Imagination vs. Creativity
想象力与创造力

I like to make a distinction between imagination and creativity that you may or may not agree with. Imagination is the ability to see known possibilities as being reachable from a situation. Creativity is the ability to manufacture new possibilities out of a situation. The two form a continuous spectrum of regimes in simple cases, but are disconnected in complex cases.
我喜欢区分想象力和创造力，你可能同意也可能不同意。想象力是指从一种情况出发，看到已知可能性能够实现的能力。创造力是指从一种情况出发，制造出新的可能性。在简单的情况下，两者形成一个连续的可能性范围，但在复杂的情况下，两者是分离的。

I’ve been playing with Legos in open-play mode lately to try and develop better intuitions about both. I’m limiting myself to a set of rectangular blocks on a base plate for now. I’m afraid so far the results are terrible.
最近我一直在玩乐高，以开放式玩法模式进行，试图培养对乐高更深入的直觉。目前我限制自己只使用一块底板上的矩形积木。但遗憾的是，到目前为止，结果很糟糕。

I can follow fairly complex instructions to build models from a kit pretty easily, but faced with a pile of bricks and no plans or goals, I come up with dull designs to build, exhibiting very little imagination and near-zero creativity. Nothing in this collage gets even a passing grade on creativity. The most imaginative thing in the collage below is the model of a FinFET — a nano-scale feature of semiconductor chips — at the bottom left. I give it a D+ on imagination because it took a minor leap of imagination to recognize that Legos can be used to model things at scales besides the familiar range of scales covered by Lego models (typically coffee-cup scale to cityscape scale). I had to let go the “habit” of only seeing normal-scale-range design possibilities. But even that minor, barely passing-grade leap felt exciting. I plan to pull out my copy of Open Circuits and model more tiny electronics parts and features.
我可以很容易地按照相当复杂的说明书组装套件中的模型，但面对一堆积木，没有图纸或目标，我只能想出一些乏味的建筑设计，几乎没有想象力和创造力。这幅拼图中没有任何东西在创造力方面能及格。下面拼图中最具想象力的部分是左下角的 FinFET 模型——一种半导体芯片的纳米级结构。我给它打了个 D+ 的想象力分数，因为它需要一点想象力才能意识到乐高可以用来模拟除乐高模型熟悉尺寸范围之外的尺寸范围（通常是咖啡杯尺寸到城市尺寸）的事物。我不得不放弃只看到正常尺寸范围设计可能性的“习惯”。但即使是这种微不足道、勉强及格的飞跃也让我感到兴奋。我计划拿出我的《开放电路》一书，并模拟更多微小的电子元件和特性。

Just to give you a sense of how pedestrian these are, consider this dragon model with 6500-7000 parts by an expert Lego builder, Donny Chen (who also designed a playable grand piano that became an official kit).
为了让你了解这些模型有多么平庸，请考虑一下这位乐高专家唐尼·陈（Donny Chen）制作的这条拥有 6500-7000 个零件的龙模型（他也是一款可演奏的三角钢琴的设计者，这款钢琴后来成为了官方套件）。

This dragon, unlike the far simpler dragon kits sold by Lego itself, uses a 2×4 oval tile for scales and a set of other parts for creating the curving spine, all from mostly unrelated kits. It’s very hard to get Lego parts to do static curves, since the grammar has a strong orthonormal bias due to the mating technique. Chen managed to pull it off:
这条龙与乐高自己出售的简单得多的龙模型不同，它使用了 2×4 的椭圆形瓷砖来制作鳞片，并使用其他零件来制作弯曲的脊柱，所有这些零件都来自大部分无关的套件。由于乐高积木的连接方式具有很强的正交性偏差，因此很难让乐高积木做出静态的曲线。陈成功地做到了：

“The dragon I promised for the Year of the Dragon—maybe a bit bigger than a bunny, LOL! I kicked off this project about a year back, right after Brickvention2023, and I’ve been working at it on and off. Started building it about a month ago, and I’m pretty happy about how it turned out. No strings, no wires, not a drop of glue, not even a flexible tube, all solid connections. It stretches a solid 2 meters when fully spread out, around 1300 scales and made up of 6500-7000 pieces.” 
“我承诺的龙年龙——可能比兔子大一点，哈哈！我大约一年前，就在 Brickvention2023 之后，开始了这个项目，断断续续地做着。大约一个月前开始建造，我对最终的结果很满意。没有绳子，没有电线，没有一滴胶水，甚至没有一根软管，都是固体连接。完全展开后，它可以伸展到 2 米，大约有 1300 个鳞片，由 6500 到 7000 块积木组成。”

Chen’s design exhibits way more of both imagination and creativity than anything I’ve ever made up in any physical construction medium. He has clearly mastered Lego to the point where working forwards from the possibilities of a set of parts, and backwards from the constraints of a vision, are part of a near-unconscious fluency in the medium. But I can dimly see radically advanced versions of my own primitive pidgin Lego compositions in Chen’s process as described in the linked video. I’m at least at conscious incompetence in the Lego medium and language. I’m aware of my own decided lack of creativity and imagination. Chen is clearly at some advanced level of unconscious competence on the shuhari developmental curve that I’ll never come close to.
陈的设计比我用任何物理建构媒介创造出来的东西都更具想象力和创造力。他显然已经精通乐高，从零件的可能性出发，从愿景的限制出发，在媒介中几乎达到了无意识的流畅。但我隐约地在陈的描述中看到了我自己的原始的、不成熟的乐高作品的极度先进版本。我在乐高媒介和语言中至少处于有意识的不称职状态。我意识到自己缺乏创造力和想象力。陈显然处于舒哈利发展曲线上的某个高级无意识胜任阶段，我永远无法接近。

Keeping Lego in mind as a reference example, what can we say about imagination versus creativity? Here’s my theory.
以乐高为例，我们能从想象力和创造力中得出什么结论？我的理论是：

Imagination is an aptitude based on analysis, and is a variety of reasoning forwards from a current state marked by freedom from habituated patterns of seeing. Creativity is an aptitude is based on synthesis, and is a variety of reasoning backwards from desired outcomes marked by closing of realizability gaps. To some extent, the two behaviors exist on the same continuous spectrum, and in most situations we alternate between forwards and backwards reasoning modes. But in complex situations, there is also a discontinuity between the two modes, which is the same as the general discontinuity and qualitative difference that separates analysis from synthesis.
想象力是一种基于分析的能力，它是一种从当前状态向前推理的思维方式，这种状态的特点是摆脱了习惯性的观察模式。创造力是一种基于综合的能力，它是一种从期望的结果向后推理的思维方式，这种状态的特点是弥合了实现差距。在某种程度上，这两种行为存在于同一个连续谱上，在大多数情况下，我们会在向前和向后推理模式之间交替。但在复杂的情况下，这两种模式之间也存在着不连续性，这与分析和综合之间的一般不连续性和质的差异相同。

Forward and backward are not symmetric. Synthesis, since it works backwards from a desired state, is strictly more expressive, since it can start from desired states that are not realizable or reachable from the current state using known techniques and patterns of behavior. It can also fail in more ways, since it might attempt impossibilities.
向前和向后是不对称的。综合，因为它从期望的状态向后工作，因此严格地说更具表现力，因为它可以从期望的状态开始，而这些状态是不可实现的，或者无法使用已知的技术和行为模式从当前状态到达。它也可能以更多的方式失败，因为它可能尝试不可能的事情。

A leap — a creative leap — may be required to connect the forward and backward regimes. Sometimes this might just manifest as a textbook technical problem that is easy to solve once you pose it correctly. You could even outsource that to an appropriate sort of technician to actually execute. Craftsmanship and skill are useful for creativity up to the point where you can see the leap that is needed, but once seen, others can often do it. The most creative people in a medium are rarely the master technicians.
可能需要一个飞跃——一个创造性的飞跃——来连接向前和向后机制。有时这可能仅仅表现为一个教科书式的技术问题，一旦你正确地提出问题，它就很容易解决。你甚至可以将它外包给合适的技术人员来执行。工艺和技能对创造力的作用，直到你能看到所需的飞跃为止，但一旦看到，其他人通常就能做到。一个媒介中最有创造力的人很少是技艺精湛的人。

I like the definition of genius as “talent hits the target others can’t hit, genius hits the target others can’t see.” Creative genius likes in seeing what others don’t see. But once you’ve actually seen it, you might be able to simply point it out to others to hit. They might even be better at hitting it than you, once you point it out.
我喜欢将天才定义为“天赋击中其他人无法击中的目标，天才击中其他人无法看到的目标”。创造性天才在于看到别人看不到的东西。但一旦你真正看到了它，你可能只需要把它指出来，让其他人去击中它。一旦你指出来，他们甚至可能比你更擅长击中它。

At other times creativity might manifest as an “invention gap,” as I’ve taken to calling it, or even a “discovery” gap — uncovering a new principle or phenomenon to harness in nature. A problem that nobody knows how to solve, or a behavior of nature that nobody has noticed, modeled, or figured out how to harness.
有时，创造力可能表现为一种“发明差距”，正如我所称的那样，甚至是一种“发现”差距——发现一个新的原理或现象，以利用自然。一个没有人知道如何解决的问题，或者一种没有人注意到、建模或弄清楚如何利用的自然行为。

Both imagination and creativity in adult forms rest on a lot of pre-existing knowledge and learned patterns, which is what distinguishes the useful adult variety from the charming but generally useless variety exhibited naturally by children. In unimaginative and uncreative (UIUC) behaviors, knowledge is deployed in an unsurprising way, and patterns are applied via some sort of simple recognition-and-look-up mode.
无论是成人形式的想象力还是创造力，都建立在大量预先存在的知识和习得的模式之上，这正是区分有用成人形式和儿童自然表现出的迷人但通常无用的形式的关键所在。在缺乏想象力和创造力（UIUC）的行为中，知识以一种毫不令人惊讶的方式被运用，模式则通过某种简单的识别和查找模式被应用。

One way to think of it is that UIUC behaviors are typically closed-loop. They rely on behaviors that have been executed before, up to parametric variation. You have lifted cups of coffee before, though you may be lifting a cup of coffee weighing exactly 102.8 grams for the first time. In computational terms, UIUC behaviors have known finite-time execution bounds. Any computations involved are embodied by relatively efficient low-level algorithms. They are protocolizable. They are solved behaviors. They are likely also to some degree efficient behaviors (they’ve been optimized somewhat according to some criteria) and thorough behaviors (they completely cover all concerns of potential interest within some scope). They can be made safely unconscious and reliable. You can expect them to operate within known bounds, and plan other things on the basis of such expectations, without uncertainty snowballing. In your brain, they can likely be stored in the lower layers, with little to no supervision from higher layers.
可以这样理解，UIUC 行为通常是闭环的。它们依赖于之前执行过的行为，最多只是参数上的变化。你之前已经举起过咖啡杯，尽管你可能第一次举起一个重量正好为 102.8 克的咖啡杯。从计算的角度来看，UIUC 行为具有已知的有限时间执行边界。任何涉及的计算都由相对高效的低级算法体现。它们是可协议化的。它们是已解决的行为。它们也可能在一定程度上是高效的行为（它们根据某些标准进行了优化）和彻底的行为（它们完全涵盖了某个范围内所有潜在的关注点）。它们可以安全地变得无意识和可靠。你可以预期它们在已知范围内运行，并在此基础上计划其他事情，而不会出现不确定性滚雪球。在你的大脑中，它们很可能存储在较低层，几乎没有来自较高层的监督。

Case data can be feed to AI models and the parametrizable (by model weights) space of UIUC behaviors can be efficiently “colonized” in automated ways.
案例数据可以被输入到 AI 模型中，并且 UIUC 行为的可参数化（通过模型权重）空间可以以自动化方式被有效地“殖民”。

Behaviors that are either imaginative, creative, or both, are typically open-loop. They will rely on behaviors without known bounds on either execution time or performance quality. You cannot plan on the basis of expectations about solutions because uncertainty will snowball. That’s why non-UIUC behaviors cause so much trouble in organizational contexts. Organizations are all about bounded-uncertainty planning on the basis of bounded expectations about low-level behaviors.
具有想象力、创造力或兼具二者的行为通常是开放循环的。它们依赖于行为，而行为的执行时间或性能质量都没有已知的界限。你无法根据对解决方案的预期进行计划，因为不确定性会像滚雪球一样不断累积。这就是为什么非 UIUC 行为在组织环境中会造成如此多麻烦的原因。组织都是围绕着基于对低级行为的有限预期进行有限不确定性规划而建立的。

A good example of a behavior that is typically imaginative but rarely creative is bug-fixing. As you are working through a tree of diagnostic possibilities, and inferring root causes from various clues, you have to imagine a lot of things, but you rarely have to solve a non-trivial problem or invent something along the way.
一个典型的例子是，修复 bug 通常需要想象力，但很少需要创造力。当你遍历诊断可能性树，并从各种线索中推断根本原因时，你必须想象很多东西，但你很少需要解决非平凡的问题或沿途发明一些东西。

Usually, once you find a bug and verify a hypothesis about it, the fix is usually relatively trivial. My favorite personal example: I once spent hours failing to fix a bug in my code, then a partner on the project spent several more hours — and discovered that the bug was a missing apostrophe, the operator that takes a matrix complement in Matlab. Net ~5-6 hours to find the bug. 5 seconds to fix it.
通常，一旦你找到一个 bug 并验证了关于它的假设，修复通常相对简单。我最喜欢的个人例子：我曾经花了几个小时试图修复代码中的一个 bug，然后项目中的一个伙伴又花了几个小时——才发现 bug 是一个缺失的单引号，这个运算符在 Matlab 中用于取矩阵的补。总共花了大约 5-6 个小时来找到 bug，而修复它只用了 5 秒。

The unpredictable and potentially unbounded execution time that makes bug-fixing an open-loop behavior is due to the imaginative difficulty of laying out and exploring an efficient troubleshooting tree. Do it badly and you could waste endless time going down unlikely pathways and possibly never even figure out the bug. Develop good instincts and a knowledge base of past cases, and you can zero in on the bug very quickly, almost all the time (but not 100% of the time — in that case it wouldn’t be bug-fixing but a UIUC closed-loop adaptive control behavior).
导致除错成为一个开环行为的不可预测且可能无限的执行时间，是由于构建和探索高效的故障排除树的想象力难度。如果做得不好，你可能会浪费无数时间走下不可能的路径，甚至可能永远无法找到错误。培养良好的直觉和对过去案例的知识库，你几乎可以始终快速定位到错误（但不是 100% 的时间——在这种情况下，它就不是除错，而是 UIUC 闭环自适应控制行为）。

Imagination to some extent is relative to training data. What for you is a leap of imagination may be a straightforward inference for someone who has seen or experienced more cases. A sufficiently trained AI model may produce behaviors indistinguishable from highly imaginative human behaviors.
想象力在一定程度上取决于训练数据。对你来说是天马行空的想象，对于见过或经历过更多案例的人来说可能只是简单的推断。一个训练充分的 AI 模型可能会产生与高度富有想象力的人类行为无法区分的行为。

Creative behaviors require imagination, but also require more something more. Imagination is necessary but not sufficient for creativity.
创造性行为需要想象力，但也需要更多的东西。想象力是必要的，但不足以创造。

Creative behaviors, I think, call for the equivalent of mutation or noise-injection into an evolutionary process. There is a non sequitur quality to creative leaps that strikes me as fundamentally non-analytical and serendipitous.
我认为，创造性行为需要类似于进化过程中的突变或噪声注入。创造性飞跃有一种非连续性的品质，在我看来，它本质上是非分析性的，是偶然发生的。

Humans typically tap into this quality with techniques like injecting literal randomness. Open a book at a random page and pick a random word with which to start your Great Novel. Or juxtaposing incongruous things: You’re stuck trying to make a dragon out of Lego parts — how about kit-bashing a jet-fighter kit and a flower-vase kit? Or to use the classic Boydian example from Destruction and Creation, if you mash-up a toy tank, a motorcycle, and skis, maybe you invent a snowmobile? (I’d like to see someone try this with appropriate Lego kits). A major source of injecting randomness is conflict. An adversary competing with you in some way is a source of mutations being injected into your behaviors that might potentially open up creative possibilities for you. Or kill you.
人类通常会利用一些技巧来激发这种品质，比如注入纯粹的随机性。随意打开一本书，翻到任意一页，选择一个随机的词作为你伟大作品的开篇。或者将不协调的事物并置：你卡住了，想用乐高积木拼出一条龙——不如试试将战斗机模型和花瓶模型拼凑在一起？或者用博伊德在《毁灭与创造》中提到的经典例子，如果你将一辆玩具坦克、一辆摩托车和滑雪板混合在一起，也许你就能发明出一辆雪地摩托？（我很想看到有人用合适的乐高积木尝试一下）。注入随机性的一个主要来源是冲突。与你竞争的对手，无论以何种方式，都是将变异注入你行为的来源，这可能会为你打开创造性的可能性。或者杀死你。

There is a connection here to strategy. The Clausewitzian notion of a coup d’oeil, or strike-of-the-eye, refers to the creative leap that lets the (necessary) analytical store of imaginative memory be transformed into a possibility newly recognized or constructed as realizable.
这里与战略有关。克劳塞维茨的“coup d’oeil”（眼击）概念指的是一种创造性的飞跃，它使（必要的）分析性想象记忆库转化为一种新认识或构建的现实可能性。

From what I can tell from the evolution of AIs, currently AIs can successfully fake imagination, but can’t yet do creativity. But there doesn’t seem to be any fundamental blocker to AI creativity. If you can inject randomness, incongruous juxtaposition, and adversaries deliberately into human behaviors, there’s no reason you can’t do that to AIs. Many of the computational processes of both GOFAI and modern deep-learning based AI already rhyme with phenomenology I’ve described above. There are forward and backward state-space models, there is injection of randomness, there are “adversarial” architectures. They just need to be leveled-up more. Making AIs more imaginative and creative is itself a problem that’s almost in the UIUC regime of software design now.
从我对人工智能演化的理解来看，目前人工智能可以成功地模拟想象力，但还无法做到创造力。但似乎并没有什么根本性的障碍阻止人工智能的创造力。如果你能将随机性、不协调的并置和故意对抗注入人类行为，那么就没有理由不能对人工智能做同样的事情。无论是传统的 GOFAI 还是现代基于深度学习的人工智能，它们的许多计算过程都与我上面描述的现象学相吻合。存在着前向和后向状态空间模型，存在着随机性的注入，存在着“对抗性”架构。它们只需要进一步提升。让人工智能更具想象力和创造力本身就是一个问题，现在几乎已经进入软件设计的 UIUC 领域。

Can you improve your imagination and creativity? I think so, but it’s primarily a grind of time investment in a specific domain. To get more imaginative and creative at building Lego models, you have to spend a lot of hours building Lego models. Clever insights from other domains you’ve already achieved some mastery in may help a bit, but ultimately it’s about domain-specific grinding. There was a study somewhere that showed that Chess masters are no better at remembering random board positions than ordinary people, but have uncanny perfect memories for board states that could actually legally occur in a game. I’d like to think my more developed writing skills can help me get better at Lego faster, but so far, that has not proved to be the case. But maybe there’s a “refactored perception” or “constructions in magical thinking” approach to Legos that I’d be able to master faster than most people, and I just haven’t figured it out yet. Maybe porting creative-imagination techniques across domains is itself a challenge of creative imagination. It would be odd if such porting problems were themselves UIUC. We’d all become generalist geniuses in short order if that were the case, bootstrapping laterally from a single mastered domain to many.
你能提高你的想象力和创造力吗？我认为可以，但这主要取决于你在特定领域投入的时间。想要在搭建乐高模型方面更有想象力和创造力，你就必须花很多时间搭建乐高模型。你已经掌握的其他领域的聪明见解可能会有所帮助，但最终还是要靠特定领域的磨练。曾经有一项研究表明，国际象棋大师在记忆随机棋盘位置方面并不比普通人强，但他们对游戏中可能合法出现的棋盘状态却有着不可思议的完美记忆。我想我的写作技巧更发达可以帮助我更快地玩转乐高，但到目前为止，事实并非如此。但也许有一种“重构感知”或“魔法思维中的构建”方法可以让我比大多数人更快地掌握乐高，只是我还没有找到。也许跨领域移植创意想象力技巧本身就是一个创意想象力的挑战。如果这种移植问题本身就是 UIUC，那就奇怪了。如果真是这样，我们都会在短时间内成为通才天才，从一个掌握的领域横向引导到多个领域。

Within a specific domain, how can you improve imagination and creativity? I think you have to alternate between imitation behaviors and open-play behaviors. In the case of Lego, you have to build existing kits to slowly build up a mental library of construction techniques, and internalize the grammar of various sorts of parts. And you have to spend time in open play to apply that learning to imagining and realizing new model possibilities from sets of parts, and working backwards from desired models by making the Lego language do things it has never done before. Which may involve going looking for specific parts in the Lego universe that are not in your kit yet. That behavior embodies the “creative leap” element perhaps. Yes, shopping for parts can be creative.
在特定领域内，如何提升想象力和创造力？我认为需要在模仿行为和开放式玩耍行为之间交替进行。以乐高为例，你需要搭建现有的套装，逐步建立起一个关于搭建技巧的心理库，并内化各种零件的语法。同时，你需要花时间进行开放式玩耍，将这些学习应用到从零件组合中想象和实现新的模型可能性，以及从想要的模型倒推，让乐高语言做它从未做过的事情。这可能需要你在乐高宇宙中寻找你套装中还没有的特定零件。这种行为可能体现了“创造性飞跃”的要素。是的，购买零件也可以是创造性的。

Lego is a fairly tightly circumscribed and low-complexity closed universe of parts and techniques, so the more you open up such domains, the harder it gets. So that’s a meta-behavior you have to overlay. For any complex domain (more complex, messy, and open than Lego), perhaps you have to start with some Lego-like subset and work to gradually broaden your command and use of the language. At least that’s what I suspect, with low-medium confidence. Or maybe a different strategy is needed for complex domains. I wouldn’t know because I don’t think I’ve truly mastered any such domain. I’m mostly a bumbling conscious-incompetent in every domain I touch, whether simple or complex.
乐高是一个相当封闭的、低复杂度的零件和技巧体系，所以你越打开这样的领域，它就越难。所以这是一种你需要叠加的元行为。对于任何复杂的领域（比乐高更复杂、更混乱、更开放），也许你必须从一些类似乐高的子集开始，并努力逐渐扩展你对语言的掌握和使用。至少这是我怀疑的，但信心不高。或者，也许复杂的领域需要不同的策略。我不知道，因为我认为我还没有真正掌握任何这样的领域。我基本上是在我接触的每个领域，无论是简单还是复杂，都是一个笨拙的、有意识的无能者。