Facebook Trains Computers to Learn More like Humans Do by Learning from Random Images

Facebook today announced an AI model called SEER, which stands for SElf-supERvised trained on a billion images that ostensibly achieves state-of-the-art results on a range of computer vision benchmarks. Unlike most computer vision models, which learn from labeled datasets, Facebook’s generates labels from data by exposing the relationships between the data’s parts — a step believed to be critical to someday achieving human-level intelligence.

After learning from these images, Seer correctly identified and categorized the dominant object in photos with an accuracy rate of 84.2%. Seer outperformed the best existing self-supervised systems by one percentage point, according to the study.

The future of AI lies in crafting systems that can make inferences from whatever information they’re given without relying on annotated datasets. Provided text, images, or another type of data, an AI system would ideally be able to recognize objects in a photo, interpret text, or perform any of the countless other tasks asked of it.