Larry's rules

I recommend this long article about Larry Page and the evolution of his role at Google.

Here are Larry's management rules (more suitable, perhaps, for a startup than to a larger, mature organization):

Don't delegate: Do everything you can yourself to make things go faster.

Don't get in the way if you're not adding value. Let the people actually doing the work talk to each other while you go do something else.

Don't be a bureaucrat.

Ideas are more important than age. Just because someone is junior doesn't mean they don't deserve respect and cooperation.

The worst thing you can do is stop someone from doing something by saying, “No. Period.” If you say no, you have to help them find a better way to get it done.

The Soul of the Research University

Basic research, whose applications may be decades in the future, is an uncertain investment for any single entity (e.g., corporation), even if it is an essential public good for the long term advancement of civilization. Consequently, basic research is mostly done at universities and government labs. Indeed, the vast majority of research in the US is led by professors and takes place on campus. Unfortunately, this crucial aspect of the mission of universities is least understood by their broad constituency.

Boosters, alumni, parents, and advocates should note that the research prowess of a great university is a large component of its institutional prestige: nearly all of the most prestigious universities in the world, those that attract the brightest and most able (and, ultimately, most successful) students, are world class research institutions.

Nicholas Lemann writes in the Chronicle of Higher Education.

Chronicle: ... building on the foundation laid by the establishment of The Johns Hopkins University, in 1876, American higher education has embraced the idea of the research university as its most cherished aspiration. Today there are about 300 American universities that confer doctoral degrees, far more than envisioned by the original proselytizers for importing the research-university model from Germany to the United States. And that number understates the importance of the model, because the core members of the faculty and senior administration at hundreds more institutions hold doctoral degrees and operate within the academic tenure system that lies at the heart of the way research universities are run.

For many people who have spent their lives working in higher education, mass higher education and research universities make for a perfect fit: Together they express both the public service and the intellectual ambitions of educators. And during most of the 20th century, especially the years between 1950 and 1975, the two big ideas grew and flourished in tandem.

But they aren’t the same idea. Mass higher education, conceptually, is practical, low cost, skills oriented, and mainly concerned with teaching. It caught on because state legislatures and businesses saw it as a means of economic development and a supplier of personnel, and because families saw it as a way of ensuring a place in the middle class for their children. Research universities, on the other hand, grant extraordinary freedom and empowerment to a small, elaborately trained and selected group of people whose mission is to pursue knowledge and understanding without the constraints of immediate practical applicability under which most of the rest of the world has to operate. Some of their work is subsidized directly by the federal government and by private donors, but they also live under the economic protection that very large and successful institutions can provide to some of their component parts. ...

Early intervention and long term outcomes

I wish I had made more of an effort to talk to James Heckman about his main research interests during the conference last week. My understanding is that while Heckman admits that cognitive ability is hard to change through early intervention, he believes that important "non-cognitive skills" (e.g., conscientiousness, rule-following, long term planning, etc.) can be inculcated. It would have been enlightening to hear more from the horse's mouth! Unfortunately for me he seemed to always be in conversation with other participants.

Brookings: ... Not one of the studies that has suggested long-term positive impacts of center-based early childhood programs has been based on a well-implemented and appropriately analyzed randomized trial, and nearly all have serious limitations in external validity. In contrast, the only two studies in the list with both high internal and external validity (Head Start Impact and Tennessee) find null or negative impacts, and all of the studies that point to very small, null, or negative effects have high external validity. In general, a finding of meaningful long-term outcomes of an early childhood intervention is more likely when the program is old, or small, or a multi-year intervention, and evaluated with something other than a well-implemented RCT. In contrast, as the program being evaluated becomes closer to universal pre-k for four-year-olds and the evaluation design is an RCT, the outcomes beyond the pre-k year diminish to nothing.

I conclude that the best available evidence raises serious doubts that a large public investment in the expansion of pre-k for four-year-olds will have the long-term effects that advocates tout.

This doesn’t mean that we ought not to spend public money to help families with limited financial resources access good childcare for their young children. After all, we spend tax dollars on national parks, symphony orchestras, and Amtrak because they make the lives of those who use them better today. Why not childcare?

It does mean that we need public debate that recognizes the mixed nature of the research findings rather than a rush to judgment based on one-sided and misleading appeals to the preponderance of the evidence.

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.

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.

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 ;-)

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.

PROGRAM

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
Neuroscience
Paul Glimcher New York University
Jonathan King National Institute on Aging
Aldo Rustichini University of Minnesota

11:30 to 1:30
Intelligence
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

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.

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).

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.

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 extra 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.

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.