As many of us in the information retrieval (IR) research community know and appreciate, search is far from being a solved problem. Millions of people struggle with tasks on search engines every day. Often, their struggles relate to the intrinsic complexity of their task and the failure of search systems to fully understand it and serve relevant results. The task motivates the search, creating the problem that searchers attempt to solve, and drives search behavior as they work through different task facets. Complex search tasks require more than support for rudimentary fact finding or re-finding. Research on methods to support complex tasks includes work on generating query and website suggestions, personalizing and contextualizing search, and developing new search experiences, including those that span time and space. The recent emergence of generative artificial intelligence and the arrival of assistive agents based on this technology have the potential to offer further assistance to searchers, especially those engaged in complex tasks. These advances have profound implications for the design of intelligent systems and for the future of search itself. This article, based on a keynote given by the author at the 2023 ACM SIGIR conference, explores these issues and how AI agents are advancing the frontier of search-system capabilities, with a special focus on information interaction and complex task completion.
Figure 3. Advancing the search frontier. Visualizing the set of possible tasks that can be tackled with search only today (that is, finding, learning, and investigating) plus the expansion on the frontier into support for higher-order task activities with the addition of AI agents (for example, adding AI support for creative inspiration, synthesis, and summarization). Source: 10.1145/3655615
Key Insights
Search is an unsolved problem; millions of people struggle with complex tasks on Web search engines every day.
The emergence of generative AI and assistive agents based on it promise to revolutionize task completion, aiding users in navigating and resolving complex search tasks.
These advancements present both challenges and extraordinary opportunities to redefine the landscape of information access and use, propelling search toward new horizons.
Gary Price (gprice@gmail.com) is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area.
He earned his MLIS degree from Wayne State University in Detroit.
Price has won several awards including the SLA Innovations in Technology Award and Alumnus of the Year from the Wayne St. University Library and Information Science Program. From 2006-2009 he was Director of Online Information Services at Ask.com.
As many of us in the information retrieval (IR) research community know and appreciate, search is far from being a solved problem. Millions of people struggle with tasks on search engines every day. Often, their struggles relate to the intrinsic complexity of their task and the failure of search systems to fully understand it and serve relevant results. The task motivates the search, creating the problem that searchers attempt to solve, and drives search behavior as they work through different task facets. Complex search tasks require more than support for rudimentary fact finding or re-finding. Research on methods to support complex tasks includes work on generating query and website suggestions, personalizing and contextualizing search, and developing new search experiences, including those that span time and space. The recent emergence of generative artificial intelligence and the arrival of assistive agents based on this technology have the potential to offer further assistance to searchers, especially those engaged in complex tasks. These advances have profound implications for the design of intelligent systems and for the future of search itself. This article, based on a keynote given by the author at the 2023 ACM SIGIR conference, explores these issues and how AI agents are advancing the frontier of search-system capabilities, with a special focus on information interaction and complex task completion.
