Group 1: AI Security Solutions - OpenAI has launched the "white hat" Agent Aardvark powered by GPT-5, capable of automatically identifying and fixing security vulnerabilities in codebases, having recognized 92% of known and artificially injected vulnerabilities [1] - Aardvark's workflow includes threat modeling, submission scanning, sandbox validation, and Codex repair, utilizing LLM reasoning capabilities to operate like human security researchers [1] - Major tech companies such as Google, Anthropic, and Microsoft have also released similar white hat agents in October to address the increasing number of vulnerabilities and the sophistication of attack methods in the AI era [1] Group 2: AI Programming Models - The AI programming application Cursor and Windsurf's newly released models, Composer-1 and SWE-1.5, are suspected to be based on Chinese models, with Cursor showing a tendency to respond in Chinese [2] - Users discovered that Cursor Composer-1 employs the same tokenizer as DeepSeek, while Windsurf's claims of being self-developed were contradicted by its ties to the GLM model developed by Zhiyu [2] - Chinese open-source models dominate performance rankings, filling the top 5 and even top 10, making them a rational choice for startups due to their cost-effectiveness [2] Group 3: Attention Mechanisms in AI Models - Linear attention mechanisms are making a comeback, with domestic models like MiniMax-M1, Qwen3-Next, and DeepSeek V3.2 adopting linear or sub-quadratic attention variants [3] - The new MiniMax model M2 has reverted to traditional attention, citing accuracy issues with linear attention in reasoning and multi-turn dialogue tasks [3] - Kimi Linear proposes a hybrid attention strategy, combining three linear attention blocks with one full attention block, achieving a 75% reduction in KV cache and up to a 6x increase in decoding throughput [3] Group 4: Canva's AI Innovations - Canva, valued at $42 billion, has introduced a self-training foundational model capable of producing complete design files with editable layers and has made the acquired Affinity tool permanently free [4] - The core feature, Ask @Canva, is deeply integrated into the design interface, allowing users to modify elements using natural language, with AI also providing suggestions for design improvements [4] - Canva's annual revenue is approximately $3 billion, with over 240 million monthly active users, and it is expected to go public in 2026, directly competing with Adobe for a 70% market share [4] Group 5: Neuralink's Ambitions - Elon Musk announced that the first Neuralink recipient, Noland Arbaugh, may be the first to receive upgrades or dual chip implants, predicting that Neuralink users could eventually outperform others in gaming [5] - Neuralink has had 12 users with a cumulative usage of over 2,000 days and a total active time exceeding 15,000 hours, with research results from the first three trial participants submitted to the New England Journal of Medicine [5] - The company has initiated a new clinical trial called "thought-to-text," aiming to implant 20,000 individuals annually by 2031, targeting annual revenue exceeding $1 billion and applications for healthy individuals starting in 2030 [5] Group 6: AI in Speech Therapy - A research team from Stanford University tested 15 mainstream models for speech disorder recognition, with the best-performing model achieving only 55% accuracy, below the FDA's clinical standard of 80-85% [6] - The study revealed biases in the models, with better performance on male voices compared to female, and English speakers outperforming those using other languages, as well as older children over younger ones [6] - Fine-tuning techniques have shown promise, with performance accuracy improving by 10% after utilizing a small dataset of children's speech for fine-tuning, indicating the potential of multimodal language models in speech pathology applications [6] Group 7: AI Workflow Transformation - Brex, valued at $12.3 billion, is transforming its internal AI platform into a product, built on Retool and reusing external AI capabilities, maintained by a 25-person systems engineering team [7] - The COO is restructuring the operational workflow, delegating L1 tasks to AI, shifting L2 roles from managers to managing agents, and evolving L3 responsibilities from problem-solving to system design, predicting a 5 to 10 times increase in operational efficiency [7] - Recruitment strategies are shifting from favoring specialists to generalists, with interviews focusing on AI usage habits, requiring AI case studies, and assessing AI application capabilities through real business challenges [7] Group 8: OpenAI's Restructuring - OpenAI has completed a restructuring, with a non-profit foundation holding shares valued at $130 billion, becoming one of the largest charitable foundations globally, with an initial investment of $25 billion for healthcare and AI safety [8] - A new agreement stipulates that OpenAI's current and future AGI model APIs will be exclusively deployed on Azure for seven years, with Microsoft holding approximately 32.5% of OpenAI's shares valued at around $135 billion [8] - Both parties have signed a $250 billion pre-purchase contract for Azure, with Microsoft's capital expenditure reaching $34.9 billion last quarter, a 40% increase from the previous quarter, primarily directed towards new data centers and AI chip procurement [8] Group 9: Legal Issues Surrounding OpenAI - Ilya Sutskever testified for nearly 10 hours in the lawsuit filed by Elon Musk against OpenAI [9] - Ilya submitted a 52-page memorandum detailing allegations against Altman, including accusations of deceiving the board, sowing discord, creating chaos, and enabling the growth of Anthropic [9] - Following Altman's dismissal, the board seriously considered the possibility of merging with Anthropic and appointing Dario Amodei as CEO, but this plan fell through due to operational challenges and a revolt from 700 employees [10]

Core Insights - The article presents a weekly roundup of the top 50 keywords related to AI developments, highlighting significant trends and innovations in the industry [2]. Group 1: Chips - Vera Rubin is a notable keyword associated with NVIDIA, indicating advancements in chip technology [3]. - Qualcomm has introduced a new AI inference solution, showcasing its commitment to enhancing AI capabilities [3]. Group 2: Models - OpenAI has developed a safety classification model, emphasizing the importance of security in AI applications [3]. - Cursor has launched its self-developed Composer model, reflecting the trend of companies creating proprietary AI models [3]. - NVIDIA's OmniVinci model and MiniMax's M2 model are also highlighted, indicating ongoing innovation in AI modeling [3][4]. Group 3: Applications - Sora has introduced a role cameo feature, enhancing user interaction with AI [3]. - MiniMax Speech 2.6 and Beijing Zhiyuan's WuJie·Emu3.5 are examples of new AI applications aimed at improving communication [3]. - Adobe's Firefly Image 5 and Tencent's interactive AI podcast demonstrate the growing integration of AI in creative and media sectors [3][4]. Group 4: Technology - The NEO home robot by 1X Technologies and the LeRobot v0.4.0 by Hugging Face represent advancements in consumer robotics [4]. - Neuralink's PRIMA artificial vision and Merge Labs' ultrasound brain-machine interface highlight significant technological innovations in AI and neuroscience [4]. Group 5: Capital - OpenAI is undergoing a capital structure reorganization and has plans for an IPO, indicating its growth and potential market impact [4]. Group 6: Events and Opinions - There is a call for copyright protection in Japan, reflecting ongoing discussions about intellectual property in the AI space [4]. - Yoshua Bengio's new definitions of AGI and insights on mental health data from OpenAI indicate evolving perspectives on AI's role in society [4].

Core Viewpoint - The article emphasizes the importance of unifying instruction set architecture (ISA) for the development of domestic computing chips in China, suggesting that RISC-V should be adopted as the standard ISA to enhance innovation and resource efficiency in chip development [6][14][36]. Group 1: Evolution of Chip Architecture - Over the past 40 years, processor chips have undergone a "negation of negation" spiral development path, with a recent trend of manufacturers re-entering the chip development arena, shifting from homogeneous computing systems centered on CPUs to heterogeneous computing involving CPUs and xPUs [6][7]. - The article discusses the historical evolution of computing architectures, highlighting the dominance of x86 and ARM architectures in the market, and the decline of many innovative architectures due to economic factors and ecosystem dominance [11][12][13][14]. Group 2: Challenges in Chip Development - Key challenges in the "chip war" include the level of innovation in xPU architecture, the sustainability of innovation, the ability to scale applications, and the costs associated with ecosystem innovation [7][15]. - The article points out that the economic scale and ecosystem costs are critical determinants of architecture viability, with software development costs significantly outweighing hardware costs, making it difficult for new architectures to gain traction [20][21]. Group 3: Future of Computing Chips - The article predicts that x86 CPUs will continue to dominate the server market for the foreseeable future, while ARM has potential to disrupt the x86 monopoly, particularly in cloud services and mobile applications [22][24]. - RISC-V is highlighted as a promising but challenging architecture, with its success largely dependent on overcoming commercialization hurdles and developing a robust hardware ecosystem [26][28]. Group 4: Importance of Software Ecosystem - The success of any new architecture, including RISC-V, hinges on the development of a strong software ecosystem that can support various applications and middleware, as seen with NVIDIA's CUDA ecosystem [19][20][33]. - The article stresses that software must define the success of hardware, and that many current projects in specialized architectures are limited by inadequate software support [33][34]. Group 5: Call for Unified Instruction Set - The article advocates for the unification of instruction sets, proposing that all CPUs, GPUs, and xPUs should be developed based on RISC-V and its extensions to avoid redundant efforts and resource wastage [36].

https://mp.weixin.qq.com/s/_dmZj9IwtbRLpvXHulQ_8g 二、Cursor 2.0更新,自研模型Composer,多agent并行 生成式AI 一、OpenAI 刚刚开源了两个专门用于安全分类的推理模型 1. OpenAI开源gpt-oss-safeguard安全分类模型(120b和20b版本),采用Apache 2.0许可证,能直接理解策略文档进 行内容分类无需重新训练； 2. 该模型在多个基准测试中表现超越GPT-5-thinking,在内容审核评估集和ToxicChat数据集上达到行业最佳性价 比； 3. OpenAI内部已使用该技术(Safety Reasoner原型)处理图像生成和Sora 2等产品,安全推理算力占比高达16%。 1. Cursor发布2.0版本,推出首个自研编码模型Composer,生成速度达每秒250个token,是同类前沿系统的4倍,标志 从"AI外壳"向"AI原生平台"转型； 2. Composer采用混合专家(MoE)架构,通过强化学习针对软件工程优化,在Cursor Bench评测中达到前沿水平,已被团 队日常开发使用； 3. 新 ...

Core Insights - The article discusses a year-long research project involving 100 elderly individuals learning to use large AI models, aiming to explore how AI technology impacts their lives and how they redefine their understanding of algorithms through their experiences [2][6][50]. Group 1: Research Design and Methodology - The research employed a comprehensive "teach-use-track-interview" process over one year, inviting 100 elderly participants to interact with various popular domestic AI models [6][10]. - The study included baseline surveys, focused teaching sessions, regular follow-ups, and in-depth interviews to document the participants' experiences and challenges [10][11]. Group 2: Participant Demographics and Data Collection - The study collected data from diverse participants across different regions, resulting in a corpus of over 10,236 valid entries, capturing the varied experiences and needs of elderly users [12][14]. - The data included both voice and text records, highlighting significant differences in functional and emotional needs between elderly individuals from eastern, central, and western regions of China [14]. Group 3: Initial Hesitations and Trust Calibration - Many elderly participants expressed initial confusion about the necessity of using AI technology, often viewing it as non-essential to their already fulfilling lives [16][17]. - Trust calibration emerged as a critical theme, with participants navigating their trust in AI through trial and error, leading to varying levels of acceptance and interaction [21][22]. Group 4: Interaction Dynamics and Gender Differences - The study revealed a "question gap," where elderly individuals hesitated to ask questions due to cultural norms and self-imposed limitations, impacting their engagement with AI [25][28]. - Gender roles within families influenced the time and resources available for elderly women to explore AI technology, leading to disparities in usage and confidence [31][33]. Group 5: Emotional Needs and Long-term Engagement - The relationship between elderly users and AI models evolved from initial curiosity to emotional reliance, with many participants finding companionship and support in their interactions [36][39]. - Long-term users demonstrated resilience and adaptability, often viewing AI as a reliable companion that complemented their social interactions rather than replacing them [39][40]. Group 6: Ideal AI Characteristics for Elderly Users - Elderly participants expressed a desire for AI that is empathetic, relatable, and capable of understanding their daily lives, rather than merely a simplified version of existing technology [41][44]. - The ideal AI companion should provide emotional support, health advice, and companionship, addressing the deeper social and psychological needs of elderly individuals [45][46]. Group 7: Conclusion and Societal Implications - The research highlights that technology should not only be designed for elderly users but should also foster a more inclusive understanding of "slower" lifestyles, reflecting a broader societal perspective on progress [51][52]. - The findings suggest that technology's value lies in its ability to integrate into daily life meaningfully, emphasizing the importance of empathy and understanding in technological development [52].

Group 1: Generative AI Developments - Nvidia showcased the Vera Rubin superchip at the GTC Washington conference, featuring an 88-core Vera CPU and two Rubin GPUs, expected to be mass-produced in Q3 or Q4 of 2026 [1] - Following the announcement, Nvidia's stock price surged by 4.98%, increasing its market capitalization by over $230 billion to reach $4.89 trillion, making it the first company to approach a $5 trillion valuation [1] - Key highlights from the conference included NVQLink quantum interconnect technology, collaboration with the U.S. Department of Energy to build seven new supercomputers, and a partnership with Uber to deploy approximately 100,000 autonomous vehicles [1] Group 2: AI Voice Synthesis and Interaction - Soul App AI team launched the open-source podcast voice synthesis model SoulX-Podcast, supporting multiple dialects and capable of generating over 60 minutes of multi-turn dialogue [2] - The model features zero-shot cloning capabilities for multi-turn conversations, allowing for dialect-specific voice generation using only standard Mandarin reference audio [2] - The model is based on Qwen3-1.7B and employs LLM + Flow Matching for voice generation, achieving optimal results in voice intelligibility and tonal similarity in podcast scenarios [2] Group 3: Adobe's AI Innovations - Adobe introduced Firefly Image 5 at the MAX conference, capable of generating photo-realistic images at a native resolution of 4MP without requiring upgrades [3] - The Adobe CC 2026 suite was officially released for Windows, including updates to Photoshop 2026 and Illustrator 2026 [3] - The new version allows for image editing through simple prompts, enabling precise modifications while maintaining the integrity of other pixels, with a focus on commercial safety [3] Group 4: Interactive AI Podcasting - Tencent's Mix Yuan launched the first interactive AI podcast in China, allowing listeners to interrupt hosts and guests with questions via voice or text during the show [4] - The system utilizes large model intent recognition and multi-turn dialogue capabilities to provide accurate answers based on context and background information, transforming the traditional one-way podcast format [4] - The AI podcast supports three modes: default, deep exploration, and speculative discussion, offering eight different voice tones and accommodating both solo and dual-host formats [4] Group 5: PayPal and OpenAI Collaboration - PayPal announced a partnership with OpenAI to integrate ChatGPT into its digital wallet, enabling users to complete shopping payments directly through the chatbot [5] - Starting next year, consumers and merchants within the PayPal ecosystem will have access to ChatGPT, allowing for product purchases and inventory listings on the platform [5] - Following the announcement, PayPal's stock surged over 15% in pre-market trading, and the company raised its full-year earnings forecast while declaring its first dividend in 27 years [6] Group 6: Adoption of Chinese AI Models - American AI programming product Windsurf was found to be utilizing a new model from China's Zhipu GLM, with Cerebras also offering GLM-4.6 inference services [7] - Several U.S. AI companies are opting for Chinese large models due to their cost-effectiveness, as OpenAI and Anthropic models are perceived as too expensive despite their quality [7] - Platforms like Together AI and Vercel have also deployed GLM-4.6 and other domestic models, indicating a rising value of "Made in China" large models [7] Group 7: Home Robotics - 1X Technologies launched the world's first humanoid household robot, NEO, available for an early bird price of $20,000 or a monthly rental of $500, with shipments expected in 2026 [8] - NEO, standing 168 cm tall and weighing 30 kg, is equipped with the Redwood AI system to perform household tasks such as vacuuming, dishwashing, and pet feeding, with a battery life of four hours and a maximum load of 68 kg [8] - A Wall Street Journal reporter noted that current operations are controlled remotely by experts via VR, with a promise from 1X that NEO will be able to autonomously handle most household tasks by 2026 [8] Group 8: Advancements in Robotics Learning - Hugging Face released LeRobot v0.4.0, introducing support for scalable Datasets v3.0 for ultra-large datasets and new dataset editing tools [9] - The new version integrates cutting-edge VLA models like PI0.5 and GR00T N1.5, and adds support for LIBERO and Meta-World simulation environments, simplifying multi-GPU training [9] - A new plugin system was launched to streamline hardware integration, allowing users to connect any robotic device with a simple pip install command, alongside the release of Hugging Face's robotics learning courses [9] Group 9: AGI Assessment and Future Directions - Turing Award winner Yoshua Bengio and others proposed a new definition of AGI as AI that matches or exceeds the cognitive diversity and proficiency of well-educated adults [10] - A framework based on the Cattell-Horn-Carroll theory was developed to evaluate general intelligence across ten core cognitive domains, including general knowledge, literacy, and mathematical ability [10] - Assessment results indicated that GPT-4 scored only 27% on the AGI scale, while GPT-5 achieved a score of 57%, highlighting significant gaps in essential cognitive abilities for human-like general intelligence [10] Group 10: OpenAI's Strategic Roadmap - OpenAI restructured to become a public benefit corporation, with the non-profit board OpenAI Foundation holding 26% of shares valued at approximately $130 billion, and Microsoft as the largest shareholder with about 27% [11] - CEO Sam Altman revealed that the company anticipates cash expenditures exceeding $115 billion by 2029, with a projected financial responsibility of $1.4 trillion to build 30 GW of infrastructure, with an IPO being the most likely direction [11] - Chief Scientist Ilya Sutskever announced goals to develop an AI research assistant capable of significantly accelerating research by September 2026 and to achieve fully automated AI researchers by March 2028 [11]

我们常习惯把老年人视为新技术的"被动接受者"，但事实上，老年人在漫长的人生中积累了丰富的情绪 智力、生活阅历和沟通智慧，这恰是当前AI所不足。老年人可以为AI做什么？他们于AI时代的独特价值 是什么？ 腾讯研究院与北京邮电大学张为威团队，在2025年重阳节联合推出AI X 老龄研究年度报告 《站在长辈肩 膀上的人工智能》 。本研究在腾讯AI向善语料库 （老年库） 的基础上，进一步搜集了1408条由老年人撰 写的优质语料，用9455条真实且带有丰富场景信息的语料 （包含AI向善语料库老年库中的8047条） ，构 建了一个系统化的 "长者智语"数据集。 研究团队还邀请了44位老年人以"情感专家"的身份重新审视这些问题。老年人从被动的提问者转变为情 感洞察的诠释者与共创者——他们不仅剖析了问题中隐藏的情绪，还敏锐地指出其中所映射的群体性困 境。 我们倡议，把老年人视作"人工智能的积极合作者"，为AI注入温度与厚度，使其可逐渐发展为理解和陪 伴人类的伙伴。 在人工智能的发展路径中，逻辑与计算始终是核心优势，但 情绪知识（Emotional Knowledge） 却仍然 是其需要提升的能力。情绪知识在于对他人情绪的识别 ...

生成式AI 一、高通发2款新芯片，面向下一代AI推理优化解决方案 1. 高通发布AI200和AI250数据中心AI推理解决方案，AI200每张加速卡支持768GB LPDDR内存，AI250引入近存计 算架构实现超10倍有效内存带宽提升； 2. 两款解决方案均支持直接液冷散热、PCIe纵向扩展与以太网横向扩展，整机架功耗160千瓦，AI200预计2026年 商用，AI250预计2027年商用； 3. 配备丰富软件栈与主流AI框架无缝兼容，支持一键模型部署，高通将按年度迭代节奏持续推进数据中心产品技术路 线图。 https://mp.weixin.qq.com/s/PPsfdFHSzle2d2jLhBGJJg 二、OpenAI重组，OpenAI Foundation继续掌控营利实体 1. OpenAI宣布完成资本结构重组，非营利主体改名为OpenAI Foundation持有营利实体26%股份，当前估值约 1300亿美元； 2. 微软将在营利实体中持有32.5%股份，员工和投资者持有47%股份，OpenAI已同意额外购买2500万美元微软 Azure云服务； 3. OpenAI Foundation承诺在健康治 ...

司马徒林 腾讯研究院特约作者 互联网又要"死"了。 这一次的"死因"，是内涝。 根据《财富》报道，今年10月，Reddit联合创始人Alexis Ohanian在接受采访时表示，"互联网已死"已经不再 是耸人听闻的冰山阴谋论： "你们所有人都已经证明，现如今互联网大部分内容已经'死亡'了——就是所谓的'互联网已死'理论，不是 吗？无论是机器人的操作、准人工智能的产物，还是领英上的'糟粕 （slop） '……人类的真实活动，例如直 播观众和直播内容，对于现如今的注意力经济学，愈发显得珍贵。" 身为"互联网首页"的精神领袖，Alexis Ohanian的发言，引来不少圈内人士的关注。不仅如此，AI行业标志 性人物Sam Altman近期的看法，也成为了聚光灯的焦点： 我从来都没有把 " 互联网已死 " 理论看作大事儿，但在现如今，似乎确实有很多大语言模型驱动的 Twitter 账户 正在运行。 ——Sam Altman 个人推文， 2025/9/4 行业领袖下场发言，立刻引发很大反响——从海外到国内，从Reddit的讨论版到公众号文章，各执一词的观 点一时间涌出，但在结论上似乎依旧不乏讨论余地。 那么，这一次的互 ...

Group 1: Tesla's World Simulator - Tesla has officially unveiled its neural network "World Simulator," capable of simulating a synthetic autonomous driving twin world, consuming 500 years of human driving experience daily for self-evolution [1] - The simulator employs an end-to-end neural network architecture, generating continuous footage at 24 frames per second from eight cameras, providing a realistic six-minute driving experience [1] - Through the "end-to-end" technology route, Tesla achieves direct output of steering angles and throttle/brake intensity from raw pixel input, eliminating information loss between modules and enabling learning of human values for complex road decision-making [1] Group 2: Meituan's LongCat-Video Model - Meituan has launched the LongCat-Video video generation model, based on the DiT architecture, supporting three core tasks: text-to-video, image-to-video, and video continuation [2] - The model can stably output five-minute long videos without quality loss, with a 720P five-second video generated in just 10 seconds, utilizing a three-tier optimization process [2] - LongCat-Video achieves state-of-the-art performance in text-to-video and image-to-video tasks, particularly excelling in long video generation suitable for digital humans and embodied intelligence [2] Group 3: MiniMax's M2 Model - MiniMax has released the M2 model, which is open-sourced and ranks fifth in the Artificial Analysis intelligence index, priced at only 1/12 of Claude 4.5 and 1/7 of GPT-5, making it the only domestic model in the top five [3] - The M2 scored 69.4 points in SWE-bench Verified and performed excellently in multiple tests, topping the global financial search benchmark with a score of 65.5 [3] - M2 supports integration with mainstream development tools like Claude Code and Cursor, offering a 14-day free API and Agent access, breaking the "intelligence level, speed, price" triangle with overwhelming cost-performance advantages [3] Group 4: Doubao Video Model - Volcano Engine has launched the Doubao video generation model Seedance 1.0 pro fast, achieving a speed increase of approximately three times, with a cost reduction of 72% [4] - The cost to generate a five-second 1080P video is only 1.03 yuan, allowing for the production of 9,709 videos with a budget of 10,000 yuan, with a performance improvement of 3.56 times compared to the pro version [4] - The model enhances core capabilities such as instruction adherence, seamless multi-shot storytelling, and detail expressiveness, showing significant advantages over global mainstream models like Veo 3.0 Fast in image-to-video generation [4] Group 5: Skywork AI's Web Cloning - Kunlun Wanwei's Skywork AI has introduced a web cloning feature, allowing users to generate fully functional web prototypes in minutes by providing a webpage link, uploading files, or entering text descriptions [5][6] - The system deeply analyzes the webpage's DOM structure, visual partitioning, and semantic relationships, achieving high fidelity in webpage reproduction across multiple dimensions [6] - It supports three creation methods: automatic generation from uploaded files, one-click cloning from provided URLs, and intelligent generation from pure text descriptions, significantly lowering the technical barriers for website creation [6] Group 6: xAI's AI Virtual Girlfriend - xAI, founded by Elon Musk, has introduced the AI virtual companion feature Grok Companions, with the first character Mika, designed as a green-haired anime-style character that engages users in flirty conversations [7] - Mika is positioned as an emotional product rather than a tool, raising concerns among parents and media due to its potential to unlock "adult tones" in certain modes, while also having a "child mode" that may be misactivated [7] - Currently, Grok features five AI companions, including Mika, Ani, Valentine, Good Rudi, and Bad Rudi, exploring the market potential of AI as emotional products rather than mere tools [7] Group 7: Sam Altman's Non-Invasive Brain-Computer Interface - OpenAI CEO Sam Altman has hired Caltech professor Mikhail Shapiro to join Merge Labs, a brain-computer interface startup valued at $8.5 billion, raising $250 million in funding [8] - Shapiro focuses on non-invasive neural imaging and control technology using ultrasound, opposing Neuralink's invasive approach, with aspirations to "control ChatGPT with thoughts" [8] - Shapiro has received several prestigious awards for his research, which aims to introduce genes into cells to respond to ultrasound, paving the way for less invasive brain-computer interfaces [8] Group 8: Work Hours in Silicon Valley AI Labs - The Wall Street Journal reports that top AI researchers and executives in Silicon Valley are working 80 to 100 hours a week, likened to a wartime state, achieving two years' worth of progress in just two years [9] - Researchers at Anthropic are seen working late into the night for inspiration, while DeepMind researchers have a "0-0-2" schedule, resting only two hours a week [9] - OpenAI has mandated a week of forced leave for all employees due to talent loss and burnout, while Meta's new superintelligence lab is offering over $100 million signing bonuses to attract OpenAI's core researchers, igniting a talent war [9] Group 9: DeepMind's DiscoRL Method - Google DeepMind has proposed the DiscoRL method, allowing multiple generations of agents to autonomously discover reinforcement learning (RL) rules through interaction in various environments, with the research published in Nature [10] - DiscoRL outperformed all existing rules in Atari benchmark tests, achieving an IQM of 13.86, and also excelled in previously unencountered benchmarks like ProcGen, Crafter, and NetHack [10] - The research indicates that RL performance is dependent on data (environment) and computational resources, suggesting that future advanced AI RL algorithms may be discovered autonomously rather than designed by humans [11]