So the figures are astronomical and there is a way to make sense of this stuff. And I'm going to use the acronym c, T Chips Data Talent. So those are the three pillars of AI development.And basically, you can kind of make sense of what all of these different AI firms are doing based on where they have weaknesses in those areas. So if you think about that, they have got the chips, they most definitely have got the data, but it's the talent where it's seeming like they're needing to pick up pace a little bit. ...

Core Insights - The hybrid cloud storage market is emerging and rapidly evolving due to the need for efficient data management and unification [2][4] - The market generated approximately $100 billion in 2024, with a projected compound annual growth rate (CAGR) of about 16% over the next six years [4] Industry Overview - Businesses are increasingly adopting hybrid cloud storage solutions to manage costs, ensure performance, and support sustainability goals [1] - A full solution is deemed necessary to support multimodal AI applications that utilize diverse datasets [3] Market Dynamics - Few vendors currently offer comprehensive solutions that support unified data across multiple storage formats and types [2] - The market opportunity is expected to grow significantly, especially with the acceleration of AI adoption and the development of large language models (LLMs) [4] Companies to Watch - Key players in the hybrid cloud storage market include Cloudian, CTERA, Hammerspace, LucidLink, Nasuni, NetApp, Panzura, and Peer Software [7]

Core Insights - Nvidia has shown remarkable growth, with a 740% increase over the past three years, significantly outperforming the market [1] - The stock is considered a strong investment opportunity, with potential for further growth even if it does not replicate past performance [2] Performance Overview - Nvidia's sales surged by 69% year-over-year in the first quarter of fiscal 2026, with earnings per share (EPS) at $0.81, despite a $0.15 impact from U.S. policy affecting shipments to China [7] - The company has a dominant position in the AI chip market, holding an estimated 70% to 95% market share, which positions it well for future growth [11] Technological Advancements - The demand for powerful chips is driven by advancements in generative AI, with Nvidia continuously developing new technologies, including the upcoming Rubin model in 2026 [10] - As large language models (LLMs) evolve, they are moving towards reasoning capabilities, which enhances their effectiveness and increases the need for Nvidia's chips [9] Market Valuation - Nvidia's stock is currently trading at 25 times next year's earnings estimates, suggesting room for price appreciation without becoming excessively expensive [12] - The stock trades at a premium valuation of 23 times trailing-12-month sales, indicating high expectations for growth [13] Growth Potential - To double its stock price over the next five years, Nvidia would need to achieve a compound annual growth rate (CAGR) of 15%, which is deemed achievable given its current performance and market opportunities [14] - There is a strong possibility that Nvidia could double its stock price even sooner than five years, considering its growth trajectory and market position [15]

Transformer vs. Mixture of Experts in LLMs, clearly explained (with visuals): ...

It was this week that WWE DC left us hanging on theory and you bring us some clarity. That's exactly right. So they did not talk about Siri features pretty much at all at WDC, other than to say they're still delayed and they'll be arriving in the coming year.So I looked into it and the new launch time frame is likely going to be as part of what will be known as iOS 26.4% and that it's currently scheduled for the spring time next year. So around March, April. And this will deliver the delayed Siri features.T ...

机器之心报道 机器之心编辑部 开源社区的人应该对 vLLM 不陌生，它是一个由加州大学伯克利分校团队开发的高性能、开源 LLM 推理和服务引擎，核心目标是提升 LLM 的推理速度（吞吐 量）和资源利用率（尤其是内存），同时兼容 Hugging Face 等流行模型库。 简单来说，vLLM 能让 GPT、Mistral、LLaMA 等主流模型系列跑得更快、消耗更少资源，取得这些效果的关键是其创新的注意力机制实现方案 —— PagedAttention。 近日，DeepSeek AI 研究者、深度学习系统工程师俞星凯 从零开始构建了一个轻量级 vLLM 实现 ——Nano-vLLM，将代码简化到了 1200 行 。 | Inference Engine | Output Tokens | Time (s) | Throughput (tokens/s) | | --- | --- | --- | --- | | vLLM | 133,966 | 98.95 | 1353.86 | | Nano-vLLM | 133,966 | 101.90 | 1314.65 | 作者简介 GitHub 地址：https://g ...

Core Viewpoint - Apple is aiming to launch an AI-powered upgrade of its Siri voice assistant in spring 2026, after multiple delays from its original target of fall 2024 [1][2][3] Group 1: Release Timeline - The initial announcement for Siri's new features was made in June 2024, with a planned launch in fall 2024 [2][3] - The release date was later pushed to spring 2025 and then postponed to sometime in the coming year [3] - Apple has indicated that the new features could be previewed in the fall before the spring release [2] Group 2: Technical Challenges - Technical challenges have necessitated a complete rebuild of Siri, leading to shifts in management responsibilities within Apple [3] - Apple CEO Tim Cook acknowledged the need for "more time" to enhance Siri's AI capabilities during a quarterly earnings call [4][5] - Employees who recently left the company reported difficulties in updating Siri with advanced large language models (LLMs) for improved responses [5] Group 3: Competitive Landscape - Apple's cautious approach to generative AI contrasts with competitors like Amazon, Google, and Microsoft, which are aggressively adopting LLMs and enterprise-scale AI solutions [6]

Xcode can now use large language models such as ChatGPT to provide coding assistance. You can ask general questions about Swift — like “tell me about swift concurrency”. And because of the integration with Xcode, the model can take your code into consideration and answer specific questions about your project, or even make changes on your behalf. ...

大模型≠随机鹦鹉！Nature子刊最新研究证明： 大模型内部存在着类似人类对现实世界概念的理解。 LLM能理解现实世界和各种抽象概念吗？还是仅仅在"鹦鹉学舌"，纯粹依靠统计概率预测下一个token? 长期以 来，AI社区对这一问题存在很大的分歧。 有一种猜测是，纯粹基于语言的形式（例如训练语料库中token的条件分布）进行训练的语言模型不会获得任何语 义。 相反，它们仅仅是根据从训练数据中收集的表面统计相关性来生成文本，其强大的涌现能力则归因于模型和训练 数据的规模。这部分人将LLM称为"随机鹦鹉"。 但现在研究证明，并非如此！ 中国科学院自动化研究所与脑科学与智能技术卓越创新中心的联合团队 在 《Nature Machine Intelligence》 发表 题为 《Human-like object concept representations emerge naturally in multimodal large language models》 的研 究。 | Received: 26 June 2024 | Changde Du@ 12, Kaicheng Fu1-2, Bincheng Wen ...

The FoundationModels framework gives you access to the on-device Large Language Model that powers Apple Intelligence, with a convenient and powerful Swift API. You can use it to enhance existing features in your apps, like providing personalized search suggestions. Or you can create completely new features, like generating an itinerary in a travel app, all on-device. You can even use it to create dialog on-the-fly for characters in a game.It is optimized for generating content, summarizing text, analyzing u ...