As sub-symbolic AI, like deep learning, continues to advance, its limitations in safety and reliability are becoming more apparent. Verification and stability are crucial in safety-critical domains such as humanoid robotics, which is rapidly evolving into a versatile tool for various applications. However, proving the correctness of AI-based self-learning algorithms is challenging due to their uncertain inferences and opaque decision-making processes.As sub-symbolic AI, like deep learning, continues to advance, its limitations in safety and reliability are becoming more apparent. Verification and stability are crucial in safety-critical domains such as humanoid robotics, which is rapidly evolving into a versatile tool for various applications. However, proving the correctness of AI-based self-learning algorithms is challenging due to their uncertain inferences and opaque decision-making processes.Robotics[#item_full_content]

By giving artificial intelligence simple associative learning rules based on the brain circuits that allow a sea slug to forage—and augmenting it with better episodic memory, like that of an octopus—scientists have built an AI that can navigate new environments, seek rewards, map landmarks and overcome obstacles.By giving artificial intelligence simple associative learning rules based on the brain circuits that allow a sea slug to forage—and augmenting it with better episodic memory, like that of an octopus—scientists have built an AI that can navigate new environments, seek rewards, map landmarks and overcome obstacles.Computer Sciences[#item_full_content]