Let's explore how AI models are designed. Designing an AI model involves several key steps. 

First, you define the problem you want the AI to solve. This could be recognizing images, translating languages, suggesting ideas, and so on. Next, you collect and prepare data. AI models are trained using a large amount of data, known as "big data." Then, you choose a learning algorithm to train the model. Based on the algorithm, AI models learn through processing big data, categorizing patterns, and finding appropriate solutions on their own.  This training process is called "Machine Learning (ML)." 

"Deep learning (DL)" is a type of advanced machine learning, which allows AI models to learn using neural networks modelled after the neural circuits of the human brain. After training, you test the model with new data to see how well it performs. If it is not accurate enough, you might adjust the model or use better data. Finally, you deploy the AI model so people can use it in apps or websites.

Unlike robots that can only operate based on pre-programmed instructions by humans, AI can find answers even in unfamiliar situations by inferring from previously learned patterns.