A Brief Taxonomy of Hybrid Intelligence

Adversarial Dynamics in Centralized Versus Decentralized Intelligent Systems

Artificial intelligence (AI) is often used to predict human behavior, thus potentially posing limitations to individuals' and collectives' freedom to act. AI's most controversial and contested applications range from targeted advertisements to crime prevention, including the suppression of civil disorder. Scholars and civil society watchdogs are discussing the oppressive dangers of AI being used by centralized institutions, like governments or private corporations. Some suggest that AI gives asymmetrical power to governments, compared to their citizens. On the other hand, civil protests often rely on distributed networks of activists without centralized leadership or planning. Civil protests create an adversarial tension between centralized and decentralized intelligence, opening the question of how distributed human networks can collectively adapt and outperform a hostile centralized AI trying to anticipate and control their activities. This paper leverages multi-agent reinforcement learning to simulate dynamics within a human-machine hybrid society. We ask how decentralized intelligent agents can collectively adapt when competing with a centralized predictive algorithm, wherein prediction involves suppressing coordination. In particular, we investigate an adversarial game between a collective of individual learners and a central predictive algorithm, each trained through deep Q-learning. We compare different predictive architectures and showcase conditions in which the adversarial nature of this dynamic pushes each intelligence to increase its behavioral complexity to outperform its counterpart. We further show that a shared predictive algorithm drives decentralized agents to align their behavior. This work sheds light on the totalitarian danger posed by AI and provides evidence that decentrally organized humans can overcome its risks by developing increasingly complex coordination strategies.

Augmenting perception: How artificial intelligence transforms sensory substitution

What happens when artificial sensors are coupled with the human senses? Using technology to extend the senses is an old human dream, on which sensory substitution and other augmentation technologies have already delivered. Laser tactile canes, corneal implants and magnetic belts can correct or extend what individuals could otherwise perceive. Here we show why accommodating intelligent sensory augmentation devices not just improves but also changes the way of thinking and classifying former sensory augmentation devices. We review the benefits in terms of signal processing and show why non-linear transformation is more than a mere improvement compared to classical linear transformation.