Amii and CIFAR are thrilled to host the Deep Learning & Reinforcement Learning Summer School in Edmonton this July 24 – August 2.

The Summer School brings together graduate students, post-doctoral fellows and industry professionals to explore the latest AI techniques and advancements, build their research networks and open new opportunities for collaboration. Participants learn directly from world-renowned AI researchers including Richard Sutton, Yoshua Bengio and Martha White. Other programming includes an AI Career Fair and social events around town.

To give this year’s attendees an idea of what to expect for this year’s Summer School, we asked three of last year’s attendees to tell us about their experiences and what they found valuable. Check out what they had to say below:

Why did you decide to apply for the 2018 Deep Learning & Reinforcement Learning Summer School?

Katya: The [Summer School] is an incredible opportunity to meet distinguished researchers in the field face-to-face, get the latest cutting-edge research and, no less important, to connect and meet fellow students, find out what they are working on and discuss potential collaborative projects. This past year we had people from the University of Toronto, Université de Montréal, McGill, University of Alberta, CMU, MIT, Stanford, Google, Duke.

Matthew: When the application went up, I was actually informed by several of my lab mates that the Summer School was something that would be extremely valuable in my future research. There was also quite a big push to apply in the RLAI [editor’s note: Reinforcement Learning & Artificial Intelligence] Lab here at University of Alberta. I joined in and was even more excited once I saw the list of speakers.

Raksha: I was aware of various summer schools, but I’d never gotten a chance to attend one. So in late winter of 2018, when I heard about the [Summer School] happening in Toronto, it seemed like the perfect opportunity. The list of speakers for the school were distinguished researchers in the area, plus the RL school component, the positive reviews from peers who had been to the 2017 version, and encouragement to attend by professors in the department, made it very exciting – and I decided to apply to it!

What did you enjoy most about the event?

Katya: People. The brain power and diversity of research backgrounds in the room were fascinating. It was also valuable that the timetable had plenty of time for networking, i.e. over lunch.

Matthew: I was struck by the amazing talent of all the participants and how many were from fields outside machine learning and reinforcement learning. Some of my favorite conversations were with physicists who were looking to apply deep learning or reinforcement learning to model the dynamics of particular physical systems. There was also a lot of opportunity to connect with people from around the world. Learning about the many avenues of research explored is awe-inspiring and a reminder that my own interests are a minuscule part of the picture.

Raksha: As the focus is both on deep learning and reinforcement learning, it was a great opportunity for me, a person who works in reinforcement learning, to get insight into state-of-the-art research in deep learning and hear different perspectives about reinforcement learning. Additionally, it was really nice to meet and interact with the extended peer/research community in a more school-like setting!

Did the Summer School affect your career and/or research trajectory? If so, how?

Katya: Terrific impact. During one of the breaks, we sat down with Rich Sutton and discussed a new project. As a result of that discussion, four months later, I am visiting the RLAI group at the University of Alberta and working with Rich on this project, which becomes an intersection of the NLP and RL. This is a great example where a conversation turned into a mind-blowing experience – and not because of the winter in Alberta (I am from Siberia!) – but by being in the “mecca of RL” and having an opportunity to learn from Rich and the group.

Matthew: I would say it emboldened my research trajectory. My interests are still the same — i.e using reinforcement learning to make predictions of the world through interactions — but I am more excited about this topic and how it relates to the wider AI community. The scope of my research has also widened. While before I was very narrow in what I thought was the way forward, I am now looking towards many communities I had not considered (or was even aware of!).

Raksha: [The] chance to discuss my research with some leading researchers, listen to their experiences and thoughts about what’s to come and where we are headed, meet and interact with peers who are pursuing interesting problems in their research, etc., has been very inspiring!

What was the most valuable thing you learned or experienced at the event?

Katya: Being able to connect with people and learn from them … [S]uch as having an opportunity to ask Graham Neubig hundreds of questions and get most practical answers, learning about RLAI Lab from the people in that group and connecting with people there, being deeply inspired by Martha White and Jamie Kiros.

Matthew: I found the most value in how my view on the field was expanded. The exposure of ideas and topics that I hadn’t yet seen sparked many ideas that I want to explore in my future research. It gave me perspective on the massive amount of work that is still left, but with that the many interesting topics still unexplored.

Raksha: I have always heard about the general [Deep Learning/Reinforcement Learning] community being large and diverse, but this was my first hands-on experience of it. It was invaluable to meet and interact with peers from various countries! The Summer School was filled with energy from day one — and we got a sneak-peak into the breadth of research in the community, in a classroom-like setting, which was really valuable!

Visit dlrlsummerschool.ca for more information.