Cited by 213Psychiatry, Mental Health, NeuroscienceArtificial Intelligence ApplicationsAI-based Problem Solving and Planning

Mapping the Landscape of Human-Level Artificial General Intelligence

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Adams, Sam S., Arel, Itamar, Bach, Joscha, Coop, Robert, Furlan, Rod, Goertzel, Ben, Hall, J. Storrs, Samsonovich, Alexei +4 more·2012·AI Magazine

Abstract

We present the broad outlines of a roadmap toward human‐level artificial general intelligence (henceforth, AGI). We begin by discussing AGI in general, adopting a pragmatic goal for its attainment and a necessary foundation of characteristics and requirements. An initial capability landscape will be presented, drawing on major themes from developmental psychology and illuminated by mathematical, physiological, and information‐processing perspectives. The challenge of identifying appropriate tasks and environments for measuring AGI will be addressed, and seven scenarios will be presented as milestones suggesting a roadmap across the AGI landscape along with directions for future research and collaboration.

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References 26 · Cited by 213

Research works printed but not linked: 17

  1. Arel, I. 2009. Working Toward Pragmatic Convergence: AGI Axioms and a Unified Roadmap. Paper presented at the Workshop on the Future of AGI, Second Conference on Artificial General Intelligence (AGI-09).
  2. Arel, I., and Livingston, S. 2009. Beyond the Turing Test. IEEE Computer 42(3): 90-91.
  3. Bengio, Y. 2009. Learning Deep Architectures for AI. Foundations and Trends in Machine Learning 2(1): 1-127.
  4. Dempster, F. N. 1981. Memory Span: Sources of Individ- ual and Developmental Differences. Psychological Bulletin 89(1): 63-100.
  5. Ferrucci, D. 2010. Build Watson: An Overview of DeepQA for the Jeopardy! Challenge. In Proceedings of the 19th International Conference on Parallel Architectures and Com- pilation Techniques (PACT '10), 1-2. New York: Association for Computing Machinery.
  6. Goertzel, B. 2010. Toward a Formal Definition of Real- World General Intelligence. In Proceedings of the Third Conference on Artificial General Intelligence (AGI-10). Paris: Atlantis Press.
  7. Goertzel, B., and Bugaj, S. V. 2009. AGI Preschool. In Pro- ceedings of the Second Conference on Artificial General Intel- ligence (AGI-09). Paris: Atlantis Press.
  8. Hutter, M. 2004. Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability. Berlin: Springer.
  9. Kitano, H. 1997. RoboCup: The Robot World Cup Initia- tive. In Proceedings of the First International Conference on Autonomous Agent (Agents-97). New York: Association for Computing Machinery.
  10. Laird, J., and Wray, R. 2010. Cognitive Architecture Requirements for Achieving AGI. In Proceedings of the Third Conference on Artificial General Intelligence (AGI-10). Paris: Atlantis Press.
  11. Laird, J.; Wray, R.; Marinier, R.; and Langley, P. 2009. Claims and Challenges in Evaluating Human-Level Intel- ligent Systems. In Proceedings of the Second Conference on Artificial General Intelligence (AGI-09). Paris: Atlantis Press.
  12. Lungarella, M.; Metta, G.; Pfeifer, R.; and Sandini, G. 2003. Developmental Robotics: A Survey. Connection Sci- ence 15(4): 151-190.
  13. Nilsson, N. J. 2005. Human-Level Artificial Intelligence? Be Serious! AI Magazine 26(4): 68-75.
  14. Piaget, J. 1953. The Origins of Intelligence in Children. Lon- don: Routledge and Kegan Paul.
  15. Steels, L. 2010. Modeling the Formation of Language in Embodied Agents: Methods and Open Challenges. In Evolution of Communication and Language in Embodied Agents, 223-233. Berlin: Springer Verlag. Sundance 2001. Teacher Guide for Just Kids. Marlborough, MA: Sundance Publishing.
  16. Vygotsky, L. 1986. Thought and Language. Cambridge, MA: The MIT Press.
  17. Winne, P. H., and Perry, N. E. 2000. Measuring Self-Regu- lated Learning, 531-566. Boston: Academic Press.

Authors
Adams, S. S., Arel, I., Bach, J., Coop, R., Furlan, R., Goertzel, B., Hall, J. S., Samsonovich, A., Scheutz, M., Schlesinger, M., Shapiro, S. C., & Sowa, J. F.
Affiliations
  • IBM (United States) (US)
  • University of Tennessee at Knoxville (US)
  • Humboldt-Universität zu Berlin (DE)
  • George Mason University (US)
  • Tufts University (US)
  • Southern Illinois University Carbondale (US)
  • University at Buffalo, State University of New York (US)
Venue
AI Magazine
Publisher
Wiley
Published
2012-03
Volume
33
Issue
1
Pages
25-41
ISSN
  • 0738-4602 (ISSN-L)
  • 2371-9621
OpenAlex
W2123285937
Cited by
213
References
26
Citations by year
2026: 13 · 2025: 25 · 2024: 18 · 2023: 26 · 2022: 25 · 2021: 30 · 2020: 20 · 2019: 12 · 2018: 20 · 2017: 5 · 2016: 5 · 2014: 8 · 2013: 3 · 2012: 3
FWCI
4.41
Type
journal article
Language
en
Open access
Locations
AI Magazine (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
Keywords
developmental psychology, information processing

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