Generative AI
The job of Predictive AI is to observe; however, Generative AI is about imagining what a user requests.
These systems can create entirely new content like text, art, music, code, and even videos by learning from massive amounts of data. You give it a prompt, and it gives you something new every time.
A few examples are:
- ChatGPT writing blogs or explaining complex topics.
- Midjourney or DALL·E creating realistic images from a few words.
- Gemini and Claude writing stories or summaries within seconds.
Generative AI feels more human because it can think creatively or at least mimic creativity.
But there is one limitation: it doesn’t take initiative. It won’t decide what needs to be done next. It only responds when you ask.
A little history of Generative AI:
The roots of generative AI go back to the 1950s and 60s, when simple rule-based systems like ELIZA simulated human conversation. Its true modern form arrived in the late 2010s, powered by GANs and transformer models. And by 2022, cloud computing made it accessible to everyone, sparking the global AI boom we see today.