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Physicist, Startup Founder, Blogger, Dad

Wednesday, November 14, 2018

London / DeepMind photos

I've been very lucky with the London weather on this trip -- in the 50s and sunny.



These were taken from the roof terrace of the Google building that houses DeepMind:





These are from the nearby area: St. Pancras, Granary Square, in King's Cross.







More photos from the area.


Photos below from The Economist (also rooftop views of London, but not as posh as GOOG) and BBC visits.

This Economist article on Genomic Prediction has been in waiting for weeks, to appear in The World in 2019 special issue. I spent a couple hours briefing their science team on what is coming in AI and genomics -- I would guess there will be more coverage of polygenic scores and health care in the future.

See also this New Scientist article on GP.

2019 may be the Year of the Designer Baby, if journos are to be believed ;-)  Of course, this is sensationalism. It is more accurate to say that 2019 will see the first deployment of advanced genetic tests which can be used to screen against complex disease and health risks. Already today ~1 million IVF embryos per year are screened worldwide using less sophisticated genetic tests for single gene disease mutations and chromosomal abnormality.

The Economist:
In 2019, ... those with the cash to do so will have an opportunity to give their offspring a greater chance of living a long and healthy life.

"Expert" opinion seems to have evolved as follows:
1. Of course babies can't be "designed" because genes don't really affect anything -- we're all products of our environment!

2. Gulp, even if genes do affect things it's much too complicated to ever figure out!

3. Anyone who wants to use this technology (hmm... it works) needs to tread carefully, and to seriously consider the ethical issues.

Only point 3 is actually correct, although there are still plenty of people who believe 1 and 2   :-(
BBC wanted me for their Radio 4 Today show. I went in and recorded some clips, but the broadcast may be delayed due to all the Brexit excitement -- Theresa May has finally revealed the proposed EU-UK agreement her administration negotiated. Angry Brexiteer Tory MPs may vote her out. I had a ringside seat to all this thanks to my friend Dominic Cummings!






Friday, November 09, 2018

DeepMind Talk: Genomic Prediction of Complex Traits and Disease Risks via Machine Learning


I'll be at DeepMind in London next week to give the talk below. Quite a thrill for me given how much I've admired their AI breakthroughs in recent years. Perhaps AlphaGo can lead to AlphaGenome :-)

Hope the weather holds up!
Title: Genomic Prediction of Complex Traits and Disease Risks via Machine Learning

Abstract: After a brief review (suitable for non-specialists) of computational genomics and complex traits, I describe recent progress in this area. Using methods from Compressed Sensing (L1-penalized regression; Donoho-Tanner phase transition with noise) and the UK BioBank dataset of 500k SNP genotypes, we construct genomic predictors for several complex traits. Our height predictor captures nearly all of the predicted SNP heritability for this trait -- actual heights of most individuals in validation tests are within a few cm of predicted heights. I also discuss application of these methods to cognitive ability and polygenic disease risk: sparsity estimates (of the number of causal loci), combined with phase transition scaling analysis, allow estimates of the amount of data required to construct good predictors. We can now identify risk outliers for conditions such as heart disease, diabetes, breast cancer, hypothyroidism, etc. using inexpensive genotyping. Finally, I discuss how these advances will affect human reproduction (embryo selection for In Vitro Fertilization (IVF); gene editing) in the coming decade.

Bio: Stephen Hsu is VP for Research and Professor of Theoretical Physics at Michigan State University. He is also a researcher in computational genomics and founder of several Silicon Valley startups, ranging from information security to biotech. Educated at Caltech and Berkeley, he was a Harvard Junior Fellow and held faculty positions at Yale and the University of Oregon before joining MSU.

Action Photos!







Wednesday, November 07, 2018

Validation of simultaneous preimplantation genetic testing (PGT) for aneuploidy, monogenic, and polygenic disorders (Dr. Nathan Treff, Genomic Prediction, Inc.)



Dr. Nathan Treff, co-founder of Genomic Prediction, at the 2018 American Society of Reproductive Medicine meeting. His talk introduces Expanded Pre-Implantation Genomic Testing (EPⓖT):
EPⓖT allows the routine, inexpensive evaluation of hundreds of thousands of genetic variants, implementing a novel combination of embryo genotyping methods not previously combined into a reproductive genetics application.

Universal coverage of common single-gene disorders, such as Cystic Fibrosis, Thalassemia, BRCA, Sickle Cell Anemia, and Gaucher Disease.

Complex disorders whose risk can be predicted include:
Type 1 Diabetes, Type 2 Diabetes, Coronary Artery Disease, Atrial Fibrillation, Breast Cancer, Hypothyroidism, Mental Disability, Idiopathic Short Stature, Inflammatory Bowel Disease
At the American Society of Human Genetics (ASHG) meeting last month in San Diego, several people recognized me and came over to marvel at Genomic Prediction.
"Look at all these people walking around, with no idea what is happening right now...  You guys are Creating the Future!"

From Comments:
John C. • 11 hours ago
What could be the use of predicting atrial fibrillation, coronary artery disease and Type 2 diabetes risk be? Tell people to not gain weight as they age?



Bobdisqus • 3 hours ago
Well as someone who had an ablation for AFIB, two stents in my RCA, and a family history that includes a brother with first heart attack at 38, and my father at 43 I would say the value is immense. Beyond that the extended family on both sides is rife with such. The number of men in my ancestry that made it past 70 is tiny. My children now have the option with a couple of rounds of egg harvesting which is well advised for my brood of daughters anyway given the human fertility curve and their plans for education to filter the worst of this scourge from our line going forward. Their sons can then anticipate fine old ages into their 90s much like the people of their Mother's line.

Tuesday, November 06, 2018

1 In 4 Biostatisticians Surveyed Say They Were Asked To Commit Scientific Fraud


In the survey reported below, about 1 in 4 biostatisticians were asked to commit scientific fraud. I don't know whether this bad behavior was more prevalent in industry as opposed to academia, but I am not surprised by the results.

I do not accept the claim that researchers in data-driven areas can be ignorant of statistics. It is common practice to outsource statistical analysis to people like the "consulting biostatisticians" surveyed below. But scientists who do not understand statistics will not be effective in planning future research, nor in understanding the implications of results in their own field. See the candidate gene and missing heritability nonsense the field of genetics has been subject to for the last decade.

I cannot count the number of times, in talking to a scientist with a weak quantitative background, that I have performed -- to their amazement -- a quick back of the envelope analysis of a statistical design or new results. This kind of quick estimate is essential to understand whether the results in question should be trusted, or whether a prospective experiment is worth doing. The fact that they cannot understand my simple calculation means that they literally do not understand how inference in their own field should be performed.
Researcher Requests for Inappropriate Analysis and Reporting: A U.S. Survey of Consulting Biostatisticians

(Annals of Internal Medicine 554-558. Published: 16-Oct-2018. DOI: 10.7326/M18-1230)

Results:
Of 522 consulting biostatisticians contacted, 390 provided sufficient responses: a completion rate of 74.7%. The 4 most frequently reported inappropriate requests rated as “most severe” by at least 20% of the respondents were, in order of frequency, removing or altering some data records to better support the research hypothesis; interpreting the statistical findings on the basis of expectation, not actual results; not reporting the presence of key missing data that might bias the results; and ignoring violations of assumptions that would change results from positive to negative. These requests were reported most often by younger biostatisticians.
This kind of behavior is consistent with the generally low rate of replication for results in biomedical science, even those published in top journals:
What is medicine’s 5 sigma? (Editorial in the Lancet)... much of the [BIOMEDICAL] scientific literature, perhaps half, may simply be untrue. Afflicted by studies with small sample sizes, tiny effects, invalid exploratory analyses, and flagrant conflicts of interest, together with an obsession for pursuing fashionable trends of dubious importance, [BIOMEDICAL] science has taken a turn towards darkness. As one participant put it, “poor methods get results”. The Academy of Medical Sciences, Medical Research Council, and Biotechnology and Biological Sciences Research Council have now put their reputational weight behind an investigation into these questionable research practices. The apparent endemicity of bad research behaviour is alarming. In their quest for telling a compelling story, scientists too often sculpt data to fit their preferred theory of the world. ...
More background on the ongoing replication crisis in certain fields of science. See also Bounded Cognition.

Wednesday, October 31, 2018

Glenn Loury and Laurence Kotlikoff on the Harvard Trial (video)



Glenn Loury is Merton P. Stoltz Professor of the Social Sciences, Department of Economics, Brown University. Laurence J. Kotlikoff is a William Fairfield Warren Distinguished Professor and Professor of Economics at Boston University.

Video should start at @49:06 Glenn: Affirmative Action undermines black students’ dignity.
@95:27

Kotlikoff: I think it's pretty obvious that at least based on the facts so far that Harvard probably did downgrade the personalities of the Asians in order to achieve ...

Glenn: [Interrupting] Well that's the ball game -- they discriminated. Civil Rights Act of 1960.
Yesterday David Card (Harvard's statistical expert in the Asian American discrimination trial) began his testimony. At least as reported in the Chronicle, he has yet to dispute Arcidiacono's (plaintiff expert) finding that among "unhooked" applicants (95% of applicants: not in the subset of legacies, recruited athletes, and major donor kids), Asian Americans are discriminated against relative to all others, including whites. I discuss this in detail here and here.

Card has questioned the legal relevance of Arcidiacono's finding (he does not want to consider unhooked applicants separately), but that is for the judge and lawyers to wrangle over (see excerpt below). As a statistical fact I have yet to see any claim from Harvard or Card that the result is incorrect.

Perhaps today's cross examination of Card will focus on this important question, which the media is largely ignoring.


From the SFFA brief:
"The task here is to determine whether “similarly situated” applicants have been treated differently on the basis of race; “apples should be compared to apples.” SBT Holdings, LLC v. Town of Westminster, 547 F.3d 28, 34 (1st Cir. 2008). Because certain applicants are in a special category, it is important to analyze the effect of race without them included. Excluding them allows for the effect of race to be tested on the bulk of the applicant pool (more than 95% of applicants and more than two-thirds of admitted students) that do not fall into one of these categories, i.e., the similarly situated applicants. For special-category applicants, race either does not play a meaningful role in their chances of admission or the discrimination is offset by the “significant advantage” they receive. Either way, they are not apples.

Professor Card’s inclusion of these applicants reflects his position that “there is no penalty against Asian-American applicants unless Harvard imposes a penalty on every Asian-American applicant.” But he is not a lawyer and he is wrong. It is illegal to discriminate against any Asian-American applicant or subset of applicants on the basis of race. Professor Card cannot escape that reality by trying to dilute the dataset. The claim here is not that Harvard, for example, “penalizes recruited athletes who are Asian-American because of their race.” The claim “is that the effects of Harvard’s use of race occur outside these special categories.” Professor Arcidiacono thus correctly excluded special-category applicants to isolate and highlight Harvard’s discrimination against Asian Americans. Professor Card, by contrast, includes “special recruiting categories in his models” to “obscure the extent to which race is affecting admissions decisions for those not fortunate enough to belong to one of these groups.” At bottom, SFFA’s claim is that Harvard penalizes Asian-American applicants who are not legacies or recruited athletes. Professor Card has shown that he is unwilling and unable to contest that claim.

[ Card and Arcidiacono have exchanged criticisms of the other's analysis already, so Card's lack of response on this specific point is worthy of attention. ]

UPDATE: The reporting below confirms what I wrote above. Card and Harvard maintain that looking specifically at unhooked applicants is irrelevant to the case, and do not dispute the statistical facts uncovered by SFFA regarding that group (95% of all applicants!). SFFA maintain (see case law cited above) that anti-Asian American discrimination in this category is itself a violation of law. Will any journalists report this part of the case, prominently discussed in the SFFA brief?
Chronicle: Card’s main objection to Arcidiacono’s model is that it omits recruited athletes, the children of alumni, the children of Harvard faculty and staff members, and students on a special list that includes children of donors. Excluding all those applicants, who are accepted at a relatively high rate, Card suggested, had skewed his counterpart’s results.

[ THIS EXCLUSION DID NOT "SKEW" THE RESULTS -- THE POINT IS THAT THIS ANALYSIS IS OF INTEREST IN AND OF ITSELF. SURELY THIS POINT WILL NOT BE LOST ON THE JUDGE. ]

Tuesday, October 30, 2018

Algorithms Rule Us All - VPRO documentary - 2018



ALGOS BAD!!!  ... and these instances prove it ... ;-)

Global R&D ~$1 trillion per annum?


Federal R&D, which skews more toward basic research, is typically somewhat less than 1% of US GDP (~$100 billion per annum). See figure below.
WSJ: ... U.S.-based companies accounted for $329 billion of a record $781.8 billion in R&D spending tallied by PwC for the year ended June 30. While Chinese R&D investment came in at $61 billion, in 2010 that figure was just $7 billion, PwC said. Today, 145 Chinese companies are among the top 1,000 R&D spenders, up from 14 a decade ago.

... PwC’s figures don’t include private companies, however, which leaves out China’s state-owned monoliths and closely held Huawei Technologies Co., the world’s largest maker of telecommunications equipment. Huawei said it spent more than $13 billion on R&D last year.

Monday, October 29, 2018

Thomas Fingar: OBOR, TPP, China economic development and foreign policy



This is a thoughtful discussion of OBOR and TPP. @44min and following, an insider's account of economic rapprochement between the US and China starting in 1978. Also, some interesting comments on political reforms necessary to escape the middle income trap.
China is heavily investing in two infrastructure routes: a “21st Century Maritime Silk Road” stretching from Southern China across the Indian Ocean to connect Southeast Asia, South Asia, and Africa to the Mediterranean; and a land-based Silk Road Economic Belt connecting Western China to Europe via Central Asia. Establishing these transcontinental trade routes will likely cost over one trillion dollars and will cover 65% of the world's population. This investment could help fill a wide “infrastructure investment gap” in China and the 68 other Asian, African, and European countries it passes through, however, traditional international development actors such as multilateral investment banks and developed nations are concerned about the outcomes, terms, and process that come with this massive investment. There are still a number of questions surrounding how China might protect the route after it's built and if the benefits will outweigh the risk. Should recipient countries be worried about political strings that might come attached to OBOR projects? What impact does this different unilateral, loan-based model have on the recipient countries? How likely is China to succeed in achieving these grand investment goals and how will a project of this scale continue to contribute to China’s own growth? What type of impact does this project have on global trade in general?

Thomas Fingar (Wikipedia)

Born January 11, 1948 (age 70)
Education Cornell University (BA) Stanford University (MA, PhD)

Charles Thomas Fingar, born January 11, 1946, is a professor at Stanford University. In 1986 Fingar left Stanford to join the State Department. In 2005, he moved to the Office of the Director of National Intelligence as the Deputy Director of National Intelligence for Analysis and concurrently served as the Chairman of the National Intelligence Council until December 2008.[1] In January 2009, he rejoined Stanford University as a Payne Distinguished Lecturer in the Freeman Spogli Institute for International Studies.

Also worth a listen:


Friday, October 26, 2018

Harvard Admissions on Trial: Enter the Statisticians


Let's see if any other media outlets cover this very essential part of the trial -- the cross examination of each side's statistical experts. As far as I understand, the plaintiff's claim that "unhooked" Asian American applicants are discriminated against by Harvard relative to applicants of other ethnicities (including white applicants) is NOT DISPUTED by Harvard, nor by their statistical expert David Card (economist at Berkeley).
Chronicle: ...A main difference between the two economists’ analyses is which types of applicants they included. Arcidiacono excluded recruited athletes, the children of alumni, the children of Harvard faculty and staff members, and students on a “Dean’s List” made up partly of children of donors. Those applicants — about 7,000 out of the roughly 150,000 students in the six-year data set — are admitted at a much higher rate than the rest of the pool, which Arcidiacono said made them difficult to compare with the other applicants.

The judge, Allison D. Burroughs of the Federal District Court, had some questions about the decision to omit that group. She wondered how many Asian-American applicants in those excluded categories are admitted. As it turned out, they are admitted at higher rates than the white applicants.

“It looks to me like what you’re arguing is you have an admissions office that’s discriminating against Asians, but they only do it in certain places,” she said. Arcidiacono agreed.
Unhooked applicants make up 95% of all applicants, but only 2/3 of admits. Recruited athletes, legacies, rich donor kids, etc. are all admitted at much higher rates than ordinary kids -- while only 5% of the applicant pool they are 1/3 of the entering class!

There has never been any claim that Asian Americans who are, e.g., nationally ranked athletes or children of billionaires are discriminated against. Eoin Hu, a Chinese American, was the star running back at Harvard when I was there! Jeremy Lin may have been denied D1 scholarships by Stanford and Berkeley despite being first-team All-State and Northern California Division II Player of the Year, but Harvard Basketball was very happy to have him.

Special status is a much stronger effect than Asian ethnicity, so including hooked applicants only dilutes the statistical effect found by Arcidiacono. Card insisted on lumping together hooked and unhooked applicants in his analysis and has not (to my knowledge) rebutted Arcidiacono's analysis. Reportedly, 86 percent of recruited athletes were admitted, 33.6 percent of legacy students were admitted, 42.2 percent of applicants on the Dean or Director’s List (major donor kids) were admitted, and 46.7 percent of children of faculty or staff were admitted. Compare this to an admit rate of ~5 percent for unhooked applicants. It is clear that these are different categories of applicants that should not be conflated.

If your kid is an unhooked applicant, you can infer much more about his or her prospects from Arcidiacono's analysis than from Card's. The former covers 95% of the pool and is not subject to large idiosyncratic and distortionary effects from the special 5% that are advantaged for reasons having nothing to do with academic merit or even personality and leadership factors.

From the SFFA brief (still uncontested?):
Professor Arcidiacono thus correctly excluded special-category applicants to isolate and highlight Harvard’s discrimination against Asian Americans. Professor Card, by contrast, includes “special recruiting categories in his models” to “obscure the extent to which race is affecting admissions decisions for those not fortunate enough to belong to one of these groups.” At bottom, SFFA’s claim is that Harvard penalizes Asian-American applicants who are not legacies or recruited athletes. Professor Card has shown that he is unwilling and unable to contest that claim.
The question of how unhooked applicants are treated has been discussed in college admissions circles for some time. From 2006:
Inside Higher Ed covers a panel called “Too Asian?” at the annual meeting of the National Association for College Admission Counseling. Particularly telling are the comments of a former Stanford admissions officer about an internal study which found evidence of higher admission rates for white applicants over Asians of similar academic and leadership qualifications (all applicants in the study were "unhooked" - meaning not in any favored categories such as legacies or athletes). 

Thursday, October 25, 2018

Backpropagation in the Brain?

Ask and ye shall receive :-)

In an earlier post I recommended a talk by Ilya Sutskever of OpenAI (part of an MIT AGI lecture series). In the Q&A someone asks about the status of backpropagation (used for training of artificial deep neural nets) in real neural nets, and Ilya answers that it's currently not known how or whether a real brain does it.

Almost immediately, neuroscientist James Phillips of Janelia provides a link to a recent talk on this topic, which proposes a specific biological mechanism / model for backprop. I don't know enough neuroscience to really judge the idea, but it's nice to see cross-fertilization between in silico AI and real neuroscience.

See here for more from Blake Richards.

David Goldman: Will China overtake the U.S. as the world's leading superpower?



David Goldman writes the Spengler column for the Asia Times. He has been a keen observer of geopolitics, economics, and finance in the Asia-Pacific region for many decades, as well as a banker and financial analyst.

This talk is an entertaining blend of insight and sensationalism ;-)

(At some moments listening to Goldman I am reminded of The Doctor Fox Lecture: A Paradigm of Educational Seduction ... But at other moments I agree with him completely ...)

I believe you can hear Sebastian Gorka in the Q&A.

Here's more Goldman, if you find him to your taste 8-)

Tuesday, October 23, 2018

MIT AGI: OpenAI Meta-Learning and Self-Play (Ilya Sutskever)



I recently noticed this lecture series at MIT, focusing on AGI. This talk by Ilya Sutskever (OpenAI) is very good. There are several more in this series: playlist.

In Q&A Sutskever notes that it is not known whether/how human brains do backpropagation, which seems central to training of deep networks. Any neuroscientists out there want to take up this question?

Sunday, October 21, 2018

The Truth Shall Make You Free


These NYTimes articles by Pulitzer Prize winner Amy Harmon, linking genetic science to racism and white supremacy, caused a sensation at ASHG 2018, a large annual meeting of genetics researchers.
Why White Supremacists Are Chugging Milk (and Why Geneticists Are Alarmed)

‘Could Somebody Please Debunk This?’: Writing About Science When Even the Scientists Are Nervous

Geneticists Criticize Use of Science by White Nationalists to Justify ‘Racial Purity’
In the second article above, Harmon writes
But another reason some scientists avoid engaging on this topic, I came to understand, was that they do not have definitive answers about whether there are average differences in biological traits across populations. And they have increasingly powerful tools to try to detect how natural selection may have acted differently on the genes that contribute to assorted traits in various populations.

What’s more, some believe substantial differences will be found. ...
One the first talks I attended at ASHG this year is summarized below. The talk was oversubscribed, so I had to sit in the overflow room. One of the slides presented showed a table of specific complex traits, cross-referenced by different ancestry groups, indicating status of recent natural selection. The authors' results imply that different population groups have been experiencing differential selection over the last ~10k years: different selection pressures in different geographical locations. There were many talks at ASHG covering related topics, with similar conclusions. Advances in computational and statistical methods, together with large datasets, make it possible now to seriously investigate differential selection in recent human evolutionary history.

Given such results, how are researchers to respond when asked to categorically exclude the possibility of genetically mediated average differences between groups? 

We are scientists, seeking truth. We are not slaves to ideological conformity.
Building genealogies for tens of thousands of individuals genome-wide identifies evidence of directional selection driving many complex human traits.

S.R. Myers 1,2; L. Speidel 1
1) Department of Statistics, University of Oxford, Oxford, United Kingdom; 2) Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom

For a variety of species, large-scale genetic variation datasets are now available. All observed genetic variation can be traced back to a genealogy, which records historical recombination and coalescence events and in principle captures all available information about evolutionary processes. However, the reconstruction of these genealogies has been impossible for modern-scale data, due to huge inherent computational challenges. As a consequence, existing methods usually scale to no more than tens of samples. We have developed a new, computationally efficient method for inferring genome-wide genealogies accounting for varying population sizes and recombination hotspots, robust to data errors, and applicable to thousands of samples genome-wide in many species. This method is >10,000 times faster than existing approaches, and more accurate than leading algorithms for a range of tasks including estimating mutational ages and inferring historical population sizes. Application to 2,478 present-day humans in the 1000 Genomes Project, and wild mice, provides dates for population size changes, merges, splits and introgressions, and identifies changes in underlying evolutionary mutation rates, from 1000 years, to more than 1 million years, ago. Using our mutational age estimates, we developed an approach quantifying evidence of natural selection at each SNP. We compared resulting p-values to existing GWAS study results, finding widespread enrichment (>2.5-fold in Europeans and East Asians) of GWAS hits among individual SNPs with low selection p-values (Z>6), stronger than the 1.5-fold increase observed at nonsynonymous mutations, and with enrichment increasing with statistical significance. We found evidence that directional selection, impacting many SNPs jointly, has shaped the evolution of >50 human traits over the past 1,000-50,000 years, sometimes in different directions among different groups. These include many blood-related traits including blood pressure, platelet volume, both red and white blood cell count and e.g. monocyte counts; educational attainment; age at menarche; and physical traits including skin colour, body mass index and (particularly in South Asian populations) height. Our approach enables simultaneous testing of recent selection, ancient natural selection, and changes in the strength of selection on a trait through time, and is applicable across a wide range of organisms.

Of course, all good people abhor racism. I believe that each person should be treated as an individual, independent of ancestry or ethnic background. (Hence I oppose Harvard's race-based discrimination against Asian Americans and favor Caltech's meritocratic approach to admissions.)

However, this ethical position is not predicated on the absence of average differences between groups. I believe that basic human rights and human dignity derive from our shared humanity, not from uniformity in ability or genetic makeup.

As a parent it is obvious to me that my children differ in innate aptitudes, preferences, and personalities. I love them equally: it would be wrong to condition this love on their specific genetic endowments.



Here is another set of ASHG talks I attended, on related issues:
Impact of Natural Selection on the Genetic Architecture of Complex Traits

Moderators: Shamil Sunyaev, Harvard Med Sch & Brigham & Women’s Hosp, Boston
Laura Hayward, Columbia Univ, New York

Evolution and maintenance of complex traits under natural selection has been a long-standing area of genetic research. Polygenic adaptation, stabilizing selection, and negative selection on new mutations can substantially impact the genetic architecture of diseases and complex traits, via direct selection on traits that are correlated with fitness and/or via pleiotropic selection. New methods are being developed to detect the action of natural selection at different time scales, including selection in contemporary humans. This session will discuss recent work on methods that analyze data from large cohorts to detect natural selection and evaluate its impact on diseases and complex traits. The application of these methods has substantially improved our understanding of polygenic disease and complex trait architectures, informing efforts to identify and interpret genetic variation affecting diseases and complex traits.

10:30 AM Polygenic architecture and adaptation of human complex traits. J. Pritchard. Howard Hughes Med Inst, Stanford.

11:00 AM Detection and quantification of the effect of selection and adaptation on complex traits. P. Visscher. Univ Queensland, Brisbane, Australia.

11:30 AM Observing natural selection in contemporary humans. M. Ilardo. Univ Utah & UC Berkeley.

12:00 PM Impact of negative selection on common variant disease architectures. A. Price. Harvard TH Chan Sch Publ Hlth, Boston.
More papers on recent natural selection and human complex traits:

https://infoproc.blogspot.com/2015/10/genetic-group-differences-in-height.html

https://infoproc.blogspot.com/2016/05/evidence-for-very-recent-natural.html

https://infoproc.blogspot.com/2017/06/complex-trait-adaptation-and-branching.html

https://infoproc.blogspot.com/2017/07/natural-selection-and-body-shape-in.html

Tuesday, October 16, 2018

ASHG 2018


See you in San Diego! American Society of Human Genetics.

My schedule is so booked with meetings that I wonder whether I will be able to attend any talks... (Everyone in the genomics world seems to be attending this event.)

Nevertheless, if you see me wandering around please say HI!

Monday, October 15, 2018

Harvard Admissions on Trial


Now that the Harvard Asian American discrimination trial has started, let me share some previous correspondence with NYTimes reporters who are covering the proceedings.
... it's far too easy for the press to just report it as "Harvard's statistician disagrees with plaintiff's statistician" when in fact there are specific and important claims (e.g., regarding unhooked applicants) that are unanswered by Harvard.

Claim: when unhooked applicants are considered, Asians are discriminated against relative to all other groups.

Unhooked applicants are 95% of the total pool, but only ~2/3 of those admitted (see below). [ Unhooked = ordinary applicant = non-legacy, non-recruited athlete, etc. ]

http://infoproc.blogspot.com/2018/06/harvard-office-of-institutional.html

From the SFFA brief:

"The task here is to determine whether “similarly situated” applicants have been treated differently on the basis of race; “apples should be compared to apples.” SBT Holdings, LLC v. Town of Westminster, 547 F.3d 28, 34 (1st Cir. 2008). Because certain applicants are in a special category, it is important to analyze the effect of race without them included. Excluding them allows for the effect of race to be tested on the bulk of the applicant pool (more than 95% of applicants and more than two-thirds of admitted students) that do not fall into one of these categories, i.e., the similarly situated applicants. For special-category applicants, race either does not play a meaningful role in their chances of admission or the discrimination is offset by the “significant advantage” they receive. Either way, they are not apples.

Professor Card’s inclusion of these applicants reflects his position that “there is no penalty against Asian-American applicants unless Harvard imposes a penalty on every Asian-American applicant.” But he is not a lawyer and he is wrong. It is illegal to discriminate against any Asian-American applicant or subset of applicants on the basis of race. Professor Card cannot escape that reality by trying to dilute the dataset. The claim here is not that Harvard, for example, “penalizes recruited athletes who are Asian-American because of their race.” The claim “is that the effects of Harvard’s use of race occur outside these special categories.” Professor Arcidiacono thus correctly excluded special-category applicants to isolate and highlight Harvard’s discrimination against Asian Americans. Professor Card, by contrast, includes “special recruiting categories in his models” to “obscure the extent to which race is affecting admissions decisions for those not fortunate enough to belong to one of these groups.” At bottom, SFFA’s claim is that Harvard penalizes Asian-American applicants who are not legacies or recruited athletes. Professor Card has shown that he is unwilling and unable to contest that claim.

[ Card and Arcidiacono have exchanged criticisms of the other's analysis already, so Card's lack of response on this specific point is worthy of attention. ]
Will Harvard contest the claim that within the set of unhooked applicants, Asian Americans are discriminated against? As far as I know they have not.
The question about how unhooked applicants are treated has been discussed in college admissions circles for some time. See this from 2006:

https://infoproc.blogspot.com/2006/11/ugly-truth.html

Inside Higher Ed covers a panel called “Too Asian?” at the annual meeting of the National Association for College Admission Counseling. Particularly telling are the comments of a former Stanford admissions officer about an internal study which found evidence of higher admission rates for white applicants over Asians of similar academic and leadership qualifications (all applicants in the study were "unhooked" - meaning not in any favored categories such as legacies or athletes). 
[ So it is strange for Card / Harvard to claim that this specific question is not worth investigating! ]
Thanks to the lawsuit the results of a Harvard internal study in 2013 have been revealed, which concluded, like the Stanford study, that there was indeed discrimination against Asian American applicants. This will undoubtedly be discussed at trial.




The Content of their Character: Ed Blum and Jian Li
.
Jian Li: "I have a message to every single Asian-American student in the country who is applying to college: your civil rights are being violated and you must speak up in defense of them. If you've suffered discrimination you have the option to file a complaint with the Office for Civil Rights. Let your voice be heard .. not only through formal means but also by simply letting it be known in your schools and your communities, in the press and on social media, that university discrimination is pervasive and that this does not sit well with you. Together we will fight to ensure that universities can no longer treat us as second-class citizens."

Sunday, October 14, 2018

Nature News on Polygenic Genomic Prediction


See also Population-wide Genomic Prediction of Health Risks.
The approach to predictive medicine that is taking genomics research by storm (Nature News)

Polygenic risk scores represent a giant leap for gene-based diagnostic tests. Here’s why they’re still so controversial.

... Supporters say that polygenic scores could be the next great stride in genomic medicine, but the approach has generated considerable debate. Some research presents ethical quandaries as to how the scores might be used: for example, in predicting academic performance. Critics also worry about how people will interpret the complex and sometimes equivocal information that emerges from the tests. And because leading biobanks lack ethnic and geographic diversity, the current crop of genetic screening tools might have predictive power only for the populations represented in the databases.

“Most people are keen to have a decent debate about this, because it raises all sorts of logistical and social and ethical issues,” says Mark McCarthy, a geneticist at the University of Oxford, UK. Even so, polygenic scores are racing to the clinic and are already being offered to consumers by at least one US company.

Peter Visscher, a geneticist at the University of Queensland, Australia, who pioneered the methods that underlie the trend, is broadly optimistic about the approach, but is still surprised by the speed of progress. “I’m absolutely convinced this is going to come sooner than we think,” he says. ...
Below are some remarks from earlier posts. Population-wide Genomic Prediction of Health Risks:
I estimate that within a year or so there will be more than 10 good genomic predictors covering very significant disease risks, ranging from heart disease to diabetes to hypothyroidism to various cancers. These predictors will be able to identify the, e.g., few percent of the population that are outliers in risk -- for example, have 5x or 10x the normal likelihood of getting the disease at some point in their lives. Risk predictions can be made at birth (or before! or in adulthood), and preventative care allocated appropriately. All of these risk scores can be computed using a genotype read from an inexpensive (< $50 per person) array that probes ~1M or so common SNPs.

Genomic Prediction of disease risk using polygenic scores:
It seems to me we are just at the tipping point -- soon it will be widely understood that with large enough data sets we can predict complex traits and complex disease risk from genotype, capturing most of the estimated heritable variance. People will forget that many "experts" doubted this was possible -- the term missing heritability will gradually disappear.

In just a few years genotyping will start to become "standard of care" in many health systems. In 5 years there will be ~100M genotypes in storage (vs ~20M now), a large fraction available for scientific analysis.

Saturday, October 13, 2018

Physics as a Strange Attractor


Almost every student who attends a decent high school will be exposed to Special Relativity. Their science/physics teacher may not really understand it very well, may do a terrible job trying to explain it. But the kid will have to read a textbook discussion and (in the internet age) can easily find more with a simple search.

Wikipedia entry on Special Relativity:
In Albert Einstein's original pedagogical treatment, it is based on two postulates:

1. The laws of physics are invariant (i.e., identical) in all inertial systems (i.e., non-accelerating frames of reference).

2. The speed of light in a vacuum is the same for all observers, regardless of the motion of the light source.
What happens next depends, of course, on the kid. I posit that above a certain (perhaps very high) threshold in g and in intellectual curiosity, almost everyone will invest some hours to think about this particular topic. Special Relativity is fundamental to our understanding of space and time and causality, and has a certain intellectual and cultural glamour. Furthermore, it is amazing that a simple empirical observation like 2 above has such deep and significant consequences. A bright individual who invests those few hours is likely to come away with an appreciation of the beauty and power of physics and the mathematical approach to natural science.

I suspect that Special Relativity, because it is easy to introduce (no mathematics beyond algebra is required), yet deep and beautiful and counterintuitive, stimulates many people of high ability to become interested in physics.

So what does it mean when you meet an educated adult who does not understand Special Relativity? Does it suggest an upper bound (albeit perhaps very high) on a combination of their cognitive ability and intellectual curiosity? I mention curiosity (perhaps better to say interest in first principles or deep knowledge) because of course some (how many?) people of high ability will simply not be interested in the topic. However, as ability level increases the amount of effort necessary to learn and retain the information decreases. So someone with very off-scale ability would have to be quite incurious not to absorb and retain some basic understanding of relativity, if only from school days.

Years ago I was discussing a particle accelerator facility with a distinguished (internationally renowned) engineering professor. I mentioned that the particles in the beam would reach a certain fraction of the speed of light. He asked me why they could not reach or surpass the speed of light. It became obvious that he had essentially zero understanding of Special Relativity, and I was shocked.

We could go a bit further. General Relativity (also an invention of Einstein) describes the dynamics of spacetime (sound interesting?), and is connected to topics in popular culture such as black holes, time travel, wormholes, galactic empires, etc. General Relativity is far more complex than Special Relativity, but can be introduced to someone who has a good grasp of multivariable calculus. For example, Dirac's lecture notes on the subject provide a pedagogical introduction in only 62 pages. Yet what fraction of adults have even a modest grasp of this topic? Perhaps one in ten or a hundred thousand at best.

What is the cognitive threshold to learn Special or General Relativity? What is the cognitive threshold to remember something about it ten or twenty years later? Is the cognitive threshold higher, or the threshold in intellectual curiosity required to ponder such things?

See also One hundred thousand brains and Quantum GDP.

Thursday, October 11, 2018

Population-wide Genomic Prediction of Health Risks


The UK is ahead of the US in the application of genomics in clinical practice. Part of this is due to their leadership in projects like the UK Biobank (500k genomes with extensive biomedical phenotyping), and part is due to having a single-payer system that can adopt obviously beneficial (and cost-beneficial) practices after some detailed study. Former Prime Minister David Cameron's son has a rare genetic disease, which contributed to his strong support of genomics research in the UK. The decentralized (broken) US health care system, which does not focus on quality of outcome, is having a hard time with no-brainer decisions like making inexpensive genotyping Standard of Care. Will insurance reimburse?

I estimate that within a year or so there will be more than 10 good genomic predictors covering very significant disease risks, ranging from heart disease to diabetes to hypothyroidism to various cancers. These predictors will be able to identify the, e.g., few percent of the population that are outliers in risk -- for example, have 5x or 10x the normal likelihood of getting the disease at some point in their lives. Risk predictions can be made at birth (or before! or in adulthood), and preventative care allocated appropriately. All of these risk scores can be computed using a genotype read from an inexpensive (< $50 per person) array that probes ~1M or so common SNPs.

In technical papers my research group anticipated years ago that even very complex traits would be predictable once a data threshold was crossed. The phenomenon is related to what physicists refer to as a phase transition in algorithm performance. The rapid appearance now of practically useful risk predictors for disease is one anticipated consequence of this phase transition. Medicine in well-functioning health care systems will be transformed over the next 5 years or so.

Test could predict risk of future heart disease for just £40 (Guardian)

Genomic Risk Score test is cheap enough to allow population-wide screening of children, researchers believe

A one-off genetic test costing less than £40 can show if a person is born with a predisposition to heart disease.

The Genomic Risk Score (GRS) test is cheap enough to allow population-wide screening of children, researchers believe. Medical and lifestyle interventions could then be employed to reduce the chances of those most at risk of suffering heart attacks in adulthood.

A study found that participants with a GRS in the top 20% were more than four times more likely to develop coronary heart disease than those with scores in the bottom 20%. Many in the “at risk” category lacked the usual heart disease indicators, such as high cholesterol and blood pressure.

Senior author Sir Nilesh Samani, the professor of cardiology at the University of Leicester and medical director of the British Heart Foundation charity, said: “At the moment, we assess people for their risk of coronary heart disease in their 40s through NHS health checks. But we know this is imprecise and also that coronary heart disease starts much earlier, several decades before symptoms develop.

“Therefore, if we are going to do true prevention, we need to identify those at increased risk much earlier. This study shows that the GRS can now identify such individuals.

“Applying it could provide a most cost-effective way of preventing the enormous burden of coronary heart disease, by helping doctors select patients who would most benefit from interventions.”

Coronary heart disease is the leading cause of death worldwide and claims 66,000 lives each year in the UK. Healthcare costs related to heart and circulatory diseases in the UK are estimated at £9bn per year. ...
Related posts:

Advances in Genomic Prediction

Genomic Prediction of disease risk using polygenic scores (Nature Genetics)

Genomic Prediction: A Hypothetical (Embryo Selection), Part 2

Wednesday, October 10, 2018

Well done, tovarishch




I thought Khabib's grappling would dominate McGregor's striking. Tony Ferguson would have given him a tougher fight.

The finish was not a choke -- probably a jaw lock. It seems that Conor almost taps, stops, then realizes his jaw is in danger (or just gives up, which is part of his MO) and taps.




Friday, October 05, 2018

Advances in Genomic Prediction

Apologies, I've been super busy lately. Below are some links of interest.

Genomic prediction of cognitive traits in childhood and adolescence (Plomin): predictor reaches correlation of ~0.33 for cognitive ability and ~0.4 for Educational Attainment.

Machine Learning to Predict Osteoporotic Fracture Risk from Genotypes. Another potentially clinically useful result. I'd guess we'll have >10 good polygenic disease risk predictors within a year.

Genomic Prediction and IVF in WSJ: Discusses eye color as well as our results.

New DNA Tool Predicts Height, Shows Promise for Serious Illness Assessment. Includes podcast interview with me.

Register for online discussion of our height / L1 optimization paper, hosted by the journal GENETICS. Thursday, October 25, 2018 12:00-1:00PM (ET)

Interview on Airtalk
(KPCC Los Angeles NPR show).


Figure from the bone fracture risk paper linked above. It appears that individuals with polygenic scores in the bottom few percent (highest risk) are ~4 times as likely to suffer a hip or other major fracture.


Sunday, September 30, 2018

Quantum Information Science Workshop at MSU


Webpage / Program / Abstracts.

My opening remarks:
On behalf of Michigan State University it is my pleasure to welcome all of you to this workshop on quantum information science.

In the fall of 1983 (my freshman year!) Feynman taught a graduate course at Caltech called Potentialities and Limitations of Computing Machines. Chapter 6 of the book developed from his lecture notes is entitled Quantum Mechanical Computers. In the prior years he had teamed with Professors Carver Mead and John Hopfield to teach a similar course. Carver Mead was the father of VLSI and coined the term "Moore's Law"! John Hopfield, no slouch, was an early pioneer of neural nets, among other things.

It was in 1981, in a paper called Simulating Physics with Computers, that Feynman proposed the idea of a Universal Quantum Simulator. He was the first to discuss the simulation of quantum systems using a quantum computer, and to point out the difficulties of using classical computations to explore what could be exponentially large Hilbert spaces. Feynman analyzed reversible (unitary) computations using quantum elements, and wrote "... the laws of physics present no barrier to reducing the size of computers until bits are the size of atoms, and quantum behavior holds dominant sway."
I recount this little bit of history because we have finally, thanks to the sweat and ingenuity of many physicists, reached the era of noisy, but useful, quantum simulators. Personally I feel that universal quantum computers -- of the type that could, for instance, implement Shor's Algorithm -- might still be far off. Nevertheless, quantum simulators are themselves an important step forward, and will likely become a very useful tool for physicists.

I can't resist making a small prediction of my own here. Some of you might know that the foundations of quantum mechanics are still in disarray. As Steve Weinberg says: "... today there is no interpretation of quantum mechanics that does not have serious flaws." Feynman himself said: "I think I can safely say that nobody understands quantum mechanics."

Most physicists, even theorists, focus their efforts on practical matters and don't worry about foundational questions. I believe that a side effect of work on quantum information and quantum computing will be a demystification of the process of measurement and of decoherence. By demystification I mean that many more physicists will develop a good understanding of something that was swept under the rug in von Neumann's Projection or Collapse postulate, which we now teach in every QM course. Once we truly understand decoherence we realize that Schrodinger evolution of the wavefunction describing both observer and system can reproduce all the usual phenomenology of quantum mechanics -- Collapse is not necessary. This was pointed out long ago by Everett, and well-appreciated by people like Feynman, Schwinger, Gell-Mann, Hawking, David Deutsch, and Steve Weinberg, although not widely understood in the broader physics community.

I apologize if these final comments are mysterious. Perhaps they will someday become clear... In the meantime, please enjoy the workshop :-)

Links:

Weinberg on quantum foundations

Schwinger on quantum foundations

Steven Weinberg: What's the matter with quantum mechanics?


Feynman and Gell-Mann

Saturday, September 29, 2018

Intuition and the two brains, revisited



Iain McGilchrist, author of The Master and His Emissary: The Divided Brain and the Making of the Western World, in conversation with Jordan Peterson.

I wrote about McGilchrist in 2012: Intuition and the two brains.
Albert Einstein:
“The intuitive mind is a sacred gift and the rational mind is a faithful servant. We have created a society that honors the servant and has forgotten the gift.”

Wigner on Einstein and von Neumann:
"But Einstein's understanding was deeper even than von Neumann's. His mind was both more penetrating and more original than von Neumann's. And that is a very remarkable statement. Einstein took an extraordinary pleasure in invention. Two of his greatest inventions are the Special and General Theories of Relativity; and for all of Jansci's brilliance, he never produced anything as original."

From Schwinger's Feynman eulogy:
"An honest man, the outstanding intuitionist of our age..."

Feynman:
"We know a lot more than we can prove."


... "if the brain is all about making connections, why is it that it's evolved with this whopping divide down the middle?"

... [chicks] use the eye connected to the left hemisphere to attend to the fine detail of picking seeds from amongst grit, whilst the other eye attends to the broader threat from predators. According to the author, "The left hemisphere has its own agenda, to manipulate and use the world"; its world view is essentially that of a mechanism. The right has a broader outlook, "has no preconceptions, and simply looks out to the world for whatever might be. In other words it does not have any allegiance to any particular set of values."

... "The right hemisphere sees a great deal, but in order to refine it, and to make sense of it in certain ways---in order to be able to use what it understands of the world and to be able to manipulate the world---it needs to delegate the job of simplifying it and turning it into a usable form to another part of the brain" [the left hemisphere]. ... the left hemisphere has a "narrow, decontextualised and theoretically based model of the world which is self consistent and is therefore quite powerful" and to the problem of the left hemisphere's lack of awareness of its own shortcomings; whilst in contrast, the right hemisphere is aware that it is in a symbiotic relationship.

Roger Sperry: ... each hemisphere is "indeed a conscious system in its own right, perceiving, thinking, remembering, reasoning, willing, and emoting, all at a characteristically human level, and . . . both the left and the right hemisphere may be conscious simultaneously in different, even in mutually conflicting, mental experiences that run along in parallel."
Much more here.

Split-brain structure (with the different hemispheres having very distinct structures and morphologies) is common to all higher organisms (as far as I know). Is this structure just an accident of evolution? Or does the (putative) split between a systematizing core and a big-picture intuitive core play an important role in higher cognition?

AGI optimists sometimes claim that deep learning and existing neural net structures are capable of taking us all the way to AGI (human-like cognition and beyond). I think there is a significant chance that neural-architectural structures necessary for, e.g., recurrent memory, meta-reasoning, theory of mind, creative generation of ideas, integration of inferences developed from observation into more general hypotheses/models, etc. still need to be developed. Any step requiring development of novel neural architecture could easily take researchers a decade to accomplish. So a timescale > 30-50 years for AGI, even in highly optimistic scenarios, seems quite possible to me.

Saturday, September 22, 2018

The French Way: Alain Connes interview


I came across this interview with Fields Medalist Alain Connes (excerpt below) via an essay by Dominic Cummings (see his blog here).

Dom's essay is also highly recommended. He has spent considerable effort to understand the history of highly effective scientific / research organizations. There is a good chance that his insights will someday be put to use in service of the UK. Dom helped create a UK variant of Kolmogorov's School for Physics and Mathematics.

On the referendum and on Expertise: the ARPA/PARC ‘Dream Machine’, science funding, high performance, and UK national strategy


Topics discussed by Connes: CNRS as a model for nurturing talent, materialism and hedonic treadmill as the enemy to intellectual development, string theory (pro and con!), US, French, and Soviet systems for science / mathematics, his entry into Ecole Normale and the '68 Paris convulsions.

France and Ecole Normale produce great mathematicians far in excess of their population size.
Connes: I believe that the most successful systems so far were these big institutes in the Soviet union, like the Landau institute, the Steklov institute, etc. Money did not play any role there, the job was just to talk about science. It is a dream to gather many young people in an institute and make sure that their basic activity is to talk about science without getting corrupted by thinking about buying a car, getting more money, having a plan for career etc. ... Of course in the former Soviet Union there were no such things as cars to buy etc. so the problem did not arise. In fact CNRS comes quite close to that dream too, provided one avoids all interference from our society which nowadays unfortunately tends to become more and more money oriented.


Q: You were criticizing the US way of doing research and approach to science but they have been very successful too, right? You have to work hard to get tenure, and research grants. Their system is very unified in the sense they have very few institutes like Institute for Advanced Studies but otherwise the system is modeled after universities. So you become first an assistant professor and so on. You are always worried about your raise but in spite of all these hazards the system is working.


Connes: I don’t really agree. The system does not function as a closed system. The US are successful mostly because they import very bright scientists from abroad. For instance they have imported all of the Russian mathematicians at some point.


Q: But the system is big enough to accommodate all these people this is also a good point.


Connes: If the Soviet Union had not collapsed there would still be a great school of mathematics there with no pressure for money, no grants and they would be more successful than the US. In some sense once they migrated in the US they survived and did very well but I believed they would have bloomed better if not transplanted. By doing well they give the appearance that the US system is very successful but it is not on its own by any means. The constant pressure for producing reduces the “time unit” of most young people there. Beginners have little choice but to find an adviser that is sociologically well implanted (so that at a later stage he or she will be able to write the relevant recommendation letters and get a position for the student) and then write a technical thesis showing that they have good muscles, and all this in a limited amount of time which prevents them from learning stuff that requires several years of hard work. We badly need good technicians, of course, but it is only a fraction of what generates progress in research. It reminds me of an anecdote about Andre Weil who at some point had some problems with elliptic operators so he invited a great expert in the field and he gave him the problem. The expert sat at the kitchen table and solved the problem after several hours. To thank him, Andre Weil said “when I have a problem with electricity I call an electrician, when I have a problem with ellipticity I use an elliptician”.

From my point of view the actual system in the US really discourages people who are truly original thinkers, which often goes with a slow maturation at the technical level. Also the way the young people get their position on the market creates “feudalities” namely a few fields well implanted in key universities which reproduce themselves leaving no room for new fields.

....

Q: So you were in Paris [ Ecole Normale ] in the best place and in the best time.

Connes: Yes it was a good time. I think it was ideal that we were a small group of people and our only motivation was pure thought and no talking about careers. We couldn’t care the less and our main occupation was just discussing mathematics and challenging each other with problems. I don’t mean ”puzzles” but problems which required a lot of thought, time or speed was not a factor, we just had all the time we needed. If you could give that to gifted young people it would be perfect.
See also Defining Merit:
... As a parting shot, Wilson could not resist accusing Ford of anti-intellectualism; citing Ford's desire to change Harvard's image, Wilson asked bluntly: "What's wrong with Harvard being regarded as an egghead college? Isn't it right that a country the size of the United States should be able to afford one university in which intellectual achievement is the most important consideration?"

E. Bright Wilson was Harvard professor of chemistry and member of the National Academy of Sciences, later a recipient of the National Medal of Science. The last quote from Wilson could easily have come from anyone who went to Caltech! Indeed, both E. Bright Wilson and his son, Nobel Laureate Ken Wilson (theoretical physics), earned their doctorates at Caltech (the father under Linus Pauling, the son under Murray Gell-Mann).
Where Nobel winners get their start (Nature):
Top Nobel-producing undergraduate institutions

Rank School                Country               Nobelists per capita (UG alumni)
1 École Normale Supérieure France       0.00135
2 Caltech                               US             0.00067
3 Harvard University            US             0.00032
4 Swarthmore College          US             0.00027
5 Cambridge University       UK             0.00025
6 École Polytechnique          France       0.00025
7 MIT                                   US              0.00025
8 Columbia University         US              0.00021
9 Amherst College               US              0.00019
10 University of Chicago     US              0.00017

Thursday, September 20, 2018

Social Credit in China



I can't vouch for the accuracy of this documentary, but I suspect the opinions of the people interviewed -- white collar mom with high social credit score, and blacklisted investigative journalist -- are representative. Probably too much emphasis on cameras and face recognition, when in fact the smartphone each person is carrying generates as much or more data about their activities. See also PanOpticon in my Pocket.

Coming soon to the US?

Black Mirror:

Sunday, September 16, 2018

"The Mouthpiece of the Party of Davos": Bannon interview with Economist Editor in Chief



Steve Bannon, former White House chief strategist, interviewed by Zanny Minton Beddoes, The Economist’s Editor-in-chief (Open Future festival in New York on September 15th 2018). In contrast, New Yorker editor David Remnick surrendered to protests and disinvited Bannon from The New Yorker Festival two weeks ago.

For almost two decades I subscribed to The Economist and The New Yorker. But these days I read them only sporadically.

Whether you like or hate Steve Bannon, this interview is worth watching. Beddoes and questioners from the audience attack Bannon vigorously, but mostly allow him time to answer in full. Opening tactic is, no surprise, to insinuate racism, which Bannon explicitly rejects for the millionth time... If your source of information about Bannon is primarily the mainstream media, you might be surprised at what comes from the horse's mouth.

Topics covered: populism, nationalism, economic war with China, immigration, class struggle, tax and tariff policy, and Duty, Honor, Country.

Bannon @15:30 (talking over interruption):
Bannon: ... you keep getting bailed out by the Deplorables -- in World War One and World War Two, in the Cold War and whatever else is in the future it is working men and women that have bailed you out ...

Editor: We have to go on to something else...
Bannon @23:15, as the racism attack morphs into sexism:
... I'm a former naval officer that served in the South China Sea in the Pacific. My daughter is a graduate of the United States Military Academy at West Point and served with the 101st airborne in Iraq. She's an army captain today after serving in Eastern Europe, probably going to be deployed back to Afghanistan. She's on the faculty at West Point. I know how to help raise an empowered woman ...
General Douglas MacArthur, 1962 speech at West Point:
Duty, Honor, Country: Those three hallowed words reverently dictate what you ought to be, what you can be, what you will be. They are your rallying points: to build courage when courage seems to fail; to regain faith when there seems to be little cause for faith; to create hope when hope becomes forlorn.

Unhappily, I possess neither that eloquence of diction, that poetry of imagination, nor that brilliance of metaphor to tell you all that they mean.

The unbelievers will say they are but words, but a slogan, but a flamboyant phrase. Every pedant, every demagogue, every cynic, every hypocrite, every troublemaker, and I am sorry to say, some others of an entirely different character, will try to downgrade them even to the extent of mockery and ridicule.

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