In a recent article, I explored the potential of embedded AI in browsers, specifically focusing on Google Chrome's integration of Gemini Nano and the broader implications of local AI models. I posited that this deep browser integration represents a fundamental shift, offering AI models unprecedented access and control within our applications and data compared to external foundation models like ChatGPT.
The reasoning is straightforward: models embedded within the browser operate on the user's behalf, bypassing the limitations imposed on external entities. Websites like Medium, for instance, often present different views to users and robots (like ChatGPT), restricting access even with browsing capabilities enabled.
A browser-based model, however, can interact with the website as the user, circumventing these restrictions. Crucially, this also means the model has access not only to the current tab but also potentially to user information, cookies, other tabs, and essentially all browsing activity, contingent on the browser's implementation. This level of access raises significant questions about data privacy and transparency.
Initial expectations might suggest that Chrome exclusively utilizes Gemini Nano locally. However, Google’s communication regarding the use of private versus public models is unfortunately opaque and inconsistent. This lack of clarity raises concerns from a privacy standpoint.
Google has publicly extolled the benefits of local models like Gemini Nano, emphasizing their privacy advantages:
Furthermore, Google has showcased various AI-powered features in Chrome, including a tab organization function, in a separate announcement:
- https://blog.google/products/chrome/google-chrome-generative-ai-features-january-2024/#help-me-write
Top comments (0)