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Physicist, Startup Founder, Blogger, Dad

Friday, January 19, 2018

Allen Institute meeting on Genetics of Complex Traits

You can probably tell by all the photos below that I am in love with their new building :-)

I was a participant in this event: What Makes Us Human? The Genetics of Complex Traits (Allen Frontiers Group), including in a small second day workshop with just the speakers and the AI leadership. This workshop will, I hope, result in some interesting new initiatives in complex trait genomics!

I'd like to thank the Allen Institute organizers for making this such a pleasant and productive 2 days. I learned some incredible things from the other speakers and I recommend all of their talks (should appear on AI site eventually).

My talk:




Action photos:








Working hard on day 2 in the little conference room :-)

Tuesday, January 16, 2018

The Jiujitsu Philosopher: John Danaher



John Danaher is one of the deepest thinkers in combat sports, MMA, and jiujitsu. He has coached a number of world champions in MMA and jiujitsu/submission grappling (Georges St. Pierre, Garry Tonon, etc.). The recent leg lock technique renaissance is largely due to Danaher and his school.

Danaher was a philosophy PhD student at Columbia before discovering BJJ through Renzo Gracie's academy in NYC. When I was a Yale professor (in the 90s) I made trips to Renzo's for training. I don't recall Danaher (who would have been a student/instructor there at the time), but I do recall Craig Kukuk, Renzo's partner in the school and the first US blackbelt instructor. Kukuk had played linebacker at Iowa State University (where I grew up), and we spent time talking about Iowa (a big wrestling hotbed) and the origins of jiujitsu and ultimate fighting in the US. I had trained in Japan and so knew quite a bit about the relationship between traditional judo and BJJ. At one time I probably knew as much as anyone about the relationship between judo, BJJ, MMA, and US folk style wrestling.

See Mama said knock you out.

What Makes Us Human? The Genetics of Complex Traits (Allen Frontiers Group)


I'll be attending this meeting in Seattle the next few days.
Recent research has led to new insights on how genes shape brain structure and development, and their impact on individual variation. Although significant inroads have been made in understanding the genetics underlying disease risk, what about the complex traits of extraordinary variation - such as cognition, superior memory, etc.? Can current advances shed light on genetic components underpinning these variations?

Personal genomics, biobank resources, emerging statistical genetics methods and neuroimaging capabilities are opening new frontiers in the field of complex trait analysis. This symposium will highlight experts using diverse approaches to explore a spectrum of individual variation of the human mind.
Paul Allen (MSFT co-founder) is a major supporter of scientific research, including the Allen Institute for Brain Science. Excerpts from his memoir, Idea Man.
We are at a unique moment in bioscience. New ideas, combined with emerging technologies, will create unprecedented and transformational insights into living systems. Accelerating the pace of this change requires a thoughtful and agile exploration of the entire landscape of bioscience, across disciplines and spheres of research. Launched in 2016 with a $100 million commitment toward a larger 10-year plan, The Paul G. Allen Frontiers Group will discover and support scientific ideas that change the world. We are committed to a continuous conversation with the scientific community that allows us to remain at the ever-changing frontiers of science and reimagine what is possible.
My talk is scheduled for 3:55 PM Pacific Weds 1/17. All talks will be streamed on the Allen Institute Facebook page.

Saturday, January 06, 2018

Institute for Advanced Study: Genomic Prediction of Complex Traits (seminar)


Genomic Prediction of Complex Traits

After a brief review (suitable for physicists) 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 -- thereby resolving the missing heritability problem. 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. Finally, I discuss how these advances will affect human health and reproduction (embryo selection for In Vitro Fertilization, genetic editing) in the coming decade.

FEATURING
Steve Hsu

SPEAKER AFFILIATION
Michigan State University

I recently gave a similar talk at 23andMe (slides at link).


Some Comments and Slides:

I tried to make the talk understandable to physicists, and at least according to what I was told (and my impression from the questions asked during and after the talk), largely succeeded. Early on, when presenting the phenotype function y(g), both Nima Arkani-Hamed (my host) and Ed Witten asked some questions about the "units" of the various quantities involved. In the actual computation everything is z-scored: measured in units of SD relative to the sample mean. I didn't realize until later that there was some confusion about how this is done for the "state variable" of the genetic locus g_i. In fact, when the gene array is read the result is 0,1,2 for homozygous common allele, heterozygous, homozygous rare allele, respectively. (I might have that backwards but you get the point.) For each locus there is a minor allele frequency (MAF) and this determines the sample average and SD of the distribution of 0's, 1's, and 2's. It is the z-scored version of this variable that appears in the computation. I didn't realize certain people were following the details so closely in the talk but I should not be surprised ;-) In the future I'll include a slide specifically on this to avoid confusion.

Looking at my slide on missing heritability, Witten immediately noted that estimating SNP heritability (as opposed to total or broad sense heritability) is nontrivial and I had to quickly explain the GCTA technique!

During the talk I discussed the theoretical reason we expect to find a lot of additive variance: nonlinear gadgets are fragile (easy to break through recombination in sexual reproduction), whereas additive genetic variance can be reliably passed on and is easy for natural selection to act on***. (See also Fisher's Fundamental Theorem of Natural Selection. More.) Usually these comments pass over the head of the audience but at IAS I am sure quite a few people understood the point.

One non-physicist reader of this blog braved IAS security and managed to attend the lecture. I am flattered, and I invite him to share his impressions in the comments!

Afterwards there was quite a bit of additional discussion which spilled over into tea time. The important ideas: how Compressed Sensing works, the nature of the phase transition, how we can predict the amount of data required to build a good predictor (capturing most of the SNP heritability) using the universality of the phase transition + estimate of sparsity, etc. were clearly absorbed by the people I talked to.

Slides


*** On the genetic architecture of intelligence and other quantitative traits (p.16):
... The preceding discussion is not intended to convey an overly simplistic view of genetics or systems biology. Complex nonlinear genetic systems certainly exist and are realized in every organism. However, quantitative differences between individuals within a species may be largely due to independent linear effects of specific genetic variants. As noted, linear effects are the most readily evolvable in response to selection, whereas nonlinear gadgets are more likely to be fragile to small changes. (Evolutionary adaptations requiring significant changes to nonlinear gadgets are improbable and therefore require exponentially more time than simple adjustment of frequencies of alleles of linear effect.) One might say that, to first approximation, Biology = linear combinations of nonlinear gadgets, and most of the variation between individuals is in the (linear) way gadgets are combined, rather than in the realization of different gadgets in different individuals.

Linear models work well in practice, allowing, for example, SNP-based prediction of quantitative traits (milk yield, fat and protein content, productive life, etc.) in dairy cattle. ...

Friday, January 05, 2018

Gork revisited, 2018

It's been almost 10 years since I made the post Are you Gork?

Over the last decade, both scientists and non-scientists have become more confident that we will someday create:

A. AGI (= sentient AI, named "Gork" :-)  See Rise of the Machines: Survey of AI Researchers.

B. Quantum Computers. See Quantum Computing at a Tipping Point?

This change in zeitgeist makes the thought experiment proposed below much less outlandish. What, exactly, does Gork perceive? Why couldn't you be Gork? (Note that the AGI in Gork can be an entirely classical algorithm even though he exists in a quantum simulation.)




Slide from this [Caltech IQI] talk. See also illustrations in Big Ed.
Survey questions:

1) Could you be Gork the robot? (Do you split into different branches after observing the outcome of, e.g., a Stern-Gerlach measurement?)

2) If not, why? e.g.,

I have a soul and Gork doesn't

Decoherence solved all that! See previous post.

I don't believe that quantum computers will work as designed, e.g., sufficiently large algorithms or subsystems will lead to real (truly irreversible) collapse. Macroscopic superpositions larger than whatever was done in the lab last week are impossible.

QM is only an algorithm for computing probabilities -- there is no reality to the quantum state or wavefunction or description of what is happening inside a quantum computer.

Stop bothering me -- I only care about real stuff like the Higgs mass / SUSY-breaking scale / string Landscape / mechanism for high-Tc / LIBOR spread / how to generate alpha. 
[ 2018: Ha Ha -- first 3 real stuff topics turned out to be pretty boring use of the last decade... ]
Just as A. and B. above have become less outlandish assumptions, our ability to create large and complex superposition states with improved technology (largely developed for quantum computing; see Schrodinger's Virus) will make the possibility that we ourselves exist in a superposition state less shocking. Future generations of physicists will wonder why it took their predecessors so long to accept Many Worlds.

Bonus! I will be visiting Caltech next week (Tues and Weds 1/8-9). Any blog readers interested in getting a coffee or beer please feel free to contact me :-)

Sunday, December 31, 2017

Happy New Year 2018


Greetings from the Central Coast of California! I've been spending part of the holiday working with the kids on their swimming. Hope to get both of them qualified for the Michigan middle school state championship meet :-)







It's hard to beat sunshine, palm trees, and an outdoor pool in December!



On the beach, New Year's Day :-)







A brief exposition on the nature of tides:



Friday, December 29, 2017

The Bonfire of the Black Public Intellectual Vanities: Economist Glenn Loury on Ta Nehisi Coates and Cornell West

Glenn Loury is Merton P. Stoltz Professor of the Social Sciences, Department of Economics, Brown University. John McWhorter is Associate Professor of English and Comparative Literature at Columbia University, where he teaches linguistics, American studies, philosophy, and music history.



(Video will start at 20:50 but the entire conversation is worth a listen.)
[20:50] ... I'm talking about 65 or 70 percent of kids born to unmarried women. You can't tell me that that doesn't matter. It matters. There could be many explanations for it, but don't try to ignore that fact. Development, the test scores? This whole edifice that we'd built of Diversity and Inclusion, it's founded on a lie, John. Because the issue is performance and the Asians have demonstrated that. The facts are so palpable that it amazes me that people can't look at them. The Asians have demonstrated -- these are people who are second generation descendants; people were born 10,000 miles from here -- it [the USA] is an open society. African-American under-representation is a reflection of African-American under-development. Now, we can go into the historical reasons for that. If the issue is who is to blame ... plenty enough blame to go around. But the fundamental imperative is to enhance the development and that won't happen unless you acknowledge the absence of it. The test scores reflect an inadequate acquisition of functional and cognitive capacities essential to functioning in the modern world and the gaps are enormous etcetera...
Now Loury gets really worked up:
[23:50] ... the Afro Studies hustle ... the avoidance of the necessity of failure against standards in order for the standards to be meaningful and for the kind of disciplines and capacities that constitute excellence to be honed and developed. It's a shell game. It's a lie, ok. That's what I'm saying. Just say that the jails are full of black people means that the criminal justice system is racist and to leave it at that when the bodies pile up in Chicago and elsewhere. To talk about Diversity / Inclusion is the way of legitimating and institutionalizing a deferential and racist withholding of judgment from African-American people to perform at the level of excellence at a place like MIT or Caltech or Brown or Columbia or Yale requires. I mean, I'm really really angry about this because people are being dishonest about this in the interest of a Coon Show, John, a Coon Show -- that's what we're talking about ...
More at [25:17] The Bonfire of the Black Public Intellectual Vanities. See earlier post Talking Ta-Nehisi Coates, Seriously?

See also Loury's Kenneth Arrow Lecture, Department of Economics, Columbia University: Persistent Racial Inequality in the US: An Economic Theorist’s Account (PDF).

Monday, December 25, 2017

Peace on Earth, Good Will to Men 2017



For years, when asked what I wanted for Christmas, I've been replying: Peace On Earth, Good Will To All Men :-)

No one ever seems to recognize that this comes from the bible, Luke 2.14 to be precise!

Linus said it best in A Charlie Brown Christmas:
And there were in the same country shepherds abiding in the field, keeping watch over their flock by night.

And, lo, the angel of the Lord came upon them, and the glory of the Lord shone round about them: and they were sore afraid.

And the angel said unto them, Fear not: for, behold, I bring you good tidings of great joy, which shall be to all people.

For unto you is born this day in the city of David a Saviour, which is Christ the Lord.

And this shall be a sign unto you; Ye shall find the babe wrapped in swaddling clothes, lying in a manger.

And suddenly there was with the angel a multitude of the heavenly host praising God, and saying,

Glory to God in the highest, and on earth peace, good will toward men.

Merry Christmas!

Two years ago today I shared the following story on this blog: Nativity 2050

For an update, see The Future is Here: Genomic Prediction in MIT Technology Review


And the angel said unto them, Fear not: for, behold, I bring you good tidings of great joy, which shall be to all people.
Mary was born in the twenties, when the tests were new and still primitive. Her mother had frozen a dozen eggs, from which came Mary and her sister Elizabeth. Mary had her father's long frame, brown eyes, and friendly demeanor. She was clever, but Elizabeth was the really brainy one. Both were healthy and strong and free from inherited disease. All this her parents knew from the tests -- performed on DNA taken from a few cells of each embryo. The reports came via email, from GP Inc., by way of the fertility doctor. Dad used to joke that Mary and Elizabeth were the pick of the litter, but never mentioned what happened to the other fertilized eggs.

Now Mary and Joe were ready for their first child. The choices were dizzying. Fortunately, Elizabeth had been through the same process just the year before, and referred them to her genetic engineer, a friend from Harvard. Joe was a bit reluctant about bleeding edge edits, but Mary had a feeling the GP engineer was right -- their son had the potential to be truly special, with just the right tweaks ...
See also [1], [2], and [3].

Tuesday, December 19, 2017

Low SES does not decrease heritability of cognitive ability (N=300k)


These researchers, from Stanford, Northwestern, and the University of Florida, analyze a large population of twins and siblings (~24k twins and ~300k children in total, born 1994-2002 in Florida). They find no evidence of SES (Socio-Economic Status) moderation of genetic influence on test scores (i.e., cognitive ability). The figure above shows the usual pattern of lower pairwise correlations in test performance between non-identical twins and ordinary sibs, consistent with strong heritability. (In figure, ICC = Intraclass Correlation = ratio of between-pair variance to total variance; SS/OS = Same/Opposite Sex.) The researchers find, via further analysis (see below), that lower SES does not decrease heritability. No large GxE effect at low SES.

Earlier work by Turkheimer and collaborators (with much smaller sample size) suggested that low SES can drastically reduce the genetic heritability of intelligence. Their result has been widely publicized, but over time evidence is accumulating against it.

Note that Economics Nobelist James J. Heckman is the editor at PNAS who handled this paper. Heckman is an expert statistician and one of the most highly cited researchers in the area of childhood education and human capital. He was also a vocal critic of The Bell Curve, but seems (now) to accept the validity of general intelligence as a construct, its heritability, and the difficulty of increasing intelligence through environmental intervention. He tends to focus on other, more trainable, factors that influence life success, such as (my interpretation) Conscientiousness, Rule Following, Pro-Sociality, etc. ("non-cognitive skills").
Socioeconomic status and genetic influences on cognitive development
PNAS doi: 10.1073/pnas.1708491114

Significance
A prominent hypothesis in the study of intelligence is that genetic influences on cognitive abilities are larger for children raised in more advantaged environments. Evidence to date has been mixed, with some indication that the hypothesized pattern may hold in the United States but not elsewhere. We conducted the largest study to date using matched birth and school administrative records from the socioeconomically diverse state of Florida, and we did not find evidence for the hypothesis.

Abstract
Accurate understanding of environmental moderation of genetic influences is vital to advancing the science of cognitive development as well as for designing interventions. One widely reported idea is increasing genetic influence on cognition for children raised in higher socioeconomic status (SES) families, including recent proposals that the pattern is a particularly US phenomenon. We used matched birth and school records from Florida siblings and twins born in 1994–2002 to provide the largest, most population-diverse consideration of this hypothesis to date. We found no evidence of SES moderation of genetic influence on test scores, suggesting that articulating gene-environment interactions for cognition is more complex and elusive than previously supposed.
From the paper. Note SS/OS = Same/Opposite Sex, SES = Socio-Economic Status.
First, Turkheimer and Horn indicate that “the between-pair variance of MZ pairs decreases in poor environments” (ref. 21, p. 63). Contrary to this relationship, we found that the between-pair variance of SS twins is actually lowest in the highest SES families. Given that SS twins are a relatively equal combination of MZ and DZ twins, one possibility is that a pattern supporting the hypothesis among MZ SS twins is masked by an even stronger pattern in the opposite direction among DZ SS twins. However, Fig. 3 shows that corresponding results for OS twins (all of whom are DZ) give no indication of such a pattern. Between-pair variances in achievement test scores for high-school educated parents of OS twins are higher in all cases than it is for parents without a high school diploma.

Second, Turkheimer and Horn report that “the within-pair variance of MZ twin pairs increases at lower levels of SES: poverty appears to have the effect of making MZ twins more different from each other” (ref. 21, p. 61). We would therefore expect in our data that the within-pair variance for SS twins whose mother did not graduate from high school would be higher than the variance for SS twins whose mother has a high school diploma. However, this is not the case in any of the SS twin comparisons shown in Fig. 3.
Via SSC -- thanks, Scott!

Added remarks about context and broader implications: This paper does not exclude SES effects on intelligence. Rather, it excludes a hypothesis (big nonlinear effect at low SES; GxE!) that has been widely discussed: In good environments individuals can achieve their full genetic potential, and consequently measured heritability is high. However, in bad environments individuals don't achieve their full genetic potential, and (perhaps) do not even realize the full effect of beneficial genetic variants, so heritability is much reduced. This reasonable sounding hypothesis is not supported by the Florida data, suggesting that genetic influence is similarly strong in both high and low SES families.

Now, just how strong is this genetic influence? Many large studies have been conducted on populations of twins (raised together and apart), adoptees (who end up resembling their biological parents much more than the adoptive parents who raised them), and ordinary siblings. The results suggest very high heritability of adult intelligence -- broad sense heritability may be as high as ~0.8!
Wikipedia: Recent twin and adoption studies suggest that while the effect of the shared family environment is substantial in early childhood, it becomes quite small by late adolescence. These findings suggest that differences in the life styles of families whatever their importance may be for many aspects of children's lives make little long-term difference for the skills measured by intelligence tests.

Monday, December 18, 2017

Quantum Computing near a Tipping Point?

I received an email from a physicist colleague suggesting that we might be near a "tipping point" in quantum computation. I've sort of followed quantum computation and quantum information as an outsider for about 20 years now, but haven't been paying close attention recently because it seems that practical general purpose quantum computers are still quite distant. Furthermore, I am turned off by the constant hype in the technology press...

But perhaps my opinion is due for an update? I know some real quantum computing people read this blog, so I welcome comments.

Here's part of what I wrote back:
I'm not sure what is meant by "tipping point" -- I don't think we know yet what qubit technology can be scaled to the point of making Shor's Algorithm feasible. The threat to classical cryptography is still very far off -- you need millions* of qubits and the adversary can always just increase the key length; the tradeoffs are likely to be in favor of the classical method for a long time.

Noisy quantum simulators of the type Preskill talks about might be almost possible (first envisioned by Feynman in the Caltech class he gave in the 1980s: Limits to Computation). These are scientifically very interesting but I am not sure that there will be practical applications for some time.

* This is from distant memory so might not be quite right. The number of ideal qubits needed would be a lot less, but with imperfect qubits/gates and quantum error-correction, etc., I seem to remember a result like this. Perhaps millions is the number of gates, not qubits? (See here.)
These are the Preskill slides I mentioned -- highly recommended. John Preskill is the Feynman Professor of Theoretical Physics at Caltech :-)



Here's a summary of current and near-term hardware capability:

Thursday, December 14, 2017

100 Billionaires In Beijing Alone



Real talk from former Australian Prime Minister Paul Keating on the strategic outlook for Australia in Asia, the rise of China, and the likely future military balance of power in the Pacific region.

More from the Australian strategic viewpoint. Balance of power in the Western Pacific.

From the YouTube transcript:
29:18 [Eventually... Total] Chinese GDP is twice as large as America's so the idea that this great massive economy is going to be a strategic client of the United States that they are kept in line by the US 7th fleet that the US 7th fleet controls its coasts six miles off the ... territorial sea is of course nonsense but this is what the Pivot was all about. This is what Hillary Clinton and Barrack Obama's Pivot was all about was about the reestablishment of US power...

... you know it's simply unreal and if we try and become remain party to that piece of nonsense you know... that's not to say we don't need the US strategically in Asia as a balancing and conciliating power we do, but if we are party to the nonsense that we will line up for the United States to maintain its strategic hegemony in Asia over China we must have troubles...

Wednesday, December 13, 2017

Nature, Nurture, and Invention: analysis of Finnish data



What is the dominant causal mechanism for the results shown above? Is it that better family environments experienced by affluent children make them more likely to invent later in life? Is it that higher income fathers tend to pass on better genes (e.g., for cognitive ability) to their children? Obviously the explanation has important implications for social policy and for models of how the world works.

The authors of the paper below have access to patent, income, education, and military IQ records in Finland. (All males are subject to conscription.) By looking at brothers who are close in age but differ in IQ score, they can estimate the relative importance of common family environment (such as family income level or parental education level, which affect both brothers) versus the IQ difference itself. Their results suggest that cognitive ability has a stronger effect than shared family environment. Again, if one just looks at probability of invention versus family income or SES (see graph), one might mistakenly conclude that family environment is the main cause of increased likelihood of earning a patent later in life. In fact, higher family SES is also correlated to superior genetic endowments which can be passed on to the children.
The Social Origins of Inventors
Philippe Aghion, Ufuk Akcigit, Ari Hyytinen, Otto Toivanen
NBER Working Paper No. 24110
December 2017

In this paper we merge three datasets - individual income data, patenting data, and IQ data - to analyze the determinants of an individual's probability of inventing. We find that: (i) parental income matters even after controlling for other background variables and for IQ, yet the estimated impact of parental income is greatly diminished once parental education and the individual's IQ are controlled for; (ii) IQ has both a direct effect on the probability of inventing an indirect impact through education. The effect of IQ is larger for inventors than for medical doctors or lawyers. The impact of IQ is robust to controlling for unobserved family characteristics by focusing on potential inventors with brothers close in age. We also provide evidence on the importance of social family interactions, by looking at biological versus non-biological parents. Finally, we find a positive and significant interaction effect between IQ and father income, which suggests a misallocation of talents to innovation.
From the paper:
... IQ has both a direct effect on the probability of inventing which is almost five times as large as that of having a high-income father, and an indirect effect through education ...

... an R-squared decomposition shows that IQ matters more than all family background variables combined; moreover, IQ has both a direct and an indirect impact through education on the probability of inventing, and finally the impact of IQ is larger and more convex for inventors than for medical doctors or lawyers. Third, to address the potential endogeneity of IQ, we focused on potential inventors with brothers close in age. This allowed us to control for family-specific time-invariant unobservables. We showed that the effect of visuospatial IQ on the probability of inventing is maintained when adding these controls.

More on the close brothers analysis (p.24).
We look at the effect of an IQ differential between the individual and close brother(s) born at most three years apart.16 This allows us to include family fixed effects and thereby control for family-level time-invariant unobservables, such as genes shared by siblings, parenting style, and fixed family resources. Table 4 shows the results from the regression with family-fixed effects. The first column shows the baseline OLS results using the sample on brothers born at most three years apart. Notice that we include a dummy for the individual being the first born son in the family to account for birth-order effects. The second column shows the results from a regression where we introduce family fixed effects. We lose other parental characteristics than income due to their time-invariant nature.17 The main finding in Table 4 is that the coefficients on "IQ 91-95" and "IQ 96-100" [ these are percentiles, not IQ scores ] in Column 2 (i.e. when we perform the regression with family fixed effects) are the same as in the OLS Column 1. This suggests that these coefficients capture an effect of IQ on the probability of inventing which is largely independent of unobserved family background characteristics, as otherwise the OLS coefficients would be biased and different from the fixed effects estimates.

Note Added: Finland is generally more egalitarian than the US, both in terms of wealth distribution and access to education. But the probability of invention vs family income graph is qualitatively similar in both countries (see Fig 1 in the paper). The figure below is from recent US data; compare to the Finland figure at top.


Thanks to some discussion (see comments) I noticed that in the Finnish data the probability of invention seems to saturate at high incomes (see top figure, red circle), whereas it continues to rise strongly at top IQ scores (middle figure above; also perhaps in the US data above?). It would be interesting to explore this in more detail...

Friday, December 08, 2017

Recursive Cortical Networks: data efficient computer vision



Will knowledge from neuroscience inform the design of better AIs (neural nets)? These results from startup Vicarious AI suggest that the answer is yes! (See also this company blog post describing the research.)

It has often been remarked that evolved biological systems (e.g., a baby) can learn much faster and using much less data than existing artificial neural nets. Significant improvements in AI are almost certainly within reach...

Thanks to reader and former UO Physics colleague Raghuveer Parthasarathy for a pointer to this paper!
A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs

Science 08 Dec 2017: Vol. 358, Issue 6368, eaag2612
DOI: 10.1126/science.aag2612

INTRODUCTION
Compositionality, generalization, and learning from a few examples are among the hallmarks of human intelligence. CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart), images used by websites to block automated interactions, are examples of problems that are easy for people but difficult for computers. CAPTCHAs add clutter and crowd letters together to create a chicken-and-egg problem for algorithmic classifiers—the classifiers work well for characters that have been segmented out, but segmenting requires an understanding of the characters, which may be rendered in a combinatorial number of ways. CAPTCHAs also demonstrate human data efficiency: A recent deep-learning approach for parsing one specific CAPTCHA style required millions of labeled examples, whereas humans solve new styles without explicit training.

By drawing inspiration from systems neuroscience, we introduce recursive cortical network (RCN), a probabilistic generative model for vision in which message-passing–based inference handles recognition, segmentation, and reasoning in a unified manner. RCN learns with very little training data and fundamentally breaks the defense of modern text-based CAPTCHAs by generatively segmenting characters. In addition, RCN outperforms deep neural networks on a variety of benchmarks while being orders of magnitude more data-efficient.

RATIONALE
Modern deep neural networks resemble the feed-forward hierarchy of simple and complex cells in the neocortex. Neuroscience has postulated computational roles for lateral and feedback connections, segregated contour and surface representations, and border-ownership coding observed in the visual cortex, yet these features are not commonly used by deep neural nets. We hypothesized that systematically incorporating these findings into a new model could lead to higher data efficiency and generalization. Structured probabilistic models provide a natural framework for incorporating prior knowledge, and belief propagation (BP) is an inference algorithm that can match the cortical computational speed. The representational choices in RCN were determined by investigating the computational underpinnings of neuroscience data under the constraint that accurate inference should be possible using BP.

RESULTS
RCN was effective in breaking a wide variety of CAPTCHAs with very little training data and without using CAPTCHA-specific heuristics. By comparison, a convolutional neural network required a 50,000-fold larger training set and was less robust to perturbations to the input. Similar results are shown on one- and few-shot MNIST (modified National Institute of Standards and Technology handwritten digit data set) classification, where RCN was significantly more robust to clutter introduced during testing. As a generative model, RCN outperformed neural network models when tested on noisy and cluttered examples and generated realistic samples from one-shot training of handwritten characters. RCN also proved to be effective at an occlusion reasoning task that required identifying the precise relationships between characters at multiple points of overlap. On a standard benchmark for parsing text in natural scenes, RCN outperformed state-of-the-art deep-learning methods while requiring 300-fold less training data.

CONCLUSION
Our work demonstrates that structured probabilistic models that incorporate inductive biases from neuroscience can lead to robust, generalizable machine learning models that learn with high data efficiency. In addition, our model’s effectiveness in breaking text-based CAPTCHAs with very little training data suggests that websites should seek more robust mechanisms for detecting automated interactions.

Wednesday, December 06, 2017

AlphaZero: learns via self-play, surpasses best humans and machines at chess


AlphaZero taught itself chess through 4 hours of self-play, surpassing the best humans and the best (old-style) chess programs in the world.
Chess24: 20 years after DeepBlue defeated Garry Kasparov in a match, chess players have awoken to a new revolution. The AlphaZero algorithm developed by Google and DeepMind took just four hours of playing against itself to synthesise the chess knowledge of one and a half millennium and reach a level where it not only surpassed humans but crushed the reigning World Computer Champion Stockfish 28 wins to 0 in a 100-game match. All the brilliant stratagems and refinements that human programmers used to build chess engines have been outdone, and like Go players we can only marvel at a wholly new approach to the game. ...
ArXiv preprint:
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

The game of chess is the most widely-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. In contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement learning from games of self-play. In this paper, we generalise this approach into a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in many challenging domains. Starting from random play, and given no domain knowledge except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a world-champion program in each case.
Excerpt:
Finally, we analysed the chess knowledge discovered by AlphaZero. Table 2 analyses the most common human openings (those played more than 100,000 times in an online database of human chess games (1)). Each of these openings is independently discovered and played frequently by AlphaZero during self-play training. When starting from each human opening, AlphaZero convincingly defeated Stockfish, suggesting that it has indeed mastered a wide spectrum of chess play.

Tuesday, December 05, 2017

How Europe lost its tech companies



Some perspectives from a Berlin tech guy who has also worked in China.

To some extent Europe is like the Midwest of the US: a source of human capital for SV and other places. Europe and the Midwest have strong universities and produce talented individuals, but lack a mature tech ecosystem which includes access to venture funding, exits (acquisition by big established companies), and a culture of risk taking and innovation.

See also The next Silicon Valley? (another German guy):
My meeting in Beijing with Hugo Barra, who runs all international expansion for Xiaomi — the cool smartphone maker and highest-valued startup in China, at around $45 billion or so — was scheduled for 11 pm, but got delayed because of other meetings, so it started at midnight. (Hugo had a flight to catch at 6:30 am after that.)

In China, there is a company work culture at startups that's called 9/9/6. It means that regular work hours for most employees are from 9 am to 9 pm, six days a week. If you thought Silicon Valley has intense work hours, think again.

For founders and top executives, it's often 9/11/6.5. That's probably not very efficient and useful (who's good as a leader when they're always tired and don't know their kids?) but totally common.

Teams get locked up in hotels for weeks before a product launch, where they only work, sleep and work out, to drive 100 percent focus without distractions and make the launch date. And while I don't think long hours are any measure of productivity, I was amazed by the enormous hunger and drive. ...

Sunday, December 03, 2017

Big Ed


Today I came across a recent interview with Ed Witten in Quanta Magazine. The article has some nice photos like the one above. I was struck by the following quote from Witten ("It from Qubit!"):
When I was a beginning grad student, they had a series of lectures by faculty members to the new students about theoretical research, and one of the people who gave such a lecture was Wheeler. He drew a picture on the blackboard of the universe visualized as an eye looking at itself. I had no idea what he was talking about. It’s obvious to me in hindsight that he was explaining what it meant to talk about quantum mechanics when the observer is part of the quantum system. I imagine there is something we don’t understand about that.  [ Italics mine ]
The picture he refers to is reproduced below.


This question has been of interest to me since I was first exposed to quantum mechanics, although I put it off for a long time because quantum foundations is not considered a respectable area by most physicists! Of course it should be obvious that if quantum mechanics is to be a universal theory of nature, then observers like ourselves can't help but be part of the (big) quantum system.

See related posts Feynman and Everett, Schwinger on Quantum Foundations, Gell-Man on Quantum Foundations, and Weinberg on Quantum Foundations.

Here's a similar figure, meant to represent the perspective of an observer inside the wavefunction of the universe (which evolves deterministically and unitarily; the degrees of freedom of the observer's mind are part of the Hilbert space of Psi; time runs vertically and Psi evolves into exp(-iHT) Psi while we are "inside" :-). The figure was drawn on the whiteboard of my University of Oregon office and persisted there for a year or more. I doubt any visitors (other than perhaps one special grad student) understood what it was about.



For some powerful Witten anecdotes like the one below, see here. (If you don't know who Ed Witten is this should clarify things a bit!)
I met him in Boston in 1977, when I was getting interested in the connection between physics and mathematics. I attended a meeting, and there was this young chap with the older guys. We started talking, and after a few minutes I realized that the younger guy was much smarter than the old guys. He understood all the mathematics I was talking about, so I started paying attention to him. That was Witten. And I’ve kept in touch with him ever since.

In 2001, he invited me to Caltech, where he was a visiting professor. I felt like a graduate student again. Every morning I would walk into the department, I’d go to see Witten, and we’d talk for an hour or so. He’d give me my homework. I’d go away and spend the next 23 hours trying to catch up. Meanwhile, he’d go off and do half a dozen other things. We had a very intense collaboration. It was an incredible experience because it was like working with a brilliant supervisor. I mean, he knew all the answers before I got them. If we ever argued, he was right and I was wrong. It was embarrassing!

(Fields Medalist Michael Atiyah, on what it was like to collaborate with Witten)
The closest thing I have read to a personal intellectual history of Witten is his essay Adventures in Physics and Math, which I highly recommend. The essay addresses some common questions, such as What was Ed like as a kid? How did he choose a career in Physics? How does he know so much Mathematics? For example,
At about age 11, I was presented with some relatively advanced math books. My father is a theoretical physicist and he introduced me to calculus. For a while, math was my passion. My parents, however, were reluctant to push me too far, too fast with math (as they saw it) and so it was a long time after that before I was exposed to any math that was really more advanced than basic calculus. I am not sure in hindsight whether their attitude was best or not.
A great video, suggested by a commenter:

Thursday, November 30, 2017

CMSE (Computational Mathematics, Science and Engineering) at MSU



At Oregon I was part of an interdisciplinary institute that included theoretical physicists and chemists, mathematicians, and computer scientists. We tried to create a program (not even a new department, just an interdisciplinary program) in applied math and computation, but failed due to lack of support from higher administration. When I arrived at MSU as VPR I learned that the faculty here had formulated a similar plan for a new department. Together with the Engineering dean and the Natural Sciences dean we pushed it through and created an entirely new department in just a few years. This new department already has a research ranking among the top 10 in the US (according to Academic Analytics).

Computational Mathematics, Science and Engineering at MSU.


IQ (Institute for Quantitative Health Science and Engineering) at MSU



Chris Contag is the founding director of the Institute for Quantitative Health Science and Engineering and the chairperson of the new Department of Biomedical Engineering in the College of Engineering.

Contag was previously a professor in the Departments of Pediatrics, Radiology, Bioengineering and Microbiology and Immunology at Stanford University. He held the titles of associate chief of Neonatal and Developmental Medicine, director of Stanford’s Center for Innovation in In Vivo Imaging and co-director of the Molecular Imaging Program. Among the new faculty recruited to IQ are researchers previously on the faculties at Stanford, Harvard, and Johns Hopkins University.

Below are some photos from the annual progress report meeting I attended yesterday.






Monday, November 27, 2017

The nuclear physics of neutron star mergers at MSU's FRIB


Science reports on MSU's Facility for Rare Isotope Beams, which will probe the properties of nuclear matter.
Science: Last month, astronomers wowed the world when they announced that they had seen two neutron stars merge, apparently creating heavy elements such as gold and platinum and spewing them into space. Nuclear physicists here at Michigan State University (MSU) also cheered the find. They are building an atom smasher, the $730 million Facility for Rare Isotope Beams (FRIB), that could decipher exactly how those elements were forged in the inferno. “We were hoping to see an event like this someday,” says Witold Nazarewicz, an MSU theorist and FRIB's chief scientist.

First proposed in 1999, the project didn't get the greenlight for construction from the Department of Energy (DOE) until 2014. But since then, progress has been rapid. In what was a grassy patch behind MSU's existing nuclear physics laboratory now stands an imposing 200-meter-long building. In its basement, technicians are installing the first section of FRIB's 500-meter-long linear accelerator, which will shoot beams of nuclei ranging from hydrogen to uranium into a graphite target to blast out short-lived new isotopes. In this context, isotope is just another word for nucleus—one that makes for a better acronym.

The accelerator at the Facility for Rare Isotope Beams will create short-lived nuclei thought to be forged in neutron star mergers.

The project is on budget and ahead of schedule, and most of the major technological puzzles have been solved, says Thomas Glasmacher, FRIB's project director. “We don't have anything that we don't know how to do,” he says. Other physicists are impressed with the progress. “The moment they could, they ran with this project,” says Kate Jones, an experimental nuclear physicist at the University of Tennessee in Knoxville. “It's very impressive when you look down in the basement and see all the kit they've got.”

FRIB's nuclei will be key to understanding how neutron-star mergers make heavy elements. Spotted by gravitational-wave detectors in the United States and Italy and telescopes around the world, the violent collision produced an afterglow that over days faded from bright blue to dimmer red (Science, 20 October, p. 282). The light show jibed with astrophysicists' model of a so-called kilonova, in which the disintegrating neutron stars fling neutron-rich matter into space. The model predicts that in the debris, a chain of nuclear interactions known as the rapid neutron process, or r-process, quickly generates most of the elements heavier than iron. (Other elements emerge from supernova explosions and the deaths of smaller stars, from cosmic ray interactions, and also as leftovers lingering from the big bang.)

For astrophysicists, the observation marked a triumph for the kilonova model. For nuclear physicists, it's just the beginning. In the r-process, a nucleus gains weight by gobbling up one neutron after another. At the same time, the nucleus can change its chemical identity through radioactive beta decay, which turns a neutron into a proton and bumps the nucleus up the periodic table of elements. Exactly how the nucleus evolves depends on the speed of the decay and the probability that it will soak up another neutron.

Those parameters are poorly known. “Honestly, the nuclear physics is not in good shape,” says MSU nuclear astrophysicist Hendrik Schatz. “Most of the nuclei involved have not been identified and the theory has not been developed.” FRIB aims to change that by making as many of the neutron-laden nuclei as possible and measuring their masses and lifetimes. That might seem like a hopeless task, as the r-process involves dozens of intermediate nuclei. However, only a few key nuclei—the slowest decayers and absorbers—should act as bottlenecks to control the process and determine which elements are made in greatest abundance, Schatz explains.

Such data would better constrain models of heavy element production in neutron star mergers. The abundances could then be compared with those observed in the universe to determine whether merging neutron stars are the only astrophysical sites of the r-process, Jones says. Many astrophysicists have suggested as much, but that's a leap, she says. “It's very easy to say, ‘Oh, we've found the site for the r-process—Well done!’ In reality this is just opening a door.”

The 1400 physicists who have signed up to use FRIB will perform many other experiments, ranging from trapping and measuring the properties of a single exotic nucleus, to measuring a hail of novel nuclei scattering off a particular target nucleus. Data from the experiments will feed into a more comprehensive theory of the structure of the nucleus, Nazarewicz says. Physicists already have a fundamental theory of the innards of protons and neutrons, particles called quarks and gluons, and how they interact. But using that theory, known as quantum chromodynamics, to predict nuclear structure is effectively impossible: It is so computationally complex that supercomputers are needed just to simulate the proton and neutron.

To model the behavior of nuclei, theorists now rely on various approximate “effective theories” that work for some nuclei but not others. FRIB's grandest goal, Nazarewicz says, is to develop a deeper understanding that will enable theorists to weave these disparate and sometimes discordant theories together into a coherent whole.

First, researchers have to finish the accelerator. In September, they fired a test beam through its first section, made of copper cavities that work at room temperature. They are now installing the main accelerating modules, which are made of superconducting niobium and must be chilled with liquid helium to 2 K. Researchers hope to send beams through the cold accelerator next year. In 2021, they plan to tear down a wall and connect the finished accelerator to the existing lab so that new experiments can begin. ...
Note this is an old schematic, from 2008 or so.

Remarks on the Decline of American Empire



Some gloomy remarks on the decline of the American Empire.

1. US foreign policy over the last decades has been disastrous -- trillions of dollars and thousands of lives expended on Middle Eastern wars, culminating in utter defeat. This defeat is still not acknowledged among most of the media or what passes for intelligentsia in academia and policy circles, but defeat it is. Iran now exerts significant control over Iraq and a swath of land running from the Persian Gulf to the Mediterranean. None of the goals of our costly intervention have been achieved. We are exhausted morally, financially, and militarily, and still have not fully extricated ourselves from a useless morass. George W. Bush should go down in history as the worst US President of the modern era.

2. We are fortunate that the fracking revolution may lead to US independence from Middle Eastern energy. But policy elites have to fully recognize this possibility and pivot our strategy to reflect the decreased importance of the region. The fracking revolution is a consequence of basic research from decades ago (including investment from the Department of Energy) and the work of private sector innovators and risk-takers.

3. US budget deficits are a ticking time bomb, which cripple investment in basic infrastructure and also in research that creates strategically important new technologies like AI. US research spending has been roughly flat in inflation adjusted dollars over the last 20 years, declining as a fraction of GDP.

4. Divisive identity politics and demographic trends in the US will continue to undermine political cohesion and overall effectiveness of our institutions. ("Civilizational decline," as one leading theoretical physicist observed to me recently, remarking on our current inability to take on big science projects.)

5. The Chinese have almost entirely closed the technology gap with the West, and dominate important areas of manufacturing. It seems very likely that their economy will eventually become significantly larger than the US economy. This is the world that strategists have to prepare for. Wars involving religious fanatics in unimportant regions of the world should not distract us from a possible future conflict with a peer competitor that threatens to match or exceed our economic, technological, and even military capability.

However, I'm not sure that OBOR (One Belt One Road) and a focus on the "world island" of Eurasia will be a winning strategy for China. Mackinder's dream of a unified or even fully economically integrated world island will have to overcome the limitations (in human capital, institutions, culture, etc.) of the under-developed middle...


More McCoy and Mackinder. RAND study on war with China mentioned by McCoy in the video above is linked here.

See also The Stages of Empire:
The empires Glubb studied had a lifespan of about ten human generations, or two hundred and fifty years, despite changing factors such as technology. Glubb describes a pattern of growth and decline, with six stages: the Ages of Pioneers, Conquest, Commerce, Affluence, Intellect and Decadence. He pointedly avoided writing about India or China, focusing rather on middle and western Eurasia, stating that his knowledge was inadequate to the task.

Note that six stages in 10 generations means that significant change can occur over one or two generations -- a nation can pass from one age to the next, as I believe we have in America during my lifetime.

... There does not appear to be any doubt that money is the agent which causes the decline of this strong, brave and self-confident people. The decline in courage, enterprise and a sense of duty is, however, gradual. The first direction in which wealth injures the nation is a moral one. Money replaces honour and adventure as the objective of the best young men. Moreover, men do not normally seek to make money for their country or their community, but for themselves. Gradually, and almost imperceptibly, the Age of Affluence silences the voice of duty. The object of the young and the ambitious is no longer fame, honour or service, but cash. Education undergoes the same gradual transformation. No longer do schools aim at producing brave patriots ready to serve their country. [ Or to discover great things for all mankind! ] Parents and students alike seek the educational qualifications which will command the highest salaries. ...

Duty, Honor, Country:

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.

The 21st century American reality (the Age of Decadence):

"Yeah, I calculated the NPV, and, you know, it's just not worth it for me. I really believe in your project, though. And, I share your passion. Good luck."

Tuesday, November 21, 2017

DOJ invokes Title VI against Harvard admissions


“Elections have consequences..." -- Barack Obama

See 20 years @15 percent: does Harvard discriminate against Asian-Americans?
CNN: The Justice Department is actively investigating Harvard University's use of race in its admissions policies and has concluded the school is "out of compliance" with federal law, according to documents obtained by CNN. ...

[Click through for DOJ letter to Harvard. Harvard refused to supply admissions data to DOJ as requested for Title VI investigation of bias against Asian-Americans.]
Wall Street Journal
WSJ: ... The Justice Department, whose Civil Rights Division is conducting the investigation into similar allegations, said in a letter to Harvard’s lawyers, dated Nov. 17 and reviewed by the Journal, that the school was being investigated under Title VI of the Civil Rights Act of 1964, which bars discrimination on the basis of race, color and national origin for organizations that receive federal funding. The letter also said the school had failed to comply with a Nov. 2 deadline to provide documents related to the university’s admissions policies and practices.

The department told Harvard it “may file a lawsuit” to enforce compliance if Harvard doesn’t hand over the documents by Dec. 1, according to a separate letter dated Nov. 17 from John M. Gore, the acting assistant attorney general for the Civil Rights Division.

... if a federal judge finds Harvard has violated Title VI, the court has broad authority to issue a remedy, such as ordering the university to change its admissions policies, the experts say.

Schools in violation of Title VI can also lose access to federal funds.
From DOJ web site:
TITLE VI OF THE CIVIL RIGHTS ACT OF 1964
42 U.S.C. § 2000D ET SEQ.
OVERVIEW OF TITLE VI OF THE CIVIL RIGHTS ACT OF 1964

Title VI, 42 U.S.C. § 2000d et seq., was enacted as part of the landmark Civil Rights Act of 1964. It prohibits discrimination on the basis of race, color, and national origin in programs and activities receiving federal financial assistance. As President John F. Kennedy said in 1963:

Simple justice requires that public funds, to which all taxpayers of all races [colors, and national origins] contribute, not be spent in any fashion which encourages, entrenches, subsidizes or results in racial [color or national origin] discrimination.

Monday, November 20, 2017

Saturday, November 18, 2017

Robot Overlords and the Academy


In a previous post Half of all jobs (> $60k/y) coding related? I wrote
In the future there will be two kinds of jobs. Workers will either

Tell computers what to do    
      or
Be told by computers what to do
I've been pushing Michigan State University to offer a coding bootcamp experience to all undergraduates who want it: e.g., Codecademy.com. The goal isn't to turn non-STEM majors into software developers, but to give all interested students exposure to an increasingly important and central aspect of the modern world.

I even invited the CodeNow CEO to campus to help push the idea. We're still working on it at the university -- painfully SLOWLY, if you ask me. But this fall I learned my kids are taking a class based on Codecademy at their middle school! Go figure.

(Image via 1, 2)

Wednesday, November 15, 2017

Behold, the Super Cow




Hmm... how do they compute the Net Merit and GTPI? (But, but, what about all of that missing heritability?)

See also

Applied genomics: the genetic "super cow"

Genomic prediction: no bull.

Attention climate virtue signalers: more efficient cows produce less methane per liter of milk! Drink milk from genetically engineered cows :-)

Friday, November 10, 2017

23andMe



I'm in Mountain View to give a talk at 23andMe. Their latest funding round was $250M on a (reported) valuation of $1.5B. If I just add up the Crunchbase numbers it looks like almost half a billion invested at this point...

Slides: Genomic Prediction of Complex Traits
Abstract: We apply methods from Compressed Sensing (L1-penalized regression; Donoho-Tanner phase transition with noise) to the UKBB 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 -- thereby resolving the missing heritability problem. Actual heights of most individuals in validation tests are within a few cm of predicted heights. I also discuss application of these methods to polygenic disease risk: sparsity estimates (of the number of causal loci), combined with phase transition scaling analysis, allow estimates of the amount of case | control data required to construct good predictors.
Here's how people + robots handle your spit sample to produce a SNP genotype:

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