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• 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.