Comparison of Classification between Artificial Intelligence and Machine Learning
Kai Crabb (Northwest Missouri State University), Mason Sipe (Northwest Missouri State University), Umair Mughal (Northwest Missouri State University)
Artificial intelligence and machine learning algorithms are often grouped into the same classification, and both models analyze data to make decisions. However, machine learning is just a branch of artificial intelligence that uses past information to make informed decisions. By comparing these topics, we can better understand the main goals and the similarities and differences between the two. Regarding accuracy, your model is only as effective as your feed data. The main goal of machine learning is to be as accurate as possible from our data. At the same time, artificial intelligence focuses on artificially creating intelligence to be able to think logically like a human, simulating the human mind in an attempt to solve complex problems. Humans, as does artificial intelligence, use all kinds of data (structured, semi-structured, unstructured) in their day-to-day lives. In contrast, machine learning only takes in structured and semi-structured data. As for timing and resources required, artificial intelligence may take longer depending on the complexity of the task at hand. It may be out of reach for a typical individual to develop their artificial intelligence model as these require lots of energy-intensive resources and may require large data centers to run, but training your machine learning model may be more manageable in terms of the resources it needs and what’s available to the average person.