Learning an instrument is an hard task, which takes time and effort.
It is exceedingly easy to manipulate your emotions, because as a teacher we are controlling the rewards.
We can monitor your state of mind, your personality by the way you play.
Because of all this, our source code is shared and can be run locally.
Ads about instruments, or music lessons will have a high CTR (because of the time spent on page), and be valuable (because of the context).
For the time being, GistNoesis will rather take donations and spend time helping you to learn music than work on maximizing the cash value I can get out of your kids.
As always with GistNoesis projects, once the grace period is over, if the project hasn't reached its objectives, it will disappear and the technology behind will be put towards more fruitful objectives.
This art project is there to highlight that in AI, the biggest challenge isn't the technical side but the alignement of incentives.
The technology behind Wisteria is quite generic, and can be applied to a wide range of domain. You can monitor your sleep, by detecting your respirations. You can monitor the local fauna, by listening and identifying birds. You can monitor suspicious noises in car engines. You can do speech-recognition. You can do voice identification. You can build the future of offline advertising : Every morning you task humans to speak the right things to other humans, and have Wisteria validate the task completion and reward accordingly.
I don't see the same result as your video :
Even though we randomize the microphone frequency response, it's likely that your microphone or recording settings are different than what the bot was trained on. Try a different microphone, or come back soon when we have better models.
Your piano might be tuned differently, come back soon we have solutions almost ready for this problem.
You are using a mobile device and the result is garbage then it's probably due to a combination of microphone and 16-bits precision computations. So come back soon, or try a different device.
It lags :
Close some other tasks to liberate memory, and stop other cpu/gpu intensive tasks. Memory usage shouldn't grow over time. It usually needs some warm-up period after the loading of a new network to reach stable processing regime. We might drop audio frames to stay in sync with real-time. Use smaller and faster networks or a better computer.
I'm not impressed by the performance :
We have built the infrastructure and use the simplest models. We are heavily constrained on the computation side. We will stay behind (~one year) last research papers : our original know-how won't be published until equivalent ideas are, because certain ideas are widely applicable and shouldn't be wasted.
I'm impressed by the performance :
Don't forget to check the Wisteria GistNoesis github page for more technical details.You can find the donate button there, as this page doesn't load external resources.You can contact us at email@example.com Thanks :)