- June 03, 2022.
The DALL-E tool is a neural network “trained” by the research company OpenAI to generate realistic images and art from natural-language text. Developers have previously discovered that DALL-E is capable of plausibly combining unrelated concepts and creating anthropomorphized versions of animals and objects.
Giannis Daras, a PhD candidate in computer science, posted examples of what he called “AI’s own language” on Twitter this week.
“Apoploe vesrreaitais” means birds, Daras wrote on Twitter. “Contarra ccetnxniams luryca tanniounons,” meaning bugs or pests.
DALL-E 2 works in conjunction with CLIP, a computer vision system that OpenAI announced in 2021.
DALL-E creates illustrations, photos and renderings based on a simple description. For example, it can depict “a cat in a bow tie” or “a daikon in a tutu walking a dog.” CLIP identifies objects and classifies them based on a simple description.
For example, this is what the result created by the neural network for the query “Cool panda rolls on a skateboard in Santa Monica
For now, the new version is only accessible to a group of users via a waiting list. Users are prohibited from uploading or creating images that “may cause harm,” including anything involving hate symbols, nudity, obscene gestures or “conspiracies or events related to current geopolitical events.” They should also reveal the role of AI in the creation of images. Later, the neural network will be added to the API so it can be used by third-party developers.
Researchers using machine-learning artificial intelligence (AI) often don’t know exactly how their algorithms solve the problems they encounter. Likewise with DALLE-2: Researchers have so far failed to understand the processes going on inside this “black box.” Having uncovered them now, the researchers came to a surprising conclusion: the artificial intelligence has created its own language to build images.
The authors noticed that the DALLE-2 system, which creates images based on typed text, will return meaningless words under certain circumstances. For example, images with the word “airplane” often return some gibberish. At first it seemed to be a bug, but then scientists concluded that the sequence of letters in the returned words was not random.
To check this, scientists simply “fed” to the neural network the previously generated words. It turned out that these sets of letters do indeed have meaning for the program, and they mean the same thing as previously entered into the system. This could mean that the neural network is converting human words into its own words for some reason, so that it can more easily generate images from them.
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