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AI for Service: Proactive Assistance with AI Glasses

Zichen Wen, Yiyu Wang, Chenfei Liao, Boxue Yang, Junxian Li, Weifeng Liu, Haocong He, Bolong Feng, Xuyang Liu, Yuanhuiyi Lyu, Xu Zheng, Xuming Hu, Linfeng Zhang

TL;DR

The paper proposes AI4Service, a paradigm for proactive, real-time assistance in daily life using AI glasses. It introduces Alpha-Service, a Von Neumann-inspired architecture with five units (Input, CPU, ALU, Memory, Output) and a dual-layer Know When / Know How framework to detect when to intervene and how to provide both generalized and personalized services. Through case studies in Blackjack, museum guidance, and fashion fitting, the authors demonstrate continuous perception, user intent inference, and timely, non-intrusive support without explicit prompts. They discuss challenges related to edge-device constraints, balancing generalization with personalization, privacy, and trust, and outline future directions including richer memory, extended tool sets, and large-scale user studies to evaluate real-world impact.

Abstract

In an era where AI is evolving from a passive tool into an active and adaptive companion, we introduce AI for Service (AI4Service), a new paradigm that enables proactive and real-time assistance in daily life. Existing AI services remain largely reactive, responding only to explicit user commands. We argue that a truly intelligent and helpful assistant should be capable of anticipating user needs and taking actions proactively when appropriate. To realize this vision, we propose Alpha-Service, a unified framework that addresses two fundamental challenges: Know When to intervene by detecting service opportunities from egocentric video streams, and Know How to provide both generalized and personalized services. Inspired by the von Neumann computer architecture and based on AI glasses, Alpha-Service consists of five key components: an Input Unit for perception, a Central Processing Unit for task scheduling, an Arithmetic Logic Unit for tool utilization, a Memory Unit for long-term personalization, and an Output Unit for natural human interaction. As an initial exploration, we implement Alpha-Service through a multi-agent system deployed on AI glasses. Case studies, including a real-time Blackjack advisor, a museum tour guide, and a shopping fit assistant, demonstrate its ability to seamlessly perceive the environment, infer user intent, and provide timely and useful assistance without explicit prompts.

AI for Service: Proactive Assistance with AI Glasses

TL;DR

The paper proposes AI4Service, a paradigm for proactive, real-time assistance in daily life using AI glasses. It introduces Alpha-Service, a Von Neumann-inspired architecture with five units (Input, CPU, ALU, Memory, Output) and a dual-layer Know When / Know How framework to detect when to intervene and how to provide both generalized and personalized services. Through case studies in Blackjack, museum guidance, and fashion fitting, the authors demonstrate continuous perception, user intent inference, and timely, non-intrusive support without explicit prompts. They discuss challenges related to edge-device constraints, balancing generalization with personalization, privacy, and trust, and outline future directions including richer memory, extended tool sets, and large-scale user studies to evaluate real-world impact.

Abstract

In an era where AI is evolving from a passive tool into an active and adaptive companion, we introduce AI for Service (AI4Service), a new paradigm that enables proactive and real-time assistance in daily life. Existing AI services remain largely reactive, responding only to explicit user commands. We argue that a truly intelligent and helpful assistant should be capable of anticipating user needs and taking actions proactively when appropriate. To realize this vision, we propose Alpha-Service, a unified framework that addresses two fundamental challenges: Know When to intervene by detecting service opportunities from egocentric video streams, and Know How to provide both generalized and personalized services. Inspired by the von Neumann computer architecture and based on AI glasses, Alpha-Service consists of five key components: an Input Unit for perception, a Central Processing Unit for task scheduling, an Arithmetic Logic Unit for tool utilization, a Memory Unit for long-term personalization, and an Output Unit for natural human interaction. As an initial exploration, we implement Alpha-Service through a multi-agent system deployed on AI glasses. Case studies, including a real-time Blackjack advisor, a museum tour guide, and a shopping fit assistant, demonstrate its ability to seamlessly perceive the environment, infer user intent, and provide timely and useful assistance without explicit prompts.
Paper Structure (24 sections, 5 figures)

This paper contains 24 sections, 5 figures.

Table of Contents

  1. Introduction
  2. Concept of AI for Service
  3. Definition and Key Layers
  4. Know When: Event Prediction and Timing
  5. Know How: Generalized and Personalized Services
  6. Architecture
  7. Overview: Von Neumann-Inspired Design
  8. Input Unit: Trigger and Streaming MLLMs
  9. Central Processing Unit: Task Orchestration and Synthesis
  10. Arithmetic Logic Unit: Tools Integration
  11. Memory Unit: Long-Term Context Storage
  12. Output Unit: Human-Friendly Synthesis
  13. Case Study
  14. Case i : Blackjack playing guide
  15. Case ii: Guided tour explanation in museum
  16. ...and 9 more sections

Figures (5)

  • Figure 1: Comparison between passive service and proactive service. Passive service indicates that AI provides service only when it is asked for, while proactive service indicates that the AI keeps observing the environment, guesses the user's thoughts, consider the user's needs, and provide services without the user having to ask.
  • Figure 2: The architecture of Alpha-Service.
  • Figure 3: A case of blackjack playing guide. After understanding that the user is playing blackjack, Alpha-Zero proactively gives guidance on playing this game : (at 13s) Hit. With a hand totaling 12 (2, 4, and 6), basic Blackjack strategy dictates you should hit. Videos are collected from the AI glasses made by Xiaomi.
  • Figure 4: A case of guided tour explanation. After understanding that the user is watching an exhibition in a museum, Alpha-Service proactively proposes to search the obtained image from websites, and then gives an introduction: (15s) The cultural relic in front of you is a bronze artifact from the Shang Dynasty — the Four-Ram Square Zun. It is a square ritual wine vessel adorned with four ram heads... The video is collected by AI glasses made by Xiaomi.
  • Figure 5: A case of fit advisor. After finding that the user has focused on one piece of clothing for a long time, Alpha-Service proactively gives advice on it: (at 18s) There are many ways to wear it: pairing jeans with a casual suit jacket can also be a bit formal... Videos are collected from the AI glasses made by Xiaomi.