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Interestingly, Google can handle these searches just fine... "birds without flight" "cars without wheels" "cats without tails" "dogs without hair" "intersections without lights" "poems without rhyme" "shirts without collars" (also "sleeves", "shoulders", "buttons", "logos", "pockets", and more)
Earlier quoted context omitted.
"there is no evidence of cancer" and "there is evidence of no cancer" are two different statements with different meaning, so it's more complex a task than just understanding the importance of "no" in a sentence. It's involves semantic analysis of the sentence. The paper I linked to below describes a technique they call "deep parsing." Check it out for more context.
While it was an interesting point, it doesn't seem completely applicable here. Presumably the classifications include context. "A shirt with no stripes" should be distinguishable from "a stripes with no shirt" in the this context.
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.
Or maybe there's just not a lot of AI in their main search product (for whatever reason). They seem to be pretty good in other areas (Translate, Cloud speech-to-text, Alexa etc.)
I'm actually not sure I expect this much from a search engine. Typically there is going to be a useful word to describe what you want without having to hope it can understand "no" or "without" (for example, without stripes -> "solid" or even "NOT striped" in many cases). Anyone with a programming background knows there is an art to forming useful search queries--it is an acquired skill. I'd personally much rather the…
That said, this seems like an obvious place for improvement where both groups can be made happy.
Earlier quoted context omitted.
"there is no evidence of cancer" and "there is evidence of no cancer" are two different statements with different meaning, so it's more complex a task than just understanding the importance of "no" in a sentence. It's involves semantic analysis of the sentence. The paper I linked to below describes a technique they call "deep parsing." Check it out for more context.
I tried to search for "cheese without holes" on Google and it yielded good results. I think the problem here is that the query is something people would rarely search.
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I couldn't quite believe your comment when I read it so I did a Google image search for "person" and the results weren't a lot better than you'd suggested. Mostly white men, a few white women, a very few black women, a handful of Asians, and multiple instances of Terry Crews. The net result of that Google search, combined with the "Shirt Without Stripes" repo, leaves me even more unimpressed with the capabilities of…
I think the skewing of results lessening your impressed-ness is the wrong takeaway. If anything, the AI is a more perfect mirror of the society it learned from than you expected. Perhaps the right way to look at it is that we are capable of producing things that we don't understand, that are more sophisticated than we realize.
- If I entered "person" I'd see a mix of images substantially similar to what I saw using google.co.uk up to and including Terry Crews, which was frankly a little weird, and otherwise mostly white
- If I entered "人", which Google Translate reliably informs me is Japanese for "person", I'd see a few white faces, but a substantial majority of Japanese people
So it seems possible that Google's trying to be smart in showing me images that reflect the ethnic makeup I might expect based on my language and location. I mean, it's doing a pretty imperfect job of it (men are overrepresented, for one) but viewed charitably it's possible that's what's going on.
Is the case for woke outrage against Google Image Search overstated? Possibly; possibly not. After these experiments I honestly don't feel like I have enough data to come to a conclusion either way, although it does seem like they may at least be trying to do a half decent job.
The point that the author is making, in a very understated way, is that all three companies have PR websites that breathlessly describe their advanced AI capabilities, yet they cannot understand a very simple query that young children can.
I disagree, "shirt without stripes" is an unusual word choice, not one that our ML models would be optimized for. Try "solid color shirt" and you'll see how much better the results are - at least on Google.
This problem is known as "attribution" - you have a "no" or "without" in the sentence, but you don't know where it belongs. One could (and one does) argue that the problem cannot be solved with statistical methods (ML), especially not in any domain where accuracy is required, such as medical recored analysis: "no evidence of cancer" and "evidence of no cancer" are very different things. Zooming out, the language fiel…
While this is indeed an example of the attribution problem, I'd argue that this particular query will never be solved. I don't search for a "shirt without stripes", I search for a "solid shirt, or a " hawai'ian shirt". I'd be curious to see how many sentences with attribution problems actually have other structural issues. If I want to write clearly and without ambiguity, I rewrite sentences that have these problems.…
What do you call a skyscraper like that if you want to refer to it? They exist, but you can't find them using that search term on Google.
I'm actually not sure I expect this much from a search engine. Typically there is going to be a useful word to describe what you want without having to hope it can understand "no" or "without" (for example, without stripes -> "solid" or even "NOT striped" in many cases). Anyone with a programming background knows there is an art to forming useful search queries--it is an acquired skill. I'd personally much rather the…
I think Google should design primarily for people who DON'T know a ton about crafting queries, even if it's at the expense of a much smaller number of folks who are experts. That said, this seems like an obvious place for improvement where both groups can be made happy.