为什么这篇谷歌论文被称为「Attention is all you need」V2

Core Insights - The article discusses a groundbreaking research paper by Google titled "Nested Learning: The Illusion of Deep Learning Architectures," which is being referred to as "Attention is All You Need" V2, emphasizing a new perspective on AI's learning capabilities [1][5]. Group 1: AI Limitations - Current large language models (LLMs) suffer from a condition termed "digital amnesia," where they forget recently learned information shortly after it is taught [2][3]. - The industry has focused on making models deeper and larger, believing that increasing scale would lead to emergent memory capabilities, but this approach has significant limitations [3][4]. Group 2: Nested Learning Paradigm - The research introduces the concept of "nested learning," which posits that effective intelligent learning requires two orthogonal dimensions: depth (model layers and capacity) and frequency (the rhythm and speed of internal component updates) [9][10]. - The paper argues that mainstream optimizers, traditionally viewed as mere training engines, actually function as associative memory systems that continuously record gradient changes [6]. Group 3: HOPE Architecture - The new architecture proposed, named HOPE, features a continuous memory system with multiple MLP modules arranged like a spectrum, each updating at different frequencies [14]. - This architecture mimics the human brain's memory processes, allowing new knowledge to be integrated without causing systemic collapse or forgetting [17][16]. Group 4: Future Implications - The value of "nested learning" lies not in immediately replacing existing models like Transformers but in providing a new design logic and framework for AI development [18]. - The exploration of memory and learning processes is still in its early stages, suggesting that future AI advancements may require systems capable of learning and evolving rather than being static repositories of knowledge [18].