Summary of Learning From a Generative Ai Predecessor — the Many Motivations For Interacting with Conversational Agents, by Donald Brinkman and Jonathan Grudin
Learning from a Generative AI Predecessor – The Many Motivations for Interacting with Conversational Agents
by Donald Brinkman, Jonathan Grudin
First submitted to arxiv on: 31 Dec 2023
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Generative conversational AI must be engaging in order to succeed, just like traditional conversational agents that have been around for decades. These agents focus on keeping a conversation going by responding to any question or comment, and they’ve been successful in motivating millions of people to engage with them. For example, Microsoft’s Zo, which was studied in this research paper, had over 2000 anonymized users who interacted with it through chat logs. The study identified over a dozen motivations that people had for interacting with Zo, such as seeking advice, seeking entertainment, and seeking companionship. Designers learned different ways to increase engagement by studying the chat logs and identifying patterns of user behavior. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative conversational AI is important because it can support productivity and creativity. Right now, there isn’t a clear revenue model for this type of AI, but it could benefit from being more engaging. The study showed that people were motivated to engage with Zo by different factors, such as its ability to provide advice or entertainment. This research can help designers create more engaging virtual companions. |
