Superhumans and the Race for AI Supremacy - Hidden Forces podcast Episode 351

 

I've been listening to Hidden Forces with Demetri Kofinas for years now. He's an excellent interviewer with interests in finance, geopolitics, technology and more.

Audio-only version.
 

In Episode 351 of Hidden Forces, Demetri Kofinas speaks with Stephen Hsu, a Professor of Theoretical Physics and Computational Mathematics, Science, and Engineering at Michigan State University. Stephen is also the co-founder of multiple companies, including Genomic Prediction, which provides preimplantation genetic screening services for human embryos, and SuperFocus.ai, which builds large language models for narrow enterprise use cases. 

This is a conversation about some of the most important advancements and trends in genomic science and artificial intelligence, including the social and ethical dilemmas arising from implementing these technologies at scale. Stephen and I discuss the competitive landscapes in both industries, how America’s geostrategic competition with China is driving tradeoffs between innovation and safety, the risks and opportunities that these revolutionary technologies pose, and how the world’s largest companies, economies, and military powers can work together to reap the benefits of this revolution while averting some of their most disastrous potential consequences.

Marc Martinez: "Dream Big" and the Golden Age of Bodybuilding — Manifold #32

 

Marc Martinez is the director of Dream Big, a documentary about Gold's Gym and the golden age of bodybuilding in Venice and Santa Monica in the 1970s. 

0:00 Introduction 

1:34 Marc's background in bodybuilding 

5:25 Bodybuilding in 70s Southern California 

25:52 Setting the record straight on steroid use 

33:40 Frank Zane 

38:23 Robby Robinson 

40:20 Butler, Gaines, and Arnold 

42:35 'Dream Big' 

48:07 Pumping Iron 

59:40 Hypersexuality in bodybuilding 

1:10:44 What's next for Marc

References: 

Watch Dream Big on Amazon: https://www.amazon.com/Dream-Big-Ken-Sprague/dp/B0B8ST5LNL/ 

Dream Big documentary: https://dreambigdoc.com/ 

Dream Big trailer: https://www.youtube.com/watch?v=X22ISDn083A 

Pumping Iron: https://www.youtube.com/watch?v=6lCCk6rgn84

Dominic Cummings: Vote Leave, Brexit, COVID, and No. 10 with Boris — Manifold #28

 

Dominic Cummings is a major historical figure in UK politics. He helped save the Pound Sterling, led the Vote Leave campaign, Got Brexit Done, and guided the Tories to a landslide general election victory. His time in No. 10 Downing Street as Boris Johnson's Chief Advisor was one of the most interesting and impactful periods in modern UK political history.  Dom and Steve discuss all of this and more in this 2-hour episode. 

0:00 Early Life: Oxford, Russia, entering politics 

16:49 Keeping the UK out of the Euro 

19:41 How Dominic and Steve became acquainted: blogs, 2008 financial crisis, meeting at Google 

27:37 Vote Leave, the science of polling 

43:46 Cambridge Analytica conspiracy; History is impossible 

48:41 Dominic on Benedict Cumberbatch’s portrayal of him and the movie “Brexit: The Uncivil War” 

54:05 On joining British Prime Minister Boris Johnson’s office: an ultimatum 

1:06:31 The pandemic 

1:21:28 The Deep State, talent pipeline for public service 

1:47:25 Quants and weirdos invade No.10 

1:52:06 Can the Tories win the next election? 

1:56:27 Trump in 2024? 

Transcript

Audio Player

References: 

Dominic's Substack newsletter: https://dominiccummings.substack.com/

The Future of Human Evolution -- excerpts from podcast interview with Brian Chau

The transcript excerpts below are from my recent conversation with Brian Chau (aka Cactus Chu) on his podcast.

Segments begin at 47m and 1h07m.

They have been lightly edited.https://t.co/RJ68FasfJchttps://t.co/TnSuNaxhnQ

— steve hsu (@hsu_steve) September 28, 2022

Full excerpts.

1. The prospect of predicting cognitive ability from DNA, and the consequences. Why the main motivation has nothing to do with group differences. This segment begins at roughly 47 minutes. 

2. Anti-scientific resistance to research on the genetics of cognitive ability. My experience with the Jasons. Blank Slate-ism as a sacralized, cherished belief of social progressives. This segment begins at roughly 1 hour 7 minutes. 

1. Starts at roughly 47 minutes. 

Okay, let's just say hypothetically my billionaire friend is buddies with the CEO of 23andMe and let's say on the down low we collected some SAT scores of 1M or 2M people. I think there are about 10M people that have done 23andMe, let's suppose I manage to collect 1-2M scores for those people. I get them to opt in and agree to the study and da da da da and then Steve runs his algos and you get this nice predictor. 

But you’ve got to do it on the down low. Because if it leaks out that you're doing it, People are going to come for you. The New York Times is going to come for you, everybody's going to come for you. They're going to try to trash the reputation of 23andMe. They're going to trash the reputation of the billionaire. They're going to trash the reputation of the scientists who are involved in this. But suppose you get it done. And getting it done as you know very well is a simple run on AWS and you end up with this predictor which wow it's really complicated it depends on 20k SNPs in the genome ... 

For anybody with an ounce of intellectual integrity, they would look back at their copy of The Mismeasure of Man which has sat magisterially on their bookshelf since they were forced to buy it as a freshman at Harvard. They would say, “WOW! I guess I can just throw that in the trash right? I can just throw that in the trash.” 

But the set of people who have intellectual integrity and can process new information and then reformulate the opinion that they absorbed through social convention – i.e., that Gould is a good person and a good scientist and wise -- is tiny. The set of people who can actually do that is like 1% of the population. So you know maybe none of this matters, but in the long run it does matter. … 

Everything else about that hypothetical: the social scientists running the longitudinal study, getting the predictor in his grubby little hands and publishing the validation, but people trying to force you to studiously ignore the results, all that has actually already happened. We already have something which correlates ~0.4 with IQ. Everything else I said has already been done but it's just being studiously ignored by the right thinking people. 

 … 

Some people could misunderstand our discussion as being racist. I'm not saying that any of this has anything to do with group differences between ancestry groups. I'm just saying, e.g., within the white population of America, it is possible to predict from embryo DNA which of 2 brothers raised in the same family will be the smart one and which one will struggle in school. Which one will be the tall one and which one will be not so tall. 

[more]

2. Starts at roughly 1 hour 7 minutes. 

I've been in enough places where this kind of research is presented in seminar rooms and conferences and seen very negative attacks on the individuals presenting the results. 

I'll give you a very good example. There used to be a thing called the Jasons. During the cold war there was a group of super smart scientists called the Jasons. They were paid by the government to get together in the summers and think about technological issues that might be useful for defense and things like war fighting. … 

I had a meeting with the (current) Jasons. I was invited to a place near Stanford to address them about genetic engineering, genomics, and all this stuff. I thought okay these are serious scientists and I'll give them a very nice overview of the progress in this field. This anecdote takes place just a few years ago. 

One of the Jasons present is a biochemist but not an expert on genomics or machine learning. This biochemist asked me a few sharp questions which were easy to answer. But then at some point he just can't take it anymore and he grabs all his stuff and runs out of the room. ...

[more]

Lunar Society (Dwarkesh Patel) Interview

 

Dwarkesh did a fantastic job with this interview. He read the scientific papers on genomic prediction and his questions are very insightful. Consequently we covered the important material that people are most confused about. 

Don't let the sensationalistic image above deter you -- I highly recommend this podcast!

0:00:00 Intro 

0:00:49 Feynman’s advice on picking up women 

0:12:21 Embryo selection 

0:24:54 Why hasn't natural selection already optimized humans? 

0:34:48 Aging 

0:43:53 First Mover Advantage 

0:54:24 Genomics in dating 

1:01:06 Ancestral populations 

1:08:33 Is this eugenics? 

1:16:34 Tradeoffs to intelligence 

1:25:36 Consumer preferences 

1:30:49 Gwern 

1:35:10 Will parents matter? 

1:46:00 Wordcels and shape rotators 

1:58:04 Bezos and brilliant physicists 

2:10:58 Elite education 

If you prefer audio-only click here.

How We Learned, Then Forgot, About Human Intelligence... And Witnessing the Live Breakdown of Academia (podcast interview with Cactus Chu)

This is a long interview I did recently with Cactus Chu, a math prodigy turned political theorist and podcaster. (Unfortunately I can't embed the podcast here.)

Steve Hsu: How We Learned, Then Forgot, About Human Intelligence... And Witnessing the Live Breakdown of Academia 

Timestamps: 

3:24 Interview Starts  

15:49 Cactus' Experience with High Math People 

19:49 High School Sports 

21:26 Comparison to Intelligence 

26:29 Is Lack of Understanding due to Denial or Ignorance? 

29:29 The Past and Present of Selection in Academia 

37:02 How Universities Look from the Inside 

44:19 Informal Networks Replacing Credentials 

48:37 Capture of Research Positions 

50:24 Progressivism as Demagoguery Against the Self-Made 

55:31 Innumeracy is Common 

1:06:53 Understanding Innumerate People 

1:13:53 Skill Alignment at Cactus' High School 

1:18:12 Free Speech in Academia 

1:21:00 You Shouldn't Fire Exceptional People 

1:23:03 The Anti-Excellence Progressives 

1:28:42 Rawls, Nozick, and Technology 

1:34:00 Freedom = Variance = Inequality 

1:37:58 Dating Apps 

1:41:27 Jumping Into Social Problems From a Technical Background 

1:41:50 Steve's High School Pranks 

1:46:43 996 and Cactus' High School 

1:50:26 The Vietnam War and Social Change 

1:53:07 Are Podcasts the Future? 

1:59:37 The Power of New Things 

2:02:56 The Birth of Twitter 

2:07:27 Selection Creates Quality 

2:10:21 Incentives of University Departments 

2:16:29 Woke Bureaucrats 

2:27:59 Building a New University 

2:30:42 What needs more order? 

2:31:56 What needs more chaos?

An automated (i.e., imperfect) transcript of our discussion.

Here's an excerpt from the podcast:

@hsu_steve on innumerate journalists, professors, and politicians:

"If you are not high [Math], you cannot reason from statistical data" pic.twitter.com/N3glUITCXH

— Cactus Chu (@cactus_chu) May 3, 2022

"The Pressure to Conform is Enormous": Steve Hsu on Affirmative Action, Assimilation and IQ Outliers (CSPI Podcast with Richard Hanania)

 

Another great conversation with Richard Hanania. 

Some rough timestamps: 

Begin: American society, growing up as child of immigrants 

18m: Russia-Ukraine conflict (eve of invasion), geopolitical implications (China, India, Germany, EU) 

38m: Affirmative Action, Harvard case at SCOTUS 

54m: Woke leftists at the university, destruction of meritocracy, STEM vs Social Justice advocacy, Sokal Hoax 

1h25m: Academic economics, 2008 credit crisis, Do economists test theories? 

1h33m: Maverick thinking, Agreeableness, Aspergers, Pressure to conform 

1h39m: Far-tail intelligence, Jeff Bezos and physics, progress in science and technology

Full transcript at Richard's substack.

Peter Shor on Quantum Factorization and Error Correction

 

This talk by Peter Shor describes the discovery of his quantum algorithm for prime factorization, and the discovery of quantum error correcting codes. The talk commemorates the first conference (Endicott House meeting) on the physics of computation in 1981. See 40 Years of Quantum Computation and Quantum Information.

Shor did not attend the 1981 meeting, where Feynman gave the keynote address Simulating Physics With Computers -- he was in his senior year at Caltech. But he recalls a talk that Feynman gave around the same time, on the possibility that negative probabilities might illuminate the EPR experiment and the Bell inequalities. 

Coincidentally, in my senior year (1986) I got Feynman to give a talk to the Society of Physics Students on this very topic! (I think I was president of SPS at the time.)

Three Thousand Years and 115 Generations of 徐 (Hsu / Xu)

Over the years I have discussed economic historian Greg Clark's groundbreaking work on the persistence of social class. Clark found that intergenerational social mobility was much less than previously thought, and that intergenerational correlations on traits such as education and occupation were consistent with predictions from an additive genetic model with a high degree of assortative mating. 

See Genetic correlation of social outcomes between relatives (Fisher 1918) tested using lineage of 400k English individuals, and further links therein. Also recommended: this recent podcast interview Clark did with Razib Khan. 

The other day a reader familiar with Clark's work asked me about my family background. Obviously my own family history is not a scientific validation of Clark's work, being only a single (if potentially illustrative) example. Nevertheless it provides an interesting microcosm of the tumult of 20th century China and a window into the deep past...

I described my father's background in the post Hsu Scholarship at Caltech:

Cheng Ting Hsu was born December 1, 1923 in Wenling, Zhejiang province, China. His grandfather, Zan Yao Hsu was a poet and doctor of Chinese medicine. His father, Guang Qiu Hsu graduated from college in the 1920's and was an educator, lawyer and poet. 

Cheng Ting was admitted at age 16 to the elite National Southwest Unified University (Lianda), which was created during WWII by merging Tsinghua, Beijing, and Nankai Universities. This university produced numerous famous scientists and scholars such as the physicists C.N. Yang and T.D. Lee. 

Cheng Ting studied aerospace engineering (originally part of Tsinghua), graduating in 1944. He became a research assistant at China's Aerospace Research Institute and a lecturer at Sichuan University. He also taught aerodynamics for several years to advanced students at the air force engineering academy. 

In 1946 he was awarded one of only two Ministry of Education fellowships in his field to pursue graduate work in the United States. In 1946-1947 he published a three-volume book, co-authored with Professor Li Shoutong, on the structures of thin-walled airplanes. 

In January 1948, he left China by ocean liner, crossing the Pacific and arriving in San Francisco. ...

My mother's father was a KMT general, and her family related to Chiang Kai Shek by marriage. Both my grandfather and Chiang attended the military academy Shinbu Gakko in Tokyo. When the KMT lost to the communists, her family fled China and arrived in Taiwan in 1949. My mother's family had been converted to Christianity in the 19th century and became Methodists, like Sun Yat Sen. (I attended Methodist Sunday school while growing up in Ames IA.) My grandfather was a partner of T.V. Soong in the distribution of bibles in China in the early 20th century.

My father's family remained mostly in Zhejiang and suffered through the communist takeover, Great Leap Forward, and Cultural Revolution. My father never returned to China and never saw his parents again. 

When I met my uncle (a retired Tsinghua professor) and some of my cousins in Hangzhou in 2010, they gave me a four volume family history that had originally been printed in the 1930s. The Hsu (Xu) lineage began in the 10th century BC and continued to my father, in the 113th generation. His entry is the bottom photo below.

Wikipedia: The State of Xu (Chinese: 徐) (also called Xu Rong (徐戎) or Xu Yi (徐夷)[a] by its enemies)[4][5] was an independent Huaiyi state of the Chinese Bronze Age[6] that was ruled by the Ying family (嬴) and controlled much of the Huai River valley for at least two centuries.[3][7] It was centered in northern Jiangsu and Anhui. ...

Generations 114 and 115:

Four volume history of the Hsu (Xu) family, beginning in the 10th century BC. The first 67 generations are covered rather briefly, only indicating prominent individuals in each generation of the family tree. The books are mostly devoted to generations 68-113 living in Zhejiang. (Earlier I wrote that it was two volumes, but it's actually four. The printing that I have is two thick books.)

Gerald Feinberg and The Prometheus Project

Gerald Feinberg (1933-1992) was a theoretical physicist at Columbia, perhaps best known for positing the tachyon -- a particle that travels faster than light. He also predicted the existence of the mu neutrino. 

Feinberg attended Bronx Science with Glashow and Weinberg. Interesting stories abound concerning how the three young theorists were regarded by their seniors at the start of their careers. 

I became aware of Feinberg when Pierre Sikivie and I worked out the long range force resulting from two neutrino exchange. Although we came to the idea independently and derived, for the first time, the correct result, we learned later that it had been studied before by Feinberg and Sucher. Sadly, Feinberg died of cancer shortly before Pierre and I wrote our paper. 

Recently I came across Feinberg's 1969 book The Prometheus Project, which is one of the first serious examinations (outside of science fiction) of world-changing technologies such as genetic engineering and AI. See reviews in Science, Physics Today, and H+ Magazine. A scanned copy of the book can be found at Libgen.

Feinberg had the courage to engage with ideas that were much more speculative in the late 60s than they are today. He foresaw correctly, I believe, that technologies like AI and genetic engineering will alter not just human society but the nature of the human species itself. In the final chapter, he outlines a proposal for the eponymous Prometheus Project -- a global democratic process by which the human species can set long term goals in order to guide our approach to what today would be called the Singularity.

See From Genotype to Phenotype: polygenic prediction of complex human traits.

   

Lianda

The videos below are about Lianda, a wartime university located in Kunming that was formed by the merger of Peking University, Tsinghua University, and Nankai University.

Lianda: A Chinese University in War and Revolution 

In the summer of 1937, Japanese troops occupied the campuses of Beijing’s two leading universities, Beida and Qinghua, and reduced Nankai, in Tianjin, to rubble. These were China's leading institutions of higher learning, run by men educated in the West and committed to modern liberal education. The three universities first moved to Changsha, 900 miles southwest of Beijing, where they joined forces. But with the fall of Nanjing in mid-December, many students left to fight the Japanese, who soon began bombing Changsha. 

In February 1938, the 800 remaining students and faculty made the thousand-mile trek to Kunming, in China’s remote, mountainous southwest, where they formed the National Southwest Associated University (Lianda). In makeshift quarters, subject to sporadic bombing by the Japanese and shortages of food, books, and clothing, students and professors did their best to conduct a modern university. In the next eight years, many of China’s most prominent intellectuals taught or studied at Lianda. ... 

Lianda’s wartime saga crystallized the experience of a generation of Chinese intellectuals, beginning with epic journeys, followed by years of privation and endurance, and concluding with politicization, polarization, and radicalization, as China moved from a war of resistance against a foreign foe to a civil war pitting brother against brother. The Lianda community, which had entered the war fiercely loyal to the government of Chiang Kai-shek, emerged in 1946 as a bastion of criticism of China’s ruling Guomindang party. Within three years, the majority of the Lianda community, now returned to its north China campuses in Beijing and Tianjin, was prepared to accept Communist rule. ...

My father attended this university at age 16, admitted via Tsinghua. Among its most famous alumni are the Nobel Prize winning theoretical physicists C.N. Yang and T.D. Lee. As the university only existed for 8 years, there are very few alumni still living.

 

I came across the 一条 Yit channel because I recently bought an Android Smart TV and it caused an increase in consumption of YouTube, etc. I got the TV to use as a big monitor but it's great for content as well. One of the most enjoyable things I do with it is watch seminars (e.g., in theoretical physics or AI)!

In case you are wondering I bought a 70inch HiSense on sale for under $400: good 4k picture and sound -- highly recommended!

Replications of Height Genomic Prediction: Harvard, Stanford, 23andMe

These are two replications of our 2017 height prediction results (also recently validated using sibling data) that I neglected to blog about previously.

1. Senior author Liang is in Epidemiology and Biostatistics at Harvard.

Efficient cross-trait penalized regression increases prediction accuracy in large cohorts using secondary phenotypes 

Wonil Chung, Jun Chen, Constance Turman, Sara Lindstrom, Zhaozhong Zhu, Po-Ru Loh, Peter Kraft and Liming Liang 

Nature Communications volume 10, Article number: 569 (2019) 

We introduce cross-trait penalized regression (CTPR), a powerful and practical approach for multi-trait polygenic risk prediction in large cohorts. Specifically, we propose a novel cross-trait penalty function with the Lasso and the minimax concave penalty (MCP) to incorporate the shared genetic effects across multiple traits for large-sample GWAS data. Our approach extracts information from the secondary traits that is beneficial for predicting the primary trait based on individual-level genotypes and/or summary statistics. Our novel implementation of a parallel computing algorithm makes it feasible to apply our method to biobank-scale GWAS data. We illustrate our method using large-scale GWAS data (~1M SNPs) from the UK Biobank (N = 456,837). We show that our multi-trait method outperforms the recently proposed multi-trait analysis of GWAS (MTAG) for predictive performance. The prediction accuracy for height by the aid of BMI improves from R2 = 35.8% (MTAG) to 42.5% (MCP + CTPR) or 42.8% (Lasso + CTPR) with UK Biobank data.

2. This is a 2019 Stanford paper. Tibshirani and Hastie are famous researchers in statistics and machine learning. Figure is from their paper.

A Fast and Flexible Algorithm for Solving the Lasso in Large-scale and Ultrahigh-dimensional Problems 

Junyang Qian, Wenfei Du, Yosuke Tanigawa, Matthew Aguirre, Robert Tibshirani, Manuel A. Rivas, Trevor Hastie 

1Department of Statistics, Stanford University 2Department of Biomedical Data Science, Stanford University 

Since its first proposal in statistics (Tibshirani, 1996), the lasso has been an effective method for simultaneous variable selection and estimation. A number of packages have been developed to solve the lasso efficiently. However as large datasets become more prevalent, many algorithms are constrained by efficiency or memory bounds. In this paper, we propose a meta algorithm batch screening iterative lasso (BASIL) that can take advantage of any existing lasso solver and build a scalable lasso solution for large datasets. We also introduce snpnet, an R package that implements the proposed algorithm on top of glmnet (Friedman et al., 2010a) for large-scale single nucleotide polymorphism (SNP) datasets that are widely studied in genetics. We demonstrate results on a large genotype-phenotype dataset from the UK Biobank, where we achieve state-of-the-art heritability estimation on quantitative and qualitative traits including height, body mass index, asthma and high cholesterol.

The very first validation I heard about was soon after we posted our paper (2018 IIRC): I visited 23andMe to give a talk about genomic prediction and one of the PhD researchers there said that they had reproduced our results, presumably using their own data. At a meeting later in the day, one of the VPs from the business side who had missed my talk in the morning was shocked when I mentioned few cm accuracy for height. He turned to one of the 23andMe scientists in the room and exclaimed 

I thought WE were the best in the world at this stuff!?

Feynman on AI

Thanks to a reader for sending the video to me. The first clip is of Feynman discussing AI, taken from the longer 1985 lecture in the second video.

There is not much to disagree with in his remarks on AI. He was remarkably well calibrated and would not have been very surprised by what has happened in the following 35 years, except that he did not anticipate (at least, does not explicitly predict) the success that neural nets and deep learning would have for the problem that he describes several times as "pattern recognition" (face recognition, fingerprint recognition, gait recognition). Feynman was well aware of early work on neural nets, through his colleague John Hopfield.  [1] [2] [3]

I was at Caltech in 1985 and this is Feynman as I remember him. To me, still a teen ager, he seemed ancient. But his mind was marvelously active! As you can see from the talk he was following the fields of AI and computation rather closely. 

Of course, he and other Manhattan project physicists were present at the creation. They had to use crude early contraptions for mechanical calculation in bomb design computations. Thus, the habit of reducing a complex problem (whether in physics or machine learning) to primitive operations was second nature. Already for kids of my generation it was not second nature -- we grew up with early "home computers" like the Apple II and Commodore, so there was a black box magic aspect already to programming in high level languages. Machine language was useful for speeding up video games, but not everyone learned it. The problem is even worse today: children first encounter computers as phones or tablets that already seem like magic. The highly advanced nature of these devices discourages them from trying to grasp the underlying first principles.  

If I am not mistaken the t-shirt he is wearing is from the startup Thinking Machines, which built early parallel supercomputers.

Just three years later he was gone. The finely tuned neural connections in his brain -- which allowed him to reason with such acuity and communicate with such clarity still in 1985 -- were lost forever.

Leif Wenar on the Resource Curse and Impact Philosophy -- Manifold Episode #49

Corey and Steve interview Leif Wenar, Professor of Philosophy at Stanford University and author of Blood Oil. They begin with memories of Leif and Corey’s mutual friend David Foster Wallace and end with a discussion of John Rawls and Robert Nozick (Wenar's thesis advisor at Harvard, and a friend of Steve's). Corey asks whether Leif shares his view that analytic philosophy had become too divorced from wider intellectual life. Leif explains his effort to re-engage philosophy in the big issues of our day as Hobbes, Rousseau, Locke, Mill and Marx were in theirs. He details how a trip to Nigeria gave him insight into the real problems facing real people in oil-rich countries. Leif explains how the legal concept of “efficiency” led to the resource curse and argues that we should refuse to buy oil from countries that are not minimally accountable to their people. Steve notes that some may find this approach too idealistic and not in the US interest. Leif suggests that what philosophers can contribute is the ability to see the big synthetic picture in a complex world.

Transcript

Leif Wenar (Bio)

Blood Oil: Tyrants, Violence, and the Rules That Run the World

John Rawls - Stanford Encyclopedia of Philosophy

Robert Nozick - Stanford Encyclopedia of Philosophy


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.

Skidelsky, Against Economics (NY Review of Books)

From the NY Review of Books, an article entitled Against Economics, which reviews the recent book by Robert Skidelsky.

Money and Government: The Past and Future of Economics

Robert Skidelsky
Yale University Press

... Before long, the Bank of England (the British equivalent of the Federal Reserve, whose economists are most free to speak their minds since they are not formally part of the government) rolled out an elaborate official report called “Money Creation in the Modern Economy,” replete with videos and animations, making the same point: existing economics textbooks, and particularly the reigning monetarist orthodoxy, are wrong. The heterodox economists are right. Private banks create money. Central banks like the Bank of England create money as well, but monetarists are entirely wrong to insist that their proper function is to control the money supply. In fact, central banks do not in any sense control the money supply; their main function is to set the interest rate—to determine how much private banks can charge for the money they create. Almost all public debate on these subjects is therefore based on false premises. For example, if what the Bank of England was saying were true, government borrowing didn’t divert funds from the private sector; it created entirely new money that had not existed before.

[[ Certainly central banks influence the money supply, but the degree to which they control animal spirits, lending practices and standards, the price of credit risk in general, etc. via a single part of the yield curve is highly debatable, dependent on many factors such as investor psychology and recent events, etc. etc.  There is no doubt this is a complex question worthy of deep analysis ... 

At any instant in time there is a certain level of tolerance for borrowing from the future (private and public debt), and merely by changing this level of tolerance one can in effect create money out of thin air ... This level of tolerance is a completely emergent phenomenon and no one fully controls it. ]]

... one of the most significant books to come out of the UK in recent years would have to be Robert Skidelsky’s Money and Government: The Past and Future of Economics. Ostensibly an attempt to answer the question of why mainstream economics rendered itself so useless in the years immediately before and after the crisis of 2008, it is really an attempt to retell the history of the economic discipline through a consideration of the two things—money and government—that most economists least like to talk about.

On the question of whether academic economists understand how the world works, I'll just reiterate that at the time of the last financial crisis (circa 2007-2008) I became aware through direct experience that many very prominent economists did not know what a Credit Default Swap was, did not know how the credit markets actually worked, did not know how credit risk was priced. Instead, their mental model consisted of coarse graining over all of this activity (quants, traders, mobs, speculators, thieves, fraudsters) as simply a (more or less) rational and efficient market not worthy of deep inspection.

They will all deny it now, of course. But I was there.


Note added: In the 1990s, in part due to the collapse of the Soviet empire and resulting mass emigration of top scientists to the West, there were very few opportunities in theoretical physics and related fields for young researchers. Consequently large numbers of extremely talented people left the field (largely against their will) and perhaps most of them ended up in finance. As might be expected a large number of big brains began thinking about previously obscure topics such as options pricing (derivatives, Black Scholes), credit risk, the yield curve, etc. Immediately it was noted, by myself and others, that methods from imaginary time quantum mechanics, path integrals, etc., could be applied to the pricing of derivatives -- especially exotic derivatives which had, up to that time, required significant computational resources to simulate.

The yield curve and credit derivatives are especially challenging problems. One reason is that they deal with a potentially infinite (if a continuous curve is assumed) number of degrees of freedom. As one of my former Caltech-Harvard collaborators (by the 1990s a quant-trader, now a hedge fund magnate) described it, modeling the yield curve compared to pricing equity derivatives is like quantum field theory compared to simple quantum mechanics.

In modeling the yield curve one immediately asks: what are the underlying dynamics? What are reasonable consistency conditions? What is the impact of a "shock" like a change in the Fed funds rate? A moment of reflection reveals that market psychology plays a huge role in setting the model parameters... A bit of historical investigation shows radical changes in the yield curve (and, consequently, the effective "money supply") over time. One can in effect create money out of thin air!

Ronin

My first visit to Japan was in 1993. Ostensibly, I was there to attend a conference on High Energy Physics at the University of Tokyo, and to give a seminar at KEK, the largest particle accelerator laboratory in Japan.

I spent the first night at the Shinagawa Prince Hotel. I had carefully chosen this hotel -- it is a short walk from Sengakugi Temple, the resting place of the 47 Ronin (see photos above and history below).

It was already late at night when I checked in and deposited my luggage in the room. I was jet-lagged, but still energetic after the long trip. Outside, the neighborhood was deserted and dark except for the harsh glare of neon streetlights. It had rained and the streets were wet and shiny. As I approached the temple I could smell the burning incense that suffused the night air ...

47 Ronin (photo above from the 1941 movie directed by Kenji Mizoguchi)

... At the death of their lord, Asano’s samurai retainers become masterless, or rōnin, and under the planning of Ōishi Kuranosuke Yoshio, Asano’s counsellor, 47 of these rōnin plot to avenge their former master. Because Kira suspects this, and spies on Ōishi, revenge is delayed as the rōnin disperse and assume other occupations, while Ōishi performs the life of a drunkard, visiting taverns and geisha. After a year and a half the rōnin return to Edo to stake out Kira’s house, and two years following Asano’s death they attack. Kira is eventually killed and his head is taken as an offering to Asano’s grave. The rōnin then turn themselves in to the Shogunate authorities. Having defied a Shogunate edict prohibiting them to avenge their master, but having followed the requirements of bushido in doing so, the rōnin are sentenced to death but allowed to die honourably by committing seppuku.

I had all but forgotten about my strange visit to Sengakuji, so long ago. But memory returned when I came across the interview below, with former special forces soldier and tactical instructor Tu Lam.

Interview

When you started out in the world of Special Operations it was pre-9/11. What was that like compared to how it is now?

TL: My understanding from birth was one of war. I was born out of war. I was born in ’74 after the fall of Saigon. In ’76 they dragged us out into the streets of Vietnam because they were trying to impose the Communist ideologies of our government. My uncles were serving in the Navy and were dragged out into the streets like animals and shot. They separated our family and imprisoned my other uncles in what they called “re-education camps.” My grandfather took his life savings and smuggled us out of the country because my mom was like, “There’s no way my two sons will grow up under Communist rule.” We left on an overstuffed wooden boat with hundreds of other refugees. First we had to be navigated past the pirating that was going on. There were a lot of bandits, pirates and everyone who was leaving country had money. These pirates would intercept the refugees, rape the women, rob the boats and kill everyone on board.

We navigated past the pirates first then made it into Indonesia where the Coast Guard stopped us. They told us we couldn’t come into their country. They anchored us down and pulled us back into the ocean on lines, then shot our motor and cut the lines, leaving us out in the middle of the waters to die. Our boat drifted further and further into the ocean. My mother told me that people were stealing from each other, fighting, and eventually dying due to the terrible conditions. We were caught up in a storm and this storm took us out into the middle of Russian waters by the grace of God. A Russian supply boat picked us up as they were crossing the Pacific Ocean into Singapore. They dropped us off at a refugee camp in Indonesia. The irony of this story is the same ideology that took me out of my country (Communism) was the same ideology that brought me to safety.

My family was gunned down like animals by a Communist government and yet the Russians, another Communist government, saved us. That was my first lesson in humanity and that everyone is truly different. The Indonesian monks came and helped us while we were in the camp. My aunt had married a Special Forces Green Beret and he expedited the paperwork to get us out of Indonesia and to the United States. At the age of eight I found myself on Ft. Bragg and my mom re-married a Sergeant who was a Green Beret. At that early age, I was indoctrinated in the ways of a Special Forces soldier. I learned how to speak different languages, learned how to take apart many different types of weapons, and learned how to properly navigate the back woods of North Carolina.

I was taught how to navigate the stars and build my own compasses. The truth is, we were just spending father and son time but he was teaching me a trade craft. Throughout my life he’d leave, come back, leave, come back and I’d equate it with seeing something bad on the news. Panama happened and he immediately went over there. I felt from a very young age, being raised as a part of that warrior class, that I had a much higher purpose. I knew what a sheep, sheep dog, and a wolf were from a very young age. My dad taught me that very early on. I asked my father how I could help protect and my dad said I’d have to pass a test to become a part of the brotherhood. At ten years old I wanted to be a Green Beret.

Like a lot of Asians, I was academically gifted at a very young age. I had scholarships and I turned them down. I made better grades than my brother and he ended up being a doctor. When I got to age 18 I went to MEPS and applied for 11B (Infantry). There was no such thing as 18X or direct entry into Special Operations. You couldn’t just come off the streets and train for Special Operations. You had to become an E5 (Sergeant) first and then do a certain amount of years. Those years could be waived and so I made E5 after a year and a half. When I went in I went into long-range reconnaissance, which took me directly into the Marines’ Amphibious School, Ranger training, and a lot of other leadership courses as well as the Army Sniper School.

See also On Japan and Learning how to fight.

Epstein and the Big Lie

The biggest Epstein conspiracy mystery is not how he died. The more important mystery is how he managed to operate out in the open for 15-20 years. Rumors concerning Epstein and leading figures like Bill Clinton have been around for at least that long. I have been following his activities, at least casually, for well over a decade.

In the 1990s I was a Bill Clinton supporter. I voted for him twice and supported his efforts to move the democratic party in a centrist, pro-business direction. But my brother is a Republican. He fed me a steady stream of anti-Clinton information that I (at the time) dismissed as crazy right-wing conspiracy theories. However, with the advent of the internet in the mid 90s it became easier to obtain information that was not filtered by corrupt mainstream media outlets. I gradually realized that at least some of my brother's claims were correct. For example, Clinton's first presidential bid was almost derailed by charges of adultery by women like Gennifer Flowers. Supporters like myself dismissed these charges as a right-wing smear. However, years later, Clinton admitted under oath that he had indeed had sex with Flowers.

My first exposure to Hillary Clinton was her appearance on 60 Minutes after the SuperBowl in 1992. This was widely regarded to be the emotive performance ("stand by your man") that saved Bill Clinton's presidential candidacy. Hillary affects a fake southern accent and (I believe) lies boldly and convincingly about Flowers to an estimated 50 million Americans. Quite a display of talent.

A side-effect of my history as a Clinton supporter (and gradual enlightenment thanks to my brother!) is that I became quite interested in the tendency of the media to hide obvious truths from the general public. We Americans accept that foreign governments (e.g., the Soviets and "ChiComs") successfully brainwash their people to believe all sorts of crazy and false things. But we can't accept that the same might be true here. (The big difference is that people in the PRC and former Soviet states  -- especially intellectuals -- know propaganda when they see it, whereas most Americans do not...)

It was natural for me to become aware of Epstein once he was linked to Bill Clinton at the very birth of the Clinton Foundation. It was easy to uncover very disturbing aspects of the Epstein story -- including details of his private island, traffic in young women, connections to the rich, the powerful, and even to leading scientists, academics (many of whom I know), and Harvard University. Almost anyone with access to the internet (let alone an actual journalist) could have discovered these things at any point in the last decade.

But just 6 months ago I could mention Epstein to highly educated "politically aware" acquaintances with absolutely no recognition on their part.

Some obvious, and still unanswered, questions:

Former Federal prosecutor and Labor Secretary R. Alexander Acosta said he was told to lay off Epstein, as he "belongs to intelligence" -- why no media followup on this? (Still don't believe in a Deep State?)

Clinton said he only flew on Epstein's plane 4 times (but 26 is also commonly reported) and never visited the island (despite many eyewitness claims to the contrary). No investigative reporting on this by mainstream media?

Epstein's partner Ghislaine Maxwell is the daughter of Robert Maxwell, a billionaire with possible Mossad connections. What were Epstein's links to Israeli intelligence and national interests? (Robert Maxwell's death is at least as mysterious as Epstein's ...)

Why did it take the FBI so long to get to Epstein's island? What have they found in Epstein's house and on his island? How much blackmail material is there and who is implicated?

Were it not for the possibility that the Epstein scandal might be damaging to Trump, would there be anything close to this level of mainstream media interest?

Why was there almost zero MSM interest in Epstein in the previous 15-20 years?

Someone was protecting Epstein (someone with influence on the DOJ, FBI, perhaps US intelligence) long before Donald Trump had political power of any kind. Why?

What other obvious scandals are hidden in plain sight? Iraq WMD? Spygate? Compromised politicians and national leaders? Blackmail by national intelligence services? Ideology-driven Social Media and Search filtering of information? Ivy League discrimination against Asian Americans? ...

Update

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.  

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

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

Steve and Corey 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.

The Quantum Theory of Fields

Excerpt from Sidney Coleman's Erice lectures. The period he describes just predates my entry into physics.

This was a great time to be a high-energy theorist, the period of the famous triumph of quantum field theory. And what a triumph it was, in the old sense of the word: a glorious victory parade, full of wonderful things brought back from far places to make the spectator gasp with awe and laugh with joy. I hope some of that awe and joy has been captured here.

Physics students learn quantum mechanics and special relativity as undergraduates, but typically do not encounter a synthesis of the two until graduate school, in a course on quantum field theory. Undergraduate quantum mechanics focuses on non-relativistic particles, moving at much less than the speed of light (e.g., the electrons in atomic systems or ordinary matter). Special relativity, as first encountered by students, is a modification of Newtonian (classical) mechanics, and ignores quantum effects.

In quantum field theory (QFT), the wave function of quantum mechanics Ψ(x) becomes a wave functional Ψ[ Φ(x) ], valued over field configurations Φ(x) which are themselves functions of spacetime coordinates. Individual particles are excitations ("quanta") of quantum fields. I think it is fair to say that almost no student really gets a deep understanding of quantum field theory when they take it for the first time. It is simply too complex to digest quickly. QFT introduces new intuitive pictures, novel calculational tricks, strange physical and mathematical constructs.

And how could it be otherwise? All of these tools are necessary to make sense of the generalization of ordinary quantum mechanics (of a finite number of degrees of freedom) to a physical system with an infinite number of degrees of freedom.

I first took quantum field theory (Physics 205) in my last year at Caltech, taught by Fredrik Zachariasen. Zachariasen used Bjorken and Drell I and II and Ramond as the main textbooks. He was what Russian theorists sometimes refer to as a "strong calculator" -- he would fill the blackboard with equations as fast as we could note them down. However, I would say his approach to the subject was rather old-fashioned by that time, and while I learned a good bit about the Dirac equation, spinors, how to compute Feynman diagrams, and even about path integrals, my overall understanding of the subject was still lacking. If I had been there the following year I would have enjoyed John Preskill's version of 205 (see below), but alas I was already in graduate school by then.

I remember that I also studied Feynman's short volume (in the Frontiers in Physics series; not to be confused with his later popular book) Quantum Electrodynamics. I was very confused at the time about the relationship between particles and fields and about so-called Second Quantization.  Also, what happened to the Schrodinger equation? At no point did Zachariasen (nor, I think, do Bjorken and Drell) clarify that while Dirac deduced his equation via relativistic generalization of Schrodinger's, the two are not on the same logical footing.

It was only some years later that I realized that Feynman himself had been confused about these things when he wrote his early papers on the subject. (Feynman, when someone explained a creation operator and Fock space to him: "How can you create an electron? It disagrees with conservation of charge!") Do Feynman diagrams describe spacetime trajectories of particles? Or are they simply graphical representations of terms in a perturbative expansion that happen to correspond, intuitively but not exactly, to physical processes?

As a first year graduate student at Berkeley I took Physics 230 from Stanley Mandelstam, a true master of the subject. This course was far more theoretical than the one I had taken the previous year. Amazingly, Stanley taught without notes. The only day he brought a single page of paper to class was when he covered the BPHZ proof of renormalizability. (Or was it the day he derived the beta function for non-Abelian gauge theories? I might be conflating two different instances.) His lectures followed no specific textbook, although the recommended one was probably Itzykson and Zuber.

My final student encounter with a QFT course was as the grader for Physics 230, taught by Martin Halpern. (I am sad to discover, in finding this link, that Marty passed away earlier this year.) Marty was a high strung chain smoker, and I recall many hours in his office going over solutions to his homework problems. He was especially on edge that fall because Vaughan Jones from the math department (who was about to share the Fields Medal with Ed Witten!) had decided to learn QFT and was sitting in on the class. As might be expected, the mathematician's insistence on clarity and precision slowed Marty down significantly. This wasn't Marty's fault -- QFT has not, even today, been placed on a completely rigorous footing (at least, not to the satisfaction of mathematicians), even though it is (in the form of Quantum Electrodynamics and the Standard Model) the most precisely tested theoretical construct in science.

This post is long enough. Perhaps I will revisit the topic in the future with a discussion of Sidney Coleman's lectures on QFT at Harvard, where I went after graduate school. It's nice to see that these lectures have been rendered into a book by his former students. For many years one could check out videotapes (Sony Betamax!) of his lectures from the physics library at Harvard. This made me think, even then, that the future of many professors might someday be as glorified teaching assistants, helping to explain and clarify recorded or streamed lectures by the true masters.

If I have kindled your interest in the subject, I recommend my friend Tony Zee's book: Quantum Field Theory in a Nutshell. Also, John Preskill's fantastic lecture notes, covering basic as well as advanced topics. It took me some time to learn to decipher his handwriting, but it was worth it!

Let me end by noting that the physics students who took these classes with me are quite a remarkable group. Among them are a number of well-known theoretical physicists, as well as the odd startup founder, AI researcher, or hedge fund billionaire. You could do worse in this life than get to know some students of quantum field theory :-)