What is artificial curiosity?
Curiosity AI, also referred to as artificial curiosity, is a form of artificial intelligence that aims to simulate human curiosity. Curiosity is a fundamental driver of self-directed learning in humans. When we encounter gaps in our knowledge, our curiosity is sparked, motivating us to seek out the missing information. By emulating the behavior of a curious human in algorithms, we can enhance the potential for self-directed machine learning, enabling AI systems to actively seek solutions to unfamiliar problems.
The incorporation of curiosity-related behaviors into algorithmic models can enable AI systems to behave more like humans in new situations. For instance, a curiosity AI system may prioritize exploration by reinforcing behaviors that lead to the discovery of new information about its environment. Behaviors that facilitate exploration can be rewarded, while those that hinder or limit exploration can be discouraged.
How AI can learn continuously?
Reinforcement learning (RL) is a technique used to motivate AI systems to exhibit desired behaviors while discouraging undesired ones. It operates by seeking observations and experiences that yield programmatic rewards, which serve as feedback signals informing the AI of progress towards its objectives. RL can be seen as a “carrot and stick” approach, using positive or negative feedback to train the AI.
RL is currently applied in various AI applications, including robotic autonomous arm programs capable of outperforming human world champions in video games. However, RL has inherent limitations. It heavily relies on substantial amounts of feedback to function optimally, struggling when there is limited feedback available. Curiosity AI aims to address this limitation by enabling AI systems to continue searching for solutions even in the absence of clear guidance or signals.
Applications of Curiosity AI
The utilization of Curiosity AI in industrial automation holds the potential for significant improvements. This is primarily due to its ability to effectively process intricate inputs while maintaining a high level of success, a crucial requirement in industrial settings. Unlike narrow AI systems, Curiosity AI excels at managing uncertainty, generalizing information, and providing explanations for complex data.
Industrial machines, in many cases, are not designed with usability as a primary consideration. However, by integrating Curiosity AI, these machines can gain newfound flexibility without the constant need for human intervention. Curiosity AI empowers them to adapt and respond autonomously to dynamic environments, contributing to enhanced efficiency and productivity in industrial automation processes.
References
Roza, F. (2020b) How can artificial intelligence become curious?, Medium. Available at: Link (Accessed: 14 July 2023).
SUN, C., QIAN, H. and MIAO, C. (no date) From Psychological Curiosity to Artificial Curiosity: Curiosity-Driven Learning in Artificial Intelligence Tasks [Preprint]. Available at: PDF (Accessed: 14 July 2023).