AI & ML: They're Different

It’s a common mistake to treat them like synonyms, but machine learning is a subset of artificial intelligence, not a different word for the same thing.

Here's the Difference

AI is a sub-discipline of computer science. It's a field of study?concerned with developing artificial systems for tasks that typically require human intelligence. We categorize AI into subsets based on functionality.?ML is one of those subsets, and it involves teaching artificial systems to learn from data and improve their performance over time.?ML-powered systems can analyze vast amounts of data incredibly quickly and accurately. ML applications are everywhere; in fact, you're probably using one as you read this article. Google, Twitter, and LinkedIn all use ML algorithms to organize search results and deliver recommendations.

Four Types of Machine Learning

These are the four most commonly discussed machine-learning techniques:

1. Supervised machine learning trains with labeled data: data classified by a human “supervisor.”?It teaches a system to predict outputs from inputs. Your iPhone uses an application of supervised learning, image classification, to recognize when you take a “selfie” and organize your photos accordingly.
2. Unsupervised machine learning trans with unlabeled data.?It’s useful for identifying patterns in large and complex datasets, which are difficult to label. Amazon uses an application of unsupervised learning called “clustering” to group customers by shared characteristics into market segments.?[1]
3. Semi-supervised learning trains with a combination of some labeled data and lots of unlabeled data. It can improve the accuracy of supervised learning models when labeled data is scarce and unlabeled data is abundant.
4. Reinforcement learning trains with simulated experiences and feedback (similar to human learning!).?It's often used in robotics to teach artificial systems to navigate complex environments and perform unstructured tasks.