Understanding the design principle of reward functions is a substantial challenge both in artificial intelligence and neuroscience. Successful acquisition of a task usually requires not only rewards for goals, but also for intermediate states to promote effective exploration. This paper proposes a method for designing 'intrinsic' rewards of autonomous agents by combining constrained policy gradient reinforcement learning and embodied evolution. To validate the method, we use Cyber Rodent robots, in which collision avoidance, recharging from battery packs, and 'mating' by software reproduction are three major 'extrinsic' rewards. We show in hardware experiments that the robots can find appropriate 'intrinsic' rewards for the vision of battery packs and other robots to promote approach behaviors.
This perspective article shares the viewpoints of two long-standing patient safety advocates who have participated first-hand in the evolution of patient engagement in healthcare quality and safety. Their involvement is motivated by a rejection of the common cruelty of institutional betrayal that compounds harm when patient safety fails. The advocates have sought to understand how it can be that fractured trust spreads so predictably after harm, just when it most needs strengthening.
Web 3.0 represents the next significant evolution of the internet that embodies the underlying decentralized network architectures, distributed ledgers, and advanced AI capabilities. Though the technologies are maturing rapidly, considerable barriers exist to high-scale adoption.
Tool use and language are highly refined human abilities which may show neural commonalities due to their potential reciprocal interaction during evolution. Recent work provided evidence for shared neural resources between tool use and syntax. However, whether activity within the tool-use network also contributes to semantic neural representations of tool nouns remains untested.
Background: Laparoscopic cholecystectomy, being a prevalent abdominal surgical procedure, has transitioned through various innovative stages aimed at reducing the procedure's invasiveness. These stages encompass Single-Incision Laparoscopic Cholecystectomy (SILC), Mini Laparoscopic Cholecystectomy (MLC), Natural Orifice Transluminal Endoscopic Surgery (NOTES), and Robotic-Assisted Laparoscopic Cholecystectomy (RALC). The purpose of this review is to trace the evolution of minimally invasive cholecystectomy techniques, assess their status, and identify emerging trends and challenges in the field.
Mastering how to convey meanings using language is perhaps the main challenge facing any language learner. However, satisfactory accounts of how this is achieved, and even of what it is for a linguistic item to have meaning, are hard to come by. Nick Chater was one of the pioneers involved in the early development of one of the most successful methodologies within the cognitive science of language for discovering meaning: distributional semantics.
