MCS Thesis Examination: Vashisht Marhwal

Abstract

    Artificial Intelligence (AI) is a rapidly evolving field that has transformed numerous industries and one of its key applications, Pattern Recognition, has been instrumental to the success of Large Language Models like ChatGPT, Bard, etc. However, scripting these advanced systems can be complex and challenging for some users. In this research, we propose a simpler scripting language to perform complex pattern recognition tasks.

    To achieve this, we introduce a new intensional programming language, MARFL, which is an extension of the Lucid family supported by General Intensional Programming System (GIPSY). Our solution focuses on providing syntax and semantics for MARFL, which enables scripting of Modular A* Recognition Framework (MARF)-based applications as context aware, where the notion of context represents fine-grained configuration details of a given MARF instance. We adapt the concept of context to provide an easily comprehensible language that can perform complex pattern recognition tasks on a demand-driven system such as GIPSY. Our solution is also generic enough to handle other machine learning backends such as PyTorch or TensorFlow in the future.

    We also provide a complete implementation of our approach, including a new compiler component and MARFL-specific execution engines within GIPSY. Our work extends the use of intensional programming to modeling and executing scripted pattern recognition tasks, which can be used for implementing different algorithmic specifications. Additionally, we utilize the demand-driven distributed computing capabilities of GIPSY to enable an efficient and scalable execution.

Examining Committee

• Dr. Yann-Gael Gueheneuc (Chair) 
• Dr. Joey Paquet & Serguei Mokhov (Supervisor)
  
• Dr. Weiyi (Ian) Shang (Examiner)
• Dr. Yann-Gael Gueheneuc (Examiner)