A CS Professor on Why Slow Learning Wins in the AI Era | CU Boulder, Tom Yeh
In a Nutshell
Tom Yeh argues that foundational skills like matrix multiplication and patient, deliberate learning remain valuable in the AI era because they are evergreen and allow rebuilding expertise as tools change. He emphasizes that ownership of knowledge comes from investing personal time and effort, not from quick answers provided by AI. Yeh warns that cheating persists because of misaligned incentives and that employers should prioritize work ethic and problem-solving over tool-specific knowledge, since AI cannot instill these human qualities.
These notes were generated by AI and may contain inaccuracies.
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 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.
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 meant to process individual words in your sentence. We are at the token number four in my sentence. 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.
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.
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.
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.
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.
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.
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.
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.
When I was teaching the intro to programming, we would really spend a lot of effort making new assignments a whole lot every semester because of Chegg, places that people share solutions, and we have all these more 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.
But at the time I was like, "Hey, well, I hope Chegg can get out of business, maybe shut down by the government, that will help us educators." And by doing Khan too, because AI became a new cheating tool on the Chegg out of business.
And then when I realized, "Hey, Chegg was out of business by doing Khan too, but problem 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 the first place. That is the main cause. It's not and Chegg and AI are just the symptoms.
So, Chegg is gone, people still cheat. I bet when AI is gone, people can still find ways to cheat. Is this AI cheating that distracts us from the bigger fundamental problem of the society's incentive system? Why are students compelled to cheat? What is this like? The the system doesn't encourage real learning that has actually have spent time.
So, when you hire somebody, what do I care about is does 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 that can actually communicate well in the world with others?
So, when you hire somebody, you going to go back to those basics. That's what you should care about. You want to keep those people as employees. And this AI thing is just going to be a byproduct. Just think about it. When we hire someone because they're a problem solver, in order to solve problem, that person is automatically just going to learn AI. You don't have to tell them.
The reason why you had forced your AI, maybe you 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 team player, because this person is team player, the person will learn how to use AI to facilitate in 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. If you're not a team player, AI is not going to make you a team player. You always look out for your own interest. 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 you can change AI.
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