Researchers from Tsinghua University, Graz University of Technology, and the National Research Council in Italy have unveiled a new artificial intelligence framework inspired by cognitive science and neuroscience, detailed in a paper published in Nature Machine Intelligence. The initiative, led by senior author Wolfgang Maass, proposes an alternative to the energy-intensive deep neural networks and large language models that currently dominate the field by replicating the brain's method of encoding information through cognitive maps. Modern AI systems require vast computational power and significant electricity, often running counter to sustainability goals, while the human brain generates complex intelligence using merely 20 watts. The proposed framework addresses this disparity by organizing data in cognitive maps, a structure observed in neural recordings where the brain encodes knowledge to enable rapid, adaptive problem-solving and intuition. Unlike traditional models that depend on extensive parameter tables, this brain-inspired architecture allows artificial neural networks to navigate unfamiliar tasks with flexibility and efficiency. The new model features a learning mechanism that operates locally, bypassing the need for resource-heavy training processes. Initial tests demonstrate that the system can plan adaptively and solve novel problems while maintaining higher interpretability than conventional black-box AI. The architecture is explicitly designed to integrate with next-generation hardware, including neuromorphic and in-memory computing chips. These devices process data directly within storage elements like memristor arrays, drastically reducing the energy consumption caused by transferring information between memory and processors. To accelerate practical application, Maass and his colleagues are collaborating with Intel and a U.S. startup to implement the algorithm on physical chips. The partnership aims to validate the system's performance in real-world settings, with future work targeting the demonstration of low-latency decision-making and automatic adaptability to new contingencies. Researchers also emphasize the framework's potential to enhance AI explainability by generating decisions grounded in concrete experiences, aligning with broader industry efforts to create transparent and biologically plausible intelligent systems.