Abstract
The development of artificial intelligence (AI) in robotics has opened up new possibilities for advanced machine learning algorithms to enhance robots' intelligence and cognitive abilities. One of the key factors in achieving these capabilities is the memory capacity of the robots. This paper examines the potential for future GPT (Generative Pre-trained Transformer) memory in chips that can be installed in robots to enhance their intelligence. We explore the current state of the art in AI and robotics, discuss the importance of memory capacity for robot intelligence, and examine the potential of GPT memory in chips for improving the cognitive abilities of robots. Finally, we discuss the possible implications of these developments for the future of robotics and AI. This paper explores the potential of GPT memory, a type of transformer-based neural network, for use in memory systems installed in robots. We discuss the architecture of GPT memory, the current state of GPT technology, and its potential applications in the field of robotics. We also consider the challenges associated with implementing GPT memory in robotic systems, including power consumption and computational requirements
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