Artificial general intelligence (AGI), also known as superintelligence, is the ultimate goal of AI research. It aims to create autonomous systems capable of performing a wide range of tasks as humans do. However, the concept of AGI is still elusive, with critics arguing that current AI systems can never achieve general intelligence. They cite limitations such as a lack of sensory perception and grounding in the natural world.
One approach towards AGI is to dissect elements of cognition and develop AI systems that mimic them. This is where the emotion of “fear” comes into play, as fear is an evolutionarily potent tool for maintaining organism safety. The amygdala, the central structure governing fear in vertebrates, scans incoming sensory information for potential threats, triggering a cascade of physical and behavioral changes when a threat is detected, known as the fear response.
Incorporating fear into AI could potentially make robots more robust and adaptable, especially important for autonomous systems such as self-driving cars. In recent years, AI has significantly advanced, yet driverless cars often fail in safety and reliability. Using a fear-based mechanism might help AI systems handle unforeseen situations better than traditional reinforcement learning (RL) techniques.
Understanding how simpler organisms process fear could also be a step towards building robust AI systems. For example, studies on Drosophila melanogaster, the common fruit fly, reveal that these creatures display defensive behaviors that resemble fear responses in mammals, indicating that even simple creatures can exhibit the fundamental building blocks of emotional states.
One study at Nanyang Technological University in Singapore sought to improve the safety of driverless cars by developing a fear-neuro-inspired reinforcement learning (FNI-RL) framework. The model translates principles of fear circuitry into a computational model for threat-sensitive driving, allowing an autonomous vehicle to learn adaptive defensive strategies in real-time.
Bio-inspired AI, a paradigm that draws inspiration from human emotional responses, signals the potential for more intelligent and adaptive AI systems in various fields. This includes the development of robotic hands with “digital nociceptors” that mimic pain receptors, enabling swift reactions to potential damage.
Advancements in neuromorphic computing are also bringing the fusion of nature-inspired AI technology and emotional states like fear or curiosity closer to reality. As research progresses, we could see autonomous machines reacting to unpredictable environmental cues with distinct human emotions. The potential impacts of this evolution in AI are immense and wide-ranging.
