近日，全球领先的AI研究实验室谷歌DeepMind宣布推出两款新的基础模型——Hawk和Griffin。这两款模型代表了AI技术的一大飞跃，它们采用了全新的神经网络架构，旨在提高机器学习和自然语言处理的性能。
Hawk模型混合了多层感知器（MLP）和循环块，这种创新的混合架构使得模型在处理复杂数据集时表现出色的学习能力和推理能力。而Griffin模型则更进一步，它不仅融合了MLP与循环块，还引入了局部注意力机制，这使得模型在处理大规模数据时更加高效。
RG-LRU层是这两个模型的核心技术之一，它是一种门控线性循环层，能够在不牺牲性能的前提下简化模型的结构。研究者们通过这一层的设计，成功地构建了新的循环块，并以此为基础开发了Hawk和Griffin模型。
这些模型的推出，标志着AI技术在基础架构上的重大进展。DeepMind的研究者们相信，Hawk和Griffin模型将为未来的AI应用开辟新的可能性，从机器翻译到智能助手，再到医疗诊断，都有望借助这些模型实现性能的提升。
英文翻译内容：
Title: Google DeepMind Unveils New Foundation Models Hawk and Griffin
Keywords: AI Models; Neural Network Architecture; Baseline Models
News content:
In a recent announcement, Google DeepMind, a global leader in AI research, introduced two new baseline models – Hawk and Griffin. These models represent a significant leap forward in AI technology, featuring a novel neural network architecture designed to enhance the performance of machine learning and natural language processing.
The Hawk model combines multi-layer perceptrons (MLPs) with a new type of recurrent block, resulting in a hybrid architecture that exhibits superior learning and reasoning capabilities when handling complex datasets. Griffin, on the other hand, goes further by integrating MLPs with recurrent blocks and introducing local attention mechanisms, making the model more efficient when dealing with large-scale data.
At the core of these models is the RG-LRU layer, a type of gating linear recurrent layer that simplifies the model structure without compromising performance. Researchers at DeepMind have utilized this layer to develop the new recurrent block, which forms the basis for the Hawk and Griffin models.
The launch of these models marks a major advancement in the infrastructure of AI technology. DeepMind researchers believe that Hawk and Griffin will open new possibilities for future AI applications, ranging from machine translation and smart assistants to medical diagnosis, all of which stand to benefit from the improved performance of these models.
【来源】https://mp.weixin.qq.com/s/RtAZiEzjRWgqQw3yu3lvcg
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