Evolution of AI Search: From Keywords to BERT and MUM
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In this riveting episode by IBM Technology, we dive headfirst into the exhilarating world of AI search. Gone are the days of simple keyword matching; we're talking about algorithms like TF-IDF and the groundbreaking PageRank by Google that revolutionized the game. But hold on to your seats, because the real action starts with models like BERT and MUM, taking context understanding to a whole new level. It's like going from a leisurely Sunday drive to blasting down the Autobahn in a high-performance supercar.
Now, let's break down how AI search, fueled by these large language models, actually works. Picture it like a thrilling four-stage race: first, there's natural language query processing, where the system deciphers your intent like a seasoned detective. Then, it's off to the retrieval stage, where vector search swoops in to find those relevant documents with the precision of a skilled marksman. And let's not forget the adrenaline-pumping answer generation phase, where the AI crafts a seamless response using snippets of information like a master storyteller.
But wait, there's more! The feedback stage adds a thrilling twist, as AI search systems learn and evolve from user interactions, fine-tuning their performance like a finely-tuned racing machine. And when we compare traditional search to this AI-powered beast, it's like pitting a classic vintage car against a cutting-edge hypercar. AI search doesn't just give you a list of links; it serves up direct answers in natural language, making the whole experience as smooth as a well-oiled engine. So buckle up, because AI search isn't just changing the game – it's rewriting the entire rulebook of online information consumption.
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