I appreciate that you've given a pretty topline overview but in the 90/10 example, if that 10% can be characterised and/or clustered can the algorithm be optimised for both groups involved? Appreciate that that's not always possible and can lead to lots of engineering overhead - curious what your thoughts are though...
Yes, often it is possible to determine where the user belongs in that 90/10 setting, but it can take a lot of time in order to be 'pretty sure'. You need a lot of 'user interaction' in order to make that assessment.
The 90/10 rule can broadly apply to things like culture: certain Latino Americans speak/write very differently. A lot of 'le' and 'la' (gendered) in there as well as a whole different set of proper names and colloquialisms.
But it can take some time to really establish if someone is 'latino' from their writing.
Even harder: some people type more precisely, some people type more loosely. You can actually adjust the probability spectrum of a predictive keyboard to match someone's style. But get this: people's style changes all the time! I noticed that when I'm tired, I type like I'm drunk. Or if I'm busy etc.. So there's even variation in style that makes it difficult.
You can 'massively decreasing returns to complexity' in these domains.
Meaning that you can do 'pretty good' with some basic algorithms.
For the next 'bump in performance' you need some complex code.
After that - you really start to have 10x larger models, or crazy complex engineering just to move the needle.
It creates a completely different set of 'Product Management' rules. It's kind of fun, unless you're a struggling startup trying to figure this out on the fly :)
Usually, someone comes along with a new approach which changes the games.
As I understand it 'Neural Networks' i.e. 'Deep Learning' style AI has changed everything voice related quite a lot.
And also - different business approaches can change the game. Google has access to zillions of phrases for properly transcribed audio phrases. This is the 'golden asset' that can underpin a really great voice recognition engine. Google voice is even better than the old industry standard - Nuance - in many scenarios and my hunch is that it's the size of their training data that has given them an edge - at least that.
Yeah, I was thinking the same thing. If you can tell which group a particular user belongs to then you can train two models and optimize them independently. Then you just select the most appropriate model for the user.
Practically this almost never works out. The 10% cluster is using a very small dataset and will produce inferior results. If you train a model based on only 10 people, you're prone to overfit that small sample
