Collaborative machine intelligence: Accelerating human knowledge

Description

Recent advances have made machines more autonomous, but much work remains for AI to collaborate with people. Emily Pavlini and Max Kleiman-Weiner share new insights inspired by the way humans accumulate knowledge and naturally work together that enable machines and people to work and learn as a team, discovering new knowledge in unstructured natural language content together.

Collaborative machine intelligence will enable new products and services that help knowledge workers discover new knowledge answering known unknowns and also helping uncover unknown unknowns. Emily and Max discuss emerging research in this area, such as active learning, dynamic knowledge graphs, imitation learning, and theory-of-mind-based approaches to collaboratively creating cumulative knowledge, and explain how semisupervised hierarchical clustering models generate intuitive interactive structures for uncovering connections and reacting to user actions. Emily and Max also dive into how collaborative agents are transforming the way we work and interact with technology. They demo an AI agent that dynamically infers a knowledge worker’s interests and intents from natural language and finds text passages and entity relations that are both relevant and novel to the user’s current state of knowledge, instead of requiring users to constantly craft queries across many different search tools. The simple act of interacting with this recommended content provides active feedback that simultaneously sharpens the model’s representation of the user’s interests and intents and broadens the system’s ability to traverse farther into the data to find new elements that the user did not yet know.