Six reasons on why Community-driven AI is the future

“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!”, says Iresh Mishra, a 4th-year student of Shri Mata Vaishno Devi University, India.

Let me start this article with a conjecture: The future of AI and Machine Learning (ML) will be driven by the community through grass root movement. This is a bottom-up model where people come together to build solutions for problems they can associate with.

But why did I make this conjecture?

ML/AI has the ability to transform our society and build products that benefit the majority. However, for that, the technology has to move from the hype phase to the correction phase. In the correction phase, the product markets to the majority, who adopt a technology when they see a clear tangible value and have trust in the technology.

Unfortunately, the adoption of AI and Machine Learning by the majority will face problems: primarily due to the mistrust among the majority and stories with the misuse of personal data.

Technology adoption life-cycle and the innovation hype cycle.

To change the perception and reality, the products need to be built by communities. When communities are given a proper platform with the right infrastructure, mentoring, support and environment, they can come together to build these products.

To prove my point, I have been working with a group of students and AI enthusiast to build a Machine Learning solution to increase adoption of rooftop solar panels (details here). These students, coming from all over India, have never met each other and are collaborating together to build the product.

Students from all over India — who have never met each other — are collaborating to build a Machine Learning product for the clean energy sector. From the top - Jitendra, Abhigyan, Raghav, Devendra, Rasika, Iresh, Jerin Paul, and Shivani.

The idea of using a community to develop a product is taken from two areas: open source development and decentralized product development. We have seen that such an approach of AI/ML product development has many advantages.

Empowering Youth and Sharing knowledge

Over the last one year, I had the opportunity to speak at over 50 events in 17 countries. In every place, I met awesome talented people — students, working professionals, and entrepreneurs. I can unequivocally say that talent is not limited to certain areas (like Bay area) but is available everywhere — be it in a small town like Novi Sad, Serbia or Odessa, Ukraine or in big cities like Ho Chi Minh, Vietnam.

With today’s technological advancement, online courses, and available tools, talent is everywhere and can be accessed easily. For example, for the machine learning project, we have around 50 highly engaged junior ML engineers contributing and developing the product. Tasks are announced in the community and students can take up those tasks, and start working on them under the supervision of a mentor. The results we have already are beyond expectation, better than any system I am aware of, on analyzing low-resolution images to identify rooftops.

Results from predictions — quite close to the target
One of the students, Abhigyan Das, who is helping to gather the data says “I think such a community model should be followed by more organizations because we as students can gain not only the first-hand experience about work but can also learn a lot of things which are not available in any course.
Shivani Bhawsar, also working on the project and full-time in a big tech firm says “all members are very helpful and we all are working as a group helping each other and also learning from each other.
While Smriti Bahugana, a recent Master’s student from IIT Guwahati adds, “It is also important to see that such impacting projects do not need a heavy setup, we are working on our laptops, at our homes, on something that we all are enthusiastic about.”

Building Trust and Respect for each other, and for the product

A community can also help to build trust. Companies that emerge from communities share common values, beliefs, and often a bigger vision that serves the long-term interests of those communities. This builds more trust and makes people more willing to use such systems and share their data, something which is receding in products built by large corporations.

According to Piyush Choudhury, a 4th-year student of IIT(ISM) Dhanbad “Since we work remotely, we need to trust each other more. Everyone understands that a person cannot be free all the time, thus we adjust our time according to others and this builds respect for each other”.
I feel working like this meets both the ends, of the working community to get the best of enthusiastic people and of the members who are just beginning to contribute in this field, to add to their learning curve by being able to work towards delivering their own product, have an understanding of the impact of their little ideas. This is only possible when there is mutual trust and respect for each other. ”, adds Smriti Bahugana.
Piyush Choudhury and Smriti Bahugana

Helping to build a truly communal product through diverse opinions

Communities across the world ingraining different values and perspectives are needed to build great products that augment us and solve pressing problems in today’s and tomorrow’s world. Products which are built from diverse opinions end up being more inclusive and appeal to a larger audience.

Raghav Saraf, a 4th year student of Vellore Institute of Technology says, everyone can share their opinions to help solve the problem and this is great as we get to look at the problem from many perspectives.
According to Devendra Barmate, a 4-th year student of Sri Shankaracharya Group of Institutions, “I quite like this way of working where people from different areas come together, share their ideas and opinions, bring a lot on the table”.

Getting Access to data

A community behind a product can give access to a large amount of data. The wisdom of the crowd, fueling diversity through people from different backgrounds and locations, can result in innovative approaches to gather and work with the data. For specific projects, members can even bring in their data through, e.g., images, music, movie recommendations, text and so on. For the solar project mentioned above, we are also using a community-driven approach to gather data.

One of the students, Rasika Joshi, says “I could focus more on building Neural Network and do training over required formatted data set just because I was working with fellow students and they provided me with the data in given time frame.”
Masked images generated by the community.

Reducing the Cost of development and democratizing AI

The salaries of data scientists are exuberantly high, for example, a data scientist in California can earn somewhere around $150,000 and up. While on the other side there are hundreds of thoughts of young junior Machin learning engineers and data scientist, who find it extremely hard to work on real-world projects. Through this approach, we can combine the best of both the worlds. A model like this can reduce the cost of development by one-third to one-fifth, thus making the technology accessible to all and democratization of AI.

Additionally, there remain major challenges with product development that cannot be solved by a single person; e.g., data gathering and preparation. So a community-driven approach works not only for development but also for data gathering.

Building a Decentralized, Equal Opportunity World

“Imagine a world where no matter where you are born or live if you are talented you get equal access to work and opportunities as anyone else living in any other part of the world.” — The future of Work: Decentralized, Transparent, and Trust Based[1]

This is the philosophy behind community-driven development. The model of people coming together to find solutions not only makes the problem to get divided and be conquered but also provides various viewpoints and different strategies to approach the problem because brainstorming gets more fruitful when people from various backgrounds and cultures come together.

This is what the future of work we want to build. A world where work is distributed, equal, transparent, and trustworthy. Our vision is to make a better work environment.


[1] The future of Work: Decentralized, Transparent, and Trust-Based, Rudradeb Mitra, April 20, 2018,

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