Introduction
A team of interdisciplinary researchers has discovered a significant parallel
between memory mechanisms in advanced AI systems and the human brain, a
breakthrough in understanding and emulating human memory within AI.
AI’s Human-like Memory Processing
Researchers from the Centre for Cognition and Sociality and the Data Science Group at the Institute for Basic Science have drawn parallels between AI memory functions and the human brain’s hippocampus. This comparison sheds light on AI’s process of transforming short-term memories into long-term ones.
The Quest for Artificial General Intelligence (AGI)
Key players like OpenAI and Google DeepMind aim to achieve AGI, focusing on
replicating human cognition. The Transformer model plays a critical role in
this endeavour.
Memory Consolidation in AI
The research emphasizes the similarity between the brain’s learning process and AI models. It focuses on the NMDA receptor in the hippocampus and its equivalent mechanisms in AI, vital for learning and memory formation.
AI’s NMDA-like Mechanism
The study found that the Transformer model uses a gatekeeping process similar to the brain’s NMDA receptor, influencing memory consolidation in AI. Adjusting the Transformer to mimic the NMDA receptor improved memory retention in the AI model.
Implications for AI and Neuroscience
This research advances AI technology and our understanding of the human brain. It indicates the potential for creating high-performing, energy-efficient AI
systems comparable to the human brain.
Conclusion
This innovative research integrates brain-inspired elements into AI models, paving the way for sophisticated, human-like AI systems and offering insights into brain memory processes through AI technology.
Reference
Institute for Basic Science. “AI’s memory-forming mechanism found to be strikingly similar to that of the brain.” ScienceDaily, 18 Dec. 2023. https://lnkd.in/e65jKcc4.
About the writer
Alaba Bukola Ogungbite is a versatile and accomplished data scientist with a rich academic background, including an MSc in Applied Data Science, an MSc in Mathematics, and a BSc in Industrial Mathematics. Her expertise spans a broad range of disciplines, from data science research, where she engages in diverse fields including health, pharmaceuticals, marketing, psychology, and business, to specialized areas like optimization and operations research. Alaba is also proficient as a data engineer and cloud engineer, adding depth to her technical capabilities. She is open to collaboration and actively seeks opportunities for engaging and innovative research across various fields of study.