Ayush

Are you inputting images, texts, or maybe even sounds?

And it's the architecture and the training data.

If you forget all the terminology,

Here's the mental model to keep.

At first, this function is basically random.

But as it sees examples and gets feedback, the network reshapes itself so that similar inputs lead to a better and more useful output.

Today, we pulled back the curtain on neural networks, which is the basic engine behind a lot of modern AI.

We talked about neurons, weights, layers, and how learning works through trial and error, and why deep just means many layers of processing.

If there's a topic you really want us to cover, make sure to let us know down in the comments.

Until then, stay curious, stay critical, and stay tuned to AI Squared.

I'm Ayush.

From smart coaching systems and AI referees to motion tracking and predictive injury prevention, AI is fundamentally transforming how athletes train, how teams strategize, and even how fans experience the game.

Let's start with training.

Athletes used to rely solely on coaches and instinct.

Today, they've got AI on their side, analyzing every move, heartbeat, and millisecond of performance.

Exactly.

By analyzing historical data from thousands of athletes, these platforms learn the subtle warning signs that a player might be at risk of injury.

Coaches can then reduce load, tweak training sessions, or focus on recovery.

And it's not just the pros.

AI is showing up in personal fitness too, from Peloton's adaptive workouts to Whoop's wearable coaching.