Recently, there's been a surge of popularity for the AI image extender feature on the Chinese TikTok platform, Douyin. However, this new trend has led to some side-splittingly funny errors that have everyone in stitches.
First, let’s talk about this girl who simply wanted to try the new feature. The AI went haywire and thought she was part of a wooden fence! Imagine her surprise!
In another instance, this young lady thought it would be fun to give the AI a whirl—but it decided she should be a horse instead. To top it off, it even added a saddle!
Two girls, dressed adorably in their fluffy pajamas, were also victims of the AI's peculiar sense of humor. It mistook them for two dogs taking selfies.
A guy decided to use his selfie, and the AI went off the rails, turning him into part of a building.
Then there were these girls having a meeting, and the AI, for some reason, assumed they were sitting in wheelchairs.
In one heartwarming Chinese wedding photo, the AI decided things weren't sweet enough and Photoshopped in another foreign boyfriend for the bride!
One girl tried the filter and hilariously ended up as a bear. The laughter continued until I saw this...
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Q: What is Douyin? A: Douyin is the Chinese version of TikTok, where various fun and experimental filters and features are popular among users.
Q: What is the AI image extender feature? A: The AI image extender is a feature that uses artificial intelligence to extend or modify images, sometimes resulting in hilarious or unexpected results.
Q: Why did the AI make such funny mistakes? A: AI algorithms can sometimes misinterpret visual data, leading to funny or bizarre outcomes.
Q: Are these AI mishaps common? A: While not extremely common, these mishaps tend to stand out due to their humorous nature and are often shared widely on social media.
Q: How do users react to these AI errors? A: Most users find these errors amusing and share them to entertain others, making such content go viral.
Q: Can these AI features be improved? A: Yes, over time, AI algorithms can be improved with more data and better training, likely reducing the frequency of such errors.
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