The Ango team prides itself in its incredible diversity. Our team has people coming from different countries, walks of life, and disciplines, each with unique interests and many stories to tell. We couldn’t just keep all these stories to ourselves. So welcome to People of Ango AI.
In this series of posts we will talk to the people who make up the Ango AI team, in conversations where they can share their stories, work, experiences, thoughts, and more.
Today we will be having a chat with our Machine Learning Engineer Balaj Saleem. Balaj was born and lives in Islamabad. He spent his childhood in many different cities in Pakistan, to then graduate from Bilkent University’s CS department in mid-2021. He has been working remotely with us ever since.
First of all, thank you for taking the time to have a chat with us. Could you please briefly introduce yourself to the readers, telling them a bit about your background and your work at Ango AI?
For quite some time I was interested in solving problems that impact society in general and specifically the tech industry. After finishing high school I felt like the best way to do it was to polish my self in the domains that I excelled and to then utilize those skills in the best way possible, for that I moved to Turkey from Pakistan, and in my last two years at Bilkent I developed a keen interest in AI and more specifically, machine learning. The way that changed the whole paradigm to be data centric and the idea of systems tuning themselves to make sense of data was truly fascinating. Around the same time, interestingly Ango AI was taking some of its first steps in the domain of AI and I thought it would be amazing to contribute here with the skills and enthusiasm that I had for the domain. Since then I have done a lot of work on systems that leverage machine learning to help make the tedious problem of data labeling faster, easier and more approachable. I have gotten to develop, learn about and contribute to some of the most cutting edge technology there in the field of Machine Learning and have had the pleasure to help teams globally meet their data needs in the best way possible.
At Ango, you’ve been spending time solving problems related to machine learning in general and data labeling specifically. What do you think is the state of data labeling right now in early 2022?
I think we’re at somewhat of a tipping point, the world of machine learning is starting to recognise the impact and importance of data, more importantly quality data. With numerous advocates and movements towards “Data-Centric AI” we have a spotlight on data labeling. ML teams all over the world are realizing that quality data is one of the most cumbersome yet important parts of any ML solution.
Yet, traditionally, we have a research orientation towards a “model-centric AI” thus the field of data labeling, academically and industrially is still in its infancy. But the work in the field is evolving at an exponential rate, and to be at the forefront of witnessing such an evolution is truly remarkable for me personally.
What are some of the biggest challenges you have worked on at Ango AI? And what do you think are the biggest challenges facing data annotation in general right now?
There are two fundamentals I have tried to focus on in my work at Ango AI: Quality and Efficiency. Many of the problems I have worked on have been closely related to solving these challenges of data annotation.
I have worked on solutions using various technical frameworks to ensure that data is labeled fast and to a pixel perfect accuracy. Apart from this I have tackled quite a bit of research oriented challenges, focused on ideas ranging from predicting data labeling difficulty and duration to implementing deep learning algorithms straight into the browser as opposed to the server.
I think one of the key problems faced by the data annotation industry is that usually the problem of data annotation is treated as a sub-problem of any ML / AI product. The focus on the process of annotation is often minimal, even though according to some estimates it takes 80% of project time / effort.
Machine Learning is a field that’s been growing considerably in the last few years, both in breadth and depth. What are your thoughts on the increasing role ML models are playing in our lives? What are their positive and potentially negative effects? What do we need to be careful about?
The overwhelming inflow of data from a myriad of sources in the past few years and the support of tech giants such as Google and Facebook to pursue Machine Learning is one of the key factors in this growth.
This makes Machine Learning a lot more accessible and supported. This means that it doesn’t have to remain for the benefit of a select few in the Industry. As more and more organizations adopt ML and AI we have solutions that affect major parts of our lives, with the way our search engines work to the way agriculture is being approached. The possibilities are practically endless as long as the data is available.
However, one caveat certainly is the concentration of data with a handful of organizations. Fundamentally, this means that compared to the level of data collection the open source access to it is very limited, this certainly hinders not only the transparency of the way data is being used but also retards the development in the field of AI and ML. I think as individuals, a keen eye on how our data may be processed and more importantly what insights may be generated from them are key issues we need to think about.
Moving now to a more personal side of the conversation: you’ve been working remotely at Ango for a while now, from a city that’s two time zones away from Ango’s main hub. How has the experience of working remotely been for you? What are its pros and cons for you?
I think working remotely has been truly delightful. I spent about 4 years in Turkey and as amazing of a host country as it was, I missed Pakistan. Being here, surrounded by family and friends, whom I had been away from for quite some time certainly has helped me get in touch with a crucial part of what makes me me.
A great thing is that the kind of work we do at Ango AI is pretty flexible. I get to work on my tasks at my own time discretion which certainly boosts productivity and promotes a work-family balance. It also helps me be more goal and task oriented and that allows me to make sure that I bring the best ideas and value to the company.
There are rarely times when the time in Pakistan can be a bit late for an important scheduled meeting, but that’s really once in a blue moon. The only true con I would say is that I miss socializing and interacting with the amazing and dynamic team. In the time I was in Ankara, due to Covid I only got to meet the team once, and since then have worked mostly remotely. Everyone is extremely supportive and encouraging from aspects of life related to and beyond work, and so I would truly be glad to meet them again and spend some time in the office.
Did you always envision a career in machine learning, or is it something you picked up along the way?
Machine learning was actually something I discovered quite later in my university and it all happened out of a simple project that we worked on together that worked to infer the emotions of a face given a picture of it, we managed to match the right emotion with the picture with nearly 70% accuracy and that truly opened my eyes to how impactful this work can be. After that I took more courses along the same lines, finally incorporating it into my final year project as well.
What do you enjoy tinkering with in your free time?
Not many people know this but I am actually really into making electronic music, it’s a great creative outlet for me. Although much of what I make goes either to friends or family or on my computer, there are some tracks that I put on soundcloud that go from chill lo-fi beats to more hip-hop / trap kind of stuff. Go ahead and find me on soundcloud if you want!
Thank you again for your time today. Do you have any final words of advice for those who are looking to start a career in ML?
The pleasure is all mine, I would just like to say that keep being inquisitive and share your passion for the discipline with like-minded people. The domain of ML is still in the process of rapid evolution and make sure you keep yourself surrounded with people who can give you the best kind of guidance.