Meta Learning in Decentralized Neural Networks: Towards More General AI

TL;DR

This paper explores meta-learning in decentralized neural networks to enhance AI's ability to adapt to unpredictable environments by using multiple models, hierarchies, and cross-modal learning.

Contribution

It introduces three novel approaches for decentralized meta-learning, advancing understanding of learning to learn in complex neural network systems.

Findings

Demonstrates effectiveness of multi-replica learning

Shows hierarchical neural networks improve adaptability

Leverages cross-modal representations for better generalization

Abstract

Meta-learning usually refers to a learning algorithm that learns from other learning algorithms. The problem of uncertainty in the predictions of neural networks shows that the world is only partially predictable and a learned neural network cannot generalize to its ever-changing surrounding environments. Therefore, the question is how a predictive model can represent multiple predictions simultaneously. We aim to provide a fundamental understanding of learning to learn in the contents of Decentralized Neural Networks (Decentralized NNs) and we believe this is one of the most important questions and prerequisites to building an autonomous intelligence machine. To this end, we shall demonstrate several pieces of evidence for tackling the problems above with Meta Learning in Decentralized NNs. In particular, we will present three different approaches to building such a decentralized learning system: (1) learning from many replica neural networks, (2) building the hierarchy of neural networks for different functions, and (3) leveraging different modality experts to learn cross-modal representations.