认知去魅：重构 AI 学习路径
在 人工智能（Artificial Intelligence: 基于机器学习的自动化计算系统）领域，由于其复杂的黑盒性质，常常令学习者望而生畏。然而，所谓模型本质上并非不可逾越的鸿沟。为了打破这种迷思，我创立了 AI by Hand 这一教育倡议，核心主张是通过手工推演 AI 模型背后的数学原理，使技术变得易于获取且具有亲和力。在进行深度学习的过程中，通过手写每一项计算，我们能够真正掌握其核心逻辑。知识的本质在于内化与所有权，拥有一个学位或证书并不等同于掌握了真知，知识所有权的归属感与你为之投入的专注时间呈正相关。AI 无法改变人的本质，但我们可以通过重构思维来驾驭 AI。
Original English Source
My name is Tom Diet. I'm a professor of computer science at the University of Colorado in Boulder. I'm also a founder of AI by Hand. It's a global education initiative to make AI inside a black box accessible and approachable. Writing out all the math with hand, in doing so, understanding that AI is not a big mystery. It's something we all can understand. What is the purpose of learning? Having an answer doesn't mean you know it. People can buy degree, buy certificate. Do you have ownership of this to take idea core and foundational that doesn't change. It's evergreen. AI cannot change people, but you can change AI. A transformer is to meant to process individual words in your sentence. And I can start with say we are at the token number four in my sentence. And then this then this is a box that I drew to show that well, each token actually has multiple numbers. In this case, simple case I say three numbers. Unfortunately, while I was a student, I missed deep learning entirely. I was a bit too old. So, I studied support vector machines, traditional machine learning method. So, right then I became a professor. All of a sudden all these people are doing deep learning. I have to learn deep learning all over again. So, what I'm doing today with AI by Hand is to share my learning journey. How I, as an old professor, trying to learn deep learning from scratch. All of this I'm sharing this being about to show my own struggle with understanding AI model math and algorithms. Only way I can get it is I get to draw or write on the paper. This is where all I actually get it. So, I want to share my drawing, share my way to map out the math. And then a lot of people resonated. And people started to comment on, "Hey, I really like your approach to breaking down by hand." I studied, "Hey, maybe I just call it AI by hand." Maybe there's a reason that people resonate with this, connect with this thing that I'm doing by hand.回归地基：工具迭代下的常青基础
为什么在 AI 能够高效完成计算的当下，我们仍需坚持“手算”？这源于对学习本质的理解。在编程教学中，我曾尝试放弃直播编码，转而使用黑板授课。这种低效的“手工”方式带来了意想不到的红利：我只能以人类书写的速度授课，学生也只能以人类理解的速度学习，这迫使双方将注意力聚焦在逻辑流转本身，而非被快速掠过的幻灯片所干扰，更杜绝了学生在课堂上使用键盘分心社交网络的可能性。这种回归，实则是为了与人的生物特性重新连接。在计算机图形学、大数据、机器学习乃至当前的 AI 浪潮中，矩阵乘法（Matrix Multiplication: 将两个矩阵结合产生新矩阵的线性代数基本运算）始终是这些技术背后的核心支柱。技术趋势如走马灯般更迭，但这种基础逻辑是常青的。正如重修烧毁的宫殿必须依靠坚固的地基，只有深耕基础，才能在不断变化的技术浪潮中，依托同一地基不断重建并升级技能架构，而非被动地在浮沙之上徒劳筑屋。
Original English Source
Why do we like to calculate this by hand when AI can do this very well? When I was teaching introduction to programming, and I have a feedback that I going too fast, I'm going too slow slides and so on, and just too fast. Cuz I really like to share a lot of my teaching and knowledge with my students. So, I decided to teach entire semester of C++ programming on a blackboard instead of doing live coding. So, I decided to do that. So, I have a whole semester of doing writing the I just my notes about I wrote it down on a piece of paper. As the semester progressing, I see a few benefits. One is that I can only go at a humanly possible speed of my writing. I cannot go any faster than I can write. Second, student can only learn at a humanly possible speed. They can only follow how much I write. And number three is that if I get my student to use their hand to copy my notes on a notebook, their hands are not on their keyboard checking their Instagram messages. So, it helps with focus as well. The by hand is really about a way to connect back to our humans. So, using your hand, you can go at a human speed. It come you in a human way. And over time I learned this I started to see this value. So, going back to the old school by hand. What is the purpose of learning? Is it about that physical or digital artifact that prove they have learning or something you feel that you actually internalize, that you own this kind of stuff? Do you have ownership of this particular idea? Well, AI can give me the answer right away. Having answer doesn't mean you know it. People can buy degree, buy certificate. Whether you own or now you own something, you value something, is actually proportional to how much time you spend on acquiring that piece of knowledge. You have to first define what learning actually means to you. I remember when I was a undergrad with learning linear algebra as part of a requirement for getting a CS degree. We have learned linear algebra and then we have learned matrix multiplication. I've no idea why it is even important. But it turns out over time computer graphics became really popular because of Jurassic Park that produced CGI up the front. And people talk, "Well, hey, everybody need to learn CGI." And CGI uses a lot of matrix multiplication. And then after a few years, there was big data movement. Then it turns out you also need matrix multiplication to do some sort of processing. And then move to the machine learning. Again, forget about data science. We should be machine learning specialist engineer. Again, matrix multiplication. And today's AP is AI. AI. Everybody need to be AI native. We should raise our kids and into AI school. Matrix multiplication. In few years, we all All we talk about quantum computing. It's possible, right? And guess what? Matrix multiplication again. So you see this a trend that every time there's something that two are keep changing, but there's always something that's core and foundational that doesn't change. It's evergreen. You could revisit a year from now, two years from now, it's still relevant. People still care a lot about. Whereas the deep seek that was popular at the time, but it's been a while now. So deep seek not as popular as before. There's a new new thing for instance like this Google Cloud, super popular. But let's see, in two two months, is it still a popular? We don't know. But I'm pretty confident transformer topic still going to be popular. Couple summers ago, I had the opportunity to visit South Korea and I got to visit where everybody else would go, the historical Gyeongbokgung. It's this a palace that's thousands of years of history. Very beautiful palace. And then what just struck me when I learned about a bit more history, the entire thing was burned down in like a 1500s except for the foundation that was made in solid rock. So in 1800s, they rebuilt the entire palace based off the same foundation. But I like to talk about the story because that reminds me of how this technology has been changing over and over again. But if you have a foundation on the maximum location, you could just apply it to AI. It doesn't really matter. Rebuild your skill based on your solid foundation. So that's why I'm focusing on foundation because I believe there's something you can rebuild. It doesn't really matter whether the new tool is it's obsolete. If you keep focus on the surface features, then tools, then forget about foundation, you just have to keep rebuilding your houses. You still never have your foundation up lower can rebuild up.身份重塑：将基础能力迁移至 AI 时代
如何将这种学习理念应用于自身？这关键在于认识到个人能力发展的连续性。无论是钢琴、国际象棋还是足球，你在获取这些高难度技能过程中所习得的“克服困难、拆解难题”的学习过程，构成了你身份认同的一部分。当新的 AI 工具出现时，这种习得技能的能力本身不会过时，它是你可以迁移的核心资产。即便当前的某个具体工具（例如某一特定 AI 模型）最终被时代淘汰，但你作为一名能够深度掌握困难知识的学习者，这一核心身份将伴随终身。作为教育者，我当前最关心的并非学生是否死记硬背了 Transformer（一种基于自注意力机制的神经网络架构）中的方程，而是他们是否愿意展现出打开黑盒、探索基础、应对挑战的意愿。这种面对未知时敢于投入时间、进行高强度思维训练的意愿，正是区分个体的关键指标。
Original English Source
So how can I apply to your own situation? Think about the way you grew up. Maybe your parents sent you to a soccer game or maybe the piano and you have something some skill you have become good at. Is it piano? Is it chess? And think about the way you acquire the skill. And that is actually something that doesn't change. So as AI tool come every day, the fact that you could acquire very difficult skill, that is something that part of your identity that doesn't change. If you continue to focus on that and you have ability to realize, "Hey, I could acquire this. I can learn this skill. I can become really good at it." That's when you could continue to apply that skill to a new AI tool. So I'm a good example. I was falling behind on deep learning for quite a while, but I have learned skills really trying to break down difficult topics by patiently writing everything down on paper. So that skill I was able to eventually catch up I call up on deep learning by found the position as way behind from people who actually been working on deep learning for a long time. So, for you, your piano skill, your soccer skill is not useless. That will be the skill that help you eventually once we figure out all this like crazy stuff. And this one tool, you just skip this tool, I think it's absolutely fine. Who knows what is going to be next. But if you skip your next piano practice, you skip your next foot soccer practice, and you give up on that, that's not fine because that is going to be eventually a long run define who you are. But not this tool. Not this just one tool. At this moment in my career as an educator, I started to care more about not that they learn this math. A lot of times when I teach you say, "Oh, you showed up, you listened to me, you trying to go through it, but I bet maybe a year from now you don't remember anything." But what you can remember is more about you are able to understand this. At the moment you are willing to come here to understand foundation, you are willing to open up the black box, that willingness is what set you apart from others. Others would never try, never attempted, never took on the challenge. That's what differentiate. It's not really about how much you remember the equation about transformer, about attention mechanism. It's really about there was once upon a time I tried hard to memorize this. I stayed in the library for hours and studied. I was successful. So, next time when there's a learning challenge, I can learn this. So, that is more important about what differentiate that people with no foundation, they implies the process 100% invested in learning it. So, that is what I value versus person who never really learn foundation implies the lack of effort, the lack of willingness to invest in time effort to take on a challenging learning task.激励反思：AI 只是不平等激励机制的映射
在教育实践中，针对学生利用工具绕过学习任务的行为（如使用 AI 或在线作业共享平台），我们不应仅关注“技术性”的防御，而应反思其背后的主因：当前的激励系统是否在鼓励真正的学习？AI 的滥用仅是激励机制失灵的表征。如果我们单纯为了消除 AI 或某个特定工具而努力，却忽视了学生被迫欺骗的根本环境，那么即便工具消失，欺骗行为仍会演变出新的形态。雇佣决策同样遵循这一逻辑：企业应摒弃对技术工具使用的执念，回归考察基础，即应聘者是否具备良好的职业素养、是否是优秀的团队协作者与问题解决者。一个本质上具备出色问题解决能力的人，会自动为了完成任务而习得如何驾驭 AI，这无需强制驱动。对于那些缺乏团队协作精神或职业道德的人，AI 绝非治愈其本性缺陷的药方。AI 不能改变人，只有人能够利用自身的智慧，去定义和引导 AI 的演进路径。