Stanford 教授 Judy Fan 登上 MIT 的舞台，剖析了为什么人类如此擅长将不可见的事物变得可见……
以及为什么 AI 实际上还没有学会像我们那样“看”世界。
这将彻底改变你对人类智能与人工智能的看法：
1. 大自然从未赋予我们直线或锐角。数轴、坐标平面，甚至基础的几何学，都是人类的发明。我们创造了自然界中本不存在的工具，仅仅是因为我们需要一种能更清晰地思考的方式。
2. Descartes 发明的坐标系解决了一个困扰数学家几个世纪的难题——将立方体体积加倍。这一工具一经发明便变得不可或缺，以至于地球上几乎所有的数学课程至今仍依赖于它。
3. 人类这样做至少已有 3 万到 8 万年的历史。人类进步的故事与在环境中做标记的故事密不可分，从洞穴岩壁到 Galileo 的望远镜，再到描绘我们肉眼永远无法看到的粒子的 Feynman 图。
4. 每一次重大的科学突破都依赖于一种能让不可见之物变为可见的视觉工具。Darwin 需要将雀鸟的插图并排对比，才能看出原本太细微而难以察觉的变异。Cajal 需要在显微镜下绘制神经元的详细图谱，以揭示神经系统的连接方式。
5. Fan 的研究小组研究了一个看似简单的问题：人们如何决定在画中保留什么、省略什么。当两个人玩画图游戏时，如果目标物体存在高度相似的“竞争对手”，画图者会描绘多得多的细节；而当不需要过多细节时，他们自然会减少笔画并缩短时间。
6. 人们不仅仅是在复制他们看到的东西。他们在不断判断什么程度的细节才能真正服务于沟通的目标，而且他们做这一切是出于本能，从未有人教过他们背后的理论。
7. 为了让人认出某物而画，与为了让人理解其工作原理而画，这之间存在着真正的差异。在一项研究中，参与者绘制的解释性图表强调了机器的运动和因果关系部分，而描绘性图画则强调了背景和整体外观，尽管他们画的是完全相同的物体。
8. 在帮助人们弄清楚如何操作机器方面，解释性绘图确实更好，但在帮助人们认出那究竟是哪台机器方面却更差。你无法同时针对这两个目标优化同一张图。沟通总是伴随着权衡。
9. 用照片训练的 AI 视觉模型在泛化到简单、稀疏的草图时表现得出奇地好，这表明基于相似性的识别不仅仅是我们的一厢情愿，现代神经网络确实能够以极高的准确率复现这一能力。
10. 但是，AI 模型识别草图的自信度与人类的自信度之间仍然存在巨大且可衡量的差距，即使两者针对相同的图像回答相同的问题。人类在判断上简直可靠得多，也一致得多。
11. 当研究人员在严格的笔画数量限制下，将人类绘制的草图与 AI 生成的草图进行对比时，在笔画额度较高的情况下，两者的可识别度相似；但随着额度减少，两者出现了严重分化。一旦资源变得稀缺，人类和 AI 系统简化图画的方式有着根本的不同。
12. 看懂图表不是一项单一的技能。它涉及感知、知道该看哪里、将该视觉信息映射到实际提出的问题上，然后将这种映射转化为答案。这些步骤中的每一个都可能独立崩溃，即使人们得出了相同的错误答案，他们犯错的潜在原因也可能大相径庭。
13. 在图表阅读任务中与人类直接对决时，包括 GPT-4V 在内的领先多模态 AI 模型表现出了显著的性能差距。即使模型的总体准确率接近人类水平，其犯错的模式也与人类实际犯错的方式毫无相似之处。
14. 人们会根据试图回答的具体问题，选择完全不同类型的图表，而不是出于对柱状图或散点图的泛泛偏好。他们的图表选择，与哪种可视化方式能真正帮助他人正确回答该特定问题高度吻合。
15. 教育研究中使用最广泛的两种图表素养测试之间表现出强烈的相关性，这表明它们测量的是重叠的技能。但当研究人员深入剖析实际的错误模式时发现，教科书中的标准分类（如“寻找最大值”或“识别集群”）在解释人们为什么会犯错时，远不如一个更基础的底层四因素模型来得有效。
16. 所有这些研究背后最深层的目标不仅仅是学术好奇心。它最终是为了帮助学生和普通人真正掌握科学和现代决策越来越依赖的视觉工具，因为每一代人都应该能够站在上一代人构建的视觉工具上，比上一代人看得更远。
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Stanford professor Judy Fan went on stage at MIT and broke down why humans are so good at making the invisible visible...
And why AI hasn't actually learned to "see" the way we do.
It completely changes how you think about Human Intelligence v/s Artificial Intelligence:
1. Nature never gave us straight lines or sharp corners. The number line, the coordinate plane, even basic geometry are all human inventions. We created tools that do not exist in nature simply because we needed a way to think more clearly.
2. The coordinate system Descartes invented solved a problem that had stumped mathematicians for centuries, doubling the volume of a cube. Once invented, this tool became so indispensable that virtually every math curriculum on Earth still depends on it.
3. Humans have been doing this for at least 30,000 to 80,000 years. The story of human progress is inseparable from the story of marking up our environment, from cave walls to Galileo's telescope to Feynman diagrams of particles we will never see with our own eyes.
4. Every major scientific breakthrough relied on a visual tool that made something invisible visible. Darwin needed side-by-side illustrations of finches to see variation that was otherwise too subtle to notice. Cajal needed detailed drawings of neurons under a microscope to map how the nervous system was wired.
5. Fan's research group studies something deceptively simple: how people decide what to put into a drawing and what to leave out. When two people played a drawing game, sketchers used far more detail when the target object had close competitors than when it stood alone, all the way down to using fewer strokes and less time when more detail was not necessary.
6. People are not just copying what they see. They are making constant judgment calls about what level of detail actually serves the goal of communication, and they do this naturally without ever being taught the theory behind it.
7. There is a real difference between drawing something so someone can identify it and drawing something so someone can understand how it works. In one study, participants drew explanatory diagrams that emphasized moving, causal parts of a machine while depictive drawings emphasized background and overall appearance, even though both were drawing the exact same object.
8. Explanatory drawings were genuinely better at helping someone figure out how to operate a machine, but worse at helping someone identify which machine it actually was. You cannot optimize a single drawing for both goals at once. Communication always involves tradeoffs.
9. AI vision models trained on photographs generalize surprisingly well to simple, sparse sketches, suggesting that resemblance based recognition is not just a story we tell ourselves. It is something modern neural networks can replicate with real accuracy.
10. But there remains a large, measurable gap between how confidently AI models recognize sketches and how confidently humans do, even when both groups answer the same questions about the same images. Humans are simply far more reliable and far more consistent in their judgments.
11. When researchers compared human-made sketches to AI-generated sketches under tight stroke budgets, both were similarly recognizable at higher budgets, but diverged sharply as the budget shrank. Humans and AI systems simplify drawings in fundamentally different ways once resources get scarce.
12. Reading a graph is not one single skill. It involves perception, knowing where to look, mapping that visual information onto the actual question being asked, and then translating that mapping into an answer. Each of these steps can independently break down, and people fail for very different underlying reasons even when they land on the same wrong answer.
13. When tested directly against humans on graph reading tasks, leading multimodal AI models, including GPT-4V, showed a meaningful performance gap. Even when a model's overall accuracy approached human levels, its pattern of mistakes looked nothing like how humans actually get things wrong.
14. People choose entirely different types of charts depending on what specific question they are trying to answer, not out of a generic preference for bar charts or scatter plots. Their chart choices closely tracked which visualization would genuinely help someone answer that specific question correctly.
15. Two of the most widely used graph literacy tests in education research turned out to correlate strongly with each other, suggesting they measure overlapping skills. But when researchers dug into the actual error patterns, the standard categories used in textbooks, like "find the maximum" or "identify a cluster," failed to explain why people got things wrong nearly as well as a more basic, underlying four-factor model did.
16. The deepest goal behind all of this research is not just academic curiosity. It is to eventually help students and everyday people develop genuine literacy with the visual tools that science and modern decision-making increasingly depend on, because every generation should be able to see further than the last by standing on the visual tools the previous generation built.
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