Machine Learning’s Evolution: Past, Present, and Future

Machine learning is hardly a new kid on the block – it's been around for decades in one form or another. But its impact has exploded in recent years, shifting from a niche academic topic to a driving force behind everyday tools and headlines. As we mark International Women's Day, it's a good moment to reflect on who is building this AI revolution.

The Past: Machine Learning Before the Revolution

The roots of machine learning go back to the mid-20th century. In 1959, IBM researcher Arthur Samuel popularized "machine learning" after creating a checkers-playing program that learned from its mistakes. Early breakthroughs like this and later inventions such as the Perceptron neural network showed that computers could learn – but only within very limited scopes. For many years, ML systems were confined to specific use cases (think spam filters or game AIs) and often required expensive hardware and highly specialized skills to develop.

Back in those "pre-revolution" days, we developers frequently had to supplement ML with manual rules to get real-world tasks done. I even remember a project where I paired a text classification ML model with some good old regular expressions to catch edge cases the model missed. This hybrid approach was common – ML could handle most of the work, but it wasn't magical. You often needed to nudge it along with human-defined rules, especially when data was scarce or the model wasn't smart enough.

The Present: The Disruptive Rise of LLMs

Fast-forward to the last few years, and we've seen the rise of large language models completely change the game. What's different this time?

The first breakthrough is more brilliant architecture. In 2017, a new neural network design called the Transformer emerged. This architecture lets models pay "attention" more effectively to different input parts. It was a breakthrough that suddenly enabled much deeper and more flexible language understanding.

Then came massive computing power. Today's models train on cloud computing clusters with thousands of GPU cores, something unimaginable back in ML's early days. Cloud computing made it cheap and easy to harness the enormous processing power needed for ML. In parallel, hardware like GPUs and TPUs became incredibly powerful, allowing anyone to use ML-driven apps.

But also huge datasets. The Internet became the playground for ML. LLMs are trained on gigantic text datasets, reading most of the web. Feeding on this wealth of digitized text (articles, books, code, you name it) gave the models a broad knowledge of language and the world. 

Thanks to these factors, we went from models that might classify your emails as spam to models that can write entire paragraphs, debug code, or converse with you. As someone who's been in the field, the leap is astounding. A few years ago, getting an ML system to handle a single task reliably took significant effort. Now, given the right prompt, an LLM can perform tasks it was never explicitly programmed to do – from generating poetry to explaining quantum physics.

This revolution also altered the course of my career. I now work for the company behind one of the world's most famous LLMs (I can't name it here due to confidentiality, but let's just say you've probably chatted with their model!). My day-to-day job is focused on making these LLMs even better at something very near to developers' hearts: programming. In particular, I'm working on techniques to help AI understand and assist with complex, multi-file coding problems.

The Future: AI's Next Big Challenge and the Role of Diversity

Looking ahead, one of AI's next big challenges won't be just about bigger models or faster hardware – they'll be about how we integrate AI into our society and who gets to build it. People often compare AI's emergence to the Industrial Revolution in terms of impact. AI has the potential to revolutionize industries on an industrial-revolution scale. But here's the thing: AI is a human creation. These models rely on us at every step, at least for now! From the researchers who invent model architectures to the engineers who curate training data to the domain experts who fine-tune and evaluate outputs. AI will only be as good (and as fair) as the human input that shapes it.

That's why diversity in AI development is not a "nice to have,". It's a must-have.

Machine learning's journey from its early days to the current boom in LLMs has taught me that progress comes in waves – and we're riding a big one right now. The best and fairest AI models of tomorrow will come from a community of builders as diverse as the society that AI is meant to serve. And explicitly, including women in that community of AI creators isn't just a nod to equality – it's how we ensure the solutions we build genuinely benefit everyone.