Avishkar Bhoopchand† a research engineer on the Game Theory and Multi-agent team, shares his journey into DeepMind and how he is working to raise the profile of deep learning across Africa.
Know more about Deep Learning Indaba 2022the annual meeting of the African AI community – taking place in Tunisia in August.
What does a typical day at work look like?
As a research engineer and technical lead, no day is the same. I usually start my day listening to a podcast or audiobook on my commute. After breakfast, I focus on emails and paperwork before starting my first meeting. These range from one-on-one with team members and project updates to diversity, equality and inclusion (DE&I) working groups.
I try to make time in the afternoon for my to-do list. These tasks may involve preparing a presentation, reading research papers, writing or reviewing code, designing and conducting experiments, or analyzing results.
When I work from home, my dog Finn keeps me busy! Teaching him is a lot like reinforcement learning (RL) – like how we train artificial agents on the job. So a lot of my time is somehow devoted to deep learning or machine learning.
How did you get interested in AI?
During a class on intelligent agents at the University of Cape Town, my teacher demonstrated a six-legged robot that had learned to walk with RL from the start. From that moment on, I couldn’t stop thinking about the possibility of using human and animal mechanisms to build systems capable of learning.
At the time, applying machine learning and research was not really a viable career option in South Africa. Like many of my fellow students, I started working as a software engineer in the financial sector. I learned a lot, especially around designing large-scale, robust systems that meet user requirements. But after six years I wanted something more.
Around that time, deep learning started to take off. First I started doing online courses like Andrew Ng’s lectures on machine learning on Coursera. Shortly after, I was lucky enough to get a scholarship to University College London, where I obtained my master’s degree in computational statistics and machine learning.
What is your involvement in the Deep Learning Indaba?
In addition to DeepMind, I am also a proud organizer and steering committee member of the Deep Learning Indaba, a movement to amplify machine learning and AI in Africa. It started in 2017 as a summer school in South Africa. We expected about 30 students to come together to learn more about machine learning, but to our surprise, we received over 700 applications! It was amazing to watch and it clearly showed the need for connection between researchers and practitioners in Africa.
Since then, the organization has grown into an annual celebration of African AI with more than 600 attendees and local IndabaX events in nearly 30 African countries. We also have research grants, thesis awards and additional programs, including a mentoring program – which I started during the pandemic to keep the community engaged.
In 2017, there were zero publications with an African author based at an African institution presented on NeurIPS, the leading conference on machine learning. AI researchers on the African continent worked in silos — some even had colleagues working on the same topic at another institution down the road and didn’t know it. Through the Indaba, we have built a thriving community on the continent and our alumni have started new collaborations, published papers on NeurIPS and all major conferences.
Many members have landed jobs at top tech companies, formed new startups on the continent and launched other amazing grassroots AI projects in Africa. While organizing the Indaba is a lot of hard work, seeing the community’s achievements and growth is worth it. I always leave our annual event feeling inspired and ready to face the future.
What brought you to DeepMind?
DeepMind was my ultimate dream company to work for, but I didn’t think I had a chance. From time to time I’ve struggled with imposter syndrome – when you’re surrounded by intelligent, capable people, it’s easy to compare yourself on one axis and feel like an impostor. Fortunately, my wonderful wife told me I had nothing to lose by applying, so I sent in my resume and finally got an offer for a research engineer role!
My previous experience in software engineering really helped me prepare for this role, as I was able to rely on my technical skills for day-to-day work while building my research skills. Not getting the dream job right away doesn’t mean that the door to that career is closed forever.
Which projects are you most proud of?
I recently worked on a project about giving artificial agents the ability to: real-time cultural transmission† Cultural transmission is a social skill that humans and certain animals possess, which allows us to learn information by observing others. It is the foundation for cumulative cultural evolution and the process responsible for expanding our skills, tools and knowledge across generations.
In this project, we trained artificial agents in a 3D simulated environment to observe an expert performing a new task, then copy and memorize that pattern. Now that we have shown that cultural transmission is possible in artificial agents, it may be possible to use cultural evolution to generate artificial general intelligence (AGI).
This was my first time working on large scale RL. This work combines machine learning and social sciences, and there was a lot for me to learn on the research side. Sometimes progress towards our goal was also slow, but we got there in the end! But really, I’m most proud of the incredibly inclusive culture we had as a project team. Even when the going was tough, I knew I could count on my colleagues for support.
Are you part of a peer group at DeepMind?
I have been really involved in a number of diversity, equality and inclusion (DE&I) initiatives. I strongly believe that DE&I in the workplace leads to better outcomes, and to build AI for everyone we need representation from a diverse set of voices.
I am a facilitator for an internal workshop on the concept of Allyship, which is about using one’s privilege and power to challenge the status quo in support of people from marginalized groups. I am involved in several working groups that aim to improve community inclusion among research engineers and diversity in hiring. I am also a mentor in the DeepMind Scholarship Programwhich has partnerships in Africa and other parts of the world.
What impact do you hope DeepMind’s work can have?
I’m especially excited about the potential of AI to have a positive impact on medicine, especially for better understanding and treating disease. Mental illnesses such as depression, for example, affect hundreds of millions of people around the world, but we seem to have a limited understanding of the causal mechanisms behind them and therefore limited treatment options. It is my hope that in the not-too-distant future, common AI systems can work with human experts to unravel the secrets of our minds and help us understand and cure these diseases.