Active Learning

Although at first glance it may appear as though artificial intelligence (AI) automatically knows everything, this couldn’t be further from the truth. Just like humans, AI goes through a rigorous learning process known as machine learning. Yes, computers have to do homework too. 

There are three different types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Active learning is a type of iterative supervised learning in which a learning algorithm can request a human user to label data with the desired outputs.

Unlike traditional supervised learning approaches, where the algorithm learns from a static dataset, active learning involves dynamically selecting instances (individual data points) from a larger pool of unlabeled data for annotation or labeling by an oracle (usually a human expert). Active learning is a type of human-in-the-loop model because it relies on human interaction and input. 

Ghassemi, F. (2023, June 26). Active Learning (ultimate guide). Medium. https://medium.com/@farnazgh73/ultimate-guide-for-active-learning-main-approaches-3cf53ce207f0

Let’s look at a simple example. Imagine you’re teaching a computer to recognize cats in pictures. Normally, you’d show the computer lots of labeled pictures of cats and non-cats so it can learn. But what if labeling those pictures is too time-consuming or expensive?

Active learning is like having a smart system that already knows which pictures would teach the computer the most. Instead of just showing random pictures, the system identifies ones it’s unsure about and asks a human to label only those. The computer then learns from those labeled examples and gets better at recognizing cats.

So, active learning helps the computer learn faster and with less effort by focusing on the most helpful examples for learning, similar to a tutor who knows exactly which questions to ask to help a student understand better.

The fundamental principle behind active learning is that the machine learning algorithm learns better and with less training when it gets to choose the data from which it wants to learn. In other words, by being active, curious, and exploratory.

It’s a bit like a child independently deciding which books or websites they explore in learning about the planets and outer space. Not all educational resources are useful or desirable to a child; an adult encyclopedia is useless to a pre-schooler who prefers to learn from an interactive app. The same idea can be applied to active learning; not all data points are equally important for training a model.  

In fact, active learning in AI follows the same principles as inquiry-based learning in human education. Based on American educator John Dewey’s philosophy that education begins with the curiosity of the learner,¹ inquiry-based learning aims to spark inquisitiveness in the learner. The inquiry-based approach encourages students to ask questions, explore new ideas, and conduct independent research, all with the objective of enhancing engagement and information retention. Just like a child might ask their teacher questions about a new topic, active learning queries a human oracle when it encounters unknown data. 

So, what’s the end goal of all this? The main objective of both human inquiry-based learning and AI active learning is that the student—be it a nine-year-old girl or a computer—eventually has the ability to learn on their own without outside help. In other words, to produce independent learners.