AI Weirdness: the strange side of machine learning
Janelle Shane

Janelle Shane

Total 840 Posts
Jukebox of weirdness

Jukebox of weirdness

Ever wish you could hear what it would have sounded like if jazz legend Ella Fitzgerald had done a cover of Baby Shark? [https://soundcloud.com/openai_audio/jazz-in-the-style-of-ella-fitzgerald] So classy, right? How did this happen? A couple of steps: 1. OpenAI trained a new neural net called Jukebox [https:
From Cat to Kitchen: a cautionary tale

From Cat to Kitchen: a cautionary tale

I’m not sure what was more unsettling: my recent failed attempt to train a neural net called StyleGAN2 to generate screenshots from The Great British Bakeoff [https://aiweirdness.com/post/613754259190808576/this-is-not-cozy-ai-attempts-the-great-british] , or my successful attempt to train StyleGAN2 to generate pictures of my cat [https://aiweirdness.com/post/
A weirdly broken AI

A weirdly broken AI

The other day someone told me about a program [https://deepai.org/machine-learning-model/text2img] that will generate scenes to match a text description. I’m always excited to test out algorithms like this because the task of “draw anything a human asks for” is so hard that even state-of-the-art results
Trained a neural net on my cat and regret everything

Trained a neural net on my cat and regret everything

Recently I had my first-ever experiment with training a neural net to generate images, when I trained StyleGAN2 to generate screenshots from The Great British Bakeoff [https://aiweirdness.com/post/613754259190808576/this-is-not-cozy-ai-attempts-the-great-british] . Although recognizable as the baking show, it was a highly distorted, nightmarish version - it would have helped
Nonexistent Easter Eggs

Nonexistent Easter Eggs

I’ve been experimenting with image-generating neural networks, which look at a bunch of images and via trial and error gradually learn to produce more like them. Or at least “like them” according to the neural net’s own interpretation of realistic, which is usually missing a lot. Last time
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