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.
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ContinueAdded 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.
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ContinueAdded 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.
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ContinueAdded by William Vorhies on December 16, 2019 at 9:24am — 5 Comments
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.
A little reminiscence, or for those of you who are only recently data scientists, a little history lesson.
Our profession of…
ContinueAdded by William Vorhies on December 4, 2019 at 3:12pm — No Comments
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Posted 12 April 2021
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