Borealis AI has access to a huge amount of financial data related to the stock market and is interested in leveraging recent developments in machine learning to better understand this data. Some potential questions emerging from this data are: (1) Given the closing price of a stock in the recent months, can we predict the […]

Millions of people post information on social media sites about their interests, preferences, opinions etc. on a daily basis. LeadSift mines this data stream in real-time to generate incredibly accurate and targeted sales leads. Given the short text and ambiguity around a social post, it gets very difficult to accurately identify intent. This project will […]

Technology is not neutral. Look at the keyboard it assumes right handedness. For example, the number pad and arrow keys are on the right. Similarly, the inherent biases of software computer programs are at best foreign entities and at worst tools of colonization when introduced to Indigenous communities. In line with CanadaCanada’s goals to promote […]

The goal of our proposal is to develop three automated processes in the field of construction using artificial intelligence. The first process is to develop a method that can convert two-dimensional drawings into three-dimensional models that can be further manipulated on a computer. The second process is to optimize the cutting of raw materials– such […]

The main objective of the project is to develop a software system to expertly capture an electronic health profile of a client with physical and/or cognitive disabilities to determine their level of functional ability, mobility and cognitive ability. Using the profile, the system will expertly assess the client’s ability to live either independently or with […]

In this project, we propose a continual learning approach to face the problem of forward transfer in complex reinforcement learning tasks. Concretely, we propose a model that learns how to combine a series of general modules in a deep learning architecture, so that generalization emerges through the composition of those modules. This is of vital […]

Recommender systems (RS) are intended to be a personalized decision support tool, where decisions can take the form of products to buy (e.g., Amazon), movies to watch (e.g., Netflix), online news to read (e.g., Google News), or even individuals to screen for a medical condition (e.g., personalized medicine). For digital users, RS play an essential […]

Vaolo est la plateforme de l’organisation non gouvernementale à but non lucratif (ONG) Village Monde. Il s’agit d’une plateforme technologique innovante dans le secteur du voyage, au croisement entre les plateformes transactionnelles, collaboratives, les réseaux sociaux et le partage. Par son positionnement unique la plateforme Vaolo est appelée à accumuler d’importantes quantités de données avec […]

Value-Based Healthcare (VBHC) is becoming a leading approach to improving patient and health system outcomes around the world. This research is to develop a new problem oriented record for the VBHC where it can analyze the Patient Progression which is the hospitalized patient journey through the required care events, actions and processes to achieve a […]

OneCup provides cattle management solutions to the livestock industry. Our AI is called BETSY – Bovine Expert Tracking and Surveillance. Using artificial intelligence, we put a rancher’s skillset into a device the size of a small book. With several types of cameras, BETSY can ID and track cattle activity 24×7. For example, she can track […]

The advantages of cloud computing have been mainly used from a service perspective. It offers virtually unlimited resources in CPU, memory and bandwidth, under a service agreement scheme, in order to use those resources to provide a service. Operators leverage on this model to achieve economies of scale, by hosting as much customers as possible […]

The inference of causal relationships is a problem of fundamental interest in science. Compared to models that rely on mere correlations, causal models allow us to anticipate the effect of a change in a system. Such causal models have applications ranging from government policy making to personalized medicine. However, learning causal models from data is […]