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@martyf, this is a great call out. We need to update the article with a response to this because I think it’s important.
Short answer: our models will never stop learning and we (at least I) will never be satisfied with performance. We will keep learning and keep pushing the ball forward. Personally, I’d like to have a similar-level breakthrough this time next year. I love this stuff and I want that to shine through so strongly that it’s obvious to everyone who touches this platform.
I like to think about these things with the self-driving analogies. Ideally, you can train the car with a huge amount of data. With this, it should be able to drive in most conditions. As more and more cars running this software log hours, the system continues to learn, improve, and become more robust.
But that learning and improvement curve slows over time.
Unless the rules of the road drastically change, you’re going to end up with a very stable–robust–model of driving.
This chart illustrates the concept: https://i.ytimg.com/vi/ayiV7mH2_Qg/maxresdefault.jpg
Y-axis would be effectiveness. X-axis would be time.
The new model will continue to learn and we will absolutely allow it to continue to seek opportunities to improve based on new data patterns. However, I don’t expect it to change much (or at all) unless the “rules of the road” drastically change.
The new model is much farther to the right of the chart than the 3.5 model is or will ever be.
This is where the real accomplishment lies–we’re able to get this model to a point where it can drive on all sorts of roads in all sorts of places very effectively.
You may ask why our 3.5 and earlier models continue to learn and change. Why haven’t they reached this same degree of maturity? There are two reasons:
1. They are built upon a narrower set of data, which is mostly technical. No matter how much of this data there is, it probably just doesn’t paint the entire picture as well as our new, broader data set.
2. They are mathematically less capable. Creating the underlying framework for developing these systems and structuring the data sets is where the hard human work goes. We have just been able to use our experience to create an environment in which a more capable model could flourish.
With all of that said, I fully expect the crypto “rules of the road” to change. We will undoubtedly see regulation, technology, and institutional adoption changes that will upset the status quo. This is a dynamic environment.
Also, as I alluded to earlier, even though our model is robust and seems to work well in most any historical situation, there can always be a better model.