Life at DeepMind
Meet Edgar Duéñez-Guzmán, a research engineer on our multi-agent research team who draws on his knowledge of game theory, computer science, and social evolution to enable AI agents to work better together.
What led you to work in IT?
For as long as I can remember, I've wanted to save the world. That's why I wanted to be a scientist. Even though I loved superhero stories, I realized that scientists are the real superheroes. They are the ones who give us clean water, medicine and an understanding of our place in the universe. As a child, I loved computers and I loved science. However, growing up in Mexico, I didn't think studying computer science was feasible. So I decided to study mathematics, seeing it as a strong foundation for computer science, and ended up doing my university thesis in game theory.
What impact have your studies had on your career?
As part of my PhD in computer science, I created biological simulations, and ended up falling in love with biology. Understanding evolution and how it shaped the Earth was exhilarating. Half of my thesis focused on these biological simulations, and I then worked in academia studying the evolution of social phenomena, like cooperation and altruism.
From there, I started working in search at Google, where I learned how to handle large-scale calculations. Years later, I brought these three elements together: game theory, the evolution of social behavior and large-scale computing. Now I use these elements to create artificially intelligent agents that can learn to cooperate with each other and with us.
What made you decide to apply to DeepMind over other companies?
It was the mid-2010s. I had been monitoring AI for over a decade and was familiar with DeepMind and some of its successes. Then Google acquired it and I was very excited. I wanted to get in, but I lived in California and DeepMind was only recruiting in London. So I continued to monitor the progress. Whenever an office opened in California, I was the first in line. I was lucky enough to be hired in the first cohort. Eventually, I moved to London to pursue research full-time.
What surprised you most about working at DeepMind?
How ridiculously talented and friendly the people are. Every person I spoke to also has an exciting side outside of work. Professional musicians, artists, fit bikers, people who have appeared in Hollywood films, Math Olympiad winners – you name it, we've got it! And we are all open and committed to making the world a better place.
How does your work help DeepMind make a positive impact?
At the heart of my research is the creation of intelligent agents that understand cooperation. Cooperation is the key to our success as a species. We can access the world's information and connect with our friends and family halfway around the world through cooperation. Our failure to confront the catastrophic effects of climate change is a failure of cooperation, as we saw at COP26.
What is the best thing about your job?
The flexibility to pursue the ideas that seem most important to me. For example, I would like to help use our technology to better understand social issues, like discrimination. I presented this idea to a group of researchers with expertise in psychology, ethics, equity, neuroscience, and machine learning, then created a research program to study how discrimination could arise from stereotypes.
How would you describe the culture at DeepMind?
DeepMind is one of those places where freedom and potential go hand in hand. We have the opportunity to pursue ideas that we think are important and there is a culture of open discourse. It's not uncommon to convey your ideas to others and form a team to make them a reality.
Are you part of any groups at DeepMind? Or other activities?
I love getting involved in extracurricular activities. I am an Allyship workshop facilitator at DeepMind, where we aim to empower participants to take action for positive change and encourage allyship in others, contributing to an inclusive and equitable workplace. I also enjoy making research more accessible and chatting with visiting students. I created public access educational tutorials to explain AI concepts to teenagers, which have been used in summer schools around the world.
How can AI maximize its positive impact?
To have the most positive impact possible, the benefits simply need to be widely shared, rather than retained by a small number of people. We should design systems that empower people and democratize access to technology.
For example, when I was working on WaveNet, the new voice of the Google Assistant, I thought it was cool to work on a technology now used by billions of people, in Google Search or on Maps. That's good, but then we did something better. We have started using this technology to restore speech to people with degenerative diseases, like ALS. There are always opportunities to do good, you just have to seize them.
What are the biggest challenges facing AI?
There are both practical and societal challenges. On a practical level, we are working hard to try to make our algorithms more robust and adaptable. As living beings, we take robustness and adaptability for granted. Slightly changing the arrangement of furniture does not make us forget what a refrigerator is for. Artificial systems have a really hard time handling this. There are promising avenues, but there is still a long way to go.
On the societal side, we must collectively decide what type of AI we want to create. We need to make sure that everything that is made is safe and beneficial. But this is particularly difficult to achieve when we don't have a perfect definition of what it means.
Which DeepMind projects do you find the most inspiring?
Right now I'm still at the top of AlphaFold, our protein folding algorithm. My background is in biology and I understand how protein structure prediction can hold promise for biomedical applications. And I'm especially proud of how DeepMind published the protein structure of all known proteins in the human body in global datasets, and now published almost all cataloged proteins known to science.
Any advice for aspiring DeepMinders?
Be playful, be flexible. I couldn't have optimized for a career leading to DeepMind (there wasn't even a DeepMind to optimize towards!) But what I could do was always allow myself to dream about the potential of technology, to create intelligent machines and improve the world with them.
Programming is exhilarating in its own right, but for me it has always been more of a means to an end. This is what allowed me to stay current as technologies came and went. I wasn't tied to the tools, I was focused on the mission. Focus not on the “what”, but on the “why”, and the “how” will manifest.