Glenn Luk has worked as an investment banker, private equity investor, and startup founder. He has closely analyzed aspects of the Chinese economy, including its GDP and high speed rail system.
Steve and Glenn discuss:
(00:00) - Introduction
(01:21) - Glenn Luk's Background: HK, Taiwan, China
(07:59) - Evolution of Chinese Companies and Economy
(14:58) - From Banking to Private Equity and Venture Capital
(23:08) - Founding a Healthcare Startup and Entrepreneurial Ventures
(26:35) - China's Development and Economic Policies
(41:17) - Comparing US and China's Economies and Cultures
(47:12) - Demographics and Consumer Behavior in China
(49:09) - China's Economy: Beyond GDP
(56:34) - High Speed Rail: huge success, or white elephant?
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.
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.
David Paul Goldman is an American economic strategist and author, best known for his series of online essays in the Asia Times under the pseudonym Spengler with the first column published January 1, 2000.
Steve and David discuss:
0:00 Introduction
2:22 David’s background in music, finance, and Asia
16:55 Looking back at the financial crisis
23:04 Rise of the Chinese economy
29:44 How Huawei’s strength is tied to China’s economic power
36:49 Competition in the global electric vehicles market
38:06 Why David thinks European countries like Germany will become closer with China
45:29 U.S. manufacturing is falling behind
52:08 Potential for war and ongoing U.S.-China competition
Tim Palmer is Royal Society Research Professor in Climate Physics, and a Senior Fellow at the Oxford Martin Institute.
He is interested in the predictability and dynamics of weather and climate, including extreme events.
He was involved in the first five IPCC assessment reports and was co-chair of the international scientific steering group of the World Climate Research Programme project (CLIVAR) on climate variability and predictability.
After completing his DPhil at Oxford in theoretical physics, Tim worked at the UK Meteorological Office and later the European Centre for Medium-Range Weather Forecasts. For a large part of his career, Tim has developed ensemble methods for predicting uncertainty in weather and climate forecasts.
In 2020 Tim was elected to the US National Academy of Sciences.
Steve, Corey Washington, and Tim first discuss his career path from physics to climate research and then explore the science of climate modeling and the main uncertainties in state-of-the-art models.
In this episode, we discuss:
00:00 Introduction
1:48 Tim Palmer's background and transition from general relativity to climate modeling
Note added: For some background on the importance of water vapor (cloud) distribution within the primitive cells used in these climate simulations, see:
Low clouds trap IR radiation near the Earth, while high clouds reflect solar energy back into space. The net effect on heating from the distribution of water vapor is crucial in these models. However, due to the complexity of the Navier-Stokes equations, current simulations cannot actually solve for this distribution from first principles. Rather, the modelers hand code assumptions about fine grained behavior within each cell. The resulting uncertainty in (e.g., long term) climate prediction from these approximations is unknown.
Raghu Parthasarathy is the Alec and Kay Keith Professor of Physics at the University of Oregon. His research focuses on biophysics, exploring systems in which the complex interactions between individual components, such as biomolecules or cells, can give rise to simple and robust physical patterns.
Raghu is the author of a recent popular science book, So Simple a Beginning: How Four Physical Principles Shape Our Living World.
Steve and Raghu discuss:
0:00 Introduction
1:34 Early life, transition from Physics to Biophysics
20:15 So Simple a Beginning: discussion of the Four Physical Principles in the title, which govern biological systems
26:06 DNA prediction
37:46 Machine learning / causality in science
46:23 Scaling (the fourth physical principle)
54:12 Who the book is for and what high schoolers are learning in their bio and physics classes
1:05:41 Science funding, grants, running a research lab
1:09:12 Scientific careers and radical sub-optimality of the existing system
It was a fascinating episode, and I immediately went out and ordered the book! One question that came to mind: given how much of the human genome is dedicated to complex regulatory mechanisms and not proteins as such, it seems unintuitive to me that so much of heritability seems to be additive. I would have thought that in a system with lots of complicated,messy on/off switches, small genetic differences would often lead to large phenotype differences -- but if what I've heard about polygenic prediction is right, then, empirically, assuming everything is linear seems to work just fine (outside of rare variants, maybe). Is there a clear explanation for how complex feedback patterns give rise to linearity in the end? Is it just another manifestation of the central limit theorem...?
steve hsu
This is an active area of research. It is somewhat surprising even to me how well linearity / additivity holds in human genetics. Searches for non-linear effects on complex traits have been largely unsuccessful -- i.e., in the sense that most of the variance seems to be controlled by additive effects. By now this has been investigated for large numbers of traits including major diseases, quantitive traits such as blood biomarkers, height, cognitive ability, etc.
One possible explanation is that because humans are so similar to each other, and have passed through tight evolutionary bottlenecks, *individual differences* between humans are mainly due to small additive effects, located both in regulatory and coding regions.
To genetically edit a human into a frog presumably requires many changes in loci with big nonlinear effects. However, it may be the case that almost all such genetic variants are *fixed* in the human population: what makes two individuals different from each other is mainly small additive effects.
Zooming out slightly, the implications for human genetic engineering are very positive. Vast pools of additive variance means that multiplex gene editing will not be impossibly hard...
Great discussion and insider views of AI/ML research.
Academics think of themselves as trailblazers, explorers — seekers of the truth.
Any fundamental discovery involves a significant degree of risk. If an idea is guaranteed to work then it moves from the realm of research to engineering. Unfortunately, this also means that most research careers will invariably be failures at least if failures are measured via “objective” metrics like citations.
Today we discuss the recent article from Mark Saroufim called Machine Learning: the great stagnation. We discuss the rise of gentleman scientists, fake rigor, incentives in ML, SOTA-chasing, "graduate student descent", distribution of talent in ML and how to learn effectively.
Topics include: OpenAI, GPT-3, RL: Dota & Starcraft, conference papers, incentives and incremental research, Is there an ML stagnation? Is theory useful? Is ML entirely empirical these days? How to suceed as a researcher, Why everyone is forced to become their own media company, and much more.
If you don't want to watch the video, read these (by Mark Saroufim) instead:
Steve and Corey talk with theoretical physicist turned hedge fund investor Vineer Bhansali. Bhansali describes his transition from physics to finance, his firm LongTail Alpha, and his recent outsize returns from the coronavirus financial crisis. Also discussed: derivatives pricing, random walks, helicopter money, and Modern Monetary Theory.
Added: Background on derivatives history here. AFAIK high energy physicist M.F.M. Osborne was the first to suggest the log-normal random walk model for securities prices, in the 1950s. Bachelier suggested an additive model which does not even make logical sense. See my articles in Physics World: 1 , 2
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.
Kaja Perina is the Editor in Chief of Psychology Today. Kaja, Steve, and Corey discuss so-called Dark Triad personality traits: Narcissism, Machiavellianism, and Psychopathy. Do these traits manifest more often in super successful people? What is the difference between Sociopathy and Psychopathy? Are CEOs often "warm sociopaths"? Can too much empathy be a liability? Corey laments Sociopathy in academic Philosophy. Kaja explains the operation of Psychology Today. Steve reveals his Hypomania diagnoses.
2:33 - Psychopathology and the Dark Triad 11:34 - Do these traits manifest more often in super successful people? 17:52 - Can too much empathy be a liability? 35:16 - Corey laments Sociopathy in academic Philosophy 50:32 - Kaja explains the operation of Psychology Today 1:01:06 - Steve reveals his Hypomania diagnoses
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.
Dom has seized the controls, but now has to operate the giant robot.
This is a unique situation: someone who understands the power of modern technology, scientific decision-making, high cognitive ability, and high functioning organizations, has significant influence in the government of one of the great nations of the world.
Please consider applying for one of these positions. Dom is a special leader -- open to new ideas and to maverick personalities, loyal to his team, and a genuinely humble and good person.
This could be a once in a lifetime opportunity to make a positive impact in the world.
High skill immigration is one of the priorities for the new UK government. You do not need to be a UK citizen or permanent resident to be considered for these positions.
There are many brilliant people in the civil service and politics. Over the past five months the No10 political team has been lucky to work with some fantastic officials. But there are also some profound problems at the core of how the British state makes decisions. This was seen by pundit-world as a very eccentric view in 2014. It is no longer seen as eccentric. ...
Now there is a confluence of: a) Brexit requires many large changes in policy and in the structure of decision-making, b) some people in government are prepared to take risks to change things a lot, and c) a new government with a significant majority and little need to worry about short-term unpopularity while trying to make rapid progress with long-term problems.
There is a huge amount of low hanging fruit — trillion dollar bills lying on the street — in the intersection of:
the selection, education and training of people for high performance,
the frontiers of the science of prediction data science,
AI and cognitive technologies (e.g Seeing Rooms, `authoring tools designed for arguing from evidence’, Tetlock/IARPA prediction tournaments that could easily be extended to consider ‘clusters’ of issues around themes like Brexit to improve policy and project management)
communication (e.g. Cialdini)
decision-making institutions at the apex of government.
We want to hire an unusual set of people with different skills and backgrounds to work in Downing Street with the best officials, some as spads and perhaps some as officials. If you are already an official and you read this blog and think you fit one of these categories, get in touch.
The categories are roughly:
Data scientists and software developers
Economists
Policy experts
Project managers
Communication experts
Junior researchers one of whom will also be my personal assistant
Weirdos and misfits with odd skills
[ Please click through and read the whole post on Dom's blog ]
Note Added: Some of the media takes on Dom's job ad are extremely uncharitable. They (and the people they quote) assume Dom is entirely naive about when mathematical and computational methods might be useful, and when they might not. I suggest these people study his other writing carefully. For example:
More important than technology is the mindset – the hard discipline of obeying Richard Feynman’s advice: ‘The most important thing is not to fool yourself and you are the easiest person to fool.’ They [quant types] were a hard floor on ‘fooling yourself’ and I empowered them to challenge everybody including me. They [quant types] saved me from many bad decisions even though they had zero experience in politics and they forced me to change how I made important decisions like what got what money. We either operated scientifically or knew we were not, which is itself very useful knowledge.
Does this sound like a person who does not understand both the strengths and limitations of data science, statistics, careful epistemology, etc. in modern politics? Underestimate him at your peril...
James Cham is a partner at Bloomberg Beta, a venture capital firm focused on the future of work. James invests in companies applying machine intelligence to businesses and society. Prior to Bloomberg Beta, James was a Principal at Trinity Ventures and a VP at Bessemer Venture Partners. He was educated in computer science at Harvard and at the MIT Sloan School of Business.
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.
I don't know whether Arnold ever read Nietzsche, but he certainly developed the Will to Power early in life. I quite like the video above -- I even made my kids watch it :-)
When I was in high school I came across his book Arnold: The Education of a Bodybuilder, a combination autobiography and training manual published in 1977. I found a copy in the remainder section of the book store and bought it for a few dollars. The most interesting part of the book is the description of his early life in Austria and his introduction to weightlifting and bodybuilding. I highly recommend it to anyone interested in golden age bodybuilding, the early development of physical training, or the psychology of human drive and high achievement. Young Arnold displays a kind of unbridled and unironic egoism that can no longer be expressed without shame in today's feminized society.
Chapter 2: Before long, people began looking at me as a special person. Partly this was the result of my own changing attitude about myself. I was growing, getting bigger, gaining confidence. I was given consideration I had never received before; it was as though I were the son of a millionaire. I'd walk into a room at school and my classmates would offer me food or ask if they could help me with my homework. Even my teachers treated me differently. Especially after I started winning trophies in the weight-lifting contests I entered.
This strange new attitude toward me had an incredible effect on my ego. It supplied me with something I had been craving. I'm not sure why I had this need for special attention. Perhaps it was because I had an older brother who'd received more than his share of attention from our father. Whatever the reason, I had a strong desire to be noticed, to be praised. I basked in this new flood of attention. I turned even negative responses to my own satisfaction.
I'm convinced most of the people I knew didn't really understand what I was doing at all. They looked at me as a novelty, a freak. ...
"Why did you have to pick the least-favorite sport in Austria?" they always asked. It was true. We had only twenty or thirty bodybuilders in the entire country. I couldn't come up with an answer. I didn't know. It had been instinctive. I had just fallen in love with it. I loved the feeling of the gym, of working out, of having muscles all over.
Now, looking back, I can analyze it more clearly. My total involvement had a lot to do with the discipline, the individualism, and the utter integrity of bodybuilding. But at the time it was a mystery even to me. Bodybuilding did have its rewards, but they were relatively small. I wasn't competing yet, so my gratification had to come from other areas. In the summer at the lake I could surprise everyone by showing up with a different body. They'd say, "Jesus, Arnold, you grew again. When are you going to stop?"
"Never," I'd tell them. We'd all laugh. They thought it amusing. But I meant it.
...
The strangest thing was how my new body struck girls. There were a certain number of girls who were knocked out by it and a certain number who found it repulsive. There was absolutely no in-between. It seemed cut and dried. I'd hear their comments in the hallway at lunchtime, on the street, or at the lake. "I don't like it. He's weird—all those muscles give me the creeps." Or, "I love the way Arnold looks—so big and powerful. It's like sculpture. That's how a man should look."
These reactions gave me added motivation to continue building my body. I wanted to get bigger so I could really impress the girls who liked it and upset the others even more. Not that girls were my main reason for training. Far from it. But they added incentive and I figured as long as I was getting this attention from them I might as well use it. I had fun. I could tell if a girl was repelled by my size. And when I'd catch her looking at me in disbelief, I would casually raise my arm, flex my bicep, and watch her cringe. It was always good for a laugh. ...
A great ~20 minutes starting at ~1:01 with UFC 155 contender Kevin Lee. Lee talks about self-confidence, growing up in an all-black part of Detroit, not knowing any white people his age until attending college, getting started in wrestling and MMA. If you don't believe early environment affects life outcomes you are crazy...
They also discuss Ability vs Practice: 10,000 hour rule is BS, in wrestling and MMA as with anything else. Lee was a world class fighter by his early twenties, having had no martial arts training until starting wrestling at age 16. He has surpassed other athletes who have had intensive training in boxing, kickboxing, wrestling, jiujitsu since childhood. It will be interesting to see him face Khabib Nurmagomedov, who has been trained, almost since birth, in wrestling, judo, and combat sambo. (His father is a famous coach and former competitor in Dagestan.)
Here are some highlights from Lee's recent domination of Edson Barboza.
The arXiv paper below looks at stochastic dynamical models that can transform initial (e.g., Gaussian) talent distributions into power law outcomes (e.g., observed wealth distributions in modern societies). While the models themselves may not be entirely realistic, they illustrate the potentially large role of luck relative to ability in real life outcomes.
We're used to seeing correlations reported, often between variables that have been standardized so that both are normally distributed. I've written about this many times in the past: Success, Ability, and All That , Success vs Ability.
But wealth typically follows a power law distribution:
Of course, it might be the case that better measurements would uncover a power law distribution of individual talents. But it's far more plausible to me that random fluctuations + nonlinear amplifications transform, over time, normally distributed talents into power law outcomes.
The largely dominant meritocratic paradigm of highly competitive Western cultures is rooted on the belief that success is due mainly, if not exclusively, to personal qualities such as talent, intelligence, skills, smartness, efforts, willfulness, hard work or risk taking. Sometimes, we are willing to admit that a certain degree of luck could also play a role in achieving significant material success. But, as a matter of fact, it is rather common to underestimate the importance of external forces in individual successful stories. It is very well known that intelligence (or, more in general, talent and personal qualities) exhibits a Gaussian distribution among the population, whereas the distribution of wealth - often considered a proxy of success - follows typically a power law (Pareto law), with a large majority of poor people and a very small number of billionaires. Such a discrepancy between a Normal distribution of inputs, with a typical scale (the average talent or intelligence), and the scale invariant distribution of outputs, suggests that some hidden ingredient is at work behind the scenes. In this paper, with the help of a very simple agent-based toy model, we suggest that such an ingredient is just randomness. In particular, we show that, if it is true that some degree of talent is necessary to be successful in life, almost never the most talented people reach the highest peaks of success, being overtaken by mediocre but sensibly luckier individuals. As to our knowledge, this counterintuitive result - although implicitly suggested between the lines in a vast literature - is quantified here for the first time. It sheds new light on the effectiveness of assessing merit on the basis of the reached level of success and underlines the risks of distributing excessive honors or resources to people who, at the end of the day, could have been simply luckier than others. With the help of this model, several policy hypotheses are also addressed and compared to show the most efficient strategies for public funding of research in order to improve meritocracy, diversity and innovation.
Here is a specific example of random fluctuations and nonlinear amplification:
Nonlinearity and Noisy Outcomes: ... The researchers placed a number of songs online and asked volunteers to rate them. One group rated them without seeing others' opinions. In a number of "worlds" the raters were allowed to see the opinions of others in their world. Unsurprisingly, the interactive worlds exhibited large fluctuations, in which songs judged as mediocre by isolated listeners rose on the basis of small initial fluctuations in their ratings (e.g., in a particular world, the first 10 raters may have all liked an otherwise mediocre song, and subsequent listeners were influenced by this, leading to a positive feedback loop).
It isn't hard to think of a number of other contexts where this effect plays out. Think of the careers of two otherwise identical competitors (e.g., in science, business, academia). The one who enjoys an intial positive fluctuation may be carried along far beyond their competitor, for no reason of superior merit. The effect also appears in competing technologies or brands or fashion trends.
If outcomes are so noisy, then successful prediction is more a matter of luck than skill. The successful predictor is not necessarily a better judge of intrinsic quality, since quality is swamped by random fluctuations that are amplified nonlinearly. This picture undermines the rationale for the high compensation awarded to certain CEOs, studio and recording executives, even portfolio managers. ...
I don't work in Big Tech so I don't know whether his numbers are realistic. If they are realistic, then I'd say careers in Big Tech (for someone with the ability to do high level software work) dominate all the other (risk-adjusted) options right now. This includes finance, startups, etc.
No wonder the cost of living in the bay area is starting to rival Manhattan!
MIT Center for Energy and Environmental Policy Research
We perform a detailed analysis of Uber and Lyft ride-hailing driver economics by pairing results from a survey of over 1100 drivers with detailed vehicle cost information. Results show that per hour worked, median profit from driving is $3.37/hour before taxes, and 74% of drivers earn less than the minimum wage in their state. 30% of drivers are actually losing money once vehicle expenses are included. On a per-mile basis, median gross driver revenue is $0.59/mile but vehicle operating expenses reduce real driver profit to a median of $0.29/mile. For tax purposes the $0.54/mile standard mileage deduction in 2016 means that nearly half of drivers can declare a loss on their taxes. If drivers are fully able to capitalize on these losses for tax purposes, 73.5% of an estimated U.S. market $4.8B in annual ride-hailing driver profit is untaxed.
Note Uber disputes this result and claims the low hourly result is due in part to the researchers misinterpreting one of the survey questions. Uber's analysis puts the hourly compensation at ~$15.
A VC-funded genomics startup I am familiar with is searching for someone to apply computational methods to complex human traits (e.g., polygenic disease risk).
The ideal candidate would be someone from Physics or CS or other quantitative discipline, interested in computational genomics and data science. Strong background in computation required. Advanced degree a plus, but not required.
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
See this jobs report, based on BLS statistics and analysis of 26 million job postings scraped from job boards, newspapers, and other online sources in 2015.
Coding jobs represent a large and growing part of the job market. There were nearly 7 million job openings in the U.S. last year for roles requiring coding skills. This represents 20% of the total market for career-track jobs that pay $15 an hour or more. Jobs with coding skills are projected to grow 12% faster than the job market overall in the next 10 years. IT jobs are expected to grow even more rapidly: 25% faster than the overall market.1
Programming skills are in demand across a range of industries. Half of all programming openings are in Finance, Manufacturing, Health Care, and other sectors outside of the technology industry.
...
Jobs valuing coding skills pay $22,000 per year more, on average, than jobs that don’t: $84,000 vs $62,000 per year. The value of these skills is striking and, for students looking to increase their potential income, few other skills open the door to as many well-paying careers. Slicing the data another way, 49% of the jobs in the top wage quartile (>$58,000/yr) value coding skills.
...
We define coding jobs as those in any occupation where knowing how to write computer code makes someone a stronger candidate and where employers commonly request coding skills in job postings. In some cases, coding is a prerequisite skill for the role, such as for Database Administrators. In other cases, such as Graphic Designers, knowing how to code may not be required in all cases, but job seekers with relevant programming skills will typically have an advantage.
Highly recommended! I blogged about this guy 5 years ago here: From physics to Goldman to Y Combinator. The book is hilarious and pretty accurate, AFAICT. I don't know much about Facebook corporate culture or that particular era of ad monetization, but the finance and startup stuff all rings true.
The reality is, Silicon Valley capitalism is very simple:
Investors are people with more money than time.
Employees are people with more time than money.
Entrepreneurs are the seductive go-between.
Startups are business experiments performed with other people's money.
I was a Berkeley PhD student in physics when the first dot-com bubble grew to bursting and popped around 2001. Between the month-long backpacking trips and the telenovela-esque romances, I switched thesis topic three times, and felt my twenty-something vitality slipping away in academic wankery. Inspired by Michael Lewis’ Liar’s Poker and the prior example of many a failed physicist, I looked for a Wall Street gig as a way out. Very improbably, I landed a job on the trading desk of Goldman Sachs, earning twice what my tenured professor made, pricing and modeling credit derivatives at ground-zero of the credit bubble. I may have owned one pair of lace-up shoes at the time, but I got used to speaking in quantities of hundreds of millions of dollars, and thinking a million was a ‘buck’, i.e., a rounding error for most purposes. I was very far away indeed from Berkeley.
Right around 2008, when Lehman Brothers and Bear Stearns blew up, I knew the financial jig would be up for a while (and possibly forever), unlike most of my colleagues, who seemed to think orgies of rapacious greed lasted forever. The only piece of the US economy that would be spared the apocalypse was clear in my mind: the Bay Area tech of my languid grad school days, and all that VC money that (hopefully) hadn’t touched the mortgage bubble..
Two weeks later, I started as employee number seventy-something at a venture-backed advertising startup so incompetent and vile I’ll save the historical distaste for later. Bookended as it was by experiences at Facebook and Goldman, my time there was instructive in its awfulness and how not to run a company. But there was one piece of upside: I learned how online advertising worked, specifically its ad exchange variants. As a ‘research scientist’ I tortured every piece of data until it confessed, and used it to predict user behavior, value of media purchased, and optimal bids in the largest ad auctions in the world. Dull stuff you might say, but it’s what pays for the Internet, and it would set me light-years ahead of anyone inside Facebook Ads, when the time came.
But we’re jumping ahead.
Along with the two best engineers at Shitty Unnamed Company, I applied and was accepted to Y Combinator, the Valley’s leading startup incubator. We pitched some wild, ridiculous idea around local businesses that was doomed from the start, which eventually morphed into a novel tool for managing Google search campaigns for small businesses. The tool was beautiful, innovative, and didn’t make us a dime. More bad news: We got vindictively and frivolously sued by Shitty Company and fought an existential legal battle we narrowly won by being lying, ruthless little shits. We couldn’t raise money. We had co- founder and morale issues. Every ill that plagues early-stage startups visited us in turn, like some admonitory biblical tale about what happens if you fuck with the Israelites. ...
... Every startup entrepreneur faces the immense disadvantage of playing a crooked, complex game for the first time, against a world composed mostly of masters. Arrayed against you is an army of wily, self-interested venture capitalists who know term sheets better than their wife’s ass. Or seductive sales execs who could make pedophilia and genocide enforceable via a legal contract. Or petulant co-founders with hidden agendas and momentarily suppressed grievances. Or ungrateful employees who are exploiting your startup until they can start their own. Or thick-headed journalists with urgent deadlines who just want you for a misleading quote. You get trounced again and again, and the only hope is that you learn something of the game before expiring. This is your principal challenge as a first-time entrepreneur: to learn the game faster than you burn cash and relationships.
After 25+ years in theoretical physics research, the pattern has become familiar to me. Talented postdoc has difficulty finding a permanent position (professorship), and ends up leaving the field for finance or Silicon Valley. The final phase of the physics career entails study of entirely new subjects, such as finance theory or machine learning, and developing new skills, such as coding.
My most recent postdoc interviewed with big hedge funds in Manhattan and also in the bay area. He has accepted a position in AI -- working on Deep Learning -- at the Silicon Valley research lab of a large technology company. His compensation is good (significantly higher than most full professors!) and future prospects in this area of research are exciting. With some luck, great things are possible.
In one of his early blog posts, Terence Tao (shown above with Paul Erdos in 1985) wrote
Does one have to be a genius to do maths? The answer is an emphatic NO. In order to make good and useful contributions to mathematics, one does need to work hard, learn one’s field well, learn other fields and tools, ask questions, talk to other mathematicians, and think about the “big picture”. And yes, a reasonable amount of intelligence, patience, and maturity is also required. But one does not need some sort of magic “genius gene” that spontaneously generates ex nihilo deep insights, unexpected solutions to problems, or other supernatural abilities.
The popular image of the lone (and possibly slightly mad) genius – who ignores the literature and other conventional wisdom and manages by some inexplicable inspiration (enhanced, perhaps, with a liberal dash of suffering) to come up with a breathtakingly original solution to a problem that confounded all the experts – is a charming and romantic image, but also a wildly inaccurate one, at least in the world of modern mathematics. We do have spectacular, deep and remarkable results and insights in this subject, of course, but they are the hard-won and cumulative achievement of years, decades, or even centuries of steady work and progress of many good and great mathematicians; the advance from one stage of understanding to the next can be highly non-trivial, and sometimes rather unexpected, but still builds upon the foundation of earlier work rather than starting totally anew. (This is for instance the case with Wiles‘ work on Fermat’s last theorem, or Perelman‘s work on the Poincaré conjecture.)
Actually, I find the reality of mathematical research today – in which progress is obtained naturally and cumulatively as a consequence of hard work, directed by intuition, literature, and a bit of luck – to be far more satisfying than the romantic image that I had as a student of mathematics being advanced primarily by the mystic inspirations of some rare breed of “geniuses”. This “cult of genius” in fact causes a number of problems, since nobody is able to produce these (very rare) inspirations on anything approaching a regular basis, and with reliably consistent correctness. (If someone affects to do so, I advise you to be very sceptical of their claims.) The pressure to try to behave in this impossible manner can cause some to become overly obsessed with “big problems” or “big theories”, others to lose any healthy scepticism in their own work or in their tools, and yet others still to become too discouraged to continue working in mathematics. Also, attributing success to innate talent (which is beyond one’s control) rather than effort, planning, and education (which are within one’s control) can lead to some other problems as well.
These are insightful comments, and deserve to be taken very seriously, coming as they do from the one of the youngest Fields Medalists in history and a legendary child prodigy.
But many readers misinterpreted Tao's remarks as minimizing the impact of native ability on success in research. Recently, Tao corrected this impression in the comment thread to his original post.
4 December, 2015 at 12:40 pm Terence Tao
It appears my previous comment may have have been interpreted in a manner differently from what I intended, which was as a statement of (lack of) empirical correlation rather than (lack of) causation. More precisely, the point I was trying to make with the above quote is this: if one considers a population of promising young mathematicians (e.g. an incoming PhD class at an elite mathematics department), they will almost all certainly have some reasonable level of intelligence, and some subset will have particularly exceptional levels of intelligence. A significant fraction of both groups will go on to become professional mathematicians of some decent level of accomplishment, with the fraction likely to (but not necessarily) be a bit higher when restricted to the group with exceptional intelligence. But if one were to try to use “exceptional levels of intelligence” as a predictor as to which members of the population will go on to become exceptionally successful and productive mathematicians, I believe this to be an extremely poor predictor, with the empirical correlation being low or even negative (cf. Berkson’s paradox).
Now, at the level of theoretical causation rather than empirical correlation, I would concede that if one were to take a given mathematician and somehow increase his or her level of intelligence to extraordinary levels, while keeping all other traits (e.g. maturity, work ethic, study habits, persistence, level of rigor and organisation, breadth and retention of knowledge, social skills, etc.) unchanged, then this would likely have a positive effect on his or her ability to be an extraordinarily productive mathematician. However, empirically one finds that mathematicians who did not exhibit precocious levels of intelligence in their youth are likely to be stronger in other areas which will often turn out to be more decisive in the long-term, at least when one restricts to populations that have already reached some level of mathematical achievement (e.g. admission to a top maths PhD program).
For instance, many difficult problems in mathematics require a slow, patient approach in which one methodically digests all the existing techniques in the literature and applies various combinations of them in turn to the problem, until one gets a deep enough understanding of the situation that one can isolate the key obstruction that needs to be overcome and the key new insight which, in conjunction with an appropriate combination of existing methods, will resolve the problem. A mathematician who is used to using his or her high levels of intelligence to quickly find original solutions to problems may not have the patience and stamina for such a systematic approach, and may instead inefficiently expend a lot of energy on coming up with creative but inappropriate approaches to the problem, without the benefit of being guided by the accumulated conventional wisdom gained from fully understanding prior approaches to the problem. Of course, the converse situation can also occur, in which an unusually intelligent mathematician comes up with a viable approach missed by all the more methodical people working on the problem, but in my experience this scenario is rarer than is sometimes assumed by outside observers, though it certainly can make for a more interesting story to tell.
Some comments on Tao's comment:
1. Individuals accepted into elite PhD programs in mathematics are already highly selected. I would guess, based on my familiarity with test scores of applicants to similar programs in theoretical physics, that a typical person in this population is well beyond +3 SD in overall cognitive (or at least mathematical) ability, which means fewer than one in a thousand in the general population. Tao doesn't say what he thinks the chances are for someone who has significantly less ability than this; I would say their chances at a research career in math are poor. Individuals with what Tao refers to as “exceptional levels of intelligence” would be at least +4 SD or more, making them fewer than one in ten thousand in the general population, or even much more rare. (To be totally frank I think a large fraction of good mathematicians are +4 SD and Tao is really talking about people who are exceptional even relative to them.)
2. Tao describes a schematic model with several quasi-independent input factors (raw cognitive ability, work ethic, maturity, breadth of knowledge, etc.) contributing to success. This is my working model as well. The claim that within the population of PhD students at top departments there might be only small or even negative correlation between factors such as raw ability and work ethic also seems plausible to me given a minimum threshold of undergraduate achievement (which can be obtained using various combinations of the individual factors) necessary for admission.
3. Tao's comments seem entirely consistent with results from SMPY (Study of Mathematically Precocious Youth), a longitudinal study of gifted children that finds increasing probability of success (e.g., STEM tenure at top research university) as ability increases from 99th to 99.99th percentile.
4. Should young people be made aware of the brute facts presented above? It seems terrible to limit one's ambitions based on some crudely measured construct like general cognitive ability or math ability. On the other hand, we do this all the time. When was the right time in my life to wise up about the fact that I would probably never make it to the NFL? After playing linebacker at 200 lbs for Division III Caltech (which doesn't even have a football team now), I was considering walking on at UC Berkeley as a 19 year old grad student. Should I have clung to my dream, or wised up about my dim future in Division I sports? :-)
5. Related to Tao's last remark the converse situation can also occur, in which an unusually intelligent mathematician comes up with a viable approach missed by all the more methodical people, see Sidney Coleman on Feynman:
"I think if he had not been so quick people would have treated him as a brilliant quasi crank, because he did spend a substantial amount of time going down what later turned out to be dead ends," said Sidney Coleman, a theorist who first knew Feynman at Caltech in the 50's.
"There are lots of people who are too original for their own good, and had Feynman not been as smart as he was, I think he would have been too original for his own good," Coleman continued. "There was always an element of showboating in his character. He was like the guy that climbs Mont Blanc barefoot just to show that it can be done."
Feynman continued to refuse to read the current literature, and he chided graduate students who would begin their work on a problem in the normal way, by checking what had already been done. That way, he told them, they would give up chances to find something original.
"I suspect that Einstein had some of the same character," Coleman said. "I'm sure Dick thought of that as a virtue, as noble. I don't think it's so. I think it's kidding yourself. Those other guys are not all a collection of yo-yos. Sometimes it would be better to take the recent machinery they have built and not try to rebuild it, like reinventing the wheel. Dick could get away with a lot because he was so goddamn smart. He really could climb Mont Blanc barefoot."
MSU will be hiring over 100 new professors (beyond ordinary hiring such as retirement replacements), primarily in science and technology areas that address key global challenges. Priority areas include Computation, Advanced Engineering, Genomics, Plant Sciences, Food/Environment, Precision Medicine, and Advanced Physical Sciences. MSU total funding from the Department of Energy and the National Science Foundation ranks in the top 10 among US universities.
Proximate to my own field of theoretical physics, we intend to build one of the best lattice QCD groups in the US. I predict that in the coming decade lattice QCD applied to low-energy nuclear physics will allow first-principles (starting from the level of quarks and gluons) calculations of important dynamical quantities in nuclear physics, such as scattering amplitudes and reaction rates. For the first time, strongly coupled nuclear systems will become amenable to direct computation using the quantum field theory of quarks and gluons.
The Department of Physics & Astronomy (PA), National Superconducting Cyclotron Laboratory (NSCL), and a new department of Computational Math Science and Engineering (CMSE) invite applications from outstanding candidates for three faculty positions at Michigan State University in the area of computational Lattice Quantum Chromodynamics (LQCD). We anticipate filling one or more of the positions at a senior level with tenure. We are looking for candidates with an excellent record in applying large-scale computing to solving cutting-edge scientific problems in the domains of nuclear physics (relevant to the Facility for Rare Isotope Beams) and high energy physics. We expect that the three hires will work together to establish an internationally prominent and well-funded activity in LQCD and its applications to high energy and nuclear physics. These positions are part of a committed multi-year effort to build the computational sciences programs at Michigan State University. Each position will be a joint appointment between the new CMSE department and PA/NSCL. Faculty will have a primary appointment in one of the three participating units (PA, NSCL, CMSE), and we anticipate one appointment in each of these units. In addition to developing a world-leading research group with strong disciplinary and interdisciplinary collaborations, the new faculty members are expected to contribute to the development of an innovative curriculum in computational sciences, at both the graduate and undergraduate levels.
BTW, I almost cried when I saw this happen! Go Green!
