外媒表示，iOS 26.4 版本的 Siri 不会像 ChatGPT 或 Claude 那样工作，但它将依赖于LLM，并且已经从 底层进行了更新。 在 iOS 26.4 中，Siri 将拥有一个以 LLM为核心的全新功能，其他所有功能都围绕它构建。Siri 不再只是 简单地将语音转换成文本并查找关键词来执行操作，而是能够真正理解用户提出的具体问题，并运用逻 辑推理来完成任务。 外媒称，苹果并未实现完整的聊天机器人交互功能，但任何升级都比现有版本更胜一筹，而且早就应该 实现了。（思瀚） 来源：环球网 【环球网科技综合报道】2月7日消息，据MacRumors报道，在今年春季即将发布的 iOS 26.4 更新中，苹 果将推出新版Siri，这将彻底改变我们与这位个人助理的交互方式以及它能够执行的操作。 ...

Cerence (NasdaqGS:CRNC) Q1 2026 Earnings call February 04, 2026 04:30 PM ET Company ParticipantsAman Gupta - Executive Director of Private EquityBrian Krzanich - CEOKate Hickman - VP of Corporate Communications and Head of Investor RelationsTony Rodriquez - CFOConference Call ParticipantsItay Michaeli - Senior AnalystJeffrey Van Rhee - Senior Research AnalystOperatorGood day, and thank you for standing by. Welcome to the Cerence First Quarter 2026 earnings call. At this time, all participants are in listen- ...

Core Insights - Google DeepMind is at the forefront of AI research, focusing on breakthroughs that impact science, business, and society, particularly in the context of the AGI race [1][3][4] - The company has made significant advancements, including the development of Gemini, which is now competitive with ChatGPT, and has roots in technologies originally developed by Google [3][4][28] - The investment made by Google in DeepMind in 2014, approximately £400 million (around $540 million), has potentially grown to hundreds of billions, highlighting the strategic importance of this acquisition [4][28] Company Overview - Google DeepMind was founded in 2010 in London by Demis Hassabis, Shane Legg, and Mustafa Suleyman, with the latter now working at Microsoft [2][3] - The company has been pivotal in Google's AI advancements, particularly with consumer-facing products like Gemini, which leverage DeepMind's foundational technologies [4][28] Technological Developments - The AI landscape has evolved significantly since the emergence of ChatGPT, with Google facing internal restructuring to adapt to the competitive environment [3][4] - DeepMind's previous breakthroughs, such as AlphaGo and AlphaFold, have set the stage for its current innovations, emphasizing the company's commitment to solving fundamental scientific problems [4][5] AGI and Future Prospects - The pursuit of AGI is a long-term mission for DeepMind, with expectations of achieving significant milestones within the next 5 to 10 years [10][11] - Current AI systems, including LLMs, face limitations in achieving true AGI, particularly in areas like continuous learning and creative hypothesis generation [7][8][10] Energy and Efficiency Challenges - There are physical limitations in AI development, particularly concerning energy consumption and computational power, which need to be addressed as the field progresses [11][12] - Innovations in model efficiency, such as the use of Distillation, are expected to enhance performance significantly, with annual improvements projected at around 10 times [12][13] Competitive Landscape - The AI industry is experiencing intense competition, with many players, including startups and established tech giants, vying for leadership [28][29] - Concerns about potential financial bubbles in the AI sector are acknowledged, with some segments showing signs of unsustainable valuations [32][33] Global AI Dynamics - The competition between the U.S. and China in AI development is intensifying, with Chinese companies like DeepSeek and Alibaba making notable advancements [35][36] - Despite rapid progress, there are questions about whether Chinese firms can achieve significant innovations beyond existing technologies [36][38] Collaboration and Integration - Google DeepMind operates as a central hub for AI research within Google, integrating technologies across various products and ensuring rapid deployment of new capabilities [41][42] - The collaboration between DeepMind and Google is characterized by a close iterative process, allowing for swift adjustments to strategic goals and product development [42][43]

2)Agent是单体LLM的升级。Agent在单体LLM基础上增加了决策编排器，并能使用网页搜索、抓取、 Python解释器、上下文数据库等外部工具，可自主规划任务、调用工具、记忆历史步骤并在执行过程中 实时调整策略，以弥补单体模型在上下文感知、幻觉问题以及实时信息获取方面的不足。因此Agent发 展将显著增加高并行任务量和工具调用量。 3)多智能体(MAS)成为Agent的新形态。以KimiK2.5为例，其在K2基础上实现Agent横向拓展，通过并行 强化学习训练能自主管理100个子智能体，执行1500次工具调用的并行工作流；较单一智能体最多可将 执行时间缩短4.5倍，使端到端运行时间减少80%。 Agent趋势下CPU将成为重要支撑 1)CPU可显著影响Agent延迟、吞吐量及功耗指标。CPU将成为Agent发展的核心支撑——CPU工具处理 占总延迟最高达90.6%，大批次场景下CPU动态能耗占比达44%，吞吐量受CPU核心超载、缓存一致 性、同步机制等制约；上述因素使CPU成为影响Agent系统性能、效率与扩展性的关键硬件。 智通财经APP获悉，中泰证券发布研报称，据IDC预计，活跃Agent的数量将从 ...

Group 1 - The core viewpoint of the article is that Yann LeCun, after leaving Meta, is launching a new company called Advanced Machine Intelligence (AMI), focusing on world models rather than large language models (LLMs) for achieving human-level intelligence [9][17][20] - LeCun criticizes Meta's product development decisions, stating that while research is acceptable, product execution has been poor, particularly under Mark Zuckerberg's leadership [2][3][15] - AMI aims to be an open-source platform, contrasting with the recent trend in Silicon Valley towards closed-source models, which LeCun believes is a misguided approach [11][13][16] Group 2 - The company will initially focus on research and development, specifically on world models, which LeCun argues are essential for building intelligent systems [17][19] - LeCun emphasizes that LLMs are not equivalent to AI and that understanding the real world is crucial for achieving human-like intelligence, which LLMs struggle to do [21][22][23] - AMI is seeking to raise €30 million (approximately 247 billion RMB) in funding, with an initial goal of €3.5 million for early financing, aiming for a total of €5 million in the first round [45][46][50] Group 3 - The company has already attracted interest from potential investors, including Cathay Innovation and Hiro Capital, indicating a shift in venture capital investment logic towards valuing founders over products [52][53][54] - LeCun is actively recruiting talent, including former Meta executives, to strengthen AMI's capabilities [40][42] - The ultimate goal of AMI is to become a leading supplier of intelligent systems, with a focus on practical applications of world models and planning capabilities [38][39]

Core Viewpoint - The article discusses the challenges faced by generative AI voice assistants, particularly in executing simple commands reliably, highlighting a gap between user expectations and actual performance [14][18]. Group 1: User Experience with AI Assistants - Users have reported frustrations with AI voice assistants like Alexa, which fail to execute basic commands such as brewing coffee or turning on lights, despite their advanced capabilities [4][8]. - The transition to generative AI has led to a situation where users experience inconsistent responses, with the AI providing creative but unhelpful reasons for not executing commands [7][16]. Group 2: Technical Limitations of Generative AI - Generative AI introduces a level of randomness that can lead to misunderstandings in command execution, making it unsuitable for tasks requiring precision and reliability [18][22]. - Traditional voice assistants operated on a template-matching basis, ensuring predictable outcomes, while generative models struggle to maintain consistency in system calls [19][23]. Group 3: Potential and Future Directions - Despite current limitations, there is recognition of the potential of generative AI to understand complex tasks and improve user interactions, suggesting a paradigm shift in capabilities [30][34]. - The article suggests that the chaos observed may not be a failure of generative AI but rather a misalignment of its application in contexts where deterministic execution is critical [44].

Summary of Key Points from the Conference Call Industry Overview - The conference call discusses the Internet sector in North America, focusing on key debates and investment opportunities for 2026, including advancements in LLM (Large Language Models), GenAI (Generative AI), hyperscaler growth, autonomous driving, and the impact of AI on various industries [1][2]. Core Themes and Arguments 1. **Thematic Debates for 2026**: - Key debates shaping the sector include LLM advances, GenAI productization, hyperscaler growth, the future of search, autonomous driving, and robotics [1][2]. - The market is expected to reward companies demonstrating positive ROIC (Return on Invested Capital) from GenAI or GPU-enabled technologies, while sectors facing disruption, such as rideshare and e-commerce, may trade at lower multiples [2]. 2. **Hyperscaler Growth**: - There is a bullish outlook on hyperscaler growth in 2026, particularly for AWS, GCP, and Azure, driven by increased AI tech adoption and diffusion across the economy [2]. 3. **Investment Opportunities**: - Key picks for 2026 include AMZN (Amazon), META (Meta Platforms), DASH (DoorDash), EBAY (eBay), and RBLX (Roblox) [1][2]. Company-Specific Insights 1. **Amazon (AMZN)**: - Target Price: $315, implying ~27% upside. - Expected to leverage both AWS and Retail to drive GenAI adoption, with a projected durable growth rate of over 20% for AWS in 2026 [3]. - Investments in AI-driven shopping assistants and logistics are expected to enhance retail growth and profitability [3]. 2. **Meta Platforms (META)**: - Target Price: $750, with ~15% upside. - Anticipated improvements in core engagement and monetization, with a focus on productizing new LLMs to drive revenue growth [7]. 3. **DoorDash (DASH)**: - Target Price: $300, indicating ~39% upside. - Investments in a unified tech infrastructure and autonomous delivery are expected to enhance ROIC and overall business performance [9]. 4. **eBay (EBAY)**: - Target Price: $112, suggesting a 23% upside. - Market skepticism about sustainability and profitability of growth is noted, but durable tailwinds in collectibles and new initiatives are expected to drive GMV growth [13]. 5. **Roblox (RBLX)**: - Target Price: $155, with ~100% upside. - Strong user-generated content platform performance is expected to drive bookings and engagement growth, despite short-term risks [14]. Additional Important Insights - **GenAI and AI Adoption**: - The call emphasizes the importance of demonstrating ROI from AI investments, particularly as companies face rising model training costs [40][41]. - The anticipated rollout of new AI models and tools is expected to enhance product monetization and engagement across platforms [49]. - **Agentic Commerce**: - The rise of agentic offerings is expected to impact e-commerce significantly, with personalized shopping experiences likely to drive consumer spending [67][69]. - **Autonomous Driving**: - 2026 is projected to be an inflection year for autonomous driving, with significant advancements expected in service availability and technology [78][90]. - **Physical AI**: - Companies are increasingly focusing on real-world data capture and physical AI, with Amazon's robotics-enabled warehouses expected to have a substantial impact on efficiency and cost savings [98][100]. This summary encapsulates the key discussions and insights from the conference call, highlighting the strategic outlook for the Internet sector and specific companies within it for 2026.

Core Insights - The article discusses the challenges and innovations in evaluating student performance in the context of AI advancements, particularly focusing on the shift from traditional assessments to AI-driven oral examinations [1][2][3]. Group 1: AI in Education - The traditional method of assessing students through written assignments has become ineffective due to the availability of AI tools that can assist students in completing their work [2][3]. - The introduction of AI-driven oral exams aims to evaluate students' real understanding and reasoning abilities, as it requires them to think on their feet without AI assistance [3][4]. Group 2: Implementation Challenges - Scaling oral exams presents logistical challenges, especially with larger class sizes, making coordination of exam schedules difficult [4][5]. - The use of AI to facilitate oral exams can streamline the process, allowing for personalized questioning and structured workflows [5][6][7]. Group 3: AI Oral Exam Structure - The AI oral exam consists of two main parts: discussing the student's project and analyzing a randomly selected case study, which tests their knowledge retention and application [9][10]. - A structured workflow with multiple AI agents is employed to ensure a smooth examination process, including identity verification and targeted questioning based on project details [11][12]. Group 4: Cost and Efficiency - The implementation of the AI oral exam system resulted in a total cost of $15 for 36 students, significantly lower than the estimated $750 for traditional human-led assessments [13][14]. - The average duration of the oral exams was 25 minutes, with a notable finding that shorter exams did not correlate with lower scores, indicating efficiency in understanding [32]. Group 5: Feedback and Assessment Quality - The AI system provides detailed feedback on students' performance, highlighting strengths and areas for improvement, which is more comprehensive than typical human feedback [29][30]. - The AI scoring system showed a high degree of consistency among different models, with a notable improvement in scoring accuracy after models reviewed each other's assessments [22][24]. Group 6: Student Reception - Student feedback indicated a preference for traditional assessments, with many feeling that AI oral exams increased pressure, yet a majority acknowledged that these exams better assessed their understanding [33][35]. - The article concludes that while the core idea of AI-driven assessments is promising, further refinement of execution details is necessary to enhance the student experience [35][36].

RT Avi Chawla (@_avichawla)I built my own ChatGPT from scratch, and you can too.Karpathy's nanochat is a single, clean, minimal, and hackable codebase to build a modern LLM.By setting this up, you'll learn how to:> train a tokenizer from the ground up> pre-training: master next-word prediction> mid-training: teach the model to hold conversations> sft: fine-tune on high-quality dialogue datasets> evaluate and log every step of the processI've done this on a LightningAI studio, and you can reproduce everythin ...

RT Nick Szabo (@NickSzabo4)@SeanParnellUSA You're just parroting word-for-word what Hegseth said. An LLM has more originality. ...