Generative artificial intelligence (AI) continues to transform consumer technology, adding new capabilities to existing digital tools. AI now provides features such as AI-generated summaries in search engines, productivity software enabling swift document creation, and AI-powered tools in social media and e-commerce for content and service discovery.
Many advanced AI systems, including chatbots and digital assistants, rely on foundation models. These models are trained on varied text, image, and audio collections sourced from the internet and public databases. This enables AI systems to handle diverse queries, create new content, and analyze documents beyond their initial data training.
However, foundation models in their generic form often struggle to provide personalized details for individual users. AI developers argue that personalization could enhance the utility and appeal of these technologies by tailoring support and information to individual needs. As AI tools aim to be more useful, developers foresee AI assistants capable of executing tasks on behalf of users, prompting discussions about balancing privacy with utility and maintaining human control and minimizing addictive tendencies.
The use of personal data to train foundation models powering AI tools is a topic of significant interest. This discussion extends to how generative AI tools utilize such data to offer personalized user experiences, raising policy-related questions.