Collective intelligence can be used to improve deep learning models by incorporating principles of self-organization and adaptability.
- Deep learning networks require sophisticated engineering and careful training schemes.
- Collective intelligence produces systems that are robust, adaptable, and have less rigid assumptions about their environment configurations.
- Active areas in modern deep learning research that incorporate collective intelligence include deep reinforcement, multi-agent, and meta learning.
- An example of collective intelligence in action is the annual Reddit art Place experiment where users collaborate and coordinate a strategy to create a meaningful design.
- Analog neural networks developed in the 1980s were much closer to natural adaptive systems and produced amazing results such as object extraction.
- Collective intelligence can be applied to image processing, generative models, deep reinforcement learning, multi-agent learning, and meta learning.