Advancing Deep Learning With Collective Intelligence: Google Brain Surveys Recent Developments

The contemporaneous development in recent years of deep neural networks, hardware accelerators with large memory capacity and massive training datasets has advanced the state-of-the-art on tasks in fields such as computer vision and natural language processing. Today’s deep learning (DL) systems however remain prone to issues such as poor robustness, inability to adapt to novel task settings, and requiring rigid and inflexible configuration assumptions. This has led researchers to explore the incorporation of ideas from collective intelligence observed in complex systems into DL methods to produce models that are more robust and adaptable and have less rigid environmental assumptions.

In the new paper Collective Intelligence for Deep Learning: A Survey of Recent Developments, a Google Brain research team surveys historical and recent neural network research on complex systems and the incorporation of collective intelligence principles to advance the capabilities of deep neural networks.

Collective intelligence can manifest in complex systems as self-organization, emergent behaviours, swarm optimization, and cellular systems; and such self-organizing behaviours can also naturally arise in artificial neural networks. The paper identifies and explores four DL areas that show close connections with collective intelligence: image processing, deep reinforcement learning, multi-agent learning, and meta-learning.

In image processing, cellular automata in learning alternative image representations can help reveal implicit relationships and recurring patterns in natural mediums. For instance, the Neural Cellular Automata (neural CA) model proposed by Mordvintsev et al. treats each individual image pixel as a single neural network cell; and can be trained to predict its colour based on the states of its immediate neighbours. Developing such a morphogenesis model for image generation enables neural networks to reconstruct entire images even when each cell lacks information about its location and relies only on local information from its neighbours.

The rapidly expanding field of deep reinforcement learning (Deep RL) equips reinforcement learning agents with deep neural networks, enabling them to address complex problems such as high dimensional continuous control or vision-based tasks from pixel observations. Earlier work on the incorporation of modularity into the agents’ evolutionary design process and more recent proposals on incorporating metamorphosis in the evolution of cell placement can produce configurations that are robust to a wider range of environments.

Along with improving adaption to changing morphologies and environments, self-organizing systems can also adapt to changes in sensory inputs. Studies have shown that sensory networks can still perform their tasks effectively even if the ordering of inputs is randomly permuted numerous times during an episode.

Deep RL has also introduced the capability of simulating and training thousands of agents in complex 3D simulation environments by leveraging parallel computing hardware and distributed computation, i.e. multi-agent learning.

Meta-learning is an active research area within DL that aims at training a meta-learner to learn a learning algorithm. The paper outlines how self-organization can be naturally applied to train neural networks to meta-learn, and proposes such meta-learning approaches could challenge the current paradigm that separates model training and model deployment and revolutionize the way neural networks are developed and deployed.

Overall, this work summarizes past and present DL research that has drawn inspiration from concepts in collective intelligence. The researchers believe this combination could one day enable the simulation of artificial civilizations that mirror aspects of our own and capture all the complexities that contribute to our collective intelligence.

The paper Collective Intelligence for Deep Learning: A Survey of Recent Developments is on arXiv.

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Author: Hecate He | Editor: Michael Sarazen

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