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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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“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]
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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
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 ›
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