Gunnari Auvinen is a staff software engineer based in Cambridge, Massachusetts, whose career has spanned software engineering, systems architecture, and technical leadership across multiple technology companies. Beginning his engineering career with General Dynamics Advanced Information Systems in 2005, he later held software engineering roles with Sonian, Turo, and Labviva, where he currently leads system design sessions, conducts code reviews, and supports architectural planning. He has also taught full-stack JavaScript through Hack Reactor, providing technical instruction to students in North America, Europe, and Asia. Gunnari Auvinen’s experience developing modern software systems and working with evolving technologies provides relevant context for understanding the distinction between artificial intelligence and machine learning, two closely related concepts that are often misunderstood.
Artificial Intelligence Vs. Machine Learning
Artificial intelligence and machine learning are often treated as interchangeable terms, especially in marketing campaigns and news coverage. In reality, they describe related but different ideas. Artificial intelligence, or AI, is the broader concept. It refers to computer systems designed to perform tasks that normally require human intelligence, such as reasoning, language understanding, problem-solving, image recognition, or decision-making. Machine learning, or ML, is a subset of AI focused on systems that improve their performance by learning from data rather than following only fixed instructions.
One way to understand the distinction is to think of AI as the goal and machine learning as one of the methods used to reach it. Traditional AI systems can rely on explicit rules programmed by humans. For example, an expert system used in medicine might follow a long series of “if-then” instructions written by specialists. Machine learning systems work differently. Instead of relying entirely on hand-written rules, they analyze large amounts of data to identify patterns and improve predictions over time.
The difference becomes clearer in everyday examples. A virtual assistant that understands spoken language and responds intelligently is considered AI because it simulates human-like interaction. The part of the system that learns your speech patterns or predicts what you might ask next is machine learning. Similarly, a self-driving car is an AI system overall, while the algorithms trained to recognize pedestrians or traffic signs are examples of machine learning.
Machine learning has become especially important in the last decade because modern computers can process enormous datasets quickly. Companies use ML systems to recommend movies, filter spam, detect fraud, and predict customer behavior. Streaming services analyze viewing history to suggest content. Banks train models to recognize suspicious transactions. Hospitals increasingly use machine learning tools to assist with medical imaging and risk assessment. These systems improve as they are exposed to more examples, which is why data quality and scale matter so much in ML development.
Another source of confusion is deep learning, which is itself a subset of machine learning. Deep learning uses multilayered neural networks inspired loosely by the structure of the human brain. These models power many recent advances in generative AI, including image generators and large language models. According to IBM and academic researchers, deep learning systems can outperform traditional machine learning approaches in areas like speech recognition and computer vision, though they also require far more computing power and training data.
The distinction between AI and ML matters because it shapes public expectations. Headlines sometimes describe all advanced software as “AI,” even when the system is actually a narrowly trained machine learning model with limited abilities. Experts consistently emphasize that machine learning is one branch within the much larger field of artificial intelligence. Not all AI depends on machine learning, and not every machine learning system demonstrates broader reasoning or human-like understanding. (IBM)
Understanding the relationship between AI and machine learning helps cut through hype. AI describes the broader ambition of creating intelligent systems. Machine learning describes one of the most powerful techniques currently driving that ambition forward.
About Gunnari Auvinen
Gunnari Auvinen is a staff software engineer with Labviva in Cambridge, Massachusetts, where he leads system design sessions, conducts code reviews, and contributes to architectural planning. His previous experience includes engineering roles with General Dynamics Advanced Information Systems, Sonian, Turo, and Hack Reactor, where he taught full-stack JavaScript. Outside of work, he volunteers with a local food pantry and enjoys hiking, weightlifting, gourmet coffee, cooking, board games, and anime.
