“That’s the biggest risk I see in the future of AI: the collection of information by a small number of companies through proprietary systems.”
This is a national security risk for states. There is a risk of dependency for investment managers and firms. When research and decision support processes are mediated through a limited variety of proprietary platforms, trust, resilience, data confidentiality and bargaining power weaken over time.
LeCun identified “federated learning” as a partial mitigating measure. In such systems, centralized models don’t require viewing the underlying data for training, but as an alternative depend on exchanged model parameters.
In principle, this permits a resulting model to “…perform as if it had been trained on the entire data set…without the data ever leaving (your domain).”
However, this isn’t a straightforward solution. Federated learning requires a brand new kind of setup with trusted orchestration between parties and central models in addition to a secure cloud infrastructure at a national or regional level. It reduces data sovereignty risk, but doesn’t eliminate the necessity for sovereign cloud capability, reliable energy supplies, or sustainable capital investments.
