Grants and Contributions

The 70-20-10 model for learning shows how people gain knowledge: 70% from on-the-job experiences, 20% from social interactions, and only 10% from formal education. Halight LMS provides the structured 30% of learning but leaves 70% untapped.

CoachOnCue fills this gap by simulating real-world job scenarios in a dynamic environment, offering behavioral and situational learning with a focus on real outcomes.

FD Inroads is a new approach to driver risk management that employs AI, gamification and short inapp driver activities called “In Roads Challenges” to directly address the need to coach drivers in response to risk warning events triggered by driver monitoring systems or other driver risk-related events.

Development cycle Pilot - PMV of automated machine configurable by AI according to user profiles + Wheel of Flavours.

The project leverages AI to further develop software that transforms 2D ultrasound images into accurate 3D reconstructions of large vessel structures and rapidly provides enriched, comprehensive measurements critical for vascular risk assessments.

This project uses artificial intelligence in the form of machine learning and LLM's to fill in gaps in a data array of electronic component manufacturers’ part numbers using the intelligence built into the part numbers, knowledge of their parametrics and attributes to address incomplete groupings of millions of components.

Through new audio streaming, speech recognition, and function-calling capabilities, the solution aims to streamline patient assessment simulations, enable multi-lingual interactions, and enhance both VR and non-VR clinical learning experiences.

The Project will support the development of a plan to leverage artificial intelligence to extensively leverage past customer behaviour (10+ years) and product availability in the pre-population of customer produce and meat boxes. This will result in both more satisfied customers (i.e. getting what they want without having to customize each week) and more accurate demand prediction (i.e. reducing out of stock from underordering and shrink from overordering).

The Project will validate AI strategies for ‘smart’ scooters.

The Project will support pilot testing of AI strategies, determine target architecture and quantitative technical objectives for implementation of an AI solution.

The objective of this project is the design and preliminary development of an automated snow crab packing machine. The project will focus on concept engineering of automated technology to receive and place individually crab sections-clusters into specified packaging.