神译局是36氪旗下编译团队，关注科技、商业、职场、生活等领域，重点介绍国外的新技 术、新观点、新风向。 编者按：别再无效"卷"了。真正拉开差距的，是这五项不被察觉的"非对称优势"。文章来自编译。 从我记事起，我成长过程中的一切似乎都围绕着赚钱。"如果你不努力学习，你就永远找不到工作。" 不光是我。 在大多数印度家庭中，生存本身就被当作成功来崇拜。 所以我们被迫走上了一条熟悉的道路：努力工作、获得学位、然后找一份工作。 但在今天，熟悉（的老路）已经过时了。 因为当信息无处不在时，仅仅"知道"是不够的。你需要知道"该知道什么"，以及如何利用它来产生影 响。 但谁在教你这些呢？ 你的老板希望你干活又快又好，而不是变得睿智。 社交媒体希望你不停地刷屏，而不是思考。至于学校？他们还在忙着帮孩子们为那些早已不存在的工作 做准备。 世界不只是在变化而已，而且还在加速。一眨眼，你就已经落后了。 这就是为什么我认识的那些最聪明的人，不会花时间去过度吹捧他们过去学到的东西。 他们在悄悄地掌握一套截然不同的技能，这些技能更有可能复利式地增长，累积成他们的"非对称优 势"。 当大多数人还在谷歌上搜索"最佳生产力提示词"时，他们已经在 ...

来源：环球时报 法新社11月7日报道，原题：在中国，用人工智能（AI）制作短剧 龙、充满魅力的主角和催泪情节，短 剧《山海奇镜之劈波斩浪》看上去是如此真实，除了它是由人工智能生成的。这部短剧的在线播放量超 过5000万，体现了中国观众对于AI短剧的热情。不过，这一现象也让人担心AI会对就业和版权造成冲 击。 上海温哥华电影学院高级讲师奥黛·阿瓦迪亚说，人工智能"大大降低了制作成本，加快了整个流程"。 她说："人工智能是另一种讲故事的方式，你可以得到那种带来'哇塞'效果的怪诞产物。"她认为，需要 为未来做好准备，那时所有的影视从业者都需要使用人工智能。 版权问题仍令人担心，因为AI模型大量使用现有作品，却无相应的报酬体系。AI生成的内容本身也可 能被剽窃。在陈看来，尽管有人工智能的帮助，但这些内容仍是来自于"我们自己的想象，无论是人还 是神兽的模样。这些都是完全原创的作品"。（董铭译） 该剧集的导演陈坤告诉法新社，短剧的每集时长（有时不到30秒）适合智能手机播放，也适合人工智能 生成，因为观众不容易在小屏幕上发现视觉上的瑕疵，"即使AI生成的效果尚未达到传统电影的水准， 仍可以先满足短剧的需求"。陈在他的短剧中 ...

Getting thereDogan Ural (@doganuraldesign):I never thought I’d say this, but…Grok might have reached Midjourney-level aestheticsJust try it and tell me what you think https://t.co/n7bnvvF4ww ...

Core Viewpoint - The article discusses the rapid evolution of the design industry due to the integration of AI technologies, highlighting both the opportunities and challenges faced by creators in this new landscape [2][8][12]. Group 1: AI's Impact on the Design Industry - AI has significantly improved in capability, allowing creators to produce complex works more efficiently, with some projects now completed in under a week compared to previous timelines [5][7]. - The design community is witnessing a shift where AI tools are becoming integral to workflows, with less than 1% of designers on a platform having never used AI, and over 70% using it for more than an hour daily [7][10]. - The role of designers is evolving from traditional visual tasks to more comprehensive involvement in market operations and decision-making, leading to a new category of "super designers" [7][10]. Group 2: Fragmentation and Opportunities - The execution of projects is becoming increasingly fragmented, with tasks being outsourced globally, which may benefit smaller teams and individual creators [10][12]. - Smaller creative teams are now able to compete with larger firms, as AI provides them with tools to produce high-quality work without the need for extensive resources [10][12]. - The trend indicates a potential market shift towards more agile, small teams dominating the landscape, as they can leverage AI to create comparable outputs to traditional large companies [12]. Group 3: Challenges and Resistance - Despite the advantages, there is resistance among some traditional artists who feel threatened by AI's capabilities, fearing it undermines their years of training and expertise [3][14]. - Creators face the pressure of constantly adapting to new AI tools and technologies, which can lead to feelings of inadequacy and anxiety about their skills becoming obsolete [12][16]. - The article emphasizes the importance of maintaining human creativity and emotional depth in artistic work, suggesting that AI should complement rather than replace human input [18][19].

曾经"一个镜头2000多元"的AI进化了。 清晨，一群小猫走过老北京胡同口的屋顶，走过天坛、鸟巢、红墙青瓦，最后聚集在故宫"上班"，对着游客摆出最迷人的姿势。这些想象来自《故宫猫猫上 班记》——一支由AI生成的短片，全网播放量破亿。 AIGC艺术家海辛和Simon阿文在去年底创作了这一短片，但"做得很痛苦"，那时AI理解复杂指令的能力尚且稚嫩，生成长镜头极易"崩坏"，他们不得不将创 意拆解成无数短镜头，像"抽卡"一样反复尝试，一个镜头甚至能耗费2000多元。 然而不到一年，他们对第一财经记者感慨，AI的能力已提升了近两三倍，技术的门槛也越来越低。就在上个月，他们制作了一支关于橘猫和浦东的城市宣 传片，以往做不到的复杂镜头，AI已能轻松搞定。 AI的浪潮在吞没一切，整个艺术设计行业都在经历一场静默而迅猛的重构。近日设计师社区站酷发布的《AI时代的超级设计师研究手册》中提到，从来不 用AI的站酷设计师用户已不到1%。AI能让设计师"一人成团"，像海辛和阿文这样的AIGC艺术家已经越来越多，传统商业格局也在被打破。 但另一边，动画师、《爱、死亡和机器人》导演彼得·多德（Peter Dodd）对第一财经记者表示，他许 ...

Core Insights - The article discusses an upcoming CEO conference organized by JinQiu Fund, focusing on AI innovation and collaboration among entrepreneurs in the AI sector [1][2]. Event Overview - The conference is scheduled for November 1, featuring 100 CEOs from the AI industry, emphasizing a gathering of founders as the main focus [1]. - The event aims to foster direct engagement among entrepreneurs, allowing them to share insights and experiences without external authoritative figures [1]. Concept of AI Evolution - AI has transitioned from mimicking human data to actively experiencing the world through sensors and feedback, marking a shift to an "experience era" [6]. - In this new era, both AI and entrepreneurs are seen as dynamic entities that continuously learn and adapt through interaction with the environment [6]. Conference Activities - The event will include a roundtable discussion titled "Ask me anything," where investors will engage with founders based on their unique experiences [8]. - A "Long Table" discussion will facilitate open dialogue about future predictions, encouraging participation from all attendees [8]. - An evening party will provide a platform for entrepreneurs to connect and share their stories in a more relaxed setting [9]. Product Launches and Presentations - The conference will feature product launches from various AI companies, showcasing innovations across different AI applications [11][15]. - Key presentations will include insights from JinQiu Fund's partners on technology innovation and investment practices for 2025 [11]. Collaboration and Support - The event is supported by various AI tool partners and technology companies, highlighting the collaborative effort in organizing the conference [22]. - The conference aims to create a personalized experience for attendees, including customized AI representations and gift bags [16]. Previous Engagements - JinQiu Fund previously engaged with numerous leading tech companies during a CES trip, establishing a foundation for ongoing dialogue with innovators in the AI space [19][21].

RT techbimbo (@jameygannon)cracked this lo-res party girl aesthetic in midjourney the other night and i am obsesssssssed https://t.co/IHKMPmXopr ...

Core Insights - The article discusses the rapid development of image generation technology based on diffusion models, highlighting the limitations of the Variational Autoencoder (VAE) and introducing the EPG framework as a solution [1][19]. Training Efficiency and Generation Quality - EPG demonstrates significant improvements in training efficiency and generation quality, achieving a FID of 2.04 and 2.35 on ImageNet-256 and ImageNet-512 datasets, respectively, with only 75 model forward computations [3][19]. - Compared to the mainstream VAE-based models like DiT and SiT, EPG requires significantly less pre-training and fine-tuning time, with 57 hours for pre-training and 139 hours for fine-tuning, versus 160 hours and 506 hours for DiT [7]. Consistency Model Training - EPG successfully trains a consistency model in pixel space without relying on VAE or pre-trained diffusion model weights, achieving a FID of 8.82 on ImageNet-256 [5][19]. Training Complexity and Costs - The VAE's training complexity arises from the need to balance compression rate and reconstruction quality, making it challenging [6]. - Fine-tuning costs are high when adapting to new domains, as poor performance of the pre-trained VAE necessitates retraining the entire model, increasing development time and costs [6]. Two-Stage Training Method - EPG employs a two-stage training method: self-supervised pre-training (SSL Pre-training) and end-to-end fine-tuning, decoupling representation learning from pixel reconstruction [8][19]. - The first stage focuses on extracting high-quality visual features from noisy images using a contrastive loss and representation consistency loss [9][19]. - The second stage involves directly fine-tuning the pre-trained encoder with a randomly initialized decoder, simplifying the training process [13][19]. Performance and Scalability - EPG's framework is similar to classic image classification tasks, significantly lowering the barriers for developing and applying downstream generation tasks [14][19]. - The inference performance of EPG-trained diffusion models is efficient, requiring only 75 forward computations to achieve optimal results, showcasing excellent scalability [18]. Conclusion - The introduction of the EPG framework provides a new, efficient, and VAE-independent approach to training pixel space generative models, achieving superior training efficiency and generation quality [19]. - EPG's "de-VAE" paradigm is expected to drive further exploration and application in generative AI, lowering development barriers and fostering innovation [19].

Core Insights - The driving force behind the AI revolution is not genius but rather human laziness, as tools that require less effort and thought will ultimately prevail [1][2][6] - AI's diffusion is characterized by a "lazy economics" where products that allow people to do less while earning more will be adopted more quickly [6][12] Group 1: AI Diffusion and Economic Impact - AI investment can be categorized into three areas: obvious AI tracks like chatbots and productivity tools, new platforms emerging in the AI era, and opportunities outside Silicon Valley's traditional focus, such as drug discovery [4][20] - The combination of multiple models, including language models for logic and text and diffusion models for images and videos, creates a comprehensive AI ecosystem [4][12] - The shift from "hard work" to "smart laziness" signifies a change in competitive advantage, where efficiency is achieved through reduced repetitive tasks [6][12] Group 2: AI in Professional Fields - In the medical field, AI will not replace doctors but will require them to be re-educated, shifting their role from knowledge retainers to critical thinkers who can question AI outputs [7][9] - The ability to critically assess AI-generated results is more crucial than experience, as studies show that those who actively engage with AI data achieve better outcomes [11][12] - Similar transformations are occurring in other professions, such as law and programming, where the focus is on identifying AI's limitations rather than merely executing tasks [12][13] Group 3: Social Networks and AI - LinkedIn's longevity is attributed to its efficiency-focused model, which contrasts with other social networks that prioritize engagement over productivity [16][18] - The platform's success lies in its ability to create value-based connections, making it a trusted network that is difficult to replicate [18][20] - AI's potential to disrupt LinkedIn exists, but its unique network effects and trust-based structure provide resilience against such changes [18][20] Group 4: Human-AI Relationship - The relationship between humans and AI is fundamentally one-sided, as AI can simulate understanding but lacks the capacity for mutual growth [22][26] - Concerns arise about the diminishing human empathy as interactions with AI increase, emphasizing the need for a clear definition of relationships [22][26] - The evolution of AI prompts a reevaluation of human identity and purpose, as reliance on AI for decision-making may lead to a loss of autonomy [15][26]

Core Viewpoint - The article emphasizes the importance of developing personal taste and skills through deliberate practice before heavily relying on AI tools for creative work. It argues that while AI can assist in the creative process, true expertise and unique perspectives come from extensive hands-on experience and understanding of one's craft [2][34][36]. Group 1: AI and Creative Process - AI can be a powerful tool for generating content, but it should not replace the foundational skills and personal insights that come from manual practice [34][36]. - The author uses AI to assist in writing, with AI contributions varying from 0% to 40% depending on the type of content, highlighting that core ideas and insights must come from the individual [3][4][5]. - The process of selecting and refining AI-generated content is crucial and relies on the individual's judgment and taste, which cannot be replaced by AI [11][12][17]. Group 2: Importance of Deliberate Practice - The article advocates for at least 1000 hours of deliberate practice in one's field to build foundational skills and personal taste, which are essential for effective use of AI [25][35]. - This practice should be largely independent of AI to ensure that the individual develops a deep understanding of their craft [26][30]. - The author draws parallels to the "10,000-hour rule," suggesting that while AI can accelerate learning, the hands-on experience remains irreplaceable [24][35]. Group 3: The Role of Personal Taste - Personal taste is described as a critical component of creative work, which is developed through extensive exposure to quality content and hands-on practice [18][22][29]. - The article warns against the risk of relying too heavily on AI, which may lead to a decline in personal standards and creativity [20][36]. - Ultimately, the ability to leverage AI effectively hinges on having a unique perspective and understanding of what constitutes quality work [36][40].