Artificial Intelligence Debate of the Ingenuity Labs Robotics and AI Symposium (RAIS2023) from October 12, 2023.



It’s no secret that AI research is heavily reliant on access to computing resources, particularly GPU computing. In the race to develop new technologies and publish new work in top journals, computational speed is incredibly important. However, GPU compute clusters are expensive to acquire and maintain and it’s difficult for university researchers to keep buying new computing hardware. Private companies, on the other hand, have more resources to acquire and maintain newer and faster equipment. Meta AI, for example, has a “22,000 NVIDIA V100 Tensor Core GPUs in a single cluster that performs 35,000 training jobs a day.” Given the disparity in ability to acquire the critical computing hardware, it begs the question: “Can universities compete with industrial labs in AI research?”

Our debate panel will include:



Dr. Ting Hu, Associate Professor, School of Computing, Queen’s University

Dr. Ting Hu received her postdoctoral and PhD training from the Geisel School of Medicine, Dartmouth College, and the Department of Computer Science, Memorial University, respectively. Her research focus lies on two inter-related areas, bio-inspired intelligent computing and bioinformatics. She is interested in 1) designing robust and interpretable evolutionary learning algorithms, a creative approach to AI, and 2) mining large-scale biomedical data using complex networks and machine learning techniques.



Dr. Dan Desjardins, CEO of Distributive

Dr. Desjardins is the CEO of Distributive, the Kingston-based company developing the Distributive Compute Protocol (DCP), a next-generation distributed computing platform built on web technology. DCP stitches together all of the computers and laptops in a room or building to form a supercomputer for use by data scientists, devops engineers, and computational scientists. Dan has a PhD in Physics from Queen’s University, and he himself needs a lot of computing power for his electrodynamics research. Prior to Distributive, Dan served 18 years in the Royal Canadian Air Force as a CC130H Hercules military search and rescue pilot and as an Assistant Professor of Physics and Space Science at the Royal Military College of Canada. Dan ranked among the top hundred candidates during Canada’s 2016 astronaut recruitment campaign.



Dr. Ahmad Beirami, Google Research

Ahmad Beirami is a research scientist at Google Research, leading research efforts on building safe, helpful, and scalable generative language models. At Meta AI, he led research to power the next generation of virtual digital assistants with AR/VR capabilities through robust generative language modeling. At Electronic Arts, he led the AI agent research program for automated playtesting of video games and cooperative reinforcement learning. Before moving to industry in 2018, he held a joint postdoctoral fellow position at Harvard & MIT, focused on problems in the intersection of core machine learning and information theory. He is the recipient of the 2015 Sigma Xi Best PhD Thesis Award from Georgia Tech.



Dr. Ali Etemad, Electrical and Computer Engineering, Ingenuity Labs Research Institute, Queen’s University

Dr. Etemad is an Associate Professor, as well as a Mitchell Professor in AI for Human Sensing & Understanding at the Department of Electrical and Computer Engineering, Queen’s University, Canada, where he leads the Ambient Intelligence and Interactive Machines (Aiim) lab. He is also a faculty member at Ingenuity Labs Research Institute. He received his MASc and PhD degrees in Electrical and Computer Engineering from Carleton University, Ottawa, Canada, in 2009 and 2014, respectively.  His main areas of research are machine learning and deep learning focused on human-centered applications with wearables, smart devices, and smart environments. Prior to joining Queen’s, he led AI and machine learning teams in the industry. 

Please visit the Aiim Lab for more information.