited by 23 · see citing works
Artificial intelligence and risk prediction in geriatric mental health: what happens next?
Graham, S. A., & Depp, C. A. (2019) · International Psychogeriatrics · doi:10.1017/s1041610219000954 · Cited by 13 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 3
- Asimov Isaac (1950). “Runaround”. Robot I (The Isaac Asimov Collection ed.). New York City: Doubleday. p. 40. ISBN 0-385-42304-7.
- Minsky ML. Logical versus analogical or symbolic versus connectionist or neat versus scruffy. AI magazine. 1991 6 15; 12(2): 34-. DOI: 10.1609/aimag.v12i2.894 · DOI
- Wechsler D (2008). Wechsler adult intelligence scale fourth edition: Technical interpretive manual. San Antonio: NCS Pearson.
Cited by
Sorted by citations
SHIFTing artificial intelligence to be responsible in healthcare: a systematic review
Siala, H., & Wang, Y. (2022) · Social Science & Medicine · doi:10.1016/j.socscimed.2022.114782 · Cited by 358 · see citing works
Jeste, D. V., Lee, E. E., Palmer, B. W., & Treichler, E. B. H. (2020) · Psychological Inquiry · doi:10.1080/1047840x.2020.1757984 · Cited by 36 · see citing works
Self-citationHow Can ChatGPT Benefit Pharmacy: A Case Report on Review Writing
Zhu, Y., Han, D., Chen, S., Zeng, F., & Wang, C. (2023) · doi:10.20944/preprints202302.0324.v1 · Cited by 21 · see citing works
Jaffar, Z. F., Alvi, A., Rahim, A., Ambreen, S., & Jaffar, M. M. (2026) · Journal of Language, Literature & Social Affairs · doi:10.63056/jllsa.2.5.2026.253
- Authors
- Jeste, D. V., Graham, S. A., Nguyen, T. T., Depp, C. A., Lee, E. E., & Kim, H.-C.
- Affiliations
- University of California San Diego (US)
- IBM Research - Almaden (US)
- Corresponding author
- Jeste, Dilip V.
- Venue
- International Psychogeriatrics
- Publisher
- Elsevier BV
- Published
- 2020-08
- Volume
- 32
- Issue
- 8
- Pages
- 993-1001
- ISSN
- 1041-6102 (ISSN-L)
- PMC
- PMC7942180
- OpenAlex
- W3038047461
- Cited by
- 100
- References
- 58
- Citations by year
- 2026: 16 · 2025: 24 · 2024: 25 · 2023: 22 · 2022: 6 · 2021: 3 · 2020: 4
- FWCI
- 0.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Psychogeriatrics (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Brain and Behavior Research Foundation
- National Alliance for Research on Schizophrenia and Depression
- Sam and Rose Stein Institute for Research on Aging
- National Institute of Mental Health (MH019934, R01MH094151-01, K23MH118435-01A1, K23MH119375-01)
- University of California, San Diego
- IBM Research
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
- Social Sciences › Psychology › Neuropsychology and Physiological Psychology › Aging and Gerontology Research
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- artificial wisdom, artificial intelligence, human wisdom, human intelligence, aging, emotion regulation, ethical AI, self-correction, interdisciplinary collaboration
- MeSH terms
- Aging, Artificial Intelligence, Humans, Intelligence, Longevity, Neurobiology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
