Making a scientist is a real superhero

Meet Edgar Dues Guzman, a research engineer with the Multi-agent Research Team, who utilizes knowledge of game theory, computer science and social evolution, to better integrate AI agents.

What led you to work in computer science?

I remember, so I wanted to save the world. That’s why I wanted to be a scientist. I loved superhero stories, but I realized that scientists are real superheroes. They are people who understand us clean water, medicine, and our place in space. As a kid, I loved computers and science. However, although I grew up in Mexico, I didn’t think studying computer science was feasible. So I decided to study mathematics and treat it as a solid foundation for computing, and write a university paper on game theory.

How did your research affect your career?

As part of my PhD in Computer Science, I created biological simulations and fell in love with biology. Understanding evolution and how it shaped the planet was exhilarating. Half of my papers are in these biological simulations, and I continued to work in academia studying the evolution of social phenomena such as cooperation and altruism.

From there I began working on searches on Google, where I learned to deal with large-scale calculations. A few years later, I put together all three: game theory, the evolution of social behavior, and large-scale calculations. Now I use those works to create artificially intelligent agents who can learn to work with themselves and us.

What made you decide to apply for a deeper mindset than other companies?

It was the mid-2010s. I’ve been keeping an eye on AI for over a decade and knew some of their successes. After that, Google got it and I was very excited. I wanted it, but I live in California and I only hired Deep Mind in London. So I continued to track progress. As soon as the office opened in California, I was first in line. I was fortunate to have been hired in my first cohort. Finally, I moved to London to pursue research full time.

What surprised you most about working at Deepmind?

How incredibly talented and friendly are those who are incredibly talented. Everybody I’ve spoken to has exciting aspects outside of work. Professional musicians, artists, super fit bikers, people who appeared in Hollywood films, winners of the Olympiad in Mathematics – you give it a name, we have it! And we are all committed to being open and making the world a better place.

How will your work help DeepMind have a positive impact?

At the heart of my research is creating intelligent agents who understand collaboration. Cooperation is key to success as a species. To cooperate, you can access information from around the world and connect with friends and family on the other side of the world. Failure to address the devastating effects of climate change, as we saw at COP26, is a failure of cooperation.

What’s the best part about your job?

The flexibility to pursue ideas that I think are most important. For example, I would like to help us use our technology to better understand social issues such as discrimination. I pitched this idea to a group of researchers with expertise in psychology, ethics, equity, neuroscience and machine learning, and then created a research program to study how distinctions arise in stereotyping.

How do you explain culture in Deepmind?

DeepMind is one of the places where freedom and possibilities are closely related. We have the opportunity to pursue ideas that we think are important and we have an open culture of discourse. It’s not uncommon to form a team around infecting others with your ideas and making them come true.

Are you part of a deep mind group? Or other activities?

I love taking part in extracurricular activities. I am a facilitator at DeepMind’s Allyship Workshops, aiming to help participants take action for positive change and encourage others’ alliances, contributing to an inclusive and equitable workplace. I also love making my research more accessible and talking to visiting students. We have created public educational tutorials to explain the concept of AI to teenagers used in summer schools around the world.

How can AI maximize its positive impact?

The most positive impact is to have a wider shared profits rather than a small number of people maintain. We need to design systems that empower people and democratize access to technology.

For example, when I worked on Wavenet, the new voice for Google Assistant, I felt it was cool to be working on Google Search, or the technology billions of people currently using on Maps. That’s good, but then we did something better. We have started using this technology to bring their voices back to people with degenerative disorders like ALS. There are always opportunities to do good things. We need to take them.

What are the biggest challenges AI faces?

There are both practical and social challenges. In terms of practical terms, we strive to make our algorithms more robust and adaptable. As living creatures, we take robustness and adaptability for granted. Even if you change the furniture arrangement slightly, you won’t let the fridge forget what it is meant to be. Artificial systems really struggle with this. There are some promising leads, but we still have a way to go.

In the social aspect, you need to collectively decide what kind of AI you want to create. We need to make sure that anything made is safe and beneficial. However, this is particularly difficult to achieve without a complete definition of what this means.

What deep projects do you think are the most exciting?

Now I’m still riding the height of Alphafold, a protein folding algorithm. I have a background in biology and understand how promising the prediction of protein structures for biomedical applications is. And I am particularly proud of how deep attitudes released the protein structures of all known proteins in the human body in the global dataset, releasing almost every cataloged protein that is now known to science.

Are there any tips for an ambitious deep minder?

It’s playful and flexible. I couldn’t optimize for a career that leads to a deep mind (I didn’t even have a deep mind to optimize!), but all I could do was always be able to dream of technology possibilities, creating intelligent machines, and improving the world with them.

Programming is exhilarating in itself, but for me it has always been a means to an end. This is what allowed me to stay up to date as technology came and went. I wasn’t tied to the tools, I was focused on the mission. Rather than focusing on “what,” “why” and “how” appear.