What is the difference between automation and augmentation in AI?

Automation aims to replace human tasks, while augmentation aims to enhance what people can do by working alongside them. In her talk at Megagon Labs, Diyi Yang argued that effective human-AI collaboration depends on designing for augmentation, grounded in real worker perspectives, rather than automating tasks for their own sake.

What is a General User Model (GUM)?

A General User Model learns about a user from their interaction signals, building an understanding of their goals, context, and preferences. This lets an AI system reason about and adapt to each person instead of treating every user the same. Diyi Yang introduced GUMs as part of her research on human-centered AI.

How can AI systems become proactive instead of reactive?

Reactive systems wait for explicit instructions, while proactive systems anticipate what a user is trying to do and act in support of it. Frameworks like Next Action Prediction infer a user's likely next action from their interaction history, helping AI move from simply responding to genuinely collaborating.

Optimizing Human-AI Collaboration: A Conversation with Stanford’s Diyi Yang

Stanford’s Diyi Yang on optimizing human-AI collaboration: how General User Models and Next Action Prediction help AI adapt to people, not just respond.

What does it take for AI to truly collaborate with humans, not just respond to them? We hosted Stanford University Assistant Professor Diyi Yang as a guest speaker at Megagon Labs to discuss just that.

Professor Yang works at the intersection of NLP and Human-Computer Interaction, focusing on how AI systems can better understand people, their goals, and their context. Her work offers important perspectives for anyone building human-centered AI.

Inside the Talk: Optimizing Human-AI Collaboration

Recent advances in LLMs have transformed human-AI interaction, but effective collaboration requires systems that can reason about users and adapt to how they work, not just strong models. In this talk, Dr. Yang examined how automation and augmentation are shaping the future of work and how grounding AI system design in real worker perspectives is essential. She introduced General User Models (GUMs), which learn about users from interaction signals, and Next Action Prediction (NAP), a framework for anticipating user intent from multimodal interaction histories.

We enjoyed the insights and discussion. As an AI research lab consistently conducting human-centered AI research and developing frameworks, we appreciated the dialogue on how systems can move from reactive systems to ones that can support meaningful, proactive collaboration.

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