Artificial Intelligence (AI) has come a long way since its inception. Its ability to recognize images and predict patterns has made it an important tool in many fields. However, the latest development in AI technology might come as a shock to many: AI can now read minds! In this blog post, we will explore the recent research that shows how Stable Diffusion can reconstruct visual images from MRI scan data and its implications.
Stable Diffusion is a deep generative model that can achieve state-of-the-art performance in several image-related tasks. Researchers from the Graduate School of Frontier Biosciences, Osaka University, and CiNet, NICT, Japan, have used Stable Diffusion to reconstruct visual experiences from MRI data. This method eliminates the need to train and fine-tune complex AI models. Instead, simple linear models are trained to map fMRI signals of the lower and upper visual brain regions to individual Stable Diffusion components.
The researchers use fMRI images from the Natural Scenes Dataset (NSD) for their experiment and test whether they can use Stable Diffusion to reconstruct what subjects saw. They show that the combination of image and text decoding provides the most accurate reconstruction. There are differences in accuracy between subjects, but these correlate with the quality of the fMRI images.
The implications of this research are immense. Stable Diffusion has the potential to transform how we understand the human brain. It can be used to reconstruct visual experiences, which can help in the diagnosis and treatment of various neurological disorders. For example, it can be used to understand how a person perceives a particular object or image, which can help in designing treatments for conditions such as visual agnosia.
Furthermore, Stable Diffusion can be used to understand the biological processes that underlie AI models. The researchers are quantitatively interpreting the image transformations at different stages of diffusion, which can contribute to a better understanding of diffusion models from a biological perspective.
