There are many here who only train indoors and on TR. 
They are sitting on over 100 million rides. I would argue that’s as diverse as it gets.
If you mark the right category it shouldn’t be an issue.
As a fellow triathlete I would argue you should do that. You won’t get more bang for your time which leaves you with more time for the swim and run.
The majority for it will be useful and likely also used. In terms of setting it up you start with a small sample and go from there.
Subjective feedback is helpful though not required. They could simply pick a subset of athletes and track their career. The context provided will help to detect patterns. Like when athletes fail workouts and how they fail (as discussed in the podcast).
We did the same thing with our branch network. AI and ML ultimately enabled us to sharpen our product profile which lead to a significant increase in revenue and customer satisfaction. We did it without subjective feedback.
100 Mio. should be enough to derive those subsets.
In terms of data sources you are right. Though the power and heart rate data along with workout compliance is already a lot. Sleep, HRV, RHR, steps, stress, fatigue, weather, weight, yada yada yada would also be super useful. I am sure they will find a way to consider that when moving forward. The subjective feedback could be a good proxy in the meantime.
I think you’ve slightly missed my point here. I’m saying lots of people use the platform in different ways. If the closed beta group consists of just one type of TR user, that being the indoor and TR only user (I think it’s actually more diverse than this) then you’re only getting a narrow viewpoint as feedback. Hence the post ride survey in the announcement doesn’t have a “finished ride elsewhere” option (I know that’s a bit ridiculous but I think the points still stands).
I agree with @Pasque, however, the closed beta is probably for a more technical stand point bugs etc. and a more open diverse beta group will be included at some stage. And a staggered roll out of features makes sense. If you ride indoors on TR only then the new features might be useful to you sooner, but as I do not fit into that category I would need to wait until they are able to analyse all rides to feed in to the new system, otherwise I would be concerned that the AT system wouldn’t be learning correctly as my training data would have blind spots that it’s not taking into account.
To be fair to them they have said they are going to address this I’m just stating that I don’t think it’ll worthwhile for me and many others until they do.
I just don’t enjoy 3+ hours on a turbo so I’m not going to do it. I’d rather put a jacket on and ride for 4+ hours. Everyone’s different and that’s a good thing and exactly what AT is trying to address.
TSS and IF are simple mathematical formulas. It might be useful to take a step back and realize how much experience you are using to derive:
Yes to someone who is familiar with TSS and IF. But the fact remains that these measures describe neither the difficulty nor the training load of a ride, and they do nothing to describe the targeted energy system.
I disagree with this but we are all just speculating until we get our hands on the goods 
That could be the case.
I get that but what influence would it have on AI and ML. As long as you don’t categorize the early end due to failure it shouldn’t affect the magic behind the scenes. They sure will offer a time-crunched or other option to select.
Yup.
I get that but they could still release it anyway. You could simply ignore the AT bit while others could already benefit from it. Delaying the release until it delivers value for everybody seems like a waste to me.
That’s all good. Though it would still be beneficial. 
Haha, yeah I think we’re pretty much on the same page now. Enough to close the debate anyway 
. Apologies, I see how my statement reads, I’m just saying you can’t force the new platform on people if all rides aren’t accounted for. By all mean fire on and get it out before that though as long as the legacy option is still available, I think it will be useful for the TR only athletes.
You can keep your super long turbo rides though!
Here is one question I have for the smart people here in regards to the inclusion of outdoor rides: Do you guys think weather conditions could “muddy the waters”, so to speak, for the ML? Because in the datasets there is essentially only heartrate and/or power (And I guess maybe temperature, depending on the head unit). Presumably performance is quite impacted by weather conditions (it for sure feels that way), so I wonder how much that impacts the ability to implement outdoor rides into the system?
Guess we always were. 
Meh, was about to invite you for a group workout. Bandeira preferably.
Needs a long answer, but the more data variability the harder it may be to fit a model and produce useful outputs (training guidance). If a riders data is all over the map, it will be hard to tell what is going on. You are perhaps describing a rare ride? It would perhaps be easiest to exclude that data and not use outliers or rare events to guide training plan modifications.
By analogy, say I have a race and have an amazing day. Produce a 20 min power PR. Am very happy with that performance, but I don’t immediately take 95% of that number and call it my new FTP. It’s an outlier ride not representative of training conditions.
Perhaps a different question is how much variability can the current model adapt to? If it works great for a homogeneous indoor training setting (meaning well controlled environment) that is a great step. Huge accomplishment. The next questions are how much and what type of data and environment variability can the model tolerate without breaking? Or rather, how much variability can it take and still be valid and useful?
TR should be able to produce guidance for what good data input looks like. Riders wanting to use the early iterations should probably stick to those guidelines. TR can automate some aspects of data cleaning. Part of asking the rider questions post-ride is to help with that data cleaning or data weighting process.
As the algorithm or model evolves, the applicable use cases and acceptable data inputs will (may) expand. Without throwing shade, that’s the important GIGO discussion.
First step of a journey - its going to be very interesting.
You don’t need to test every duration every month. If you have some hard group rides or races that is enough to keep the short stuff ‘tight enough’.
Everything you listed should be accounted for today with AT.
But of course, if someone wants to do what they want to do, then we can’t stop them.
A far as other people doing “AI Training” I like to think of the cooking analogy.
Machine learning output is a combination of data + data engineering.
Just like a good meal is a combination of good ingredients and a good chef.
Data = Ingrendients
Chef = data engineering
You need both to have a great outcome. You can’t simply say “Well, this restaurant cooks food too! It’s already been done!” and expect it to be the same.
What if you also told us what your body was telling you? 
The goal is to beat a coach in terms of picking workouts.
We’ve already seen this on the closed beta. A coach had a “breakthrough” workout assigned to an athlete. They showed the progression data to the coach and they adjusted the workout to what AT suggested.
Coaches will still be very valuable for accountability, skills, racing tactics, and a sounding board. I don’t see them going away but I see them being used in tandem with AT.
This already happens with the progression system. Except instead of changing your FTP it puts you in a different point in your progression.
This is happening in our internal system with FTP predictions. I suspect we’ll run future models to optimize certain levels in specific workout zones. This will automatically take into account repeatability which is awesome.
This is what the survey data post-workout is for. If you stop early we get to know why you stopped. This is also why we can’t have the system tell if you’re overtraining at the moment because we can’t tell why you stopped a workout.
I think the real question is; is it better than what we have today? And then is next month better than last month?
For TR employees, the answer is it’s way better than what we have today. And I know it will continue to be improved.
It’s not a binary “Yes” or “No”.
We just adjust your progression level so you get the correct workouts afterwards.
We like this approach because your levels during a phase will also change a bunch. This makes more sense compared to constantly changing your FTP.
You can think of FTP more as “Ramp Test Result”.
CTL and ATL so far haven’t been as useful as other things in our ML model.
It’s because TSS doesn’t tell a big enough picture. IE 4x10 min at threshold is the same as 1x40 in terms of TSS but are very different workouts.
There are orders of magnitude more data for people not directly or not following plans at all.
Very rigid plan compliance over a season is extremely rare. And half of our users don’t follow a plan at all. Then we have all of the outside data plus people leaving and rejoining TR seasonly.