6.S963 Final Paper 7/1/2024

Eunhae Lee
Massachusetts Institute of Technology

Abstract

One application area of LTM capabilities with increasing traction is personal (or personalized) AI companions and assistants. With the ability to retain and contextualize past interactions and adapt to user preferences, personal AI companions and assistants promise a profound shift in how we interact with AI and are on track to become indispensable in personal and professional settings. However, this advancement introduces new challenges and vulnerabilities that require careful consideration regarding the deployment and widespread use of these systems.

The goal of this paper is to explore the broader implications of building and deploying personal AI applications with LTM capabilities using a holistic evaluation approach (Spector et al., 2022). This will be done in three ways: 1) reviewing the technological underpinnings of LTM in LLMs, 2) surveying current personal AI companions and assistants, and 3) analyzing critical considerations and implications of deploying and using these applications.

Full Paper

Disclaimer

This paper was written for Alfred Spector’s MIT Spring 2024 course 6.S963 Beyond Models – Applying Data Science/AI Effectively. It has not been peer-reviewed, and it may contain errors.