“I believe that deep learning is going to be able to do everything, but I think it’s going to be a while before it does everything.” – Geoffrey Hinton
Artificial Intelligence (AI) is a branch of computer science dedicated to creating systems capable of tasks that typically require human intelligence, such as problem solving, learning, language processing, and creativity. While definitions vary, one of the most concise comes from John McCarthy, the computer scientist who coined the term in 1956: “Artificial Intelligence is the science and engineering of making intelligent machines.” In the Canadian context, AI has moved beyond theory into practical applications that touch healthcare, finance, energy, and even municipal services, positioning Canada as a global leader in responsible AI development.
Defining AI in Practice
AI is best understood along a spectrum. Narrow AI systems perform specific tasks such as spam filtering or medical image recognition. Broader forms, still largely aspirational, aim to mimic general human reasoning. Geoffrey Hinton, a Toronto-based researcher often called the “Godfather of Deep Learning,” has emphasized the transformative nature of the technology: “Deep learning will do better than people on many tasks. The question is, how do we make sure it benefits society?” Canada’s early investment in deep learning research, particularly through Hinton’s work at the University of Toronto and the Vector Institute, has placed the country at the forefront of AI innovation.
Large Language Models (LLMs)
Large Language Models are designed to process and generate human-like language by training on vast datasets. Models such as GPT-4 or LLaMA (Large Language Model Meta AI) are capable of answering questions, writing essays, and assisting with coding. These systems operate not by understanding language in the human sense, but by predicting the most likely sequence of words. For example, Canadian news outlets such as The Globe and Mail have experimented with LLMs to generate first drafts of financial reports, which are then fact-checked by journalists. This demonstrates how LLMs can accelerate workflows while requiring human oversight.
Generative AI
Generative AI encompasses systems that can create new content, including text, images, audio, and video. A widely known example is DALL·E, which generates digital images from textual descriptions. In Canada, Shopify has piloted generative AI tools to help merchants quickly produce product descriptions and marketing materials. This illustrates the commercial potential of generative AI in improving efficiency for small and medium-sized businesses, which are the backbone of the Canadian economy. As Yoshua Bengio, founder of Mila in Montreal, stated: “Generative models are not just tools for creativity, they are tools for discovery.”
Natural Language Processing (NLP)
Natural Language Processing is the sub-field of AI focused on enabling machines to understand and interact with human language. In Canada, where official bilingualism requires services in both English and French, NLP has been crucial in improving translation tools. The Canadian Broadcasting Corporation (CBC/Radio-Canada) has invested in AI-driven translation systems to ensure content is available across linguistic communities. Applications such as sentiment analysis also allow Canadian firms to gauge public reaction to policies or campaigns in real time, helping shape more inclusive communication strategies.
GPT
Generative Pre-trained Transformer, or GPT, is a particular implementation of LLMs that has set benchmarks in natural language tasks. GPT systems are pre-trained on massive corpora of text and then fine-tuned for specific applications. In Canada, educational institutions like McGill University have studied GPT’s potential in tutoring environments. Students in remote northern communities, for instance, can access AI-powered support for math and science, helping bridge the gap in access to quality education. However, this raises questions about dependency and digital literacy, requiring careful policy responses.
Machine Learning
Machine learning is the foundation of modern AI. It involves algorithms that learn patterns from data to make predictions. A prominent Canadian example is RBC, which has deployed machine learning systems to detect fraudulent credit card activity. By identifying unusual patterns in spending, the bank can intervene more quickly to protect customers. As RBC’s Chief Data Officer Greg Grice noted in an interview, “Machine learning allows us to fight fraud in real time and improve customer confidence.” This demonstrates how ML can support both security and trust in financial systems.
Deep Learning
Deep learning, a subset of machine learning, uses multi-layered artificial neural networks to identify patterns in large, complex datasets. Its strength lies in its ability to automatically extract features from raw inputs such as images or sound. In Canadian healthcare, deep learning is being used to interpret radiology images, helping physicians detect diseases like cancer earlier and with greater accuracy. A study conducted at the University Health Network in Toronto demonstrated that deep learning models could identify tumours on CT scans with performance comparable to expert radiologists, offering a glimpse into the future of medical diagnostics.
Artificial General Intelligence (AGI)
Artificial General Intelligence refers to the hypothetical future stage when machines can perform any intellectual task a human can. While not yet achieved, AGI is central to ethical debates. Yoshua Bengio has cautioned: “AGI could be the last invention humanity needs to make. We must prepare carefully.” In Canada, policymakers are beginning to anticipate AGI’s implications. The federal government’s Pan-Canadian Artificial Intelligence Strategy, launched in 2017, is unique in explicitly linking AI development with ethics and governance. This forward-looking approach reflects Canada’s attempt to lead globally in AI safety discussions.
Risks of AI
AI carries risks alongside its benefits. Algorithmic bias is a major concern. For instance, an AI recruiting tool tested by a Canadian company was found to favour male applicants over female candidates, reflecting bias in historical training data. Generative AI can also spread misinformation by producing convincing but false content, a problem particularly acute during elections. The Canadian government’s proposed Artificial Intelligence and Data Act (AIDA), part of Bill C-27, seeks to mitigate these risks by regulating high-impact AI systems. Privacy Commissioner Philippe Dufresne summarized the challenge: “We must ensure that AI respects fundamental rights and freedoms while fostering innovation.”
Use Cases in Canada
AI is reshaping Canadian industries in tangible ways. In healthcare, AI-powered triage chatbots have been piloted in Ontario hospitals to reduce emergency wait times. In energy, Hydro-Québec is testing AI to forecast demand and optimize renewable integration. Municipalities such as Vancouver are exploring AI-driven leak detection in water networks to conserve resources. The arts are also touched by AI: the National Film Board of Canada has experimented with AI-assisted editing tools to support documentary filmmaking. These examples demonstrate the breadth of AI’s impact, from critical infrastructure to cultural expression.
Comparison and Contrast
The AI landscape can be visualized as layered. Machine learning and deep learning form the foundation, providing the computational methods. NLP applies these methods to language, while LLMs are large-scale models within NLP. GPT is a branded example of LLMs, optimized through transformer architecture. Generative AI encompasses LLMs but extends further to visual and auditory domains. AGI stands apart as the aspirational goal of human-level intelligence. Understanding these distinctions is essential for Canadian businesses and policymakers who must choose where to invest, regulate, or deploy.
Summary
Artificial Intelligence is no longer confined to research labs; it is woven into the fabric of Canadian society. From fraud detection at RBC to medical imaging at Toronto hospitals, AI demonstrates its capacity to solve problems and unlock new opportunities. Yet risks remain, including bias, misinformation, and the ethical challenges posed by future AGI. By combining innovation with regulation, Canada has the chance to lead globally in responsible AI adoption. As Geoffrey Hinton once remarked, “We should be very careful about how we build these systems, because once they are smarter than us, we may not be able to control them.” For Canada, the challenge lies in balancing opportunity with caution, ensuring that AI reflects Canadian values of equity, inclusivity, and trust.
About the Author:
Michael Martin is the Vice President of Technology with Metercor Inc., a Smart Meter, IoT, and Smart City systems integrator based in Canada. He has more than 40 years of experience in systems design for applications that use broadband networks, optical fibre, wireless, and digital communications technologies. He is a business and technology consultant. He was a senior executive consultant for 15 years with IBM, where he worked in the GBS Global Center of Competency for Energy and Utilities and the GTS Global Center of Excellence for Energy and Utilities. He is a founding partner and President of MICAN Communications and before that was President of Comlink Systems Limited and Ensat Broadcast Services, Inc., both divisions of Cygnal Technologies Corporation (CYN: TSX).
Martin served on the Board of Directors for TeraGo Inc (TGO: TSX) and on the Board of Directors for Avante Logixx Inc. (XX: TSX.V). He has served as a Member, SCC ISO-IEC JTC 1/SC-41 – Internet of Things and related technologies, ISO – International Organization for Standardization, and as a member of the NIST SP 500-325 Fog Computing Conceptual Model, National Institute of Standards and Technology. He served on the Board of Governors of the University of Ontario Institute of Technology (UOIT) [now Ontario Tech University] and on the Board of Advisers of five different Colleges in Ontario – Centennial College, Humber College, George Brown College, Durham College, Ryerson Polytechnic University [now Toronto Metropolitan University]. For 16 years he served on the Board of the Society of Motion Picture and Television Engineers (SMPTE), Toronto Section.
He holds three master’s degrees, in business (MBA), communication (MA), and education (MEd). As well, he has three undergraduate diplomas and seven certifications in business, computer programming, internetworking, project management, media, photography, and communication technology. He has completed over 60 next generation MOOC (Massive Open Online Courses) continuous education in a wide variety of topics, including: Economics, Python Programming, Internet of Things, Cloud, Artificial Intelligence and Cognitive systems, Blockchain, Agile, Big Data, Design Thinking, Security, Indigenous Canada awareness, and more.