The distinction between machine learning and artificial intelligence often confuses business leaders and technologists alike. Hassan Taher, founder of Taher AI Solutions in Los Angeles, has spent years clarifying these concepts for clients across healthcare, finance, and manufacturing sectors. His work demystifying technical subjects has made him a trusted voice for organizations implementing AI technologies.
Taher’s approach emphasizes practical understanding over technical jargon. Rather than treating machine learning as an abstract concept, he frames it as a specific methodology within the broader field of artificial intelligence. This perspective helps decision-makers evaluate which technologies suit their operational needs without getting lost in buzzwords or marketing claims.
What Is Machine Learning?
Machine learning refers to computational systems that improve their performance on specific tasks through exposure to data, without being explicitly programmed for every possible scenario. Unlike traditional software that follows predetermined instructions, these systems identify patterns and adjust their behavior based on examples.
The core mechanism involves algorithms that process input data, extract relevant features, and generate outputs or predictions. As the system encounters more examples, it refines its internal parameters to minimize errors. This learning process mirrors certain aspects of human cognition, though the underlying mechanics differ substantially.
Hassan Taher has noted that machine learning applications now permeate daily life, from email spam filters to recommendation engines on streaming platforms. These systems operate without constant human intervention, making autonomous decisions based on their training. Financial institutions use machine learning to detect fraudulent transactions by recognizing patterns that deviate from typical customer behavior.
The technology’s value lies in its ability to handle tasks that would be impractical to code manually. Writing explicit rules for identifying every possible type of spam email would be nearly impossible, but machine learning systems can learn to classify messages after processing thousands of examples. This adaptability makes the technology particularly useful for complex, variable environments where rigid programming falls short.
How Machine Learning Works
The learning process begins with data collection. Organizations gather relevant information that represents the problem they want to solve. For a system designed to predict equipment failures, this might include sensor readings, maintenance logs, and failure reports spanning several years.
Practitioners then prepare this data by cleaning inconsistencies, handling missing values, and selecting relevant features. This preprocessing stage significantly impacts the system’s eventual performance. Poor quality data yields poor results, regardless of algorithmic sophistication—a principle Taher emphasizes in his consulting work with Taher AI Solutions.
Once prepared, the data feeds into a chosen algorithm. The algorithm contains adjustable parameters that influence how it interprets information. During training, the system makes predictions, compares them to known correct answers, and calculates the difference. This error measurement guides parameter adjustments through mathematical optimization techniques.
Training continues iteratively. Each pass through the data refines the parameters slightly, gradually improving accuracy. The process requires careful monitoring to avoid overfitting, where the system memorizes training examples rather than learning generalizable patterns. Hassan Taher has written about this challenge, noting that models must balance specificity with broader applicability.
After training, practitioners evaluate the system using separate test data it has never encountered. This validation step reveals whether the model can handle new situations effectively. Successful models then deploy into production environments, where they process real-world inputs and generate predictions or classifications.
Types of Machine Learning
Supervised learning represents the most common approach. These systems learn from labeled examples where both inputs and desired outputs are known. A supervised system trained to recognize handwritten digits receives thousands of digit images alongside their correct numerical values. Through repeated exposure, it learns to associate visual patterns with specific numbers.
Classification and regression tasks both fall under supervised learning. Classification assigns inputs to discrete categories—determining whether an email is spam or legitimate, for instance. Regression predicts continuous values, such as estimating a home’s selling price based on its characteristics.
Unsupervised learning operates without labeled outputs. These algorithms find hidden structure in unlabeled data by identifying clusters, patterns, or relationships. Customer segmentation often uses unsupervised methods, grouping buyers based on purchasing behavior without predefined categories. The system discovers natural groupings that marketers can then target with tailored campaigns.
Reinforcement learning takes a different approach entirely. These systems learn through interaction with an environment, receiving rewards or penalties based on their actions. Rather than learning from examples, they discover effective strategies through trial and error. Robotics applications frequently employ reinforcement learning, as physical systems must adapt to dynamic conditions that cannot be fully specified in advance.
Semi-supervised learning combines labeled and unlabeled data, useful when labeling examples proves expensive or time-consuming. Medical imaging applications sometimes use this approach, as obtaining expert annotations for thousands of scans requires substantial resources. The system leverages a small set of labeled images alongside a larger unlabeled collection to improve performance beyond what either dataset alone would provide.
Machine Learning vs Artificial Intelligence
Artificial intelligence encompasses any system that exhibits intelligent behavior—reasoning, learning, problem-solving, perception, or language understanding. Machine learning represents one method for achieving artificial intelligence, but not the only approach. Rule-based expert systems, for example, demonstrate intelligent behavior through carefully crafted logical rules rather than learning from data.
The relationship resembles that between fruit and apples. All apples are fruit, but not all fruit is apples. Similarly, all machine learning is artificial intelligence, but artificial intelligence includes techniques beyond machine learning. Hassan Taher has addressed this distinction in his writings, emphasizing that organizations should select technologies based on their specific requirements rather than chasing trends (https://www.hassantaherauthor.com/).
Traditional AI systems encoded human expertise directly into software. Programmers interviewed domain experts, extracted their knowledge, and translated it into logical rules. These systems could explain their reasoning clearly but struggled with ambiguous situations or cases falling outside their rule sets. Maintaining and updating them required significant programming effort.
Machine learning shifts this burden. Rather than encoding expertise manually, practitioners provide examples and let algorithms extract patterns. This approach handles ambiguity better and scales more effectively to large, complex datasets. However, machine learning systems often function as “black boxes” whose internal decision-making processes resist straightforward explanation.
Some modern AI systems combine multiple approaches. A medical diagnosis platform might use machine learning to analyze patient data while employing rule-based reasoning to verify that recommendations align with established clinical guidelines. This hybrid strategy leverages each technique’s strengths while mitigating weaknesses.
Hassan Taher’s consulting practice helps organizations navigate these choices. Through Taher AI Solutions, he advises clients on matching technologies to business objectives, considering factors like available data, interpretability requirements, and maintenance capabilities. His emphasis on ethical implementation ensures that systems operate transparently and align with stakeholder values.
The field continues advancing as researchers develop new algorithms and techniques. Deep learning, a machine learning subset using neural networks with many layers, has driven recent breakthroughs in image recognition and natural language processing. Yet even as capabilities expand, fundamental principles remain constant: systems learn from data, improve through experience, and require careful design to function reliably in real-world contexts.
