When people hear Artificial Intelligence (AI), they often think of chatbots like ChatGPT. But AI is much broader than language models. It spans technologies that recognize images, drive cars, detect fraud, or recommend products.

At the core of many AI applications is Machine Learning (ML), where systems learn from data to improve over time. Neural networks, now central to ML, have existed for decades but have advanced dramatically thanks to modern computing power. ML includes both supervised learning (learning from labeled data) and unsupervised learning (discovering patterns without labels).

What sets today’s systems apart is their scale and complexity. Modern models describe intricate relationships between variables, make accurate predictions, and often combine classical statistical and chemometric techniques — such as regression, PCA, and clustering — with Bayesian methods for better uncertainty estimation.

These concepts are set to radically change the way we work with complex measurement data — unlocking insights that were previously out of reach.