An update on Signal Data Science (an intensive data science training program)
In December 2015, Robert Cordwell and I cofounded Signal Data Science (website), which we announced on Less Wrong.
Our first cohort has just concluded, and overall went very well. We're planning another one in Berkeley from May 2nd – June 24th. The program is a good fit for people who are both excited to learn how to extract insights from data sets and looking to prepare for industry data science jobs. If you're interested attending the next cohort, we would love to hear from you. You can apply here, or contact us at signaldatascience@gmail.com.
We offer inquiry-based learning and an unusually intellectually curious peer group. Unlike typical college classes, Signal Data Science focuses on learning by doing. You’ll learn from a combination of lectures, short knowledge-reinforcement problems, and longer, more open-ended assignments focusing on analyzing real datasets. (That’s your chance to discover something new!) Don’t worry if that sounds daunting: our instructors will be there to support you every step of the way.
You’ll learn both the theory and the application of a wide array of data science techniques. We offer a pair programming-focused curriculum, allowing students to learn from each other’s strengths. We cover everything from basic linear regression to advanced, industry-relevant methods like support vector machines and dimensionality reduction. You’ll do an advanced, self-directed project at the end of the course. Curious? Check out our showcase of past students’ final projects. Whatever your interests are—from doing something with real-world, industry-relevant applicability to applying cutting-edge neural nets—we’ll work with you to find a project to match your interests and help you showcase it to prospective employers.
Less Wrong readers might be especially interested by Olivia Schaefer's project, which describes results of doing some natural language processing on the Less Wrong comment corpus, explaining how the words pictured in different colors below are at opposite ends of an axis.

Announcing the Signal Data Science Intensive Training Program
Note: We now have a website with up to date information here: http://signaldatascience.com/.
(This post is coauthored with Robert Cordwell.)
We’re writing to announce the inaugural run of Signal Data Science’s intensive training program.
The program will train students in the core skills needed to work as a professional data scientist:
- Scraping and cleaning data
- Exploring and analyzing data using statistics
- Presenting findings
- Interviewing
By the end of the course, you’ll will be able to start with raw data and produce analyses like the one in Bayesian Adjustment of Yelp Ratings. More to the point, you’ll understand why Jonah structured the analysis the way he did and be able to do the same yourself.
You’ll also be able to produce cool visualizations like this automatic grouping of Slate Star Codex posts by topic, as shown below.
Why data science?
Making inferences from data is fundamental to understanding the world, and there’s a growing unmet need in industry for people with the relevant skills. With good instruction and peer group, smart, motivated people can quickly develop enough proficiency to get jobs in the tech sector (starting compensation ~$115k in the San Francisco Bay Area).
Why us?
The Program
We offer inquiry-based learning (no boring lecturers or unmotivating problem sets!) and an unusually intellectually curious peer group. Far from what’s typical of college classes, our model has more in common with the Math Olympiad Summer Program, where daily lectures are interspersed with on-the-spot problems and followed by long-form problems designed to build on the lesson.
Robert Cordwell is an IMO gold medalist and educational startup veteran who’s working a Facebook data science job despite his limited, self-taught experience. He’s going to be teaching math problem solving, overall presentation skills, and how to break interviews.
Jonah Sinick is a data scientist with 13 years of experience making advanced math accessible to beginners, a PhD in math from University of Illinois, and an extensive body of published work. He’ll be teaching a comprehensive technical curriculum.
Who is this for?
If you:
- Are interested in data science
- Passionate about learning new things
- Would benefit from a social environment with others working toward the same goal
- Have the programming skills to solve simple algorithms problems
- Plan on applying for data science jobs after the program
our program will be a good fit for you.
Where / When
The first cohort will run in Berkeley for 6 weeks, from Feburary 1st – March 18th. This will be a compressed version of the standard course that we’ll be offering in the future, and is targeted at students who have a high degree of comfort with math.
In the future we’ll be offering longer courses that cover the mathematical / statistical material at a gentler pace.
Cost
For students in our first 6 week cohort, we offer two options:
- Payment of $8,000 at the start of the program.
- A “pay later” model where students pay 8% of their first year’s salary (pretax, spaced over 6 months), contingent on getting a data science job.
This is roughly 50% of the standard price for coding /data science bootcamps.
Next steps
If you’re interested in exploring participating in our first cohort, or keeping posted, please be in touch with us at signaldatascience@gmail.com.
Beyond Statistics 101
Is statistics beyond introductory statistics important for general reasoning?
Ideas such as regression to the mean, that correlation does not imply causation and base rate fallacy are very important for reasoning about the world in general. One gets these from a deep understanding of statistics 101, and the basics of the Bayesian statistical paradigm. Up until one year ago, I was under the impression that more advanced statistics is technical elaboration that doesn't offer major additional insights into thinking about the world in general.
Nothing could be further from the truth: ideas from advanced statistics are essential for reasoning about the world, even on a day-to-day level. In hindsight my prior belief seems very naive – as far as I can tell, my only reason for holding it is that I hadn't heard anyone say otherwise. But I hadn't actually looked advanced statistics to see whether or not my impression was justified :D.
Since then, I've learned some advanced statistics and machine learning, and the ideas that I've learned have radically altered my worldview. The "official" prerequisites for this material are calculus, differential multivariable calculus, and linear algebra. But one doesn't actually need to have detailed knowledge of these to understand ideas from advanced statistics well enough to benefit from them. The problem is pedagogical: I need to figure out how how to communicate them in an accessible way.
Advanced statistics enables one to reach nonobvious conclusions
To give a bird's eye view of the perspective that I've arrived at, in practice, the ideas from "basic" statistics are generally useful primarily for disproving hypotheses. This pushes in the direction of a state of radical agnosticism: the idea that one can't really know anything for sure about lots of important questions. More advanced statistics enables one to become justifiably confident in nonobvious conclusions, often even in the absence of formal evidence coming from the standard scientific practice.
IQ research and PCA as a case study
The work of Spearman and his successors on IQ constitute one of the pinnacles of achievement in the social sciences. But while Spearman's discovery of IQ was a great discovery, it wasn't his greatest discovery. His greatest discovery was a discovery about how to do social science research. He pioneered the use of factor analysis, a close relative of principal component analysis (PCA).
The philosophy of dimensionality reduction
PCA is a dimensionality reduction method. Real world data often has the surprising property of "dimensionality reduction": a small number of latent variables explain a large fraction of the variance in data.
This is related to the effectiveness of Occam's razor: it turns out to be possible to describe a surprisingly large amount of what we see around us in terms of a small number of variables. Only, the variables that explain a lot usually aren't the variables that are immediately visible – instead they're hidden from us, and in order to model reality, we need to discover them, which is the function that PCA serves. The small number of variables that drive a large fraction of variance in data can be thought of as a sort of "backbone" of the data. That enables one to understand the data at a "macro / big picture / structural" level.
This is a very long story that will take a long time to flesh out, and doing so is one of my main goals.
Intrinsic motivation is crucial for overcoming akrasia
tl;dr: If you struggle with motivational problems, it's likely that the problem is not intrinsic to you, but instead that you haven't yet found work that you find very interesting.
How I discovered how to do great work
Last winter I did something that I had never done before. I spent ~1500 hours working on genuinely original scientific research.
I had done research for my PhD in pure math, but faced squarely, the problems that I worked on were of very little interest to anyone outside of the fields, and I was not very engaged with my research. Pure math is very heavily stacked with talent, and the low hanging fruit has been plucked, so unless you're one of the most talented people in the world, your prospects for doing anything other than derivative work are very poor.
What I did last fall was entirely different. As I trained to be a data scientist, I found that there's far more low hanging fruit in the field than there is in pure math, and found myself working on novel problems that are of broad interest almost immediately.
Having very high intrinsic motivation made a huge difference. I found myself spending all waking hours (~90 hours / week) working on it obsessively, almost involuntarily. Once I emerged, I realized that what I had done over the past ~3.5 months was far more significant than all of the other work that I had done over the span of my entire life combined. I was astonished to find myself having ascended to the pantheon of those who have made major contributions to human knowledge, something that I hadn't imagined possible in my wildest dreams.
The problem isn't "laziness"
Many of the most interesting people who I know are achieving at a level far below their potential. They often have major procrastination problems, and believe this to correspond to them having a character flaw of "laziness". I've become convinced that these people's problems don't come from them being insufficiently disciplined.
Their problems come from them spending their time trying to do work that they find boring. If you find your work boring, it's very likely that you should be doing something else.
References
My position is not unique to me: it's common to extremely high functioning people.
[1] Steve Jobs created Apple, which owns ~0.1%+ of the world's wealth. In his 2005 Stanford commencement address he said:
I'm convinced that the only thing that kept me going was that I loved what I did. You've got to find what you love. And that is as true for your work as it is for your lovers. Your work is going to fill a large part of your life, and the only way to be truly satisfied is to do what you believe is great work. And the only way to do great work is to love what you do. If you haven't found it yet, keep looking. Don't settle. As with all matters of the heart, you'll know when you find it. And, like any great relationship, it just gets better and better as the years roll on. So keep looking until you find it. Don't settle.
[2] Bill Thurston is one of the greatest mathematicians of the 20th century. He formulated the geometrization conjecture, which subsumes the 100 year old Poincare conjecture, considered one of the ~7 most important unsolved problems. He describes his own character as follows:
My attention is more inward than that of most people: it can be resistant to being captured and directed externally. Exercises like these mathematics lessons were excruciatingly boring and painful (whether or not I had "mastered the material"). I used to think my wandering attention and difficulty in completing assignments was a defect, but now I realize my "laziness" is a feature, not a bug. Human society wouldn't function well if everyone were like me, but society is better with everyone not being alike.
[3] Scott Alexander / Yvain is widely regarded as a great writer. Political celebrity Ezra Klein characterized his blog as fantastic. Scott wrote:
On the other hand, I know people who want to get good at writing, and make a mighty resolution to write two hundred words a day every day, and then after the first week they find it’s too annoying and give up. These people think I’m amazing, and why shouldn’t they? I’ve written a few hundred to a few thousand words pretty much every day for the past ten years.
But as I’ve said before, this has taken exactly zero willpower. It’s more that I can’t stop even if I want to. Part of that is probably that when I write, I feel really good about having expressed exactly what it was I meant to say. Lots of people read it, they comment, they praise me, I feel good, I’m encouraged to keep writing, and it’s exactly the same virtuous cycle as my brother got from his piano practice.
[4] Paul Graham is the co-founder of Y-Combinator, a seed funder with a portfolio of combined value exceeding $30 billion (with investees including Dropbox, AirBnB and Stripe). In What You'll Wish You Had Known he wrote
One of the most dangerous illusions you get from school is the idea that doing great things requires a lot of discipline. Most subjects are taught in such a boring way that it's only by discipline that you can flog yourself through them.
Now I know a number of people who do great work, and it's the same with all of them. They have little discipline. They're all terrible procrastinators and find it almost impossible to make themselves do anything they're not interested in. One still hasn't sent out his half of the thank-you notes from his wedding, four years ago. Another has 26,000 emails in her inbox.
I'm not saying you can get away with zero self-discipline. You probably need about the amount you need to go running. [...] But once they get started, interest takes over, and discipline is no longer necessary.
Do you think Shakespeare was gritting his teeth and diligently trying to write Great Literature? Of course not. He was having fun. That's why he's so good.
Autism, or early isolation?
I've often heard LWers describe themselves as having autism, or Asperger's Syndrome (which is no longer considered a valid construct, and was removed from the Diagnostic and Statistical Manual of Mental Disorders two years ago.) This is given as an explanation for various forms of social dysfunction. The suggestion is that such people have a genetic disorder.
I've come to think that the issues are seldom genetic in origin. There's a simpler explanation. LWers are often intellectually gifted. This is conducive to early isolation. In The Outsiders Grady Towers writes:
The single greatest adjustment problem faced by the intellectually gifted, however, is their tendency to become isolated from the rest of humanity. Hollingworth points out that the exceptionally gifted do not deliberately choose isolation, but are forced into it against their wills. These children are not unfriendly or ungregarious by nature. Typically they strive to play with others but their efforts are defeated by the difficulties of the case... Other children do not share their interests, their vocabulary, or their desire to organize activities. [...] Forms of solitary play develop, and these, becoming fixed as habits, may explain the fact that many highly intellectual adults are shy, ungregarious, and unmindful of human relationships, or even misanthropic and uncomfortable in ordinary social intercourse.
Most people pick up a huge amount of tacit social knowledge as children and adolescents, through very frequent interaction with many peers. This is often not true of intellectually gifted people, who usually grew up in relative isolation on account of lack of peers who shared their interests.
They often have the chance to meet others similar to themselves later on in life. One might think that this would resolve the issue. But in many cases intellectually gifted people simply never learn how beneficial it can be to interact with others. For example, the great mathematician Robert Langlands wrote:
Bochner pointed out my existence to Selberg and he invited me over to speak with him at the Institute. I have known Selberg for more than 40 years. We are on cordial terms and our offices have been essentially adjacent for more than 20 years.This is nevertheless the only mathematical conversation I ever had with him. It was a revelation.
At first blush, this seems very strange: much of Langlands' work involves generalizations of Selberg's trace formula. It seems obvious that it would be fruitful for Langlands to have spoken with Selberg about math more than once, especially given that the one conversation that he had was very fruitful! But if one thinks about what their early life experiences must have been like, as a couple of the most brilliant people in the world, it sort of makes sense: they plausibly had essentially nobody to talk to about their interests for many years, and if you go for many years without having substantive conversations with people, you might never get into the habit.
When intellectually gifted people do interact, one often sees cultural clashes, because such people created their own cultures as a substitute for usual cultural acclimation, and share no common background culture. From the inside, one sees other intellectually gifted people, recognizes that they're very odd by mainstream standards, and thinks "these people are freaks!" But at the same time, the people who one sees as freaks see one in the same light, and one is often blind to how unusual one's own behavior is, only in different ways. Thus, one gets trainwreck scenarios, as when I inadvertently offended dozens of people when I made strong criticisms of MIRI and Eliezer back in 2010, just after I joined the LW community.
Grady Towers concludes the essay by writing:
The tragedy is that none of the super high IQ societies created thus far have been able to meet those needs, and the reason for this is simple. None of these groups is willing to acknowledge or come to terms with the fact that much of their membership belong to the psychological walking wounded. This alone is enough to explain the constant schisms that develop, the frequent vendettas, and the mediocre level of their publications. But those are not immutable facts; they can be changed. And the first step in doing so is to see ourselves as we are.
Accomplishing things takes a long time
At age 17, my future looked very promising. I had overcome a crippling learning disability, and discovered how to do research level math on my own. I knew that the entire K-12 infrastructure had failed to figure out how to teach the skills that I developed, and so I felt empowered to help others learn how to think about the world mathematically.
Things didn't go as I had been hoping they would. My years between 18 and 28 consisted of a long string of failed attempts to help people learn math, and to promote effective altruism. I learned a lot along the way, but I didn't have the outsized impact that I aspired to. On the contrary, I was only marginally functional, and I alienated most of the people who I tried to help. I found this profoundly demoralizing, and struggled with chronic depression. If I had died at age 28, my life would have been a tragedy.
Fortunately, at age 29, I'm still alive, and after spending a decade wandering in a wilderness, I've gotten my act together, and am back on my feet.
What I finally realized out is that my failures had come from me having very poor communication skills, something that I had been oblivious to until very recently. Recognizing the problem was just the first step. It's still the case that most of what I try to communicate is lost in translation. I know that the issue is not going to go away overnight, or even over the next 6 months. Sometimes it's frustrating, because my self-image is so closely tied with my desire to help people, and even now, in practice, most of my efforts are fruitless.
But I'm not concerned about that. I probably still have 30 or 40 productive years ahead of me. I'm ok with the fact that no matter how hard I try, I fail most of the time. Y-Combinator founder Paul Graham emphasizes the importance of relentless resourcefulness. Every failure is a learning opportunity. I know that if I keep experimenting and learning, eventually I'll succeed. Figuratively speaking, I know that even if I lose dozens of battles over the next four decades, in the end, I'll win the war. And that's enough to keep me going.
Something analogous is true of everyone who has a strong passion, and is willing and able to learn from failure. Steve Jobs expressed a similar view in his 2005 Stanford commencement address (transcript | video):
Sometimes life hits you in the head with a brick. Don't lose faith. I'm convinced that the only thing that kept me going was that I loved what I did. You've got to find what you love. And that is as true for your work as it is for your lovers. Your work is going to fill a large part of your life, and the only way to be truly satisfied is to do what you believe is great work. And the only way to do great work is to love what you do. If you haven't found it yet, keep looking. Don't settle. As with all matters of the heart, you'll know when you find it. And, like any great relationship, it just gets better and better as the years roll on. So keep looking until you find it. Don't settle.
Social class amongst the intellectually gifted
Something that I've come to realize is that as a practical matter, intellectually gifted people who haven't developed very strong ability in a quantitative subject tend to be at a major disadvantage relative to those who have. The quantitative subjects that I have in mind as "quantitative subjects" are primarily math, physics, theoretical computer science and statistics, though others such as electrical engineering may qualify. [1]
This point is usually masked over by the fact that people who don't have very strong technical ability are often reasonable functional by the standards of mainstream society, and don't realize how far they're falling short of their genetic potential. They tend to have jobs that don't fully use their strengths, and experience cognitive dissonance around being aware on some level of far they are from utilizing their core competencies.
Consider the following:
- The Google co-founders met as computer science graduate students at Stanford. Sergei Brin double majored in math and physics and was an NSF graduate fellow. He comes from a mathematical family: his father was a math professor at University of Maryland.
- Bill Gates took Math 55 as a freshman at Harvard, which is the class designed for the most mathematically talented students at Harvard. During his sophomore year he did research which he later published a paper on with well known theoretical computer science professor Christos Papadimitriou.
- James Simons comes close to being the only elite mathematician to leave academia for the business world. He founded the hedge fund Renaissance Technologies and made ~$12.5 billion.
- Charles Munger, the Vice-Chairman of Berkshire Hathaway (net worth ~$1.3 billion) often quotes the maxim of the 19th century mathematician Carl Gustav Jacob Jacobi Invert, Always Invert, and characterizes him using that concept to solve difficult business problems
I can't give a brief justification for this, but I have good reason to believe that the ~10000x+ differential in net worth comes in large part from the people having had unusually good opportunities to conducive to becoming very technically proficient, that resulted in them developing transferable reasoning abilities and having had an intellectually elite peer group to learn from.
I know a fair number of brilliant people who didn't have such advantages. The situation actually seems to me like one in which amongst intellectually gifted people, there's an "upperclass" of people who had opportunities to develop very strong technical ability and an "underclass" of people who who could have developed them under more favorable environmental circumstances, but haven't. Many intellectually gifted people who didn't have the chance to develop the abilities mistakenly believe that they lack the innate ability to do so. And people who did have the opportunities to develop them often look down on those who didn't, unaware of how much of their own relative success is due to having had environmental advantages earlier in their lives.
[1] James Miller points out that graduates of elite law schools may have analogous advantages – that's a population that I haven't had exposure to.
The value of learning mathematical proof
The social justice movement espouses the notion that people who are privileged are often unfairly judgmental of those who were less privileged. Until recently, what they said didn't resonate with me. I knew that I had major advantages out of virtue of having been born a white, middle class male. But I recently realized that there were other privileges that I hadn't acknowledged as having benefited enormously from. In particular, I had the unusual experience of growing up with a very intellectually curious father, which gave me a huge head start in intellectual development.
I used to get annoyed when LWers misread my posts in ways that they wouldn't have if they had been reading more carefully. I conceptualized such commenters as being undisciplined, and being unwilling to do the work necessary to maintain high epistemic standards. I now see that my reading was in many cases uncharitable, analogous to many of my teachers having misread my learning disability as reflecting laziness. Many of my readers have probably never had the opportunity to learn how to read really carefully.
How did I myself learn? I don't remember in detail, but the one factor that seems most significant is my study of the mathematical subject of real analysis. A number of strongest thinkers who I know characterized the experience as a turning point in their development as well. It's the subject where one goes through rigorous proofs of the theorems of calculus.
Consider the extreme value theorem:
If a real-valued function f is continuous in the closed and bounded interval [a,b], then f must attain a maximum and a minimum, each at least once.
The theorem may seem obvious, but almost no undergraduate math majors would be able to come up with a logically impeccable proof from scratch. This ties in with why I almost never try to present rigorous arguments. If it's not clear to you that it might be very difficult to construct a rigorous proof of the extreme value theorem, you'd probably benefit intellectually from more exposure to mathematical proof. The experience of seeing how difficult it can be to offer rigorous proofs of even relatively simple statements trains one to read very carefully, and not make any unwarranted assumptions.
If you've studied calculus, haven't yet had the experience of proving theorems from first principles beyond high school geometry, and would are interested, I would recommend:
- Abbott's Understanding Analysis
- Rosenlicht's Introduction to Analysis (as a less expensive second choice)
- Gelbaum and Olmsted's Counterexamples in Analysis
The last of these books is great for developing a sense for how superficially plausible statements are often false.
Realistic epistemic expectations
When I state a position and offer evidence for it, people sometimes complain that the evidence that I've given doesn't suffice to establish my position. The situation is usually that I'm not trying to give a rigorous argument for my position, and I don't intend to claim that the evidence that I provide suffices to establish my position.
My goal in these cases is to offer a high-level summary of my thinking, and to provide enough evidence so that readers have reason to Bayesian update and to find the view sufficiently intriguing to investigate further.
In general, when a position is non-obvious, a single conversation is nowhere near enough time to convince a rational person that it's very likely to be true. As Burgundy recently wrote:
When you ask Carl Shulman a question on AI, and he starts giving you facts instead of a straight answer, he is revealing part of his book. The thing you are hearing from Carl Shulman is really only the tip of the iceberg because he cannot talk fast enough. His real answer to your question involves the totality of his knowledge of AI, or perhaps the totality of the contents of his brain.
If I were to restrict myself to making claims that I could substantiate in a mere ~2 hours, that would preclude the possibility of me sharing the vast majority of what I know.
In math, one can give rigorous proofs starting from very simple axioms, as Gauss described:
I mean the word proof not in the sense of lawyers, who set two half proofs equal to a whole one, but in the sense of mathematicians, where 1/2 proof = 0, and it is demanded for proof that every doubt becomes impossible'.
Even within math, as a practical matter, proofs that appear to be right are sometimes undercut by subtle errors. But outside of math – the only reliable tool that one has at one's disposal is Bayesian inference. In 2009, charity evaluator GiveWell made very strong efforts to apply careful reasoning to identify its top rated charity, and gave a "conservative" cost-effectiveness estimate of $545/life saved, which turned out to have been wildly optimistic. Argumentation that looks solid on the surface often breaks down on close scrutiny. This is closely related to why GiveWell emphasizes the need to look at giving opportunities from many angles, and gives more weight to robustness of evidence than to careful chains of argumentation.
Eliezer named this website Less Wrong for a reason – one can never be certain of anything – all rational beliefs reflect degrees of confidence. I believe that discussion advances rationality the most when it involves sharing perspectives and evidence, rather than argumentation.
Learning takes a long time
I recently realized that I had greatly underestimated the inferential distance between most of my readers and myself. Thinking it over, I realize that the bulk of the difference comes from a difference in perspectives on how long it takes to learn substantive things.
People often tell me that they're bad at math. I sometimes respond by saying that they didn't spend enough time on it to know one way or the other. I averaged ~25+ hours a week thinking about math when I was 16 and 17, for a total of ~2,500+ hours. I needed to immerse myself in the math to become very good at it, in the same way that I would need to live in French speaking country to get very good at French. If my mathematical activity had been restricted exclusively to coursework, I never would have become a good mathematician.
Math grad students who want to learn algebraic geometry often spend spend two years going through Hartshorne's dense and obscure textbook. it's not uncommon for students to learn interesting applications only after having gone through it. I find this practice grotesque, and I don't endorse it. I bring it up only to explain where I'm coming from. With the Hartshorne ritual as a standard practice, it's felt to me like a very solid achievement to present substantive material that readers can understand after only ~10 hours of reading and reflecting deeply.
It was so salient to me that one can't hope to become intellectually sophisticated without engaging in such activity on a regular basis that it didn't occur to me that it might not be obvious everyone. I missed the fact that most of my readers aren't in the habit of spending ~10 hours carefully reading a dense article and grappling with the ideas therein, so that even though I felt like I was making things accessible, I was still in the wrong ballpark altogether.
Thinking it over, I'm bemused by the irony of the situation. Even as I was exasperated by some readers' apparent disinclination to read articles very carefully and think about them deeply, I was blind to the fact that I was failing because I hadn't put thousands of hours into learning how to communicate to a general audience. Seeing how large my blindspot was made me realize "Oh... just as I had no idea how much time I need to put into developing my communication abilities to reach my readers, some of my readers who appeared to me to be trolling probably just had no way of knowing of how much time it takes to learn really deep things."
The tens of thousands of hours that I put into developing intellectually didn't feel like a slog – it was fascinating. It was the same for all of the deepest thinkers who I know. If you haven't had this experience, and you're open to it, you're in for a wonderful treat.
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