L2M – DeepSimu: Next Generation of Deep Learning Frameworks

DeepSimu, the next generation of Deep Learning (DL) frameworks, is proposed to deal with the challenges which Artificial Intelligence (AI) practitioners are facing nowadays when using the available Machine Learning (ML) tools such as Tensorflow, Pytorch, Keras and ScikitLearn. While these frameworks necessitate high level python programming skills like object-oriented programming, DeepSimu has made a huge shortcut in the process of learning AI by eliminating Python from it. This novel framework presents a block diagram environment where different elements of a deep learning model such as Dense layers, Convolutional layers, Recurrent layers, and Attention layers are represented in the form of some blocks. The user can easily arrange these blocks according to the desired architecture and link them by drawing some lines between them. While Canada is considered as the birthplace of DL and modern AI in the world, it plays no significant role in the business of DL frameworks. As countries around the world race to leverage artificial intelligence (AI), a recent study found that Canada is falling behind. In a recent study from KPMG, Canada ranked 44th in AI training and literacy out of 47 countries, and 28th among 30 advanced economies. The study surveyed over 48,000 people in 30 advanced economies and 17 emerging economies, including 1,025 people in Canada. DeepSimu is believed to be a milestone for Canada in the business of AI. It is expected to facilitates and accelerates the process of learning and teaching AI in Canada. This innovation will result in increasing the number of people knowing how to work with AI in Canada and generally speaking would make a dramatic change in the public confidence toward AI.

Faculty Supervisor:

Mohsen Mohammadi

Student:

Partner:

Springboard Atlantic Inc.

Discipline:

Computer science

Sector:

Artificial Intelligence; Education

University:

University of New Brunswick

Program: