Empowered by the rapid advancement of digital technologies, including Big Data, artificial intelligence (AI), and virtual reality, human society has transformed from the era of information to the era of digital intelligence. Unlike previous social formations, the digital-intelligent society has disrupted many long-held consensus norms and introduced numerous difficult challenges. To cultivate adaptive talents with general literacy of digital intelligence and specific professional competences, psychology, as one of the foundations of social sciences, must launch a revolution in future-oriented education. In higher education, the two principal components, defined by their nature and objective, are knowledge-oriented and research-oriented teaching. The former is designed to provide an introduction to the fundamental principles and basic knowledge of psychology for freshmen and sophomores, while the latter is intended to equip junior and senior undergraduates with the skills necessary for conducting scientific research. First, it is both possible and necessary to integrate AI throughout the processes of knowledge-oriented teaching. In this article, we propose a “loop model” to demonstrate the applications of AI in the knowledge-oriented phase. Furthermore, to provide a reference criterion for nurturing innovative and research-oriented students, we present a theoretical framework of “chimeric research” to provide a comprehensive overview of psychology research in the era of AI. In conclusion, psychology education needs to be aligned with the demands of modern society and embrace digital intelligence in both knowledge- and research-oriented teaching phases.

The Ministry of Education of the People’s Republic of China has proposed the deep integration of digital intelligence (DI) technologies into the higher education system to achieve a fundamental transformation. In response to the global imperative for digital transformation in higher education, the research investigates how Wuhan University systematically implements DI technologies across teaching, management, and service to cultivate innovative talents. With a focus on talent development, Wuhan University has built an integrated teaching platform and developed a DI education evaluation system. The research offers practical insights for higher education institutions navigating digital transitions and advancing global DI education practices. By fully integrating DI technologies and concepts into all aspects of teaching, management, and service, the reform aims to create a new synergy between DI technologies and the higher education system. This integration enhances the university’s abilities to seize opportunities and meet challenges in the DI era, thereby providing comprehensive support for cultivating top innovative talents.

Researchers have discovered how an ion channel in the brain’s neurons has a kind of ‘molecular memory’, which contributes to the formation and preservation of lifelong memories. The researchers have identified a specific part of the ion channel at which new drugs for certain genetic diseases could be targeted. The study, led from Linköping University in Sweden, has been published in Nature Communications.

Scientists have mapped the neural mechanisms that allow organisms to generalize learning from past experiences to new situations. The comprehensive review traces these pathways from hippocampus to cortex across rodents, primates, and humans, offering new insights into how different brain regions cooperate to create flexible thinking.

A brand-new photonic accelerator is proposed to achieve efficient and accurate decision-making machine for reinforcement learning, which can assist advanced integrated circuits in enhancing operation speed and reducing energy consumption. The photonic accelerator is established based on a broadband nonlinear optoelectronic oscillator, whose operation bandwidth can easily reach up to tens of GHz. Hence, it is no longer governed by the Amdahl’s law and the Dennard Scaling at the post Moore’s era.

The information age is built on mathematics. From finding the best route between two points, over predicting the future load on a national power grid or tomorrow's weather, to identifying ideal treatment options for diseases, algorithms share a common structure: they take input data, process it through a series of calculations, and deliver an output. Powering the ongoing AI revolution are increasingly sophisticated algorithms, often composed of millions of lines of code. And the more steps a model goes through before presenting a solution, the costlier it is in the number of physical computing units, time, and energy required.

Optimizing these mathematical models is at the heart of the work of the Machine Learning and Data Science Unit (MLDS) at the Okinawa Institute of Science and Technology (OIST). Led by Professor Makoto Yamada, the unit strives to unlock the full potential of machine learning (ML) and improve efficiency, optimizing not just data science but also education and the scholarly output within the unit through a distributed hierarchy.