谷歌DeepMind近日在其最新研究中推出了两款全新的基础模型——Hawk和Griffin，标志着人工智能领域的又一重大突破。这两款模型是基于研究团队提出的创新性RG-LRU层设计的，该层是一种独特的门控线性循环层，旨在替代传统的多查询注意力（MQA）机制。
RG-LRU层的引入为循环神经网络带来了新的活力，它优化了信息的处理和存储，提高了模型的效率和性能。基于这一新层，DeepMind构建了两个不同架构的模型。Hawk模型融合了MLP（多层感知机）和RG-LRU循环块，旨在平衡计算效率和复杂性，以适应各种任务需求。而Griffin模型则更进一步，不仅结合了MLP和循环块，还引入了局部注意力机制，这使得Griffin在处理复杂和细致的信息时能展现出更高的精确度。
这两款模型的诞生，预示着深度学习和自然语言处理技术的持续演进，为未来的人工智能应用提供了更强大的工具。谷歌DeepMind的这一创新，无疑将对学术界和工业界产生深远影响，推动人工智能在理解、学习和适应复杂信息流方面的能力达到新的高度。
英语如下：
**News Title:** “Google DeepMind Unveils Innovative Models Hawk and Griffin: Redefining the Foundations of Artificial Intelligence”
**Keywords:** DeepMind, Hawk, Griffin
**News Content:** Google’s DeepMind recently unveiled two groundbreaking foundational models, Hawk and Griffin, marking a significant advancement in the field of artificial intelligence. These models are built upon the research team’s innovative RG-LRU layer design, a distinctive gated linear recurrent unit that aims to replace conventional multi-query attention (MQA) mechanisms.
The introduction of the RG-LRU layer rejuvenates recurrent neural networks, optimizing information processing and storage, thus enhancing the efficiency and performance of the models. Based on this new layer, DeepMind has constructed models with distinct architectures. The Hawk model integrates MLPs (Multi-Layer Perceptrons) with RG-LRU recurrent blocks, striking a balance between computational efficiency and complexity to cater to various task requirements. On the other hand, the Griffin model pushes the boundaries further, combining MLPs and recurrent blocks alongside local attention mechanisms. This allows Griffin to exhibit higher precision when dealing with complex and intricate information.
The birth of these models signals the ongoing evolution of deep learning and natural language processing technologies, providing more powerful tools for future AI applications. Google DeepMind’s innovation is set to have a profound impact on both academia and industry, propelling AI’s capabilities in understanding, learning, and adapting to complex information flows to new heights.
【来源】https://mp.weixin.qq.com/s/RtAZiEzjRWgqQw3yu3lvcg
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