161–170 of 291 posts
This happened to both Google Photos and Flickr too. Which makes it an inexcusable mistake to make in 2021 - how are you not testing for this? Google Photos in 2015: https://www.wired.com/story/when-it-comes-to-gorillas-google... Flickr in 2015: https://www.independent.co.uk/life-style/gadgets-and-tech/ne...
This reminds me of a favorite tweet from 2013: "Then Google Maps was like, 'turn right on Malcolm Ten Boulevard' and I knew there were no black engineers working there" -- https://twitter.com/alliebland/status/402990270402543616 Facebook, like a lot of tech companies, has long had problems with diversity in engineering. Here's an article from April that discusses specific incidents and the broader background: https:/…
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What’s this about soap dispensers?
FB had a soap dispenser that didn't recognize black people. https://gizmodo.com/why-cant-this-soap-dispenser-identify-da...
(And when you click "why" you get a picture of Arabic text, which can't be copy/pasted into translation software)
Shame on you for distributing false information, it's a good thing we have facebook protecting The Truth. /s
I think the negative reaction is reasonable. Clearly, if a human did this it would a problem, so why should it be acceptable for an automated system to do the same thing? The fact that it is unintentional doesn't negate the fact that it's an embarrassing mistake. On the other hand, imagine a world where these labels were applied by a massive team of humans instead of a deep learning algorithm. At Facebook's scale, wo…
That raises the question, is it embarrassing or an expected mistake to be learned from. Many things are mislabel many things are labelled properly but we never say AI must feel pride at the good labeling job why would we give emotions to an emotionless system?
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This isn't a problem with diversity. Everybody knows how to pronounce Malcom X. And it's not like just because a google engineer was black that he was like "oh, let's try and see if Malcom X is pronounced correctly because he's black and I'm black too". This only happens in white people's brain.
In what universe is "Ten" a more common pronunciation of "X" than "X"? You might have an argument for "II" or "III", but I'll be shocked if any street in USA is named after the tenth generation of really unimaginative namers.
Or do you think they had a team (on a completely different project or perhaps company) write a text to speech function that wasn't well suited for directions.
Streets have lots of numbers after all. People frequently have numbers in their name.
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This isn't a problem with diversity. Everybody knows how to pronounce Malcom X. And it's not like just because a google engineer was black that he was like "oh, let's try and see if Malcom X is pronounced correctly because he's black and I'm black too". This only happens in white people's brain.
In what universe is "Ten" a more common pronunciation of "X" than "X"? You might have an argument for "II" or "III", but I'll be shocked if any street in USA is named after the tenth generation of really unimaginative namers.
So to answer your question, this one. This universe.
And no, nobody "programmed this". I don't know how anyone of decent technical competence can think that. The workaround has likely been hardcoded in some way, if anything. How can you possibly think that someone at google decided it would be funny to program, on purpose, a wrong pronunciation of Malcolm X, and even more so SOMEHOW NOT GET CAUGHT.
By the way, there are an incredibly large amount of streets in the US alone, so no it would not be surprising. Here in Europe we have kings going up into the twenties of their name, and those ML models have to pronounce those too.
I just... I just fucking can't, with comments like yours, anymore.
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> The thing is that humans are generally excellent at these sorts of pattern recognition Humans with a lot of experience are. Would kids be? I once referred to firefighter as robots as a kid.
Our ability for pattern recognition w/ human faces developed over the course of many thousands of years. People in modern fire fighting gear weren't present through that process. A kid thinking a firefighter is a robot is not the same class of problem. Even kids are good at the type of tasks we're talking about.
https://en.wikipedia.org/wiki/Cross-race_effect
So at some level it breaks down for us too.
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I wonder why this was downvoted. It's an interesting hypothesis.
My guess is that the poster was making an assumption that a large part of facebook's images are bigoted content. I am neither agreeing or disagreeing. But apparently some people got a little emotional about the platform being associated with maybe having a heightened amount of bigot content.
In my experience there are a lot of bigoted things on Facebook. If these are serving as source data, and are sufficiently distinguished from other training material, it may well be user behavior the ML system would replicate.
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This reminds me of a favorite tweet from 2013: "Then Google Maps was like, 'turn right on Malcolm Ten Boulevard' and I knew there were no black engineers working there" -- https://twitter.com/alliebland/status/402990270402543616 Facebook, like a lot of tech companies, has long had problems with diversity in engineering. Here's an article from April that discusses specific incidents and the broader background: https:/…
This is a contrarian take that may get me downvoted and unfairly labeled, but I encourage critical thinking instead: I've struggled with people telling me that these FAANG companies have "diversity problems," as a person of color myself. A majority of software engineers are female and male immigrants from East Asia and South Asia. These population centers are some of the most diverse regions of the world. The enginee…
South Asia and SE Asia, maybe. But East Asia (NE China, Korea, Japan) has actually one of the most ethnically "pure" populations in the world.
Have we considered AI and ML as a general brain replacement is a failed idea? That we humans feel we are so smart we can recreate or exceed millions of year evolution of a human brain?
I'd never call AI a waste, it's not. But getting it to do human things just may be.
Even a child can tell the difference between a human of any color and an ape. How many billions have been spent trying, and failing, to exceed the bar of the thoughts of a human child?