Cracking Arithmetic: The Indonesian Way to Train AI

Arithmetic isn't just about numbers. it's about how we teach machines to think. A team recently explored if human mathematics teaching methods could sharpen AI's arithmetic reasoning. They turned to the GASING method, a math pedagogy from Indonesia, to see if its step-by-step, causal approach could enhance language models' number-crunching prowess.

Going Small, Thinking Big

What might surprise you is that they didn't opt for a massive AI model. Instead, they trained a modestly sized GPT-2 variant, sporting just 86 million parameters. They didn't bother with the usual bells and whistles like reinforcement learning. The focus was solely on predicting the next token, using a syllable-focused tokenizer designed for Indonesian.

Now, here's the kicker: Despite its size, this model managed to hit over 80% accuracy on problems it hadn't seen before. It even held its own against larger models. So, does size really matter? Or have we been too fixated on the 'bigger is better' mantra?

The Learning Curve

Throughout the training, researchers identified three distinct learning phases. It was like watching a child grow from scribbles to full-blown calculations. Intriguingly, the AI first learned to follow a step-by-step procedure, but then it developed an associative, almost instinctive, ability to solve arithmetic without spelling out each step.

Think of it this way: it wasn't just memorizing multiplication tables. it was internalizing the process. That's akin to a human doing mental arithmetic. It’s a shift from memorizing to understanding, something we often wish our schools would emphasize more.

Why This Matters

So why should we care about an 86M parameter model acing arithmetic? Well, it challenges the notion that only behemoth models can achieve high accuracy. More importantly, it suggests that if we align AI training with effective human teaching methods, we could develop smarter, more efficient models without the hefty infrastructure costs.

Here's the real story: This success could be a major shift for educational tech, especially in regions where computing resources are limited. Smaller, more efficient models mean more accessibility and potential for widespread adoption.

The press release might shout AI transformation. But internally, teams are likely buzzing about how this approach could recalibrate AI training paradigms. Are we about to see a shift in how AI is taught arithmetic? It's possible. And for educators and technologists alike, that's a conversation worth having.