OpenAI與DeepMind的Scaling Laws之爭
["OpenAI和DeepMind在Scaling Laws研究中的觀點和方法不同","Scaling Laws能預測大模型在參數量、數據量和計算量變動時的損失變化","競爭將推動人工智能發展和人機共存的未來塑造","大語言模型的預訓練涉及模型大小、數據量、訓練成本的權衡博弈","Scaling Laws可以幫助優化設計決策","DeepMind提出模型大小和數據量應按相等比例擴展，OpenAI傾向選擇更大的模型","DeepMind開發了AlphaGo和AlphaFold，展示深度強化學習和神經網絡潛力","OpenAI開發了GPT系列模型，展示在生成式模型上的能力","研究結論表明影響模型性能的三個要素相互影響","DeepMind的Chinchilla模型優於之前的大語言模型","國內百川智能和明德大模型也在Scaling Laws研究中有所貢獻","DeepMind提出Levels of AGI分類方法，揭示人工智能不同發展階段"]
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