There’s a reason for that: some of the most prominent AI breakthroughs in the past decade have relied on enormous data sets. Image classification made enormous strides in the 2010s thanks to the ...

In a previous paper, MIT researchers had introduced a technique to “distill” giant data sets into tiny ones, and as a proof of concept, they had compressed MNIST down to only 10 images.The ...

High-throughput imaging generates massive data sets that are difficult to quantitatively analyze by hand. Peng et al. describe customizable software for visualizing and working with multi-gigabyte ...

Researchers only extracted 94 direct matches and 109 perceptual near-matches out of 350,000 high-probability-of-memorization images they tested (a set of known duplicates in the 160 million-image ...

Using the fMRI data from the 1,050 unique faces, they trained the AI model to convert the brain imaging results into actual images. (It works like a more primitive version of DALL-E 2 or Stable ...

Ingrained: An Automated Framework for Fusing Atomic-Scale Image Simulations into Experiments Data and Software for “Ingrained: An Automated Framework for Fusing Atomic-Scale Image Simulations into ...