Introduction
Chatbots like OpenAI’s ChatGPT, Google’s Bard, and Anthropic’s Claude provide us with interesting and exciting new ways to interact with information. These products respond to users queries by transforming a statistical analysis of patterns existing in a large amount of information—a large language model (LLM)—into a natural language response that mimics human intelligence. Mimic is the key word here: these platforms do not understand the data they are analyzing and interpreting in the same ways that people do.
The problem that these products represent for sophisticated consumers of information, such as analysts, academics, and journalists, lies in their design: to date, LLMs preclude insight into or an understanding of the basis for the answers they generate. Users are being asked to trust the technology, but they are not given the opportunity to verify the way the underlying algorithms weigh information (or even what information is, or is not, being used in the formulation of answers). In short, both the “dots” and the connections between those dots exist within a black box at a time when organizations like the IC continue to work toward greater
transparency about the underpinnings of their judgments and actions.
The opportunity in front of us lies beyond the words often used to describe these technologies. The idea of developing artificial intelligence dates back to the 1940s and 1950s. Today’s chatbots are not intelligent, but they are innovative, exciting, and full of potential in the context of the volumes and varieties of information the IC collects, processes, triages, and
uses in support of its global mission. The challenges and opportunities for organizations looking to implement generative AI (GenAI) start with the breadth, depth, richness, and cleanliness of the data itself.