Google의 타이탄: 트랜스포머 이후의 AI 시대?

In this video, we dive into the groundbreaking research paper "Titans: Learning to Memorize at Test Time" by Google Research.

This paper introduces a new model architecture called Titans, inspired by how memory operates in the human brain.

Titan models show promising results, sparking curiosity about their potential impact on the future of AI.

We explain the deep neural long-term memory module at the core of Titan models and explore different Titan architectures: Memory as a Context (MAC), Memory as a Gate (MAG), Memory as a Layer (MAL), and LMM.

Finally, we review results from the paper, demonstrating the potential of Titans.

Paper - https://arxiv.org/abs/2501.00663

Titans written review - https://aipapersacademy.com/titans/

Review of NVIDIA's Hymba, which was referred in the video - https://aipapersacademy.com/hymba/

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The video was edited using VideoScribe - https://tidd.ly/44TZEiX

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Chapters:

0:00 Introduction

1:56 Deep Neural Long-Term Memory

5:18 MAC Titan Architecture

7:27 MAG Titan Architecture

8:09 MAL & LMM Titan Architectures

9:06 Results