I was interested not only in computer science but in seeing if there might be a new way to master the skills needed in work and life. — *location: 171* ^ref-45872 --- Spaced-repetition software is an advanced flash card algorithm first developed by the Polish researcher Piotr Woźniak in the 1980s.4 Woźniak’s algorithm was designed to optimally time when you need to review material in order to remember it. Given a large database of facts, most people will forget what they learn first, needing to remind themselves of it again and again for it to stick. The algorithm fixes this problem by calculating the optimal time for reviewing each fact so you don’t waste energy overdrilling the same information, but also so you don’t forget what you’ve already learned. — *location: 257* ^ref-51932 --- What differentiated de Montebello wasn’t that he thought he could go from near-zero experience to the finalist for the World Championship in six months. Rather, it was his obsessive work ethic. His goal wasn’t to reach some predetermined extreme but to see how far he could go. — *location: 704* ^ref-44126 >Top 10 toastmaster after 7 months practice. Phiosophy of how far not a goal. --- There are nine universal principles that underlie the ultralearning projects described so far. Each embodies a particular aspect of successful learning, and I describe how ultralearners maximize the effectiveness of the principle through the choices they make in their projects. They are: Metalearning: First Draw a Map. Start by learning how to learn the subject or skill you want to tackle. Discover how to do good research and how to draw on your past competencies to learn new skills more easily. Focus: Sharpen Your Knife. Cultivate the ability to concentrate. Carve out chunks of time when you can focus on learning, and make it easy to just do it. Directness: Go Straight Ahead. Learn by doing the thing you want to become good at. Don’t trade it off for other tasks, just because those are more convenient or comfortable. Drill: Attack Your Weakest Point. Be ruthless in improving your weakest points. Break down complex skills into small parts; then master those parts and build them back together again. Retrieval: Test to Learn. Testing isn’t simply a way of assessing knowledge but a way of creating it. Test yourself before you feel confident, and push yourself to actively recall information rather than passively review it. Feedback: Don’t Dodge the Punches. Feedback is harsh and uncomfortable. Know how to use it without letting your ego get in the way. Extract the signal from the noise, so you know what to pay attention to and what to ignore. Retention: Don’t Fill a Leaky Bucket. Understand what you forget and why. Learn to remember things not just for now but forever. Intuition: Dig Deep Before Building Up. Develop your intuition through play and exploration of concepts and skills. Understand how understanding works, and don’t recourse to cheap tricks of memorization to avoid deeply knowing things. Experimentation: Explore Outside Your Comfort Zone. All of these principles are only starting points. True mastery comes not just from following the path trodden by others but from exploring possibilities they haven’t yet imagined. — *location: 726* ^ref-47793 --- Instrumental learning projects are those you’re learning with the purpose of achieving a different, nonlearning result. — *location: 860* ^ref-37123 --- Intrinsic projects are those that you’re pursuing for their own sake. — *location: 864* ^ref-36057 --- If you’re pursuing a project for mostly instrumental reasons, it’s often a good idea to do an additional step of research: determining whether learning the skill or topic in question will actually help you achieve your goal. — *location: 868* ^ref-37309 --- During the MIT Challenge, I recognized that the most important resource for being able to eventually pass the classes wasn’t having access to recorded lectures, it was having access to problem sets. Yet, in the years since this project, when I am asked for help by students, they often decry the absence of lecture videos from some classes, only rarely complaining about incomplete or insufficient problem sets. This makes me think that most students view sitting and listening to a lecture as the main way that they learn the material, with doing problems that look substantially similar to those on the final exam as being a superficial check on their knowledge. Though first covering the material is often essential to begin doing practice, the principle of directness asserts that it’s actually while doing the thing you want to get good at when much of learning takes place. The exceptions to this rule are rarer than they may first appear, and therefore directness has been a thorny problem in the side of education for over a century. — *location: 1315* ^ref-4509 --- Haskell suggests that a major reason is that transfer tends to be harder when our knowledge is more limited. As we develop more knowledge and skill in an area, they become more flexible and easier to apply outside the narrow contexts in which they were learned. — *location: 1383* ^ref-28158 --- Unfortunately, transfer is also something that, despite more than a century of intense work and research, has largely failed to occur in formal education. The psychologist Robert Haskell has said in his excellent coverage of the vast literature on transfer in learning, “Despite the importance of transfer of learning, research findings over the past nine decades clearly show that as individuals, and as educational institutions, we have failed to achieve transfer of learning on any significant level.” He later added, “Without exaggeration, it’s an education scandal.” — *location: 1343* ^ref-47375 --- In chemistry, there’s a useful concept known as the rate-determining step. This occurs when a reaction takes place over multiple steps, with the products of one reaction becoming the reagents for another. The rate-determining step is the slowest part of this chain of reactions, forming a bottleneck that ultimately defines the amount of time needed for the entire reaction to occur. — *location: 1549* ^ref-27820 --- Ultralearners, in contrast, frequently employ what I’ll call the Direct-Then-Drill Approach. The first step is to try to practice the skill directly. This means figuring out where and how the skill will be used and then trying to match that situation as close as is feasible when practicing. Practice a language by actually speaking it. Learn programming by writing software. Improve your writing skills by penning essays. This initial connection and subsequent feedback loop ensure that the transfer problem won’t occur. The next step is to analyze the direct skill and try to isolate components that are either rate-determining steps in your performance or subskills you find difficult to improve because there are too many other things going on for you to focus on them. From here you can develop drills and practice those components separately until you get better at them. The final step is to go back to direct practice and integrate what you’ve learned. — *location: 1584* ^ref-23159 --- Consider that over the last twenty years, the amount of knowledge easily accessible from a quick online search has exploded. Nearly any fact or concept is now available on demand to anyone with a smartphone. Yet despite this incredible advance, it is not as if the average person is thousands as times as smart as people were was a generation ago. Being able to look things up is certainly an advantage, but without a certain amount of knowledge inside your head, it doesn’t help you solve hard problems. — *location: 1811* ^ref-29034 --- Hermann Ebbinghaus, in one of the first psychological experiments in history, spent years memorizing nonsense syllables, much in the same way Richards memorizes Scrabble words, and carefully tracking his ability to recall them later. From this original research, later verified by more experimentally robust studies, Ebbinghaus discovered the forgetting curve. This curve shows that we tend to forget things incredibly quickly after learning them, there being an exponential decay in knowledge, which is steepest right after learning. However, Ebbinghaus noted, this forgetting tapers off, and the amount of knowledge forgotten lessens over time. Our minds are a leaky bucket; however, most of the holes are near the top, so the water that remains at the bottom leaks out more slowly. — *location: 2213* ^ref-53651 --- Over the intervening years, psychologists have identified at least three dominant theories to help explain why our brains forget much of what we initially learn: decay, interference, and forgotten cues. Though the jury is still out on the exact mechanism underlying human long-term memory, these three ideas likely form some part of explaining why we tend to forget things and, conversely, provide insight into how we might better retain what we’ve learned. — *location: 2218* ^ref-48060 --- One group, however, did not show such a steep decline in forgetting: those who had taken calculus. This suggests that moving up a level to a more advanced skill enabled the earlier skill to be overlearned, thus preventing some forgetting. — *location: 2388* ^ref-41860 >If you want to learn algbra forever, learn calculus --- Even his magical intuition for physics had its explanation: “I had a scheme, which I still use today when somebody is explaining something that I’m trying to understand: I keep making up examples.” Instead of trying to follow an equation, he would try to imagine the situation it described. As more information was given, he’d work it through on his example. Then whenever his interlocutor made a mistake, he could see it. “As they’re telling me the conditions of the theorem, I construct something which fits all the conditions. You know, you have a set (one ball)—disjoint (two balls). Then the balls turn colors, grow hairs, or whatever, in my head as they put more conditions on. Finally they state the theorem, which is some dumb thing about the ball which isn’t true for my hairy green ball thing, so I say, ‘False!’”4 — *location: 2498* ^ref-63848 --- The challenge of thinking you understand something you don’t is unfortunately a common one. Researcher Rebecca Lawson calls this the “illusion of explanatory depth.”11 — *location: 2588* ^ref-16544 --- When I first read about Feynman, I was inspired to try to formulate many of these different observations into a concrete method I could apply to my own studies. What resulted was something I named the Feynman Technique and applied extensively during my MIT Challenge. The purpose of using this technique is to help develop intuition about the ideas you are learning. It can be used when you don’t understand an idea at all or simply when you understand something a little but really want to turn it into a deep intuition. The method is quite simple: Write down the concept or problem you want to understand at the top of a piece of paper. In the space below, explain the idea as if you had to teach it to someone else. If it’s a concept, ask yourself how you would convey the idea to someone who has never heard of it before. If it’s a problem, explain how to solve it and—crucially—why that solution procedure makes sense to you. When you get stuck, meaning your understanding fails to provide a clear answer, go back to your book, notes, teacher, or reference material to find the answer. — *location: 2648* ^ref-42771 --- When people hear about geniuses, especially the iconoclastic ones such as Feynman, there’s a tendency to focus on their gifts and not their efforts. I have no doubt that Feynman possessed gifts. But perhaps his greatest one was his ability to merge tenacious practice and play. He approached picking locks with the same enthusiasm for solving puzzles that he did for unraveling the secrets of quantum electrodynamics. It’s this spirit of playful exploration that I want to turn to in the final principle of ultralearning: experimentation. — *location: 2713* ^ref-60612 ---