Terrestrial contaminated sites – such as abandoned oilfields, chemical spill sites, or former industrial zones – are a major environmental problem in Canada and around the world. Environmental Material Science has created new environmental monitoring equipment that generates high-resolution spatially and temporally explicit data on environmental quality. The data must be visualized and then used to make decisions regarding if site remediation needs to occur, or if occurring, if site remediation should stop.
Scrawlr is a platform for unconstrained, global interaction with all internet content and users. Scrawlr allows for user evaluation and unconstrained classification of any Scrawlr-hosted or non-Scrawlr content. For non-Scrawlr content, this evaluation and classification allowance will be first at the URL level but will subsequently be provided at the individual content component level. Scrawlr will require the capacity to, in multiple languages, identify equivalent and similar content.
The recent advances in machine learning based on deep neural networks, coupled with the availability of phenomenal storage capacity, are transforming the industrial landscape. However, these novel machine learning approaches are known to be data hungry, as they need to tune a huge number of parameters in order to perform well. As more and more AI based applications are being deployed to learn from personal data, privacy concerns are rising, and more specifically on sensible domains like medicine, finance or mobile related data.
The Earth Data Store project provides a cloud-based repository for information about our earth. This encompasses satellite information, environmental models, sensors, and many other types of data that allow users to ask questions of this data to help inform their understanding of the world. The outputs of the project will allow economic development to be balanced with environmental protection.
Insider threat is one of the most damaging security threats to the safety of data, systems, and intellectual property of institutions. Typical threats caused by malicious insiders are trade secrets / intellectual property theft, disclosure of classified information, theft of personal information and system sabotage. Malicious actions of insider threats are performed by authorized personnel of organizations, which may be familiar with the organizational structure, valued properties, and security layers.
Hospitals strive to perform cutting edge medical treatment, treat all patients fairly, and reduce operating costs, while also enabling caregivers to spend more time interacting with patients. Artificial intelligence and machine learning promise these things. However, medical data provides unique challenges for machine learning. Currently, if a hospital wants to include an algorithm for automated decision making, they must either secure approval to collect additional patient data or change their care practices to replicate those at other institutions.
In the recent years, blockchain technologies have shown promise as infrastructure for decentralized trustless anonymous digital asset exchange. The technology promises to transform how the data is shared in many areas including financial sector, insurance and gaming industries. Yet several obstacles prevent mainstream adoption of this technology - one of these challenges is security.
Estimates of the population density of marine mammals in an area and the change in population over space and time are critical inputs for managing the interactions of human activity and mammal populations. Visual surveys from boats, shore stations, and aircraft have served as the basis for most population estimates currently used by managers. However, these survey methods are generally only performed in good weather conditions and require many trained observers. These factors make visual surveys expensive and reduce the temporal and spatial coverage of population estimates.
In this project we want to develop a few AI algorithms for the security and public health monitoring applications that can be implemented on a mobile camera. This camera can be either mounted on the autonomous mobile robot or on the wearable devices that security guards are equipped with. This project is aiming to solve the following challenges: understanding the location of the camera based on the footage, anomaly detection, and action recognition. These goals can be achieved through a combination of deep learning, traditional computer vision, and machine learning methods.
Precision.ai is building solutions to minimize chemical consumption while maintaining weed control through Intelligent UAV based application. Precision.ai has working survey drones that can fly a field, capture images and use AI to map weeds to be sprayed later. Precision.ai also has “See & spray” drones that can fly a field, identify weeds and spray them. We now need to scale our capabilities through drone swarming. The required speed and coverage will require an autonomous and collaborative swarm of drones (or a combination of more capable drones and/or more efficient field coverage).