171–180 of 639 posts
Earlier quoted context omitted.
Because you want to sell shirts without stripes to customers who want them.
I expect most users genuinely looking for such a product would just search "plain shirt"
Earlier quoted context omitted.
It would be really nice if the search engines would automatically apply the "-" when it sees the work "without".
I’m not so sure. What about a search query for “Doctors without Borders“ or “Men Without Hats“? Surely interpreting “without“ as the negative operator would ruin those searches.
Though I somewhat agree, I don't think that an average user even knows about existence of search operators in the first place, let alone being aware of this specific one and when to use it.
https://www.google.com/search?q=plain+shirt https://www.amazon.com/s?k=plain+shirt on edit: https://www.google.com/search?q=shirt+-stripes
Probably not surprising that most people don’t know, but negative keyword search works on all these platforms: shirt -stripes
it actually does work though. keywords will need to be generated whether manually, from the manufacturer, or from image analysis.
The author's intent exceeds both the capabilities and intended use case of search engines.
The query "shirts without stripes" if interpreted by human would require any search system to not only analyse the keywords and tags (of the products/images), but also the content, which is an infeasible task given its dynamic nature.
So the author wants: select all shirts where content analysis of returned images yields no stripes.
This is a context-sensitive image/product search based on arbitrary, dynamically created criteria and shows that user isn't aware of what the search functionality does as opposed to exposing weak "AI". [edit]To clarify: you cannot add all possible keywords/criteria in advance[/edit]
Why is everything a git repo when it could have been a blog post?
Earlier quoted context omitted.
Because you want to sell shirts without stripes to customers who want them.
I expect most users genuinely looking for such a product would just search "plain shirt"
You can't assume that customers would type one thing or another - you need to gather lots of query log data and see what you find. You'd be surprised how much variation there is, but once you do have this data you can then find patterns to cover lots of (but not all) cases.
Earlier quoted context omitted.
Every systems sucks at NLP. It is an extremely hard problem and we are nowhere near matching the human cerebral cortex.
Generally, yes. But you can go very far and cover a lot of cases when you stick to a narrow domain and common patterns that customers of your product will typically use.
The point of this isn't asking how to apply boolean search operators, it's showing that the largest AI-focused companies in the world absolutely suck at NLP.
Why would you really apply NLP to a search engine though? Generally speaking a weighted keyword search is good enough 95% of the time and requires significantly less resources to perform.
I'm guessing one of those reflections looks like a turtle? Or maybe a pattern on the floor, wall, or rug?
Although there are examples where I'm unsure if the AI is dumber than my 4yo or smarter than me. This is a result for "truck": https://i.imgur.com/JcgXZAG.jpg
Even (especially?) my 4yo knows those are Brio trains, not trucks. However, trains have components called trucks! https://en.wikipedia.org/wiki/Steam_locomotive_components I'm unsure whether or not any of the wheel assemblies on these toy trains are considered trucks, so either the AI is extremely smart or slightly dumber than a 4yo.
If it's meaningful for some reason, then it works:
https://www.google.com/search?q=woman+without+makeup&tbm=isc...
If it's an user error (like a dumb query) it fails and it shouldn't be a surprise:
