Summary: Reinforcement Learning (RL) is going to be critical to achieving our AI/ML technology goals but it has several barriers to overcome. While reliability and a reduction in training data may be achievable within a year, the nature of RL as a ‘black box’ solution will bring scrutiny for its lack of transparency.
Added by William Vorhies on December 30, 2019 at 11:18am — No Comments
Summary: For all the hype around winning game play and self-driving cars, traditional Reinforcement Learning (RL) has yet to deliver as a reliable tool for ML applications. Here we explore the main drawbacks as well as an innovative approach to RL that dramatically reduces the training compute requirement and time to train.
Added by William Vorhies on December 23, 2019 at 7:30am — No Comments
Summary: This may be the golden age of deep learning but a lot can be learned by looking at where deep neural nets aren’t working yet. This can be a guide to calming the hype. It can also be a roadmap to future opportunities once these barriers are behind us.