Stanford CS25: V2 I Represent part-whole hierarchies in a neural network, Geoff Hinton

This is a lecture by Geoffrey Hinton introducing a hypothetical neural network system called "Glom" [04:16]. Glom aims to address how the brain represents "part-whole" hierarchies (e.g., a face composed of a nose, mouth, etc.) [00:28]. Its core idea involves "islands of agreement" between representations at different levels [04:52]: at higher levels, different parts belonging to the same object (e.g., a "face") converge to the same vector representation [05:05]. The model draws inspiration from contrastive learning [20:43] and Transformer-like attention mechanisms [26:16] to resolve ambiguities in visual perception and form unified representations.

You can watch the full video here: http://www.youtube.com/watch?v=CYaju6aCMoQ