Abi Olvera
When you click a button in Microsoft Word, you likely know the exact outcome. That’s because each user action leads to a predetermined result through a path that developers carefully mapped out, line by line, in the program’s source code. The same goes for many often-used computing applications available up until recently. But artificial intelligence systems, particularly large language models that power the likes of ChatGPT and Claude, were built and thus operate in a fundamentally different way. Developers didn’t meticulously program these new systems in a step-by-step fashion. The models shaped themselves through complex learning processes, training on vast amounts of data to recognize patterns and generate responses.
When a user enters a prompt, chatbots powered by these models may, in text applications, predict what the next word in a sentence might be and output text that can feel remarkably human. Similarly, image-generation models like DALL-E and Midjourney create visuals by training on billions of image-text pairs, without following explicit drawing instructions.
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