Machine Learning vs AI | Whats The Difference?

Artificial intelligence (AI) and machine learning (ML) aren't synonymous. It’s a common mistake to use these terms interchangeably, but ML is a subset of AI, not a different word for the same idea. Learning the difference between ML and other types of AI is a step toward tech-literacy worth taking — especially if you’re a business leader, working professional, or student — because you almost certainly encounter them every day. 

Artificial Intelligence vs. Machine Learning

Artificial intelligence is a sub-discipline of computer science concerned with developing artificial systems capable of performing tasks that typically require human intelligence. We categorize AI into different subsets based on functionality. One of those subsets is machine learning, which involves teaching artificial systems to learn from data and improve their performance over time.  ML-enabled systems can analyze vast amounts of data incredibly quickly and accurately. ML applications are everywhere; in fact, you probably used one to find this blog. Google, Twitter, and LinkedIn leverage ML algorithms to order search results and deliver recommendations.

Four Types of Machine Learning

You should familiarize yourself with these four common types of machine learning:

1. Supervised machine learning requires labeled data, or data that has been 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 uses unlabeled data. It’s particularly 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 uses some labeled data alongside 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 a system on simulated experiences and feedback. Its objective is to teach a system to make decisions that achieve the best outcome in a dynamic situation. Reinforcement learning enables robots to discover the optimal behaviors for navigating new environments and completing unstructured tasks.
   

Photo by Shaun Meintjes on Unsplash

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