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Robin and I discuss many of the ideas in his book The Self-Assembling Brain: How Neural Networks Grow Smarter. The premise is that our DNA encodes an algorithmic growth process that unfolds information via time and energy, resulting in a connected neural network (our brains!) imbued with vast amounts of information from the "start". This contrasts with modern deep learning networks, which start with minimal initial information in their connectivity, and instead rely almost solely on learning to gain their function. Robin suggests we won't be able to create anything with close to human-like intelligence unless we build in an algorithmic growth process and an evolutionary selection process to create artificial networks.
0:00 - Intro
3:01 - The Self-Assembling Brain
21:14 - Including growth in networks
27:52 - Information unfolding and algorithmic growth
31:27 - Cellular automata
40:43 - Learning as a continuum of growth
45:01 - Robustness, autonomous agents
49:11 - Metabolism vs. connectivity
58:00 - Feedback at all levels
1:05:32 - Generality vs. specificity
1:10:36 - Whole brain emulation
1:20:38 - Changing view of intelligence
1:26:34 - Popular and wrong vs. unknown and right
