Inference Market & KV Cache Importance - Inference spending is projected to surpass training spending, highlighting its growing significance in the AI landscape [2] - KV cache is crucial for understanding context in prefill stages and augmenting tokens in decode stages during inference [3][4] - Utilizing DDN as a KV cache can potentially save hundreds of millions of dollars by retrieving previously computed contexts instead of recomputing them [5] Disaggregated Inference & Performance - Disaggregated inference, running prefill and decode on different GPUs, improves efficiency, requiring a global KV cache for information dissemination [6] - DDN's fast storage delivers KV caches at extremely high speeds, leading to massive efficiency gains [9] - DDN's throughput is reportedly 15 times faster than competitors, resulting in a 20 times faster token output [10] Productivity & Cost Efficiency - Implementing a fast shared KV cache like DDN can lead to a 60% increase in output from GPU infrastructure [12] - DDN aims to deliver a 60% increase in tokens output per watt, per data center, per GPU, and per capital dollar expenditure [13] - Using DDN offers the strongest improvement in GPU productivity over the next five years by accelerating inference models [12]

Language Model Importance - Having a language model tailored to a specific country is crucial for building infrastructure for its people [1] - Using generic models built elsewhere may not adequately empower a country's economy due to a lack of cultural fluency [1] Localization Benefits - A language model that understands the local context, dialect, and culture is essential for empowering the people of a country [1]

Listing Announcement - LBank 将上线 $LLM1 (Latina Language Model) [1] - $LLM token，或 "Latino Language Model"（拉丁语语言模型），幽默地以语言模型为中心，专门针对拉丁裔社区 [1] Token Details - $LLM token 专注于拉丁语社区 [1]

Product Launch - OpenAI launched its most advanced language model, GPT-5 [1] - GPT-5 is expected to enhance ChatGPT [1] Valuation - OpenAI reportedly nears a $500 billion valuation [1]

Hiring Opportunity - The company is hiring to build autonomous agents for understanding language model behaviors [1] - The focus is on identifying and understanding interesting language model behaviors [1]

闻乐 发自 凹非寺 量子位 | 公众号 QbitAI 为什么语言模型能从预测下一个词中学到很多，而视频模型却从预测下一帧中学到很少？ 这是UC伯克利大学计算机副教授 Sergey Levine 最新提出的灵魂一问。 他同时是Google Brain的研究员，参与了Google知名机器人大模型PALM-E、RT1和RT2等项目。 Sergey Levine在谷歌学术的被引用次数高达18万次。 "柏拉图洞穴"是一个很古老的哲学比喻，通常被用来说明人们对世界认知的局限性。 Sergey Levine的这篇文章以《柏拉图洞穴中的语言模型》为题，又想要揭示AI的哪些缺陷呢？ 在文章的开头，作者提到人工智能就是在研究能够反映人类智能的灵活性和适应性的假想智能。 一些研究者推测，人类心智的复杂性和灵活性源自于大脑中应用的一个 单一算法 ，通过这个算法可以实现所有多样化的能力。 也就是说，AI如果能复现这个终极算法，人工智能就能通过经验自主获取多元能力，达到人类智能的高度。 在这个探索过程中，语言模型取得了非常成功的突破。 甚至，LLMs实现能力跃升背后的算法（ 下一词预测+强化学习微调 ），也非常简单。 单一终极算法 假设 ...