Just watched the podcast #568. Thank you for publishing!
What are the inputs into the new machine learning model described as the 8D model in the podcast? Please define the primary 8 inputs into the “8D model”. Assuming I understood the podcast correctly.
I don’t think they meant there’s litterly 8 inputs. Jonathan said it’s like 2d vs 3d, and Nate jokingly said it’s 8d, implying far larger complexity difference, and they both laughed.
It was under the context of comparing workout levels, a static representation of the overall difficulty/progression for that workout, which is the same for everyone, to the workout recommendations, which factor in far more context such as: your level of fatigue, what type of rider you are, recent training history, etc.
This helps to explain discrepancies in the workout levels and what the AI recommends, and addresses beta tester’s comment about “workout levels being a random number generator”.
Hope this helps