Concepts: AI Learning Techniques

few-shot, zero-shot, meta and in-context learning. Dive in!

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Ksenia Se & Haziqa Sajid
December 11, 2024

Welcome to our fifth set of flashcards, designed to make ML concepts easy and intuitive. If you know all the terms – you are a champ! But also, you might have some people around you who aren’t as familiar.

Last time, we examined some techniques for training models using extensive labeled, unlabeled, or both types of datasets. These techniques create accurate, robust models but require long training sessions. This time, we’ll examine some advanced learning techniques that adapt models to new tasks or classes using minimal datasets and training. These techniques are hot! And you need to understand what they mean.

Our cards explore Zero-shot Learning, Few-shot learning, Meta-Learning, and In-context Learning to facilitate AI training. Each of these techniques is designed to efficiently teach the model new information without building enormous datasets or training from scratch. While some of these are more popular than others, our cards are designed to provide easy, intuitive explanations.

Here are the AI learning techniques:

Now let’s clarify what’s the difference between semi-supervised learning and few-shot learning. For some they might sound very similar. And then → to meta learning and in-context learning!

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The flashcards series is a pure experiment, and it will be evolving with time and your feedback. If you want to help create them or can recommend cool tools that could assist with this – let me know at [email protected]

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