Generative artificial intelligence (AI) is the art of creating new things inspired by the old. To understand what generative AI does, ask yourself what you would do if you wanted to create a new hit song. You wouldn't want to start from scratch. Instead, you'd listen to 100 popular songs, analyze the rhythms, melodies, and chord progressions. You would learn how these elements are structured and combined. Then, with this knowledge you would use your creativity to mix and match them in new and exciting ways—resulting in a fresh, original song that may be the next big hit. That's essentially what generative AI does. It devours massive amounts of training data, whether it's music, images, text, or scientific data. It learns the underlying patterns and relationships within that data. Then, it uses this knowledge to generate entirely new content that shares characteristics with the training data, but with a unique twist. Generative AI operates like a master mimic, following a three-step process to create entirely new content. 1. Find the building blocks. As mentioned, generative AI begins by analyzing vast amounts of training data. The goal is to identify the underlying building blocks and structures that define the data. For example, different rooms in a house contain different objects—kitchens have different items than bedrooms. Think of it like understanding the grammar of a language or the architectural styles that make up different cityscapes. For example, generative AI trained on cat images would learn to recognize distinct features like ears, whiskers, and fur patterns. 2. Decipher the unwritten rules. Next, it calculates the conditional probability distribution of these structures. In other words, it finds the order and relationship between various structures and the building blocks. Imagine that kitchen again. We know that knives are more likely to be found near cutting boards than, say, pillows, and that a sofa is typically found in front of a TV in the living room. Generative AI quantifies such relationships which allows it to then understand the underlying rules of how likely certain elements are to co-occur and in what order. 3. Perform a bold and creative remix. Finally, armed with this knowledge of building blocks and the rules of their order and structure, generative AI can create entirely new samples. It combines the learned structures and their conditional probabilities in novel ways, producing something entirely new yet rooted in the representations extracted from the training data. This could be a photorealistic image of a cat with unique markings, a catchy song inspired by popular hits, or even a compelling scientific hypothesis based on existing research.
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