In cellular automata, simple rules create elaborate structures. Now researchers can start with the structures and reverse-engineer the rules.
A new study by MIT CSAIL researchers maps the challenges of AI in software development, identifying bottlenecks and highlighting research directions to move the field forward, aiming to allow humans to focus on high-level design while automating routine tasks.
"We present a systematic review of some of the popular machine learning based email spam filtering approaches."
"Our review covers survey of the important concepts, attempts, efficiency, and the research trend in spam filtering."
"Our review covers survey of the important concepts, attempts, efficiency, and the research trend in spam filtering."
This paper introduces Cross-Layer Attention (CLA), an extension of Multi-Query Attention (MQA) and Grouped-Query Attention (GQA) for reducing the size of the key-value cache in transformer-based autoregressive large language models (LLMs). The authors demonstrate that CLA can reduce the cache size by another 2x while maintaining nearly the same accuracy as unmodified MQA, enabling inference with longer sequence lengths and larger batch sizes.
