Only eating with a fork. A full system would require more data than that. We tested on real people in real-world conditions who were not part of the training dataset. If someone ate in a different style we could add just a little bit of annotated training data for the eating style, run the toolchain overnight and the algorithm would be noticeably better for that person and everyone else. The reason why I’m so confident in our algorith was because ① it required very little data to do updates and ② I had lots of experience in the field which meant I knew exactly what quality level was and wasn’t acceptable to customers.
To update the code in response to user feedback we would have to push the new code. Building an update system was theoretically straightforward. It was a (theoretically) solved problem with little technical risk. But it was not a problem that we had personally built a toolchain for and the whole firmware update system involved more technical maintenance than I wanted to commit myself to.
Only eating with a fork. A full system would require more data than that. We tested on real people in real-world conditions who were not part of the training dataset. If someone ate in a different style we could add just a little bit of annotated training data for the eating style, run the toolchain overnight and the algorithm would be noticeably better for that person and everyone else. The reason why I’m so confident in our algorith was because ① it required very little data to do updates and ② I had lots of experience in the field which meant I knew exactly what quality level was and wasn’t acceptable to customers.
To update the code in response to user feedback we would have to push the new code. Building an update system was theoretically straightforward. It was a (theoretically) solved problem with little technical risk. But it was not a problem that we had personally built a toolchain for and the whole firmware update system involved more technical maintenance than I wanted to commit myself to.