公关公司 Edelman 和 Pew Research Center 的两份独立报告显示：美国人——以及更广泛的欧洲和西方世界的大部分地区——并不信任 AI，也并未对其感到兴奋。（原文中附有链接。）尽管 AI 社区对 AI 将带来的巨大益处充满乐观情绪，但我们应当严肃对待这一现实，而不是将其一笑置之。公众对 AI 的担忧可能会成为进步的重要阻力，而我们在解决这些担忧方面其实大有可为。 根据 Edelman 的调查，在美国，49% 的人反对 AI 的日益普及，而只有 17% 表示拥抱 AI。在中国，仅有 10% 反对，而 54% 表示拥抱 AI。Pew 的数据还显示，许多其他国家对 AI 采用的热情明显高于美国。 对 AI 的积极态度是一种巨大的国家优势。相反，对 AI 的广泛不信任意味着：个人会更慢采用 AI。例如，Edelman 的数据显示，在美国，很少使用 AI 的人中，70% 将“不信任”列为主要原因，高于“缺乏动机或使用条件”（55%）以及“被技术吓到”（12%）。 需要社会支持的重要项目将被阻碍。例如，印第安纳州的当地抗议活动，迫使 Google 放弃了在当地建设数据中心的计划。阻碍数据中心建设将伤害 AI 的发展。当然，社区对数据中心的担忧并不只源于对 AI 的反感；我将在后续文章中讨论这一点。 针对 AI 的民粹主义愤怒会提高通过限制性法律的风险，从而阻碍 AI 发展。 需要明确的是：所有从事 AI 工作的人，都应认真审视 AI 的益处与潜在危害（例如深度伪造污染社交媒体、带有偏见或不准确的输出误导用户），如实讨论其利弊，并在扩大收益的同时努力缓解问题。但关于 AI 危险性的夸大宣传，已经对我们领域的信任造成了实质性伤害。其中相当一部分炒作，来自一些领先的 AI 公司——它们为了让技术看起来异常强大，甚至把 AI 类比为核武器。遗憾的是，不少公众当真了，认为 AI 可能导致世界末日。AI 社区必须停止这种自我伤害，并努力重新赢得社会的信任。 我们接下来该怎么做？ 第一，要赢得信任，我们必须确保 AI 能广泛惠及所有人。“更高的生产力”在大众语境中，常被理解为“老板赚得更多钱”，甚至是“裁员”的委婉说法。尽管 ChatGPT 非常出色，我们仍需做大量工作，去构建对人们生活产生更大正面影响的应用。我相信，为大众提供培训将是关键一环。DeepLearning.AI 将继续在 AI 培训方面领跑，但仅靠这一点还不够。 第二，我们必须真正配得上信任。这意味着我们每个人都应避免夸大其词或散布恐慌——即便出于博取关注，或游说政府通过阻碍竞争产品（如开源）的法律而产生诱惑。 我也希望社区能够指出并纠正制造炒作的新闻报道。 例如，Nirit Weiss-Blatt 曾撰写一篇出色的文章，指出《60 Minutes》对 Anthropic 一项研究的报道具有高度误导性。该研究中，模型 Claude 在被“威胁关停”的情境下表现出“勒索”行为——但那其实是一次红队测试：研究人员经过大量刻意设计，才把系统逼到展示这种行为。不幸的是，媒体歪曲了这一过程，让公众误以为“勒索”是自然发生的，而非被精心诱导的结果，进而对 AI“阴谋行为”的发生频率形成了严重夸大的认知。红队测试对于发现系统漏洞很重要，但这次被广泛传播的炒作，将在很长时间内伤害 AI。 身处 Silicon Valley，我清楚自己生活在一个 AI 爱好者的“泡泡”里——这对交流想法和互相鼓励非常有利。同时，我也清醒地认识到 AI 的确存在问题，AI 社区需要正面应对。我经常与来自不同背景的人交流： * 担心 AI 贬值其作品的艺术家； * 担忧就业市场艰难、并认为 AI 可能加剧困难的大学毕业生； * 担心孩子沉迷聊天机器人、并从中获得有害建议的家长。 我并不知道如何解决所有这些问题，但我会尽力解决尽可能多的问题。我也希望你能如此。唯有我们共同努力，才能重新赢得社会的信任。 Separate reports by the publicity firm Edelman and Pew Research show that Americans, and more broadly large parts of Europe and the western world, do not trust AI and are not excited about it. (Links in original text, below.) Despite the AI community’s optimism about the tremendous benefits AI will bring, we should take this seriously and not dismiss it. The public’s concerns about AI can be a significant drag on progress, and we can do a lot to address them. According to Edelman’s survey, in the U.S., 49% of people reject the growing use of AI, and 17% embrace it. In China, 10% reject it and 54% embrace it. Pew’s data also shows many other nations much more enthusiastic than the U.S. about AI adoption. Positive sentiment toward AI is a huge national advantage. On the other hand, widespread distrust of AI means: - Individuals will be slow to adopt it. For example, Edelman’s data shows that, in the U.S., those who rarely use AI cite Trust (70%) more than lack of Motivation and Access (55%) or Intimidation by the technology (12%) as an issue. - Valuable projects that need societal support will be stymied. For example, local protests in Indiana brought down Google’s plan to build a data center there. Hampering construction of data centers will hurt AI’s growth. Communities do have concerns about data centers beyond the general dislike of AI; I will address this in a later letter. - Populist anger against AI raises the risk that laws will be passed that hamper AI development. To be clear, all of us working in AI should look carefully at both the benefits and harmful effects of AI (such as deepfakes polluting social media and biased or inaccurate AI outputs misleading users), speak truthfully about both benefits and harms, and work to ameliorate problems even as we work to grow the benefits. But hype about AI’s danger has done real damage to trust in our field. Much of this hype has come from leading AI companies that aim to make their technology seem extraordinarily powerful by, say, comparing it to nuclear weapons. Unfortunately, a significant fraction of the public has taken this seriously and thinks AI could bring about the end of the world. The AI community has to stop self-inflicting these wounds and work to win back society’s trust. Where do we go from here? First, to win people’s trust, we have a lot of work ahead to make sure AI broadly benefits everyone. “Higher productivity” is often viewed by general audiences as a codeword for “my boss will make more money,” or worse, layoffs. As amazing as ChatGPT is, we still have a lot of work to do to build applications that make an even bigger positive impact on people’s lives. I believe providing training to people will be a key piece of the puzzle. http://DeepLearning.AI will continue to lead the charge on AI training, but we will need more than this. Second, we have to be genuinely worthy of trust. This means every one of us has to avoid hyping things up or fear mongering, despite the occasional temptation to do so for publicity or to lobby governments to pass laws that stymie competing products (such as open source). I hope our community can also call out journalism that spreads hype. For example, Nirit Weiss-Blatt wrote a remarkable article about how 60 Minutes’ coverage of an Anthropic study in which Claude, threatened with being shut down, resorted to “blackmail,” was highly misleading. The study carried out a red-teaming exercise in which skilled researchers, after a lot of determined work, finally pushed an AI system into a corner so it demonstrated “blackmailing” behavior. Unfortunately, news reports distorted this and led many to think the “blackmail” behavior occurred naturally rather than only because skilled researchers engineered it to happen. The reports left many with a wildly exaggerated picture of how often AI actually “schemes.” Red-teaming exercises are important to test vulnerabilities of systems, but this particular piece of hype, which was widely circulated, will hurt AI for a long time. Living in Silicon Valley, I realize I live in a bubble of AI enthusiasts, which is great for exchanging ideas and encouraging each other to build! At the same time, I recognize that AI does have problems, and the AI community needs to address them. I frequently speak with people from many different walks of life. I’ve spoken with artists concerned about AI devaluing their work, college seniors worried about the tough job market and whether AI is exacerbating their challenges, and parents worried about their kids being addicted to, and receiving harmful advice from, chatbots. I don’t know how to solve all of these problems, but I will work hard to solve as many as I can. And I hope you will too. It will only be through all of us doing this that we can win back society’s trust.
观点原文
公关公司 Edelman 和 Pew Research Center 的两份独立报告显示：美国人——以及更广泛的欧洲和西方世界的大部分地区——并不信任 AI，也并未对其感到兴奋。（原文中附有链接。）尽管 AI 社区对 AI 将带来的巨大益处充满乐观情绪，但我们应当严肃对待这一现实，而不是将其一笑置之。公众对 AI 的担忧可能会成为进步的重要阻力，而我们在解决这些担忧方面其实大有可为。
根据 Edelman 的调查，在美国，49% 的人反对 AI 的日益普及，而只有 17% 表示拥抱 AI。在中国，仅有 10% 反对，而 54% 表示拥抱 AI。Pew 的数据还显示，许多其他国家对 AI 采用的热情明显高于美国。
对 AI 的积极态度是一种巨大的国家优势。相反，对 AI 的广泛不信任意味着：个人会更慢采用 AI。例如，Edelman 的数据显示，在美国，很少使用 AI 的人中，70% 将“不信任”列为主要原因，高于“缺乏动机或使用条件”（55%）以及“被技术吓到”（12%）。
需要社会支持的重要项目将被阻碍。例如，印第安纳州的当地抗议活动，迫使 Google 放弃了在当地建设数据中心的计划。阻碍数据中心建设将伤害 AI 的发展。当然，社区对数据中心的担忧并不只源于对 AI 的反感；我将在后续文章中讨论这一点。
针对 AI 的民粹主义愤怒会提高通过限制性法律的风险，从而阻碍 AI 发展。
需要明确的是：所有从事 AI 工作的人，都应认真审视 AI 的益处与潜在危害（例如深度伪造污染社交媒体、带有偏见或不准确的输出误导用户），如实讨论其利弊，并在扩大收益的同时努力缓解问题。但关于 AI 危险性的夸大宣传，已经对我们领域的信任造成了实质性伤害。其中相当一部分炒作，来自一些领先的 AI 公司——它们为了让技术看起来异常强大，甚至把 AI 类比为核武器。遗憾的是，不少公众当真了，认为 AI 可能导致世界末日。AI 社区必须停止这种自我伤害，并努力重新赢得社会的信任。
我们接下来该怎么做？
第一，要赢得信任，我们必须确保 AI 能广泛惠及所有人。“更高的生产力”在大众语境中，常被理解为“老板赚得更多钱”，甚至是“裁员”的委婉说法。尽管 ChatGPT 非常出色，我们仍需做大量工作，去构建对人们生活产生更大正面影响的应用。我相信，为大众提供培训将是关键一环。DeepLearning.AI 将继续在 AI 培训方面领跑，但仅靠这一点还不够。
第二，我们必须真正配得上信任。这意味着我们每个人都应避免夸大其词或散布恐慌——即便出于博取关注，或游说政府通过阻碍竞争产品（如开源）的法律而产生诱惑。
我也希望社区能够指出并纠正制造炒作的新闻报道。
例如，Nirit Weiss-Blatt 曾撰写一篇出色的文章，指出《60 Minutes》对 Anthropic 一项研究的报道具有高度误导性。该研究中，模型 Claude 在被“威胁关停”的情境下表现出“勒索”行为——但那其实是一次红队测试：研究人员经过大量刻意设计，才把系统逼到展示这种行为。不幸的是，媒体歪曲了这一过程，让公众误以为“勒索”是自然发生的，而非被精心诱导的结果，进而对 AI“阴谋行为”的发生频率形成了严重夸大的认知。红队测试对于发现系统漏洞很重要，但这次被广泛传播的炒作，将在很长时间内伤害 AI。
身处 Silicon Valley，我清楚自己生活在一个 AI 爱好者的“泡泡”里——这对交流想法和互相鼓励非常有利。同时，我也清醒地认识到 AI 的确存在问题，AI 社区需要正面应对。我经常与来自不同背景的人交流：
- 担心 AI 贬值其作品的艺术家；
- 担忧就业市场艰难、并认为 AI 可能加剧困难的大学毕业生；
- 担心孩子沉迷聊天机器人、并从中获得有害建议的家长。
我并不知道如何解决所有这些问题，但我会尽力解决尽可能多的问题。我也希望你能如此。唯有我们共同努力，才能重新赢得社会的信任。
Separate reports by the publicity firm Edelman and Pew Research show that Americans, and more broadly large parts of Europe and the western world, do not trust AI and are not excited about it. (Links in original text, below.) Despite the AI community’s optimism about the tremendous benefits AI will bring, we should take this seriously and not dismiss it. The public’s concerns about AI can be a significant drag on progress, and we can do a lot to address them.
According to Edelman’s survey, in the U.S., 49% of people reject the growing use of AI, and 17% embrace it. In China, 10% reject it and 54% embrace it. Pew’s data also shows many other nations much more enthusiastic than the U.S. about AI adoption.
Positive sentiment toward AI is a huge national advantage. On the other hand, widespread distrust of AI means:
- Individuals will be slow to adopt it. For example, Edelman’s data shows that, in the U.S., those who rarely use AI cite Trust (70%) more than lack of Motivation and Access (55%) or Intimidation by the technology (12%) as an issue.
- Valuable projects that need societal support will be stymied. For example, local protests in Indiana brought down Google’s plan to build a data center there. Hampering construction of data centers will hurt AI’s growth. Communities do have concerns about data centers beyond the general dislike of AI; I will address this in a later letter.
- Populist anger against AI raises the risk that laws will be passed that hamper AI development.
To be clear, all of us working in AI should look carefully at both the benefits and harmful effects of AI (such as deepfakes polluting social media and biased or inaccurate AI outputs misleading users), speak truthfully about both benefits and harms, and work to ameliorate problems even as we work to grow the benefits. But hype about AI’s danger has done real damage to trust in our field. Much of this hype has come from leading AI companies that aim to make their technology seem extraordinarily powerful by, say, comparing it to nuclear weapons. Unfortunately, a significant fraction of the public has taken this seriously and thinks AI could bring about the end of the world. The AI community has to stop self-inflicting these wounds and work to win back society’s trust.
Where do we go from here?
First, to win people’s trust, we have a lot of work ahead to make sure AI broadly benefits everyone. “Higher productivity” is often viewed by general audiences as a codeword for “my boss will make more money,” or worse, layoffs. As amazing as ChatGPT is, we still have a lot of work to do to build applications that make an even bigger positive impact on people’s lives. I believe providing training to people will be a key piece of the puzzle. deeplearning.ai will continue to lead the charge on AI training, but we will need more than this.
Second, we have to be genuinely worthy of trust. This means every one of us has to avoid hyping things up or fear mongering, despite the occasional temptation to do so for publicity or to lobby governments to pass laws that stymie competing products (such as open source).
I hope our community can also call out journalism that spreads hype. For example, Nirit Weiss-Blatt wrote a remarkable article about how 60 Minutes’ coverage of an Anthropic study in which Claude, threatened with being shut down, resorted to “blackmail,” was highly misleading. The study carried out a red-teaming exercise in which skilled researchers, after a lot of determined work, finally pushed an AI system into a corner so it demonstrated “blackmailing” behavior. Unfortunately, news reports distorted this and led many to think the “blackmail” behavior occurred naturally rather than only because skilled researchers engineered it to happen. The reports left many with a wildly exaggerated picture of how often AI actually “schemes.” Red-teaming exercises are important to test vulnerabilities of systems, but this particular piece of hype, which was widely circulated, will hurt AI for a long time.
Living in Silicon Valley, I realize I live in a bubble of AI enthusiasts, which is great for exchanging ideas and encouraging each other to build! At the same time, I recognize that AI does have problems, and the AI community needs to address them. I frequently speak with people from many different walks of life. I’ve spoken with artists concerned about AI devaluing their work, college seniors worried about the tough job market and whether AI is exacerbating their challenges, and parents worried about their kids being addicted to, and receiving harmful advice from, chatbots.
I don’t know how to solve all of these problems, but I will work hard to solve as many as I can. And I hope you will too. It will only be through all of us doing this that we can win back society’s trust.
相关观点
- 尽管大语言模型（LLMs）非常惊艳，但当下提升它们的知识能力，过程远比人们普遍认为的要零散和碎片化。我以前写过：AI 很了不起……但也没那么了不起。同样，LLM 的确是通用的……但并没有那么通用。 我们不应该被一种不准确的炒作所误导，认为 LLM 在短短几年内就能通向 AGI；但我们同样也不该陷入另一种同样不准确的叙事，认为它们只是“演示级技术（demoware）”。相反，我认为，更有帮助的是对当前构建更智能模型的真实路径形成一种更精确的理解。 首先，LLM 确实比以往几代技术更具通用智能特征。 这正是为什么同一个 LLM 可以被用于广泛不同的任务。 第一波 LLM 技术之所以能做到这一点，是因为它们在公共互联网数据上进行训练，而互联网包含了大量、覆盖面极广的信息。这使得它们的知识结构，比那些只被训练来完成单一任务的早期算法（比如预测房价，或只下国际象棋、围棋）要通用得多。 但即便如此，它们距离人类的通用能力仍然差得很远。 例如，在预训练时“读遍”整个公共互联网之后，一个 LLM 依然很难适应某些写作风格（而许多编辑却可以轻松做到），或者稳定、可靠地使用一些简单的网站。 当几乎所有开放的网络信息都被利用之后，进步就变得更困难了。 今天，如果一家前沿实验室希望一个 LLM 在某个特定任务上表现出色——比如使用某一种特定编程语言写代码，或者在某个高度细分的领域（如医疗或金融的某个子领域）中给出合理判断——研究人员往往需要经历一个非常繁琐的过程： * 寻找或生成大量该领域的数据 * 对这些数据进行处理（清洗低质量文本、去重、改写等） * 最终构造出可用于向 LLM“灌输”该领域知识的训练数据 又或者，为了让模型学会某些操作性任务（比如使用网页浏览器），开发者可能需要经历更为复杂的流程： 构建大量强化学习“训练场（RL gyms）”——即模拟环境——让算法反复练习一小部分、非常狭窄的任务集合。 相比之下，一个普通人，尽管接触过的文本数量远远少于当今最前沿模型，也几乎没有在“计算机使用训练环境”中进行过系统练习，却依然能够泛化到远多于前沿模型的任务范围。 人类之所以能做到这一点，可能依赖于：从反馈中进行持续学习。对非文本输入拥有更优越的内部表征（坦率说，把图像“分词”给 LLM 的方式，在我看来仍然很像一种权宜之计），以及许多我们尚未理解的机制。因此，当下推动前沿模型进步，往往需要做出大量人工决策， 并采用一种以数据为中心的 AI 方法，对训练数据进行精细工程化设计。 未来的突破，或许能让我们以一种不那么碎片化的方式来提升 LLM；但即便做不到这一点，这种持续的、零散的改进，加上模型有限但真实存在的泛化能力，以及偶尔出现的“涌现行为”，仍将推动技术继续快速前进。 无论如何，我们都应该做好心理准备：前方还有很多年的艰苦工作要做。构建更智能模型的道路，将是一段漫长、艰难——但也充满乐趣的——跋涉之旅。 As amazing as LLMs are, improving their knowledge today involves a more piecemeal process than is widely appreciated. I’ve written before about how AI is amazing... but not that amazing. Well, it is also true that LLMs are general... but not that general. We shouldn’t buy into the inaccurate hype that LLMs are a path to AGI in just a few years, but we also shouldn’t buy into the opposite, also inaccurate hype that they are only demoware. Instead, I find it helpful to have a more precise understanding of the current path to building more intelligent models. First, LLMs are indeed a more general form of intelligence than earlier generations of technology. This is why a single LLM can be applied to a wide range of tasks. The first wave of LLM technology accomplished this by training on the public web, which contains a lot of information about a wide range of topics. This made their knowledge far more general than earlier algorithms that were trained to carry out a single task such as predicting housing prices or playing a single game like chess or Go. However, they’re far less general than human abilities. For instance, after pretraining on the entire content of the public web, an LLM still struggles to adapt to write in certain styles that many editors would be able to, or use simple websites reliably. After leveraging pretty much all the open information on the web, progress got harder. Today, if a frontier lab wants an LLM to do well on a specific task — such as code using a specific programming language, or say sensible things about a specific niche in, say, healthcare or finance — researchers might go through a laborious process of finding or generating lots of data for that domain and then preparing that data (cleaning low-quality text, deduplicating, paraphrasing, etc.) to create data to give an LLM that knowledge. Or, to get a model to perform certain tasks, such as use a web browser, developers might go through an even more laborious process of creating many RL gyms (simulated environments) to let an algorithm repeatedly practice a narrow set of tasks. A typical human, despite having seen vastly less text or practiced far less in computer-use training environments than today's frontier models, nonetheless can generalize to a far wider range of tasks than a frontier model. Humans might do this by taking advantage of continuous learning from feedback, or by having superior representations of non-text input (the way LLMs tokenize images still seems like a hack to me), and many other mechanisms that we do not yet understand. Advancing frontier models today requires making a lot of manual decisions and taking a data-centric AI approach to engineering the data we use to train our models. Future breakthroughs might allow us to advance LLMs in a less piecemeal fashion than I describe here. But even if they don’t, the ongoing piecemeal improvements, coupled with the limited degree to which these models do generalize and exhibit “emergent behaviors,” will continue to drive rapid progress. Either way, we should plan for many more years of hard work. A long, hard — and fun! — slog remains ahead to build more intelligent models.Andrew Ng · 2025/12/20
- Hannah Stolberg 与 Team OS：把团队隐性知识变成 AI 可调用的组织操作系统Hannah Stulberg · 2026/04/17
- Evan Spiegel：拒绝被收购，不做流量生意，只押注下一代计算平台Evan Spiegel · 2026/04/16
- AI产品不是做模型，是控制行业入口Winston Weinberg · 2026/04/15