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AI Memory Crisis: The Answer Was in Biology All Along
Why do AI systems still struggle to remember and generalize like humans do?
In this episode, we dive into one of AI's most pressing challenges: memory. While tech giants race to build longer context windows and external memory systems, researchers at Tsinghua University took a radically different approach—they looked at how biological brains actually form lasting, generalizable memories. Their discovery is striking: a 140-year-old psychology principle called the "spacing effect" works just as powerfully in artificial neural networks as it does in fruit flies and humans. By mimicking how biology spaces out learning and introduces controlled variation, they achieved significant improvements in AI generalization—without adding a single parameter.
Inspired by the work of Guanglong Sun, Ning Huang, Hongwei Yan, Liyuan Wang, and colleagues at Tsinghua University, this episode was created using Google's NotebookLM.
Read the original paper here: https://www.biorxiv.org/content/10.64898/2025.12.18.695340v1.full
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By Anlie Arnaudy, Daniel Herbera and Guillaume FournierWhy do AI systems still struggle to remember and generalize like humans do?
In this episode, we dive into one of AI's most pressing challenges: memory. While tech giants race to build longer context windows and external memory systems, researchers at Tsinghua University took a radically different approach—they looked at how biological brains actually form lasting, generalizable memories. Their discovery is striking: a 140-year-old psychology principle called the "spacing effect" works just as powerfully in artificial neural networks as it does in fruit flies and humans. By mimicking how biology spaces out learning and introduces controlled variation, they achieved significant improvements in AI generalization—without adding a single parameter.
Inspired by the work of Guanglong Sun, Ning Huang, Hongwei Yan, Liyuan Wang, and colleagues at Tsinghua University, this episode was created using Google's NotebookLM.
Read the original paper here: https://www.biorxiv.org/content/10.64898/2025.12.18.695340v1.full