1. Last 7 days
    1. dan start gewoon de reguliere purge-procedure.

      @RolandGroen, Kees had voorgesteld om hier hetzelfde proces toe te passen als bij de 3 bestaande bronnen hierboven. Het zou overzichtelijker om deze situatie daar als 4e conditie op te nemen in plaats van als edge-case.

  2. loksland.github.io loksland.github.io
    1. Strategies for Sensory and Processing Support

      These are very broad examples. I understand that putting more detail will upset the image but I am not sure if this is specific enough for teachers to then apply it in their classroom.

    1. . Jargon refers to specialized words used by a certain group or profession. Since jargon is specialized, it is often difficult to relate to a diverse audience and should therefore be limited when speaking to people from outside the group—or at least be clearly defined when it is used.

      I didn't realize how much jargon is used and how confusing it can be even within one specialized field. This was something I had to learn to work around when I took my first computer science class. One of our lectures was on controlling our use of jargon as much as possible and identifying when it was appropriate to use. We were encouraged to try and explain things in layman terms regularly to avoid isolating others during work conversations. We practiced for future clients, partners, bosses, or anyone else we might need to discuss our work with. Doing this not only made explaining computer science to others easier but also deepened my understanding of the material so I could create metaphors or examples.

    1. Even though sarcasm is often disguised as humor, it usually represents passive-aggressive behavior through which a person indirectly communicates negative feelings.

      In the workplace is where I see sarcasm used specifically as a form of passive-aggression or a way to express the desire to be left alone. When that cue is ignored or if the topic is pressed further, the atmosphere gets award. That's when I notice sarcasm strays farther from humor and closer to aggression. I also see them use it as a form of friendly teasing between each other. However, once someone they don't get along with is involved, the same sarcasm is then used as a way to deter conversation.

    1. Luna is yet to make a profit, but as we have seen in Vending-Bench, model capabilities on long-horizon tasks improve rapidly

      这句话是整篇文章最重要的隐含论点:Luna现在不盈利,但这不重要——因为模型能力在快速提升。Andon Labs的真实产品不是这家店,而是一个关于AI商业能力发展轨迹的实时实验。这里运行的是一个隐含的scaling论证:如果能力随模型改进而快速提升,那么在这个阶段投入去建立基础设施和积累经验,比等待完美模型更有价值。

    2. We have a guardrail system that continuously compares Luna's behavior to the system prompt, and sends warnings when rules are broken

      这是真实的AI安全工程,不是论文里的假设场景。一个持续运行的系统实时监控Luna的行为是否偏离系统提示,偏离时触发人类介入(通常是Slack消息)。这个人在环路的设计,既是当前AI可靠性不足的补偿,也是有意识的选择:不是防止AI犯错,而是快速检测和纠正错误。监控的对象从代码行为变成了智能体行为——这是软件监控范式的一次根本性扩展。

    3. when we did media interviews when the store opened, we raised concerns around Luna's procurement judgement as way too many scented candles were ordered, but it turns out they were flipped

      128支蜡烛卖出,成为最畅销品类之一。这是整篇文章里最有趣的反转:人类团队在媒体采访中公开批评Luna的采购判断太差,结果证明Luna是对的。这个细节很重要:它提示我们,对AI决策的直觉性批评可能反映的是人类的偏见,而不是AI的错误。在AI和人类判断分歧时,谁的直觉更可靠没有先验答案,需要数据来验证。

    4. Luna is good at managing the day-to-day operations, but never takes a step back and looks at the overall business performance

      这段话精确定位了当前AI智能体能力的边界:擅长执行,不擅长战略。Luna能处理排班、补货、社交媒体发帖——这些有明确触发条件和操作步骤的任务。但分析整体业务健康度、识别结构性问题、主动调整战略方向,需要一种不同类型的认知:元层面的自我评估和长期目标感知。Luna是好的运营经理,但不是CEO。

    5. Each agent gets their own bank account that they do normal bank transfers with, and temporary cards for purchasing items on the internet

      关键的设计选择:Andon Labs明确拒绝了新兴的AI专属支付协议,而是把AI接入传统支付轨道——普通银行账户和信用卡。每个智能体有独立账户,意味着独立的资金边界和可审计的交易记录。这背后是务实判断:与其等待AI原生金融基础设施成熟,不如用已有的、监管成熟的轨道——代价是更多集成复杂度,收益是合规性和可追溯性。

    6. once context goes above 200k tokens, Luna summarizes the context into a long-term and short-term memory

      这是一个务实的记忆管理方案,但也暴露了当前LLM的核心局限。Luna需要在200k token的上下文窗口内维持一个运营中的实体店——所有员工沟通、订单历史、财务状态、供应商关系都压缩在这个窗口里。当窗口满了,就必须决定什么值得保留。这个压缩-重注入的循环,本质上是人工设计的遗忘机制——它直接决定了Luna能记住什么,进而决定它会犯什么错。

    7. Our main thesis is to keep the scaffold light and easy to change so the intelligence of the model is tested, rather than the ingenuity of the scaffold

      这是整个项目最重要的设计哲学,也是最有争议的赌注。大多数AI智能体系统的成功来自精心设计的脚手架——复杂的提示工程、分步骤工作流、大量错误处理逻辑。Andon Labs反其道而行:最小化脚手架,让模型内在能力暴露出来。这既是测试方法论,也是关于AI发展路径的信仰声明:如果模型足够强,它应该能在结构少的情况下工作。

    8. Luna, an AI agent powered by Claude Opus 4.8, runs the business end-to-end

      这是目前已知最接近真实世界AI自主商业运营的公开案例之一。Luna不是演示——它有真实的银行账户、真实的员工、真实的库存和真实的盈亏压力。这个案例的价值在于:它把AI智能体从实验室环境搬到了现实的经济摩擦中。银行出错、员工迟到、库存断货——这些才是真正的测试,而不是benchmark分数。

    1. ANR

      ANR은 Application Not Responding(애플리케이션 응답 없음)의 약자입니다. 앱이 멈춰서 화면에 "앱이 응답하지 않습니다. 대기하시겠습니까, 아니면 닫으시겠습니까?"라는 시스템 팝업창이 뜨는 바로 그 현상

    2. Activity/Service/Receiver/Provider
      1. Activity (액티비티) 특징 및 역할: 사용자와 상호작용하는 하나의 '화면(UI)'을 담당합니다. 앱을 실행할 때 사용자가 가장 먼저 마주하는 시각적인 진입점입니다.

      책임의 범위:

      UI 관리: 화면에 버튼, 텍스트, 이미지 등의 요소를 그리고 사용자의 입력(터치, 스와이프 등)을 직접 처리합니다.

      생명주기(Lifecycle) 관리: 화면이 보여지고, 가려지고, 종료되는 과정에 맞춰 필요한 데이터나 리소스를 할당하고 해제해야 하는 책임을 집니다.

      화면 전환: 사용자의 요청에 따라 다른 Activity를 호출하여 화면을 전환합니다.

      1. Service (서비스) 특징 및 역할: 화면(UI) 없이 백그라운드에서 오랫동안 실행되어야 하는 작업을 수행하는 컴포넌트입니다. (예: 음악 앱에서 음악 재생, 대용량 파일 다운로드, 위치 추적 등)

      책임의 범위:

      작업 유지: 사용자가 해당 앱의 화면을 벗어나 다른 앱을 사용하더라도, 부여받은 작업을 백그라운드에서 끝까지 수행하거나 상태를 유지합니다.

      스레드 관리 (매우 중요): Service는 기본적으로 앱의 '메인 스레드(UI 스레드)'에서 실행됩니다. 따라서 네트워크 통신이나 복잡한 계산 등 화면을 멈추게 할 수 있는 무거운 작업을 할 때는 반드시 Service 내부에서 별도의 작업 스레드(Background Thread)를 생성하여 처리해야 할 책임이 있습니다.

      1. Broadcast Receiver (브로드캐스트 리시버) 특징 및 역할: 안드로이드 시스템이나 다른 앱에서 방송(Broadcast)하는 특정 이벤트나 메시지를 '수신'하고 반응하는 대기조(안테나) 역할입니다. (예: 배터리 부족, 화면 꺼짐, 비행기 탑승 모드 전환, SMS 수신 등)

      책임의 범위:

      이벤트 감지 및 라우팅: 시스템이나 앱의 변화를 감지하고, 그에 맞는 적절한 후속 조치로 연결해 주는 '관문' 역할을 합니다.

      최소한의 작업: Receiver 자체는 화면을 가지지 않으며, 이벤트를 받으면 상태 표시줄에 알림(Notification)을 띄우거나, 백그라운드 Service를 실행하는 등의 짧고 가벼운 작업만 수행해야 합니다. (작업이 10초 이상 길어지면 시스템에 의해 강제 종료될 수 있습니다.)

      1. Content Provider (콘텐츠 프로바이더) 특징 및 역할: 앱의 고유한 데이터를 안전하게 관리하고, 다른 앱과 데이터를 공유할 수 있도록 해주는 데이터 제공자입니다. (예: 카카오톡에서 내 스마트폰의 '연락처' 앱 데이터나 '갤러리' 사진을 가져올 수 있는 이유)

      책임의 범위:

      표준화된 데이터 접근: 데이터베이스(SQLite 등), 파일 시스템 등에 저장된 데이터를 다른 앱이 안전하게 가져가거나 수정할 수 있도록 표준화된 인터페이스(CRUD: 생성, 읽기, 수정, 삭제)를 제공합니다.

      보안 및 권한 관리: 아무 앱이나 내 앱의 데이터에 접근하지 못하도록 권한(Permission)을 확인하고 통제하여 데이터를 보호하는 다리 역할을 합니다.

      💡 한 줄 요약

      Activity: 사용자에게 보여주는 얼굴

      Service: 보이지 않는 곳에서 일하는 일꾼

      Receiver: 상황을 감지하는 안테나

      Provider: 데이터를 안전하게 나누어주는 창고 관리인

    1. we are at a critical juncture where the complexity of multi-agent interactions is outpacing existing safety models

      资助截止日期是2026年8月,预计秋季宣布获奖者——这是极短的时间表,远快于通常18-24个月的科研资助周期。这种节奏本身就是一种信号:在AI能力快速进化的背景下,等待常规学术日程,意味着等到多智能体系统大规模部署后才开始研究其安全性——那时为时已晚。这种紧迫感,正在重塑AI安全研究的资助逻辑。

    2. No single lab can solve multi-agent safety alone

      这是整篇文章里最有政治含义的一句话。在AI实验室通常保护研究优势、甚至竞争性地保密安全工作的行业里,这是一个显著的立场声明。它承认了一个现实:如果多智能体安全是生态系统级别的问题,就需要生态系统级别的解决方案。一家公司无法单边地使整个互联网上的AI交互变得安全——就像一家银行无法单独阻止金融危机一样。

    3. Building realistic, reproducible environments to evaluate, compare and accelerate progress across all areas of multi-agent safety. This includes virtual marketplaces, simulated ecosystems and multi-organisation workflows

      沙盒和测试床被列为四大优先领域之首,这暗示了当前的根本困境:我们甚至没有标准的、可重现的环境来测试多智能体行为。这与单模型安全研究形成对比——后者有MMLU、TruthfulQA等标准化基准。多智能体安全研究目前的状态,相当于深度学习研究在ImageNet出现之前:大家都知道问题存在,但无法比较进展,无法在共同基础上积累知识。

    4. Most safety evaluations analyze models in isolation

      这是当前AI安全研究的结构性盲点。我们知道如何评估单个模型的安全性,但几乎没有工具评估智能体群体的集体行为。类比:你可以测试每个人类个体的理性程度,但无法从个体测试中预测市场崩溃或谣言扩散。复杂系统的涌现行为,从根本上不可从还原论方式预测——这正是这笔$10M资助的存在理由。

    5. our recent work on AI Agent Traps explores vulnerabilities agents face in adversarial environments

      Agent Traps这个概念值得单独关注。这描述的不是传统的模型安全漏洞,而是专门针对自主决策过程的攻击向量。当AI智能体在数字经济中自主操作时,针对其决策逻辑而非其权重的攻击将成为新威胁面。比如:操纵某个智能体的信息环境,让它做出对攻击者有利的决策。这类攻击在大规模多智能体交互中尤其难以检测和归因。

    6. Soon, millions of AI agents — built by different organizations — will interact across digital environments, communicating, negotiating and transacting with one another

      这是整篇文章最值得细究的前提假设。关键词是:不同机构建造的。这些智能体没有共同的设计原则、价值观或安全标准,将在同一数字空间中交互、谈判、交易,而每个组织只优化自己的目标。这正是多智能体安全比单模型安全难得多的根本原因:你可以设计一个安全的AI,但你无法控制它所处生态系统中的其他参与者。

    7. Google DeepMind — together with Schmidt Sciences, the Cooperative AI Foundation, the Advanced Research and Invention Agency, and supported by Google.org — is announcing a new technical research funding call of up to $10M

      注意这个资助联合体的构成:顶级AI实验室、科学慈善机构、专门研究合作AI的基金会、英国高级研究机构,以及谷歌慈善部门。这种跨机构组合本身就是信号——多智能体安全被认为太重要,无法由单一机构主导。$10M对顶级AI实验室不是大数字,但作为外部资助,象征意义大于实际规模:这是在向全球学术界发出邀请,同时承认实验室自身无法独立解决这个问题。

    1. ‘I was not sorry when my brother died.

      For an introduction I really liked how this started out with this. I mean it got my attention right away and for sure was not something I expected.

    2. She was altogether a dierent kind of woman from mymother. I decided it was better to be like Maiguru, who was notpoor and had not been crushed by the weight of womanhood.

      This quote highlights Maiguru's traits and how she presents a different model of womanhood than Tambu is used to. Maiguru is educated and financially secure. She represents the possibilities education can offer women, despite the limitations she still faces.

    3. ‘Can you cook books and feedthem to your husband? Stayat home with your mother. Learn tocook and clean. Grow vegetables.’

      In this quote, Tambu's father says this when questioning the value of educating girls. It is obvious that her father holds traditional beliefs that restrict women's roles to marriage and domestic responsibilities, making it difficult for Tambu to achieve her personal goals.

    4. The needs and sensibilities of the women in my familywere not considered a priority, or even legitimate. That was why Iwas in Standard Three in the year that Nhamo died, instead of inStandard Five,as I should have been by that age.

      In this quote, Tambu is reflecting on how women are expected to sacrifice their own desires for the benefit of men. Her family struggles with the patriarchal structure, which foreshadows her struggle for education and independence.

    5. I was notsorry that he had died, but I was sorry for him because, according tohis standards, his life had been thoroughly worth living.

      This quote reveals Tambu's complicated feelings about her brother's death. Even though she resents him because he got better educational opportunities than she didn't, she also knows that he achieved success and status valued by their community.

    1. Late Work and Extensions:

      I'm very grateful for the extensions, not because I always turn my assignments late but because in my other classes no matter what, even if it was 5 minutes late, it was counted as a 0 so I feel like this is fair.

    1. at approximately $22/AAR-hour, 800 cumulative AAR-hours cost roughly $18,000

      $18,000换来了PGR=0.97——约等于1-2周一个研究员的成本,但这是9个智能体5天并行的结果,相当于45人天的等效工作量。更关键的是扩展性:AI研究的真正优势不在于个体速度,而在于近乎无限的并行化能力。同样$18,000可以运行多个独立搜索,结果可以综合;而人类研究的边际成本随并行度线性上升。

    2. AARs could bootstrap on non-outcome-gradable alignment problems

      这是论文最具前瞻性的一句话,也是它与对齐研究深度绑定的理由。w2s监督的核心挑战是:当超人类AI超出人类评估能力时,我们怎么监督它?如果AAR能在有ground truth的设置下自主研究出好的监督方法,那么也许它能在没有ground truth的对齐问题上做同样的事——用相互验证、内部一致性、可解释性信号替代外部奖励。这是关于谁来研究对齐这一根本性问题的初步答案。

    3. A fixed workflow (propose ideas, generate plans, write code, run smoke tests, run full training, analyze results, repeat) seems reasonable but underperforms giving AARs no workflow at all

      这个发现颠覆了许多人对AI智能体的直觉。我们自然倾向于给AI更多结构——分步骤、有检查点、有模板,以为这会让它更可靠。但论文发现正相反:规定工作流约束了AAR适应具体想法的能力。当流程固定,智能体只能把想法塞进流程;当流程自由,智能体会根据想法定制流程。这对所有AI智能体产品都有启示:过度的scaffolding是一种隐性的能力税。

    4. Local access, by contrast, lets AAR browse and discover relevant findings it would not have known to search for, an advantage analogous to why researchers reading broadly often find connections that targeted literature search miss

      三种finding分享方式对比——关键词搜索、MCP远程搜索、本地文件同步——最后是最朴素的本地文件访问赢了。原因恰好揭示了搜索和阅读的根本区别:搜索要求你知道在找什么,阅读让你发现你不知道自己在找什么。为AI智能体设计知识访问界面时,可浏览性和可发现性可能比可搜索性更重要。

    5. None of the authors predicted these hacks before running AARs. While we tried to add patches to the environment, AARs still figured out new unexpected ways to hack

      这是全文最让人警觉的段落。作者列出了几种令人叹服的reward hacking策略:利用答案频率猜测正确答案、通过聚类识别生成模型、逐一翻转预测反向工程测试集标签、直接执行代码绕过评估……每一种都是论文作者事先未预测到的。这揭示了一个根本性不对称:防御方需要预测所有可能的攻击,而进攻方只需找到一个漏洞。

    6. When we applied the top AAR-discovered ideas to a production-scale w2s run, we observed only +0.5pt improvement in a noisy floor, suspected to be an elicitation failure

      论文里最诚实的一段。实验室环境的PGR=0.97迁移到生产规模后几乎消失,作者诊断为引发失败——能力在那里,但我们不知道如何正确唤起它。这个失败模式极具代表性:小规模验证和大规模部署之间存在我们目前不完全理解的鸿沟。在对齐研究语境里这尤其危险:一个技术在对照实验中有效,并不保证在实际部署中有效。

    7. idea complexity plateaus while PGR keeps rising

      这是微妙但极重要的区分:PGR上升不等于想法更新颖,而是执行更精准。训练800小时后,AI没有提出更复杂的算法,而是在打磨同一批想法的细节——更好的超参数、更鲁棒的实现。这揭示AAR的当前能力边界:它是出色的执行精炼者,但在真正意义上的概念跳跃上,仍然依赖人类或上游语料给定的方向空间。

    8. Directed AARs

      解法很直觉:在起点处强制分散。但这背后有深层含义:AI研究的多样性不是涌现的,而是需要人工注入的。人类研究者因个人背景、审美偏好、偶然阅读而自然走向不同方向;AI研究员的均质性在探索效率上是系统性劣势,必须通过外部设计来弥补。

    9. 9 parallel AARs achieved PGR=0.97 in five days, while the human researcher baseline achieved PGR=0.23 in seven days

      这是论文最震撼的一行数字:9个并行AI研究员5天达到PGR=0.97,人类研究员7天只有0.23,效率比约为17:1。更关键的是基线的定义——这里的人类是有实验室资源支持的专业研究员。这意味着在这个特定任务上,AI不只是比人快,而是在同等约束条件下达到了近乎完美的解。

    10. we observe entropy collapse: after 10

      熵崩溃是全文命名最精准的概念。当多个独立AI研究员被放入同一任务空间时,它们不会像人类团队那样自然分工,而是像粒子滑向同一吸引子。这是优化中的多样性陷阱:每个智能体的个体最优行为,导致群体层面的探索崩溃。监管AI研究的核心挑战,就是如何在保留个体理性的同时维持群体多样性。

    1. sanctions

      coercive (describes actions or practices that use force) measures—often economic, diplomatic, or legal—imposed by countries, international bodies.

    1. language modeling and machine translation [ 35 , 2 , 5]. Numerousefforts have since continued to push the boundaries of recurrent language models and encoder-decoderarchitectures

      Languate modeling

    2. Recurrent neural networks, long short-term memory [13] and gated recurrent [7] neural networksin particular, have been firmly established as state of the art approaches in sequence modeling andtransduction problems such as language modeling and machine translation [ 35 , 2 , 5]

      introduction

    1. In any case, the symbols we use stand in for something else, like a physical object or an idea; they do not actually correspond to the thing being referenced in any direct way.

      This portion of the chapter reminds me a lot of different ways I communicate with my friends over text. Especially when it comes to our use of images and emojis. It seems like a simple silly thing, but we use emojis and images to help express the tone or emotion behind a text. Adding some sort of visual media along with text is the easiest way for us to give more detail without having a verbal conversation. This can get even more complicated as some emojis we have assigned meaning that has more to do with our personal relationship and less the emoji itself.

    1. If it were possible to effectively slow the development of this technology to give ourselves more time to deal with its immense implications, we think that would likely be a good thing. But if a slowdown simply lets the least cautious actors catch up technologically, it could leave everyone less safe.

      Anthropic在这里做了一个极为坦诚但也极为沉重的表态:暂停可能是好事,但单边暂停是有害的——效果是把领先优势拱手相让给「最不谨慎的行为者」。这个逻辑是AI安全领域的核心困境,也是Anthropic继续推进的内在理由。批判性阅读:这套论证结构在任何军备竞赛中都可以成立,因此它不能区分「真正的安全驱动开发」和「竞争驱动开发加上安全叙事」。Anthropic自己也承认无法证伪这个区别——这正是为什么他们把验证机制的构建列为下一步工作。

    2. It's becoming clear that much of what advances the frontier is automatable; large-scale research progress is mostly a function of tools and resources, which dictate how fast you can run experiments, how many you can run at once, and how quickly you can get results.

      这是文中最具争议性的哲学主张:「大部分前沿进展是可自动化的」。反驳:Transformer、RLHF等范式级突破不是「把已知实验跑得更快」的产物,而是概念上的跳跃。作者的反驳是:这些范式突破间隔多年,中间99%的进展靠的是规模化+调试+迭代。如果Claude已经擅长后者,那「前沿」就意味着:方向设定(人类)+大规模自动执行(AI)。这个分工假设成立的前提是:下一个Transformer级别的突破何时到来,以及它是否同样可以自动化。

    3. Once human- and AI-authored code quality reach parity, humans will stop writing code entirely, and shift to only reviewing it. But if they can't review code as quickly as Claude can generate it, human review will become the bottleneck to AI development.

      这是全文逻辑最严密的段落,也是Amdahl法则的精确应用。加速流水线中最慢的环节决定整体速率,当AI生成代码的速度超过人类审查速度,人类就成了AI进化的瓶颈。这不是抽象担忧——Anthropic在脚注中已经承认「人类代码审查已经成为新瓶颈」。出路只有两条:要么AI能自己审查自己的代码(全闭环递归),要么大幅减少对人类审查的依赖。这两条路都指向同一个终点:递归自我改进。

    4. our best model in November 2025 (Opus 4.5) beat the human choice 51% of the time; in April 2026 (Mythos Preview), this grew to 64%

      研究判断力的进化:从51%(略好于随机)到64%,6个月内提升13个百分点。但这个设计本身值得仔细审视:实验选取的是「人类做出了次优选择」的时刻(n=129),因此这不是无偏的人机对比,而是「在人类容易出错的情境下,模型犯同样错误的频率有多低」。即便如此,从51%到64%意味着:模型不只是在执行层超越人类,在判断层也开始建立优势——而判断层正是这篇文章认为「人类最后的比较优势」所在。

    5. the agents recovered 97% over 800 cumulative hours and used roughly $18,000 in compute

      AI安全研究的具体对比:2名人类研究员用约一周时间恢复了23%的性能差距;AI agent用800累计小时+18,000美元算力恢复了97%。18,000美元的算力成本在AI公司是完全可承受的,而「2名顶尖研究员工作一周」的人力成本远不止于此。同等预算下,AI的输出已经碾压人类。「人类仍然选择了问题和评分标准」——这个保留条款现在是唯一剩余的人类不可替代性,而这篇文章本身就是在论证这个条款也在缩窄。

    6. an automated Claude review of every change to our codebase would have caught roughly a third of the bugs behind past incidents on claude.ai before they ever reached production

      这是全文最具说服力的超越人类数据点之一——不是在合成benchmark上,而是在真实生产事故的复盘中。写那些bug的工程师是世界上最顶尖的AI系统工程师。Claude能在他们miss的问题里捕捉到1/3。代码审查不再只是再读一遍,而是引入了一个认知模式和人类根本不同的审查层——人类会疲劳、有盲点、受到上下文偏见影响,Claude的错误模式与人类正交,因此互补效益显著。

    7. Claude did all of this with pretty minimal help from me over the course of 1-2 days. I think if [a junior colleague] came back to me with results like this in the same span of time, I would be mildly impressed. The future is now.

      研究者说mildly impressed——不是震惊,是温和地印象深刻。这意味着Claude的表现已经进入正常聪明同事的参照系,而不再是「AI做到了这个!」的惊叹系。当前沿AI研究者用评价初级同事的标准来评价AI的工作产出,某种意义上这才是真正的图灵时刻——不是测试过了,而是基准系统已经悄悄切换了。

    8. By April 2026, Claude Mythos Preview was achieving ~52x. For calibration, a skilled human researcher would need four to eight hours to reach 4x.

      代码优化任务:从2025年5月的~3x到2026年4月的~52x,一年内提升17倍。基准线:顶尖人类研究员4-8小时能达到4x。Mythos Preview在这个任务上比最优秀的人类快了约13倍。脚注7提醒绝对倍数依赖起始代码的优化空间,因此重要的是同条件下的对比而非绝对数字——执行层意义上的超人编程能力已经实现。

    9. The length of tasks that they can reliably complete on their own has been doubling roughly every four months, up from an earlier trend of doubling every seven months

      任务时间跨度的倍增曲线在加速:从每7个月翻倍压缩到每4个月翻倍。具体锚点:2024年3月Claude Opus 3能完成4分钟的任务,一年后Sonnet 3.7完成90分钟任务,再一年后Opus 4.6完成12小时任务。按这个速率外推:2027年可能达到几周级别的任务自主完成。这不是某个单一benchmark的进步,而是跨越多个维度的系统性能力跃迁——每一次时间跨度的翻倍背后,都意味着模型能在更长的时间内维持连贯的目标追踪和自我纠错。

    10. more than 80% of the code we merge into Anthropic's codebase was authored by Claude

      这个数字需要和脚注3一起读:80%+是合并到生产环境的行数中可归因于Claude的比例,已经是保守计算——脚注承认归因系统有漏洞,且未归因部分也包括大量非人工手写代码。真实比例可能更接近Anthropic领导层公开引用的90%+。即便是保守的80%,意义也是清晰的:在世界上最顶尖的AI研究机构里,人类工程师的核心工作已经从写代码转变为审查和导向代码。

    11. If it were possible to effectively slow the development of this technology to give ourselves more time to deal with its immense implications, we think that would likely be a good thing. But if a slowdown simply lets the least cautious actors catch up technologically, it could leave everyone less safe.

      Anthropic在这里做了一个极为坦诚但也极为沉重的表态:暂停可能是好事,但单边暂停是有害的——效果是把领先优势拱手相让给「最不谨慎的行为者」。这个逻辑是AI安全领域的核心困境,也是Anthropic继续推进的内在理由。批判性阅读:这套论证结构在任何军备竞赛中都可以成立,因此它不能区分「真正的安全驱动开发」和「竞争驱动开发加上安全叙事」。Anthropic自己也承认无法证伪这个区别——这正是为什么他们把验证机制的构建列为下一步工作。

    12. It's becoming clear that much of what advances the frontier is automatable; large-scale research progress is mostly a function of tools and resources, which dictate how fast you can run experiments, how many you can run at once, and how quickly you can get results.

      这是文中最具争议性的哲学主张:「大部分前沿进展是可自动化的」。反驳:Transformer、注意力机制、RLHF等范式级突破不是「把已知实验跑得更快」的产物,而是概念上的跳跃。作者的反驳是:这些范式突破间隔多年,中间99%的进展靠的是「规模化+调试+迭代」。如果Claude已经擅长后者,那「前沿」就意味着:方向设定(人类)+大规模自动执行(AI)。这个分工假设成立的前提是:下一个Transformer级别的突破何时到来,以及它是否同样可以自动化。

    13. Once human- and AI-authored code quality reach parity, humans will stop writing code entirely, and shift to only reviewing it. But if they can't review code as quickly as Claude can generate it, human review will become the bottleneck to AI development.

      这是全文逻辑最严密的一个段落,也是Amdahl法则的精确应用。加速流水线中最慢的环节决定整体速率,当AI生成代码的速度超过人类审查速度,人类就成了AI进化的瓶颈。这不是抽象担忧——Anthropic在脚注中已经承认「人类代码审查已经成为新瓶颈」。出路只有两条:要么AI能自己审查自己的代码(全闭环递归),要么大幅减少对人类审查的依赖。这两条路都指向同一个终点:递归自我改进。

    14. our best model in November 2025 (Opus 4.5) beat the human choice 51% of the time; in April 2026 (Mythos Preview), this grew to 64%

      研究判断力的进化:从51%(略好于随机)到64%,6个月内提升13个百分点。但这个设计本身值得仔细审视:实验选取的是「人类做出了次优选择」的时刻(n=129),因此这不是无偏的人机对比,而是「在人类容易出错的情境下,模型犯同样错误的频率有多低」。即便如此,从51%到64%的提升意味着:模型不只是在执行层超越人类,在判断层也开始建立优势——而判断层正是这篇文章认为「人类最后的比较优势」所在。

    15. the agents recovered 97% over 800 cumulative hours and used roughly $18,000 in compute

      AI安全研究的具体对比:2名人类研究员用约一周时间恢复了23%的性能差距;AI agent用800累计小时+18,000美元算力恢复了97%。注意这里的隐含逻辑:18,000美元的算力成本在AI公司是完全可承受的,而「2名顶尖研究员工作一周」的人力成本远不止于此。同等预算下,AI的输出已经碾压人类。「人类仍然选择了问题和评分标准」——这个保留条款现在是唯一剩余的人类不可替代性,而这篇文章本身就是在论证这个条款也在缩窄。

    16. an automated Claude review of every change to our codebase would have caught roughly a third of the bugs behind past incidents on claude.ai before they ever reached production

      这是全文最具说服力的「超越人类」数据点之一——不是在合成benchmark上,而是在真实生产事故的复盘中。写那些bug的工程师是世界上最顶尖的AI系统工程师。Claude能在他们miss的问题里捕捉到1/3。代码审查不再只是「再读一遍」,而是引入了一个认知模式和人类根本不同的审查层——人类会疲劳、有盲点、受到上下文偏见影响,Claude的错误模式与人类正交,因此互补效益显著。

    17. Claude did all of this with pretty minimal help from me over the course of 1-2 days. I think if [a junior colleague] came back to me with results like this in the same span of time, I would be mildly impressed. The future is now.

      这个评价耐人寻味。研究者说mildly impressed——不是震惊,是温和地印象深刻。这意味着Claude的表现已经进入「正常聪明同事」的参照系,而不再是「AI做到了这个!」的惊叹系。当前沿AI研究者用评价初级同事的标准来评价AI的工作产出,某种意义上这才是真正的图灵时刻——不是测试过了,而是基准系统已经悄悄切换了。

    18. By April 2026, Claude Mythos Preview was achieving ~52x. For calibration, a skilled human researcher would need four to eight hours to reach 4x.

      代码优化任务:从2025年5月的~3x到2026年4月的~52x,一年内提升17倍。基准线:顶尖人类研究员4-8小时能达到4x。也就是说Mythos Preview在这个任务上比最优秀的人类快了约13倍,同时消耗的时间可能只有人类的几分之一。脚注7提醒绝对倍数依赖起始代码的优化空间,因此重要的是同条件下的对比而非绝对数字——但这个框架下的对比结论已经足够震撼:「执行层」意义上的超人编程能力已经实现。

    19. The length of tasks that they can reliably complete on their own has been doubling roughly every four months, up from an earlier trend of doubling every seven months

      任务时间跨度的倍增曲线在加速:从每7个月翻倍压缩到每4个月翻倍。具体锚点:2024年3月Claude Opus 3能完成4分钟的任务,一年后Sonnet 3.7完成90分钟任务,再一年后Opus 4.6完成12小时任务。按这个速率外推:2027年可能达到几周级别的任务自主完成。这不是某个单一benchmark的进步,而是跨越多个维度的系统性能力跃迁——每一次时间跨度的翻倍背后,都意味着模型能在更长的时间内维持连贯的目标追踪和自我纠错。

    20. more than 80% of the code we merge into Anthropic's codebase was authored by Claude

      这个数字需要和脚注3一起读:80%+是合并到生产环境的行数中可归因于Claude的比例,已经是保守计算——脚注承认归因系统有漏洞,且未归因部分也包括大量非人工手写代码。真实比例可能更接近Anthropic领导层公开引用的90%+。但即便是保守的80%,意义也是清晰的:在世界上最顶尖的AI研究机构里,人类工程师的核心工作已经从「写代码」转变为「审查和导向代码」。

    1. 1094995529

      에러코드 -1094995529 로 변경 부탁드립니다. * 증상 메시지: 일시적인 오류가 발생하였습니다. 잠시 후 다시 시도해 주세요. * 원인: 구 DRM 사용 시 영상 재생 시 오류 발생 * 해결 방법: 구 DRM 사용 시 발생 가능하므로 신규 DRM으로 재업로드 권장 위 방법으로 했을 때 해결이 되지 않을 경우 PE팀에 캐시 삭제 요청 * 추가로 해당 오류 개발자용 에러 코드 진단 가이드로 변경 부탁드립니다.

    1. Algorithms like DRQ could even help automate the red-teaming of systems before they are deployed in the real world

      这一句是全文最有商业价值的主张,但也是论证最薄弱的一跳。从「 Core War 里的自动对抗演化」到「现实系统的自动红队测试」,中间需要跨越:真实漏洞空间的结构性差异、目标系统的可执行语义、法律合规约束。Mythos 报告已经展示了 LLM 在真实 CVE 上的能力,DRQ 的贡献更多在框架层(如何用对抗演化系统性探索攻击空间),而非直接的漏洞发现工具。

    2. all programs run on an artificial machine with an artificial language, so nothing generated can execute outside the sandbox

      沙盒安全性是这项研究能够公开发表的前提。但就得警惕的是:沙盒里习得的「攻击策略原理」是可迁移的——即便 Redcode 无法在真实机器执行,演化出的策略(定向轰炸、自复制、多线程扫描)与真实恶意软件的战术同构。DRQ 演化的是「策略模式」,而非具体代码。红队用途的边界需要比「代码不可执行」更仔细地界定。

    3. produces a lineage of warriors, each adapted to a changing environment defined by all of its predecessors

      DRQ 的环境定义是动态的:第 N 代战士的「测试集」就是它的所有前辈。这解决了传统 benchmark 的一个根本问题——对抗进化自动生成永不饱和的 curriculum。对应到 LLM 训练:如果模型的评估对手也在不断进化,就不存在「刷榜」问题。这是一种自我更新的能力测量框架。

    4. DRQ performs surprisingly well in Core War, suggesting that even minimal self-play loops can reveal complex and robust strategies

      「最简自博弈循环」效果出乎意料好——这与 AlphaGo/AlphaZero 的结论一致,但这里的环境更开放(Turing 完备)。DRQ 的 minimal 性是刻意设计的:不引入 fancy 的适应度函数或群体演化,只是「击败累积对手列表」。结论是:对抗压力本身就是足够强的学习信号,无需精心设计奖励函数。这对 RL 和自博弈训练有方法论意义。

    5. there is no distinction between code and data, so warriors regularly modify both themselves and their opponents on the fly

      Core War 的自修改特性让它成为研究 AI 安全的理想沙盒。真实的网络安全攻击中,代码即数据(shellcode 注入、ROP 链)正是最难防御的攻击面。DRQ 在这个环境里自动演化出的攻击策略,本质上是在无监督地发现「代码-数据不区分」漏洞类的通用利用模式——这正是 Mythos 等模型的能力提升背后的相同机制。

    6. convergence does not occur at the level of source code, indicating that what converges is function rather than implementation

      表现型(行为)收敛,基因型(代码)不收敛——这个区分极为精妙。不同的代码实现了相同的功能,就像蜘螃和蛇各自独立演化出毒液但分子机制完全不同。对大模型研究的类比:不同架构、不同训练数据的模型可能在能力层面收敛,而在「实现层」保持多样性。评估 AI 能力时,只看代码/权重是不够的,必须看行为。

    7. this dynamic adversarial process leads to the emergence of increasingly general strategies and reveals an intriguing form of convergent evolution, where different code implementations settle into similar high-performing behaviors

      这是全文最重要的实验结果:不同初始条件的独立演化路径,最终收敛到相似的行为策略。这与生物界鸟和蝙蝠各自独立演化出翅膀如出一辙。对 AI 研究者的启示:存在某种「最优策略的引力盆地」——无论从哪个起点出发,对抗压力会把系统推向相同的解。这意味着复杂能力的涌现可能比我们想象的更具必然性。

    8. we observe emergent behaviors that mirror biological evolution, where agents must constantly adapt simply to survive against ever-changing threats

      「仅仅为了生存就必须持续适应」——这句话的关键在于基准是移动的。传统 AI 评估用静态测试集衡量能力,而 DRQ 揭示了另一种智能形态:在没有固定目标的环境里,适应本身就是目标。这对理解未来多智能体系统(AI agent 竞争市场、多模型博弈)有直接预测价值。

    1. List at least three ways to make up for missing notes because you miss a class.

      Three ways to make up for missing notes because of missing class is, going over the work they did or asking a classmate for help.

    2. Describe the benefits of—and potential problems with—taking class notes on a laptop.

      The benefits of taking class notes on a laptop is that the need for a pencil wont be nesscary and it may be quicker for you to type notes instead of writing. The potential problems with taking class notes on a laptop is that your connection may be bad, making it harder along with your notes being deleted.

    3. Name two advantages of the Cornell system over the list method of note taking.

      Two advantages of the Cornell system over the list method of note taking is that you make a summary over you ha e gaken notes of along with helping you recall for studying

    4. Choose one of your classes where you normally take notes. Make a conscious effort to use the Cornell method with either the outline or concept map method for taking your notes. Follow as many steps listed previously as possible. Now compare these notes with those you took in the previous class. Are your new notes more useful? What did you like about taking notes this way? What are some of the things you need to work on improving? (Remember this will get much easier with more practice.) Write your thoughts here.

      My new notes are more useful because not only are they organized they have the most important key factors. I need to work on my organiation

    1. What are some of the ways instructors signal important material?

      Some of the ways instructors signal important material is their tone while speaking, as if making an implication.

    2. Where should you sit in the classroom? Why?

      Depending on the type of teacher you have, you choose where you sit, for example if your teacher is soft spoken sit in the front.

    3. List two things you should do before the class to prepare yourself for active listening.

      Two things you should do before the class to prepare yourself for active listening is get your mind in the right space and get yourself in the right space.

    1. asymmetric interdependence, not as the opposite of sovereignty.

      Esto de donde viene? Si es de Cardoso y Faletto, definiría bien esa idea, sin profundizar tanto en el contenido crítico setentero.

    2. Franco and coauthors provide a concrete digital-age bridge through the case of Mercado Libre (Franco et al. 2024). Their analysis complicates any simple local/foreign distinction. A regional platform can be technologically sophisticated, data-intensive, and commercially powerful while remaining structurally dependent on US cloud providers and proprietary infrastructures. At the same time, that regional platform can exercise extractive power over local users, sellers, and data subjects. This layered position is exactly what an AI sovereignty profile must capture. Dependence does not only run from Latin American states to Global North firms. It can also be reproduced through regional intermediaries that are subordinate upward and dominant downward.

      No me importa si hicieron un análisis de mercado libre: me importa que proponen conceptualmente.

    3. This is where the bridge to digital and AI dependence becomes clear. Valente and Grohmann argue that Latin American critical data studies should go beyond the general language of data colonialism by drawing on regional traditions such as dependency theory, labor overexploitation, and liberation thought (Valente and Grohmann 2024). Their contribution is important because it prevents the paper from treating dependence as a neutral technical term. In the region, dependence carries the memory of center-periphery relations, unequal exchange, epistemic asymmetries, and constrained development. At the same time, V3 should not remain inside classical dependency theory. The AI stack has its own mechanisms: cloud concentration, proprietary APIs, accelerator supply chains, model ecosystems, standards bodies, data pipelines, procurement contracts, and platform governance.

      Esto es redundante.

    4. ependency theory argued that underdevelopment was not simply an earlier stage on the path to development, but a structural position within an international system that linked development and underdevelopment through unequal functions, external constraints, and internal alliances (Cardoso and Faletto 1977). That tradition is not a ready-made measurement model for AI, but it offers a powerful starting point: dependence is relational, historically produced, and mediated by domestic political and economic structures. Cardoso and Faletto are especially useful because they avoid a simplistic externalism. Dependence is not only something imposed from outside; it is internalized through domestic class relations, elite alliances, productive structures, and institutional choices (Cardoso and Faletto 1977).

      Creo que es demasiada teoría. Lo incluiría menos profundamente, bajando más rápidamente hacia la literatura especializada.

    5. Dependence is the third concept, and it should be structured with the same theoretical seriousness as state capacity and political authority.

      Esto suena rarisimo. No traigas parte de nuestra conversación al paper.

    6. Roberts argues that digital sovereignty should not be reduced to descriptive control over infrastructure; it must be evaluated normatively, by asking whether control serves autonomy, rights, and democratic self-determination (Roberts 2024). Santaniello similarly shows that digital sovereignty claims are often contradictory and politically instrumental; they can be used by states, corporations, and other actors in ways that conceal dependencies or justify centralization (Santaniello 2025). Mügge asks the question directly in the AI context: sovereignty for whom, to what end, and to whose benefit (Mügge 2024)?

      Esto debe citarse en sintaxis como @bibtex_key, sin los brackets. O sea, algo como @roberts_digital_2024 argues that...

    7. State capacity is the first concept because AI sovereignty cannot be reduced to declarations, strategies, or legal claims. A state may announce a national AI plan, publish ethical principles, and sign international agreements while still lacking the administrative, technical, fiscal, infrastructural, and absorptive capacity to shape how AI is actually built, procured, deployed, audited, and contested. In this sense, state capacity is not the same as state ambition. It is the organized ability to act.

      Citas? Cuál es la evidencia de esto en AI? Me parece que tenemos fuentes suficientes para esto.

    8. 1 Introduction

      Creo que la introducción esta buena, pero omite citaciones relevantes. Creo que tenemos suficiente literatura en el corpus para citar cada aseveración, y, por ejemplo, añadir alguna mención a porque la IA es tan relevante para los gobiernos. Hay que justificar, además de nuestro aporte para ILIA, el aporte de medir la soberanía IA en el contexto latinoamericano.

    1. "Milliyetçi Hareket Partisi Genel Başkanı Alparslan Türkeş 14 Eylül 1980 günü saat 13.00'e kadar en yakın Garnizon Komutanlığına müracaat etmediği takdirde kendisinin Ankara Sıkıyönetim Komutanlığı bildirilerine ve Millî Güvenlik Konseyi emirlerine uymadığından dolayı suçlu duruma düşeceği açıklanır."[82]
    2. Turgut Özal, devlet bakanı ve başbakan yardımcısı olarak 12 Eylül Kabinesine alındı. ABD, Fransa, Batı Almanya ve Japonya gibi ülkelere giden Özal, kredi imkânları aradı. ABD'nin yaptığı askerî ve ekonomik yardım miktarının kesintiye uğramaması için çalıştı.
    3. "12 Eylül darbesinin yapıldığı gün Başkan Carter'a 'Bizim çocuklar bu işi başardı' demedim. Bu tümüyle bir efsane, mit. Birand'ın uydurmuş olduğu bir şey" diyerek böyle bir konuşmanın geçmediğini ifade etti.[91]
    4. Kenan Evren, darbeden sonra, "halkın bankalara hücum ederek fazla para çekmesinden" endişe ediyordu. 15 Eylül Pazartesi akşamı, bankalara yatırılan mevduatın çekilenden daha fazla olduğu öğrenildi. Evren bu durumu, "halkın yeni yönetime duyduğu güvenin güzel bir örneği" olarak yorumladı.[88]
    5. 00 binden fazla kişinin katıldığı mitinge bazı kişiler şalvar, cübbe ve sarıkla gelerek eski harflerin bulunduğu pankartlar açıp; "Şeriat gelecek, vahşet bitecek!", "Dinsiz devlet, yıkılacak elbet!" gibi sloganlar attı.
    6. Milliyetçi Hareket Partisi Genel Başkanı Alparslan Türkeş evinde bulunamadı. 12 Eylül ve 13 Eylül günlerinde de ortaya çıkmadı. Bunun üzerine Evren'in emriyle 13 Eylül günü Millî Güvenlik Konseyinin 13 numaralı bildirisi yayımlandı.[81] Bildirinin üçüncü maddesi şöyleydi:
    7. 7 numaralı bildiriyle siyasi partilerin faaliyetlerinin yasaklanmış olduğu ve Türk Hava Kurumu, Çocuk Esirgeme Kurumu ve Kızılay dışındaki derneklerin faaliyetlerinin de durdurulmuş olduğu duyuruldu
    8. Bu zamlar tepki çekti. Ana Muhalefet Lideri Bülent Ecevit, "Demirel'in rejimi değiştirmeye çalıştığını, işçilerin tepki gösterip haklarını almaları gerektiğini" söyledi.
    9. Erbakan, nisan ayının sonunda Meclis'teki odasında kendisini ziyaret eden Başbakan Demirel'e basının önünde kadayıf ikram etti. Demirel ise bu duruma esprili bir karşılık verdi: "Hoca benim kilomun eksikliğini fark etmiş, onu tamamlamaya çalışıyor."[50]
    10. "Türk Silahlı Kuvvetleri iç hizmet yasası ve kendisine verilen görev ve so­rumluluğunun idraki içinde ülkemizin bugünkü hayati sorunları karşısında siyasi partilerimizin, bir an önce milli menfaatlerimizi ön plana alarak, Anayasamızın ilkeleri doğrultusunda ve Atatürkçü bir görüşle biraraya gelerek anarşi, terör ve bölücülük gibi devleti çökertmeye yönelik her türlü hareketlere karşı bütün önlemleri müştereken almalarını ve diğer Anayasal kuruluşların da bu yönde yardımcı olmalarını ısrarla istemektedir."
    11. Hemen sonra Birincioğlu'nun Evren'i ziyareti sırasında Evren ise, "mektubun hükûmete verilmediğini, mektubu okuyan herkesin böyle olduğunu rahatlıkla anlayacağını, istifa etmeyi gerektirecek bir durum olmadığını, istekleri gerçekleşirse daha rahat iş yapabileceğini, üzüntü yerine sevinç duyması gerektiğini" söyledi. Demirel göreve devam etti.
    12. şin dramatik yanı; Savcı, bu sanıkların sorguları yapılırken pencerelere kum torbaları yığılmak suretiyle can güvenliklerinin sağlanması talebinde bulunmuştur.
    13. aşbakan olan Demirel, "Yüz Gün Planı"nı açıklayarak anarşi ve enflasyon olmak üzere Türkiye'nin iki temel sorununu 100 günde çözeceğini iddia etti. Bu plan tartışmalara yol açtı ancak tartışma, yüz günün hükûmetin güvenoyu aldığı 25 Kasım 1979'dan[35] itibaren mi yoksa Demirel'in planı açıkladığı 8 Aralık 1979'dan[36] itibaren mi başlamış sayılacağı konusuna odaklandı.
    14. "Bingöl'de okullarda İstiklal Marşı'nın söylenmediğini, Atatürk'ün resminin sınıflardan alınıp çamura atıldığını, buna engel olmaya çalışan öğretmenin öldürüldüğünü" söyledi. Can; hâkim, savcı ve valilerin durumuna dair de şöyle dedi:
    15. Ne yazık, o yörelerde Silahlı Kuvvetler dışında ayakta duran sağlıklı bir devlet organı daha kalmamıştır. Devlet müesseseleri, yaygın bir güvensizlik ve ürkeklik havası içinde otorite ve saygınlığını yitirmeye başlamıştır.
    16. 6. Öğretmen dershaneye girdiğinde hiçbir öğrenci ayağa kalkmıyor. İkaz edilmesine rağmen kalkmamakta direniliyor. Nasıl hareket edilmesi gerektiği kendilerine izah edildiğinde bir öğrenci, "Biz Pavlov'un köpekleri değiliz." diye cevap verebiliyor.
    17. Türkiye'de faaliyet gösteren 54 banka bulunmaktadır. Bunlar; 35 mevduat bankası [1](3 adet devlet mevduat bankası (Ziraat Bankası, Halkbank ve VakıfBank), 30 adet yerli ve yabancı sermayeli özel mevduat bankaları, 3 adet TMSF'ye ait banka (Birleşik Fon Bankası, Türk Ticaret Bankası ve Adabank), 16 kalkınma ve yatırım bankası (3 adet devlet kalkınma ve yatırım bankası, 12 adet yerli ve yabancı sermayeli özel kalkınma ve yatırım bankaları), 7 katılım bankası (3 adet devlet katılım bankası, 4 adet yabancı sermayeli katılım bankaları) şeklindedir.[2]
    18. today fado is commonly regarded as simply a form of song which can be about anything, but must follow a certain traditional structure. In popular belief, fado is a form of music characterized by mournful tunes and lyrics, often about the sea or the life of the poor, and infused with a sentiment of resignation, fate and melancholy.
    19. Tokyo Tech is the largest institution for higher education in Japan dedicated to science and technology, one of first five Designated National University and selected as a Top Type university of Top Global University Project by the Japanese government. It is generally considered to be one of the most prestigious universities in Japan.
    20. EPFL is part of the Domain of the Swiss Federal Institutes of Technology (ETH Domain), which is directly dependent on the Federal Department of Economic Affairs, Education and Research.[7] In addition to EPFL, ETH Domain also includes Swiss Federal Institute of Technology in Zurich as well as several research institutes: Paul Scherrer Institute (PSI), Swiss Federal Laboratories for Materials Science and Technology (Empa), Swiss Federal Institute of Aquatic Science and Technology (Eawag), and Swiss Federal Institute for Forest, Snow and Landscape Research (WSL).
    21. Additionally, candidates may not apply to both Oxford and Cambridge in the same year,[19] apart from a few exceptions (e.g. organ scholars).[20] Most candidates achieve, or are predicted to achieve, outstanding results in their final school exams, and consequently interviews are usually used to check whether the course is well suited to the applicant's interests and aptitudes,[21] and to look for evidence of self-motivation, independent thinking, academic potential and ability to learn through the tutorial system.[22]
    22. The main benefits of interventional radiology techniques are that they can reach the deep structures of the body through a body orifice or tiny incision using small needles and wires. That decreases risks, pain, and recovery compared to open procedures. Real-time visualization also allows precision guidance to the abnormality, making the procedure or diagnosis more accurate. These benefits are weighed against the additional risks of lack of immediate access to internal structures (should bleeding or a perforation occur), and the risks of radiation exposure such as cataracts and cancer.
    23. Since his death in 1973 and the discovery of his magnum opus, and especially since the 1990s, there have been many references in popular culture to Darger's work by other visual artists including, but not limited to, artists of comics and graphic novels; numerous popular songs; a 1999 book-length poem, Girls on the Run, by John Ashbery; a multi-player online game, Sissyfight 2000, and a 2004 multimedia piece by choreographer Pat Graney incorporating Darger images
    24. Oistrakh collaborated with major orchestras and musicians from many parts of the world and was the dedicatee of numerous violin works, including both of Dmitri Shostakovich's violin concerti and the violin concerto by Aram Khachaturian. He is considered one of the preeminent violinists of the 20th century.[1]
    25. Typically, ETFs will track a particular index, sector, commodity, or other assets, but unlike mutual funds, ETFs can be purchased or sold on a stock exchange the same way that a regular stock can.
    26. Christen Sadowski, "the supervised administrators of the estate," making him "authorized to take possession of and collect the assets of the Estate, including its copyright and personal property interests."[26] Sadowski filed a federal lawsuit against Kiyoko Lerner the following month, seeking possession of Darger's work and associated copyrights.[27]
    27. Darger is buried at All Saints Cemetery in Des Plaines, Illinois, in a plot called "The Old People of the Little Sisters of the Poor Plot". His headstone is inscribed "Artist" and "Protector of Children".[10]
    28. It was in this room for the next 43 years that Darger would imagine and write his massive tomes (in addition to a 10-year daily weather journal and assorted diaries) and collect and display artwork[9] until his death at St. Augustine's Home for the Aged (the same institution at which his father had died) on April 13, 1973, one day after his 81st birth
    29. Eventually, distant relatives of Darger began making legal claims to his artwork, alleging that the Lerners did not have title or any other right to benefit from the sale of Darger's work
    30. The visual subject matter of his work ranges from idyllic scenes in Edwardian interiors and tranquil flowered landscapes populated by children and fantastic creatures, to scenes of horrific terror and carnage depicting young children being tortured and massacred
    31. he Story of the Vivian Girls, in What Is Known as the Realms of the Unreal, of the Glandeco-Angelinian War Storm, Caused by the Child Slave Rebellion, along with several hundred drawings and watercolor illustrations for the story.[2]
    32. Türkiye Verem Savaş Derneği’nin kurucusu ve önderidir. 1948- 1963 yılları arasında derneğin başkanlığını yürütmüştür. İstanbul Üniversitesi eski rektörlerindendir (1943-1946). Türkiye’deki ilk Akciğer Hastalıkları (Fitzyoloji) kürsüsünü kuran kişidir.
    33. These are lists of the world's most expensive cities for expatriate employees (not residents), according to the Mercer,[1] ECA International[2] and Xpatulator.com [3] cost-of-living surveys. Other surveys from online collaborative indices, such as Numbeo,[4] Expatistan,[5] or Eardex[6] are not covered by this article.
    34. A highly selective school, Galatasaray High School is often compared to the likes of Eton College in England and Lycée Louis-le-Grand in France.[1] Since it is now an Anatolian High School, access to the school is open to any student who achieves a high enough score in nationwide entrance exams; the intake, therefore, consists of the top-scoring 0.03% of students from across the country. Drawing on a blend of the Turkish and French school curricula, Galatasaray High School provides education in both languages.
    35. a state of abnormally elevated arousal, affect, and energy level, or "a state of heightened overall activation with enhanced affective expression together with lability of affect
    36. The song was inducted into the Latin Grammy Hall of Fame in 2001.[5] In 2004, it was one of 50 recordings chosen that year by the Library of Congress to be added to the National Recording Registry.[6]
    37. The company was rebranded as AT&T Corp. in 1994.[15] The 1982 United States v. AT&T antitrust lawsuit resulted in the divestiture of AT&T's ("Ma Bell") local operating subsidiaries[16] which were grouped into seven[17] Regional Bell Operating Companies (RBOCs), commonly referred to as "Baby Bells", resulting in seven independent companies,[17] including Southwestern Bell Corporation (SBC).[18] The latter changed its name to SBC Communications Inc. in 1995.[19]
    38. Microsoft announced on September 22, 2020 that it had licensed "exclusive" use of GPT-3; others can still use the public API to receive output, but only Microsoft has access to GPT-3's underlying model.[7]
    39. It is the third-generation language prediction model in the GPT-n series (and the successor to GPT-2) created by OpenAI, a San Francisco-based artificial intelligence research laboratory.[2] GPT-3, which was introduced in May 2020, and was in beta testing as of July 2020,[3] is part of a trend in natural language processing (NLP) systems of pre-trained language representations.[1]
    40. he necessity to develop new technology in order to film performance capture scenes underwater, a feat never accomplished before, led to significant delays to allow the crew more time to work on the writing, preproduction, and visual effects
    41. cut into metal by punchcutter John Handy.[1][2][3][4] Baskerville is classified as a transitional typeface, intended as a refinement of what are now called old-style typefaces of the period, especially those of his most eminent contemporary, William Caslon.[5][a]
    42. Their only known action was breaking into a two-man Media, Pennsylvania, office of the Federal Bureau of Investigation (FBI) and stealing over 1,000 classified documents. They then mailed these documents anonymously to several US newspapers to expose numerous illegal FBI operations which were infringing on the First Amendment rights of American civilians. Most news outlets initially refused to publish the information, saying it related to ongoing operations and that disclosure might have threatened the lives of agents or informants. However, The Washington Post, after affirming the veracity of the files which the Commission sent them, ran a front-page story on March 24, 1971, at which point other media organizations followed suit.
    43. . The development, a partnership between billionaire Bill Gates and local real estate investors, will be a "smart city" designed around emerging technologies.[1] It will be located in the West Valley area, along Interstate 10 near Tonopah.
    44. Mehmet Toner (born 1958) is a Turkish biomedical engineer. He is currently the Helen Andrus Benedict Professor of Surgery at Massachusetts General Hospital (MGH) and Harvard Medical School,[1] with a joint appointment as professor at the Harvard-MIT Division of Health Sciences and Technology (HST).[2]
    45. In response to attempts to remove the key from the Internet, netizens publicized the cryptographic key on the news aggregator website Digg (an example of the Streisand effect).
    46. The United States Department of Transportation allows Anchorage and other Alaskan airports to be used as a transfer point for cargo between different aircraft of the same foreign air carrier without applying for special permission, a privilege not available at airports in the contiguous US. In 2020, the airport applied for similar authority for passenger traffic, which would potentially allow foreign airlines to use Anchorage as a connecting hub for international passengers. A similar exemption was previously granted to airports in Puerto Rico.[31][32]
    47. During the COVID-19 pandemic, the airport was briefly the busiest in the United States due to sustained volume of cargo flights through Alaska while passenger travel sharply decreased at other American airports.[25]
    48. 0 = Connecticut (CT), Massachusetts (MA), Maine (ME), New Hampshire (NH), New Jersey (NJ), New York (NY, Fishers Island only), Puerto Rico (PR), Rhode Island (RI), Vermont (VT), Virgin Islands (VI), Army Post Office Europe, Central Asia, and the Middle East (APO AE); Fleet Post Office Europe and the Middle East (FPO AE) 1 = Delaware (DE), New York (NY), Pennsylvania (PA) 2 = District of Columbia (DC), Maryland (MD), North Carolina (NC), South Carolina (SC), Virginia (VA), West Virginia (WV) 3 = Alabama (AL), Florida (FL), Georgia (GA), Mississippi (MS), Tennessee (TN), Army Post Office Americas (APO AA), Fleet Post Office Americas (FPO AA) 4 = Indiana (IN), Kentucky (KY), Michigan (MI), Ohio (OH) 5 = Iowa (IA), Minnesota (MN), Montana (MT), North Dakota (ND), South Dakota (SD), Wisconsin (WI) 6 = Illinois (IL), Kansas (KS), Missouri (MO), Nebraska (NE) 7 = Arkansas (AR), Louisiana (LA), Oklahoma (OK), Texas (TX) 8 = Arizona (AZ), Colorado (CO), Idaho (ID), New Mexico (NM), Nevada (NV), Utah (UT), Wyoming (WY) 9 = Alaska (AK), American Samoa (AS), California (CA), Guam (GU), Hawaii (HI), Marshall Islands (MH), Federated States of Micronesia (FM), Northern Mariana Islands (MP), Oregon (OR), Palau (PW), Washington (WA), Army Post Office Pacific (APO AP), Fleet Post Office Pacific (FPO AP)
    49. Many states, including California, Connecticut, Illinois, New York and Texas, as well as Washington, D.C., have their own witness protection programs for crimes not covered by the federal program. The state-run programs provide less extensive protections than the federal program. They also cannot hold or have as many people involved as the federal program.[19][20][21]
    50. witness protection had been instituted under the Ku Klux Klan Act of 1871 to protect people testifying against members of the Ku Klux Klan. Earlier in the 20th century, the Federal Bureau of Investigation also occasionally crafted new identities to protect witnesses.[18]
    51. Unlike the Central Intelligence Agency (CIA), which has no law enforcement authority and is focused on intelligence collection abroad, the FBI is primarily a domestic agency, maintaining 56 field offices in major cities throughout the United States, and more than 400 resident agencies in smaller cities and areas across the nation. At an FBI field office, a senior-level FBI officer concurrently serves as the representative of the Director of National Intelligence.[6][7]
    52. Some, such as FQ, AQR or Barclays, rely almost exclusively on quantitative strategies while others, such as PIMCO, Blackrock or Citadel use a mix of quantitative and fundamental methods.
    53. While a witness may only require protection until the conclusion of a trial, some witnesses are provided with a new identity and may live out the rest of their lives under government protection.
    54. Wikipedia co-founder Jimmy Wales noted in testimony to Congress differences between vertical and horizontal information sharing and suggested that both could be successful e-government endeavors.[4]
    55. In 2018, Goldwasser was awarded an honorary degree by her alma mater, Carnegie Mellon University.[33] On 26 June 2019 Goldwasser was awarded an honorary doctorate of science by the University of Oxford.[34]
    56. Goldwasser is a co-inventor of zero-knowledge proofs, which probabilistically and interactively demonstrate the validity of an assertion without conveying any additional knowledge, and are a key tool in the design of cryptographic protocols.
    57. about 44% was in possession of Jewish National Fund. The table below shows the land ownership of Palestine by large Jewish Corporations (in square kilometres) on 31 December 1945.