If it's technically possible then presumably it's a deliberate product choice to not have better search results for "shirt without stripes". And that seems entirely plausible.

Google is already by far the most widely used search engine, so they don't really need to innovate or improve the search product very much in order to attract and retain users. Presumably capturing more advertising spending from the companies paying for ads is a bigger priority.

Microsoft under Satya Nadella has been all about enterprise and cloud, and I doubt Bing is a strategic priority any more, so it's not surprising that they wouldn't put a lot of resources into making it better.

Amazon is a little surprising. You'd think they'd have a lot to gain from making it easier for people to find what they're looking for. But maybe less than perfect search results are deliberate? Maybe it's like how supermarkets put basic items in the back of the store and high-margin impulse buys in the front - so you have to walk past chocolates and chips if you want to buy a carton of milk.

If Amazon is deliberately nerfing search results then maybe Google would stand to benefit from having better shopping-related results - people would get frustrated trying to find a shirt without stripes on Amazon and just use Google instead, letting Google profit from advertising in the process. But maybe people selling shirts aren't willing to pay much for ads, so there isn't much money for Google to make by getting better at finding specific types of shirts.

I dunno if any of these conjectures are anywhere near accurate, but it's interesting to think about.

I feel like that's stretching the definition of NLP though. Technically you are processing natural language but it seems like you've found that doing essentially a keyword match but using certain keywords as more advanced filters rather than just search terms.

I expect most users genuinely looking for such a product would just search "plain shirt"

Yeah the examples would probably do better if they had a front layer model determining the general topic "clothing" then shipping the query off to the clothing and fashion specific model for details.

Lots of focus on a general purpose mono-model, but I think a collection of specialized subsystems is a better representation and would produce better results, faster.

My reaction to Siri is either amazement at how well it worked or disgust in its failure on seemingly simple queries.

Don't get ahead of yourself: it's SELECT with GROUP BY.

Not that but maybe someone can explain if the technology is "AI" why it still takes tens of thousands of hours to implement a project? Shouldn't we call it AI when the frameworks can be implemented with relative ease? My point is that the level of customization required to achieve AI, in my opinion, doesn't make it very intelligent.

One of the primary methods to join multiple learned models is a decision tree (or combination of decision trees making a decision forest), which can be simplified as a series of if statements/conditionals. So if you join a 'is it a shirt' model with a 'is it striped' model you get two sets of things, and with how big data approaches this it is something you can do quickly. As other people have pointed out the issue here is that the NLP of the actual search is not creating a negation of two sets, it is returning the intersect of sets 'is it a shirt', 'is it striped' and shrugging its shoulders and either intersecting it with 'things with the text without', or throwing up its hands entirely based on the context because it wasn't programmed to do something smarter.

Yes, each of which wraps a "Not Hotdog" app. Turns out that "Not Hotdog" is quite hard already.

Either that or vague network of trial and error generalizations (deep learning/gut learning).

The term has been bastardized. AI is now any app that uses if statements, so long as it's obscured to the user. So now "AGI" exists, because apparently the word "general" now means "intelligent", unlike "intelligence".

At some point in the future marketers will learn about AGI, and we'll have to make yet another term, maybe artificial general practical intelligence?