Tableau Software : Three common machine learning misconceptions

Artificial intelligence and machine learning are different, but related concepts. One way to think about the relationship of AI and ML is that the former is a problem while the latter is one solution attempting to solve it. If the end goal is that a computer can solve a problem with the cognitive abilities of (human) intelligence, the process of algorithms through data to apply to new, and larger situations is one method of getting there.

To help with the distinction, consider AI as tackling problems that are easy for humans and difficult for machines, like computer vision. To expand on our donut example, let's introduce a new challenge: to teach a computer to discern bagels from donuts. This is something much simpler for a human, but more challenging for a computer. Here, AI is the machine's successful ability to recognize bagels vs. donuts (the problem), whereas ML is the way the computer may learn to reach a conclusion when shown a new photograph (the solution).

Conversely, ML shines and is often employed in situations that are easier for machines than humans-like executing complex, mathematical algorithms or using probabilistic calculations. The computational power of machines helps quickly execute more challenging tasks, or uncover patterns that might be missed by a human.