Asking a chatbot for help on any number of issues has become part of daily life for many. But today’s query-based AI tools built on large language models (LLMs) are limited to responding to questions users ask.
These tools can pull queried info from a database, but they can’t automatically push insightful and contextualized knowledge to help a user complete a task without being asked.
DARPA’s Knowledge Management at Scale and Speed (KMASS) program, which kicked off in 2022 and recently held its final principal investigator meeting, has developed a variety of new tools that can automatically ingest knowledge sources and disseminate specific “nuggets” of info relevant to a workflow — whether the user requests the knowledge or not — exactly when needed, while avoiding irrelevant or already known information. This personalized knowledge sharing concept is a core tenet of KMASS referred to as “JustINs” — i.e., just in time, just enough, and just for me.
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“Using LLMs is like casting a magical spell; you need to find the right words to get the model to do what you want. Popular LLMs give you a haystack of info to search through when you ask a question, whereas KMASS will push the needle in the haystack to you as you’re working on a task, even if you don’t know the right question to ask,” said Matthew Marge, a leading AI expert at DARPA and KMASS program manager. “KMASS overcomes the input/output knowledge bottleneck, which occurs when knowledge producers don’t have easy ways to document knowledge and knowledge consumers don’t have easy ways to get at that knowledge.”
Source: Defense Advanced Research Projects Agency (DARPA)
