2025-08-09 13:34:35

2025-08-09 13:18:00
‘It’s missing something’: AGI, superintelligence and a race for the future OpenAI's CEO Sam Altman describes the latest upgrade to ChatGPT as a significant step toward artificial general intelligence (AGI) but notes important limitations remain. Competitors like Meta, Google, and Anthropic are also advancing in AI技术研发领域，但都尚未达到AGI的理论状态。科技分析师Benedict Evans指出，在缺乏对生成式AI模型为何有效及如何实现AGI的理解的情况下，当前的研发更像是在进行“思想实验”。同时，Aaron Rosenberg认为若将AGI定义为至少80%经济相关数字任务中的人类水平表现，则这一目标可能在本十年末达成。Matt Murphy则表示AGI的定义会不断演变，并预计竞争将继续多年。尽管距离实现AGI尚远，现有的生成式AI系统已开始盈利并引发全球科技巨头巨额投资竞赛。 A significant step forward but not a leap over the finish line . That was how Sam Altman, chief executive of OpenAI, described the latest upgrade to ChatGPT this week. The race Altman was referring to was artificial general intelligence (AGI), a theoretical state of AI where, by OpenAI’s definition, a highly autonomous system is able to do a human’s job. Describing the new GPT-5 model, which will power ChatGPT , as a “significant step on the path to AGI”, he nonetheless added a hefty caveat. “[It is] missing something quite important, many things quite important,” said Altman, such as the model’s inability to “continuously learn” even after its launch. In other words, these systems are impressive but they have yet to crack the autonomy that would allow them to do a full-time job. OpenAI’s competitors, also flush with billions of dollars to lavish on the same goal, are straining for the tape too. Last month, Mark Zuckerberg, chief executive of Facebook parent Meta , said development of superintelligence – another theoretical state of AI where a system far exceeds human cognitive abilities – is “now in sight”. Google’s AI unit on Tuesday outlined its next step to AGI by announcing an unreleased model that trains AIs to interact with a convincing simulation of the real world, while Anthropic, another company making significant advances, announced an upgrade to its Claude Opus 4 model. So where does this leave the race to AGI and superintelligence? Benedict Evans, a tech analyst, says the race towards a theoretical state of AI is taking place against a backdrop of scientific uncertainty – despite the intellectual and financial investment in the quest. Describing AGI as a “thought experiment as much as it is a technology”, he says: “We don’t really have a theoretical model of why generative AI models work so well and what would have to happen for them to get to this state of AGI.” He adds: “It’s like saying ‘we’re building the Apollo programme but we don’t actually know how gravity works or how far away the moon is, or how a rocket works, but if we keep on making the rocket bigger maybe we’ll get there’. “To use the term of the moment, it’s very vibes-based. All of these AI scientists are really just telling us what their personal vibes are on whether we’ll reach this theoretical state – but they don’t know. And that’s what sensible experts say too.” However, Aaron Rosenberg, a partner at venture capital firm Radical Ventures – whose investments include leading AI firm Cohere – and former head of strategy and operations at Google’s AI unit DeepMind, says a more limited definition of AGI could be achieved around the end of the decade. “If you define AGI more narrowly as at least 80th percentile human-level performance in 80% of economically relevant digital tasks, then I think that’s within reach in the next five years,” he says. Matt Murphy, a partner at VC firm Menlo Ventures, says the definition of AGI is a “moving target”. He adds: “I’d say the race will continue to play out for years to come and that definition will keep evolving and the bar being raised.” Even without AGI, the generative AI systems in circulation are making money. The New York Times reported this month that OpenAI’s annual recurring revenue has reached $13bn (£10bn), up from $10bn earlier in the summer, and could pass $20bn by the year end. Meanwhile, OpenAI is reportedly in talks about a sale of shares held by current and former employees that would value it at about $500bn, exceeding the price tag for Elon Musk’s SpaceX . Some experts view statements about superintelligent systems as creating unrealistic expectations, while distracting from more immediate concerns such as making sure that systems being deployed now are reliable, transparent and free of bias. “The rush to claim ‘superintelligence’ among the major tech companies reflects more about competitive positioning than actual technical breakthroughs,” says David Bader, director of the institute for data science at the New Jersey Institute of Technology. skip past newsletter promotion after newsletter promotion “We need to distinguish between genuine advances and marketing narratives designed to attract talent and investment. From a technical standpoint, we’re seeing impressive improvements in specific capabilities – better reasoning, more sophisticated planning, enhanced multimodal understanding. “But superintelligence, properly defined, would represent systems that exceed human performance across virtually all cognitive domains. We’re nowhere near that threshold.” Nonetheless, the major US tech firms will keep trying to build systems that match or exceed human intelligence at most tasks. Google’s parent Alphabet, Meta, Microsoft and Amazon alone will spend nearly $400bn this year on AI, according to the Wall Street Journal , comfortably more than EU members’ defence spend . Rosenberg acknowledges he is a former Google DeepMind employee but says the company has big advantages in data, hardware, infrastructure and an array of products to hone the technology, from search to maps and YouTube. But advantages can be slim. “On the frontier, as soon as an innovation emerges, everyone else is quick to adopt it. It’s hard to gain a huge gap right now,” he says. It is also a global race, or rather a contest, that includes China. DeepSeek came from nowhere this year to announce the DeepSeek R1 model, boasting of “powerful and intriguing reasoning behaviours” comparable with OpenAI’s best work. Major companies looking to integrate AI into their operations have taken note. Saudi Aramco, the world’s largest oil company, uses DeepSeek’s AI technology in its main datacentre and said it was “really making a big difference” to its IT systems and was making the company more efficient. According to Artificial Analysis, a company that ranks AI models, six of the top 20 on its leaderboard – which ranks models according to a range of metrics including intelligence, price and speed – are Chinese. The six models are developed by DeepSeek, Zhipu AI, Alibaba and MiniMax. On the leaderboard for video generation models, six of the top 10 – including the current leader, ByteDance’s Seedance – are also Chinese. Microsoft’s president, Brad Smith, whose company has barred use of DeepSeek, told a US senate hearing in May that getting your AI model adopted globally was a key factor in determining which country wins the AI race. “The number one factor that will define whether the US or China wins this race is whose technology is most broadly adopted in the rest of the world,” he said, adding that the lesson from Huawei and 5G was that whoever establishes leadership in a market is “difficult to supplant”. It means that, arguments over the feasibility of superintelligent systems aside, vast amounts of money and talent are being poured into this race in the world’s two largest economies – and tech firms will keep running. “If you look back five years ago to 2020 it was almost blasphemous to say AGI was on the horizon. It was crazy to say that. Now it seems increasingly consensus to say we are on that path,” says Rosenberg.

2025-08-09 13:09:30
随着生成AI系统的发展，它们的能源消耗大大增加，预计将在五年内翻一番，占全球用电的3％。科学家正在探索受脑启发的芯片或生物芯片，这些芯片将神经器官与先进的硬件相结合，可能比传统处理器具有更高的效率和适应性。约翰·霍普金斯大学（Johns Hopkins University）的David Gracias等研究人员正在开发可以与生物组织相互作用的生物芯片，从而证明了机器人技术，假肢和疾病建模的潜在应用。但是，扩展这些技术面临挑战，例如脆弱性，维护要求以及对自主生活支持系统的需求。像FinalsPark这样的公司旨在在十年内开发可远程接近的生物艺术家，旨在使硅处理器的性能匹配，但能源效率呈指数提高。作为生成的AI

2025-08-09 13:02:25
机器学习（ML）通过利用患者数据预测和改善预后来优化急性呼吸衰竭的治疗策略有望。但是，其整合到临床实践中面临挑战，例如数据质量问题，系统异质性，临床医生接受和健康公平问题。关键目标包括预测呼吸衰竭的发作和进展，尤其是关于侵入性机械通气的需求，重点是早期干预。成功的ML实施需要强大的验证策略，解决偏见，确保电子健康记录和分析平台之间的互操作性，以及促进透明度以获得临床医生的信任。建议进行预期的多中心研究，以进行更广泛的接受和部署。

2025-08-09 12:30:00
人工智能通过使工作自动化并降低成本来改变全球劳动力，并有可能使整个角色过时。Google X前首席业务官Mo Gawdat对AI的影响表示怀疑，警告说，尽管它可以提高效率，但可能会误导过度乐观。他指出，他的冒险艾玛（Emma.love）是由三名工程师在AI援助的情况下建造的，而不是由350个开发人员组成的团队。一份麦肯锡报告预测，到2030年，有60％的美国就业机会可以自动化。雷·达利奥（Ray Dalio）强调需要平衡AI的权力与人类潜力，而Microsoft报告建议，尽管AI可以支持研究和沟通等任务，但它不能完全替代任何职业中的人类。

2025-08-09 12:00:00
来自IBM和ModernA的研究人员采用了量子模拟算法来预测60-核苷酸长的mRNA序列的二级蛋白质结构，这标志着使用量子计算机实现的最长预测。传统上，预测依赖于Google DeepMind的Alphafold等古典计算机和AI模型，但是由于计算限制，这些预测通常排除了复杂的功能，例如伪诺。这项新研究表明，量子计算通过利用量子位对分子进行建模，超过了基于量子的模拟中的序列长度和复杂性的先前基准测试，从而增强蛋白质折叠预测的潜力。

2025-08-09 11:55:00
法律学者维多利亚·汉曼（Victoria Haneman）倡导美国法律为死人的财产提供有限的数字删除权，以防止AI剥削数字遗体。她在她的文章“数字复活定律”中指出，随着个人数据存储的增加，通过AI生成的个人娱乐活动的潜在滥用也会增加。诸如Rufadaa这样的当前法律几乎没有保护，而欧洲隐私规则则包括对已故个人遗忘的权利，亨曼认为，在美国不会通过宪法审查。她提出了一个十二个月的删除窗口，以作为社会和个人利益之间的妥协。

2025-08-09 11:41:36
###研究文章摘要＃＃＃＃ 标题：通过自然语言处理揭示健康对不良怀孕结果的影响的社会决定因素####作者：Nidhi Soley，Makhaila Bentil，Jash Shah，Masoud Rouhizadeh，Casey Overdy Taylor＃＃＃＃ 抽象的：这项研究旨在了解使用自然语言加工（NLP）的卫生社会决定因素（SDOH）对不良怀孕结果的影响。作者从模仿数据库中收集和注释的电子健康记录（EHR），以识别与压力，药物滥用，家族史等有关的SDOH特征。他们使用Clinicalbert嵌入和机器学习算法进行特征提取和分类。####关键点：1。**目标**： - 分析健康的社会决定因素如何影响不良怀孕的结果。2。**方法**： - 数据收集：收集了来自MIMIC-IV数据库的EHR。 - 注释：使用NLP管道对SDOH功能进行注释。 - 特征提取：应用临床重嵌入和否定范围划界技术来提取相关信息。 - 评估：使用机器学习模型（例如，逻辑回归，SVM）根据提取的SDOH特征对不良妊娠结局进行分类。3。**结果**： - 该研究成功地确定了患者笔记中的各种SDOH因素，并将其与不良妊娠结局相关联，例如早产，低出生体重等。 - 机器学习模型可以从提取的SDOH数据中预测不良结果时获得合理的准确性。4。**结论**： - 健康的社会决定因素显着影响不良怀孕的结果。-NLP技术可以有效地从临床注释中提取这些因素，并为医疗保健提供者提供宝贵的见解。####意义： - 该研究强调了考虑在母亲和胎儿保健中考虑社会决定因素的重要性。 - 它证明了NLP自动提取SDOH数据的潜力，从而增强了妇产科的决策过程。###贡献1。**概念化与设计**：-Masoud Rouhizadeh（MR），Casey Overby Taylor（COT）和Nidhi Soley（NS）。2。**数据收集和分析**：-NS执行了NLP管道的数据收集，注释和实现。3。**评估和注释**：-NS，MB，JS有助于评估和注释临床注释。4。**评论和最终确定**： - 所有作者均审查并完成了手稿。###道德声明 - **竞争利益**：作者没有宣称没有竞争利益。 - **人类受试者保护**：约翰·霍普金斯大学医学院（IRB00467867）正在审查协议。###权利和权限本文在创意共享属性下获得许可，noncrifict-noderivatives 4.0国际许可，网址为：http：//creativecommons.org/licenses/by-nc-nc-nd/4.0/---如果您需要更多详细的信息或特定部分（例如方法，结果），请告诉我！

2025-08-09 11:30:50
特斯拉（Tesla）正在中止其内部超级计算机项目Dojo，该项目旨在用于高级驾驶员辅助系统的计算机视觉处理。该项目的负责人将离开，团队将重新分配到其他项目。首席执行官埃隆·马斯克（Elon Musk）将资源部门作为这一决定的原因，偏爱像Nvidia，AMD和三星这样的外部技术合作伙伴而不是制造AI芯片。尽管发生了这种转变，但特斯拉仍致力于为推理目的开发自己的AI芯片。该公司面临挑战，包括人才丧失，与自动驾驶软件有关的法律问题以及在推出Robotaxi服务方面遇到的困难。

2025-08-09 11:04:04
Meta声称AI在Facebook团体中的机器人增强了对话，但是像Buffy Sobol Johnson这样的居民质疑他们的价值，认为他们侵蚀了真正的社区互动。这些“ AI助手”经常被卡通人物的个人资料图片掩饰，涉嫌发布重复性问题以产生点击而不是促进有意义的对话。尽管Meta断言主持人可以禁用AI功能，但在认为这些机器人破坏邻里群体的真实性和目的的用户中，怀疑论仍然很高。研究人员指出，区分人类海报和人工智能已经变得越来越困难，这引起了人们对社交媒体平台的准备，以负责任地管理这项新技术。