Manifold Episode 10: Ron Unz on the Subprime Mortgage Crisis, The Unz Review, and the Harvard Admissions Scandal

Ron Unz is the publisher of the Unz Review, a controversial but widely read alternative media site hosting opinion outside of the mainstream, including from both the far right and the far left. Unz studied theoretical physics at Harvard, Cambridge and Stanford. He founded the software company Wall Street Analytics, acquired by Moody’s in 2006, and was behind the 1998 ballot initiative that ended bilingual education in California.

Podcast transcript

The Unz Review

The Myth of American Meritocracy - How corrupt are Ivy League admissions?

The Myth of American Meritocracy and Other Essays


man·i·fold /ˈmanəˌfōld/ many and various.

In mathematics, a manifold is a topological space that locally resembles Euclidean space near each point.

Steve Hsu and Corey Washington have been friends for almost 30 years, and between them hold PhDs in Neuroscience, Philosophy, and Theoretical Physics. Join them for wide ranging and unfiltered conversations with leading writers, scientists, technologists, academics, entrepreneurs, investors, and more.

Steve 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.

Corey Washington is Director of Analytics in the Office of Research and Innovation at Michigan State University. He was educated at Amherst College and MIT before receiving a PhD in Philosophy from Stanford and a PhD in a Neuroscience from Columbia. He held faculty positions at the University Washington and the University of Maryland. Prior to MSU, Corey worked as a biotech consultant and is founder of a medical diagnostics startup.

Harvard Business Review: AI and the Genetic Revolution (podcast)

Harvard Business Review podcast with Azeem Azhar (Exponential View).

AI and the Genetic Revolution

Michigan State University senior vice president Stephen Hsu, a theoretical physicist and the founder of Genomic Prediction, demonstrates how the machine learning revolution, combined with the dramatic fall in the cost of human genome sequencing, is driving a transformation in our relationship with our genes. Stephen and Azeem Azhar explore how the technology works, what predictions can and cannot yet be made (and why), and the ethical challenges created by this technology.

In this podcast, Azeem and Stephen also discuss:

FDA approval of the first genetic treatment for monogenic conditions and the work towards developing treatments for polygenic conditions like diabetes and cancer.

How this technology might exacerbate existing social inequalities or create new ones; is it just an issue of access, or does it go further?

Developing best practice protocols for governance and regulation of genomic technologies.

In the interview I mention that the number of genomics papers on polygenic risk scores has exploded just in the last year or so:

Embryo Screening: Polygenic Traits and Disease Risk

Several people asked me to comment on this paper, which appeared recently on biorxiv. It seems to be an update of earlier (simulation) analyses by Gwern [16] and Shulman and Bostrom [15] (cited in the paper) on potential gains from embryo selection using quantitative trait predictors (e.g., height, cognitive ability). In the paper the authors analyze real families using actual genetic and phenotype data.

The main limitations given current technology are the number of embryos available from which to select, and the accuracy of the polygenic predictors. The latter will almost certainly improve significantly for some traits in the near future, and for all traits eventually. The number of embryos available for selection may also increase if new methods allow oocytes (eggs) to be produced using stem cell technology (already demonstrated in mice; video).

Screening human embryos for polygenic traits has limited utility
E. Karavani et al.

Genome-wide association studies have led to the development of polygenic score (PS) predictors that explain increasing proportions of the variance in human complex traits. In parallel, progress in preimplantation genetic testing now allows genome-wide genotyping of embryos generated via in vitro fertilization (IVF). Jointly, these developments suggest the possibility of screening embryos for polygenic traits such as height or cognitive function. There are clear ethical, legal, and societal concerns regarding such a procedure, but these cannot be properly discussed in the absence of data on the expected outcomes of screening. Here, we use theory, simulations, and real data to evaluate the potential gain of PS-based embryo selection, defined as the expected difference in trait value between the top-scoring embryo and an average, unselected embryo. We observe that the gain increases very slowly with the number of embryos, but more rapidly with increased variance explained by the PS. Given currently available polygenic predictors and typical IVF yields, the average gain due to selection would be ≈2.5cm if selecting for height, and ≈2.5 IQ (intelligence quotient) points if selecting for cognitive function. These mean values are accompanied by wide confidence intervals; in real data drawn from nuclear families with up to 20 offspring each, we observe that the offspring with the highest PS for height was the tallest only in 25% of the families. We discuss prospects and limitations of PS-based embryo selection for the foreseeable future.

The authors of the paper seem to define "utility" in terms of expected gain in trait value. However, there is also utility in eliminating very negative outcomes, even if they have small probability. This does not shift the average very much but may still be highly desirable. For example, the odds of my house being destroyed by fire or earthquake in the next decade are small, but the outcome is negative enough that I will act to insure against it. If there is a 1% chance of a $100k house being destroyed, the expected loss is only $1k over the period. But without insurance the outcome might be devastating to a family.

One can compare this to screening for Down Syndrome, which has an incidence of roughly 1% (depending on parental age, etc.) but very serious consequences (see podcast discussion below).

At Genomic Prediction we have focused on screening against disease risk rather than on selection for quantitative traits, for both ethical and practical reasons. Even noisy (imperfect) predictors allow the identification of individuals who are high risk outliers -- e.g., are 5x times more likely to get the disease than a typical person.

When considering disease risk the key metric is not the polygenic score itself, because odds ratios are nonlinear functions of the score (or score percentile). For example (note, this is entirely hypothetical), consider 3 embryos with disease risk percentile scores (e.g., Breast Cancer, Type 1 Diabetes, Atrial Fibrillation, Coronary Artery Disease) given by column:

    #1    33   57   64   51

    #2    62   39   36   49

    #3    26   22   52   99.5

Even though the linear averages of the four risk percentiles for all three embryos are similar (contrived to be near 50), embryo #3 has unusually high risk for one condition (e.g., Coronary Artery Disease) and embryos #1 and #2 might be preferred.

Quantifying the utility to the family from this kind of screening is much more complex than for quantitative traits such as height or cognitive ability.

For more on ethical questions related to genetic engineering and embryo selection, see this podcast discussion with Sam Kerstein, chair of the philosophy department at the University of Maryland.

Janelia (HHMI) talk: Genomic Prediction of Complex Traits and Disease Risks via AI and Large Genomic Datasets

Janelia is the research campus of the Howard Hughes Medical Institute (HHMI), located near Washington DC. It is reputed to be heaven on earth for scientists :-)

I'll be visiting there next week (see title and abstract below). If you're at Janelia and want to meet with me there is still a little space on my schedule. Or just come to the talk and try to grab me afterward.

My talk is Tuesday May 7 12:30 – 1:30.

Genomic Prediction of Complex Traits and Disease Risks via AI and Large Genomic Datasets

Abstract: The talk is divided into two parts. The first gives an overview of the rapidly advancing area of genomic prediction of disease risks using polygenic scores. We can now identify risk outliers (e.g., with 5 or 10 times normal risk) for about 20 common disease conditions, ranging from diabetes to heart diseases to breast cancer, using inexpensive SNP genotypes (i.e., as offered by 23andMe). We can also predict some complex quantitative traits (e.g., adult height with accuracy of few cm, using ~20k SNPs). I discuss application of these results in precision medicine as well as embryo selection in IVF, and give some details concerning genetic architecture. The second part covers the AI/ML used to build these predictors, with an emphasis on "sparse learning" and phase transitions in high dimensional statistics.

Manifold Episode #9: Philosopher S. Kerstein on the Morality of Genome Engineering

Corey and Steve speak with Samuel Kerstein, Professor of Philosophy and expert in Medical Ethics at the University of Maryland. They discuss the ethics of genome engineering and preimplantation embryo selection, and the inequality and narrowing of human diversity that might result from widespread adoption of these technologies. Among the topics covered: Why genome engineering at this time is immoral. Should we always pick the healthiest embryo? In the future will parents have a moral obligation to engineer their children? Will there be an arms race between countries to engineer their populations? Is Star Trek’s Khan a more advanced person (Steve) or just another smart psychopath (Sam) or both?

Samuel J. Kerstein

How to Treat Persons by Samuel J. Kerstein

CRISPR Babies – Episode #1

Transcript


man·i·fold /ˈmanəˌfōld/ many and various.

In mathematics, a manifold is a topological space that locally resembles Euclidean space near each point.

Steve Hsu and Corey Washington have been friends for almost 30 years, and between them hold PhDs in Neuroscience, Philosophy, and Theoretical Physics. Join them for wide ranging and unfiltered conversations with leading writers, scientists, technologists, academics, entrepreneurs, investors, and more.

Steve 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.

Corey Washington is Director of Analytics in the Office of Research and Innovation at Michigan State University. He was educated at Amherst College and MIT before receiving a PhD in Philosophy from Stanford and a PhD in a Neuroscience from Columbia. He held faculty positions at the University Washington and the University of Maryland. Prior to MSU, Corey worked as a biotech consultant and is founder of a medical diagnostics startup.

Dialogs

In a high corner office, overlooking Cambridge and the Harvard campus.

How big a role is deep learning playing right now in building genomic predictors?

So far, not a big one. Other ML methods perform roughly on par with DL. The additive component of variance is largest, and we have compressed sensing theorems showing near-optimal performance for capturing it. There are nonlinear effects, and eventually DL will likely be useful for learning multi-loci features. But at the moment everything is limited by statistical power, and nonlinear features are even harder to detect than additive ones. ...

The bottom line is that with enough statistical power predictors will capture the expected heritability for most traits. Are people in your field ready for this?

Some are, but for others it will be very difficult.

Conference on AI and Genomics / Precision Medicine (Boston).

I enjoyed your talk. I work for [leading AgBio company], but my PhD is in Applied Math. We've been computing Net Merit for bulls using SNPs for a long time. The human genetics people have been lagging...

Caught up now, though. And first derivative (sample size growth rate) is much larger...

Yes. It's funny because sperm is priced by Net Merit and when we or USDA revise models some farmers or breeders get very angry because the value of their bull can change a lot!

A Harvard Square restaurant.

I last saw Roman at the Fellows spring dinner, many years ago. I was back from Yale to see friends. He was drinking, with serious intent. He told me about working with Wilson at Cornell. He also told me an old story about Jeffrey and the Higgs mechanism. Jeffrey almost had it, soon after his work on the Goldstone boson. But Sidney talked him out of it -- something to the effect of "if you can only make sense of it in unitary gauge, it must be an artifact" ... Afterwards, at MIT they would say When push comes to shove, Sidney is wrong. ...

Genomics is in the details now. Lots of work to be done, but conceptually it's clear what to do. I wouldn't say that about AGI. There are still important conceptual breakthroughs that need to be made.

The Dunster House courtyard, overlooking the Charles.

We used to live here, can you let us in to look around?

I remember it all -- the long meals, the tutors, the students, the concerts in the library. Yo Yo Ma and Owen playing together.

A special time, at least for us. But long vanished except in memory.

Wheeler used to say that the past only exists as memory records.

Not very covariant! Why not a single four-manifold that exists all at once?

The Ritz-Carlton.

Flying private is like crack. Once you do it, you can't go back...

It's not like that. They never give you a number. They just tell you that the field house is undergoing a renovation and there's a naming opportunity. Then your kid is on the right list. They've been doing this for a hundred years...

Card had to do the analysis that way. Harvard was paying him...

I went to the session on VC for newbies. Now I realize "valuation" is just BS... Now you see how it really works...

Then Bobby says "What's an LP? I wanna be an LP because you gotta keep them happy."

Let me guess, you want a dataset with a million genomes and FICO scores?

I've helped US companies come to China for 20+ years. At first it was rough. Now if I'm back in the states for a while and return, Shenzhen seems like the Future. The dynamism is here.

To most of Eurasia it just looks like two competing hegemons. Both systems have their pluses and minuses, but it's not an existential problem...

Sure, Huawei is a big threat because they won't put in backdoors for the NSA. Who was tapping Merkel's cellphone? It was us...

Humans are just smart enough to create an AGI, but perhaps not smart enough to create a safe one.

Maybe we should make humans smarter first, so there is a better chance that our successors will look fondly on us. Genetically engineered super-geniuses might have a better chance at implementing Asimov's Laws of Robotics.  

The Economist Babbage podcast: The future of genetic engineering

Babbage Podcast by The Economist. (21 minutes. Sorry, can't embed the player.)

Economist Senior Editor Kenneth Cukier takes a look at what it means to be human. He speaks to leading scientists, doctors and philosophers to ask if ethics and regulations are able to keep up with the technology of genetic engineering.

Great interviews with so many people I admire: @EricTopol and @JamieMetzl and @Ella_Road and @GulzaarBarn and @hsu_steve! Produced by the brilliant @simonjarvis1981 https://t.co/mh8XTGJ4UO

— Kenneth Cukier (@kncukier) April 24, 2019

Backpropagation in the Brain? Part 2

If I understand correctly the issue is how to realize something like backprop when most of the information flow is feed-forward (as in real neurons). How do you transport weights "non-locally"? The L2 optimization studied here doesn't actually transport weights. Rather, the optimized solution realizes the same set of weights in two places...

See earlier post Backpropagation in the Brain? Thanks for STS for the reference.

Center for Brains, Minds and Machines (CBMM)
Published on Apr 3, 2019
Speaker: Dr. Jon Bloom, Broad Institute

Abstract: When trained to minimize reconstruction error, a linear autoencoder (LAE) learns the subspace spanned by the top principal directions but cannot learn the principal directions themselves. In this talk, I'll explain how this observation became the focus of a project on representation learning of neurons using single-cell RNA data. I'll then share how this focus led us to a satisfying conversation between numerical analysis, algebraic topology, random matrix theory, deep learning, and computational neuroscience. We'll see that an L2-regularized LAE learns the principal directions as the left singular vectors of the decoder, providing a simple and scalable PCA algorithm related to Oja's rule. We'll use the lens of Morse theory to smoothly parameterize all LAE critical manifolds and the gradient trajectories between them; and see how algebra and probability theory provide principled foundations for ensemble learning in deep networks, while suggesting new algorithms. Finally, we'll come full circle to neuroscience via the "weight transport problem" (Grossberg 1987), proving that L2-regularized LAEs are symmetric at all critical points. This theorem provides local learning rules by which maximizing information flow and minimizing energy expenditure give rise to less-biologically-implausible analogues of backproprogation, which we are excited to explore in vivo and in silico. Joint learning with Daniel Kunin, Aleksandrina Goeva, and Cotton Seed.

Chess with the human body

Keenan Cornelius is a world class blackbelt in jiujitsu. His skill level is sufficiently high that he can verbalize his tactics in real time as he rolls with lower belts.

"He's trying to bump me off mount, so I'm shifting my weight to my left knee to keep my weight off of his hips. But once he gives me an opening I'm going to slide under his lapel to finalize the choke..." Sometimes he is several moves ahead of his opponent!

Roy Dean does something similar, but with narration added in post-production, here. Roy's video is more precise (for one thing, he's not out of breath) but what Keenan is doing is super impressive :-)

Manifold Episode #8 -- Sabine Hossenfelder on the Crisis in Particle Physics and Against the Next Big Collider

Manifold Show Page    YouTube Channel

Hossenfelder is a Research Associate at the Frankfurt Institute of Advanced Studies. Her research areas include particle physics and quantum gravity. She discusses the current state of theoretical physics, and her recent book Lost in Math: How Beauty Leads Physics Astray.

The Uncertain Future of Particle Physics (NYT editorial)

Lost in Math: How Beauty Leads Physics Astray

Transcript

man·i·fold /ˈmanəˌfōld/ many and various.

In mathematics, a manifold is a topological space that locally resembles Euclidean space near each point.

Steve Hsu and Corey Washington have been friends for almost 30 years, and between them hold PhDs in Neuroscience, Philosophy, and Theoretical Physics. Join them for wide ranging and unfiltered conversations with leading writers, scientists, technologists, academics, entrepreneurs, investors, and more.

Steve 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.

Corey Washington is Director of Analytics in the Office of Research and Innovation at Michigan State University. He was educated at Amherst College and MIT before receiving a PhD in Philosophy from Stanford and a PhD in a Neuroscience from Columbia. He held faculty positions at the University Washington and the University of Maryland. Prior to MSU, Corey worked as a biotech consultant and is founder of a medical diagnostics startup.

Genomic prediction of student flow through high school math curriculum

Compute polygenic EA scores for 3000 US high school students of European ancestry. Track individual progress from 9th to 12th grade, focusing on mathematics courses. The students are out-of-sample: not used in training of predictor. In fact, a big portion (over half?) of individuals used in predictor training are not even from the US -- they are from the UK/EU.

Results: predictor captures about as much variance as family background (SES = Social Economic Status). Students with lower polygenic scores are less likely to take advanced math (e.g., Geometry and beyond).

Typical education paths of individuals with, e.g., bottom few percentile polygenic score are radically different from those in the top percentiles, even after controlling for SES. For example, consider only rich kids or kids at superior schools and compare educational trajectory vs polygenic score. Looks like (bottom figure) odds ratio for taking Geometry in 9th grade is about 4-6x higher for top polygenic score kids.

Genetic Associations with Mathematics Tracking and Persistence in Secondary School

K. Paige Harden and Benjamin W. Domingue, et al.

...we address this question using student polygenic scores, which are DNA-based indicators of propensity to succeed in education8. We integrated genetic and official school transcript data from over 3,000 European-ancestry students from U.S. high schools. We used polygenic scores as a molecular tracer to understand how the flow of students through the high school math pipeline differs in socioeconomically advantaged versus disadvantaged schools. Students with higher education polygenic scores were tracked to more advanced math already at the beginning of high school and persisted in math for more years...

...including family-SES and school-SES as covariates attenuated the association between the education-PGS and mathematics tracking in the 9th-grade only by about 20% (attenuated from b = 0.583, SE = .034, to b = 0.461, SE = .036, p < 2 × 10-16, Supplementary Table S3). Note that the association with genetics was roughly comparable in magnitude to the association with familySES...

See also Game Over: Genomic Prediction of Social Mobility (some overlap in authors with the new paper).

A talk by the first author:

Manifold Episode #7 -- David Skrbina on Ted Kaczynski, Technological Slavery, and the Future of Our Species

Manifold Show Page    YouTube Channel

David Skrbina is a philosopher at the University of Michigan. He and Ted Kaczynski published the book Technological Slavery, which elaborates on the Unabomber manifesto and contains about 100 pages of correspondence between the two which took place over almost a decade. Skrbina discusses his and Kaczynski's views on deep problems of technological society, and whether violent opposition to it is justified.

David Skrbina's Featured Publications
https://www.davidskrbina.com/

Photos of Ted Kacynski
http://murderpedia.org/male.K/k/kaczynski-photos-3.htm

David Skrbina, Pen Pal of the Unabomber, on Ted Kaczynski's Philosophy
https://www.youtube.com/watch?v=4dQd7d3XxkA

Tribe by Sebastian Junger
http://www.sebastianjunger.com/tribe-by-sebastian-junger

Joe Rogan Experience #975 - Sebastian Junger
https://www.youtube.com/watch?v=W4KiOECVGLg


man·i·fold /ˈmanəˌfōld/ many and various.

In mathematics, a manifold is a topological space that locally resembles Euclidean space near each point.

Steve Hsu and Corey Washington have been friends for almost 30 years, and between them hold PhDs in Neuroscience, Philosophy, and Theoretical Physics. Join them for wide ranging and unfiltered conversations with leading writers, scientists, technologists, academics, entrepreneurs, investors, and more.

Steve 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.

Corey Washington is Director of Analytics in the Office of Research and Innovation at Michigan State University. He was educated at Amherst College and MIT before receiving a PhD in Philosophy from Stanford and a PhD in a Neuroscience from Columbia. He held faculty positions at the University Washington and the University of Maryland. Prior to MSU, Corey worked as a biotech consultant and is founder of a medical diagnostics startup.

Big Chickens (Economist video)

Big chickens! Modern breeds are about four times larger than those raised in the 1950s. I wonder how many population SDs of change that represents? About 40?

Interview with Genetic Engineering & Biotechnology News

Polygenic Risk Scores and Genomic Prediction: Q&A with Stephen Hsu

In this exclusive interview, Stephen Hsu (Michigan State University and co-founder of Genomic Prediction) discusses the application of polygenic risk scores (PRS) for complex traits in pre-implantation genetic screening. Interview conducted by Julianna LeMieux (GEN).

GEN: What motivated you to start Genomic Prediction?

STEVE HSU: It has a very long history. Laurent Tellier is the CEO and we’ve known each other since 2010. We’d been working on the background science of how to use machine learning to look at lots of genomes and then learn to predict phenotypes from that information.

We were betting on the continuing decline in cost for genotyping, and it paid off because now there are millions of genotypes available for analysis. We’d always thought that one of the best and earliest applications of this would be embryo selection because we can help families have a healthy child.

GEN: How did you first get interested in genomics in general, given your educational background in physics?

HSU: I was interested in genetics and evolution, molecular biology, since I was a kid. I grew up in the ’70s and ’80s and already at that time there was a lot of attention focused on the molecular biology revolution, recombinant DNA. We were always told physics is a very mature subject and biology is the subject of the future and it will just explode eventually with these new molecular techniques.

When I got to college and I took some classes in molecular biology, I realized that a lot of the deep questions—like how do you actually decipher a genome and figure out which pieces of the genetic code have direct consequences in phenotypes or complex traits?—would not be answerable with the technology of that time. So I put it aside and did theoretical physics, but got re-interested around the time I met Laurent. I became aware of the super exponential cost curve for genotyping, sequencing in particular. I realized, if this continues for another ten years or so, we’re going to be able to answer all these interesting questions I’ve been thinking about since I was a kid.

...

GEN: How do you generate a polygenic risk score for different diseases? Of the eight diseases listed on the Genomic Prediction website, are those diseases that your lab has basically generated that data for?

HSU: Many of them were produced by my research group, but the current best-performing breast cancer predictor actually comes from a large international consortium that works on breast cancer…

We use the same data that people would use for GWAS [genome-wide association studies]. For example, we might have 200,000 controls and 20 or 30,000 cases of people in their 50s and 60s who are old enough that they would have been diagnosed for diabetes (or something) if they had it. The algorithm knows which ones are the cases and which are the controls, and it also has about 1 million SNPs from each person, typically what you get from an Affymetrix or an Illumina array.

It is a learning algorithm that tries to tune its internal model so that it best predicts whether someone is actually a case or a control. There’s a bunch of fancy math involved in this—a high-dimensional optimization. You are basically finding the model that best predicts the data.

It is different from GWAS because GWAS is very simple—you look at a particular gene or SNP and you say is there statistical evidence that this particular SNP is associated with whether you have diabetes? You get a yes/no answer. If the P value is significant enough then you say we found a hit.

That problem mathematically is very different from the problem we solved. We are actually doing an optimization in a million-dimensional space to find simultaneously all the SNPs that should be activated in our predictor. This is all in the technical weeds but it is just different mathematics…

We think we can actually predict risk by doing this high-dimensional optimization. Initially, people just thought we were crazy. We wrote theoretical papers predicting how much data would you need to be able to accurately predict height or something like that. ... [ AND THOSE PREDICTIONS WERE CORRECT ... ]

James Salter: A Sport and a Pastime (documentary)

The documentary James Salter: A Sport and a Pastime is now available free to Amazon Prime subscribers.

This 54-minute documentary traces the writer James Salter's lifelong love affair with France, unforgettably expressed in his 1967 masterpiece, A Sport and a Pastime. The film captures the great purity of Salter's prose and the essence of his power to evoke the erotic. Salter's own reflections on his writing and life offer rich insights for reader and writer alike.

If you enjoy it you may also like this history of The Paris Review, also free on Prime video.

See also James Salter, James Salter (1925-2015), and The Life of this World.

The excerpt below is from his 1993 interview for Paris Review's The Art of Fiction.

INTERVIEWER

When A Sport and a Pastime came out you were hailed as “celebrating the rites of erotic innovation” and yet also criticized for portraying such “vigorous ‘love’ scenes.” What did you think of all that?

SALTER

The eroticism is the heart and substance of the book. That seems obvious. I meant it to be, to use a word of Lorca’s, “lubricious” but pure, to describe things that were unspeakable in one sense, but at the same time, irresistible. Having traveled, I also was aware that voyages are, in a large sense, a search for, a journey toward love. A voyage without that is rather sterile. Perhaps this is a masculine view, but I think not entirely. The idea is of a life that combines sex and architecture—I suppose that’s what the book is, but that doesn’t explain it. It’s more or less a guide to what life might be, an ideal.

INTERVIEWER

People seem to have different opinions of what the book is about.

SALTER

I listen occasionally to people explaining the book to me. Every few years there’s an inquiry from a producer who would like to make a movie of it. I’ve turned the offers down because it seems to me ridiculous to try and film it. To my mind the book is obvious. I don’t see the ambiguity, but there again, you don’t know precisely what you are writing. Besides, how can you explain your own work? It’s vanity. To me it seems you can understand the book, if there’s been any doubt, by reading the final paragraph:

As for Anne-Marie, she lives in Troyes now, or did. She is married. I suppose there are children. They walk together on Sundays, the sunlight falling upon them. They visit friends, talk, go home in the evening, deep in the life we all agree is so greatly to be desired.

That paragraph, the final sentence, is written in irony, but perhaps not read that way. If you don’t see the irony, then the book is naturally going to have a different meaning for you.

INTERVIEWER

It has been said that Dean’s desire for Anne-Marie is also a desire for the “real” France. It’s a linked passion.

SALTER

France is beautiful, but his desire is definitely for the girl herself. Of course she is an embodiment. Even when you recognize what she is, she evokes things. But she would be desirable to him even if she didn’t.

INTERVIEWER

There’s a postmodern side to the book. The narrator indicates that he’s inventing Dean and Anne-Marie out of his own inadequacies.

SALTER

That’s just camouflage.

INTERVIEWER

What do you mean?

SALTER

This book would have been difficult to write in the first person—that is to say if it were Dean’s voice. It would be quite interesting written from Anne-Marie’s voice, but I wouldn’t know how to attempt that. On the other hand, if it were in the third person, the historic third, so to speak, it would be a little disturbing because of the explicitness, the sexual descriptions. The question was how to paint this, more or less. I don’t recall how it came to me, but the idea of having a third person describe it, somebody who is really not an important part of the book but merely serving as an intermediary between the book and the reader, was perhaps the thing that was going to make it possible; and consequently, I did that. I don’t know who this narrator is. You could say it’s me; well, possibly. But truly, there is no such person. He’s a device. He’s like the figure in black that moves the furniture in a play, so to speak, essential, but not part of the action.

...

INTERVIEWER

What do you think is the ultimate impulse to write?

SALTER

To write? Because all this is going to vanish. The only thing left will be the prose and poems, the books, what is written down. Man was very fortunate to have invented the book. Without it the past would completely vanish, and we would be left with nothing, we would be naked on earth.

MSU Research Update (video)

Remarks at a recent Michigan State University leadership meeting. MSU is currently #1 in the US in annual Department of Energy (DOE) and DOE + NSF (National Science Foundation) funding. There are ~30 institutions in the US with larger annual research expenditures than MSU, however in all but a few cases (e.g., MIT and UC Berkeley) this is due to a large medical research complex and significant NIH (National Institutes of Health) funding. I discuss MSU's strategy in this direction: a new biomedical research complex and new $450M McLaren hospital on our campus.

#RussiaHoax is the new WMD

No, there was never any Russian Collusion. But there was illegal spying on the political opposition by the Obama intelligence services. With the Mueller investigation now out of the way, I hope to see important, previously hidden, information declassified in the near future:

1. Multiple FISA applications to spy on anyone within "two hops" of Carter Page (i.e., the entire Trump campaign and transition team)

2. Originating Electronic Communication (EC) from CIA Director John Brennan to FBI Director James Comey. The two-page EC gives Brennan's justification for operation “Crossfire Hurricane” to investigate the Trump campaign (July 31, 2016).

3. Sworn testimony by Strzok, Ohr, Page, McCabe, etc. etc.

See, e.g., Spygate in 20 Minutes and Deep State Update.

If you took the #RussiaHoax seriously, and have any pretensions to rationality, then you must update your priors concerning the reliability of the media, and of our security and intelligence services.

Below, an excerpt from Matt Taibbi's forthcoming book Hate Inc.

It's official: Russiagate is this generation's WMD

The Iraq war faceplant damaged the reputation of the press. Russiagate just destroyed it

Nobody wants to hear this, but news that Special Prosecutor Robert Mueller is headed home without issuing new charges is a death-blow for the reputation of the American news media.

As has long been rumored, the former FBI chief’s independent probe will result in multiple indictments and convictions, but no “presidency-wrecking” conspiracy charges, or anything that would meet the layman’s definition of “collusion” with Russia.

With the caveat that even this news might somehow turn out to be botched, the key detail in the many stories about the end of the Mueller investigation was best expressed by the New York Times:

A senior Justice Department official said that Mr. Mueller would not recommend new indictments.

The Times tried to soften the emotional blow for the millions of Americans trained in these years to place hopes for the overturn of the Trump presidency in Mueller. Nobody even pretended it was supposed to be a fact-finding mission, instead of an act of faith.

The Special Prosecutor literally became a religious figure during the last few years, with votive candles sold in his image and Saturday Night Live cast members singing “All I Want for Christmas is You” to him featuring the rhymey line: “Mueller please come through, because the only option is a coup.”

The Times story today tried to preserve Santa Mueller’s reputation, noting Trump’s Attorney General William Barr’s reaction was an “endorsement” of the fineness of Mueller’s work:

In an apparent endorsement of an investigation that Mr. Trump has relentlessly attacked as a “witch hunt,” Mr. Barr said Justice Department officials never had to intervene to keep Mr. Mueller from taking an inappropriate or unwarranted step.

Mueller, in other words, never stepped out of the bounds of his job description. But could the same be said for the news media?

For those anxious to keep the dream alive, the Times published its usual graphic of Trump-Russia “contacts,” inviting readers to keep making connections. But in a separate piece by Peter Baker, the paper noted the Mueller news had dire consequences for the press:

It will be a reckoning for President Trump, to be sure, but also for Robert S. Mueller III, the special counsel, for Congress, for Democrats, for Republicans, for the news media and, yes, for the system as a whole…

This is a damning page one admission by the Times. Despite the connect-the-dots graphic in its other story, and despite the astonishing, emotion-laden editorial the paper also ran suggesting “We don’t need to read the Mueller report” because we know Trump is guilty, Baker at least began the work of preparing Times readers for a hard question: “Have journalists connected too many dots that do not really add up?”

The paper was signaling it understood there would now be questions about whether or not news outlets like itself made galactic errors by betting heavily on a new, politicized approach, trying to be true to “history’s judgment” on top of the hard-enough job of just being true. Worse, in a brutal irony everyone should have seen coming, the press has now handed Trump the mother of campaign issues heading into 2020.

Here is the Wall Street Journal:

WSJ: Mueller Is Done. Now Probe the Real Scandal

Attorney General William Barr has reported to Congress that special counsel Robert Mueller has cleared President Trump and his campaign team of claims of conspiring with Russia during the 2016 election. This is more than an exoneration. It’s a searing indictment of the Federal Bureau of Investigation, as well as a reminder of the need to know the story behind the bureau’s corrosive investigation.

Mr. Mueller’s report likely doesn’t put it that way, but it’s the logical conclusion of his no-collusion finding. The FBI unleashed its powers on a candidate for the office of the U.S. presidency, an astonishing first. It did so on the incredible grounds that the campaign had conspired to aid a foreign government. And it used the most aggressive tools in its arsenal—surveillance of U.S. citizens, secret subpoenas of phone records and documents, even human informants.

... None of this should ever have happened absent highly compelling evidence—from the start—of wrongdoing. Yet from what we know, the FBI operated on the basis of an overheard conversation of third-tier campaign aide George Papadopoulos, as well as a wild “dossier” financed by the rival presidential campaign. Mr. Mueller’s no-collusion finding amounts to a judgment that there never was any evidence. The Papadopoulos claim was thin, the dossier a fabrication.

Which is all the more reason Americans now deserve a full accounting of the missteps of former FBI Director James Comey and his team—in part so that this never happens again. That includes the following: What “evidence” did the FBI have in totality? What efforts did the bureau take to verify it? Did it corroborate anything before launching its probe? What role did political players play? How aware was the FBI that it was being gulled into a dirty-trick operation, and if so, how did it justify proceeding? How intrusive were the FBI methods? And who was harmed?

...

Manifold Episode #6: John Hawks on Human Evolution, Ancient DNA, and Big Labs Devouring Fossils

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John Hawks on Human Evolution, Ancient DNA, and Big Labs Devouring Fossils – Episode #6

Hawks is the Vilas-Borghesi Distinguished Achievement Professor of Anthropology at the University of Wisconsin – Madison. He is an anthropologist and studies the bones and genes of ancient humans. He’s worked on almost every part of our evolutionary story, from the very origin of our lineage among the apes, to the last 10,000 years of our history.

Links:

John Hawks Weblog

Ghosts and Hybrids: How ancient DNA and new fossils are changing human origins (Research Presentation)

Transcript

man·i·fold /ˈmanəˌfōld/ many and various.

In mathematics, a manifold is a topological space that locally resembles Euclidean space near each point.

Steve Hsu and Corey Washington have been friends for almost 30 years, and between them hold PhDs in Neuroscience, Philosophy, and Theoretical Physics. Join them for wide ranging and unfiltered conversations with leading writers, scientists, technologists, academics, entrepreneurs, investors, and more.

Steve 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.

Corey Washington is Director of Analytics in the Office of Research and Innovation at Michigan State University. He was educated at Amherst College and MIT before receiving a PhD in Philosophy from Stanford and a PhD in a Neuroscience from Columbia. He held faculty positions at the University Washington and the University of Maryland. Prior to MSU, Corey worked as a biotech consultant and is founder of a medical diagnostics startup.

Annals of Psychometry: 35 years of talent selection

David Lubinski kindly shared the recent paper linked below. He and I will both be at ISIR 2019, the annual meeting of the International Society for Intelligence Research.

Psychological Constellations Assessed at Age 13 Predict Distinct Forms of Eminence 35 Years Later (Psychological Science 2019, Vol. 30(3) 444–454).

The paper studies two populations:

1. 13 year olds identified through talented and gifted programs, all of whom scored in the top 1% in at least one of Mathematical or Verbal ability (based on SAT score; some scored at the 1 in 10k level). They were also assessed using a preference inventory (SOV = Study of Values). About 10% of this cohort of 677 were identified 35 years later as having achieved "eminence" in their careers -- e.g., full professor at R1 university, senior executive status, ...

2. Exceptional STEM graduate students at top 15 PhD programs, evaluated using GRE and SOV. If I'm not mistaken many or all of these students were NSF Graduate Fellows. About 20% of this population of 605 had achieved STEM eminence 25 years later.

I would estimate that only about one in a thousand individuals drawn randomly from the general population attains eminence as defined in the paper. Thus, the talent selection used to form cohorts 1&2 (e.g., SAT administered at age 13) produced success rates as much as 100 times higher than in the base population.

See related posts: 1 2 3

Psychological Constellations Assessed at Age 13 Predict Distinct Forms of Eminence 35 Years Later

Psychological Science 2019, Vol. 30(3) 444–454

Brian O. Bernstein, David Lubinski, and Camilla P. Benbow
Department of Psychology & Human Development, Vanderbilt University

Abstract
This investigation examined whether math/scientific and verbal/humanistic ability and preference constellations, developed on intellectually talented 13-year-olds to predict their educational outcomes at age 23, continue to maintain their longitudinal potency by distinguishing distinct forms of eminence 35 years later. Eminent individuals were defined as those who, by age 50, had accomplished something rare: creative and highly impactful careers (e.g., full professors at research-intensive universities, Fortune 500 executives, distinguished judges and lawyers, leaders in biomedicine, award-winning journalists and writers). Study 1 consisted of 677 intellectually precocious youths, assessed at age 13, whose leadership and creative accomplishments were assessed 35 years later. Study 2 constituted a constructive replication—an analysis of 605 top science, technology, engineering, and math (STEM) graduate students, assessed on the same predictor constructs early in graduate school and assessed again 25 years later. In both samples, the same ability and preference parameter values, which defined math/scientific versus verbal/humanistic constellations, discriminated participants who ultimately achieved distinct forms of eminence from their peers pursuing other life endeavors.

Note that even within both cohorts SAT / GRE were useful in predicting achievement outcomes. Click figures below for larger versions.

Othram: the future of DNA forensics

I've blogged frequently about the impact of the genomic revolution on embryo selection in IVF and precision health (complex disease risk prediction).

DNA forensics -- the use of DNA for identification of criminals, victims, military remains, etc. -- will also be transformed by inexpensive genotyping and powerful informatics.

The existing FBI standard (CODIS) for DNA identification uses only 20 markers (STRs -- previously only 13 loci were used!). By contrast, genome wide sequencing can reliably call millions of genetic variants. For the first time, the cost curves for these two methods have crossed: modern sequencing costs no more than extracting CODIS markers using the now ~30 year old technology.

What can you do with millions of genetic markers?

1. Determine relatedness of two individuals with high precision. This allows detectives to immediately identify a relative (ranging from distant cousin to sibling or parent) of the source of the DNA sample, simply by scanning through large DNA databases. In many cases this narrows the pool of suspects to ~100 or fewer individuals. With only 20 CODIS markers this is not possible. Some notorious cold cases have already been solved using this method, with more examples every few weeks.

2. Phenotype and Ancestry reports: in addition to ethnicity, we can now predict cosmetic traits such as hair color, eye color, skin tone (i.e., light to dark), baldness, height, BMI, and bodyfat percentage. (The last two are the least accurate, although outlier ares still identifiable.) Again, not remotely possible using CODIS markers.

I'm a co-founder of Othram, a startup providing 1&2 above to law enforcement, the military, and other customers.

Recently I visited Othram's brand new 4000 square foot lab which will be entirely dedicated to forensic applications of advanced sequencing and genomic prediction. The lab has specialized air handling and sample processing infrastructure, and will soon be home to an Illumina NovaSeq.

On the legal status of large DNA databases, such as those of 23andMe and Ancestry: these firms have genotyped 5M and 10M individuals, respectively, with both numbers set to double in the next year. These datasets are large enough to, e.g., immediately return a first- or second-cousin match for most searches on DNA from someone of primarily European heritage. With such resources the majority of crimes with DNA evidence become easy to solve. The Genomic Panopticon is nearly a reality.

Both 23andMe and Ancestry have, on grounds of customer privacy, resisted law enforcement requests to search for matches to forensic DNA. However, one of their smaller competitors, FamilyTreeDNA, revealed that it is routinely collaborating with FBI. I do not believe that 23andMe or Ancestry can resist a court order if vigorously pursued. The situation is similar to that of ISPs and web email providers in the early days of the internet. They also resisted cooperation with law enforcement on privacy grounds, but in the end had to capitulate. All such firms today have compliance departments that process law enforcement queries on a routine basis. I would be very surprised if 23andMe and Ancestry don't end up with a similar accommodation, despite their current posture.

Human Nature (film) at SXSW

I'll be at SXSW for the premiere of this documentary on CRISPR and genetic engineering. First screening is March 10 (Sun) at the Atom Theatre; I'll participate in a Q&A afterwards.

There is a launch party that evening for which I have an extra ticket. Taking bids from interested parties 8-)

Human Nature SXSW Schedule.

Manifold Episode #5: Kaiser Kuo of Sinica on Modern China and US-China relations

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Kaiser Kuo of Sinica on Modern China and US-China relations -- Episode #5

Kaiser Kuo is a host and co-founder of Sinica, a current affairs podcast originally based in Beijing. Sinica guests include prominent journalists, academics, and policy makers who participate in uncensored discussions about Chinese political, economic, and cultural affairs.

Tags: China, Globalization, US-China Relations, East Asia, Xi Jinping

Links:
Sinica Podcast
Social science study of Xi Jinping's anticorruption campaign

man·i·fold /ˈmanəˌfōld/ many and various.

In mathematics, a manifold is a topological space that locally resembles Euclidean space near each point.

Steve Hsu and Corey Washington have been friends for almost 30 years, and between them hold PhDs in Neuroscience, Philosophy, and Theoretical Physics. Join them for wide ranging and unfiltered conversations with leading writers, scientists, technologists, academics, entrepreneurs, investors, and more.

Steve 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.

Corey Washington is Director of Analytics in the Office of Research and Innovation at Michigan State University. He was educated at Amherst College and MIT before receiving a PhD in Philosophy from Stanford and a PhD in a Neuroscience from Columbia. He held faculty positions at the University Washington and the University of Maryland. Prior to MSU, Corey worked as a biotech consultant and is founder of a medical diagnostics startup.

Kip Thorne on Caltech and Black Holes

See LIGO Detects Gravity Waves and The Christy Gadget.

Techno-pessimists should note that detecting gravity waves is much, much harder than landing on the moon. LIGO measured a displacement 1/1000 of a neutron radius, in a noisy terrestrial background, accounting even for quantum noise.

https://www.ligo.caltech.edu/: 9/14/15 detection of BH-BH (~ 30 solar masses) merger at distance 1.3 Gy. The energy in the gravitational wave signal was ~3 solar masses!

Here is the paper http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.116.061102

When I was an undergraduate, I toured the early LIGO prototype, which was using little car shaped rubber erasers as shock absorbers. Technology has improved since then, and the real device is much bigger.

As Kip makes clear in his talk, the detection of gravity waves was a ~50 year project involving large numbers of very smart physicists and engineers, with the sustained support of some of the most impressive scientific institutions in the world (Caltech, MIT, NSF, Moscow State University). Entirely new technologies and areas of theoretical and experimental physics had to be developed to bring this dream to fruition.

I learned general relativity from Kip when I was at Caltech. The photo below was taken in Eugene, Oregon. Physics as a Strange Attractor.

Mootoo Kimura: "do something in genetics ... like theoretical physics"

I have written previously about James Crow and R.A. Fisher. Now to Mootoo Kimura.

Wikipedia: Motoo Kimura (木村 資生 Kimura Motō) (November 13, 1924 – November 13, 1994) was a Japanese biologist best known for introducing the neutral theory of molecular evolution in 1968.[2][3] He became one of the most influential theoretical population geneticists. He is remembered in genetics for his innovative use of diffusion equations to calculate the probability of fixation of beneficial, deleterious, or neutral alleles.[4] Combining theoretical population genetics with molecular evolution data, he also developed the neutral theory of molecular evolution in which genetic drift is the main force changing allele frequencies.[5] James F. Crow, himself a renowned population geneticist, considered Kimura to be one of the two greatest evolutionary geneticists, along with Gustave Malécot, after the great trio of the modern synthesis, Ronald Fisher, J. B. S. Haldane and Sewall Wright.[6]

What is the fate of the neutral theory? I suppose the fundamental question is what fraction of molecular changes (mutations) have significant phenotypic effects (i.e., effects on fitness). If the fraction is very small then one could, at the molecular level, adopt the neutral theory as a first approximation. (At the level of phenotypes, I can't see drift dominating unless the effective population size is very small.) Still unsettled?

Wikipedia: ... According to Kimura, the theory applies only for evolution at the molecular level, and phenotypic evolution is controlled by natural selection, as postulated by Charles Darwin. The proposal of the neutral theory was followed by an extensive "neutralist-selectionist" controversy over the interpretation of patterns of molecular divergence and polymorphism, peaking in the 1970s and 1980s. Since then, much evidence has been found for selection at molecular level.

The article Kimura & Crow: Infinite alleles, appeared in Genes to Genomes, the blog of the Genetics Society of America. (See also here ;-)

... Kimura was originally trained as a plant cytologist; he had been fascinated by plants since boyhood, and cytogenetics had been the hot field in Japan at the time. But his interest in chromosomes waned as he began yearning to “do something in genetics like what the theoretical physicists were doing in physics.” This ambition was buoyed by Kimura’s regular, hunger-fueled excursions to the house of his cousin-in-law Matsuhei Tamura, a mathematical physicist. Kimura visited almost every Sunday, partly because he was intensely interested in the quantum physicist’s stories, and partly because he needed to fill his belly during the post-war food shortages.

Kimura joined the lab of Japan’s most famous cytogeneticist, Hitoshi Kihara, who recognized the quiet young man’s talent for theory and left him mostly to his own devices. So, while his friends picked apart the chromosomes of wheat and watermelon, Kimura indulged in the more abstract pleasures of population genetics. He would travel the full-day’s train journey to Tokyo to copy out by hand the papers of Sewall Wright, one of the founders of the field. Determined to understand Wright’s papers, Kimura haunted the math department, attending classes, asking questions, learning from books, until he gradually gained the sophistication to follow Wright’s arguments, and eventually, critique and extend them.

But this new intellectual world was isolating. Kimura’s lab mates took a dim view of his absorption in mathematics and the situation only worsened when he took a job at the newly founded National Institute of Genetics. The facility was housed in the makeshift and uncomfortable office of a wartime aircraft factory. There was no library, no access to foreign journals, and no colleague who could understand his work. The only geneticist there who saw its value was zoologist Taku Komai, who had studied in the fly lab of genetics superstar T. H. Morgan in the United States. Komai recommended Kimura extend his training overseas and introduced him to an American scientist working for the Atomic Bomb Casualty Commission. Before long Kimura had a scholarship, a Fulbright travel award, and a ticket to Seattle.

Once they met, Crow immediately took Kimura under his wing. He invited Kimura over for dinner to meet his idol Sewall Wright. Crow probed Kimura about the paper he had just written on the Pacific voyage and was impressed that it neatly reduced a formidably complex equation down to a simple relationship used by physicists to describe heat conduction. He encouraged Kimura to submit the paper to GENETICS, where Crow was an editor (the paper was later effusively and uncharacteristically praised by its reviewer, Wright).

Always an Eccentric? A Brief Biography of Mootoo Kimura:

To Predict the Future it is useful to understand the Past

Dominic Cummings on genomics, healthcare, and innovation in the UK:

Britain could contribute huge value to the world by leveraging existing assets, including scientific talent and how the NHS is structured, to push the frontiers of a rapidly evolving scientific field — genomic prediction — that is revolutionising healthcare in ways that give Britain some natural advantages over Europe and America. We should plan for free universal ‘SNP’ genetic sequencing as part of a shift to genuinely preventive medicine — a shift that will lessen suffering, save money, help British advanced technology companies in genomics and data science/AI, make Britain more attractive for scientists and global investment, and extend human knowledge in a crucial field to the benefit of the whole world.

Those that are interested in the history of science, or in understanding its future, would do well to look at what was being written 10 or so years ago about genomics of complex traits. Whose predictions came true? Whose were dead wrong?

Dominic Cummings: ... Hsu predicted that very large samples of DNA would allow scientists over the next few years to start identifying the actual genes responsible for complex traits, such as diseases and intelligence, and make meaningful predictions about the fate of individuals. Hsu gave estimates of the sample sizes that would be needed. His 2011 talk contains some of these predictions and also provides a physicist’s explanation of ‘what is IQ measuring’. As he said at Google in 2011, the technology is ‘right on the cusp of being able to answer fundamental questions’ and ‘if in ten years we all meet again in this room there’s a very good chance that some of the key questions we’ll know the answers to’. His 2014 paper explains the science in detail. If you spend a little time looking at this, you will know more than 99% of high status economists gabbling on TV about ‘social mobility’ saying things like ‘doing well on IQ tests just proves you can do IQ tests’.

In 2013, the world of Westminster thought this all sounded like science fiction and many MP said I sounded like ‘a mad scientist’. Hsu’s predictions have come true and just five years later this is no longer ‘science fiction’. (Also NB. Hsu’s blog was one of the very few places where you would have seen discussion of CDOs and the 2008 financial crash long BEFORE it happened. I have followed his blog since ~2004 and this from 2005, two years before the crash started, was the first time I read about things like ‘synthetic CDOs’: ‘we have yet another ill-understood casino running, with trillions of dollars in play’. The quant-physics network had much better insight into the dynamics behind the 2008 Crash than high status mainstream economists like Larry Summers responsible for regulation.)

His group and others have applied machine learning to very large genetic samples and built predictors of complex traits. Complex traits like general intelligence and most diseases are ‘polygenic’ — they depend on many genes each of which contributes a little (unlike diseases caused by a single gene).

‘There are now ~20 disease conditions for which we can identify, e.g, the top 1% outliers with 5-10x normal risk for the disease. The papers reporting these results have almost all appeared within the last year or so.’

(One might ask what fraction of PhD economists knew in 2008 what a CDO was or how they were constructed or priced... I was there, and the answer is: very, very few.)

As the deep learning pioneer Jurgen Schmidhuber has emphasized,

... machine learning is itself based on accurate credit assignment. Good learning algorithms assign higher weights to features or signals that correctly predict outcomes, and lower weights to those that are not predictive. His analogy between science itself and machine learning is often lost on critics.

Therefore, to decide how to weight current claims about the future (such as: accurate genomic prediction of many disease risks and complex traits, even including cognitive ability, are right around the corner), one should carefully study the track record of those offering predictions.


Google TechTalk 2011:




Allen Institute meeting on Genetics of Complex Traits (2018):