「blog」タグの記事が4件件あります

The monetization strategy for private AI is evolving. Do we focus on hosting secure re-training servers, or is the value in providing 'Private Models' as a service? Alternatively, perhaps a data-privacy-first exchange allows for anonymized datasets to be contributed back to the collective model in exchange for reduced costs.

Lots of new information to assimilate, this first-of-many post focuses on private agency related thoughts including putting the puzzle together for Private Agency for My House.

Soumith Chintala (co-creator of PyTorch) provided insights into the evolution of local inference and distributed training, which are foundational for home-based ML.

Ingredients to the bake Private Agency in the home?​

Ignorance is dangerous, so let's take a look at what are the known-knowns, known-unknowns, and unknown-unknowns.

The AI Engineering Summit was a definite eye-opener to the speed with which IT is transforming mundane "busy" work and lowering the startup cost for exploring new ideas.

Take aways for a private agent:​

• Do Agents need to be local to be private?
• Locally on a MacBook Pro (using frameworks like Ollama or llama.cpp)
• Hosted in a secure enclave on AWS / Azure / GCP using Trusted Execution Environments (TEEs)—hardware-isolated areas of a processor that ensure data and code are protected from the host operating system or cloud provider during computation.
• Or can some sort of formal proof be done to leave a multi-tenant agent in the cloud with data privacy? (e.g., exploring Zero-Knowledge Proofs or Fully Homomorphic Encryption)
• For either of the above, how to manage the balance of cost?
• How are we protecting state?
• Where are we storing state?