Machine Learned vs. Machine Learning: The Hidden Pitfall in AI Investments

In today’s fast-paced digital landscape, machine learning (ML) has become a buzzword promising innovation and efficiency. However, many systems marketed as "machine learning" do not truly fit its definition. According to Tom Mitchell, a pioneer in the field, “The study of algorithms that enable computers to learn from data and improve their performance on tasks without being explicitly programmed”.

Most businesses unknowingly invest in systems that fall under "machine learned" rather than "machine learning." These pre-trained models, while leveraging ML during their development, remain static once deployed. They perform tasks based on past training data but lack the ability to evolve dynamically. In these systems, any improvement comes as a patch—explicitly programmed updates that must be manually incorporated by developers. Such machine learned system is not diffrent from the traditional rule based applications.

In contrast, true machine learning systems, such as adaptive algorithms or continuous online learning models, offer dynamic performance improvement, allowing businesses to respond proactively to market changes. This distinction is critical for organizations aiming to maintain a competitive edge.

To determine if a system is machine learned, one simple test can be performed. Start by observing whether the system provides a continuous feedback loop after each result is delivered. If it does, try re-entering the same input after providing feedback, and see if the results remain unchanged. If the output is the same both times, it likely indicates that the system is machine learned—unable to adapt based on new feedback. This basic check can help businesses evaluate whether they are truly investing in adaptive, future-ready AI solutions or simply maintaining static systems.

Understanding the difference between machine learning and machine learned systems empowers businesses to choose solutions that deliver long-term value, innovation, and adaptability. In a rapidly evolving marketplace, adopting truly learning systems is not just an option—it’s a necessity for sustained AI capabilities.