In medicine, medical doctors read scientific papers or, at least, executive summaries of said papers. The papers contribute useful knowledge. However, even the best software practitioners can go years without reading any research, assuming that they ever read any. I believe that it is because they rightly feel that the papers will not teach them much about the real world.

You would probably be upset if you learned that your medical doctor is unaware of the latest clinical research concerning your condition. However, how concerned would a manager at Facebook be if he learned that a software engineer is not up-to-date with computer science research?

The problem comes from the fact that computer scientists rarely work with models or, rather, that they are confused about what a scientific model is.

A model in science is an algorithm that enables you to make falsifiable predictions about the real world. In computer science, it might go as follows:

Software X will be faster than software Y on data Z.

A model can be falsified. In my computer-science example, you should be able to run X and Y and you check which is faster. Because they make actual predictions about the world, models are tremendously useful.

Not all scientific models are cleanly falsifiable. For example, natural evolution is a scientific model. The belief that unit testing makes software more reliable is part of another. However, scientific models are always sustained by real-world observations as opposed to our own mental constructions.

But that is not what computer science offers you typically. Instead, computer scientists tend to make statement that avoid scientific falsifiability:

According to some cost model that may or may not be indicative of actual running speed, algorithm X is better than algorithm Y on data Z.

Oldberg proposed to reserve the word model for the genuine scientific models, and to use the French word modèle for the other kind.

You can compare a modèle with reality (what Oldberg calls evaluating), but you can never prove it wrong. A modèle is true as long as it is logically consistent, irrespective of reality.

Computer scientists love their modèles!

The problem is made worse by the fact that researchers working on modèles more easily get the upper hand. They are never wrong. They can endlessly refine their modèles and re-evaluate them. As long as there is no actual problem to be solved, the modèles will tend to displace the models. Cargo cult science wins.

Of course, the reverse phenomenon may exist within industry. People working with modèles are at a disadvantage. They can’t make useful predictions. They can only explain, in retrospect, what is observed. All their sophistication fails to help them when real-world results are what matters.

The truth, of course, is that open source software is just another example of the invisible college. Whenever some occupation requires advanced knowledge, its practitioners tend to network and freely exchange. This is often done under the radar.

This is not to say that people won’t withhold information or be fiercely competitive. But rather that, on the whole, people will tend to share freely with their guild. It is not altruism: this collaboration is key to long-term success as individuals. In science, we have formalized this process: to exist as a scientist is to share your results regularly in the form of papers.

Well before there was anything known as open source, programmers would share with other programmers. At some point, the likes of Bill Gates asserted their copyright over code which prevented the free exchange of code. However, not the free exchange of ideas: if you can get a Microsoft engineer in a bar, and you are an established programmer, he will typically share internal ideas with you. In fact, as long as it cannot be traced by to them by their employer, most programmers will freely share with other programmers, even if they are from competing companies.

In this sense, open source code is just the visible part of the invisible college of programmers. Because open source is an expression of the invisible college, we have that open source code is meant to benefit or impress other programmers. For example, the most popular projects on GitHub right now is Bootstrap which has as its motto: “by nerds, for nerds”.

And that is the key to be effective at open source. You have to be a programmer and you have to address yourself to other programmers.

Starting an open source browser or word processor meant for end-users is not the best choice. Failure probability is high. You need many ressources beyond coding that are expensive. This means that you need a business strategy. Got an MBA? However, you can build a faster JavaScript engine, a better XSLT engine, and so on. These will be highly valued by your (programmer) peers. In effect, do not confuse business activities based on open source, with what open source is. Genuine open source is about the invisible college. It is about helping other programmers.

Recently, many successful businesses are just based on a collection of open source tools put together quickly. And it is exactly why I started this post by saying that open source is a great innovation. In effect, no other community of practitioners has established such a rich invisible college. This makes programmers some of the most effective employees from a cost-value ratio.

Hence, despite a sluggish recovery, software engineers enjoy a 2% unemployment rate and some of the best salaries an engineer can get. And I suspect that open source code and the free exchange of ideas among programmers has a lot to do with these good results.

Of course, programmers who are not participating in this invisible college risk being left in the dust. If you work in isolation, never sharing… you will have a harder time leveraging the strength of the community.

Further reading: Alexia Gaudeul, Open Source Licensing in Mixed Markets, or Why Open Source Software Does Not Succeed, 2008. Eric S. Raymond, The Social Context of Open-Source Software (chapter from the Cathedral and the Bazaar).

One of the better known problems in Computer Science is the P versus NP problem. It is often related to the following question: do all problems for which we can check the correctness of a solution quickly can also be solved quickly.

Most computer scientists believe that P is different from NP. In colloquial terms, this means that we believe that there are problems whose solution can be checked quickly but such that it is very difficult to find the solution.

One relevant problem is the Hamiltonian path problem: given a set of roads and cities, is there a path that visits every city exactly once? It is easy to check any given solution, but it is thought to be quite hard (in general) to find such a solution.

A typical application for our belief that P is different from NP is to show that a given problem is difficult. Proving that something is difficult does not sound very important and practical at first, but it can save your job!

There is a million dollar prize to whoever can resolve the P is equal to NP question. This fact alone has attracted much attention to the problem. Whoever solves it would get not only instant fame, but quite a bit of wealth as well.

Lance Fortnow has spent a few years on a book that recounts all the fascinating adventures related to this problem. He explains the problem in depth in an accessible book entitled The Golden Ticket. The book is thoroughly researched and reviewed. Anyone from a smart high school student to a computer scientist is sure to get a lot of this book. The presentation is beautiful. There are few formulas but lots of facts. The book is of course kept simple, so hard-core computational complexity expert might be disappointed, but I think many of them will enjoy the stories Fortnow offer.

Disclosure: I received a free review copy of the book. I am otherwise unrelated to Fortnow.

Excel does have one tremendous benefit: it is accessible. Most people using spreadsheets don’t even realize that they are programming. In the Reinhart-Rogoff case, this accessibility was a great virtue: it allowed a regular PhD student to verify the computations.

However, there are several critical problems with a tool like Excel that need to be widely known:

• Spreadsheets do not support testing. For anything that matters, you should validate and test your code automatically and systematically.
• Spreadsheets make code reviews impractical. To inspect the code, you need to look at every cell. In practice, this means that you cannot reasonably ask someone to read over your formulas to make sure that there is no mistake.
• Spreadsheet encourage redundancies. Spreadsheets encourage copy-and-paste. Though copying and pasting is sometimes the right tool, it also creates redundancies. These redundancies make it very difficult to update a spreadsheet: are you absolutely sure that you have changed the formula throughout?

Unfortunately, spreadsheet programming is far more common in research than we would like to admit. I keep reviewing research manuscripts where the figures were obviously made with Excel. It is also very widespread in business: decisions worth millions (if not billions) of dollars are taken on the basis of a spreadsheet all the time.

Professionals should avoid spreadsheets for activities where mistakes matter. Reinhart and Rogoff should have used a bona fide programming language with proper testing, code review and documentation.

Further reading: Lotus Improv was an early attempt to build a spreadsheet tool that did not have some of these problems. It was a market failure. (Credit: Preston L. Bannister)

Two very reputed professors at Harvard University, Carmen Reinhart and Kenneth Rogoff, published online a working paper in 2010 called Growth in a time of debt. The paper has since been cited in the scientific literature about 500 times, or about once every two days. They later wrote a best-seller book on the same topic. They built a web site to promote their ideas where they shared some of their data.

Their research paper is remarkably easy to understand: countries with large debt tend to have lesser economic growth. This claim is entirely credible. On the one hand, countries with poor economies may tend to grow large debts because they have weak revenues. On the other hand, large debts may lead to tight fiscal policies (e.g., high taxes) or lax monetary policies (e.g., printing money) that may reduce economic growth.

The part that is less credible is that Reinhart and Rogoff were able to find a magical threshold (debt-to-GDP ratio of 0.9) that triggered low growth. In effect, they implicitly claimed that any debt-to-GDP ratio in excess of 0.9 would reduce your economic growth. This gave politicians a scientific justification for austerity measures (e.g., raising taxes and cutting back on government services).

The story then becomes interesting. Thomas Herndon, a graduate student, grabbed data from the Reinhart-Rogoff web site and tried to reproduce their results. He couldn’t. He then asked the authors for help. Reinhart and Rogoff shared the Excel spreadsheet they used with Herndon. He then promptly found basic flaws in their data processing. For example, the two professors ran sums over the wrong cells. It seems that they made several odd choices when processing the data. His paper is freely available online.

Things took a turn for the worse when, faced with this opposition, Reinhart-Rogoff balked and asserted that these errors did not impact their work. Thankfully, they did not deny their mistakes.

This whole incident shows the importance of sharing data and software. Reinhart and Rogoff were almost exemplary regarding data in this case: they widely shared their data. Their mistake was in not widely distributing their software (in the form a spreadsheet) earlier. Had the spreadsheet been available from the start, they would be in a much better position.

Of course, another version of the story could be that, had Reinhart and Rogoff not shared their data and software, they would have plausible deniability and their work could still be credible. But this only means that you, as a reader, should put more trust in work where the data and software are available.

In any case, by keeping the software private, the best that Reinhart and Rogoff could have hoped for was to delay the inevitable: once your work is the basis for public policy, it is bound to come under intense scrutiny and significant mistakes will be discovered. By sharing your software, you establish your good faith.

There are other minor points that I find interesting in this story:

• All this work is posted online. To my knowledge, no journal has been directly involved.
• Usually, negative results are unpublishable: journals are not interested. It is unclear that the paper by Herndon et al. can be published in a conventional journal even though it is obviously important work.
• It seems that Reinhart and Rogoff are credited for much of the suffering due to the austerity measures in Europe. This seems entirely ridiculous to me. I think that Reinhart and Rogoff acted in good faith.

Further reading: Influential Reinhart-Rogoff economics paper suffers spreadsheet error (via Scott Guthery) and My take on Reinhart and Rogoff by David Henderson.

Graeber is an anarchist, and so am I. I think that we should always have as little formal organization as we can. So I immediately liked Graeber. He has also this whole underdog angle: he was denied tenure at Yale despite an impressive public record. I like underdogs! Like Graeber, I am upset that our governments bail out the financial sector. Bankers think that the world owe them great salaries without any possible risk that they might lose their jobs. This is the 1% that Graeber was denouncing: people who believe that they are owed great things.

But though I liked Graeber, it soon became clear that Graeber can be harsh! When Venkatesh Rao wrote a review of his book, here is how Graeber answered: A book is only as good as its readers. In effect, if you don’t like his book, you are not good. Oh! Oh!

By itself, such remarks should not be taken as an indication that Graeber is a difficult person. It is not uncommon, or even particularly unhealthy, to have a big ego as a scholar. I would say that it is almost required.

However, more recently, Graeber publicly complained about his so-called academic exile. Indeed, following a denial of his tenure at Yale, American and Canadian colleges did not approach David Graeber with a job offer. He sent out 17 job applications and got no interview! So he had to settle for a professorship at the London School of Economics with a salary that surely puts him in the top 1%. These are his exact words:

It’s not just that I didn’t get a job. 17 applications and no offer could have just been a run of bad luck, after all. The remarkable thing is that at not one of those 17 places was I even formally considered.

I do understand that the academic job market is bad. It is, to tell the truth, rather horrible, especially in the humanities. Simply put, we train too many PhDs. Places like Yale can find dozens of great anthropologists, as deserving as Graeber. In fact, if you look at the web site of the anthropology department at Yale, you will find that half of the professors are assistant (non-tenured) professors. This means that the job market is such that Yale can afford to hire assistant professors, keep them for the duration of their contract and then let most of them go.

Graeber had better luck abroad than in the US. This can be frustrating, but it is hardly uncommon. Scholars move around quite a bit: just go to your local college and listen to the professors. Many of them are foreigners. Having to move to a different country is par for the course if you want to be an academic scholar. Yes: sometimes your ideas are better received elsewhere.

Is he being shunned in the US because of his political views? It is possible. But before one starts imagining that capitalists are having anti-capitalists shunned from American colleges, one has to realize that American colleges are overwhelmingly leftist, especially in the humanities. And hiring decisions are made by other academics, not bankers.

Graeber did get seven years as a professor in an Ivy League American school. Isn’t it fair for others to get this chance as well? I would think that a fairer system is one where, after you had a good run in academia, you have to leave your place to others so that they, too, can write books and promote their ideas.

A fairer system would be also one where you are not allowed to limit yourself to the top schools: you have to offer your services to lesser colleges (often attended by poorer students). In fact, maybe the Yale professors should have to move to second-rate colleges after 7 years. This would be fairer, wouldn’t it? It would expose poorer students to more prestigious professors and I am sure it would help reduce inequalities.

Graeber answered: “I certainly thought of applying to working-class universities (…) Everyone told me don’t bother (…) they wouldn’t even look at me.”

A man who genuinely wants to teach working-class kids would apply for jobs at public colleges irrespective of what his friends tell him. He would not give the impression that he is too good for such a college. He would use the full force of his charisma to convince public colleges to give him a chance.

But Graeber is a man who wants to be part of the elite. He wants to teach to the children of the 1%, to the children of the bankers. And this is also the man behind “we are the 99 percent”. It smells of cognitive dissonance.

Of the participants of Occupy Wall Street, very few can afford to have Graeber as a professor because he will only work in top schools. How is that fair?

Credit: Thanks to Seb Paquet for a pointer to this story. Thanks to William Tozier for giving me Graeber’s book.

Whereas early research typically showed a decline in productivity after the ages of 40 to 45 years, this decline has been absent in more recent studies. (Stroebe, 2010)

It does seem that older scientists are less likely to come up with new paradigms. But this may have nothing to do with biological decay:

(…) at a relatively early stage both the accumulation of knowledge and the establishment of fixed habits of thought may begin to reduce the ability to create radical new abstract ideations that is key to important conceptual innovations. (Weinberg, 2005)

It seems to me that you have options if you want to plan to be an older intellectual:

• As you grow older, seek to best exploit your existing knowledge: leave radical new ideas to the younger generation.
• From time to time, drastically change your intellectual habits to keep your brain young.

Further reading: Fluid and crystallized intelligence and Scientific Productivity, Age, and Field (via P. Turney).

Game designers instinctively know about cognitive stretching: most video games are designed to taunt the player with the possibility that they might be so much more. Yes, cognitive stretch is stressful, but it is also stimulating… and fun!

How do you know whether you are getting enough cognitive stretching? There is a simple test: do you know everything about a given problem? What thousands of years of scholarship have shown is that there is almost no end to what we can learn about any given issue. If you have the feeling that you know it all, then it is maybe time to meet new people, to read some new books, to change school or to change job.

What defines our current era, more than anything else, is that it is obviously apparent to anyone that we live in an open world. We are sentenced to permanent cognitive stretching. To think that you know everything about programming, you have to work hard to avoid reading Stack Overflow where thousands of new perplexing questions are asked every day.

I think that there are a few sane ways to deal with cognitive stretching:

• Perfectionism is the first casualty of cognitive stretching: let go of the idea that you could perfectly master anything.
• Cognitive stretching is fun, if you let it be: you should never have to feel bored anymore.
• If you are a coach, a leader, a teacher or a manager: stop trying to give the illusion that you know it all.
• If you are a student, assume that you know little and will always know little. Take pleasure in what you learn and stop worrying about everything you haven’t learned.

Cognitive stretching is not without controverse however. For example, many dedicated teachers have a somewhat difficult relationship with it. Introducing students to ideas that are likely to remain beyond them for years is often considered a bug. It is as if we were happy to let you make erotic movies, as long these movies ended up showing full frontal nudity. As the volume of porn on the web indicates, keeping nothing mysterious can be profitable. In this sense, these teachers have a point: it does please the crowds to reveal everything. People think they like straight-forward answers. People want to learn everything there is about software programming in 10 days. They want to learn the 7 secrets of the great managers.

But the tension between full knowledge, and partial knowledge, is what keeps us going. I cannot remember a more dangerous intellectual experience than finishing my first course on classical mechanics and concluding that classical mechanics was simple. A few years later, I took another classical mechanics course where I failed the first test. I did end up with a good grade, but only after radically changing my appreciation of Newton’s work. In a very real sense, my first course failed me. It gave me no appreciation for everything I did not know.

If you are reasonable, you have to expect ever greater changes in the next ten years. Maybe you think you are open-minded and can think clearly about the future. Then close your eyes and imagine a world…

• … where the PC and Windows are all but a memory…
• … where driving a car yourself is considered reckless…
• … where paper books are like vinyl records…
• … where the last TV station closed down years ago…
• … where the notion of putting kids in a classroom to learn at fixed hours is considered barbaric…
• … where working more than 10 hours a week for money is considered strange…
• … where corporations have been declared obsolete…

Can you imagine a world where these things are true? I bet you cannot.

Scifi novels become obsolete very fast because scifi writers have failures of imagination. For example, most novels written before 1990 missed the Web. (It is very annoying to read a novel about year 2150 where the Web has not been invented yet.) If scifi writers fail to imagine our future, regular workers are bound to think that whatever they do now will still be needed in 10 or 20 years. And their reaction is entirely predictable: point out the obvious limitations of whatever new technology could threaten their livelihood.

• When blogs first took hold, journalisms almost unanimously ridiculed them. Many journalists never could adapt, and many more lost their great working conditions. You can still sell good pictures to your local newspapers, but who is going to pay $5000 when any kid can snap a picture on his cell phone and broadcast it on Twitter within seconds? Of course, the quality is not the same. Of course, average bloggers can’t replace true journalists.
• Teachers are quick to point out the limitations of cheap online learning. I sometimes like to brag that I took a class with the greatest geometer of the 20th century. Taking an online course with him would not have been the same. Watching him tear apart a student who asked about applications was frightening. You just can’t simulate this on YouTube.

Arguing with people who refuse to see their own doom can be fun, but tiring. They often simply cannot see it. Thankfully, Simon Law pointed out an effective way to fight this cognitive bias. To know whether you will endure, look in the past, not in the future. Ask who you replaced before denying that someone could replace you.

• Newspapers replaced news ballads. That is right: whenever some important event needed to be disseminated, people wrote a song about it. Far more artistic and entertaining than the average news report on CNN. It took real talent to write compelling ballads. I am sure that when the first newspapers came along, whoever wrote news ballads was unimpressed.
• Almost all teachers today work in an industrial setting, with students organized in classes, with standard testing. If you go back to the pre-Gutenberg era, teachers were scholars paid by the students for actual tutoring. Socrate would have been horrified by how philosophy is taught today. Certainly, he would not have approved of the way we sit the students in a classroom and give a grade to each student for each topic.

Maybe the greatest challenge we face as a species is that we need to get a lot better at imagining the future. Some people have wondered, for example, why I have been so critical of our response to global warming. But look at how people too often react: the Earth is warming, so let us create new taxes.

How original! Can you imagine the admiral James T. Kirk: “Spock, what shall we do, the Earth is warming!”… Spock: “Surely, we should try carbon taxes. Let us not do anything crazy involving computers or any technology beyond tax collection… that would be illogical…”

Given any problem today, if you suggest that we out-innovate it with technology, you are perceived as a dangerous man who think that we are bound by history to push forward toward an undiscovered country. Crazy talk!

People are concerned that technology is allowing the very best to get richer than was ever possible before. So, everyone, from politicians to union leader, is stuck on the idea that we must, somehow, create more jobs. Maybe we need to grant more university degrees. Maybe we need more PhDs. Maybe we need more government workers. Maybe we need more subsidies. Because, you know, 40-hour jobs is what we had so it must be what we will have. But nobody, it seems, can conceive a world where most of us work far less for money than we do now. Nobody can imagine a world where millions of people post design and publish cool videos of their dog to pass time while we keep on finding clever ways to rise our standard of living. Somehow, that’s unthinkable. We must find a way to keep people busy at a desk 40 hours a week. No matter what the cost.

I am seriously thinking about writing a scifi novel about people unable to imagine the future.

Kirk: “What shall we do, these transporters will make the the shuttle pilots nearly obsolete…” Bones: “Do not worry, we are preparing a medical advisory against this new dangerous technology.” Lady in the back: “Sirs? Can you please shut down your tricorders, they are forbidden on Star Ships along with any other portable electronics…”

Further reading: Technology is Eating Your Job.

One of the most important talk you can watch about managing your email is Inbox Zero by Merlin Mann. This talk is sheer genius. However, in practice, it has limitations. Like many others, I have developed a few “advanced” email management techniques based on a few key observations.

Even with spam filters, most email is junk.

I skip most emails. When emails are long, I will often read only the first few lines.

Email has become time sensitive.

It is silly but if you don’t reply within the 24 hours, people get nervous. They assume that you have not gotten their email. They feel ignored or rejected. Yet I sometimes need to work undisturbed for many hours. Maybe I am in meetings, maybe I am working with a student, maybe I need to crank out code. Hence, if the email requires more time that I have, I write back that I will get back to it later. I never use automated responders as I think that they are spam. I actually write a personalized (but short) answer.

Email can reproduce.

There is a relationship between how many emails you send and how many you receive. The more you use email to communicate with others, the more others will use email to communicate with you. I think it is best to send few emails, to few people and to keep them short.

Credit: Thanks to Philippe Beaudoin for rising this issue on Google+.

I want to make two important points as an answer to this question:

1. Each successive generation has been getting smarter

It shouldn’t be shocking to think that our children will be smarter. We are smarter than our parents. A thousand years ago, if you knew how to read or write, you were a scholar (by definition). A few centuries ago, anyone who could read silently (without having to vocalize the words) was regarded with awe. Fifty years ago, people who could use computers for their daily tasks were wizards.

A common mistake is to think that “intelligence” is made of the piece of meat in your brain. Your intelligence is actually an aggregate of your brain with your environment and the tools and ideas around you. Tools extend our intelligence… with computers and robots being obvious examples. Physical tools are not the only things making us smarter however. If you study the work of Newton, the way he presented it himself, it may well be impossible to understand. Newton had to work with relatively weak intellectual tools: he had to do everything through geometry because that’s how people did mathematics at the time. We can now do mathematics much more effectively. That is why there are millions of people who master calculus whereas it was once considered leading edge knowledge, only accessible to the great minds.

In some sense, true mathematics is about constructing mental tools so we can be smarter. So mathematicians have been busy making humanity smarter for centuries.

Many college students, if transported back a century or two in the past, would be phenomenal geniuses. Some might object to that statement. After all, the brains of these teenager is nothing extraordinary. But they are! Our brains are wired in ways that are vastly different. To learn is to rewire your brain. How would you differentiate a genius from someone who has visited the future long enough to steal the best ideas and train in their understanding? You simply couldn’t! As Alan Kay put it: A change of perspective is worth 80 IQ points.

Of course, we are going to hack directly into our brains in the near future. I am still waiting for a chip that will give me access to the web at the speed of the thought. But this will not be a radical departure from what we have been doing for thousands of years: getting smarter faster and faster.

2. We absolutely need to get smarter at an accelerating pace.

Unfortunately, it is not a given that we are going to get much smarter: we could also go extinct or our civilization could collapse. It has happened before. To maintain a sophisticated civilization, we have to keep out-innovating our problems. You may have heard that our civilization is not sustainable. We burn too much fossil oil, we pollute too much, there are too many of us, and so on. This is all true. If we are going to keep on surviving, let alone get better, we need to keep on getting smarter at a rate that exceeds our growing problems. We are not just getting smarter for fun, we are getting smarter as a matter of survival.

In any case, the idea is on the table. Should we pursue genetic-engineering policies that would make future generations smarter? Should we try to create superb engineers and scientists through genetic manipulations?

Let us leave aside the obvious ethical considerations involved. (Or what it tells us about Americans that they are entertaining such fears about China.) Let us just ask whether it would work.

The core problem with this project is that our intelligence is not limited or defined by our brains. So far, computers have made us much smarter much faster than biology could. We have no idea how to design a brain that has Wikipedia at its virtual finger tips, or that can compute the first 10,000 digits of pi in less than a second… yet most of us have this power right now. A cheap smart phone can do these things easily. Our brains can’t.

As time passes, the fraction of our intelligence that is born out by our brain is less and less. This fraction was never 100%. Human beings build tools, they form communities. When times come to solve problems, they use this environment in their favor.

In many ways, equipped with Google Scholar, I am a much better scholar than 99.999% of all scholars that lived before we had the Web. In turn, the scholars that had access to large libraries (e.g., the Library of Alexandria) were much better than those who did not.

In a very real way, we are getting smarter and smarter, even if our brains are standing still biologically.

What if, after a couple of generations (say 50 years), China could rise the average IQ of its population by 5 points using genetic engineering? How much would that matter? Probably not much at all. In 2060, the fraction of our intelligence that will depends on our brains will be tiny, assuming biological brains are even still relevant.

Some might object that intelligence is more than just having access to information. They would object that there are brains that are truly superior in ways that computers cannot mimick. Let us take an example: chess. Playing a good game of chess was once the summon of raw intelligence. And the greatest chess player of all time is still with us: Garry Kasparov. I am sure his brain is quite unique. Yet it did not stop computers from beating him. Again and again.

Scientists will object that they are nothing like chess players. Their work is at a higher cognitive level. Maybe so, but Kasparov was beaten by a computer fifteen years ago. And back then, he accused IBM of cheating, because he saw true intelligence in how the computer was playing. It is absolutely certain that in 20 to 50 years, computers will generate original mathematics and science that will leave human beings behind.

It does not mean that biology is uninteresting. I am generally favorable to any biological technology that can enhance intelligence. For example, it appears that amphetamines can boost intellectual performance substantially… at least for a time. I would be a user if it did not also have the side-effect of destroying your sex life. Short of using such strong drugs, I drink coffee and, thankfully, it looks like taking coffee once per day does make you smarter.

Yet I don’t know any biological trick that can boost my intelligence like computers can. I also think that any long-term intelligence improvement strategy has to take into account that we are become hybrids, part machine, part human beings…

In short: if China is really investing in a costly program to boost the intelligence of future generations using genetic engineering, they are wasting their time. I imagine my descendants having computer gear wired directly in their brains, or maybe living directly inside computers.

Disclosure: I am a computer scientist, not a biologist.

Further reading: The reinvention of self.

Credit: Allen Knutson and John Baez.

As a kid, I was told that this was meant to help farmers. But farmers have traditionally opposed daylight saving time (though they no longer do). The real argument is that daylight saving time saves energy.

Does it really save energy? This is disputed somewhat:

We estimate a cost to Indiana households of $9 million per year in increased electricity bills. We also estimate social costs of increased pollution emissions between $1.7 to $5.5 million per year. (Kotchen and Grant, 2011)

What is not disputed is that the change to daylight saving time increases significantly heart attacks:

Overall, we found an elevated incidence ratio of 1.039 (95% confidence interval, 1.003–1.075) for the first week after the spring clock shift forward. The higher risk tended to be more pronounced among individuals taking cardiac medications and having low cholesterol and triglycerides. (Janszky et al. 2012)

But if daylight saving time part of the year saves energy, wouldn’t year-long daylights saving time saves even more? Many people think so (e.g., see Ahuja and SenGupta, 2012).

The objections to a permanent switch to daylight saving time comes from parents who fear that dark morning might increase accidents involving kids. Yet the evidence that daylight saving time is bad for students does not stand.

Thus, Calandrillo and Buehler conclude that it is time to switch year-round daylight saving time:

(…) year-round daylight saving time would save hundreds of lives annually by decreasing motor vehicle and pedestrian fatalities. Furthermore, extra light in the evening hours reduces criminal activity and results in energy savings from decreased peak electricity demand. Finally, year-round daylight saving time would eliminate the negative effects caused by the current spring and fall time changes. (…)

I see no evidence in favor of the current system. It is insane.

If you are American, please sign the petition to stop this insanity.

Unfortunately, in real life, predicates either evaluate to true, or they don’t. C. J. Date showed that NULL markers end up giving you inconsistent semantics. So our civilization runs on database systems that can be inconsistent!

Yet the NULL markers were introduced for a reason: some things do remain unknown or are non applicable. We can handle these issues with more complicated schemas, but it is not practical. So database designers do allow NULL markers.

How did Codd react when it was pointed out to him that NULL markers make his model inconsistent? He essentially told us that NULL markers are in limbo:

(…) the normalization concepts do NOT apply, and should NOT be applied, globally to those combinations of attributes and tuples containing marks. (…) The proper time for the system to make this determination is when an attempt is made to replace the pertinent mark by an actual db-value.

So the mathematical rigor does not apply to NULL markers. Period.

This sounds pretty bad. I am rather amazed that Codd could get away with this.

But how bad is it in real life?

Let us consider WordPress, the blog engine I am using. As part of the core database schema, only the tables wp_postmeta, wp_usermeta and wp_commentmeta allow NULL markers. These tables are exclusively used to store metadata describing blog posts, users and comments. If this metadata is somehow inconsistent, the blog engine will not fall apart. It may hurt secondary features, such as advanced navigation, but the core data (posts, users and comments) will remain unaffected.

Date was repeatedly asked to prove that NULL markers were indeed a problem. I do not think that he ever conclusively showed that they were a real problem. Anyhow, our civilization has not collapsed yet.

Does anyone has any evidence that NULL markers are a bona fide problem in practice? Oh! Sure! Incompetent people will always find a way to create problems. So let us assume we are dealing with reasonably smart people doing reasonable work.

Credit: This post is motivated by an exchange with A. Badia from Louisville University.

Example of SQL’s inconsistency:

We are given two tables: Suppliers (sno,city) and Parts(pno,city). The tables have both a single row; (S1,’London’) and (P1,null) respectively. That is, we have one supplier in London as well as one part for which the location is left unspecified (hence the null marker).

We have the following query:

Select sno, pno
From Suppliers, Parts
Where Parts.city <> Suppliers.city
Or Parts.city <> ‘Paris’;

In SQL, this query would return nothing due to Codd’s 3-value logic because the where clause only selects row when the predicate is true.

Yet we know that if a physical part is actually located somewhere, it is either not in London or not in Paris. So the answer is wrong.

Let us consider another interpretation: maybe the part P1 is fictitious. It is not physically available anywhere. In such a case, the SQL query still fails to return the correct answer as the part P1 is not in London.

Maybe we could assume instead that the part P1 is available everywhere: this later interpretation is also incorrect
because the query

Select * from Parts where Parts.city = ‘Paris’

will return nothing.

Private businesses seem remarkably uninterested in tackling serious problems such as energy despite soaring prices and evident problems. In the rare cases where someone may be attempting to solve these problems, one often finds the heavy hand of government funding, for better or worse.

Many years ago, I pursued a Ph.D., not to become a professor or even a government researcher, but rather to pursue industrial R&D. I had vague dreams about working in some corporate research laboratory. I imagined I would be working on cool new technology to solve important problems. I never even questioned that such job existed… until I went looking for them!

I was disappointed. Instead, I ended up starting my own business. Amazingly, it worked! We got several contracts to do leading-edge work that still determines my research agenda to this day. I acquired a taste for problems that matter. However, much of our funding (maybe 40%) came indirectly from the government.

“Where are the big problem jobs?” I would say that they are either supported by the government or, better yet, driven by entrepreneurship.¹

The problem with government R&D is that it has a bad track record at helping the economy, outside of a few areas such as agriculture. Baumol made a similar point in the Free-Market Innovation Machine: Imperial China, the Roman Empire and even the USSR had great scholars and no shortage of technological innovation. However, they lacked the means to reap the benefits of this research. Government R&D often comes about as a mix of bureaucratic campuses, bureaucratic government laboratories and bureaucratic corporations. That’s only exciting if you have a perverse sense of humor.

Want to kill a good idea? Assign it to a committee.

What Imperial China, the Roman Empire and the USSR lacked, was entrepreneurship. I conjecture that the number of science graduates who become entrepreneurs is a better predictor of economic progress than the number of new Ph.D.s. And if you are a young woman or man who wants to work on big problems, you would be better off preparing yourself for some form of entrepreneurship… if you want your work to matter.

Too many scientists think that science is created in a laboratory and then turned into a product by puny engineers. The reverse process is at least as likely to happen. It is by trying to solve important problems that significant science comes about.

Yet we are not used to think of scientists as entrepreneurs. We are not used of thinking of science as a process where you get money, hire people and make a profit. In some ways, the idea of patenting a new refrigerator seems contrary to science. Yet Einstein held such a patent that he sold for profit.

Our idea of the scientist has too much to do with the Mandarins of Imperial China and not enough to do with Sergey Brin.

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¹– There are corporate exceptions. For example, Google seems to be working on a few big problems, such as the self-driving car and Google Glass.