In recent years, the terms artificial intelligence (AI) and machine learning (ML) have become commonplace in technology discussions and publications. While often used interchangeably, experts contend that there are nuanced distinctions between the two concepts, though disagreements among professionals about these differences are not uncommon.
Artificial Intelligence vs. Machine Learning
The definition of AI varies, but a widely accepted description is "the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem-solving, and pattern recognition." Fundamentally, AI revolves around the concept that machines can exhibit intelligence.
At the core of an AI-based system lies its model, a program enhancing its knowledge through learning from observations about its environment. This learning-based model falls under supervised learning, with other models categorized as unsupervised learning models.
The term "machine learning" has its origins in 1959 when Arthur Samuel defined it as "the ability to learn without being explicitly programmed." Samuel's work led to the creation of a computer checkers application—one of the earliest programs capable of learning from mistakes and improving over time.
Although ML experienced a period of reduced interest, it regained popularity in the 1990s with the emergence of data mining. ML, going beyond data mining, adapts its program's behavior based on learned patterns. Image recognition, a popular ML application, requires training, where humans label images to teach the system to identify various objects.
Many web-based companies leverage ML for recommendation engines, shaping content suggestions on platforms like Facebook, Amazon, and Netflix. These recommendations derive from predictions based on existing data patterns.
While "ML" and "AI" are key terms, the field introduces additional terms such as "cognitive computing," often used interchangeably with AI by companies like IBM.
Distinct terms include "neural networks" or "neural nets," designed to process information akin to biological brains. Neural nets excel in machine learning, sometimes causing confusion between the two. Neural nets form the foundation for "deep learning," a specific ML approach employing algorithms in multiple layers, facilitated by systems utilizing GPUs for processing extensive data sets.
Understanding these terms can be challenging, as their precise definitions remain subjects of ongoing debate among computer scientists. With continuous investments in AI and ML research, the industry may witness the emergence of new terms, adding further complexity to the discourse.