Many people justify their choice of pursuing graduate school by a desire to explore new ideas and satisfy their intellectual curiosity, an euphemism for goofing off.

These students use graduate school as a cultural hack… they turn what might be perceived as an asocial behaviour (e.g., spending 4 years writing a Perl compiler in JavaScript using Bayesian inference) into something that is tolerated and even encouraged.

I think we need outlets where such smart people can go and do their things for a time. Indeed, most corporations do not want to collect employees who write Perl compilers in JavaScript using Bayesian inference.

College can be a good place to park smart people until they find something productive to do. And the hopeless ones become college professors.

Your intuition is probably that large well-funded research laboratories produce more research…

One part of this intuition is flat out wrong. Small teams are consistently more productive:

(…) small sized laboratories are more productive. This result is consistent with the results previously obtained in the literature (…) (Carayol and Matt, 2004)

And while abundant funding does help to produce more papers, there are not necessarily better ones. And the increase in productivity (a few percentage points per million dollars) is lower than you might think. Moreover, not all funding sources have a positive outcome. Earmarked or infrastructure funding may lead to lower quality research.

(…) the receipt of an NIH research grant (worth roughly $1.7 million) leads to only one additional publication over the next five years, which corresponds to a 7% increase. (…) (Jacob and Lefgren, 2011)

Public contractual funding (per permanent researcher) is the only significant funding variable: It plays positively on the publication intensity (not weighted for impact) [but] its impact is rather low. (Carayol and Matt, 2006)

As a first approximation, increasing federal research funding on the margin results in more, but not necessarily higher quality, research output. (Payne and Siow, 2003)

Conclusion: Small teams receiving maybe modest amounts of funding are probably a winning recipe most of the times. It is also a model that is compatible with professors doing hands-on work and having time to teach. We should be skeptical of large centralized projects in research.

Roger Schank is a famous computer science professor. His take on lectures is just brilliant:

We still have lectures for one main reason. They are the lazy person’s approach to education. Both lectures and listeners agree that neither of them wants to do much work. Real work, and real doing, and real conversation, is all that matters for learning, but education is really not about learning.

Scott Alexander wrote a series of great posts on genetic determinism. He recounts how he believes he learned English without any effort but could not learn Calculus. He explains that a 7-feet-tall healthy individual is almost guaranteed to earn millions of dollars as a basketball player. He believes that height and intelligence are alike.

I disagree.

Firstly, let me briefly recount my own little story. In kindergarten, I failed most tests. I could not count up to 10. I could not tie my shoelaces. I could not recall my phone number. I was put in a special class. For some of my primary education, I was mediocre. Then I grew more interested in academic excellence. In the private high school I attended, I routinely finished first: I have the plaques to prove it. I ended up with a prestigious and generous scholarship to what might be the best Canadian University. I have screwed up many times throughout the years… but what is certain is that I can be both smart and dumb. This is not uncommon: IQ results do vary throughout a child’s life.

As a computer scientist, I view the brain as nothing more than a computer, a Turing machine of sort. Computers are made of software and hardware. There are the electrical components (e.g., the neurons) and then you have the software (how the electrical components interact).

At a high level, all Turing machines are equivalent. The main difference between computers is their speed.

So how do you get smarter computers? By improving the software. Once you set aside the speed, the main difference between computers from the 1970s and computers today is the software. And what difference does it make! Marc Andreessen famously wrote that software is eating the world to illustrate how powerful new software can be.

The great thing about software is that it is portable: once you have designed and implemented an algorithm, you can port it to other machines with relative ease. Much of the software you use today on your fancy laptop can run on a $35 Rasberry Pi.

So, as a computer scientist, I am biased to think that any two individuals with healthy brains facing a new problem differ mostly by how fast they can learn cognitive tasks. If one person can learn something, another should too.

How people differ most in a typical setup is by their software. The smart individuals have a lot of clever software running in their head.

It is easy to say that only a select few can be physicists or song writers. After all, only a few people can do it, isn’t it proof that their brain must be somehow fundamentally different?

If we go back in time, few people could read and write. Even kings were illiterate. It would have been insane to expect, back then, that everyone could know how to read and write one day. Most people simply did not have the brains to read and write. Skills that we take for granted today, like reading without speaking out the words, were reserved for prodigies.

If academic results were mostly genetically determined, then all countries with genetically similar populations would fare similarly. Yet relatively small changes to the education system can bring large changes on how students fare academically. A country like Finland went from any other European country, to a worldwide champion of academic success in a few years. (It now looks like they have fallen back again, but that is irrelevant. Poland also achieved the same feat, with a poorer and larger population.)

Europe took over the world. We got the British Empire. The greatest inventions were European. None of it had to do with genetics: the Europeans got the best software patches first. Their people adopted the scientific thought. This made all the difference in the world.

I am told that a world Chess champion in the nineteenth century would barely be able to enter national competitions today. We have long believed that great Chess players had enhanced memory… and though it is true that they can remember Chess games much better than most people, this skill is specific to Chess. They have learned to remember Chess games. They have great Chess software.

It is undeniable that we come to the world with different brains. If you have kids, you know that they have very specific traits that are visible in the first few weeks of their life. And you keep recognizing them throughout their life. One kid might laugh all the time: he started out doing just that when he was barely a month old. Another kid is going to be grumpy all his life, he started out this way as a baby. But these differences are relatively minor. By fitting the right software in their head, both can become Chess players or song writers.

Of course, at the margin, biological differences matter. On a spelling bee competition involving millions of kids, the winner needs to have an ideally suited brain and great training. But on most tasks, most of us are very far from reaching our full potential.

There are certainly physical problems that will prevent you from becoming a good speller. But if you have a healthy brain and the right training, you should be able to become a good speller.

It seems undeniable that women have a greater innate ability with language. But being a boy is no excuse to write poorly. You may have work harder to get the language patches installed, but they should be available to you.

The real problem is that nobody knows much about how to help people learn. We spend billions of dollars on education every year, but we still do not know how to install a software patch so that boys can spell as well as girls.

As for basketball and being 7-feet tall… We live in a world were men without legs can outrun most of us. It is a failure of imagination to think that we need to be limited by our physical attributes. We just need to think differently, as Steve Jobs would say.

I was asked by one of my students how to become an expert programmer. Peter Norvig has already given an excellent answer: work hard for ten years.

Let me revisit the main points that should be common knowledge by now:

  • Do a lot of it. I have published over a thousand blog posts. I have published dozens of research papers as a primary author. I must have written at least 100,000 lines of code. While the rule that says you need many hours of practice to become an expert is debated at the margin, the core truth remains. Most people need a lot of time and practice to become good.

    I will add one point that is often glossed over: use it or lose it. Once you stop using a skill at a high level, it starts to slowly deteriorate.

  • Constantly push yourself. You can setup 10,000 web sites for clients, and still be a bad programmer. And God knows that you can write 10,000 research papers and still be a mediocre writer. You have to constantly raise the bar.

    As a rule, the more effort you make, the more you learn. The problem is that as you get better, it takes harder problems to challenge you.

Notice how generic this strategy is. Almost anybody can do these two things: work a lot for a long time and push oneself. But notice something else: the earlier you start working hard, the earlier you can become good. For most people, there is no shortcut, you need time and effort.

Note: The exact question the student asked me was about becoming an expert in Java programming. I am not an expert in Java even though I have been using the language since 1998. When I try to answer language quizzes online, I routinely fail 20% of the technical questions. E.g., I rarely use abstract classes in practice and inner classes confuse me. I do not know the list of reserved keywords in Java. There are still large sections of the standard API that I have very little experience with. But my generic reply is still valid. If I wanted to become a Java expert, I would study hard for years, constantly working on harder problems, passing ever more difficult tests.

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