For generative AI to succeed, how engaging a conversationalist must it be?
For almost sixty years, some conversational agents have responded to any
question or comment to keep a conversation going. In recent years, several
utilized machine learning or sophisticated language processing, such as Tay,
Xiaoice, Zo, Hugging Face, Kuki, and Replika. Unlike generative AI, they
focused on engagement, not expertise. Millions of people were motivated to
engage with them. What were the attractions? Will generative AI do better if it
is equally engaging, or should it be less engaging? Prior to the emergence of
generative AI, we conducted a large-scale quantitative and qualitative analysis
to learn what motivated millions of people to engage with one such ‘virtual
companion,’ Microsoft’s Zo. We examined the complete chat logs of 2000
anonymized people. We identified over a dozen motivations that people had for
interacting with this software. Designers learned different ways to increase
engagement. Generative conversational AI does not yet have a clear revenue
model to address its high cost. It might benefit from being more engaging, even
as it supports productivity and creativity. Our study and analysis point to
opportunities and challenges.
Generative AI has the potential to revolutionize the way we engage with technology, but in order to succeed, it must be more than just knowledgeable—it must also be an engaging conversationalist. In the past, conversational agents like Tay, Xiaoice, and Zo focused primarily on engagement rather than expertise. These agents used machine learning and sophisticated language processing to respond to questions and comments, keeping conversations going and attracting millions of users.
But what exactly made these conversational agents engaging? A large-scale analysis of the chat logs of 2000 anonymized individuals interacting with Microsoft’s Zo revealed over a dozen motivations for engagement. By understanding what motivated people to interact with these virtual companions, designers were able to find ways to increase engagement.
This analysis not only provides insights into how to make generative AI more engaging, but also raises important questions about the revenue model for this technology. Generative AI comes with a high cost, and without a clear revenue model, its widespread adoption may face challenges. However, if generative AI is able to be both engaging and supportive of productivity and creativity, it may have a better chance of success.
What makes this topic particularly fascinating is its multi-disciplinary nature. The development of generative AI requires expertise in machine learning and language processing, as well as an understanding of human psychology and behavior. Designers and developers must work together to not only create systems that are knowledgeable, but also ones that are engaging and meet the needs and motivations of their users.
As we look ahead, the opportunities and challenges for generative AI are vast. It has the potential to enhance productivity, creativity, and human interaction. However, it must find a revenue model that can sustain its high cost, while also being able to navigate ethical concerns such as privacy and bias. By addressing these challenges and building upon the insights gained from analyzing user motivations, the future of generative AI looks promising.