Beyond artificial intelligence: exploring artificial wisdom

被引:25
|
作者
Jeste, Dilip V. [1 ,2 ,3 ]
Graham, Sarah A. [1 ,2 ]
Nguyen, Tanya T. [1 ,2 ]
Depp, Colin A. [1 ,2 ,4 ]
Lee, Ellen E. [1 ,2 ,4 ]
Kim, Ho-Cheol [5 ]
机构
[1] Univ Calif San Diego, Dept Psychiat, La Jolla, CA 92093 USA
[2] Univ Calif San Diego, Sam & Rose Stein Inst Res Aging, La Jolla, CA 92093 USA
[3] Univ Calif San Diego, Dept Neurosci, La Jolla, CA 92093 USA
[4] VA San Diego Healthcare Syst, San Diego, CA USA
[5] IBM Res Almaden, AI & Cognit Software, San Jose, CA USA
关键词
cognitive activity; aging; PREDICTION; OLDER; AGE; NEUROBIOLOGY; SCIENCE; TRIAL;
D O I
10.1017/S1041610220000927
中图分类号
B849 [应用心理学];
学科分类号
040203 ;
摘要
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
引用
收藏
页码:993 / 1001
页数:9
相关论文
共 50 条
  • [21] Beyond the Buzzwords: Artificial Intelligence in Laryngology
    Melley, Lauren E.
    Sataloff, Robert T.
    JOURNAL OF VOICE, 2022, 36 (01) : 2 - 3
  • [22] ARTIFICIAL INTELLIGENCE : LEGAL ISSUES (AND BEYOND)
    Gazzetta, Cristina
    REVISTA GENERAL DE DERECHO PUBLICO COMPARADO, 2024, (36): : 317 - 342
  • [23] Artificial intelligence for science-bridging data to wisdom
    Xu, Yongjun
    Wang, Fei
    An, Zhulin
    Wang, Qi
    Zhang, Zhao
    INNOVATION, 2023, 4 (06):
  • [24] ChatGPT: A Threat to the Natural Wisdom from Artificial Intelligence
    Tripathi, Manjul
    Chandra, Sarat P.
    NEUROLOGY INDIA, 2023, 71 (03) : 416 - 417
  • [25] Exploring the Nuances of Designing (with/for) Artificial Intelligence
    Stoimenova, Niya
    Price, Rebecca
    DESIGN ISSUES, 2020, 36 (04) : 45 - 55
  • [26] Exploring the intersection of mechanobiology and artificial intelligence
    Roger Oria
    Kashish Jain
    Valerie M. Weaver
    npj Biological Physics and Mechanics, 2 (1):
  • [27] Exploring Artificial Intelligence Utilizing BioArt
    Simou, Panagiota
    Tiligadis, Konstantinos
    Alexiou, Athanasios
    ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2013, 2013, 412 : 687 - 692
  • [28] Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom
    Lee, Ellen E.
    Torous, John
    De Choudhury, Munmun
    Depp, Colin A.
    Graham, Sarah A.
    Kim, Ho-Cheol
    Paulus, Martin P.
    Krystal, John H.
    Jeste, Dilip, V
    BIOLOGICAL PSYCHIATRY-COGNITIVE NEUROSCIENCE AND NEUROIMAGING, 2021, 6 (09) : 856 - 864
  • [29] Beyond boundaries: exploring a generative artificial intelligence assignment in graduate, online science courses
    Ganjoo, Rohini
    Rankin, James
    Lee, Benjamin
    Schwartz, Lisa
    JOURNAL OF MICROBIOLOGY & BIOLOGY EDUCATION, 2024, 25 (03)
  • [30] Artificial intelligence learning platform in a visual programming environment: exploring an artificial intelligence learning model
    Chang, Jui-Hung
    Wang, Chi-Jane
    Zhong, Hua-Xu
    Weng, Hsiu-Chen
    Zhou, Yu-Kai
    Ong, Hoe-Yuan
    Lai, Chin-Feng
    ETR&D-EDUCATIONAL TECHNOLOGY RESEARCH AND DEVELOPMENT, 2024, 72 (02): : 997 - 1024