Omni - Startup collab
Introduction
Omni brings information retrieval into the mechanics of creation. Whereas traditional workflows rely on using keyword and other heuristics to navigate the internet and one’s own documentation to find relevant content, Omni leverages machine learning to automate the process of finding relevant materials. Like Zettelkasten, a note-taking system that proposes two-way linking to organize one’s notes into a navigable graph, Omni’s systems automate creating what is effectively omni-directional linkage between personal notes, websites, textbooks, and ultimately the internet. It offers many integrations, for example a Chrome extension that brings up the most relevant information as the user reads or writes in their browser.
The goal of this specific project is to automate certain processes of thought, such as contradiction, finding examples of a claim, assumption questioning, and so forth; and to integrate this automation feature into the Omni Chrome extension. For example, given a highlighted claim, e.g. highlighting “The earth is flat” on a YouTube comment, we aim to find and display sources that either a) contradict the claim, b) question assumptions of the claim, c) find a good example of the claim, and so on. The user should then be able to choose which process of thought they want to display results of via a dropdown menu in the Omni toolbar. This feature would supercharge a user’s ability to think critically as they read or write about any topic in real-time.
This project will be headed by Brian Chen (brianchen.chen@mail.utoronto.ca) and Matthew Ao (matthew.ao@mail.utoronto.ca). We hope to form a team of ~6 members. Members should have a strong demonstrated background in good software engineering practices, machine learning, and software project work in a professional setting. NLP experience is preferred. Members are expected to commit a minimum of 4-5 hours a week.
