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Autonomous development and learning in artificial intelligence and robotics: Scaling up deep learning to human-like
1Inria and Ensta Paris-Tech,33405 Talence,France.pierre-yves.oudeyer@inria.frhttp://www.pyoudeyer.com.
The Behavioral and Brain Sciences
|January 19, 2018
Summary
Human lifelong learning differs from deep learning. Integrating AI mechanisms like curiosity, social learning, and embodiment with deep learning offers new possibilities for autonomous development.
Area of Science:
- Artificial Intelligence
- Cognitive Science
- Developmental Robotics
Background:
- Human lifelong development and learning remain distinct from current deep learning capabilities.
- Existing AI research has explored key components for autonomous learning, including curiosity, intrinsic motivation, social learning, and embodiment.
Purpose of the Study:
- To explore the integration of human-like learning mechanisms into deep learning systems.
- To bridge the gap between human autonomous development and artificial intelligence.
Main Methods:
- Conceptual analysis of existing AI research in curiosity, social learning, and embodiment.
- Review of deep learning architectures and their limitations in autonomous development.
Main Results:
- Identification of curiosity, intrinsic motivation, social learning, and embodiment as crucial for autonomous learning.
- Highlighting the potential of these mechanisms to guide exploration and goal selection in AI.
Conclusions:
- Integrating these AI-derived mechanisms with deep learning presents a promising avenue for achieving more autonomous and human-like lifelong learning systems.
- This integration can lead to AI systems capable of independent exploration and goal-directed behavior.
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