Understanding Neural Networks: Mechanistic Interpretability Explained
Explore how researchers decode neural networks with mechanistic interpretability. Learn how AI models recognize images and the importance of understanding AI decisions.
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Research
Shaping the Future of AI with Transformer History
Hyung Won Chung discusses the history and future of Transformer architectures, emphasizing the role of cheaper compute in AI advancements. Learn about encoder-decoder, encoder-only, and decoder-only models.
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Grokking Transformers: Achieving Near-Perfect Causal Reasoning
Explore the groundbreaking concept of grokking transformers and learn how they achieve near-perfect causal reasoning by identifying hierarchical structures in human sentences.
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New Discovery: LLMs Have a Performance Phase
Discover the phenomenon of ‘grokking’ in LLMs, where extended training leads to superior generalization and complex reasoning capabilities. Learn about the implications for AI training.
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Control Theory of LLM Prompting
Aman Bhargava and Cameron Witkowski discuss their groundbreaking paper on applying control theory to LLMs, exploring prompt engineering and collective intelligence.
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Omni-Modal AI Revolution introduced with GPT-4o
OpenAI’s GPT-4o leads the Omni-Modal AI Revolution, offering real-time, multilingual, and efficient human-computer communication.
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