AI That Adapts to You, Privately
Understanding how Album-as-Device 2.0³ uses AI to enhance your experience while respecting your data.
Key AI Innovations
Federated RLHF Loop
Your device learns your musical preferences locally from your interactions (e.g., thumb taps). Only anonymized model improvements (gradients), not your personal data, are optionally shared to enhance the global AI model for everyone, creating a cycle of collective intelligence.
- Local LoRA adapter fine-tunes on-device.
- Opt-in sharing of encrypted, differentially private gradients.
- Listeners can earn micro-credits for contributing valuable gradients.
Differential Privacy
Before any model improvement is shared, carefully calibrated noise is added. This mathematical technique ensures that individual user contributions cannot be reverse-engineered from the aggregated data, protecting your specific interactions and preferences.
- Strong mathematical guarantees of privacy.
- Protects against inference attacks on the global model.
- Balances privacy with the utility of collective learning.
The 'Pleasure Prior'
This is the core principle of our Reinforcement Learning from Human Feedback (RLHF). The AI starts with a general understanding of music and then fine-tunes itself based on what *you* find engaging or pleasurable, as indicated by your interactions. It learns your unique taste.
- Aligns AI behavior with individual user satisfaction.
- Drives the Mind-Mix engine to create truly personalized variations.
- Moves beyond simple explicit ratings to understand nuanced preferences.
The Federated RLHF Learning Loop
This diagram illustrates how individual devices learn locally and can optionally contribute to a global model update in a privacy-preserving manner.
A Different Approach to AI
| Feature | Album-as-Device 2.0³ | Typical Streaming/Server AI |
|---|---|---|
| Data Locus | On-Device Learning | Centralized Server Processing |
| User Privacy | Differential Privacy, Opt-In Sharing | Data often aggregated, less user control |
| Algorithm Transparency | Potential for open firmware inspection | Opaque, 'black-box' algorithms |
| Personalization Source | Direct user interaction (Pleasure Prior) | Broad demographics, listening history |
| User Control | Opt-in for gradient sharing, local model | Limited control over data usage/AI |
| Risk of Homogenization | Lower, due to personalized local models | Higher, due to global algorithmic trends |