AI year in review

What this post added

This post details advancements in AI learning through semi-supervised and unsupervised training, including new methods for training NMT models on unsupervised data and expanding automatic translation services to more languages. It also highlights the use of user-supplied hashtags for image recognition training and the development of omni-supervised learning through data distillation. The post further elaborates on accelerating the transition from AI research to production with the release of PyTorch 1.0, which integrates Caffe2 and ONNX for a streamlined AI development pipeline. New tools and platforms extending PyTorch's capabilities, such as QNNPACK, FBGEMM, PyText, and Horizon (an RL platform), are introduced. Additionally, Glow, a framework for hardware acceleration of ML, and the Big Basin v2 ML-optimized server design are discussed. The transition of Oculus Research to Facebook Reality Labs and new explorations in AI and AR/VR, including the DeepFocus project, are also mentioned.