Workshop Overview - The workshop focuses on building AI agents using Amazon's agent technologies [1] - Participants will gain hands-on experience in building sophisticated AI agents [1] - The workshop is 2-hour long [1] Technologies Highlighted - Amazon Nova Act is used for reliable web navigation [1] - Model Context Protocol (MCP) connects agents to external data sources and APIs [1] - Amazon Bedrock Agents orchestrates complex workflows [1] Skills Acquired - Participants will learn to build agents that can navigate the web like humans [1] - Participants will learn to perform complex multi-step tasks [1] - Participants will learn to leverage specialized tools through natural language commands [1]

Hi, I'm Philip and I'm the CEO at Wordware. Today I want to talk to you about what sucks about chatbased interfaces, how documents can actually solve those issues and how do they lead to um background agents that do tasks for you in the or in the background. So firstly, let's start with what are the problems with chatbased systems.When I interact with um cla or open AI, it all seems very affirmal. um I end up often creating workflows for myself using projects or just copy pasting stuff and in that way when ...

Dublin, May 14, 2025 (GLOBE NEWSWIRE) -- The "Artificial Intelligence (AI) in Clinical Trials - Market Insights, Competitive Landscape, and Market Forecast - 2032" has been added to ResearchAndMarkets.com's offering. The Artificial Intelligence (AI) in clinical trials market is projected to experience robust growth from USD 1.35 billion in 2024 to USD 3.33 billion by 2032, reflecting a compound annual growth rate (CAGR) of 12.04% from 2025 to 2032. This expansion is largely driven by the increasing global b ...

Group 1 - Preferred Networks is recognized as a "true AI" company due to its reliance on deep learning, NLP, and generative models, along with its self-developed models and AI frameworks [1][3][4] - The company has a strong product versatility, offering solutions across various sectors including industrial automation, healthcare, and education, with over 435 global patents [5][6] - Despite its initial ambitions for international expansion, Preferred Networks has reverted to a domestic focus, raising concerns for other Japanese tech firms considering overseas ventures [2][10] Group 2 - Preferred Networks was founded in 2014 and developed the deep learning framework Chainer, which was once positioned alongside TensorFlow and PyTorch [3][11] - The company has shifted its strategy to collaborate with major Japanese corporations like Toyota and Nissan, focusing on customized AI systems rather than pursuing a broader international presence [13][18] - The company has established a new subsidiary, Preferred Elements, aimed at foundational technology development, indicating a potential shift towards a more open approach [14][16] Group 3 - PKSHA Technology, another prominent Japanese AI firm, has shown strong profitability with significant revenue growth, serving various industries including retail and finance [24][25][26] - Unlike Preferred Networks, PKSHA retains ambitions for international collaboration, partnering with companies like Microsoft and Tencent [26] - The early establishment of AI companies in Japan, such as PKSHA and Preferred Networks, was driven by a combination of engineering talent and industry demand for automation [28][30] Group 4 - The Japanese AI industry is characterized by a closed-loop system where startups primarily serve large domestic corporations, limiting their growth potential and innovation [44][45] - The government and large companies emphasize project-based AI solutions, which diminishes the drive for exploratory or innovative AI developments [44][45] - Cultural factors contribute to the lack of ambition for developing universal AI platforms, contrasting with the more aggressive approaches seen in other countries [30][43]

近年来，Transformer 架构在自然语言处理领域取得了巨大成功，从机器翻译到文本生成，其强大的建模能力为语言理解与生成带来了前所未有的突破。 然而，随着模型规模的不断扩大和应用场景的日益复杂，传统 Transformer 架构逐渐暴露出缺陷，尤其是在处理长文本、关键信息检索以及对抗幻觉等任 务时，Transformer 常常因过度关注无关上下文而陷入困境，导致模型表现受限。 为攻克这一难题，来自微软和清华的研究团队提出了 DIFF Transformer ，一种基于差分注意力机制的创新基础模型架构。 其核心思想是通过计算两组 Softmax 注意力图的差值来放大对关键上下文的关注，同时消除注意力噪声干扰。DIFF Transformer 具备以下显著优势： 在语言建模任务中，DIFF Transformer 在模型大小、训练 token 数量等方面展现出了卓越的可扩展性，仅需约 65% 的模型规模或训练 token 数量即可 达到与传统 Transformer 相当的性能，大幅提升了语言模型通用表现。 在长文本建模、关键信息检索、数学推理、对抗幻觉、上下文学习、模型激活值量化等一系列任务中，DIFF T ...