Sparse and Expandable Network for Google's Pathways

TL;DR

This paper introduces SEN, a new AI architecture that improves multi-task learning by handling multiple tasks efficiently and avoiding interference.

Contribution

The novel SEN model enables sparse and expandable learning for multi-modal tasks in AI systems.

Findings

SEN effectively manages task interference and forgetting in multi-task learning.

The model integrates data from various modalities while maintaining sparsity.

SEN provides an efficient solution for lifelong learning in AI systems.

Abstract

Recently, Google introduced Pathways as its next-generation AI architecture. Pathways must address three critical challenges: learning one general model for several continuous tasks, ensuring tasks can leverage each other without forgetting old tasks, and learning from multi-modal data such as images and audio. Additionally, Pathways must maintain sparsity in both learning and deployment. Current lifelong multi-task learning approaches are inadequate in addressing these challenges.

To address these challenges, we propose SEN, a Sparse and Expandable Network. SEN is designed to handle multiple tasks concurrently by maintaining sparsity and enabling expansion when new tasks are introduced. The network leverages multi-modal data, integrating information from different sources while preventing interference between tasks.

The proposed SEN model demonstrates significant improvements in multi-task learning, successfully managing task interference and forgetting. It effectively integrates data from various modalities and maintains efficiency through sparsity during both the learning and deployment phases.

SEN offers a straightforward yet effective solution to the limitations of current lifelong multi-task learning methods. By addressing the challenges identified in the Pathways architecture, SEN provides a promising approach for developing AI systems capable of learning and adapting over time without sacrificing performance or efficiency.

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