Dual-use foundational models are AI constructs that have broad capabilities, making them applicable across a range of tasks, industries, and contexts. The term "dual-use" underscores the potential for these models to be employed in ways that can have significant positive impacts, such as advancing scientific research, enhancing medical diagnoses, or improving efficiency in various sectors. Simultaneously, there is a risk that these same models can be adapted for harmful purposes, including privacy violations, misinformation spread, and automated surveillance. The governance and ethical considerations around these models are complex, involving discussions on the responsible development, deployment, and regulation of AI technologies to prevent misuse while promoting innovation and public benefit.
Historical overview: The concept of dual-use technology is not new and has been discussed in various fields such as cybersecurity and biotechnology for many years. However, the specific application of this concept to foundational AI models has gained prominence with the rise of advanced machine learning techniques and large language models in the 21st century, particularly post-2010s, as these models have demonstrated remarkable versatility and power.
Key contributors: Given the broad nature of the topic, key contributors span a wide range of fields including computer science, ethics, policy, and law. Organizations like OpenAI, DeepMind, and various academic institutions play crucial roles in advancing the technology behind foundational models, while policy makers and ethical scholars contribute to the discussions on governance and ethical use.