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Model: "cope"
Contextual Position Encoding (CoPE)
cope gemini-1.5-flash gemini-1.5-pro claude gpt-3 meta-ai-fair google-deepmind anthropic perplexity-ai langchain openai positional-encoding transformers counting copying language-modeling coding external-memory tool-use model-evaluation inference-speed model-benchmarking scaling research-synthesis jason-weston alexandr-wang karpathy arav-srinivas
Meta AI researcher Jason Weston introduced CoPE, a novel positional encoding method for transformers that incorporates context to create learnable gates, enabling improved handling of counting and copying tasks and better performance on language modeling and coding. The approach can potentially be extended with external memory for gate calculation. Google DeepMind released Gemini 1.5 Flash and Pro models optimized for fast inference. Anthropic announced general availability of tool use for Claude, enhancing its ability to orchestrate tools for complex tasks. Alexandr Wang launched SEAL Leaderboards for private, expert evaluations of frontier models. Karpathy reflected on the 4th anniversary of GPT-3, emphasizing scaling and practical improvements. Perplexity AI launched Perplexity Pages to convert research into visually appealing articles, described as an "AI Wikipedia" by Arav Srinivas.