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Recent advancements in AI technology have introduced new methods to enhance the performance of large language models (LLMs). Google AI has developed a machine learning method that allows transformer-based LLMs to handle infinitely long inputs. Additionally, researchers at GoogleDeepMind have created a 'Mixture of Depths' technique to improve the speed and efficiency of transformers. These innovations are part of a broader trend in AI research focusing on improving the scalability, efficiency, and application of LLMs in various tasks, including reasoning, summarization, and RAG (Retrieval Augmented Generation) applications. The rise of LLMs has also made vector embeddings and VectorDB immensely popular in AI applications.
The next generation of models seem to mostly target infinite context and adaptive compute per token. Basically, these two papers: Google: Mixture of Depths Google: Infini-Attention
In this issue: New research on appropriate reliance on generative AI; Power management opportunities for LLMs in the cloud; LLMLingua-2 improves task-agnostic prompt compression; Enhancing COMET to embrace under-resourced African languages. https://t.co/yvIIwmpI9t https://t.co/pnWeZK0XTB
🏎 @GoogleDeepMind researchers developed a Mixture of Depths technique to improving the speed and efficiency of transformers. Learn how they did it: https://t.co/cQTtE3HDLF #GenAI #GenerativeAI #ML #MachineLearning #AI #ArtificialIntelligence #LLM #DataEngineering #DataScience
