correct, ml requires data, but you don't need to capture every possible position to do a good prediction
only a handful would suffice and let's be honest, how many presets do you really need (average guitarist probably uses 5?)
This is very different and more narrow use-case.
Also they have tone match like forever, you build signal chain close to your amp, then add tone match block, which applies ML to voice your digital signal chain close to recording.
Here is example: https://www.youtube.com/watch?v=hZnZ1nJODLo
Also in this example he didn't profile actual amp, but actual AC/DC recording, and result is very good I think.
> how many presets do you really need (average guitarist probably uses 5?)
But how you find this preset for your signal chain (guitar + speakers)? That's one of big points of frustration with kemper: one needs to go through hundreds profiles (not necessary good quality) to find one which will sound good with his signal chain.
With fractal: you take some basic preset, and change knobs to your tastes and goal as with real amp.
I believe you can already create your own impulse responses (from your personal amps).