How image search works at Dropbox // By Thomas Berg • May 11, 2021
Photos are among the most common types of files in Dropbox, but searching for them by filename is even less productive than it is for text-based files. When you re looking for that photo from a picnic a few years ago, you surely don t remember that the filename set by your camera was
2017-07-04 12.37.54.jpg.
Instead, you look at individual photos, or thumbnails of them, and try to identify objects or aspects that match what you’re searching for whether that’s to recover a photo you’ve stored, or perhaps discover the perfect shot for a new campaign in your company’s archives. Wouldn’t it be great if Dropbox could pore through all those images for you instead, and call out those which best match a few descriptive words that you dictated? That’s pretty much what our image search does.
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Machine learning faces its toughest challenge yet: Discerning donuts from bagels
Machine learning may border on magic, and provide many of the biggest technical benefits we ve enjoyed in the last decade, but it has plenty of weak spots. One of Google s biggest concerns is that models are often trained using example data that s too easy to interpret, making them unprepared for the greater ambiguity of the real world. Case in point: Telling a donut from a bagel.
It s an easy mistake to make, they share so many characteristics: Both are round, they ve got a hole in them, and sometimes a visible texture on the top. It may even be something you or I could have difficulty recognizing the difference between in the right circumstance, but it doesn t actually end up being that much of an issue for us, often thanks to context.