Researchers Uncover AI’s Human-Like Memory Formation
An interdisciplinary team has made an astonishing breakthrough in the field of artificial intelligence (AI) by discovering that AI models, specifically the Transformer, process memory in a manner that closely resembles the human brain’s hippocampus. This groundbreaking finding not only offers insights into human memory mechanisms but also has the potential to advance AI memory functions.
Memory consolidation, the process of transforming short-term memories into long-term ones, is a crucial aspect of human brain function. By studying memory processing in the hippocampus, researchers from the Center for Cognition and Sociality and the Data Science Group within the Institute for Basic Science (IBS) have identified striking similarities between AI memory consolidation and the human brain.
In the pursuit of Artificial General Intelligence (AGI), leading entities like OpenAI and Google DeepMind are racing to develop AI systems that replicate human-like intelligence. The Transformer model, a fundamental component of these systems, has now become the subject of extensive research aimed at furthering our understanding of its principles.
Central to the development of powerful AI systems is comprehending how they learn and remember information. In an innovative approach, the research team applied principles of human brain learning, particularly focusing on memory consolidation through the NMDA receptor in the hippocampus, to AI models.
The NMDA receptor acts as a metaphorical smart door in the brain, facilitating learning and memory formation. When a brain chemical called glutamate is present, the nerve cell is excited. However, a magnesium ion acts as a gatekeeper, blocking the door. Only when this ionic gatekeeper moves aside can substances flow into the cell, enabling memory formation. The researchers have found that the Transformer model incorporates a gatekeeping process similar to the brain’s NMDA receptor.
Beyond this revelation, the team explored whether the Transformer’s memory consolidation can be controlled by a mechanism akin to the NMDA receptor’s gating process. In the animal brain, low magnesium levels are known to weaken memory function. The researchers discovered that mimicking the gating action of the NMDA receptor in the Transformer model led to enhanced memory, similar to the impact of changing magnesium levels in the brain. This breakthrough suggests that established neuroscience knowledge can explain how AI models learn.
C. Justin LEE, a neuroscientist director at the institute, commented, This research makes a crucial step in advancing AI and neuroscience. It allows us to delve deeper into the brain’s operating principles and develop more advanced AI systems based on these insights.
CHA Meeyoung, a data scientist in the team and at KAIST, highlighted the significance of this development, stating, The human brain is remarkable in how it operates with minimal energy, unlike the large AI models that need immense resources. Our work opens up new possibilities for low-cost, high-performance AI systems that learn and remember information like humans.
This study stands out for its integration of brain-inspired nonlinearity into an AI construct, representing a significant milestone in simulating human-like memory consolidation. By bridging the gap between human cognitive mechanisms and AI design, not only can low-cost, high-performance AI systems be developed, but valuable insights into the workings of the brain can also be gleaned through AI models.
With its potential to revolutionize AI memory functions and deepen our understanding of memory mechanisms, this research has far-reaching implications. As the quest for AGI continues, this groundbreaking discovery will undoubtedly shape the future of artificial intelligence and propel us closer to unlocking the secrets of human memory formation.
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