Tech
Gunnari Auvinen: Understanding the Differences Between AI, Machine Learning, and Neural Networks
Gunnari Auvinen is a software engineer based in Cambridge, Massachusetts, with extensive experience in systems engineering, software development, and technical leadership. Currently serving as a staff software engineer at Labviva, he oversees code reviews, system architecture discussions, and technical planning while helping engineers navigate complex software systems. Over the course of his career, Gunnari Auvinen has worked with organizations including General Dynamics Advanced Information Systems, Sonian, Turo, Hack Reactor, and Labviva, where he contributed to platform modernization, educational programming, and large-scale engineering initiatives. His professional background spans full-stack JavaScript development, system integration, and contemporary application architecture. With experience implementing modern technical frameworks and supporting advanced software systems, his career aligns naturally with discussions surrounding AI, machine learning, and neural networks and their growing role in technology and business operations.
Understanding the Differences Between AI, Machine Learning, and Neural Networks
Artificial intelligence (AI), machine learning, and neural networks are computer science terms that people sometimes use interchangeably. In reality, the three terms describe three distinct technologies. Understanding these nuanced differences is increasingly important as they shape business and daily life.
From a broad perspective, individuals can think of these terms as a series of AI systems that go from largest to smallest, with each technology existing as a subset of the preceding one: AI is the largest system, machine learning is an AI subset, and neural networks provide a framework for deep learning algorithms, which exist as a subfield of machine learning.
AI is a diverse group of machine-based technologies designed to imitate human intelligence as closely as possible. Successful AI systems replicate specific cognitive functions, such as problem-solving or learning. AI technology uses these functions to make predictions, automate tasks, and optimize processes.
AI systems typically fall under one of three primary categories: artificial narrow intelligence (ANI), artificial general intelligence (AGI), and artificial superintelligence (ASI). ANI, or weak AI, handles specific tasks, such as chess, chatbots, or virtual assistants like Alexa and Siri. Multiple ANI systems can support complex technologies like self-driving vehicles.
Stronger AI systems more closely mimic human behaviors. Effective AGI systems should behave comparably to human agents, while ASI would exceed humans in terms of both intelligence and ability. However, strong AI systems do not yet exist in the real world.
As a subset of AI, machine learning refines AI systems, allowing developers to optimize ANI performance. Successful machine learning frameworks allow for more accurate predictions and reduced errors, such as a retail website making product recommendations to consumers after reviewing a shopper’s purchase history.
Like AI, machine learning exists in several forms, most prominently classic machine learning and deep learning. Classic machine learning, or non-deep machine learning, requires human input; otherwise, the computer system cannot interpret the required data, outline patterns, or execute the desired tasks. Human operators establish a hierarchy of features, allowing machine learning programs to distinguish between different data sets and provide accurate results.
Deep learning, by comparison, learns more rapidly than classic learning setups, allowing the system to automate and learn with minimal human intervention. Deep learning also performs better than classic machine learning when faced with very large data sets.
Finally, neural networks, also known as artificial neural networks and simulated neural networks, function as a subset of machine learning. They are particularly important for structuring deep learning algorithms. The term “neural” refers to how the networks imitate neurons in the human brain, delivering information to one another via a series of rapid and complex signals.
Neural networks comprise several node layers, including an input layer, a number of hidden layers, and an output layer. Each node in each layer functions as an artificial neuron, connected to the next node in the chain. When a node’s output exceeds its programmed threshold value, it activates and delivers data to the next layer.
Developers use training data sets to teach neural networks, allowing systems to become more accurate with time. After refining an algorithm’s performance, developers can use deep learning and neural networks to create more robust AI systems capable of quickly cataloging and interpreting large, complex data sets. Speech and image recognition tools rely on neural networks, taking only a few minutes to complete a task that would take a human agent several hours.
About Gunnari Auvinen
Gunnari Auvinen is a Massachusetts-based software engineer with experience spanning systems integration, platform engineering, and full-stack application development. He currently works as a staff software engineer at Labviva, where he leads architectural planning, system design sessions, and technical reviews. Previously, he held engineering and educational roles with organizations including General Dynamics, Sonian, Turo, and Hack Reactor. Outside of work, he volunteers with Rice Sticks & Tea and enjoys hiking, gourmet coffee, cooking, weightlifting, and board games.
