An Artificial Intelligence Browser Architecture (AIBA) For Our Kind and Others: A Voice Name System Speech implementation with two warrants, Wake Neutrality and Value Preservation of Personally Identifiable Information
This work addresses privacy and data utility issues in AI-driven health monitoring for applications like COVID-19 and dementia, though it appears incremental as it builds on existing voice and browser technologies.
The authors tackled the limitations of current conversational AI systems, which lack wake neutrality and cannot fully utilize personally identifiable information (PII) due to ethical constraints, by proposing a voice browser-and-server architecture that enables wake neutrality and PII value preservation, successfully capturing over 200,000 COVID-19 cough samples.
Conversational commerce, first pioneered by Apple's Siri, is the first of may applications based on always-on artificial intelligence systems that decide on its own when to interact with the environment, potentially collecting 24x7 longitudinal training data that is often Personally Identifiable Information (PII). A large body of scholarly papers, on the order of a million according to a simple Google Scholar search, suggests that the treatment of many health conditions, including COVID-19 and dementia, can be vastly improved by this data if the dataset is large enough as it has happened in other domains (e.g. GPT3). In contrast, current dominant systems are closed garden solutions without wake neutrality and that can't fully exploit the PII data they have because of IRB and Cohues-type constraints. We present a voice browser-and-server architecture that aims to address these two limitations by offering wake neutrality and the possibility to handle PII aiming to maximize its value. We have implemented this browser for the collection of speech samples and have successfully demonstrated it can capture over 200.000 samples of COVID-19 coughs. The architecture we propose is designed so it can grow beyond our kind into other domains such as collecting sound samples from vehicles, video images from nature, ingestible robotics, multi-modal signals (EEG, EKG,...), or even interacting with other kinds such as dogs and cats.