Grants and Contributions

Research and develop an intelligent “insights platform” which uses Machine Learning or other in-house algorithms to automate the derivation of important network insights.

The objective of this project is to create a prototype that uses machine learning and AI to analyze social media posts across restaurant social media accounts and platforms for our targeted user base of business travellers.

Key results are to automate customer acquisition, customer success, reduce support costs, improve retention and improve customer results by leveraging AI (artificial intelligence) and machine learning.

Rattlehub Digital is adapting its personal data enrichment platform, called permyssion, for North America and other international locations by incorporating data engineering and content recognition, as well as AI, machine learning, and natural language processing capabilities.

The AutoDefence Micro-net research objective is to develop trustworthy technologies for autonomous human-machine systems applied in dynamic and contested defence environments.
To address the above practical challenges, the AutoDefence Micro-net will focus on the following three major research themes:
1)      Cognitive Platform for Trusted Support: Novel methodologies to design trustworthy decision-support systems will be developed.
2)      Cognitive Cycle of Machine Learning: Focus on cognitive load determination for semiautonomous systems in contested environments, autonomous learning systems for defence robots, unmanned systems, reliable target recognition, autonomous decision-support, and real-time battle-field awareness and cognition.
3)      Distributed/Networked Autonomous Systems: Development of innovative machine learning and signal processing solutions for attack/intrusion modeling, detection, and isolation focusing on autonomy, distributed cooperation, and event-based nature of multi-agent systems.

The main objective of this Project is to conduct a computational study of
novel battery materials using computer models, simulations, and machine
learning. The Recipient will perform density functional theory (DFT)
simulations and machine learning to design and create proof-of-concept
battery materials, including cathode and electrolyte materials.

The workhorse density-functional theory (DFT) computational methods are time-consuming and of limited accuracy for strongly correlated systems like the metal-oxides commonly used for catalysis. (Classical) Machine learning (ML) is showing great promise in reducing the number of expensive DFT calculations in the design process. Quantum computing (focusing on hybrid approaches using Noisy Intermediate Scale Quantum (NISQ) computers for parts of the problem) is showing great promise to contribute to opening both bottlenecks by facilitating the use of more accurate quantum chemical methods and accelerating machine-learning approaches beyond the possibilities of classical ML. The proposed research will be directed along two complementary streams: i) quantum computing for quantum chemistry and ii) quantum machine learning.

Quantum Machine Works will design and install the first commercial scale lithium ion battery in the Yukon as an energy storage system.

The objective of this project is to develop leading edge geophysical and geological data augmentation tools (Artificial Intelligence and machine learning) to improve the practicality of deep learning methods applied to mineral prospectivity mapping, particularly focusing on the Southern Glennie Domain in Saskatchewan to produce mineral prospectivity maps of gold occurrences and electromagnetic anomalies.

SnapCyte proposes to use computer vision and machine learning to develop an integrated AI powered platform for cell analysis.