AI Insights

Four Powerful Tips for Working on an Omdena Real-World AI Project

June 15, 2020

article featured image

I’m about to finish up my first Omdena AI project Challenge (Mars Omdena), and I am happy to report to everyone that it has been an incredibly positive experience, full of learning, discovery, and wonder. Working on any Omdena AI Projects is a unique experience in of itself, and as such, you can never really be ready for it. Nonetheless, below are some tips I have learned from this great experience I wished I had known before starting:

Tip 1: Status Calls Are The Heartbeat of these AI Projects

First of all, the number one thing that amazed me the most about the Mars Omdena challenge is how the balance between chaos and order turns out to be crucial for making advances on the problem.

By chaos, I mean that there are 30 individuals all with their own ideas and theories and all these ideas get mixed around serendipitously. The freedom of this unstructured approach allows for creativity and initiative-taking, and it ultimately means that the best ideas win out in the end.

However, life is all about balance, and these AI projects do require some structure for these creative ideas to crystallize. This is where the status calls come into play, where all the teams present their progress to one another.

Whatever you do, do not allow yourself to miss these meetings. They create pressure to deliver results, which turn fanciful theories into concrete progress. Also during the meetings, you will learn other approaches that can help you with your own. The meetings are very focused on question answering as well, so ask as many as you can!

To summarize this point, we can say that the weekly meetings, if attended religiously, will be the driving force into turning your ideas into real-world results. Set a weekly time and, whatever you do, stick to it!

Tip 2: Read around the topic

Photo by Raj Eiamworakul on Unsplash

Photo by Raj Eiamworakul on Unsplash

One of the coolest things about Omdena projects is that they deal with all sorts of different fields and topics. Hunger reduction, fighting PTSD, segmenting trees, or discovering life on Mars, just to name a few. However, it is highly likely that when you first work on a project, you will not be an expert in that field.

One key thing that really helped us to tackle our Mars project was using the first couple of weeks just to get accustomed to the jargon and technical vocabulary related to Space Probes and Interplanetary exploration. We needed to understand words like Technosignature and the difference between a landing site and a crash site, as well as become familiarized with the industry-specific JP2 file format because our raw data was in that format. Furthermore, we had to brush up on the history and context of space probes to understand the problem better, we had to understand how the previous Mars missions had gone and what the HiRISE satellite actually was, and how it worked (because that´s where we would be getting all our data from).

All in all, when you start learning about a new field, there is always specific technical vocabulary that will trip you up at the beginning and that can cause you and your team confusion.

In the first weeks of this AI project(s), I recommend you spent half your time researching, learning, and familiarization yourself with that industry rather than just diving straight into some algorithm optimization. Trust me, this will make life a lot easier later on.

Tip 3: Omdena is a do-ocracy

Photo by George Pagan III on Unsplash

Photo by George Pagan III on Unsplash

Omdena projects like the Mars one we worked on have a strict flat hierarchy. This means that there is little ordering around and nobody is going to tell you what you are supposed to do. People naturally self-organize into groups where they do what they are the best at or what they are the most curious about.

This mode of operating has a name, it´s called a “Do-ocracy” (a play on the world Democracy). During the project, if someone has an idea, we are not going to vote on who should carry out that task or take on that role. The first person who states that they will do the task is entitled to do it. If there are several people, then they should share the role. Simple as.

Responsibilities are attached to people who do the work, rather than elected or selected individuals. For many, this way of working is pretty alien, but you will learn to embrace it and make good use of it. It becomes very empowering very quickly. And it is key to Omdena´s flexibility on which it thrives.

Tip 4: Ask for Help

Photo by Tim Marshall on Unsplash

Photo by Tim Marshall on Unsplash

Nobody knows everything. And Omdena prides itself on having a large group of people from a variety of backgrounds. Your specialty might be someone’s weakness. And vice versa.

One of the reasons why I believe Machine Learning projects excite so many people is because they are truly multidisciplinary challenges. If you were doing this challenge on your own, you would need to use high-level mathematics, be able to code proficiently, understand cognitive human behavior, be an expert in data scraping, and have some scientific/technical knowledge of the task at hand.

Of course, that´s almost impossible for a mere mortal, but a team of 20–30 people can cover for all those needs. However, the only way that your skills can be complemented by the mass brain is by asking for help.

Without a shadow of a doubt, you will reach a point in the challenge where you ́ll feel overwhelmed and completely unsure of how to progress. This is normal. This how we learn.

In such cases, ask for help! Make a general post to all participants explaining your issue, or directly contact a participant who you know has strong skills in what you need. This is the only way that these AI projects by Omdena can progress well. And you will learn so much from the answer of the other members.

So never be afraid of calling out for help, it’s what you are expected to do.

Related Articles

media card
Leading a Local Chapter Challenge in My Home Country Nepal to Understand the Voices of Women, Youth and Marginalized Groups
media card
How I Established Myself as a Machine Learning Researcher
media card
Leveraging AI to Drive Positive Change: How Becoming an Omdena Top Talent Shaped My Career in AI
media card
From Omdena Collaborator to Head of OmdenaSchool, to a Ph.D Scholarship in Germany