1. What can AI do?
Getting familiar with AI means understanding what it can do, the types of problems it can solve, and the challenges it still faces.
AI is capable of making predictions, recognizing patterns in data, optimizing complex processes, and automating repetitive tasks. While this isn’t a complete list, these represent some of AI’s most impactful applications.
One of the most well-known areas of AI is Machine Learning, which allows systems to learn from past data and make decisions. Predictions and inferences are two key tasks within Machine Learning. Predictions help forecast future events, like high-precision weather forecasting. Inferences focus on determining an outcome based on existing data, such as recommending a book you might enjoy based on your past preferences.
More broadly, AI excels at pattern recognition, which involves analyzing data to identify trends and make informed decisions. AI-powered clustering techniques help businesses segment customers into groups with similar behaviors, making it easier to tailor marketing strategies. In security and finance, anomaly detection spots unusual transactions or activities that might indicate fraud. Generative AI, a rapidly growing field, uses patterns in existing data to create new content, such as text, images, or music.
Beyond pattern recognition, AI is a powerful tool for solving optimization problems. It helps delivery services determine the fastest routes, supports energy grids in operating efficiently, and enables travel companies to set dynamic pricing strategies that maximize revenue. Retailers and brands also use AI to plan discount campaigns that drive sales by analyzing customer behavior.
Another key AI application is automation, where machines follow predefined rules to complete tasks independently. While automation itself isn’t AI, since it doesn’t involve learning or reasoning, AI enhances automation by improving efficiency. AI can quickly analyze and classify large batches of documents or images, screen job applications, and manage warehouse logistics with robotic systems.
Despite its impressive capabilities, AI has limitations. Conversational AI, like chatbots, can answer questions but struggles with social nuances such as emotional intelligence and empathy. AI-powered recommendation systems may have difficulty handling new and unfamiliar situations, like suggesting a brand-new product with no prior customer data.
Bias is another challenge, as AI systems can produce unfair predictions if trained on biased data, favoring certain groups over others based on factors like gender, age, or ethnicity. And ultimately, AI is only as good as the data it learns from. Without high-quality data, even the most advanced AI models become unreliable.
Now, let’s put AI into action!
2. Let's practice!