Don’t Be Afraid to Go at Your Own Pace

It’s okay to learn slowly.

That might sound funny from a guy who is known (perhaps even notorious) for trying to learn things faster, but it’s true.

My motivation for doing the MIT Challenge was (a) to see if I could do it and (b) if I could, hopefully help other people imagine self-education goals that they’d like to pursue. The same was true for the year without English.

But the intention was never to make people feel inferior if they don’t decide to take on all the exams for an undergraduate degree in one year, or don’t try to learn four languages back-to-back. Indeed, given that it takes me almost a year of planning and I work hard, more than full-time, during the challenges, it’s absurd to feel that the takeaway of those challenges is that everyone should try to do exactly the same thing I did.

My intention was that people might take inspiration, or even some methods, from my, admittedly, extreme examples, and apply it to a project that fits their own lives.

Many people have done that. I’ve heard from people who have taken on subsets of the MIT Challenge curriculum at a slower pace and have learned semesters’ worth of courses for free, entirely on their own. I’ve heard from people who have started using the no-English rule to try to reach the next level in just one language they care about.

But occasionally I get emails from people that seem to miss the point.

An interesting one was from a father of two, working full-time, asking me whether he could complete the MIT Challenge in his spare time in one year.

Now, I’m not one to quickly jump to saying certain accomplishments are impossible, but my guess is that if he had the stamina and intelligence to do that, he wouldn’t have to ask me.

Do You Need to Learn a Language in Three Months?

It’s easy to dismiss the previous example as someone who either didn’t read anything about my challenge (where it’s clearly posted the hours I worked), or is simply overly optimistic. But I have gotten other, more nuanced misunderstandings of my message, which I think do deserve an actual clarification.

My principle Korean teacher is in her forties and three years ago started learning English. Comparing linguistic ability with different languages is problematic, but her level of English is definitely better than my Korean and likely better than my Chinese. It would be harder to compare against the European languages I know, but that’s not really fair since they were easier to learn.

She spoke with me candidly about her thoughts on people like myself and Benny Lewis claiming to reach functional levels in just a few months. She said it disagreed with her experience. Languages, in her view, take years to learn to fluency, not just a few months.

The truth is, I agree with her. But I also agree with Benny Lewis and my own experience that a language can be learned in a short time. Isn’t that a contradiction?

The reason for the seeming contradiction is that the question is ill-posed. “How long does it take to learn a language?” isn’t a meaningful question.

Yes, languages do take years to learn. This is particularly true of so-called “hard” languages (Chinese, Korean) compared to “easy” ones (Spanish, French).

My teacher already speaks English well enough to sustain full conversations and read books. But what she really wants to be able to do is speak with excellent English, without the tell-tale mistakes of grammar and pronunciation that plague non-native speakers. She also wants to be able to read works of literature in English like Woolf and Joyce.

I speak Chinese well enough to have one-on-one conversations about most things and I can read intermediate texts. But I’d like to be to read books like the Dao De Jing and modern Chinese novels. I’d also like to be able to give presentations in Chinese and have question-and-answer sessions about topics I’m knowledgeable in.

Those are both goals which will take years of continued practice to realize.

However, yes, languages also can be learned in a matter of months. After three months of immersion in China, I was able to have full conversations about Tibetan politics, differences in cultural attitudes between the East and West and recreational drug use with people who can’t speak English. In Spanish, due to its relative ease, three months was more than enough time to be able to discuss essentially any topic and read most books.

The problem isn’t that my Korean teacher has an ineffective method for learning, or that people who claim to learn languages quickly are charlatans. It’s simply that different goals take different amounts of time. Sometimes learning “slowly” is best and sometimes learning “quickly” can be more efficient.

Why Does the MIT Challenge Have to be Done in 12 Months?

Why does it have to be the complete curriculum, including classes that have nothing to do with computer science? Why focus on passing exams instead of building real-world applications? Why does it even have to be MIT instead of a coding bootcamp?

The answer of course is that there is no reason. I simply picked what seemed compelling to me at the time.

Just like the decision to challenge a language over three months or three decades depends a lot on what you want to accomplish (and, indeed, the two aren’t mutually exclusive), the decision of how to learn computer science also has considerable flexibility.

If I wanted to learn programming in order to land a job as a developer, I’d probably join a coding bootcamp. That way I’d know I’d be learning the most current technologies and have access to referrals for employment later.

If I was already a programmer, but wanted to have a stronger background in theory, I might do the reduced MIT curriculum I mentioned here, which omits all the non-computer science courses I took as part of the challenge.

If I simply wanted to experience a world-class education that would cover not only computer science, but physics, economics, biology, chemistry, logic, philosophy and be taught at a level that doesn’t dumb down for its students, I might take on the entire challenge.

But even those goals don’t presuppose the pace I took. It’s perfectly reasonable to take it over four years or forty, to skip the ones that don’t interest you or spend your time lavishly on the ones which do.

When Should You Learn Slowly?

I like looking at outliers. Achievements that seem to break my model of what is possible, so that I can question some of my own methods. I do this for a lot more than just learning.

I’m fascinated by cases like Tim Ferriss gaining muscle quickly, or Ramit Sethi building a blog into an empire in only a few years. Even if I decide not to follow their particular methods, there’s usually something I can learn from studying those unusual successes.

But that doesn’t mean I need to go at the same pace, or that if I don’t, I’m somehow a failure. I’m fine with going to the gym over several years, steadily improving my fitness. I’m fine working on growing my audience and business at my own pace, making regular improvements.

Sometimes I can’t follow their pace because I lack the resources or abilities they have. Perhaps Tim’s extensive self-knowledge about fitness and previous athletic experience make gaining muscle unusually quickly something that works for him but not me. Perhaps Ramit’s incredible business growth is due to an uncanny business intelligence, mentors or focus.

Sometimes I don’t follow their pace because it isn’t the best strategy for my goals. My fitness goals are more modest, so going slower and having more sustainable improvement matters more for me, even if it sacrifices speed. My career and life goals aren’t the same as Ramit’s so I’m content to reach my own milestones in keeping with them.

I would offer the same advice to the people who have shown an interest in my own unusual endeavors. That I hope you can gain some interesting ideas to apply to your own learning, or perhaps even consider learning goals you hadn’t before. But that you’ll pursue them at the pace which works for you, and if you can’t or don’t go quite as fast, that it’s certainly nothing to be ashamed of.


Building, Searching and the Algorithm for Finding the Best Spouse

Let me tell you about my favorite mathematical proof.

It’s for a puzzle called the marriage problem (alternatively called the secretary problem or the sultan’s dowry). It’s my favorite for belonging to a rare category of “mathematical proofs which also have to do with sex.”

The problem is very simple:

Suppose you are looking to get hitched. You have many people you could potentially marry. How do you find the best spouse?

Well, as a mathematical puzzle, rather than a human one, we need to define the process for finding and evaluating suitors a bit more rigidly to answer that question.

The rules of this particular puzzle are defined as follows:

  1. Each potential suitor you meet, you can go on a date (or a few) with them. During this time, you’re able to evaluate their suitability.
  2. After each dating period, assuming the person is willing to propose to you, you can either accept or reject the suitor.
  3. If you accept the person, great, you’re now hitched.
  4. If you reject the person, move onto the next.
  5. In either case, accept or reject, you’re not allowed to go back and change your mind later. Once you reject someone, you can’t go back to them if you realize they were the one for you. Once you accept someone, you can’t change your mind if you meet someone else.
  6. You aren’t allowed to date more than one person at a time.

This puzzle has a more formal, mathematical definition, that can work as an analogy for a lot more than just spouses. You could, for example, also think of this as an employer looking for the best employee, but is required to either hire or reject each applicant, one at a time.

Interestingly enough, this puzzle has a solution. As in, there is a mathematically provable optimal algorithm for deciding the best spouse.

The Algorithm for the Best Spouse

The algorithm has two parts: what I’ll call a “rejection” phase and a “choosing” phase.

During the initial rejection phase, you reject every single applicant who proposes to you. It doesn’t matter how good they are, you just reject them. (In the formal definition you do this for the first n/e candidates, or roughly the first 40% of people.)

Then, after the rejection phase, you enter a new, choosing phase. Now you agree to marry the first candidate who is better than every other suitor you dated who also agreed to marry you.

With this algorithm, you can demonstrate that you will, in fact, select the best possible spouse a whopping 37% of the time, regardless of whether there are ten billion applicants or only ten.

No, You Can’t Actually Use This Algorithm to Find a Spouse

Obviously the mathematical solution to this puzzle won’t work strictly in real life. Many of the assumptions of the model are violated: you don’t know how many people you might potentially date, you don’t know whether the suitability of the suitors is time-dependent, you have access to information about people you aren’t currently dating which can inform you of the relative merits of those you are, etc.

It also goes without saying that basing your love life on an algorithm is a pretty poor way to live.

However, there is something I like about this algorithm, and I believe it can offer an analogy, if not a solution, for thinking about many areas of life.

Searching and Building

In short, the algorithm does two things. First, it has a searching capability. It spends a certain amount of time not making a decision at all, but simply gathering information about the overall range of suitability of the different options.

Second, it has a deciding capability. This is where you have gathered enough information and now need to make a choice.

While the algorithm only deals with the decision phase, most areas of life also have a follow-up part. They have not only a part where you must choose the best option, but also a point when you have to build on that choice you’ve made. A good marriage isn’t just selecting the right spouse, after all, but years of investment into building a relationship with that person.

Therefore, in a real context, I’d describe the split as being between searching and building. Searching, when you lack enough experience to know what to choose, and building, when you have enough data and now need to just make a choice and run with it.

What interests me about the ideal algorithm is that it divides itself neatly into these two phases. Search for awhile and then, abruptly, switch to deciding (or in our real world case, building).

Should You Search or Build?

Unfortunately, real life doesn’t offer a precise point to switch from one phase to the other, like in our idealized mathematical problem. But I do think the puzzle does illustrate the need for both searching and building in different areas of life.

Consider many decisions: which city to live in, what career to go into, what friends to associate with or habits to create. In many ways, they suffer from the same problems as the original puzzle I outlined: you have many options and you don’t know which to pick. Yet, at the same time, you know that once you do pick you’ll have to put in a lot of effort to make them work anyways.

Ask yourself whether your problem is a lack of information? If so, entering a searching phase where you don’t choose anything but explore options might be best. Spend some time living in different cities before picking a home. Spend some time in different jobs before picking a career. Spend some time with different groups of people before finding a tribe.

Do you have information, but can’t make a choice? If so, maybe you need to stop shuffling around, pick something and start building on it. Fretting over what is the right business idea? Maybe you already have enough information and just need to make a choice and commit to it.

What interests me about the algorithm is that the ideal solution may have two distinct phases, depending on where you sit. Which is better depends crucially on how much information you already possess, hence the seemingly endless contradictory advice between gathering more information and taking action.

Please note the “reject the first 40% of all applicants” really only applies in this formal puzzle. If you got married early or late, live in your hometown or haven’t settled into one by middle age, that shouldn’t imply you made an incorrect choice. Changing any of the assumptions can lead to very different outcomes for the algorithm.

That being said, look over the areas of your life. Could they benefit from more searching or do they need commitment? Share your thoughts in the comments.