Life in DeepMind
Avishkar Bhoopchand, a research engineer in the Game Theory and Multi-agent team, shares his journey at DeepMind and how he is working to raise the profile of deep learning across Africa.
Learn more about Deep Learning Indaba 2022the annual gathering of the African AI community – taking place in Tunisia this August.
What is a typical day at work like?
As a research engineer and technical lead, no day is the same. I usually start my day by listening to a podcast or an audiobook on my commute to the office. After breakfast, I focus on emails and admin before starting my first meeting. These range from one-on-one with team members and project briefings to diversity, equity and inclusion (DE&I) working groups.
I try to make time for my to do list in the afternoon. These tasks could include preparing a presentation, reading research papers, writing or reviewing code, designing and running experiments, or analyzing results.
When I work from home, my dog Finn keeps me busy! Teaching it is very similar to reinforcement learning (RL) – like how we train artificial agents at work. So a lot of my time is spent thinking about deep learning or machine learning in one way or another.
How are you interested in artificial intelligence?
During a course on intelligent agents at the University of Cape Town, the lecturer presented me with a six-legged robot that had learned to walk from scratch using RL. 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, machine learning application and research was not really a viable career option in South Africa. Like many of my fellow students, I ended up working in the financial industry as a software engineer. I learned a lot, especially about designing large-scale, robust systems that meet user requirements. But after six years, I wanted something more.
Around then, deep learning started to take off. I first started doing online courses like Andrew Ng’s machine learning lectures on Coursera. Soon after, I was lucky enough to get a scholarship to University College London, where I got my Masters in Computational Statistics and Machine Learning.
What is your participation in the Deep Learning Indaba?
Beyond DeepMind, I am also a proud organizer and member of its steering committee Deep Learning Indaba, a movement to boost machine learning and artificial intelligence in Africa. It started in 2017 as a summer school in South Africa. We expected about 30 students to come together to learn about machine learning – but to our surprise, we received over 700 applications! It was amazing to see and clearly demonstrated the need for linkage between researchers and practitioners in Africa.
Since then, the organization has grown into an annual celebration of African AI with more than 600 participants and local IndabaX events held in nearly 30 African countries. We also have research grants, thesis awards and complementary 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 in an African institution, presented in NeuroIPS, the premier machine learning conference. AI researchers across the African continent were working in silos – some even had colleagues working on the same topic at another institution down the road and didn’t know it. Through Indaba, we have built a thriving community on the continent and our alumni have continued to forge new partnerships, publishing papers in NeurIPS and all the major conferences.
Many members have landed jobs at leading tech companies, created new startups on the continent, and launched other amazing grassroots AI projects in Africa. Although organizing the Indaba is a lot of hard work, it is worth it seeing the achievements and growth of the community. I always leave our annual event inspired and ready to take on the future.
What brought you to DeepMind?
DeepMind was my absolute dream company to work for, but I didn’t think I had the chance. At times, I’ve struggled with imposter syndrome – when I’m surrounded by smart, capable people, it’s easy to compare yourself on a single axis and feel like a fraud. Luckily, my lovely wife told me I had nothing to lose by applying, so I sent my CV in and eventually got an offer for a research engineer role!
My previous experience in software engineering really helped prepare me for this role, as I could draw on my engineering skills for day-to-day work while building my research skills. Not getting your dream job right away doesn’t mean the door is closed for that career.
What projects are you most proud of?
I’ve recently been working on a project to empower artificial agents cultural broadcast in real time. Cultural transmission is a social skill possessed by humans and some animals that enables us to learn information by observing others. It is the basis for cumulative cultural evolution and the process responsible for extending our skills, tools and knowledge over many generations.
In this work, we trained artificial agents in a 3D simulation environment to observe an expert perform a novel task, then copy that pattern and remember it. Now that we have shown that cultural transmission is possible in artificial agents, it may be possible to use cultural evolution to help create artificial general intelligence (AGI).
This was my first time working on large scale RL. This work combines machine learning and social science, and I had a lot to learn on the research side. At times, progress towards our goal was also slow, but we got there in the end! But really, I’m very proud of the incredibly inclusive culture we had as a project team. Even when things were tough, I knew I could count on my colleagues for support.
Are you a member of a DeepMind peer group?
I have been really involved in a number of diversity, equity and inclusion (DE&I) initiatives. I strongly believe that DE&I in the workplace leads to better outcomes, and to build AI for all, we need representation from a diverse set of voices.
I am a facilitator of an internal workshop on the concept of Alliance, which is about using one’s position of privilege and power to challenge the status quo to support people from marginalized groups. I participate in various working groups aimed at improving community inclusion among research engineers and diversity in recruitment. I am also a mentor at DeepMind Fellowship Programwhich has partnerships in Africa and other parts of the world.
What impact do you hope DeepMind’s work will have?
I am particularly enthusiastic about the potential of artificial intelligence to have a positive impact on medicine, especially for better understanding and treatment of diseases. For example, mental health conditions such as depression affect hundreds of millions of 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 in tandem with human experts to unlock the secrets of our minds and help us understand and treat these diseases.