Discover how Google Photos uses supervised learning to recognize faces in your photos. Learn the key concepts behind this powerful AI capability.
Table of Contents
Question
You train Google Photos to identify people in photographs. As a result, Google Photos can recognize their faces.
What kind of learning is this?
A. Unsupervised learning
B. Supervised learning
C. Transitory learning
D. Reinforcement learning
Answer
B. Supervised learning
Explanation
Google Photos uses supervised learning. By training the AI to identify a person and their facial patterns, the AI is able to recognize and categorize pictures of that person.
The scenario described, where Google Photos is trained to identify people in photographs and can subsequently recognize their faces, is an example of supervised learning.
In supervised learning, the AI system is provided with labeled training data – in this case, photos where the faces have been manually tagged with the identities of the individuals. The system learns the mapping between the input data (the pixels of the face images) and the output labels (the identities). After being trained on many such labeled examples, the system can then recognize those same individuals in new, unseen photos.
This is different from unsupervised learning, where the AI looks for patterns in unlabeled data. It’s also distinct from reinforcement learning, where the AI learns through trial-and-error interactions with an environment. “Transitory learning” is not a standard term used in machine learning.
So in summary, because Google Photos is explicitly trained on labeled examples of faces and their identities, this is a clear case of supervised learning being used to enable facial recognition.
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