2025-10-02 08:00:00

以下是Alexei Efros演讲内容的中文简化总结，采用Markdown格式：

我们（依然）低估了数据的重要性

在Sutter Hill Ventures的AI演讲系列中，UC Berkeley的Alexei Efros提出：视觉计算领域的AI进步由数据而非算法驱动。核心观点如下：

数据的核心作用

• 长期被忽视的真相：
  • 学术界长期推崇算法创新，临近研究尾声才匆忙收集数据
  • 这种"科学自恋"阻碍了进步——人类总将突破归功于自身智慧而非数据
• 认知科学的启示：
  • 人脑依赖海量经验数据（如能从莫奈模糊笔触中识别蒸汽机，因童年记忆补全了细节）
  • "意识本质是数据的涌现属性" ——Lance Williams
• 关键案例佐证：
  • 三篇人脸检测里程碑论文：算法完全不同（神经网络/朴素贝叶斯/级联分类器），性能却相近——真正突破在于引入"非人脸"负样本数据
  • Efros团队用200万Flickr图片+最近邻搜索实现图像补全："最笨的方法却有效"
  • 相同数据下，复杂神经网络与简单最近邻算法表现相当——解决方案始终藏在数据中

插补≠智能

• 人类记忆的局限性：
  • 对自然图像记忆超强，但对随机纹理几乎无法识别
  • 我们只记住"有意义经验流形"上的信息
• AI的本质反思：
  • 现有人工智能更像是"文化技术"（如印刷术），通过压缩人类知识实现高效交互
  • "高维空间中的插补与魔法无异"——但魔力来自数据而非算法
  • 视觉/文本空间可能比想象中更小（例如200个PCA主成分就能建模全人类面孔）
• 发展现状评估：
  • 文本数据充足→性能优异；图像数据较少→逐步提升；视频/机器人数据稀缺→进展缓慢
  • 当前AI只是"蒸馏机器"，将人类数据压缩为模型

真正智能的未来

• 核心挑战：
  • 需摆脱对人类文明产物的依赖，从原始驱动力（饥饿/嫉妒/快乐）自主演化
  • "AI不是计算机会写诗，而是计算机会想要写诗"

关键结论：突破性进展将来自高质量数据集的构建，而非算法层面的微创新

在Sutter Hill Ventures举办的AI演讲系列中，加州大学伯克利分校的Alexei Efros提出，推动视觉计算领域AI发展的核心是数据而非算法。以下是我对其演讲《我们（依然）未能充分重视数据》的笔记：

海量数据是必要条件但非充分条件。我们必须学会保持谦逊，给予数据应有的认可。视觉计算领域长期存在的算法偏见掩盖了数据的基础性作用，认清这一现实对预判AI突破方向至关重要。

数据的核心地位

• 学术界直到近年才重视数据，研究者常年钻研算法，最后关头才匆忙收集数据集
• 这种思维模式长期阻碍着领域发展
• AI领域的科学自恋：我们更愿将成就归功于人类智慧而非数据作用
• 人类理解力高度依赖存储的经验，而不仅是即时感官数据
• 人们能从莫奈模糊笔触中辨识蒸汽机细节——蒸汽机其实存在于观者脑中，童年经历导致每人看到不同版本
• 人类大脑能轻松解析高度像素化的画面，自动补全缺失信息
• "心智本质上是数据的涌现属性"——Lance Williams
• 三篇人脸检测里程碑论文用完全不同算法（神经网络、朴素贝叶斯、级联增强）达到了相近效果
• 真正突破在于意识到需要负样本（不含人脸的图像），但25年后我们仍在称赞花哨算法
• Efros团队用200万Flickr图片配合基础最近邻查找实现图像补洞："最笨的方法却奏效了"
• 相同数据集对比显示，复杂神经网络与简单最近邻方法表现相当
• 所有解决方案都藏在数据里，精密算法往往只是快速查找器——因为查找本身就包含问题答案

插值与智能

• MIT的Aude Oliva实验揭示人类记忆自然图像的惊人能力
• 但记忆具有选择性：对自然有意义图像识别率极高，对随机纹理则接近随机猜测
• 我们不具备照相记忆，只记住符合自然经验流形的事物
• 这表明人类智能高度依赖数据，但聚焦于有意义经验
• 心理学家Alison Gopnik将AI重构为文化技术：如同印刷机般收集人类知识并优化交互界面
• 它们并非创造新事物，而是精密的插值系统
• "高维空间的插值与魔法无异"，但魔法存在于数据而非算法
• 或许视觉与文本空间比想象中小，这解释了数据的强大效力
• 200个人脸PCA主成分就能建模全人类面部，此原理可扩展至像素之外的模型权重线性子空间
• 初创算法黄金问："该问题是否有足够数据？"文本：数据充足，表现优异；图像：数据较少，逐步提升；视频/机器人：数据稀缺，进展缓慢
• 现有系统是将人类数据压缩成模型的"蒸馏机器"
• 真正智能或需从零开始：剥离人类文明痕迹，从饥饿、嫉妒、快乐等原始欲望启动
• "AI不是计算机会写诗的时刻，而是计算机渴望写诗的时刻"

In his AI Speaker Series presentation at Sutter Hill Ventures, UC Berkeley's Alexei Efros argued that data, not algorithms, drives AI progress in visual computing. Here's my notes from his talk: We're (Still) Not Giving Data Enough Credit.

Large data is necessary but not sufficient. We need to learn to be humble and to give the data the credit that it deserves. The visual computing field's algorithmic bias has obscured data's fundamental role. recognizing this reality becomes crucial for evaluating where AI breakthroughs will emerge.

The Role of Data

• Data got little respect in academia until recently as researchers spent years on algorithms, then scrambled for datasets at the last minute
• This mentality hurt us and stifled progress for a long time.
• Scientific Narcissism in AI: we prefer giving credit to human cleverness over data's role
• Human understanding relies heavily on stored experience, not just incoming sensory data.
• People see detailed steam engines in Monet's blurry brushstrokes, but the steam engine is in your head. Each person sees different versions based on childhood experiences
• People easily interpret heavily pixelated footage with brains filling in all the missing pieces
• "Mind is largely an emergent property of data" -Lance Williams
• Three landmark face detection papers achieved similar performance with completely different algorithms: neural networks. naive Bayes, and boosted cascades
• The real breakthrough wasn't algorithmic sophistication. It was realizing we needed negative data (images without faces). But 25 years later, we still credit the fancy algorithm.
• Efros's team demonstrated hole-filling in images using 2 million Flickr images with basic nearest-neighbor lookup. "The stupidest thing and it works."
• Comparing approaches with identical datasets revealed that fancy neural networks performed similarly to simple nearest neighbors.
• All the solution was in the data. Sophisticated algorithms often just perform fast lookup because the lookup contains the problem's solution.

Interpolation vs. Intelligence

• MIT's Aude Oliva's experiments reveal extraordinary human capacity for remembering natural images.
• But memory works selectively: high recognition rates for natural, meaningful images vs. near-chance performance on random textures.
• We don't have photographic memory. We remember things that are somehow on the manifold of natural experience.
• This suggests human intelligence is profoundly data-driven, but focused on meaningful experiences.
• Psychologist Alison Gopnik reframes AI as cultural technologies. Like printing presses, they collect human knowledge and make it easier to interface with it
• They're not creating truly new things, they're sophisticated interpolation systems.
• "Interpolation in sufficiently high dimensional space is indistinguishable from magic" but the magic sits in the data, not the algorithms
• Perhaps visual and textual spaces are smaller than we imagine, explaining data's effectiveness.
• 200 faces in PCA could model the whole of humanity's face. Can expand this to linear subspaces of not just pixels, but model weights themselves.
• Startup algorithm: "Is there enough data for this problem?" Text: lots of data, excellent performance. Images: less data, getting there. Video/Robotics: harder data, slower progress
• Current systems are "distillation machines" compressing human data into models.
• True intelligence may require starting from scratch: remove human civilization artifacts and bootstrap from primitive urges: hunger, jealousy, happiness
• "AI is not when a computer can write poetry. AI is when the computer will want to write poetry"

2025-09-30 08:00:00

最近我有幸在O'Reilly的《现实世界中的生成式AI》播客中与Ben Lorica对话，探讨了AI时代下软件应用的变革。我们讨论了以下主题：

- **应用定义的重构**：从"代码+数据库"模式转向"URL+模型"的新范式  
- **技术演进的规律**：这一转变类似Web和移动端的平台迁移，初期应用看似简陋但会持续进化  
- **AI原生数据库**：专为AI智能体而非人类设计的数据库系统运作方式  
- **开发流程的颠覆**：AI编程助手使团队能先快速构建原型，再完善设计与产品集成  
- **职业影响**：设计师和工程师需要新技能，但同时也获得更多创作机会  
- **人文价值**：审美品味与人类监督在AI系统中的重要性  
- **其他洞见**：更多精彩观点...

完整29分钟访谈《Generative AI in the Real World: Luke Wroblewski谈数据库的智能体语言》可在O'Reilly官网收听。感谢节目组的邀请。

最近我有幸在O'Reilly的《现实世界中的生成式AI》播客节目中与Ben Lorica畅谈AI时代下软件应用的变革。我们探讨了多个话题，包括：

• 应用程序定义从"运行代码+数据库"向"URL+模型"的转变
• 这种转型如何呼应了早期网络和移动平台的发展轨迹——初始应用看似简陋，但随时间推移实现了质的飞跃
• 专为AI代理而非人类设计的数据库系统运作机制
• "逆向"软件开发流程：AI编程助手让团队能先快速构建可行原型，再进行产品化设计与集成
• 这对设计和工程角色的影响：需要新技能组合，但也创造了更多创作机会
• AI系统中审美判断与人类监督的重要性
• 以及其他洞见...

您可以在O'Reilly官网收听这期29分钟的播客《现实世界中的生成式AI：Luke Wroblewski谈数据库的智能体语言》。感谢节目组的邀请。

I recently had the pleasure of speaking with Ben Lorica on O'Reilly's Generative AI in the Real World podcast about how software applications are changing in the age of AI. We discussed a number of topics including:

• The shift from "running code + database" to "URL + model" as the new definition of an application
• How this transition mirrors earlier platform shifts like the web and mobile, where initial applications looked less robust but evolved significantly over time
• How a database system designed for AI agents instead of humans operates
• The "flipped" software development process where AI coding agents allow teams to build working prototypes rapidly first, then design and integrate them into products
• How this impacts design and engineering roles, requiring new skill sets but creating more opportunities for creation
• The importance of taste and human oversight in AI systems
• And more...

You can listen to the podcast Generative AI in the Real World: Luke Wroblewski on When Databases Talk Agent-Speak (29min) on O-Reilly's site. Thanks to all the folks there for the invitation.

2025-09-26 08:00:00

以下是整理后的中文摘要，采用Markdown格式：

# 如何用AI解决正确的问题——Dave Crawford演讲笔记

在Future Product Days的演讲中，Dave Crawford分享了如何有效将AI整合到现有产品中并避免常见陷阱的见解：

## 核心观点
* 许多团队接到"加入AI"的指令时，容易陷入"AI万能锤"陷阱——把每个问题都看作AI钉子
* 关键不是"能用AI做什么"，而是"用AI做什么有意义"，应聚焦AI能为用户创造最大价值的场景

## AI交互模式
用户主要通过四种方式接触AI：
1. **发现型AI**：替代搜索功能，帮助发现信息和建立连接
2. **分析型AI**：通过数据分析提供洞察（如医疗影像诊断）
3. **生成型AI**：创造图像、文本、视频等内容
4. **功能型AI**：直接执行任务或与其他服务交互

### 交互上下文谱系（用户负担由高到低）：
- **开放式对话框**（如ChatGPT）：需用户提供全部上下文，负担高
- **侧边栏体验**：掌握部分应用上下文，仍需切换
- **嵌入式**：深度融入用户工作流
- **后台型**：自主运行无需交互

## AI产品开发原则
* **简约思维**：提供明确价值，让用户清楚能获得什么
* **情境思维**：利用现有上下文定制体验
* **全局思维**：从大处着眼，逆向推导
* **挖掘-推理-推断**：充分利用用户提供的信息
* **主动思维**：预测用户需求提前行动
* **责任思维**：考虑环境成本影响
* **价值优先于体验**：核心是交付价值

## 适合AI解决的问题
✓ 用户厌烦的枯燥任务  
✓ 当前外包的复杂活动  
✓ 耗时的冗长流程  
✓ 造成痛苦的挫败体验  
✓ 可自动化的重复工作  

## 重要提醒
× 不要用AI解决已有简单方案的问题  
× 并非所有AI都需要聊天界面，传统UI可能更优  
⚠️ 用户对AI的容错率极低：一次糟糕体验平均需要8个月恢复信任  
🔍 当前重点应是"寻找正确问题"而非"寻找完美解决方案"，应解决真实痛点而非炫技

（注：根据中文阅读习惯对部分案例表述进行了本地化调整，保留了核心术语的英文标注，采用分层缩进结构增强可读性）

在Future Product Days大会的《如何用AI解决正确的问题》演讲中，Dave Crawford分享了如何有效将AI整合到成熟产品中而不落入常见陷阱的见解。以下是我的演讲笔记：

• 许多团队都接到过"去给产品添加些AI功能"的指令。对于AI这项技术，很容易陷入"拿着AI锤子看什么都是钉子"的陷阱
• 我们需要专注于在能为用户带来最大价值的地方使用AI。重点不是我们能拿AI做什么，而是用AI做什么才合理

AI交互模式

• 人们通常通过四种主要交互类型接触AI
• 发现型AI：帮助人们查找、连接和学习信息，常替代搜索功能
• 分析型AI：通过数据分析提供洞察，例如从医学扫描中检测癌症
• 生成型AI：创建图像、文本、视频等内容
• 功能型AI：直接执行操作或与其他服务交互，实际完成任务
• AI交互模式存在于从高用户负担到低用户负担的上下文谱系中
• 开放式文本框聊天：用户必须提供所有上下文（如ChatGPT、Copilot）- 用户操作成本高
• 侧边体验：对应用其他部分有一定上下文感知，但仍需切换上下文
• 嵌入式：高度情境化的AI，直接出现在用户工作流程中
• 后台型：自主执行任务而无需用户直接交互的智能体

AI产品开发原则

• 简单思考：打造有意义且能提供明确价值的产品。用户需要清楚能从你的AI体验中获得什么
• 情境思考：能否利用现有上下文让体验更相关？在用户工作流中定制体验
• 大胆思考：AI能力强大，不妨先设想大场景再逐步收敛
• 挖掘·推理·推断：充分利用用户提供的信息
• 前瞻思考：在用户开口前，你能为他们做些什么？
• 责任思考：考虑使用AI对环境和经济成本的影响
• 我们应优先关注价值交付而非愉悦体验

适合AI解决的问题

• 用户觉得枯燥乏味的任务
• 用户当前外包给其他服务的复杂活动
• 耗时过长的冗长流程
• 造成用户困扰的挫败体验
• 可自动化的重复性工作
• 不要用AI解决已有简单方案的问题
• 并非所有AI都需要聊天界面。有时传统UI比AI更合适
• 用户对AI的容忍度和宽容度极低。一次糟糕体验后，用户平均需要8个月才会再次尝试AI产品
• 我们现在应该寻找正确的问题去解决，而非为问题寻找正确的解决方案。构建真正解决问题的产品，而非仅仅展示AI能力

In his How to solve the right problem with AI presentation at Future Product Days, Dave Crawford shared insights on how to effectively integrate AI into established products without falling into common traps. Here are my notes from his talk:

• Many teams have been given the directive to "go add some AI" to their products. With AI as a technology, it's very easy to fall into the trap of having an AI hammer where every problem looks like an AI nail.
• We need to focus on using AI where it's going to give the most value to users. It's not what we can do with AI, it's what makes sense to do with AI.

AI Interaction Patterns

• People typically encounter AI through four main interaction types
• Discovery AI: Helps people find, connect, and learn information, often taking the place of search
• Analytical AI: Analyzes data to provide insights, such as detecting cancer from medical scans
• Generative AI: Creates content like images, text, video, and more
• Functional AI: Actually gets stuff done by performing actions directly or interacting with other services
• AI interaction patterns exist on a context spectrum from high user burden to low user burden
• Open Text-Box Chat: Users must provide all context (ChatGPT, Copilot) - high overhead for users
• Sidecar Experience: Has some context about what's happening in the rest of the app, but still requires context switching
• Embedded: Highly contextual AI that appears directly in the flow of user's work
• Background: Agents that perform tasks autonomously without direct user interaction

Principles for AI Product Development

• Think Simply: Make something that makes sense and provides clear value. Users need to know what to expect from your AI experience
• Think Contextually: Can you make the experience more relevant for people using available context? Customize experiences within the user's workflow
• Think Big: AI can do a lot, so start big and work backwards.
• Mine, Reason, Infer: Make use of the information people give you.
• Think Proactively: What kinds of things can you do for people before they ask?
• Think Responsibly: Consider environmental and cost impacts of using AI.
• We should focus on delivering value first over delightful experiences

Problems for AI to Solve

• Boring tasks that users find tedious
• Complex activities users currently offload to other services
• Long-winded processes that take too much time
• Frustrating experiences that cause user pain
• Repetitive tasks that could be automated
• Don't solve problems that are already well-solved with simpler solutions
• Not all AI needs to be a chat interface. Sometimes traditional UI is better than AI
• Users' tolerance and forgiveness of AI is really low. It takes around 8 months for a user to want to try an AI product again after a bad experience
• We're now trying to find the right problems to solve rather than finding the right solutions to problems. Build things that solve real problems, not just showcase AI capabilities

2025-09-26 08:00:00

# 利用行为科学设计用户体验的核心要点

在"未来产品日"的演讲《揭示驱动用户行为的隐藏力量》中，Sarah Thompson分享了如何运用行为科学打造更有效用户体验的见解：

## 人类大脑的永恒特性
* 尽管AI技术呈指数级发展，但人类大脑已有约4万年未经历实质性进化
* 我们仍在为"穴居人大脑"做设计
* 这种不变的人类特性为设计提供了稳定的基础

## 行为科学的重要性
* 在技术快速迭代的今天，行为科学比以往更重要
* 现代工具让我们能以空前速度扩大产品影响

## 决策的神经科学原理
* 所有决策本质都是感性的
* 大脑的感性系统（系统1）会先于意识10秒做出决定
* 系统1思维特点：
  - 快速/自动化
  - 依赖直觉反应（进化遗留）
  - 运用180+种认知偏差简化复杂判断

## 成本收益评估机制
* 每次决策时，感性大脑会即时评估：
  - 成本>收益 → 放弃行动
  - 收益>成本 → 采取行动
* 评估仅关注6个核心维度：

| 维度       | 特征                      | 设计启示                          |
|------------|---------------------------|-----------------------------------|
| **心理**   | "思考是耗能的"            | 减少选择项/采用默认设置/即时理解  |
| **社交**   | "归属需求根深蒂固"        | 营造安全感/避免尴尬或排斥感       |
| **情感**   | "视觉刺激触发最快"        | 通过图像设定情感基调              |
| **体力**   | "天生节约体力"            | 推行刷脸支付/可穿戴设备等便利设计 |
| **物质**   | "稀缺思维模式"            | 运用稀缺话术/提供对等回报         |
| **时间**   | "渴望即时满足"            | 减少等待/强调省时效果             |

## 核心结论
**设计者无法改变穴居人大脑，但可以为其设计。**

在Future Product Days大会的演讲《揭示驱动用户行为的隐藏力量》中，Sarah Thompson分享了如何运用行为科学打造更高效用户体验的洞见。以下是我的演讲笔记：

• 虽然AI技术呈指数级发展，但人类大脑已有约4万年未经历重大更新，因此我们仍在为"穴居人大脑"做设计
• 这种不变的人类特质为设计提供了稳定基础，不会随技术浪潮更迭而改变
• 行为科学比以往更重要，因为我们现在的工具能实现前所未有的规模化
• 所有决策都是情绪化的——大脑中负责决策的系统一（情绪部分）会提前10秒激活，早于人们意识到自己已做决定
• 系统一思维快速、自动，曾帮我们凭直觉生存。如今它仍主导决策，但会使用捷径和180多种已知认知偏差应对复杂性
• 每次决策时，情绪脑会立即预判行动带来的成本与收益。成本更高？放弃。收益更大？前进
• 情绪脑只关注六类直觉成本与收益：内在（心理、情感、身体）和外在（社交、物质、时间）
• 心理成本："思考很费力"进化让我们节省脑力——选项过多时人们会放弃，倾向默认设置。用户能否立即明白该做什么？
• 社交成本："我们天生需要归属"进化使我们将社交成本视为生死问题。设计让用户感到安全、被关注、有归属吗？还是引发尴尬或被排斥？
• 情感成本："自动触发"图像是设定情感基调的最快方式。这个设计可能触发什么积极/消极的自动反应？
• 身体成本："我们本能节省体力"拍付、人脸识别、可穿戴设备带来身体收益。能否消除实际或感知的体力消耗？
• 物质成本："大脑形成于匮乏环境""Bob三分钟前预订了"等稀缺策略能促发行动。我们在要求用户放弃什么？或给予什么回报？
• 时间成本："我们渴望即时回报"任何等待都会导致流失。能否提供即时奖励或让用户感觉节省时间？
• 你无法摆脱穴居人大脑，但可以为它设计。

In her talk Reveal the Hidden Forces Driving User Behavior at Future Product Days, Sarah Thompson shared insights on how to leverage behavioral science to create more effective user experiences. Here's my notes from her talk:

• While AI technology evolves exponentially, the human brain has not had a meaningful update in approximately 40,000 years so we're still designing for the "caveman brain"
• This unchanging human element provides a stable foundation for design that doesn't change with every wave of technology
• Behavioral science matters more than ever because we now have tools that allow us to scale faster than ever
• All decisions are emotional because there is an system one (emotional) part of the brain that makes decisions first. This part of the brain lights up 10 seconds before a person is even aware they made a decision
• System 1 thinking is fast, automatic, and helped us survive through gut reactions. It still runs the show today but uses shortcuts and over 180 known cognitive biases to navigate complexity
• Every time someone makes a decision, the emotional brain instantly predicts whether there are more costs or gains to taking action. More costs? Don't do it. More gains? Move forward
• The emotional brain only cares about six intuitive categories of costs and gains: internal (mental, emotional, physical) and external (social, material, temporal)
• Mental: "Thinking is hard" We evolved to conserve mental effort - people drop off with too many choices, stick with defaults. Can the user understand what they need to do immediately?
• Social: "We are wired to belong" We evolved to treat social costs as life or death situations. Does this make users feel safe, seen, or part of a group? Or does it raise embarrassment or exclusion?
• Emotional: "Automatic triggers" Imagery and visuals are the fastest way to set emotional tone. What automatic trigger (positive or negative) might this design bring up for someone?
• Physical: "We're wired to conserve physical effort" Physical gains include tap-to-pay, facial recognition, wearable data collection. Can I remove real or perceived physical effort?
• Material: "Our brains evolved in scarcity" Scarcity tactics like "Bob booked this three minutes ago" drive immediate action. Are we asking people to give something up or are we giving them something in return?
• Temporal: "We crave immediate rewards" Any time people have to wait, we see drop off. Can we give immediate reward or make people feel like they're saving time?
• You can't escape the caveman brain, but you can design for it.

2025-09-25 18:00:00

# 凯特·莫兰谈AI功能使用率低的三大原因

在哥本哈根"未来产品日"的演讲《AI采用鸿沟：为何优秀功能无人问津》中，NN/g副总裁凯特·莫兰揭示了用户不使用数字产品中AI功能的深层原因：

## 核心洞察
1. **用户研究的黄金法则**  
   - 理解用户的最佳方式：直接交流+观察产品使用行为
   - 大多数用户并不主动寻找或期待AI功能，他们只关注完成任务

2. **AI功能遭冷遇的三大主因**  
   - ❌ 缺乏使用动机（"我为什么要用？"）
   - ❌ 功能不可见（"根本没注意到"）
   - ❌ 使用门槛高（"不知道怎么用"）

3. **企业场景的特殊性**  
   - 信任问题成为额外障碍（但非主要原因）

## 关键发现
- **技术≠价值**：用户只关心结果，而非底层技术。"AI驱动"不是卖点，解决问题才是
- **亚马逊案例**：购物助手"Rufus"虽功能强大（能结合购买历史提供建议），但因命名晦涩和入口隐蔽导致使用率低
- **交互设计误区**：
  - 用户将对话界面当作"智能搜索"，仅输入关键词而非完整上下文
  - 设计师常犯基础错误（命名/图标/呈现方式），这些问题与AI无关

## 专业概念区分
- **可发现性** vs **可寻性**  
  - 可寻性：定位已知目标的能力  
  - 可发现性：意外发现未知功能的可能性

## 成功案例
- **轻量级AI功能**表现最佳（如自动摘要），因其：  
  - 零交互成本  
  - 无缝融入现有工作流

## 重要结论
⚠️ 这些采用障碍并非AI独有，适用于所有新功能。因此传统设计技能在AI时代依然至关重要！

（注：NN/g指Nielsen Norman Group，全球知名用户体验咨询公司，凯特·莫兰现任该公司副总裁）

在哥本哈根未来产品日大会上的演讲《AI采用鸿沟：为何卓越功能无人问津》中，凯特·莫兰分享了用户为何不使用数字产品中AI功能的洞见。以下是我的演讲笔记：

• 理解数字产品目标用户的最佳方式，就是与他们交谈并观察他们使用产品的过程。
• 多数用户并不主动寻找或期待AI功能。他们专注于完成任务，只想尽快解决问题然后离开。
• 用户不使用AI功能的三大主因：没有使用理由、看不见功能入口、不知如何使用。
• 企业级应用中还存在信任等问题，但上述三点最为关键。
• 用户不关心技术原理，只在乎结果。"AI驱动"不是增值，解决用户问题才是。
• 亚马逊测试购物助手时很受欢迎，因其掌握丰富上下文：历史购买、当前浏览记录等
• 但用户既找不到这个功能，也不会使用。按钮标注"Rufus"——没人联想到这是购物问答助手
• 可寻性指定位目标功能的能力，可发现性指偶然遇见非目标功能的能力
• 在熟悉界面中，用户常错过新功能——尤其是混在众多操作中的单一入口
• 设计师常犯与AI无关的基础错误（命名、图标、呈现方式）
• 虽然用户声称对话界面最易用，但实际会将开放文本框当作智能搜索，未能发挥AI全部潜力
• 用户已习惯在文本框输入简略关键词，而非提供充足上下文让AI生成更优结果
• 无需交互的小型AI功能（如自动摘要）表现良好，因其无缝融入现有工作流
• 这些采用挑战并非AI独有，适用于所有新功能。因此现有设计技能对AI功能依然极具价值

In her The AI Adoption Gap: Why Great Features Go Unused talk at Future Product Days in Copenhagen, Kate Moran shared insights on why users don't utilize AI features in digital products. Here's my notes from her talk:

• The best way to understand the people we're creating digital products for is to talk to them and watch them use our products.
• Most people are not looking for AI features nor are they expecting them. People are task-focused, they're just trying to get something done and move on.
• Top three reasons people don't use AI features: they have no reason to use it, they don't see it, they don't know how to use it.
• There are other issues like in enterprise use cases, trust. But these are the main ones.
• People don't care about the technology, they care about the outcome. AI-powered is not a value-add. Solving someone's problem is a value-add.
• Amazon introduced a shopping assistant that when tested, people really liked because the assistant has a lot of context: what you bought before, what you are looking at now, and more
• However, people could not find this feature and did not know how to use it. The button is labeled "Rufus" people don't associate this with something that helps them get answers about their shopping.
• Findability is how well you can locate something you are looking for. Discoverability is finding something you weren't looking for.
• In interfaces that people use a lot (are familiar with), they often miss new features especially when they are introduced with a single action among many others
• Designers are making basic mistakes that don't have anything to do with AI (naming, icons, presentation)
• People say conversational interfaces are the easiest to use but it's not true. Open text fields feel like search, so people treat them like smarter search instead of using the full capability of AI systems
• People have gotten used to using succinct keywords in text fields instead of providing lots of context to AI models that produce better outcomes
• Smaller-scope AI features like automatic summaries that require no user interaction perform well because they integrate seamlessly into existing workflows
• These adoption challenges are not exclusive to AI but apply to any new feature, As a result, all your existing design skills remain highly valuable for AI features.

2025-09-25 17:00:00

以下是Tobias Ahlin在哥本哈根"未来产品日"演讲《产品创造者的未来》的核心观点总结（Markdown格式）：

### 核心论点
AI系统实现有效产出的关键缺失因素并非单纯能力，而是**分歧观点与辩论机制**。

---

### 现状观察
- **2025年趋势预测**：自主AI工作流将成为产品开发日常，AI在标准化测试（阅读/写作/数学/编程等）已超越人类
- **百名实习生困境**：AI个体虽更聪明，但集体缺乏方向性

---

### 当前AI系统局限性
1. **基础逻辑缺陷**  
   - 例：能计算石头剪刀布概率，却无法理解后出手的固有劣势
2. **现实应用失误**  
   - 建议用有毒胶水粘披萨、推荐吃石头补矿物质等致命错误
3. **性能天花板**  
   - 人类能持续进步，AI在初期成功后即陷入平台期
4. **基准测试 vs 现实表现**  
   - Monitor研究显示：63%AI生成代码测试失败，0%无需人工干预即可运行

---

### 推理的社会性本质
- **辩论优于孤立思考**：法庭对抗制证明冲突能优化结论
- **团队张力创造价值**  
  - 设计师（拓展性）vs 开发者（效率）vs 产品经理（平衡）的天然冲突驱动创新
- **AI创造力悖论**  
  - 康奈尔研究：GPT-4创意能力超越90.6%人类，其想法进入TOP10%概率是人类的7倍
  - **新瓶颈**：人类评估/整合海量创意的能力成为天花板

---

### 未来AI代理发展方向
1. **从生产到评估**  
   - 当前AI侧重生产，未来需同等重视评估与整合
2. **制度化否定机制**  
   - 建立类似学术同行评审的争议驱动系统
3. **对抗性代理循环**  
   - 设计相互质疑的AI工作流（如：生成代理 vs 批判代理），突破性能平台期
4. **超越线性思维**  
   - 真正的推理将来自设计性对抗循环，而非简单链式思考

在哥本哈根未来产品日大会的演讲《产品创造者的未来》中，托比亚斯·阿林提出：要实现人工智能系统的有效产出，除了原始能力外，分歧观点与辩论才是当前缺失的关键要素。以下是我的演讲笔记：

• 许多人描绘的未来图景中，并行智能体将按需创造产品和功能
• 2025年标志着代理工作流成为日常产品开发的一部分。AI代理在标准化测试中量化超越人类：阅读、写作、数学、编程乃至专业领域
• 但我们面临"百名实习生问题"：管理这些个体更聪明却"毫无方向感"的代理

现有系统的局限性

• 基础推理缺陷：AI模型存在根本性推理漏洞。例如能计算石头剪刀布概率，却无法理解后出手的固有劣势
• 现实应用中的致命错误：建议用有毒胶水粘披萨、推荐吃石头补充矿物质
• 性能停滞问题：与人类持续进步不同，AI代理初期成功后便进入平台期，即使给予更多时间也无法实质性突破
• 现实表现与基准测试差距：Monitor研究显示63%的AI生成代码测试失败，0%能在无人干预下直接运行

推理的社会属性

• 真正的推理本质上是社会性功能，"为辩论交流优化，而非孤立思考"
• 司法体系就是例证：对抗性论证通过冲突相互磨砺提升
• 当经过批判性审视系统结构化时，个体偏见可以相互补充
• 团队天然产生利益冲突：设计师追求丰富性，开发者倾向效率，产品经理平衡范围——这种张力催生更好结果
• AI在创造力测试中显著超越人类。康奈尔研究中GPT-4在创意生成上优于90.6%的人类，其创意进入前10%的可能性是人类的七倍
• 创意生成成本正趋近于零，但人类评估与整合这些创意的能力仍是瓶颈

AI代理的未来

• 当前代理主要辅助生产，但未来生产力需要同等投入评估与整合环节
• 制度化证伪：建立类似科学同行评审的争议驱动澄清机制
• 设计循环争议代理：一个代理编写代码，另一个评估代码，形成能突破性能瓶颈的反馈系统
• 真正的推理将来自设计为循环争议的代理，而非简单的思维链方法

In his talk The Future of Product Creators at Future Product Days in Copenhagen, Tobias Ahlin argued that divergent opinions and debate, not just raw capability, are the missing factors for achieving useful outcomes from AI systems. Here are my notes from his presentation:

• Many people are exposing a future vision where parallel agents creating products and features on demand.
• 2025 marked the year when agentic workflows became part of daily product development. AI agents quantifiably outperform humans on standardized tests: reading, writing, math, coding, and even specialized fields.
• Yet we face the 100 interns problem: managing agents that are individually smarter but "have no idea where they're going"

Limitations of Current Systems

• Fundamental reasoning gaps: AI models have fundamental reasoning gaps. For example, AI can calculate rock-paper-scissors odds while failing to understand it has a built-in disadvantage by going second.
• Fatal mistakes in real-world applications: suggesting toxic glue for pizza, recommending eating rocks for minerals.
• Performance plateau problem: Unlike humans who improve with sustained effort, AI agents plateau after initial success and cannot meaningfully progress even with more time
• Real-world vs. benchmark performance: Research from Monitor shows 63% of AI-generated code fails tests, with 0% working without human intervention

Social Nature of Reasoning

• True reasoning is fundamentally a social function, "optimized for debate and communication, not thinking in isolation"
• Court systems exemplify this: adversarial arguments sharpen and improve each other through conflict
• Individual biases can complement each other when structured through critical scrutiny systems
• Teams naturally create conflicting interests: designers want to do more, developers prefer efficiency, PMs balance scope.This tension drives better outcomes
• AI significantly outperforms humans in creativity tests. In a Cornell study, GPT-4 performed better than 90.6% of humans in idea generation, with AI ideas being seven times more likely to rank in the top 10%
• So the cost of generating ideas is moving towards zero but human capability remains capped by our ability to evaluate and synthesize those ideas

Future of AI Agents

• Current agents primarily help with production but future productivity requires and equal amount of effort in evaluation and synthesis.
• Institutionalized disconfirmation: creating systems where disagreement drives clarity, similar to scientific peer review
• Agents designed to disagree in loops: one agent produces code, another evaluates it, creating feedback systems that can overcome performance plateaus
• True reasoning will come from agents that are designed to disagree in loops rather than simple chain-of-thought approaches