It seems like it's no longer enough to just do our jobs; we have to upskill, reskill, and stay ahead of the curve. But who has time for that, right? And that's where metalearning comes in. This was mentioned in the book "Ultralearning" by Scott Young, where he took what typically takes four years to go through the computer science curriculum at MIT and completed it in one year. He took three months to learn Spanish, Portuguese, and Korean, which took him a month to learn how to speak at a conversational level. Taking what seems like lightning speed to go through things that people take three years, five years, or a lifetime to learn, what's he doing differently? One of the key differentiators is metalearning. Metalearning is different from traditional learning, right? We take out that textbook with all the exercises—it's really inefficient and usually very demotivating, even if we're curious. It's really hard to sit through a boring textbook. But metalearning is self-directed. It's about learning smart, so not taking what other people tell you to learn but finding what you want to know directionally and getting the right resources to be able to achieve what you want to do. The secret in metalearning, as the name suggests, is learning how to learn. As we see with artificial intelligence and machine learning, metalearning is a key to replicating that human intelligence. Think about it this way: there's deep learning, which requires large data sets for the machine to learn what the patterns are. But in real life, there are so many times when there isn't a big data set, so what do you do then? Well, you can leverage existing data sets that you've already learned from. But instead of learning the content, you learn about how you learned this process and apply it to something that's structurally similar, but the content is different. In other words, we are extracting frameworks from our previous learnings and applying them to something new. This is, of course, directly linked to framework thinking, something I mentioned in this video and is a part of this channel. And if we think about it, we spend so much time learning in our lifetime, so why not leverage the things we've learned and extract the frameworks from them so we can more efficiently learn going forward? In Scott Young's framework, we spend 10% of our time focusing on metalearning, especially the why, the what, and the how of better learning. So let's get into them: The "Why" of Metalearning Why do we want to learn this? At the highest level, there are two reasons. The first one is something very practical, or what Young calls instrumental. So, I want to learn quantum physics because I want to be a theoretical physicist. That's something very practical with a very clear goal. But not all of our curiosities are so practical. Sometimes there's just a pure desire to learn something new, and Young calls this intrinsic. Say I'm interested in quantum physics not because I want to be a physicist, but because I think physics is just another form of philosophy, and I'm curious about what quantum physics thinks about, let's say, time. The outcome here will be very different, and that is why we want to know why we want to learn. Too often, we actually create very strange expectations for ourselves because we don't think about why we're learning in the first place. Let me throw in another example, a classic Asian one. A lot of parents put their kids through piano lessons, and they go through the curriculum of the practical grade piano exam. So, it's a lot of focus on classical music, and there are certain pieces you have to play for different grades. But the goal for their children is not to become a professional or a piano teacher; it's to learn about the joy of music, to get a practical skill out of a musical instrument. So, you don't necessarily have to start with the graded exam curriculum if that was your goal. The "What" of Metalearning Since we're not starting with a textbook and going to chapter one, what do we do to learn? Here, you want to define what you want to achieve and ask yourself three questions: 1) What concepts do I need to understand? 2) What facts do I have to memorize? 3) What procedures do I have to practice? And as you can imagine, for the theoretical physicist versus the curious philosopher, the things you need to do here are very, very different. And that's how you can focus on the things that truly matter, same with the professional musician versus just the joyous curious amateur. The things you have to understand, the things that you have to memorize, and the things you have to practice are very different. Now, of course, the key thing is to understand well what concepts I really need to know, what facts I need to memorize, and how I know what I need to practice. We're learning something new, so how do I even know these things? This is where frameworks come in. They are the shortcuts, the cheats that will help you develop your memory. Let me give you an example. It's a traditional word; it's like learning how to count from one to ten. And metalearning is about taking what you know about one to ten, extrapolating it to getting to 100, getting to a thousand, getting to a million. So how do you do that with frameworks? It's the difference between memorizing number one to ten versus understanding the concepts, the frameworks of, okay, there is addition. So when you add different numbers together, the numbers change. There is the framework of order: how do you order the numbers from small to big? And based on these concepts, how do you add more concepts, right? Like multiplication: how do you get from one to ten to a hundred? And I really like the analogy of the knowledge tree. I think Elon Musk is one who also refers to this, which is that you want to be able to come back to the trunk of the knowledge, come back to the first principles, something I talked about in this video as well. And once you know that, you just need to add on, or you know, as Charlie Munger says, create that lattice work of mental models and hang your knowledge onto it. So, where do you find these frameworks, these models? Well, that leads us to the "How" of Metalearning. The "How" of Metalearning Scott Young outlined two steps. The first one is benchmarking, figuring out the most common path to get to the outcome that you want. If you want to become a theoretical physicist, you have to get a degree, right? And there are courses that you must take. So, all you have to do is look at the curriculum at a university of your choice and figure out what the path is. If you are that curious philosopher who wants to understand what time means, well, then do some Google searching, maybe look at some YouTube videos, maybe look at some online courses, maybe look at some books to see how people get from nowhere to understanding the concepts of time. Unlike traditional learning, it's not as clear-cut, but the good thing is it's about exploration. It's about building your own way to an outcome that you want. It's like going on a treasure hunt, right? You know it's somewhere; how do you get there? And so you piece together different resources and start to go through them because, remember, we're spending 10% of our time just to figure this out. It's not handed to us, and that's a good thing. We have time to piece this together; we have time to experiment, which leads us to step number two that Young mentioned, which is to emphasize or exclude. Because you know your outcome best, when you go through the resources within that 10% of time, you'll see some are really giving you what you want, in which case you want to emphasize that resource, right? You want to see where it leads, what is it mentioning, what books, other resources, other people they're mentioning, and figure those out. Whereas there will be other resources that may not be as relevant as you thought they were. They may be teaching in a way that you find it very difficult to absorb. Then it's okay to exclude them, to de-prioritize. Coming back to the idea of the knowledge tree, right, we want to get to the trunk. And once we have that foundational understanding, how we branch out is up to us. It's okay to not know everything. Unlike with traditional learning, we want to self-direct where we want to go, and it doesn't mean we will never circle back; we're just following the path we are deciding for ourselves. I'm sure you're realizing, as well as we go through this, that the most powerful thing about metalearning is this self-directed thinking, thinking for yourself. That's different from traditional learning; it's really about that learn, unlearn, and relearn process. And with that in mind, check out this framework thinking video here to learn more about how to add more frameworks to your cognitive toolbox, and I'll see you in the next video. Bye.