How AI Learns Without Labels: Predicting the Future with Contrastive Predictive Coding
Imagine a program that studies sound, pictures and text, then figures out what might come next — without anyone telling it what things are.
This idea is called unsupervised learning and one way to do it is to make the system build a small, useful picture of the data and try to guess the future from that.
The method, named Contrastive Predictive Coding, trains the machine by predicting the future inside that compact picture, and it picks what matters by comparing good guesses with wrong ones.
Because it learns from raw signals the system finds useful representations that make other tasks easier later.
It works not only for voice, but also for photos, words and games, showing strong results on speech, images, text and 3D environments.
The approach lets AI learn from lots of unlabeled data, so fewer human labels are needed.
This could let future apps understand and adapt faster, learning patterns humans might overlook.
Read article comprehensive review in Paperium.net:
Representation Learning with Contrastive Predictive Coding
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