Vitalik Buterin Tests AI-Driven Personalized Recommendations with Enhanced Privacy Measures

Ethereum co-creator Vitalik Buterin has revealed a personal experiment in using AI to provide personalized diet and exercise recommendations. He aimed to avoid leaking private information to the models, instead using a local model to orchestrate and powerful remote models via tool calls. Buterin employed a three-layer approach to privacy, including avoiding personally identifiable information, using zkAPI to prevent identity exposure through payment channels, and Tor to provide network and IP-level privacy. The experiment involved a skill file that taught the local model how to construct minimally data-revealing requests to remote models. Buterin noted that the main deficiencies in his approach include the limitations of Tor and the skill file's request construction strategies. He also pointed out the trade-off between careful data sharing and the potential benefits of AI assistance. The experiment worked, and Buterin received personalized recommendations that were improved using information from the frontier models. He emphasized the need for a comprehensive approach to privacy in AI applications. Buterin's vision for Ethereum remains unchanged, with him envisionsing it as a 'cryptographic world computer' in the years ahead. He noted that the Pectra hard fork might be Ethereum's last 'normal' fork, after which the platform will undergo significant changes, including recursive STARKs, automated formal verification, and highly optimized consensus algorithms.