ProjectBlog: Join the global AI community and solve big social problems like PTSD, hunger

Blog: Join the global AI community and solve big social problems like PTSD, hunger

Community members learn from each other, solve challenges collaboratively, and make the world a better place.

Any real-world problem could be best solved if a group of people comes together to put in their dedicated efforts. When it comes to the collective efforts of dedicated individuals, Success is bound to occur!”, – Iresh Mishra, a 4th-year student of Shri Mata Vaishno Devi University, India.

Great things in life often happen by accident. This is exactly what happened to me six months ago when I started working with a group of 40–50 students from remote parts of India.

Their mission: Build a Machine Learning model to identify rooftop areas for solar panel installation.

Challenges were huge: Lack of data, inexperienced developers with no prior work experience, building a team with people who never met each other, and a small amount of money for product development.

How can one bring such a group of ‘novices’ to build anything, let alone a complicated Machine Learning product?

Despite all of the challenges, there was one thing that all had in common. The motivation and desire to build a real-world product that has a social value they could connect with.

What happened over the next six months has been amazing. Not only the members put long hours to work on the project, but also started collaborating and helping each other. The more experienced developers started sharing their work and helping the not so experienced developers, and the ones who are new started to generate data.

Within 6 months we got results, which were beyond my expectations (the article with the results).

Create Collaboration and not Competition

We call this approach of community-driven AI development Collaborative AI. This can be a new model of innovation where individuals solve problems, share data and build solutions collaboratively. Why does this make sense?

  1. Generates more Data: Now, in the world of Machine Learning, data is the key. It is not a sophisticated algorithm or better team, but it’s the team with the better (and more) data that wins. A community behind a problem is also able to generate more data.
  2. Makes AI inclusive by utilizing all available resources: Due to the flood of online courses, education, especially of emerging technologies like AI and ML, became easily accessible. Now, one does not have to go to MIT or Stanford or Cambridge, to get top class education. In addition, there is a ton of open source codes, libraries, tools, and infrastructure that can be used by anyone. Collaborative AI means to provide anyone with the opportunity to work on real-world projects and gain skills that are needed to build a meaningful career in AI.
  3. Builds Ethical AI systems: A community provides crowd wisdom, diversity, and inclusion united in one project community. This builds more trust and increases adoption.
  4. Solves social problems: By connecting organizations and impactful problems with an intrinsically driven community, we make this technology accessible to solving broader problems and contribute to the democratization of AI.

In a collaborative environment, individuals driven by high intrinsic motivation form a community and the community members collaborate to build AI and Machine learning models. This makes the whole bigger than the sum of its parts.

This model works because of Self Organized Learning

In a TED talk titled ‘School in the cloud’, Sugata Mitra says that the best form of education is where the teacher just encourages the students and lets the students learn from each other having access to the internet. He called then Self-Organized Learning Environments (SOLE). This is exactly what we are doing. When we bring people with high motivation together under a group of mentors driven by solving a problem, they start learning and working together for their own good. This also creates future organizational structures.

What if we could create organizations structures and practices that didn’t need empowerment because, by design, everybody was powerful and no one powerless? — Reinventing Organizations

If you want to join us, feel free to select one of the following problems and apply via our website.

Challenge 1: Solving Post Traumatic Stress Disorder

Project leader: Christoph von Toggenburg

Christoph suffered from severe Post Traumatic Stress Disorder (PTSD) following a violent armed ambush in Central Africa. Following that incident, he developed personal strategies to overcome my trauma. Whilst working in the Middle East for the UN from 2012–2014 he was in charge of some of the largest refugee camps in the region. Within the first few months, he realized that there were countless minors in the camps who were severely traumatized by the conflict. In order to respond to this crisis, he developed with his teams a systematic response plan helping those young people to deal with their trauma. The highly successful approaches helped them to regain a more positive outlook for the future. These strategies were very successful and recovered fully. We will build a Machine Learning model to imitate those strategies.

To apply, click here.

Challenge 2: Crops classification for the United Nations, Nepal

Project leader: Saurav Suman

Photo by Matt Artz on Unsplash

Assisting 91.4 million people in around 83 countries each year, the World Food Programme (WFP) is the leading humanitarian organization saving lives and changing lives, delivering food assistance in emergencies and working with communities to improve nutrition and build resilience. As the international community has committed to ending hunger, achieve food security and improved nutrition by 2030, one in nine people worldwide still do not have enough to eat. Food and food-related assistance lie at the heart of the struggle to break the cycle of hunger and poverty. This is a project with the UN in Nepal to identify types of crops to help make better crop yield prediction.

To apply, click here.

Challenge 3: Identifying trees on satellite images to prevent fire and power outages

Project leader: Arshak Ulubabyan, Sergiu Iliev, and Eric Langenskiöld

We want 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.

To apply, click here.

Challenge 4: Mars surface anomalies detection for possible signs of extraterrestrial life

Project leader: Daniel Angerhausen

This project takes you to Mars by working on building an anomalies detection model for Mars surface in collaboration with the University of Bern. Mentor of this project is also a mentor of NASA Frontier Development Labs.

To apply, click here.

We welcome AI enthusiasts around the globe to be part of our Collaborative Omdena Family!

If you want to receive updates on our AI Challenges, receive expert interviews, and practical tips to boost your AI skills, subscribe here to our monthly newsletter.

Source: Artificial Intelligence on Medium

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