FAIR turns five: What we’ve accomplished and where we’re headed

This post details FAIR's five-year progress in AI research, highlighting key contributions: 1. Memory Networks: Developed to address the limitation of neural networks in storing long-term information, enabling them to answer general knowledge questions and refer to previous statements. This evolved into StackRNN, bAbI datasets (now part of ParlAI), end-to-end memory networks, and key-value memory networks. 2. Self-supervised learning and Generative Models: Explored Generative Adversarial Networks (GANs) starting in 2015, publishing papers on DCGANs, LAPGANs, and AGDLs to demonstrate their effectiveness in generating realistic images and video. Introduced Wasserstein GAN (WGAN) in 2017 for more robust and stable adversarial training. 3. Text Classification: Developed fastText in 2016 for rapid text classification and word representation learning, assigning vectors to subword units for scalability and faster training. fastText is now used for text classification across 19 languages at Facebook and is a common baseline for word embedding systems.