Assessing Human Intelligence Augmentation Strategies Using Brain Machine Interfaces and Brain Organoids in the Era of AI Advancement

Abstract

The rapid advancement of Artificial Intelligence (AI) technologies, including the potential emergence of Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI), has raised concerns about AI surpassing human cognitive capabilities. To address this challenge, intelligence augmentation approaches, such as Brain Machine Interfaces (BMI) and Brain Organoid (BO) integration have been proposed. In this study, we compare three intelligence augmentation strategies, namely BMI, BO, and a hybrid approach combining both. These strategies are evaluated from three key perspectives that influence user decisions in selecting an augmentation method: information processing capacity, identity risk, and consent authenticity risk. First, we model these strategies and assess them across the three perspectives. The results reveal that while BO poses identity risks and BMI has limitations in consent authenticity capacity, the hybrid approach mitigates these weaknesses by striking a balance between the two. Second, we investigate how users might choose among these intelligence augmentation strategies in the context of evolving AI capabilities over time. As the result, we find that BMI augmentation alone is insufficient to compete with advanced AI, and while BO augmentation offers scalability, BO increases identity risks as the scale grows. Moreover, the hybrid approach provides a balanced solution by adapting to AI advancements. This study provides a novel framework for human capability augmentation in the era of advancing AI and serves as a guideline for adapting to AI development.