BM25 uses the relative statistical frequency of words to identify relevant material, along with some adjustments. It doesn't use ML at all, but it works very well, especially for technical content.
SPLADE is capable for some areas but is slow, and often times it doesn't present much of a benefit (or is worse) versus BM25 for technical searches, where specific technical words don't have many synonyms that it would be able to pull.
The best search systems today use a mix of semantic search and BM25 or SPLADE, depending on the type of material and the speed required.
We've tested various hybrid approaches as well, but that's too much to go into in once post.
It seems inevitable that search boxes will become for prompts rather than just keywords, and become conversational and include the context of previous searches.
Why would a company want to provide this? Modern-day Amazon doesn't win when you find out that what you want isn't there; they "win" when their search is so bad that you spend a long time browsing counterfeit or not-quite-there results that you might buy. The future of an Amazon-designed chatbot that I see would be for it to try actively to snowball me into buying an inappropriate product, not to help me quickly discover that what I want isn't there.
At least that's my headcanon, who knows. And it seems like the cool preserved latrinae were in herculanum anyway. Still, fun to think about
