Fine tuning walkthrough alone makes it worth bookmarking.
MACHINE LEARNING
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BERT CPU Model Powers Smart Home Interface with Function Calling
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It's super fun to use a BERT-sized model that can run on a CPU and power a smart home interface. Also, we ranted a bit about function calling templates with a comparison between JSON and Python. Hope you enjoy it! 🙂
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Claude’s performance linked to XML-heavy training data
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Had no idea Claude was trained on XML heavy content. Makes total sense now why this works so well.
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LLMs Learn In Context via Nearest Neighbor on Learned Manifold
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It's no mystery why LLMs can learn in context. They're just doing nearest neighbor on the manifold learned by pretraining. See: https://
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LLMs Learn In Context Through Pretraining Manifold Nearest Neighbor
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It's no mystery why LLMs can learn in context. They're just doing nearest neighbor on the manifold learned by pretraining. See:
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NIPS 2027 Test of Time Award Selection Challenge Approaches
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Picking the NIPS-2027 Test of Time Award paper will be really hard.
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Prompt injection risk everywhere for AI systems
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There are prompt injections everywhere for those with AIs to see
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Chart Shows RL Results Are Not Impressive
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not a greaaaat chart to say "RL is really working" haha
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Halting Problem and Fractal Boundaries in Algorithm Analysis
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The halting problem refers to the difficulty to determine whether an algorithm leads into an infinite loop (for all algorithms), whereas the fractal demonstrates a function where that boundary can always be discovered with arbitrary precision.