How Deep Learning is Expected to Develop in 2017

We have seen other great developments such as with image recognition, where we can one day expect to see computers that will be able to read X-ray, MRI and CT scans more efficiently than radiologists, enabling the quicker diagnosis of cancer. This is just one example of how the progress of deep learning is rapidly advancing and impacting the world we live in, from the way we shop to predicting energy sources to shaping modes of transport. We asked some of our influential speakers, who will be presenting at our deep learning summits this year, for their predictions for deep learning in 2017. In 2017, we will probably see further rapid exploration of applications of current deep learning techniques, as well as further theoretical advances, improving robustness and sample efficiency. We will also see various fun new applications of deep learning to image and voice resynthesis.