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: There have been several stories over the last several months around the theme that AI is about to hit a wall. That the rapid improvements we’ve experienced and the benefits we’ve accrued can’t continue at the current pace. It’s worth taking a look at these arguments to see if we should be adjusting our plans and expectations.
Summary: A little history lesson about all the different names by which the field of data science has been called, and why, whatever you call it, it’s all the same thing.
Our profession of…Continue
Added by William Vorhies on December 4, 2019 at 3:12pm — No Comments