A CS Professor on Why Slow Learning Wins in the AI Era | CU Boulder, Tom Yeh

My name is Tom Yeh. 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 the black box accessible and approachable. Writing all the maths with hand, in doing so, understanding that AI is not a big mystery is something we all can understand. What is the purpose of learning? Having the answer doesn't mean you know it. People can buy a degree, buy a certificate. Do you have ownership of this particular idea? Core and foundational that doesn't change is evergreen. AI cannot change people, but you can change AI. A transformer is meant to process individual words in your sentence. And I can start with, say, where are the token number four in my sentence. And then this is a box I draw to show that, well, each token actually has multiple numbers. In this case, simple case, it has 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 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 the things I'm sharing is 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 when, oh, I actually get it. So when I share my drawing, share my way to map out the math, and a lot of people resonate it. And people started to comment, now, hey, I really like your approach to breaking down by hand. I started, hey, maybe I just call it AI by hand. There's a reason that people resonate with this, connect with this thing that I'm doing by hand. 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 feedback like I'm going too fast, I'm going to slides, and so on, and just too fast. Because I really like to share a lot of my teaching and knowledge with my students. So I decided to teach the entire semester of C++ programming on the blackboard instead of doing light coding. So I decided to do that. So I have a whole semester of doing writing my notes about, I wrote it down on a piece of paper. As the semester progressed, I see a few benefits. One is that I can only go at the humanly possible speed of my writing. I 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 help 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 can come here 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 this about that physical or digital artifact that proves they have learning or something you feel like you actually internalize, you actually own this kind of stuff? Do you have ownership of this particular idea? Well, AI gives me an answer right away. Having the answer doesn't mean you know it. People can buy a degree, buy a certificate. Whether or not you own something, you value something, it's actually proportional to how much time you spend acquiring that piece of knowledge. You have to first define what learning actually means to you. I remember when I was an undergrad, we're learning linear algebra as part of a requirement for getting a CS degree. We have learned linear algebra and we have learned maximum application. I have no idea why it is even important. But it turns out over time, computer graphics became really popular because of Jurassic Park. They put this CGI up the front and people thought, hey, everybody needs to learn CGI. And CGI uses a lot of maximum application. And then after a few years, there was big data movement. Then it turns out you also need maximum application to do some sort of processing. And then you move into machine learning. Again, forget about data science. We should be machine learning specialists, engineers. Again, maximum application. And today's AP is AI, AI. Everybody needs to be AI native. We should raise our kids and send them to AI school. Maximum application. In a few years, all we talk about is quantum computing. It's possible, right? And guess what? Maximum application again. So you see this trend that every time there's something that will keep changing, but it'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, it was popular at the time, but it's been a while now. So deep seek is not as popular as before. There's a new thing, for instance, like this Goldman Cloud, super popular. But let's see, in two months, is this still popular? We don't know. But I'm pretty confident transformer topic is still going to be popular. A couple of summers ago, I had the opportunity to visit South Korea and I got to visit where everybody else will go. It's called Gyeongbokgung. There's this palace that has thousands of years of history. Very beautiful palace. And what just struck me when I learned about a bit more history, the entire thing was burned down in like 1500s, except for the foundation that was made in Saudi Iraq. So in 1800s, they rebuilt the entire palace based off the same foundation. I would like to talk about this story because that reminds me of how this technology has been changing over and over again. But if you had a foundation on the maximum application, 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 obsolete. If you keep focusing on the surface features, the tools, then forget about foundation. You just have to keep rebuilding your houses. You still never have your foundation on the floor you can build upon. So how can it 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 this skill. And that is actually something that doesn't change. So as AI tool come every day, the fact that you can 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'm able to eventually catch up, I caught up on deep learning from the position that's 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 skill will help you eventually. Once we figure out all this crazy stuff, and this one tool, you just skip this tool, I think it's absolutely fine. Who knows what you're gonna do next. But if you skip your next piano practice, you skip your next soccer practice, and you give up on that, that's not fine, because that is going to be eventually in the long run define who you are. But not this tool, not just one tool. At this moment in my career as an educator, I've started to care more about, not that they learned this math. A lot of times when I teach, it's like, oh, you showed up, you listened to me, you're 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 sets you apart from others. Others will never try, never attempt it, never took out a challenge. That's why I 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 to study it. 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 who know foundation, they implies the process, how the person invested in learning it. So that is what I value versus person who would never really learn foundation implies the lack of effort, the lack of willingness to invest in time and effort to take on a challenging learning task. When I was teaching the intro to programming, we really spent a lot of effort making new assignments a whole every semester because of check places that people share solutions. And we have all these technical solutions. We're trying to check the IP addresses, see whether people are accessing this. And we even put some kind of a trap on the site. If somebody access that, we know they access this. At the time I was like, Hey, well, I hope check can get all the business, maybe shut down by the government that they'll help us educators. And my dream came true because AI became the new cheating tool and the check all the business. And then when I realized, Hey, check was all the business, my dream came true, but problem's still there. What's going on? So it reminds me again, we should keep thinking going back to the source. There was the reason why people have to cheat at first place. That is the main cause. And check and AI are just the symptoms. So check is gone, people still cheat. I bet when AI is gone, people can still find ways to cheat. It's this AI cheating that distract us from the bigger fundamental problem of the society's incentive system. Why are students compelled to cheat? It's like the system doesn't encourage real learning that has actually have spent time. So when you hire somebody, what do I care about? Is the student have a good work ethics? What I care about, is the student a good problem solver? Is this the person, a team player, they can actually communicate, willing to work with others. So when you hire somebody, it can go back to those basics. That's why you actually care about it. You want to keep those people as employees. And this AI thing is just going to be a by-product of this. Think about it. When we hire someone because there's a problem solver, in order to solve problem, that person's automatically just going to learn AI. You don't have to tell them. The reason why you had forced your AI, maybe you had to go back, maybe you didn't hire the right person. You forgot to emphasize on person being a problem solver. Again, similarly, if you are hiring this person because this person is a team player, because that person is a team player, the person will learn how to use AI to facilitate team collaboration. So you don't even have to tell the person. The person will automatically do that as well. Trust your instinct. Continue to hire people like that because those people would automatically adopt AI. You're not a team player. AI is not going to make you a team player. You always look out for your own interests. You do not respect others. AI is not going to fix that. How can AI fix that? AI cannot change people. Only you. You can change AI.