AI training methods

Supervised vs. Reinforced Learning

Now that we’ve talked about how AI is trained, let’s take a closer look at how supervised learning and reinforcement learning show up in real-world AI systems.

Supervised learning is everywhere. When you use a recommendation system that suggests what movie to watch or what product to buy, that system was trained with tons of labeled data—like “users who watched X also liked Y.” In robotics, supervised learning helps machines recognize objects, classify environments, and understand commands. For example, a warehouse robot trained on thousands of labeled images learns to identify boxes, forklifts, and pallets, so it can move around without crashing into things.

Because collecting perfectly labeled real-world data is often difficult and expensive, many AI models are trained using curated or processed datasets that have been carefully cleaned and annotated to make learning more efficient.

Reinforcement learning, on the other hand, shines when AI needs to figure things out by doing. Think about a robot learning how to navigate a maze or a self-driving car deciding how to merge onto a busy highway. These systems are trained through trial and error—testing different actions, seeing what works, and getting better over time. Reinforcement learning is also a key method behind AI that plays games like chess or Go, where strategic decision-making unfolds over a series of moves.

Both methods are powerful in their own ways. But while supervised learning relies heavily on large, labeled datasets, reinforcement learning demands a lot of time and simulations for the AI to “practice” safely before it’s ready for the real world.