AI at Play: Explaining AI Through Sports

Explaining artificial intelligence can be challenging — even to a six-year-old. While AI dominates conversations, most people lack a clear understanding of what it actually is, despite its growing influence on daily life.

Think of AI as…

Imagine a sports bettor named John. Both John and AI engage in prediction by:

• Processing large datasets and historical information
• Identifying patterns and evaluating variables
• Making informed forecasts about future outcomes
• Refining strategies through continuous learning from successes and failures

The comparison acknowledges limitations: just as unexpected events in a sports game — like a sudden injury or a surprising play — can derail the most well-researched bet, AI systems struggle with anomalies absent from training data. Neither achieves perfect accuracy, requiring balance between knowledge and acceptance of unpredictability.

Types of AI Models

There are six primary AI model categories:

Classification Models: Categorize data into predefined classes (spam detection, fraud identification)

Regression Models: Predict continuous numerical outcomes such as stock prices or rainfall amounts

Anomaly Detection Models: Identify unusual patterns or outliers crucial for fraud prevention and security

Generative Models: Produce new synthetic data instances resembling training data, including GANs and VAEs for creating images or text

Reinforcement Learning Models: Operate on reward-penalty systems for complex decision-making in robotics and strategic gameplay

Segmentation Models: Divide data into meaningful subgroups for targeted analysis

Model selection depends on specific application requirements and constraints. Understanding these categories helps construction professionals identify which AI approaches best fit their workflow challenges.