How To Learn Artificial Intelligence Collaboratively By Saving The Planet

Omdena and Ciencia y Datos have partnered up for a new Artificial Intelligence Challenge if you want to be one of the 50 AI enthusiasts to acquire hands-on skills by solving a meaningful and global problem you need to read this article.

Aren't you tired of competing with everyone to solve a problem? Don't get me wrong, competition is important and drives innovation, but if you want to learn, maybe it's not the best way to do it.

That's why when the people at omdena.com contacted us at Ciencia y Datos to work with them in a collaborative challenge for learning AI we said YEAAH!!

Before going into details about the challenge and how to apply, let me talk about briefly on collaborative learning.

Collaborative Learning

Learning a new topic it's always challenging, but there are ways of making it easy. A little why ago, in my article about the biggest mistakes people make when learning data science, I mentioned that if you want to really learn something you need to practice, be serious about learning and also that it's much better to do it with other people interested in the same field.

I'm not saying here that you need to start a course with your BFFs, but you should make use of what the online platforms are giving us today.

A community is formed because its members share common values, interests, and goals. And right now there's a lot of people learning AI. A lot! And there's a good reason:

This was said 2 years ago by the great Andrew Ng and it's more true now than ever.

A lot of people using AI in a way or another call themselves Data Scientists, Machine Learning Engineers, AI Engineers or something like that. And as I proposed before:

Data scientists don't exist alone, they need a team, this team will make things possible for developing intelligent solutions. Collaboration is a big part of science, and data science should not be an exception.
Collaborative learning is a proven fact, the educational experiences that are active, social, contextual, engaging, and student-owned lead to deeper learning.

Learning is an active, constructive process. To learn new information, ideas or skills, the collaborative learning approach states the students have to work actively with others in purposeful ways. They need to integrate this new material with what they already know-or use it to reorganize what they thought they knew.

Also, because people in real life projects have diverse backgrounds, learning styles, experiences, and aspirations, when we work together, we get a direct and immediate sense of how we and others are learning, and what experiences and ideas they bring to their work. We need to take advantage of diversity while learning.

AI can be very complicated, but bringing more minds and using our differences and similitudes will help to solve hard problems in a much more efficient way. But we need structure and guidance. So let me explain a little better what is this challenge about.

The AI Challenge: Identifying trees on satellite images

What will you do?

You will work on a social impact project with a global scope. The minimum output is to have a proof of concept. Depending on your skill-set, you will be involved in different roles such as defining the project scope, preparing the data, and building the AI model.

The goal of the project is to build a model for tree identification on satellite images. The solution will prevent power outages and fires sparked by falling trees and storms. This will save lives, reduce CO2 emissions, and improve infrastructure inspection.

What are the requirements to apply?

Everyone is welcome to apply for the challenge, but here we are looking for AI enthusiasts who have done at least one online course in machine learning or data science.

Here are the actual requirements:

Good English and access to a computer with internet
A good/very good grasp in computer science and/or mathematics
Student, junior developer or a developer who is changing the work field
Programming experience with C/C++, C#, Java, Python, R, Javascript or similar
(Basic) Understanding of ML and Deep learning algorithms.
Passed or started an online course in machine learning/deep learning.
Availability of around 7�??15 hours a week.

What's in it for me?

You'll be working with real images and apply deep learning algorithms while collaborating with leading mentors, receive project certifications, and boost your skills together with like-minded people around the world.

But if that's not enough, we have one more thing...

In addition to a unique learning experience, the project community will receive collective price money of 3000 USD upon the successful completion of the challenge. The money will be split equally among all engaged community members for solving the problem through collaboration, community, and shared learning.

How to apply?

It's super easy, just apply here:

http://omdena.com/trees/

After that, we will select a group of AI enthusiasts for the challenge. And at the end of the project, the results will be presented to the world.

Important note:

There's limited space for the challenge, so no more than 50 people will be accepted. Don't hesitate and apply now! The challenge starts next week!

We at Ciencia y Datos are very happy to be working in this project with Omdena, while we help then building the biggest platform in the world for learning and getting experience in AI, data science and much more.

Hopefully we will be working together in this great project where you will learn, have fun and help save the planet, what else can you ask for?

If you have any questions please just add me on Twitter and I'd be happy to help you:

Source: HOB

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