The DOI matches its Crossref record; 24 of the 25 references it cites were found in a registry or an index.
Chatbots, Humbots, and the Quest for Artificial General Intelligence
Abstract
What began as a quest for artificial general intelligence branched into several pursuits, including intelligent assistants developed by tech companies and task-oriented chatbots that deliver more information or services in specific domains. Progress quickened with the spread of low-latency networking, then accelerated dramatically a few years ago. In 2016, task-focused chatbots became a centerpiece of machine intelligence, promising interfaces that are more engaging than robotic answering systems and that can accommodate our increasingly phone-based information needs. Hundreds of thousands were built. Creating successful non-trivial chatbots proved more difficult than anticipated. Some developers now design for human-chatbot (humbot) teams, with people handling difficult queries. This paper describes the conversational agent space, difficulties in meeting user expectations, potential new design approaches, uses of human-bot hybrids, and implications for the ultimate goal of creating software with general intelligence.
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In the citing paper
“human-chatbot teams which handle challenging service queries better than chatbots alone and more efficiently than humans alone.”
From Future directions for chatbot research: An interdisciplinary research agenda (2021) · cites this paper as [45]
In the citing paper
“Google's Duplex, an AI system capable of making phone calls and scheduling appointments on behalf of users, faced criticism for not always disclosing its AI identity during interactions [135], [136], highlighting the importance of transparency and ethical considerations in AI development.”
From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [135]
Citations
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Cited by
Sorted by citations
Huang, W., Hew, K. F., & Fryer, L. K. (2022) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12610 · Cited by 830
Rapp, A., Curti, L., & Boldi, A. (2021) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2021.102630 · Cited by 733
Future directions for chatbot research: An interdisciplinary research agenda
Følstad, A., Araujo, T., Law, E. L.-C., Brandtzaeg, P. B., Papadopoulos, S., Reis, L., Baez, M., Laban, G., McAllister, P., Ischen, C., et al. (2021) · Computing · doi:10.1007/s00607-021-01016-7 · Cited by 247
“We hold that such collaboration will benefit from being implemented while reflecting on human collaboration, and in line with relevant empirical evidence of chatbot research—in linewith reflections byGrudin and Jacques [45].”
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Metadata
- Authors
- Grudin, J., & Jacques, R.
- Affiliations
- Microsoft (United States) (US)
- ORCID
- Grudin, Jonathan
- Venue
- Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2019-05-02
- Pages
- 1-11
- OpenAlex
- W2940884072
- Cited by
- 275
- References
- 25
- Citations by year
- 2026: 13 · 2025: 40 · 2024: 51 · 2023: 53 · 2022: 41 · 2021: 43 · 2020: 28 · 2019: 6
- FWCI
- 11.29
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- chatbots, artificial general intelligence, task-oriented chatbots, intelligent assistants, chatbot design, user expectations, low-latency communication
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