CC-AI Shared Knowledge Database

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How to use 📃

This repository is the CCAI project’s knowledge database. We use it to:

• Store resources (code, tools, news articles and so on)
• Distribute tasks according to specific domains
• Manage the project (workflow)

Contributing 🤝

Goal 🥅

We cannot stress enough how collaborative and open this project should be. This means that you may (and should) bring to the discussion anything you believe can be improved, suggest modifications to the workflow and share resources.

At first, it may seem pointless to just store a link as a line in this repository. But in the long run it will help the community a lot as nothing will be lost and it will be easy to refer new-comers to existing knowledge.

Issues 💥

We use this repository’s issues to discuss everything. Have a look at the workflow to better understand how we use them.

Keep in mind that both domain-specific issues (like questions/discussions about a GAN model or about a website feature) and meta issues (about how we manage this project, broken links, typos etc.) are relevant.

Getting started ⚡️

New to Github? 🦎

We have compiled a shor list of basic things for you to know in order to make the most out of Github without needing to become a software engineer first -> Github at cc-ai

Experienced contributor 🦖

The workflow’s Readme contains everything you need to know. Go through it at least once to get a feeling of how we want to work before you do your usual magic. Again, if you find some useless friction, open a domain:meta issue to discuss it.

According to your background and skills, you will find the list of everything we need help with in domains/ along with relevant issues

In any case

Add yourself to our Who’s Who? if you plan on contributing !

About 💡

In case you stubbled upon this repository inadvertantly, here is our story.

The CCAI project is an interdisciplinary project aimed at creating images of accurate, vivid, and personalized outcomes of climate change. Our goal is to use cutting-edge machine learning techniques to produce images of how neighborhoods and houses will look like following the effects of global warming. By creating a more visceral understanding of the effects of climate change, we aim to strengthen public support for necessary actions and motivate people to make impactful decisions. As a prototype, we first focus on modeling flood consequences on homes.

For a more detailed motivation explanation, read through our 2 pages executive summary

Also, you might be interested in oour ICLR 2019 Ai For Social Good workshop paper (~link to be added~)

Contact ✉️

This project is lead by Sasha Luccioni (@sashavor), Karthik Mukkavilli (@mukkavilli) and Victor Schmidt (@vict0rsch) from Mila (@Mila-iqai), overseen by Yoshua Bengio and Jennifer Chayes.

You can contact us via email: {luccionis | mukkavis | schmidtv}@mila.quebec

We have a Slack workspace, get in touch with us so we add you to it!