Machines that Learn Through Action: The Future of AI • Julie Pitt • YOW! 2017

Active inference is proposed as a new framework for developing autonomous AI agents that can operate effectively in real-world environments, moving beyond traditional reinforcement learning approaches

Key challenges for robot/AI agents include:

• Ability to generalize across different situations
• Risk sensitivity and damage avoidance
• Balancing exploration vs exploitation
• Handling uncertainty and recovering from unexpected situations
• Operating with imperfect sensors and noisy data

Traditional reinforcement learning has limitations:

• Requires many training examples
• Relies on manually designed reward functions
• Poor generalization to new environments
• Lacks purposeful exploration
• Slow convergence in real-world scenarios

The free energy principle suggests agents should:

• Minimize prediction error
• Maintain a small set of preferred states
• Balance accuracy vs complexity in their models
• Naturally explore to reduce uncertainty
• Have intrinsic rather than external rewards

Benefits of active inference framework:

• Emergent intelligent behaviors
• Natural balance of exploration and exploitation
• Built-in risk sensitivity
• Parallel processing of multiple goals
• Better handling of uncertainty
• More biologically plausible learning approach

Moving from “Software 2.0” to “Software 3.0” requires:

• New paradigms beyond deep learning
• Focus on experience rather than just datasets
• Better handling of real-world complexity
• More efficient learning approaches
• Integration of domain expertise