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Wednesday, April 23, 2014

No Exit

The gritty life of a startup founder in 2014 Silicon Valley.
WIRED: ... you’re getting a lot of people starting companies who shouldn’t be starting companies. Another investor I talked to called this “buying a cheap call option on a guy who doesn’t know that’s what you’re doing”—on a guy, that is, who thinks you’re investing in his success, not betting on the high-risk, high-yield chances of it. You know the odds on any given company’s success are long, but that’s why you make a lot of bets. In the first dotcom boom, the risk was largely carried by the investors, but now the risk has been returned to the youth.

Without mentioning the name of the company, I told him about Boomtrain, about what the past few weeks and months had been like for them. About how quickly they’d aged, how much weight they’d lost, the Airbnb-ing, the heavy mask of confidence, the number of mornings they’d woken up at 5 am grinding their teeth. Martino was sympathetic but unmoved. He didn’t expect them to make it. “They ran an experiment. None of their lives have been ruined.” He knew they’d get good jobs, even if it meant the life of a project manager at Yahoo. “And none of their investors’ lives have been ruined either. When they close up shop, their investors will say, ‘That’s one more off the books. I don’t need to help them anymore. I get my time back.’”

Martino watched the game for a minute, then turned back to me and held my gaze. He could tell I’d come to like and admire and root for the Boomtrain guys. I could understand the risk they thought they were taking. I was glad it looked like they’d finally found the momentum they so badly needed. “Let me tell you what the worst thing would be. The worst thing is that these guys get their funding tomorrow and are stuck doing this for another year. So far, they only lost one.” ...

All the while, Martino’s ultimate warning—that they might someday regret actually getting the money they wanted—would still hang over these two young men, inherent to a system designed to turn strivers into subcontractors. Instead of what you want to build—the consumer-facing, world-remaking thing—almost invariably you are pushed to build a small piece of technology that somebody with a lot of money wants built cheaply. As the engineer and writer Alex Payne put it, these startups represent “the field offices of a large distributed workforce assembled by venture capitalists and their associate institutions,” doing low-overhead, low-risk R&D for five corporate giants. In such a system, the real disillusionment isn’t the discovery that you’re unlikely to become a billionaire; it’s the realization that your feeling of autonomy is a fantasy, and that the vast majority of you have been set up to fail by design.

Sunday, April 20, 2014

Two cultures in Chicago

Action photos from the conference on Human Capital, Genetics and Behavior at the University of Chicago. See also Cognition uber alles.

This was a small, intimate meeting and, overall, very enjoyable. The two cultures represented were behavior genetics and economics, which I believe have a lot to say to each other. Greg Cochran and I were the theoretical physics interlopers ;-)

Video from most of the talks will be available online -- I will post a link.

Greg Cochran and Henry Harpending lead the opening discussion. Steven Durlauf is the moderator on the left.

Tuesday, April 15, 2014

Cognition über alles

Slides for a brief introduction to my panel at the University of Chicago Conference on Genetics and Behavior later this week. See also One hundred thousand brains.

Some relevant comments, from an essay by David Deutsch:
It is uncontroversial that the human brain has capabilities that are, in some respects, far superior to those of all other known objects in the cosmos. It is the only kind of object capable of understanding that the cosmos is even there, or why there are infinitely many prime numbers, or that apples fall because of the curvature of space-time, or that obeying its own inborn instincts can be morally wrong, or that it itself exists. Nor are its unique abilities confined to such cerebral matters. The cold, physical fact is that it is the only kind of object that can propel itself into space and back without harm, or predict and prevent a meteor strike on itself, or cool objects to a billionth of a degree above absolute zero, or detect others of its kind across galactic distances.

But no brain on Earth is yet close to knowing what brains do in order to achieve any of that functionality. ...

... the answer, conceived in those terms, cannot be all that difficult. For yet another consequence of understanding that the target ability is qualitatively different is that, since humans have it and apes do not, the information for how to achieve it must be encoded in the relatively tiny number of differences between the DNA of humans and that of chimpanzees. So in one respect I can agree with the AGI-is-imminent camp: it is plausible that just a single idea stands between us and the breakthrough. But it will have to be one of the best ideas ever.

[ AGI = ‘artificial general intelligence’ ]
We are, after all, merely machines that dream we are awake ;-)

Friday, April 11, 2014

Human Capital, Genetics and Behavior

See you in Chicago next week :-)
HCEO: Human Capital and Economic Opportunity Global Working Group

Conference on Genetics and Behavior

April 18, 2014 to April 19, 2014

This meeting will bring together researchers from a range of disciplines who have been exploring the role of genetic influences on socioeconomic outcomes. The approaches taken to incorporating genes into social science models differ widely. The first goal of the conference is to provide a forum in which alternative frameworks are discussed and critically evaluated. Second, we are hopeful that the meeting will trigger extended interactions and even future collaboration. Third, the meeting will help focus future genetics-related initiatives by the Human Capital and Economic Opportunity Global Working Group, which is pursuing the study of inequality and social mobility over the next several years.


9:00 to 11:00
Genes and Socioeconomic Aggregates
Gregory Cochran University of Utah
Steven Durlauf University of Wisconsin–Madison
Henry Harpending University of Utah
Aldo Rustichini University of Minnesota
Enrico Spolaore Tufts University

11:30 to 1:30
Population-Based Studies
Sara Jaffee University of Pennsylvania
Matthew McGue University of Minnesota
Peter Molenaar
Jenae Neiderhiser

2:30 to 4:30
Genome-Wide Association Studies (GWAS)
Daniel Benjamin Cornell University
David Cesarini New York University
Dalton Conley New York University/NBER
Jason Fletcher University of Wisconsin–Madison
Philipp Koellinger University of Amsterdam

APRIL 19, 2014

9:00 to 11:00
Paul Glimcher New York University
Jonathan King National Institute on Aging
Aldo Rustichini University of Minnesota

11:30 to 1:30
Stephen Hsu Michigan State University
Wendy Johnson University of Edinburgh
Rodrigo Pinto The University of Chicago

2:30 to 4:30
Role of Genes in Understanding Socioeconomic Status
Gabriella Conti University College London
Steven Durlauf University of Wisconsin–Madison
Felix Elwert University of Wisconsin–Madison
James Lee University of Minnesota

Wednesday, April 09, 2014

The essential difference

This is a recent talk at NIH, which contains some unpublished results.

In the final part of the talk (you can skip there via this link), Paabo discusses the genetic variants (~30k SNPs) that are fixed in essentially all modern humans, but are not present in the Neanderthal genomes sequenced thus far. These variants are presumably responsible for the differences between Neanderthals and moderns. Paabo obviously believes that enhanced cognition is one of the main differences, and he discusses the archaeological evidence for this. He also discusses functional investigations in genetically engineered mice, and advocates for large GWAS that might identify rare humans with "back-mutations" to the Neanderthal variant. Such studies could identify phenotypical effects.

In his recent book, Paabo wrote
(p.213) ... we estimated that the total number of DNA sequence positions at which Neanderthals differed from all humans living today will be on the order of 100,000. This will represent an essentially complete answer to the question of what makes modern humans "modern," ...

(p.253) [last paragraph of the book!] ... One can imagine putting such changes into cell lines, and into mice [or monkeys] ... in order to "humanize" or "neanderthalize" biochemical pathways or intracellular structures ... One day, we may understand what set the replacement crowd [moderns] apart from their archaic contemporaries, and why, of all the primates, modern humans spread to all corners of the world and reshaped, both intentionally and unintentionally, the environment on a global scale ...
See also The genetics of humanness, The Neanderthal Problem, and Genetic engineering of monkeys using CRISPR.

Saturday, April 05, 2014

Measuring Wealth Inequality

Recent increases in wealth inequality mainly due to top 0.01%, not top 1%? See this article (The Atlantic) and also here.

The method used to obtain these results is not without uncertainties. From these slides by Saez and Zucman. (Using flows to estimate accumulations.)
We develop a new technique to estimate the distribution of wealth

We capitalize income tax returns

Use IRS data on individual dividends, interest, rents...
Compute rates of return by asset class (Flow of Funds / NIPA)
Combine income and rates of return to obtain wealth

The capitalization method works for foundations
For which we observe both income and wealth
See also Inside the 1 percent:
Net worth distribution within the population of top wealth holders (assets > $2M; about top 1% of adult population): having $10M puts you in the 90th percentile (so, top 0.1% of total population) and $50M puts you in the 99th percentile (top 0.01% of total population).

Friday, April 04, 2014

CRISPR symposium at MSU

CRISPR Symposium, Saturday April 5, 8:30-4:00, Snyder Theater, C20 Snyder Hall.

Sponsored by the Office of the Vice-President for Research.
Speaker Information:

Dan Bauer is a lecturer in Pediatrics at Harvard Medical School. He is first author on the October 2013 Science paper “An erythroid enhancer of BCL11A subject to genetic variation determines fetal hemoglobin level”. He received his MD and PhD from the University of Pennsylvania and his BS from Brown University.

Patrick Hsu is a graduate student in Feng Zhang’s lab at the Broad Institute at MIT and Harvard and the McGovern Institute for brain research at MIT. In the past year he has contributed to 8 papers from the Zhang lab on CRISPR and genome engineering. He received his BS from Berkeley in Cellular and Molecular Biology.

Ophir Shalem is a postdoctoral research fellow in Feng Zhang’s lab at the Broad Institute of MIT and Harvard and the McGovern Institute for brain research at MIT. He is the first author on the January 2014 Science paper “Genome-scale CRISPR-Cas9 knockout screening in human cells” from the Zhang lab. He received his PhD from the Weizmann institute of Science in Biology and Computer Science and his BS from Ben Gurion University in Bioinformatics and Computer Science.

Jian-Kang Zhu is Distinguished Professor in the Departments of Biochemistry and Horticulture and Landscape Architecture at Purdue University. Recent work in his lab, which includes publications in Nature, PLOS Genetics and PNAS, has focused on RNA binding, genome engineering and DNA methylation. He received his BS from Beijing Agricultural University and his PhD from Purdue. He was a post-doctoral researcher at Rockefeller University.

Here's some recent CRIPSR coverage, focused on a method for measuring editing accuracy:
Recently a powerful new technology has emerged (called CRISPR) that allows researchers to make small, precise and permanent changes in the DNA of animal and human cells. It builds on the concept of genome editing that is key to generating cells, cell lines or even whole animals such as laboratory mice, containing specific genetic changes for study. With CRISPR, however, researchers can generate in days or weeks experimental models that usually take months or years. As a result, they can quickly assess the effect of a particular gene by deleting it entirely, or experiment with repeated, tiny changes to its DNA sequence.

According to a recent New York Times article, scientists roundly agree that CRISPR is revolutionary. At least three companies have been launched in the mere 18 months since the first results were reported by researchers at the University of California, Berkeley and Umea University in Sweden, and more than 100 research papers based on the technique have been published. But, although it’s highly specific, it’s (sadly) not perfect. According to the New York Times piece:
Quick is not always accurate, however. While Crispr is generally precise, it can have off-target effects, cutting DNA at places where the sequence is similar but not identical to that of the guide RNA.
Obviously it’s important to know when (and how frequently) this happens. Unfortunately, that’s been difficult to assess.

Enter researchers in the laboratory of pediatric cancer biologist Matthew Porteus, MD, PhD. Porteus’s lab is interested in (among other things) learning how to a particular type of genome editing called homologous recombination to treat diseases like sickle cell anemia, thalassemia, hemophilia and HIV. They’ve devised a way to monitor the efficiency of genome editing by CRISPR (as well as other more-traditional genome editing technologies) that could be widely helpful to researchers worldwide. Their technique was published today in Cell Reports. As postdoctoral researcher Ayal Hendel, PhD, told me:
We have developed a novel method for quantifying individual genome editing outcomes at any site of interest using single-molecule real-time (also known as SMRT) DNA sequencing. This approach works regardless of the editing technique used, and in any type of cell from any species.
See also here:
MIT scientists report the use of a CRISPR methodology to cure mice of a rare liver disorder caused by a single genetic mutation. They say their study (“Genome editing with Cas9 in adult mice corrects a disease mutation and phenotype”), published in Nature Biotechnology, offers the first evidence that this gene-editing technique can reverse disease symptoms in living animals. CRISPR, which provides a way to snip out mutated DNA and replace it with the correct sequence, holds potential for treating many genetic disorders, according to the research team.

Thursday, April 03, 2014

Implications of cosmological tensor modes

New paper! What can we conclude about high energy physics from the BICEP2 observations of a cosmological tensor mode background? See earlier post Patterns on the Sky.
Does the BICEP2 Observation of Cosmological Tensor Modes Imply an Era of Nearly Planckian Energy Densities?  (arXiv:1404.0745)

Chiu Man Ho, Stephen D. H. Hsu

BICEP2 observations, interpreted most simply, suggest an era of inflation with energy densities of order ($10^{16}\, {\rm GeV})^4$, not far below the Planck density. However, models of TeV gravity with large dimensions might allow a very different interpretation involving much more modest energy scales. We discuss the viability of inflation in such models, and conclude that existing scenarios do not provide attractive alternatives to single field inflation in four dimensions. Because the detection of tensor modes strengthens our confidence that inflation occurred, it disfavors models of large extra dimensions, at least for the moment.

Tuesday, April 01, 2014

Sequencing and GWAS

A very nice discussion of the challenges associated with sequence data, as opposed to SNP array output, in GWAS. All of these issues are familiar to our team as we work with our high cognitive ability sample at BGI.
8 Realities of the Sequencing GWAS

For several years, the genome-wide association study (GWAS) has served as the flagship discovery tool for genetic research, especially in the arena of common diseases. The wide availability and low cost of high-density SNP arrays made it possible to genotype 500,000 or so informative SNPs in thousands of samples. These studies spurred development of tools and pipelines for managing large-scale GWAS, and thus far they’ve revealed hundreds of new genetic associations.

As we all know, the cost of DNA sequencing has plummeted. Now it’s possible to do targeted, exome, or even whole-genome sequencing in cohorts large enough to power GWAS analyses. While we can leverage many of the same tools and approaches developed for SNP array-based GWAS, the sequencing data comes with some very important differences.


These caveats of the sequencing GWAS, while important, should not detract from the advantages over SNP array-based experiments. Sequencing studies enable the discovery, characterization, and association of many forms of sequence variation — SNPs, DNPs, indels, etc. — in a single experiment. They capture known as well as unknown variants.

Sequencing also produces an archive that can be revisited and re-analyzed in the future. That’s why submitting BAM files and good clinical data to public repositories — like dbGaP — is so important. Single analyses and meta-analyses of sequencing GWAS may ultimately help us understand the contribution of all forms of genetic variation (common, rare, SNPs, indels) to important human traits.

Sunday, March 30, 2014

Why does GCTA work?

This paper, by two of my collaborators, examines the validity of a recently introduced technique called GCTA (Genome-wide Complex Trait Analysis). GCTA allows an estimation of heritability due to common SNPs using relatively small sample sizes (e.g., a few thousand genotype-phenotype pairs). The new method is independent of, but delivers results consistent with, "classical" methods such as twin and adoption studies. To oversimplify, it examines pairs of unrelated individuals and computes the correlation between pairwise phenotype similarity and genotype similarity (relatedness). It has been applied to height, intelligence, and many medical and psychiatric conditions.

When the original GCTA paper (Common SNPs explain a large proportion of the heritability for human height) appeared in Nature Genetics it stimulated quite a lot of attention. But I was always uncertain of the theoretical justification for the technique -- what are the necessary conditions for it to work? What are conservative error estimates for the derived heritability? My impression, from talking to some of the authors, is that they had a mainly empirical view of these questions. The paper below elaborates significantly on the theory behind GCTA.
Conditions for the validity of SNP-based heritability estimation

James J Lee, Carson C Chow
doi: 10.1101/003160


The heritability of a trait ($h^2$) is the proportion of its population variance caused by genetic differences, and estimates of this parameter are important for interpreting the results of genome-wide association studies (GWAS). In recent years, researchers have adopted a novel method for estimating a lower bound on heritability directly from GWAS data that uses realized genetic similarities between nominally unrelated individuals. The quantity estimated by this method is purported to be the contribution to heritability that could in principle be recovered from association studies employing the given panel of SNPs ($h^2_\textrm{SNP}$). Thus far the validity of this approach has mostly been tested empirically. Here, we provide a mathematical explication and show that the method should remain a robust means of obtaining $h^2_\textrm{SNP}$ under circumstances wider than those under which it has so far been derived.

Saturday, March 29, 2014

The truth about social mobility

See also Not (all) in our genes?

The Truth About Social Mobility

Full audio (55 min including Q&A; video is only 22 min highlights)

Many people assume that it is much easier to move between social classes today than at any point in humankind.

However, new research from Gregory Clark, professor of economics at the University of California, Davis, reveals that mobility rates are lower than conventionally estimated and surprisingly resistant to social policies.

By tracking family names over generations to measure social mobility across periods and countries, Clark reveals that more than ever, the only sure route to success is to be born to the right parents. And so we need to come up with new ways to tackle the entrenched force of inherited advantage and avoid creating winner-take-all societies.

Speaker: Gregory Clark, professor of economics, University of California, Davis

Bitcoin dynamics

Good discussion of Bitcoin by Mark Andreesen in the Times Dealbook: Why Bitcoin matters. There are also good points made in the comments (see Reader Picks). For example:
... Bitcoin is arguably more like a commodity than a currency as it has no physical assets or central bank behind it. As a commodity should it not therefore be liable to be taxed for any capital gains/losses should the regulators define Bitcoin as a commodity, as in Finland recently? (The example of the guy buying a second-hand Tesla with Bitcoin raises the question, at what price did he originally purchase the Bitcoin? I'm sure the IRS would be 'investigating'.) To conclude, for the 'regular' guy there is very little advantage of getting involved with Bitcoin, apart from speculating on its price.
... Andreessen handwaves away the most important problem: there's no reason for anyone to want to hold bitcoins, particularly given the bitcoin-dollar rate volatility. He waves this away by saying that people can just conduct transactions in Bitcoin and then immediately convert the bitcoins into dollars, as though actual money is just transmuted into this special form while it's sent over the wire and then both parties cash out. Well and good; but if that's the case there has to be a market-maker out there. You need someone with a large dollar reserve who can convert dollars to bitcoins (and vice-versa), and who can even balance international currency flows. That's a valuable service; it's foolish to think it'll remain free forever.

Similarly, the math behind Bitcoin requires a huge amount of electricity and computing power to record current and future transactions. Right now that's covered by speculators who are basically paid in bitcoin seignorage; but any time you're consuming real resources and expecting to get them for free you're being either naive or dishonest.

And honestly, if we just wanted a new payment system, we could do that with far less technical and physical overhead than Bitcoin requires.

In short, I'm sure lots of people would love to have financial transactions be free. That doesn't mean they actually will be.
Has anyone worked out the equilibrium compensation required for miners to maintain the transaction record (this involves the cost of energy, etc.)? It seems that if there are lots of micro transactions the cost of maintaining the record might exceed the expected benefit to the miners. The volume of trades is in principle independent of the total amount of value in the system, so there seem to be bad regions of parameter space. I read the original Bitcoin paper a long time ago but haven't followed any of the theoretical developments since then. Don't forget: bits = carbon!  :-)

My question is partially addressed in the Bitcoin FAQ -- they reserve the right to charge transaction fees to help compensate miners. Who, exactly, is "they"?

Wednesday, March 26, 2014

Piketty's Capital

One of the jarring figures in Piketty's book Capital in the Twenty-first Century shows the population fraction over time that inherited more money than the average laborer earns in a lifetime. This fraction is larger than I expected -- roughly 5-10 percent in France.

Piketty's grande idée is very simple: if returns to capital r exceed GDP growth g, and if ownership of capital is concentrated, then runaway inequality will result. He argues that throughout most of history, r > g. His solution: redistribution via a wealth tax. (Don't most countries already have inheritance taxes? Perhaps they just need to be tightened up.)

See also The Normaliens.
New Yorker: ... Piketty, who teaches at the Paris School of Economics, has spent nearly two decades studying inequality. In 1993, at the age of twenty-two, he moved to the United States to teach at M.I.T. A graduate of the élite École Normale Supérieure, he had recently completed his doctorate, a dense mathematical exploration of the theory behind tax policies. Plenty of bright young European scholars move across the Atlantic, of course, and many of them end up staying. Piketty was not to be one of them. “It was the first time I had set foot in the United States,” he recalls in the introduction, “and it felt good to have my work recognized so quickly. Here was a country that knew how to attract immigrants when it wanted to! Yet I also realized quite soon that I wanted to return to France and Europe, which I did when I was twenty-five. Since then, I have not left Paris, except for a few brief trips.”

... much of the economics that Piketty encountered at M.I.T. seemed arid and pointless. “I did not find the work of U.S. economists entirely convincing,” he writes. “To be sure, they were all very intelligent, and I still have many friends from that period of my life. But something strange happened: I was only too aware of the fact that I knew nothing at all about the world’s economic problems.”

... Eventually, Piketty says, we could see the reëmergence of a world familiar to nineteenth-century Europeans; he cites the novels of Austen and Balzac. In this “patrimonial society,” a small group of wealthy rentiers lives lavishly on the fruits of its inherited wealth, and the rest struggle to keep up. For the United States, in particular, this would be a cruel and ironic fate. “The egalitarian pioneer ideal has faded into oblivion,” Piketty writes, “and the New World may be on the verge of becoming the Old Europe of the twenty-first century’s globalized economy.”

... Some people claim that the takeoff at the very top reflects the emergence of a new class of “superstars”—entrepreneurs, entertainers, sports stars, authors, and the like—who have exploited new technologies, such as the Internet, to enlarge their earnings at the expense of others in their field. If this is true, high rates of inequality may reflect a harsh and unalterable reality: outsized spoils are going to go to Roger Federer, James Patterson, and the WhatsApp guys. Piketty rejects this account. The main factor, he insists, is that major companies are giving their top executives outlandish pay packages. His research shows that “supermanagers,” rather than “superstars,” account for up to seventy per cent of the top 0.1 per cent of the income distribution. ...

... Defenders of big pay packages like to claim that senior managers earn their vast salaries by boosting their firm’s profits and stock prices. But Piketty points out how hard it is to measure the contribution (the “marginal productivity”) of any one individual in a large corporation. The compensation of top managers is typically set by committees comprising other senior executives who earn comparable amounts. “It is only reasonable to assume that people in a position to set their own salaries have a natural incentive to treat themselves generously, or at the very least to be rather optimistic in gauging their marginal productivity,” Piketty writes.

... Income from capital has always played a key role in capitalism. Piketty claims that its role is growing even larger, and that this helps explain why inequality is rising so fast. Indeed, he argues that modern capitalism has an internal law of motion that leads, not inexorably but generally, toward less equal outcomes. The law is simple. When the rate of return on capital—the annual income it generates divided by its market value—is higher than the economy’s growth rate, capital income will tend to rise faster than wages and salaries, which rarely grow faster than G.D.P.

... Piketty takes some well-aimed shots at economists who seek to obfuscate this reality. “In studying the eighteenth and nineteenth centuries it is possible to think that the evolution of prices and wages, or incomes and wealth, obeys an autonomous economic logic having little or nothing to do with the logic of politics or culture,” he writes. “When one studies the twentieth century, however, such an illusion falls apart immediately. A quick glance at the curves describing income and wealth inequality or the capital/income ratio is enough to show that politics is ubiquitous and that economic and political changes are inextricably intertwined and must be studied together.” ...
See figures here and here.

Tuesday, March 25, 2014

Hail to the Chief

MSU President Simon: Steve's a physicist who also knows genomics. He's doing great things here.

President Obama: He looks like someone who can do great things!

See earlier posts The Presidents and Obama in Oregon.

Obama picked the Spartans to win the NCAA tournament this year :-)

Saturday, March 22, 2014

Svante Paabo: Neanderthal Man

I finally got a copy of Paabo's recent book. See earlier profile in the New Yorker.
Neanderthal Man: In Search of Lost Genomes

(p.213) ... we estimated that the total number of DNA sequence positions at which Neanderthals differed from all humans living today will be on the order of 100,000. This will represent an essentially complete answer to the question of what makes modern humans "modern," ...

(p.253) [last paragraph of the book!] ... One can imagine putting such changes into cell lines, and into mice [or monkeys] ... in order to "humanize" or "neanderthalize" biochemical pathways or intracellular structures ... One day, we may understand what set the replacement crowd [moderns] apart from their archaic contemporaries, and why, of all the primates, modern humans spread to all corners of the world and reshaped, both intentionally and unintentionally, the environment on a global scale ...
The essential difference between moderns and pre-moderns is likely a qualitative increase in cognitive ability. See Neanderthals dumb?

See also The genetics of humanness, The Neanderthal Problem, and Genetic engineering of monkeys using CRISPR.

Tuesday, March 18, 2014

Patterns on the sky

I'm busy reviewing ~200 promotion and tenure cases for my day job, so I don't have much time to post about the BICEP2 observation of primordial gravitational waves via their effect on the polarization of the cosmic microwave background (CMB).

Instead, I refer you to Sean Carroll, Lubos Motl and Liam McAllister (guest poster at Lubos' blog).

Assuming the result holds up, it strongly supports inflationary cosmology, and indicates that the inflation scale is only about 2 orders of magnitude below the Planck scale ~ 10^19 GeV (which would, presumably, turn out to be the true scale of quantum gravity).

In inflationary cosmology the gravitational waves which left the polarization signal arise from quantum fluctuations in de Sitter space. As with the CMB temperature, observers on different branches of the wavefunction of the universe see distinct polarization patterns on the sky. Since the CMB temperature fluctuations track energy density, these different observers also see distinct patterns of galaxy formation. In fact, whether or not an observer (a planet or galaxy) exists in a particular region of spacetime depends on the branch of the wavefunction (i.e., on a measurement outcome). I can't tell a Copenhagen story that makes sense of this -- there is no way to place observers like ourselves outside of the quantum state describing the CMB!

I guess I've said this all before 8-)
In fact, the interpretation of quantum mechanics is not entirely disconnected from practical issues in cosmology. The cosmic microwave background data favors inflationary cosmology, in which the density perturbations in the early universe originate in the quantum fluctuations of the inflaton field itself. It is very hard to fit this into the Copenhagen view -- what "collapses" the wavefunction of the inflaton? There are no "observers" in the early universe, and the locations of "observers" (such as humans) are determined by the density perturbations themselves: galaxies, stars and planets are found in the overdense regions, but quantum mechanics itself decides which regions are overdense; there is no classical system "outside" the universe! It seems much more natural to note that differential scattering of gravitons due to more or less energy density in a particular region separates the inflaton wavefunction into decoherent branches. (The gravitons decohere the inflaton state vector through interactions.) But this is accomplished through unitary evolution and does not require von Neumann projection or "collapse". Other observers, living on other branches of the wavefunction, see a different CMB pattern on the sky.

Thursday, March 13, 2014

Icahn Institute for Genomics and Multiscale Biology

Spent the day at Mt. Sinai in NYC. Their Icahn Institute is very impressive -- an academic unit that feels like a startup! See earlier post Cancer Genomics.

Wednesday, March 12, 2014

Slouching towards the Valley

NYTimes magazine: Silicon Valley's Youth Problem. See also: Rivalry and Habituation, Hardware vs Software and Slouching Towards Bethlehem.
NYTimes: ... A few weeks ago, a programmer friend and I were talking about unhappiness, in particular the kind of unhappiness that arises when you are 21 and lavishly educated with the world at your feet. In the valley, it’s generally brought on by one of two causes: coming to the realization either that your start-up is completely trivial or that there are people your own age so knowledgeable and skilled that you may never catch up.

The latter source of frustration is the phenomenon of “the 10X engineer,” an engineer who is 10 times more productive than average. It’s a term that in its cockiness captures much of what’s good, bad and impossible about the valley. At the start-ups I visit, Friday afternoons devolve into bouts of boozing and Nerf-gun wars. Signing bonuses at Facebook are rumored to reach the six digits. In a landscape where a product may morph several times over the course of a funding round, talent — and the ability to attract it — has become one of the few stable metrics.

Yet for all the glitz and the glory and the newfound glamour, there is a surprising amount of angst in Silicon Valley. Which is probably inevitable when you put thousands of ambitious, talented young people together and tell them they’re god’s gift to technology. It’s the angst of an early hire at a start-up that only he realizes is failing; the angst of a founder who raises $5 million for his company and then finds out an acquaintance from college raised $10 million; the angst of someone who makes $100,000 at 22 but is still afraid that he may not be able to afford a house like the one he grew up in.

Tech is fun now, deliriously so, but this fun comes with a built-in anxiety that it must lead to more. As an engineer, coding should be your calling, not just a job, so you are expected to also do it in your time off. Interviewers will ask about side projects — a Firefox browser add-on maybe, or an Android version of your favorite iPhone app — which are supposed to indicate your overflowing enthusiasm for building software. Tech colloquialisms have permeated every aspect of life — hack your diet, your fitness, your dates — yet in reality, very little emphasis is placed on these activities. In a place with one of the best gender-ratios in the country for single women, female friends I talk to complain that most of the men are, in fact, not available; they are all busy working on their start-ups, or data-crunching themselves. They have prioritized self-improvement and careers over relationships.

... This past Christmas, my family went to dinner with another family, the Yangs, whose son, Andrew, was a sophomore at the University of Chicago and trying to decide on a major. He was interested in computer science, having taken the online version of CS50, Harvard’s introductory computer-science course, in his spare time. But his parents, both software engineers, wanted him to choose finance. They thought that being a software engineer meant drowning in a technical quagmire, being someone else’s code monkey. Their view of tech was shaped by their years of experience at old-guard companies, where a few cynosures (Bill Gates, Steve Jobs, Larry Ellison, etc.) got most of the money and the glory. I tried to explain to them how the tech world that their son would be joining is so very different. For all the industry’s drawbacks, I have never seen it as anything less than potential filled.

I’m not sure that they were convinced. But there is no doubt that, regardless, young talent will keep flocking to the valley. Some of us will continue to make the web products that have generated such vast wealth and changed the way we think, interact, protest. But hopefully, others among us will go to work on tech’s infrastructure, bringing the spirit of the new guard into the old. ...
Tech is more fun if you have an elite pedigree like the author of the piece (a Harvard grad) and can call on a network of friends and alumni to help you obtain funding, talented employees, and press coverage. Note, though, that the "10X engineer" is no myth. In fact, I believe there are 100X engineers ...

Monday, March 10, 2014

GMO the old fashioned way

Monsanto gives up on GMO but uses phenotype-genotype modeling to crossbreed vegetables.
WIRED: ... Furthermore, genetically modifying consumer crops proved to be inefficient and expensive. Stark estimates that adding a new gene takes roughly 10 years and $100 million to go from a product concept to regulatory approval. And inserting genes one at a time doesn’t necessarily produce the kinds of traits that rely on the inter­actions of several genes. Well before their veggie business went kaput, Monsanto knew it couldn’t just genetically modify its way to better produce; it had to breed great vegetables to begin with. As Stark phrases a company mantra: “The best gene in the world doesn’t fix dogshit germplasm.”

What does? Crossbreeding. Stark had an advantage here: In the process of learning how to engineer chemical and pest resistance into corn, researchers at Monsanto had learned to read and understand plant genomes—to tell the difference between the dogshit germplasm and the gold. And they had some nifty technology that allowed them to predict whether a given cross would yield the traits they wanted.

The key was a technique called genetic marking. It maps the parts of a genome that might be associated with a given trait, even if that trait arises from multiple genes working in concert. Researchers identify and cross plants with traits they like and then run millions of samples from the hybrid—just bits of leaf, really—through a machine that can read more than 200,000 samples per week and map all the genes in a particular region of the plant’s chromosomes.

They had more toys too. In 2006, Monsanto developed a machine called a seed chipper that quickly sorts and shaves off widely varying samples of soybean germplasm from seeds. The seed chipper lets researchers scan tiny genetic variations, just a single nucleotide, to figure out if they’ll result in plants with the traits they want—without having to take the time to let a seed grow into a plant. Monsanto computer models can actually predict inheritance patterns, meaning they can tell which desired traits will successfully be passed on. It’s breeding without breeding, plant sex in silico. In the real world, the odds of stacking 20 different characteristics into a single plant are one in 2 trillion. In nature, it can take a millennium. Monsanto can do it in just a few years.

And this all happens without any genetic engineering. Nobody inserts a single gene into a single genome. (They could, and in fact sometimes do, look at their crosses by engineering a plant as a kind of beta test. But those aren’t intended to leave the lab.) Stark and his colleagues realized that they could use these technologies to identify a cross that would have highly desirable traits and grow the way they wanted.  ...

Friday, March 07, 2014

Weinberg on Weinberg

Great interview with Steve Weinberg about his life in science.

See also Weinberg on quantum foundations.

Thursday, March 06, 2014

Not (all) in our genes?

Economic historian Greg Clark describes his latest research (and new book) in the NYTimes:
Your Ancestors, Your Fate: ... my colleagues and I estimate that 50 to 60 percent of variation in overall status is determined by your lineage. The fortunes of high-status families inexorably fall, and those of low-status families rise, toward the average — what social scientists call “regression to the mean” — but the process can take 10 to 15 generations (300 to 450 years), much longer than most social scientists have estimated in the past.

We came to these conclusions after examining reams of data on surnames, a surprisingly strong indicator of social status, in eight countries — Chile, China, England, India, Japan, South Korea, Sweden and the United States — going back centuries. Across all of them, rare or distinctive surnames associated with elite families many generations ago are still disproportionately represented among today’s elites.

... Our findings suggest, however, that the compulsion to strive, the talent to prosper and the ability to overcome failure are strongly inherited. We can’t know for certain what the mechanism of that inheritance is, though we know that genetics plays a surprisingly strong role. Alternative explanations that are in vogue — cultural traits, family economic resources, social networks — don’t hold up to scrutiny.

... Our findings were replicated in Chile, India, Japan, South Korea and, surprisingly, China, which stands out as a demonstration of the resilience of status — even after a Communist revolution nearly unparalleled in its ferocity, class hatred and mass displacement.

... The notion of genetic transmission of “social competence” — some mysterious mix of drive and ability — may unsettle us. But studies of adoption, in some ways the most dramatic of social interventions, support this view. A number of studies of adopted children in the United States and Nordic countries show convincingly that their life chances are more strongly predicted from their biological parents than their adoptive families. In America, for example, the I.Q. of adopted children correlates with their adoptive parents’ when they are young, but the correlation is close to zero by adulthood. There is a low correlation between the incomes and educational attainment of adopted children and those of their adoptive parents.

These studies, along with studies of correlations across various types of siblings (identical twins, fraternal twins, half siblings) suggest that genetics is the main carrier of social status. ...  [ Italics mine ]
These results, and the original working paper in which they first appeared, were discussed in an earlier post:
While at UC Davis to give a colloquium earlier this week, I had the pleasure of meeting economic historian Greg Clark in person. Here's a sample of his latest work, which suggests that convergence of social classes has been surprisingly slow: averaged parent-child correlations of variables such as wealth, education and occupation are in the 0.7 -- 0.8 range over the last 200 years, the same as found in India, with its caste system! IIRC, Greg said he got the idea of using rare surnames from Nicholas Wade during an interview :-)
Correlations as high as 0.7 -- 0.8 are implausible from genetic factors alone without highly assortative mating. Traits such as height and IQ have narrow sense heritabilities as large as h2 ~ 0.6, so fraction of variance accounted for is ~ 60%, and midparent-child correlation as high as ~ 0.8, but under even somewhat random mating the parental midpoint is significantly closer to average than the phenotype of the more exceptional parent. This would cause children to regress to the mean much faster in height and IQ than in social status as indicated in Clark's data. It's also important to note that social status itself is only imperfectly correlated to observable phenotypes such as IQ, Conscientiousness or Extraversion. See Intergenerational mobility: Bowles, Gintis, Clark for more.

It seems likely that there is a social component which boosts the correlation due to genetic factors. This is not implausible (i.e., kids get a leg up in social status due to parenting or purely financial factors), but somewhat vitiates Clark's conclusion in the NYTimes essay above.

Satoshi Nakamoto is ... Satoshi Nakamoto!

Newsweek breaks the story! This man has hundreds of millions of dollars in bitcoins :-)

The original proposal. I once did some modest internet sleuthing to figure out who "Satoshi Nakamoto" really was, but to no avail.

As usual, like the guy behind the World Wide Web (Berners-Lee), discovery of DNA structure (Crick), laser (Townes), atomic bomb, transistor (Shockley), electronic computer (Atanasoff et al.), etc., etc., Nakamoto is a ...
"You want to know about my amazing physicist brother?" says Arthur Nakamoto, Satoshi Nakamoto's youngest sibling, who works as director of quality assurance at Wavestream Corp., a maker of radio frequency amplifiers in San Dimas, Calif.

"He's a brilliant man. I'm just a humble engineer. He's very focused and eclectic in his way of thinking. Smart, intelligent, mathematics, engineering, computers. You name it, he can do it."

... Just after graduating college, Nakamoto went to work on defense and electronics communications for Hughes Aircraft in southern California. "That was just the beginning," says Arthur, who also worked at Hughes. "He is the only person I have ever known to show up for a job interview and tell the interviewer he's an idiot - and then prove it."
See also Prometheus in the basement.

UPDATE: Maybe it's not him ...

Wednesday, March 05, 2014

How we live now

Hyperparenting and the upper-middle (striver) class. This essay is about the book The XX Factor: How the Rise of Working Women Has Created a Far Less Equal World by Alison Wolf.

See also Kids these days and Having it all.
NYBooks: ... What most differentiates them is their total absorption in two things—their careers and their children. They devote extremely long hours to their professions, which often require them to be electronically available at almost all hours. According to Wolf’s data, upper-middle-class couples now work on average more hours per day than the rest of the population, and unlike the lower classes, they have no more leisure time now than they did in the 1960s. Contrary to what one might expect, upper-middle-class women usually return to work full-time after childbirth, whereas other women more often stop paid work at least temporarily or return only part-time. As Wolf points out, for upper-middle-class women to interrupt their careers means large sacrifices of opportunity. Moreover, their income is usually sufficient to cover the considerable expense of hiring nannies or other forms of child care. But even more important than the money is the fact that for these women, their sense of identity is tied to their professions. They are full participants in what James Surowiecki recently called “the cult of overwork.”

The commitment of power couples to their professions is outweighed only by their extraordinary involvement with their children. Wolf titles a section on children “Willing Slaves,” and begins with a one-sentence paragraph, “And then there are the children.” The next paragraph starts, “Young children dominate the lives of their parents not just emotionally but by completely upturning their lives.” Against all logic, as documented by Wolf, upper-middle-class couples somehow manage to spend more interactive time (not just being in the same room) with their children than any group in history—with or without careers, rich or poor.

True, they have fewer children; in fact, their fertility rate is so low that they don’t even replace themselves. But the few children they have are at the center of their lives, and fathers are often just as much involved as mothers. They spend enormous amounts of money on them, and employ a vast network of experts to help—beginning with childbirth classes and lactation consultants, and continuing through tutors to help them get into the best schools, athletic coaches to help the children make the team, teachers to help them develop their musical and dramatic talents, and so forth. Nannies alone cost on the order of tens of thousands of dollars per year. Children are also incorporated into their parents’ social lives ...

... The consequences of hyperparenting are unknown, since the phenomenon is only a few decades old. My views are shaped largely by observing my own family and friends, and that is not much to rely on, but I will speculate anyway. I see great advantages for the children, but also some warning signs. Young upper-middle-class children are, indeed, remarkably precocious. Since they have been exposed to adult conversations almost constantly from birth, they are much more articulate and broadly knowledgeable than children were a generation ago. They are also remarkably at ease with other people, both adults and children, because they are with them so much—with their parents’ friends, in early preschool, and in playgroups often organized among nannies. And having endured little frustration or isolation, they seem to me happier and more affectionate than children were in earlier generations. They love being with their parents (and why not?). They don’t go “up the street” to do “nothing,” as my friends and I did. They stick close to home, and their best friends are their parents.

[ Italics mine ]

Tuesday, March 04, 2014

Becoming a behaviorialist

Becoming a behaviorialist (i.e., overcoming autism in economics ;-). Audio interview.
Leading behavioral economist Richard Thaler, the Ralph and Dorothy Keller Distinguished Service Professor of Behavioral Science and Economics, talks about how he found his way into this then-nonexistent field and how he came to study irrational economic behavior at UChicago, the home of efficient markets and rational expectations.
Lots of other good interviews in this Chicago series (iTunes): Heckman, Sargent on the euro, Hansen on risk.

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