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Socially situated artificial intelligence enables learning from human interaction
Ranjay Krishna1, Donsuk Lee1, Li Fei-Fei1
1Computer Science Department, Stanford University, Stanford, CA 94305.
Summary
Artificial intelligence (AI) agents can now learn from social interactions to ask better questions in new situations. This socially situated learning approach improves AI
Area of Science:
- Artificial Intelligence
- Human-Computer Interaction
- Machine Learning
Background:
- Artificial intelligence (AI) agents struggle with novel situations not present in training data.
- Socially situated learning, acquiring information from others, is crucial for human development but challenging for AI.
- Existing AI methods often assume human availability and willingness to answer any query.
Purpose of the Study:
- To formalize socially situated learning for AI as a reinforcement learning problem.
- To develop an AI agent that learns to ask informative natural language questions to acquire missing information.
- To enable AI agents to adapt their social interaction strategies based on observed human norms.
Main Methods:
- Framed socially situated learning as a reinforcement learning task.
- Developed an interactive agent that learns to ask natural language questions about images.
- Deployed the agent on a social network, using observed social interactions as rewards.
Main Results:
- The agent improved its visual recognition performance by 112% over an 8-month deployment with 236,000 users.
- A controlled experiment showed the agent outperformed an active-learning baseline by 25.6%.
- The agent successfully adapted its question-asking behavior based on user engagement norms.
Conclusions:
- Socially situated learning is a viable approach for enhancing AI adaptability in open environments.
- AI agents can learn to interact socially to acquire necessary information, respecting human interaction norms.
- This research opens avenues for continuously improving AI agents through social learning.
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