Chatbots, Humbots, and the Quest for Artificial General Intelligence

Grudin, Jonathan; Jacques, Richard

doi:10.1145/3290605.3300439

Cited by 275Speech and dialogue systemsAI in Service InteractionsSocial Robot Interaction and HRI

Chatbots, Humbots, and the Quest for Artificial General Intelligence

Grudin, Jonathan, Jacques, Richard·2019·Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

DOI 10.1145/3290605.3300439

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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 (humbots) handle challenging service queries better than chatbots alone and more efficiently than humans alone.
“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]
Google's Duplex faced criticism for not always disclosing its AI identity during interactions.
“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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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
DOI: 10.1145/3290605.3300439
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
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: CHI Conference on Human Factors in Computing Systems (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
Physical Sciences › Computer Science › Artificial Intelligence › 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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Chatbots, Humbots, and the Quest for Artificial General Intelligence

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