Avishkar Bhoopchand, A research engineer on the Game Theory and multi-agent team, shares his journey to deepMind and how to raise his profile in deep learning across Africa.
Find out more about Deep Learning Indaba 2022, the annual gathering of the African AI community – taking place in Tunisia this August.
What’s a typical day like at work?
As a research engineer and technical lead, no day is the same. I usually start my day by listening to a podcast or audiobook on my commute into the office. After breakfast, I focus on emails and admin before jumping into my first meeting. These vary from one-on-ones with team members and project updates to diversity, equity, and inclusion (DE&I) working groups.
I try to carve out time on my to-do list this afternoon. These tasks can involve preparing a presentation, reading research papers, writing or reviewing code, designing and running experiments, or analyzing results.
When working from home, my dog Finn keeps me busy! Teaching him is a lot like reinforcement learning (RL) – like how we train at artificial agents. So, a lot of my time is spent thinking about deep learning or machine learning one way or another.
How did you get in AI?
During a course at the University of Cape Town, my lecturer demoed a six-legged robot that had a walk-through scratch using RL. From that moment on, I can stop thinking about the potential of building systems for human and animal mechanisms.
At the moment, machine learning application and research is really a viable career option in South Africa. Like many of my fellow students, I ended up working in the finance industry as a software engineer. I learned a lot, especially around designing large-scale, robust systems that meet user requirements. But six years later, I wanted something more.
Around then, deep learning begins to take off. First I started doing online courses like Andrew Ng’s Coursera on machine learning lectures. After Soon, I was fortunate enough to get a scholarship at University College London, where I got my masters in computational statistics and machine learning.
What’s your involvement in Deep Learning Indaba?
Beyond DeepMind, I am also a proud organizer and steering committee member of Deep Learning Indaba, a movement to strengthen machine learning and AI in Africa. It started in 2017 as a summer school in South Africa. We expected 30 or so students to get together to learn about machine learning – but to our surprise, we received over 700 applications! It was amazing to see, 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 over 600 attendees, and local IndabaX events across nearly 30 African countries. We also have research grants, thesis awards, and complementary programs, including a mentorship program – which keeps the community engaged when I started the pandemic.
In 2017, there were zero publications with an African author, based at an African institution, presented at NeurIPS, a leading machine learning conference. AIs across the African continent were working in silos – some even had colleagues working on the same subject at another company down the road and did not know. Through the Indaba, we’ve built a thriving community with continents and our alumni who have gone on to form new collaborations, publishing papers at NeurIPS and all the major conferences.
Many members have gotten jobs at top tech companies, formed continents on new startups, and launched other amazing grassroots AI projects in Africa. Although organizing the Indaba is a lot of hard work, it’s worthwhile to see the achievements and growth of the community. I always leave our annual event feeling inspired and ready to take on the future.
What brought you to DeepMind?
DeepMind was my ultimate dream company for work, but I didn’t think I stood a chance. From time-to-time, I’ve struggled with imposter syndrome – when surrounded by intelligent, capable people, it’s easy to compare oneself with a single axis and feel like an imposter. Luckily, my wonderful wife told me I had nothing to lose by applying, so I sent my CV and finally got a CV for the offer!
In my previous experience, software engineering really helped me prepare for this role, as I could lean on my engineering skills while building my research skills. Not getting the dream job right away doesn’t mean the door’s closed.
What projects are you most proud of?
I recently worked on a project about real-time cultural exchange of artificial agents. Cultural transmission is a social skill that humans and certain animals possess, which gives us the ability to learn from others. It’s the basis of cumulative cultural evolution and the process of responsible for expanding our skills, tools, and knowledge across multiple generations.
In this project, we have a new task to observe and perform a 3D simulated environment, then copy that pattern, and remember it. Now that we’ve 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 the first time I worked on a large-scale RL. This work combines machine learning and social science, and there was a lot to learn on the research side. At times, our goal towards progress is slow but we got there in the end! But really, I’m most proud of the incredibly inclusive culture we’ve had as a project team. Even when things were difficult, I knew I could rely on my colleagues for support.
Are you part of any peer groups at DeepMind?
I’ve been really involved with a number of diversity, equity, and inclusion (DE&I) initiatives. I’m a strong believer that DE&I has a diverse set of voices from the workplace leads to better outcomes, and to build AI, we must have representation.
I’m a facilitator for an internal workshop on the concept of allyship, which is about using one’s position to challenge the privilege and power of the status quo in support of marginalized groups of people. I’m involved in various working groups that aim to improve community inclusion amongst research engineers and diversity. I’m also a mentor in the DeepMind scholarship program, which has partnerships in Africa and other parts of the world.
What impact do you have on DeepMind’s work?
I am interested in the possibilities of AI making a positive impact on medicine, especially for better understanding and treating diseases. For example, mental health conditions such as depression affect many people worldwide, but we seem to have limited understanding of the causal mechanisms behind it, and therefore, limited treatment options. I hope that in the not-too-distant future, general AI systems can work with conjunction with human experts to unlock the secrets of our minds and help us understand and cure these diseases.