Transformer as an Incomplete Cognitive Architecture: What It Captures Well and What It Misses (A11 Perspective)
The article discusses the transformer architecture's role in artificial intelligence and its limitations. While it excels in knowledge representation and comprehension, it lacks a persistent internal will and struggles with integrity in processing contradictions. The author proposes a hierarchical cognitive framework, Structure A11, to address these shortcomings.
- ▪The transformer architecture is foundational in modern AI, particularly for its self-attention mechanism.
- ▪It effectively models knowledge and comprehension but lacks a stable internal will and genuine living experience.
- ▪Structure A11 offers a hierarchical framework to enhance cognitive processing in AI by emphasizing integrity and the acknowledgment of contradictions.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3703831) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Алексей Гормен Posted on May 26 Transformer as an Incomplete Cognitive Architecture: What It Captures Well and What It Misses (A11 Perspective) #ai #architecture #llm #machinelearning Since its introduction, the transformer architecture has become the cornerstone of modern artificial intelligence. Its ability to model complex dependencies through self-attention has delivered impressive results across countless tasks.
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