Machine learning and artificial intelligence (AI) are often used interchangeably, but they represent different concepts within the broader field of computer science and technology. While both are integral components of modern computing, they serve different purposes and operate on distinct principles. This topic came to us from More Than Digital in their article, “Artificial intelligence and machine learning: what’s the difference?

Machine learning is a subset of AI that focuses on the development of algorithms allowing computers to learn patterns from data and make decisions or predictions without explicit programming. The essence of machine learning lies in the ability of machines to improve their performance over time by learning from experience.

AI, on the other hand, is a broader concept encompassing machines or systems designed to emulate human intelligence. It goes beyond the scope of learning from data and extends into areas like problem-solving, speech recognition, planning and natural language understanding. AI aims to create intelligent agents capable of perceiving their environment, reasoning and making decisions to achieve specific goals.

While machine learning and AI share common ground, understanding their distinctions is crucial for grasping the nuances of contemporary technology. The biggest challenge is that most organizations have little knowledge regarding how AI systems make decisions or how to interpret AI and machine learning results. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.