Muggoos and overfitting

Back when I was a student, there was this (rather large) species of students who we used to call “muggoos”. They were called that because they would have a habit of “mugging up the answers” – basically they would learn verbatim stuff in the textbooks and other reading material, and then just spit it out during the exams.

They were incredibly hardworking, of course – since the volume of stuff to mug was immense – and they would make up for their general lack of understanding of the concepts with their massive memories and rote learning.

On average, they did rather well – with all that mugging, the downside was floored. However, they would stumble badly in case of any “open book exams” (where we would be allowed to carry textbooks into the exams) – since the value of mugging there was severely limited. I remember having an argument once with some topper-type muggoos (with generally much better grades than me ) on whether to keep exams in a particular course open book or closed book. They all wanted closed book of course.

This morning, I happened to remember this species while chatting with a friend. He was sending me some screenshots from ChatGPT and was marvelling at something which it supposedly made up (I remembered it as a popular meme from 4-5 years back). I immediately responded that ChatGPT was simply “overfitting” in this case.

Since this was a rather popular online meme, and a lot of tweets would have been part of ChatGPT’s training data, coming up with this “meme-y joke” was basically the algorithm remembering this exact pattern that occurred multiple times in the training set. There was no need to intuit or interpolate or hallucinate – the number of occurrences in the training set meant this was an “obvious joke”.

In that sense, muggoos are like badly trained pieces of artificial intelligence (well, I might argue that their intelligence IS artificial) – they haven’t learnt the concepts, so they are unable to be creative or hallucinate. However, they have been “trained” very very well on the stuff that is there in the textbooks (and other reading material) – and the moment they see part of that it’s easy for them to “complete the sentences”. So when questions in the exams come straight out of the reading materials (as they do in a LOT of indian universities and school boards) they find it easy to answer.

However, when tested on “concepts”, they now need to intuit – and infer based on their understanding. In that sense, they are like badly trained machine learning models.

One of the biggest pitfalls in machine learning is “overfitting” – where you build a model that is so optimised to the training data that it learns quirks of the data that you don’t want it to learn. It performs superbly on the training dataset. Now, when faced with an unknown (“out of syllabus”) test set, it underperforms like crazy. In machine learning, we use techniques such as cross validation to make sure algorithms don’t overfit.

That, however, is not how the conventional Indian education system trains you – throughout most of the education, you find that the “test set” is a subset of the “training set” (questions in examinations come straight out of the textbook). Consequently, people with the ability to mug find that it is a winning strategy to just “overfit” and learn the textbooks verbatim – the likelihood of being caught out by unseen test data is minimal.

And then IF they get out into the real world, they find that a lot of the “test data” is unknown, and having not learnt to truly learn from the data, they struggle.

PS: Overfitting is not the only way machine learning systems misbehave. Sometimes they end up learning the entirely wrong pattern!