I thought about my short academic career [1]. If I had been wildly successful, the most I could hope for would be to be one of these laureates. And yet I wouldn’t trade places with any of them. I’d rather do what I’m doing now than have an endowed chair at some university. Consulting suits me very well. I could see teaching again someday, maybe in semi-retirement, but I hope to never see another grant proposal.

***

[1] I either left academia once or twice, depending on whether you count my stint at MD Anderson as academic. I’d call my position there, and even the institution as a whole, quasi-academic. I did research and some teaching there, but I also did software development and project management. The institution is a hospital, a university, a business, and a state agency; it can be confusing to navigate.

]]>At the Heidelberg Laureate Forum I has a chance to interview John Tate. In his remarks below, Tate briefly comments on his early work on number theory and cohomology. Most of the post consists of his comments on the work of Alexander Grothendieck.

***

**JT**: My first significant work after my thesis was to determine the cohomology groups of class field theory. The creators of the theory, including my thesis advisor Emil Artin, didn’t think in terms of cohomology, but their work could be interpreted as finding the cohomology groups H_{0}, H_{1}, and H_{2}.

I was invited to give a series of three talks at MIT on class field theory. I’d been at a party, and I came home and thought about what I’d talk about. And I got this great idea: I realized I could say what all the higher groups are. In a sense it was a disappointing answer, though it didn’t occur to me then, that there’s nothing new in them; they were determined by the great work that had already been done. For that I got the Cole prize in number theory.

Later when I gave a talk on this work people would say “This is number theory?!” because it was all about cohomology groups.

**JC**: Can you explain what the great reformulation was that Andrew Wiles spoke of? Was it this greater emphasis on cohomology?

**JT**: Well, in the class field theory situation it would have been. And there I played a relatively minor part. The **big **reformulation of algebraic geometry was done by Grothendieck, the theory of schemes. That was really such a great thing, that unified number theory and algebraic geometry. Before Grothendieck, going between characteristic 0, finite characteristic 2, 3, etc. was a mess.

Grothendieck’s system just gave the right framework. We now speak of *arithmetic* algebraic geometry, which means studying problems in number theory by using your geometric intuition. The perfect background for that is the theory of schemes. ….

Grothendieck ideas [about sheaves] were so simple. People had looked at such things in particular cases: Dedekind rings, Noetherian rings, Krull rings, …. Grothendieck said take *any* ring. … He just had an *instinct* for the right degree of generality. Some people make things too general, and they’re not of any use. But he just had an instinct to put whatever theory he thought about in the most general setting that was still useful. Not generalization for generalization’s sake but the *right* generalization. He was unbelievable.

He started schemes about the time I got serious about algebraic geometry, as opposed to number theory. But the algebraic geometers classically had affine varieties, projective varieties, … It seemed kinda weird to me. But with schemes you had a *category*, and that immediately appealed to me. In the classical algebraic geometry there are all these *birational* maps, or *rational maps*, and they’re not defined everywhere because they have singularities. All of that was cleared up immediately from the outset with schemes. ….

There’s a classical algebraic geometer at Harvard, Joe Harris, who works mostly over the complex numbers. I asked him whether Grothedieck made much of a difference in the classical case — I knew for number theorists he had made a tremendous difference — and Joe Harris said yes indeed. It was a revolution for classical algebraic geometry too.

]]>When I mentioned that some people had reacted to the original post saying the scheme was too hard, Blum said that he has taught the scheme to a couple children, 6 and 9 years old, who can use it.

He also said that many people have asked for his slide summarizing the method and asked if I could post it. You can save the image below to get the full-sized slide.

This slide and my blog post both use a 3-digit password for illustration, though obviously a 3-digit password would be easy to guess by brute force. I asked Blum how long a password using his scheme would need to be so that no one with a laptop would be able to break it. He said that 12 digits should be enough. Note that this assumes the attacker has access to many of your passwords created using the scheme, which would be highly unlikely.

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Mathematics and art restoration

Studying algorithms to study problems

]]>It’s well known that software needs to be maintained; most of the work on a program occurs after it is “finished.” Proof maintenance is common as well, but it is usually very informal.

Proofs of any significant length have an implicit hierarchical structure of sub-proofs and sub-sub-proofs etc. Sub-proofs may be labeled as lemmas, but that’s usually the extent of the organization. Also, the requirements of a lemma may not be precisely stated, and the propositions used to prove the lemma may not be explicitly referenced. Lamport recommends making the hierarchical structure more formal and fine-grained, extending the sub-divisions of the proof down to propositions that take only two or three lines to prove. See his paper How to write a 21st century proof.

When proofs have this structure, you can see which parts of a proof need to be modified in order to produce a proof of a new related theorem. Software could help you identify these parts, just as software tools can show you the impact of changing one part of a large program.

]]>**JC**: Orthogonal polynomials are kind of a lost art, a topic that was common knowledge among mathematicians maybe 50 or 100 years ago and now they’re obscure.

**DS**: The first course I taught I spent a few lectures on orthogonal polynomials because they kept coming up as the solutions to problems in different areas that I cared about. Chebyshev polynomials come up in understanding solving systems of linear equations, such as if you want to understand how the conjugate gradient method behaves. The analysis of error correcting codes and sphere packing has a lot of orthogonal polynomials in it. They came up in a course in multi-linear algebra I had in grad school. And they come up in matching polynomials of graphs, which is something people don’t study much anymore. … They’re coming back. They come up a lot in random matrix theory. … There are certain things that come up again and again and again so you got to know what they are.

***

More from my interview with Daniel Spielman:

What is smoothed analysis?

Studying algorithms to study problems

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I believe there is, in mathematics, in contrast to the experimental disciplines, a character which is nearer to that of free creative art.

There is evidence that the relation of artistic beauty and mathematical beauty is more than an analogy. Michael Atiyah recently published a paper with Semir Zeki *et al* that suggests the same part of the brain responds to both.

These may be new. If they were here last year, I didn’t notice them.

There are several other points along the Old Bridge that have locks but nowhere are there very many.

Photo credit: Disdero via Wikimedia Commons

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For several years I’ve thought about the interplay of statistics and common sense. Probability is more abstract than physical properties like length or color, and so common sense is more often misguided in the context of probability than in visual perception. In probability and statistics, the analogs of optical illusions are usually called paradoxes: St. Petersburg paradox, Simpson’s paradox, Lindley’s paradox, etc. These paradoxes show that common sense can be seriously wrong, without having to consider contrived examples. Instances of Simpson’s paradox, for example, pop up regularly in application.

Some physicists say that you should always have an order-of-magnitude idea of what a result will be before you calculate it. This implies a belief that such estimates are usually possible, and that they provide a sanity check for calculations. And that’s true in physics, at least in mechanics. In probability, however, it is quite common for even an expert’s intuition to be way off. Calculations are more likely to find errors in common sense than the other way around.

Nevertheless, common sense is vitally important in statistics. Attempts to minimize the need for common sense can lead to nonsense. You need common sense to formulate a statistical model and to interpret inferences from that model. Statistics is a layer of exact calculation sandwiched between necessarily subjective formulation and interpretation. Even though common sense can go badly wrong with probability, it can also do quite well in some contexts. Common sense is necessary to map probability theory to applications and to evaluate how well that map works.

]]>This evening I ran across a couple lines from Ed Catmull that are more accurate than the vet’s quote.

Do not fall for the illusion that by preventing errors, you won’t have errors to fix. The truth is, the cost of preventing errors is often far greater than the cost of fixing them.

From Creativity, Inc.

]]>The inequality is strict unless all the *x*‘s are zero, and the constant *e* on the right side is optimal. Torsten Carleman proved this theorem in 1923.

We fear bad things that we’ve seen on the news because they make a powerful emotional impression. But the things rare enough to be newsworthy are precisely the things we should not fear. Conversely, the risks we should be concerned about are the ones that happen too frequently to make the news.

]]>- R
- Version control
- Linear algebra
- Advanced math
- Bayesian statistics
- Category theory
- Foreign languages
- How to not waste time
- Women

IgorCarron‘s response didn’t fit into the list above. He said “I wish I had known that sensing all the way to machine learning is about approximating the identity” and gave a link to this post.

]]>I like the term “Data Scientist” for now. I expect that term will be meaningless in 5 years.

Sounds about right.

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**Related post**: Take chances, make mistakes, and get messy

You can rent time on a virtual machine for around $0.05 per CPU-hour. You could pay more or less depending on on-demand vs reserved, Linux vs Windows, etc.

Suppose the total cost of hiring someone — salary, benefits, office space, equipment, insurance liability, etc. — is twice their wage. This implies that a minimum wage worker in the US costs as much as 300 CPUs.

This also implies that **programmer time is three orders of magnitude more costly than CPU time**. It’s hard to imagine such a difference. If you think, for example, that it’s worth minutes of programmer time to save hours of CPU time, you’re grossly under-valuing programmer time. It’s worth **seconds** of programmer time to save hours of CPU time.

**Update**: Use promo code KeenCon-JohnCook to get 75% off registration.

**What would Donald Knuth do**? Do a depth-first search on all technologies that might be relevant, and write a series of large, beautiful, well-written books about it all.

**What would Alexander Grothendieck do**? Develop a new field of mathematics that solves the problem as a trivial special case.

**What would Richard Stallman do**? Create a text editor so powerful that, although it doesn’t solve your problem, it does allow you to solve your problem by writing a macro and a few lines of Lisp.

**What would Larry Wall do**? Bang randomly on the keyboard and save the results to a file. Then write a language in which the file is a program that solves your problem.

What would you add to the list?

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Compare Cost and Performance of Replication and Erasure Coding

Hitachi Review Vol. 63 (July 2014)

John D. Cook

Robert Primmer

Ab de Kwant

Discussions about technology choices seldom consider who we become by using a tool. Different tools encourage different ways of thinking. Over time, different tools lead to different habits of mind.

]]>**Cheer 1**: He’s not being secretive, fearing that someone will scoop his results. There have been a few instances of one academic scooping another’s research, but these are rare and probably not worth worrying about. Besides, a public GitHub repo is a pretty good way to prove your priority.

**Cheer 2**: Rather than being afraid someone will find an error, he’s inviting a world-wide audience to look for errors.

**Cheer 3**: He’s writing a dissertation that someone might actually want to read! That’s not the fastest route to a degree. It’s even actively discouraged in some circles. But it’s generous and great experience.

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The podcast was posted this afternoon here.

**Related post**: Looking like you know what you’re doing

I’ve stopped posting to @DailySymbol. It was a fun experiment, but it was time to wrap it up.

My most popular account, @CompSciFact, now has over 100,000 followers. It’s interesting how some Twitter accounts take off and some don’t. CompSciFact has done quite well but I’ve shut down several other accounts that never gained much of a following.

You can find a list of my accounts here with a very brief description of each. Some of the accounts are a little broader than the name implies.

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The AirConf events will be broadcast via G+ hangouts.

]]>Most of the rides involve sitting in an inner tube and floating down a course with rapids, waterfalls, swells, etc. At many points there are back currents. You could be headed toward a fall but then find yourself reversing direction. It’s surprising to have to work to make yourself go downhill. At most if not all these points there are employees standing in the water to grab hold of rafts and pull people in the right direction who need a little help.

One question I had is **what causes the back currents**. Ultimately you could solve Navier-Stokes equations, but it would be nice to understand at a more rule-of-thumb level how these currents work. It would also be interesting to see **whether a park could reduce the number of guides** while keeping the rides as fun. The guides also serve as lifeguards, so the park may need to position people in all the same spots even if they didn’t need as many guides.

The slowest person in the family was consistently yours truly. I’d start out in front and inevitably end up bringing up the rear. I was curious **how I could be so inept at a mostly passive activity**.

I was also curious **how they designed the rapids to be so safe**. You’re repeatedly tossed straight toward rocks — perfectly smooth artificial rocks, but still not not things you want to hit your head on — at a fairly high speed, and yet you never hit one. It has something to do with how they position jets to push you away from the rocks, but that would be interesting to understand in more detail.

Another thing I was curious about is **what the park does with its water in the off-season**. Schlitterbahn in New Braunfels is actually two parks, an older park that uses untreated water from the Comal river, and a newer park that uses treated water. When the parks close for the season, the older park must just let its water return to the river. (At least one of the rides ends in the river, so they’re already returning water to the river.)

The question of **what to do with the treated water** in the new park is more interesting. I assume they cannot just dump a huge volume of chlorinated water into the river. Aside from ecological consequences, I wonder whether they’d even want to dump the water. Is it economical to store the water somewhere when the park closes for the year? If not, do they store it anyway because they have no way to dispose of it, or do they treat it so that they can dispose it? I suppose they could circulate the water occasionally while the park is closed, though that seems expensive. I wonder whether different waterparks solve this problem different ways.

If I could propose a new ride for Schiltterbahn, it would be a video presentation about how the park was designed followed by Q&A with a couple engineers. This would be a terrible business decision, but a few visitors would love it.

]]>]]>It’s amazing how much cleaner your code looks the third time writing it. First time, hack; Second over-engineer; Third = goldilocks.

]]>Now the discovery of ideas as general as these is chiefly the willingness to make a brash or speculative abstraction, in this case supported by the pleasure of purloining words from the philosophers: “Category” from Aristotle and Kant, “Functor’ from Carnap …, and “natural transformation” from the current informal parlance.

Computing: the only industry that becomes less mature as more time passes.

The immaturity of computing is used to excuse every ignorance. There’s an enormous body of existing wisdom but we don’t care.

I don’t know whether computing is becoming less mature, though it may very well be on average, even if individual developers become more mature.

One reason is that computing is a growing profession, so people are entering the field faster than they are leaving. That lowers average maturity.

Another reason is chronological snobbery, alluded to in Fogus’s second tweet. Chronological snobbery is pervasive in contemporary culture, but especially in computing. Tremendous hardware advances give the illusion that software development has advanced more than it has. What could I possibly learn from someone who programmed back when computers were 100x slower? Maybe a lot.

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