Intel yesterday announced a three-year strategic research and co-innovation collaboration with Mila, a Montreal-based artificial intelligence (AI) research institute. As part of this collaboration, more than 20 researchers from Intel and Mila will work on the development of advanced AI techniques to address global challenges such as climate change, the discovery of new materials, and digital biology.
“Accelerating research and development of advanced AI to solve some of the world’s most critical and challenging problems requires a responsible approach to AI, and the ability to scale computing technology,” the partners said in a statement. “As leaders in computing and AI, and with alignment on being a positive, powerful agent of change in our world, Intel and Mila will be able to double down on projects started in 2021, add a third track and significantly increases the support to drive tangible results.”
“In the face of current global challenges, we must push for a culture of open science between academia and industry to successfully advance AI applications for the benefit of society,” said Yoshua Bengio, Founder and Chief Scientific Officer of Mila. “We are thrilled to collaborate with Intel to rapidly explore novel and needed materials to improve carbon capture, accelerate drug discovery and enable a more sustainable future.”
“Solving complex problems like climate change and new materials discovery requires deep AI research coupled with domain expertise and a commitment to advancing state-of-the-art computing technologies,” said Kavitha Prasad, vice president and general manager, data center delivery and strategies, AI and cloud computing at Intel. “Today’s announcement will play a critical role in surfacing key insights for researchers and driving forward the technological innovations. We look forward to teaming up with Mila to tackle the challenges we face today and create a better world for future generations with technology.”
This extended collaboration will focus on:
- Automating AI-driven discovery of novel materials
- The application of causal machine learning to climate science
- Accelerating the study of molecular drivers of disease and drug discovery.
