UI / UX

Hardware IxD

Integration

Moment Memo

An AI note-taker to draw insights from conversations.

Technology Design Foundation, Master of Design at UC Berkeley, 2 Weeks, Dec 2024

Currently working on the second prototype as a startup project

Challenge

The offline in-person conversations can lead to highly relevant insights that are rarely documented. LLMs (Large Language Models) can understand and generate natural languages and thus, are the ideal agent to tackle the note-taking problem. The question is: how do we develo a proper interaction?

Solution

We have not yet found a final answer. However, we believe in the hard way—developing local LLM-empowered AI hardware to generate structured, personalized notes.

Major milestones

These are major appearance and interactive prototype concepts in April 2024.

Functional prototype shown to the public on the Innovation Day at UC Berkeley.

Left: Raspberry 5 Local LLM Prototype;
Right: appearance model.

"Take me to the next!"

Circle Hire
Pouring Sounds

Process

The inspiration

From within

The original idea came from the daily interactions while working in the first semester of Master of Design program at UC Berkeley:

Within an interdisciplinary cohort of students, we constantly discuss over topics of mutual interest - these casual, unplanned encounters sparkle fleeting insights that can inspire the next project. Yet, we don't have a good way to capture them every time.

From without

The memex is an envisioned device meant to enhance human memory by allowing users to store and retrieve documents linked by associations, resembling the way the human mind works. Inspired by such a foreboding concept from the 1945 paper, As We May Think by Vannevar Bush, we wanted to create a device for us to record daily conversations and consult whenever needed.

As We May Think - Wikipedia

Design with no precedents

The paradigm shift in AI-enabled hardware introduces challenging hardware design principles. As we prototype and communicate with potential users, we are also verifying our conjectures:

AI with warmth

The concept of "AI" feels distant to most people. An important step in embracing AI applications is to introduce emotion design elements.

The LED matrix can show customized facial expressions;
The simple UI can also show other important states

Co-pilot over auto-pilot

AI's capabilities are far from  perfect for many tasks; even when it does, it takes time for people to build trust. In this, user intervention in any task is needed.

This concept introduces the "rewind" UI to redo the most recent notes
The latest concept emphasizes on co-notetaking (the annotation button) in addition

An ongoing journey

The validation process will be an ongoing journey with new theories to prove or disprove. These may be as simple as a design change and as hard as tweaking LLM models. More importantly, it will be a conversational one as we talk with people to get feedback.

If you happen to read this, don't hesitate to connect to express your interest. We would love any input to inspire us. Thank you.

Takeaway

The hard way to develop an AI application?

LLM models excel at specialized tasks because they can process and analyze vast amounts of data quickly and accurately. BLLMs can develop deep expertise in particular domains by training on specific dataset.

On the other hand, AI security is a paramount concern. Proper industrial design along with locally processed data can provide much stronger confidence in data integrity than any pure software solution.

Local LLM and hardware are the hard ways to develop an AI application. However, we believe this to be the easy and sustainable approach in the long run.

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