In the ever-evolving landscape of artificial intelligence, Ring Attention is a groundbreaking discovery has shattered the constraints that have long limited the capabilities of AI models.
“Ring Attention with Blockwise Transformers for Near-Infinite Context,” is a research paper published on the Cornell University website that promises to supercharge the AI model’s memory.
“Ring Attention,” is an algorithm that processes vast amounts of data with unprecedented ease.
Currently, state-of-the-art AI models, such as ChatGPT, are constrained by memory limitations, limiting their capacity to ingest and analyze data. While they can handle a few thousand words at best, larger models can extend their reach to approximately 75,000 words, or 100,000 tokens, as the industry refers to them.
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Ring Attention is a game-changer
However, the real game-changer is the “Ring Attention” method developed by a team of researchers, including Hao Liu, a UC Berkeley PhD student and part-time researcher at Google DeepMind. Collaborating with Databricks CTO Matei Zaharia and UC Berkeley professor Pieter Abbeel, the trio devised a solution to overcome the memory bottleneck that has plagued AI models for years.
