#ai #biology #machinelearning
Neural Cellular Automata are models for how living creatures can use local message passing to reach global consensus without a central authority. This paper teaches pixels of an image to communicate with each other and figure out as a group which digit they represent. On the way, the authors have to deal with pesky side-effects that come from applying the Cross-Entropy Loss in combination with a Softmax layer, but ultimately achieve a self-sustaining, stable and continuous algorithm that models living systems.
OUTLINE:
0:00 – Intro & Overview
3:10 – Neural Cellular Automata
7:30 – Global Agreement via Message-Passing
11:05 – Neural CAs as Recurrent Convolutions
14:30 – Training Continuously Alive Systems
17:30 – Problems with Cross-Entropy
26:10 – Out-of-Distribution Robustness
27:10 – Chimeric Digits
27:45 – Visualizing Latent State Dimensions
29:05 – Conclusion & Comments
Paper:
My Video on Neural CAs:
Abstract:
Growing Neural Cellular Automata [1] demonstrated how simple cellular automata (CAs) can learn to self-organise into complex shapes while being resistant to perturbations. Such a computational model approximates a solution to an open question in biology, namely, how do cells cooperate to create a complex multicellular anatomy and work to regenerate it upon damage? The model parameterizing the cells’ rules is parameter-efficient, end-to-end differentiable, and illustrates a new approach to modeling the regulation of anatomical homeostasis. In this work, we use a version of this model to show how CAs can be applied to a common task in machine learning: classification. We pose the question: can CAs use local message passing to achieve global agreement on what digit they compose?
Authors: Ettore Randazzo, Alexander Mordvintsev, Eyvind Niklasson, Michael Levin, Sam Greydanus
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