I gave a talk last night at MTLDATA about using Fuzzy.ai for task prioritization. It’s a really interesting subject for me, because tasks are an atomic part of our life and our work. How and when we complete tasks is an important part of any person’s productivity, and artificial intelligence that helps us do that effectively is really valuable.
Most of us use a task management system — whether it’s part of a project management system like Github issues or JIRA, or a personal system like Todoist, todo.txt or Google Tasks. All of them have ways to define important data about our tasks, but very few of them make intelligent recommendations for what to do now.
Intelligent organizations are starting to work on this problem today. After all, having people inside your company work on tasks that are timely, impactful, actionable and personal can make a huge difference. Tying the performance of tasks to metrics within the organization can drive much better relevance tracking.
To test out these ideas, we built a cool task prioritization project using the Todoist API. If you log into the site at https://tasks.fuzzy.ai/ with Todoist authorization, it will automatically score and rank your upcoming tasks, putting the most relevant items first.
Todoist has a relatively simple schema for tasks. We used that data for building a fuzzy.ai agent that uses time, priority, and other features to determine the task’s relevancy.
We think there’s some interesting future developments here. Keyword matching with previously relevant tasks is a big one. Providing other backends, like Github issues, is another. (I use Todo.txt, so I’m especially interested in this.) Most of all, building in different kinds of performance metrics to see if your tasks really matter is going to be the biggest.
Our code is open source. Feel free to fork and try on your own, or send pull requests. We’re always interested in what people figure out to do with Fuzzy.ai.