Artificial Intelligence vs Machine Learning
Within the last decade, the terms artificial intelligence (AI) and machine learning (ML) have become buzzwords that are often used interchangeably. While AI and ML are inextricably linked and share similar characteristics, they are not the same thing. Rather, ML is a major subset of AI. AI and ML technologies are all around us, from the digital voice assistants in our living rooms to the recommendations you see on Netflix Despite AI and ML penetrating several human domains, there’s still much confusion and ambiguity regarding their similarities, differences and primary applications. Here’s a more in-depth look into artificial intelligence vs. machine learning, the different types, and how the two revolutionary technologies compare to one another. What is artificial intelligence (AI)? AI is defined as computer technology that imitate(s) a human’s ability to solve problems and make connections based on insight, understanding and intuition. The field of AI rose to prominence in the 1950s. However, mentions of artificial beings with intelligence can be identified earlier throughout various disciplines like ancient philosophy, Greek mythology and fiction stories. One notable project in the 20th century, the Turing Test, is often referred to when referencing AI’ history. Alan Turing, also referred to as “the father of AI,” created the test and is best known for creating a code-breaking computer that helped the Allies in World War II understand secret messages being sent by the German military. The Turing Test, is used to determine if a machine is capable of thinking like a human being. A computer can only pass the Turing Test if it responds to questions with answers that are indistinguishable from human responses. Three key capabilities of a computer system powered by AI include intentionality, intelligence and adaptability. AI systems use mathematics and logic to accomplish tasks, often encompassing large amounts of data, that otherwise wouldn’t be practical or possible. What is machine learning (ML)? Machine learning (ML) is considered a subset of AI, whereby a set of algorithms builds models based on sample data, also called training data. The main purpose of an ML model is to make accurate predictions or decisions based on historical data. ML solutions use vast amounts of semi-structured and structured data to make forecasts and predictions with a high level of accuracy. In 1959, Arthur Samuel, a pioneer in AI and computer gaming, defined ML as a field of study that enables computers to continuously learn without being explicitly programmed. An ML model exposed to new data continuously learns, adapts and develops on its own. Many businesses are investing in ML solutions because they assist them with decision-making, forecasting future trends, learning more about their customers and gaining other valuable insights. Identifying the differences between AI and ML Much of the progress we’ve seen in recent years regarding AI and ML is expected to continue. ML has helped fuel innovation in the field of AI. AI and ML are highly complex topics that some people find difficult to comprehend. Despite their mystifying natures, AI and ML have quickly become invaluable tools for businesses and consumers, and the latest developments in AI and ML may transform the way we live.