Physicist, Startup Founder, Blogger, Dad

Wednesday, March 14, 2018

Stephen Hawking (1942-2018)

Roger Penrose writes in the Guardian, providing a scientifically precise summary of Hawking's accomplishments as a physicist (worth reading in full at the link). Penrose and Hawking collaborated to produce important singularity theorems in general relativity in the late 1960s.

Here is a nice BBC feature: A Brief History of Stephen Hawking. The photo above was taken at Hawking's Oxford graduation in 1962.
Stephen Hawking – obituary by Roger Penrose

... This radiation coming from black holes that Hawking predicted is now, very appropriately, referred to as Hawking radiation. For any black hole that is expected to arise in normal astrophysical processes, however, the Hawking radiation would be exceedingly tiny, and certainly unobservable directly by any techniques known today. But he argued that very tiny black holes could have been produced in the big bang itself, and the Hawking radiation from such holes would build up into a final explosion that might be observed. There appears to be no evidence for such explosions, showing that the big bang was not so accommodating as Hawking wished, and this was a great disappointment to him.

These achievements were certainly important on the theoretical side. They established the theory of black-hole thermodynamics: by combining the procedures of quantum (field) theory with those of general relativity, Hawking established that it is necessary also to bring in a third subject, thermodynamics. They are generally regarded as Hawking’s greatest contributions. That they have deep implications for future theories of fundamental physics is undeniable, but the detailed nature of these implications is still a matter of much heated debate.

... He also provided reasons for suspecting that the very rules of quantum mechanics might need modification, a viewpoint that he seemed originally to favour. But later (unfortunately, in my own opinion) he came to a different view, and at the Dublin international conference on gravity in July 2004, he publicly announced a change of mind (thereby conceding a bet with the Caltech physicist John Preskill) concerning his originally predicted “information loss” inside black holes.
Notwithstanding Hawking's premature 2004 capitulation to Preskill, information loss in black hole evaporation remains an open question in fundamental physics, nearly a half century after Hawking first recognized the problem in 1975. I read this paper as a graduate student, but with little understanding. I am embarrassed to say that I did not know a single person (student or faculty member) at Berkeley at the time (late 1980s) who was familiar with Hawking's arguments and who appreciated the deep implications of the results. This was true of most of theoretical physics -- despite the fact that even Hawking's popular book A Brief History of Time (1988) gives a simple version of the paradox. The importance of Hawking's observation only became clear to the broader community somewhat later, perhaps largely due to people like John Preskill and Lenny Susskind.

I have only two minor recollections to share about Hawking. The first, from my undergraduate days, is really more about Gell-Mann: Gell-Mann, Feynman, Hawking. The second is from a small meeting on the black hole information problem, at Institut Henri Poincare in Paris in 2008. (My slides.) At the conference dinner I helped to carry Hawking and his motorized chair -- very heavy! -- into a fancy Paris restaurant (which are not, by and large, handicapped accessible). Over dinner I met Hawking's engineer -- the man who maintained the chair and its computer voice / controller system. He traveled everywhere with Hawking's entourage and had many interesting stories to tell. For example, Hawking's computer system was quite antiquated but he refused to upgrade to something more advanced because he had grown used to it. The entourage required to keep Hawking going was rather large (nurses, engineer, driver, spouse), expensive, and, as you can imagine, had its own internal dramas.

Saturday, March 10, 2018

Risk, Uncertainty, and Heuristics

Risk = space of outcomes and probabilities are known. Uncertainty = probabilities not known, and even space of possibilities may not be known. Heuristic rules are contrasted with algorithms like maximization of expected utility.

See also Bounded Cognition and Risk, Ambiguity, and Decision (Ellsberg).

Here's a well-known 2007 paper by Gigerenzer et al.
Helping Doctors and Patients Make Sense of Health Statistics

Gigerenzer G1, Gaissmaier W2, Kurz-Milcke E2, Schwartz LM3, Woloshin S3.

Many doctors, patients, journalists, and politicians alike do not understand what health statistics mean or draw wrong conclusions without noticing. Collective statistical illiteracy refers to the widespread inability to understand the meaning of numbers. For instance, many citizens are unaware that higher survival rates with cancer screening do not imply longer life, or that the statement that mammography screening reduces the risk of dying from breast cancer by 25% in fact means that 1 less woman out of 1,000 will die of the disease. We provide evidence that statistical illiteracy (a) is common to patients, journalists, and physicians; (b) is created by nontransparent framing of information that is sometimes an unintentional result of lack of understanding but can also be a result of intentional efforts to manipulate or persuade people; and (c) can have serious consequences for health. The causes of statistical illiteracy should not be attributed to cognitive biases alone, but to the emotional nature of the doctor-patient relationship and conflicts of interest in the healthcare system. The classic doctor-patient relation is based on (the physician's) paternalism and (the patient's) trust in authority, which make statistical literacy seem unnecessary; so does the traditional combination of determinism (physicians who seek causes, not chances) and the illusion of certainty (patients who seek certainty when there is none). We show that information pamphlets, Web sites, leaflets distributed to doctors by the pharmaceutical industry, and even medical journals often report evidence in nontransparent forms that suggest big benefits of featured interventions and small harms. Without understanding the numbers involved, the public is susceptible to political and commercial manipulation of their anxieties and hopes, which undermines the goals of informed consent and shared decision making. What can be done? We discuss the importance of teaching statistical thinking and transparent representations in primary and secondary education as well as in medical school. Yet this requires familiarizing children early on with the concept of probability and teaching statistical literacy as the art of solving real-world problems rather than applying formulas to toy problems about coins and dice. A major precondition for statistical literacy is transparent risk communication. We recommend using frequency statements instead of single-event probabilities, absolute risks instead of relative risks, mortality rates instead of survival rates, and natural frequencies instead of conditional probabilities. Psychological research on transparent visual and numerical forms of risk communication, as well as training of physicians in their use, is called for. Statistical literacy is a necessary precondition for an educated citizenship in a technological democracy. Understanding risks and asking critical questions can also shape the emotional climate in a society so that hopes and anxieties are no longer as easily manipulated from outside and citizens can develop a better-informed and more relaxed attitude toward their health.

Wednesday, March 07, 2018

The Ballad of Bedbug Eddie and the Golden Rule

This is a bedtime story I made up for my kids when they were small. See also Isabel and the dwarf king.
Once upon a time, there was a tiny bedbug named Eddie, who was no bigger than a sesame seed. Like all bedbugs, Eddie lived by eating the blood of humans. Every night he crawled out of the bedding and bit his sleeping victim.

One night a strange idea entered Eddie's mind. Are there little bugs that bite me when I sleep? he wondered. That would be terrible! (Little did Eddie know that there was a much smaller bug named Mini who lived in his left antenna, and who drank his blood! But that is another story...)

Suddenly, Eddie had an inspiration. It was wrong to bite other people and drink their blood. If I don't like it, he thought, I shouldn't do it to other people!

From that moment on, Eddie resolved to never bite another creature. He would have to find a source of food other than blood!

Eddie lay in his bedding nest and wondered what he would do next. He had never eaten any other kind of food. He realized that to survive, he would have to search out a new kind of meal.

When the sun came up, Eddie decided he should leave his bed in search of food. He wandered through the giant house, with its fuzzy carpeting and enormous potted plants. Finally he came upon the cool, smooth floor of the kitchen. Smelling something edible, he continued toward the breakfast table.

Soon enough, he encountered the biggest chunk of food he had ever seen. It was a hundred times bigger than Eddie, and smelled of peanut butter -- it was a crumb of toast! Then Eddie realized the entire floor under the table was covered with crumbs -- bread, cracker, muffin, even fruit and vegetable crumbs!

Eddie jumped onto the peanut butter toast crumb and started to eat. He was very hungry after missing his usual midnight meal. He ate until he was very full. It took some getting used to peanut butter -- not his usual blood meal! But he would manage.

Suddenly, a huge crumb fell from the sky and almost crushed Eddie. He barely managed to jump out of the way of the huge block of cereal, wet with milk. Looking up, he saw a giant figure on a chair, who was spraying crumbs all around as he gobbled up his breakfast.

The Crumb King! exclaimed Eddie. The Crumb King provides us with sustenance!

Hello Crumb King, shouted Eddie. Look out below! You almost crushed me with that cereal! he yelled.

Between crunches of cereal, Max heard a tiny voice from below. Surprised, he looked down at the small black dot, no bigger than a sesame seed. Are you a bug? he asked.

I am bedbug Eddie! responded Eddie. Don't crush me with crumbs! he shouted.

From that day on, Eddie and Max were great friends.

Eddie became a vegetarian and devoted his life to teaching the Golden Rule: "Do unto others as you would have them do unto you.” (Matthew 7:12)

Better to be Lucky than Good?

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.

Talent vs Luck: the role of randomness in success and failure

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. ...

Saturday, March 03, 2018

Big Tech compensation in 2018

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!

Anyone care to comment?

Meanwhile, in the low-skill part of the economy:
The Economics of Ride-Hailing: Driver Revenue, Expenses and Taxes

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.

How NSA Tracks You (Bill Binney)

Anyone who is paying attention knows that the Obama FBI/DOJ used massive government surveillance powers against the Trump team during and after the election. A FISA warrant on Carter Page (and Manafort and others?) was likely used to mine stored communications of other Trump team members. Hundreds of "mysterious" unmasking requests by Susan Rice, Samantha Powers, etc. were probably used to identify US individuals captured in this data.

I think it's entirely possible that Obama et al. thought they were doing the right (moral, patriotic) thing -- they really thought that Trump might be colluding with the Russians. But as a civil libertarian and rule of law kind of guy I want to see it all come to light. I have been against this kind of thing since GWB was president -- see this post from 2005!

My guess is that NSA is intercepting and storing big chunks of, perhaps almost all, US email traffic. They're getting almost all metadata from email and phone traffic, possibly much of the actual voice traffic converted to text using voice recognition. This used to be searchable only by a limited number of NSA people (although that number grew a lot over the years; see 2013 article and LOVEINT below), but now available to many different "intel" agencies in the government thanks to Obama.

Situation in 2013: https://www.npr.org/templates/story/story.php?storyId=207195207

(Note Title 1 FISA warrant grants capability to look at all associates of target... like the whole Trump team.)

Obama changes in 2016: https://www.nytimes.com/2016/02/26/us/politics/obama-administration-set-to-expand-sharing-of-data-that-nsa-intercepts.html
NYT: "The new system would permit analysts at other intelligence agencies to obtain direct access to raw information from the N.S.A.’s surveillance to evaluate for themselves. If they pull out phone calls or email to use for their own agency’s work, they would apply the privacy protections masking innocent Americans’ information... ” HA HA HA I guess that's what all the UNmasking was about...
More on NSA capabilities: https://en.wikipedia.org/wiki/LOVEINT (think how broad their coverage has to be for spooks to be able to spy on their wife or girlfriend)

See also FISA, EO 12333, Bulk Collection, and All That.
Wikipedia: William Edward Binney[3] is a former highly placed intelligence official with the United States National Security Agency (NSA)[4] turned whistleblower who resigned on October 31, 2001, after more than 30 years with the agency.

He was a high-profile critic of his former employers during the George W. Bush administration, and later criticized the NSA's data collection policies during the Barack Obama administration. 
From the transcript of Binney's talk:
ways that they basically collect data
first it's they use the corporations
that run the fiber-optic lines and they
get them to allow them to put taps on
them and I'll show you some of the taps
where they are and and if that doesn't
work they use the foreign government to
go at their own telecommunications
companies to do the similar thing and if
that doesn't work they'll tap the line
anywhere they can get to it and they
won't even know it you know the
government's know that communications
companies will even though they're
tapped so that's how they get into it
then I get into fiber lines and this is
this is a the prism program ...

that was published
out of the Snowden material and they've
all focused on prism well prism is
really the the minor program I mean the
major program is upstream that's where
they have the fiber-optic taps on
hundreds of places around in the world
that's where they're collecting off the
fiber lined all the data and storing it
2016 FISC reprimand of Obama administration. The court learned in October 2016 that analysts at the National Security Agency were conducting prohibited database searches “with much greater frequency than had previously been disclosed to the court.” The forbidden queries were searches of Upstream Data using US-person identifiers. The report makes clear that as of early 2017 NSA Inspector General did not even have a good handle on all the ways that improper queries could be made to the system. (Imagine Snowden-like sys admins with a variety of tools that can be used to access raw data.) Proposed remedies to the situation circa-2016/17 do not inspire confidence (please read the FISC document).

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