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Why the #EUAIAct might be bad for #AI #Research in the #EU and why we should care. A long š§µ 1/15
Disclaimer:
*Iām not a legal expert, but I work with #AI models
*Iām happy to discuss if you have some insights that I have missed
*I am part of #LAION
*All opinions are my own
Disclaimer:
*Iām not a legal expert, but I work with #AI models
*Iām happy to discuss if you have some insights that I have missed
*I am part of #LAION
*All opinions are my own
Here is the draft: 2/15
europarl.europa.eu/meetdocs/2014_ā¦
europarl.europa.eu/meetdocs/2014_ā¦
Problem: The #EUAIAct puts a regulation on providers of foundation models or General-purpose AI systems defined as
ā... trained on broad data at scale, is designed for generality of output, and can be adapted to a wide range of distinctive tasksā t.ly/D-ibR
3/15
ā... trained on broad data at scale, is designed for generality of output, and can be adapted to a wide range of distinctive tasksā t.ly/D-ibR
3/15
šIntroducing RoentGen, a generative vision-language foundation model based on #StableDiffusion, fine-tuned on a large chest x-ray and radiology report dataset, and controllable through text prompts!
@PierreChambon6 @Dr_ASChaudhari @curtlanglotz
š§µ#Radiology #AI #StanfordAIMI
@PierreChambon6 @Dr_ASChaudhari @curtlanglotz
š§µ#Radiology #AI #StanfordAIMI
#RoentGen is able to generate a wide variety of radiological chest x-ray (CXR) findings with fidelity and high level of detail. Of note, this is without being explicitly trained on class labels.
Built on previous work, #RoentGen is a fine-tuned latent diffusion model based on #StableDiffusion. Free-form medical text prompts are used to condition a denoising process, resulting in high-fidelity yet diverse CXR, improving on a typical limitation of GAN-based methods.
