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Physicist, Startup Founder, Blogger, Dad

Monday, February 29, 2016

Moore's Law and AI

By now you've probably heard that Moore's Law is really dead. So dead that the semiconductor industry roadmap for keeping it on track has more or less been abandoned: see, e.g., here, here or here. (Reported on this blog 2 years ago!)

What I have not yet seen discussed is how a significantly reduced rate of improvement in hardware capability will affect AI and the arrival of the dreaded (in some quarters) Singularity. The fundamental physical problems associated with ~ nm scale feature size could take decades or more to overcome. How much faster are today's cars and airplanes than those of 50 years ago?

Hint to technocratic planners: invest more in physicists, chemists, and materials scientists. The recent explosion in value from technology has been driven by physical science -- software gets way too much credit. From the former we got a factor of a million or more in compute power, data storage, and bandwidth. From the latter, we gained (perhaps) an order of magnitude or two in effectiveness: how much better are current OSes and programming languages than Unix and C, both of which are ~50 years old now?


HLMI = ‘high–level machine intelligence’ = one that can carry out most human professions at least as well as a typical human. (From Minds and Machines.)

Of relevance to this discussion: a big chunk of AlphaGo's performance improvement over other Go programs is due to raw compute power (link via Jess Riedel). The vertical axis is ELO score. You can see that without multi-GPU compute, AlphaGo has relatively pedestrian strength.


ELO range 2000-3000 spans amateur to lower professional Go ranks. The compute power certainly affects depth of Monte Carlo Tree Search. The initial training of the value and policy neural networks using KGS Go server positions might have still been possible with slower machines, but would have taken a long time.

Saturday, February 27, 2016

Trump on Trump



Someone sent me a link to this 1990 Playboy interview with Donald Trump. There's much more in the interview than what I have excerpted below. See also Crazy like a fox.
What satisfaction, exactly, do you get out of doing a deal? I love the creative process. I do what I do out of pure enjoyment. Hopefully, nobody does it better. There's a beauty to making a great deal. It's my canvas. And I like painting it. I like the challenge and tell the story of the coal miner's son. The coal miner gets black-lung disease, his son gets it, then his son. If I had been the son of a coal miner, I would have left the damn mines. But most people don't have the imagination-or whatever-to leave their mine. They don’t have "it."

Which is? "It" is an ability to become an entrepreneur, a great athlete, a great writer. You're either born with it or you're not. Ability can be honed, perfected or neglected. The day Jack Nicklaus came into this world, he had more innate ability to play golf than anybody else.

Do you suppose your children inherited "it" from you? Statistically, my children have a very bad shot. Children of successful people are generally very, very troubled, not successful. They don't have the right shtick. You never know until they're tested. But I do well with my children.

Do you think they will have to make it? I would love them to be in business with me, but ninety-five percent of those children fail in a sophisticated big business. It takes confidence, intelligence, shtick. If any one of these traits is missing, you're not going to make it.

Your older brother, Fred, who died from heart failure brought on by acute alcoholism, had a more difficult time with him [Trump's father], didn't he? Take one environment and it will work completely differently on different children. Our family environment, the competitiveness, was a negative for Fred. It wasn't easy for him being cast in a very tough environment, and I think it played havoc on him. I was very close to him and it was very sad when he died . . . toughest situation I've had ...

What did you learn from his experience? [Pauses] Nobody has ever asked me that. But his death affected everything that has come after it. ... I think constantly that I never really gave him thanks for it. He was the first Trump boy out there, and I subconsciously watched his moves.

And the lesson? I saw people really taking advantage of Fred and the lesson I learned was always to keep up my guard one hundred percent, whereas he didn't. He didn't feel that there was really reason for that, which is a fatal mistake in life. People are too trusting. I'm a very untrusting guy. I study people all the time, automatically; it's my way of life, for better or worse.

Why? I am very skeptical about people; that's self-preservation at work. I believe that, unfortunately, people are out for themselves. At this point, it's to many people’s advantage to like me. Would the phone stop ringing, would these people kissing ass disappear if things were not going well? I enjoy testing friendship .... Everything in life to me is a psychological game, a series of challenges you either meet or don't. I am always testing people who work for me.

How? I will send people around to my buyers to test their honesty by offering them trips and other things. I've been surprised that some people least likely to accept a trip from a contractor did and some of the most likely did not. You can never tell until you test; the human species is interesting in that way. So to me, friendship can be really tested only in bad times. I instinctively mistrust many people. It is not a negative in my life but a positive. Playboy wouldn't be talking to me today if I weren't a cynic. So I learned that from Fred, and I owe him a lot. . . . He could have ultimately been a happy guy, but things just went the unhappy way.

How large a role does pure ego play in your deal making and enjoyment of publicity? Every successful person has a very large ego.

Every successful person? Mother Teresa? Jesus Christ? Far greater egos than you will ever understand.

A favorite word of yours, tough. How do you define it? Tough is being mentally capable of winning battles against an opponent and doing it with a smile. Tough is winning systematically.

Life? Or death? Both. We're here and we live our sixty, seventy or eighty years and we’re gone. You win, you win, and in the end, it doesn't mean a hell of a lot. But it is something to do-to keep you interested.

Do you agree with the T-shirt that says, WHOEVER HAS THE MOST TOYS WINS? Depends on your definition of winning. Some of my friends are unbelievably successful and miserable people. I truly believe that someone successful is never really happy, because dissatisfaction is what drives him. I've never met a successful person who wasn't neurotic. It's not a terrible thing ... it's controlled neuroses.

Do you think George Bush is soft? I like George Bush very much and support him and always will. But I disagree with him when he talks of a kinder, gentler America. I think if this country gets any kinder or gentler, it's literally going to cease to exist. I think if we had people from the business community-the Carl Icahns, the Ross Perots-negotiating some of our foreign policy, we'd have respect around the world.

You categorically don't want to be President? I don't want to be President. I’m one hundred percent sure. I'd change my mind only if I saw this country continue to go down the tubes.

Postdoc Position

Please help me fill this position! This search is a bit out of sync with the regular postdoc application process, so I need some help spreading the word.
Theoretical Physics Postdoc at Michigan State University 
Stephen Hsu, Vice-President for Research and Professor of Physics at MSU, anticipates filling a postdoctoral position to start in the summer or fall of 2016. The successful applicant will have broad interests in theoretical physics and good computational skills. In addition to research in particle physics and cosmology, there will be opportunities to work on problems in machine learning and computational genomics.

The High Energy Theory group at MSU currently consists of eight faculty members: Sekhar Chivukula, Jon Pumplin, Wayne Repko, Carl Schmidt, Elizabeth Simmons, Dan Stump, C.-P. Yuan and Stephen Hsu, as well as postdoctoral fellows and several graduate students. Ongoing research encompasses QCD theory and phenomenology, electroweak symmetry breaking mechanisms, supersymmetry and other beyond-the-standard-model scenarios, cosmology, and collider phenomenology. Recently, a new group of 3 theorists have been hired in the area of lattice QCD. The Physics/Astronomy Department at MSU has 60 faculty members; it has strong research programs in Condensed Matter Physics, Nuclear Physics, and Astronomy, in addition to High Energy Physics (http://www.pa.msu.edu/hep/hept.html).

See MSU Applicant Page http://www.hr.msu.edu/hiring/msujobs.htm , posting 2859 (PA). Applications should be uploaded to MSU’s online job application site, https://jobs.msu.edu and should include a CV, research plan and publication list. In addition, three letters of recommendation should be submitted electronically by the recommenders through this application system. Review of applications will begin immediately and will continue until the position is filled. MSU is an affirmative action, equal opportunity employer MSU is committed to achieving excellence through cultural diversity. The university actively encourages applications and/or nominations of women, persons of color, veterans and persons with disabilities.

Friday, February 26, 2016

Crazy like a fox


Trump Unstoppable? Matt Taibbi writes in Rolling Stone. (Does Rolling Stone still exist? It's only a matter of time as the lawsuit(s) over the UVA fraternity rape hoax work their way through the system.)

See also Trump: the Master Persuader and American and Chinese Oligarchies.
How America Made Donald Trump Unstoppable: ... It turns out we let our electoral process devolve into something so fake and dysfunctional that any half-bright con man with the stones to try it could walk right through the front door and tear it to shreds on the first go.

And Trump is no half-bright con man, either. He's way better than average.

... in an insane twist of fate, this bloated billionaire scion has hobbies that have given him insight into the presidential electoral process. He likes women, which got him into beauty pageants. And he likes being famous, which got him into reality TV. He knows show business.

That put him in position to understand that the presidential election campaign is really just a badly acted, billion-dollar TV show whose production costs ludicrously include the political disenfranchisement of its audience. Trump is making a mockery of the show, and the Wolf Blitzers and Anderson Coopers of the world seem appalled. How dare he demean the presidency with his antics?

But they've all got it backward. The presidency is serious. The presidential electoral process, however, is a sick joke, in which everyone loses except the people behind the rope line. And every time some pundit or party spokesman tries to deny it, Trump picks up another vote.

... Trump's basic argument is the same one every successful authoritarian movement in recent Western history has made: that the regular guy has been screwed by a conspiracy of incestuous elites. The Bushes are half that conspiratorial picture, fronts for a Republican Party establishment and whose sum total of accomplishments, dating back nearly 30 years, are two failed presidencies, the sweeping loss of manufacturing jobs, and a pair of pitiable Middle Eastern military adventures – the second one achieving nothing but dead American kids and Junior's re-election.

[ CAN YOU BLAME WORKING CLASS AMERICANS FOR FEELING THIS WAY? When a majority of citizens disagrees with economic elites and/or with organised interests, they generally lose.  ]

... Trump picked on Jeb because Jeb is a symbol. The Bushes are a dissolute monarchy, down to offering their last genetic screw-up to the throne.

Jeb took the high road for most of the past calendar year, but Trump used his gentlemanly dignity against him. What Trump understands better than his opponents is that NASCAR America, WWE America, always loves seeing the preening self-proclaimed good guy get whacked with a chair. In Greenville, Trump went after Jeb this time on the issue of his brother's invasion of Iraq.

"The war in Iraq was a big f ... fat mistake, all right?" he snorted. He nearly said, "A big fucking mistake." He added that the George W. Bush administration lied before the war about Iraq having WMDs and that we spent $2 trillion basically for nothing.

[ WHO CAN DISAGREE? ]

... Reporters have focused quite a lot on the crazy/race-baiting/nativist themes in Trump's campaign, but these comprise a very small part of his usual presentation. His speeches increasingly are strikingly populist in their content.

His pitch is: He's rich, he won't owe anyone anything upon election, and therefore he won't do what both Democratic and Republican politicians unfailingly do upon taking office, i.e., approve rotten/regressive policies that screw ordinary people.

[ VOTERS ARE NOT RATIONAL, AND TRUMP KNOWS IT. ]

...Cheryl Donlon says she heard the tariff message loud and clear and she's fine with it, despite the fact that it clashes with traditional conservatism.

"We need someone who is just going to look at what's best for us," she says.

I mention that Trump's plan is virtually identical to Dick Gephardt's idea from way back in the 1988 Democratic presidential race, to fight the Korean Hyundai import wave with retaliatory tariffs.

Donlon says she didn't like that idea then.

Why not?

"I didn't like him," she says.

[ TRUMP WILL EAT HILLARY ALIVE. ]

... At a Democratic town hall in Derry, New Hampshire, Hillary's strangely pathetic answer about why she accepted $675,000 from Goldman to give speeches – "That's what they offered" – seemed doomed to become a touchstone for the general-election contest. Trump would go out on Day One of that race and blow $675,000 on a pair of sable underwear, or a solid-gold happy-face necktie. And he'd wear it 24 hours a day, just to remind voters that his opponent sold out for the Trump equivalent of lunch money.

... The triumvirate of big media, big donors and big political parties has until now successfully excluded every challenge to its authority. But like every aristocracy, it eventually got lazy and profligate, too sure it was loved by the people. It's now shocked that voters in depressed ex-factory towns won't keep pulling the lever for "conservative principles," or that union members bitten a dozen times over by a trade deal won't just keep voting Democratic on cue.

Trump isn't the first rich guy to run for office. But he is the first to realize the weakness in the system, which is that the watchdogs in the political media can't resist a car wreck. The more he insults the press, the more they cover him: He's pulling 33 times as much coverage on the major networks as his next-closest GOP competitor, and twice as much as Hillary.

Trump found the flaw in the American Death Star. It doesn't know how to turn the cameras off, even when it's filming its own demise. ...

Monday, February 22, 2016

DeepMind and Demis Hassabis



Recent profile in the Guardian; 15 facts about Hassabis. The mastery of Atari games through reinforcement learning deep neural nets is described here (Nature). See also Deep Neural Nets and Go: AlphaGo beats European champion.
Guardian: ... “We’re really lucky,” says Hassabis, who compares his company to the Apollo programme and Manhattan Project for both the breathtaking scale of its ambition and the quality of the minds he is assembling at an ever increasing rate. “We are able to literally get the best scientists from each country each year. So we’ll have, say, the person that won the Physics Olympiad in Poland, the person who got the top maths PhD of the year in France. We’ve got more ideas than we’ve got researchers, but at the same time, there are more great people coming to our door than we can take on. So we’re in a very fortunate position. The only limitation is how many people we can absorb without damaging the culture.”

That culture goes much deeper than beanbags, free snacks and rooftop beers. Insisting that the Google acquisition has not in any way forced him to deviate from his own research path, Hassabis reckons he spends “at least as much time thinking about the efficiency of DeepMind as the algorithms“ and describes the company as “a blend of the best of academia with the most exciting start-ups, which have this incredible energy and buzz that fuels creativity and progress.” He mentions “creativity” a lot, and observes that although his formal training has all been in the sciences, he is “naturally on the creative or intuitive” side. “I’m not, sort of, a standard scientist,” he remarks, apparently without irony. Vital to the fabric of DeepMind are what he calls his “glue minds”: fellow polymaths who can sufficiently grasp myriad scientific areas to “find the join points and quickly identify where promising interdisciplinary connections might be, in a sort of left-field way.” Applying the right benchmarks, these glue people can then check in on working groups every few weeks and swiftly, flexibly, move around resources and engineers where required. “So you’ll have one incredible, genius researcher and almost immediately, unlike in academia, three or four other people from a different area can pick up that baton and add to it with their own brilliance,” he describes. “That can result in incredible results happening very quickly.” The AlphaGo project, launched just 18 months ago, is a perfect case in point.

... “just thinking time. Until three or four in the morning, that’s when I do my thinking: on research, on our next challenge, or I’ll write up an algorithmic design document.” ... It’s not so much actual AI coding, he admits, “because my maths is too rusty now.  [ Quel dommage! ]  It’s more about intuitive thinking. Or maybe strategic thinking about the company: how to scale it and manage that. Or it might just be something I read in an article or saw on the news that day, wondering how our research could connect to that.”
See also Don’t Worry, Smart Machines Will Take Us With Them: Why human intelligence and AI will co-evolve.

Sunday, February 21, 2016

Missing Heritability and GCTA: Update on PNAS dispute

GCTA is a statistical method for estimating the heritability of a complex trait using (phenotype | genotype) data from unrelated individuals. It has been applied to many human phenotypes, including disease conditions and behavioral traits. GCTA results tend to be consistent with earlier twin and family studies of heritability, and suggest that significant heritability is due to common genetic variants that will be identified in the future through increased statistical power (sample size).

A recent PNAS paper by researchers at Stanford claims to identify many problems with GCTA. The conclusions of this paper have been hotly contested by the GCTA authors and others.
Earlier post (January 1, 2016) on PNAS paper Limitations of GCTA as a solution to the missing heritability problem. (See also: many posts on this blog which mention GCTA.)

Detailed comments and analysis here and here by Sasha Gusev. Gusev claims that the problems identified in Figs 4,7 are the result of incorrect calculation of the SE (4) and failure to exclude related individuals in the Framingham data (7).

GCTA authors Visscher, Yang, et al. respond to PNAS paper -- they accept none of the criticisms (February 13, 2016 biorxiv).

PNAS authors reply to Visscher, Yang, et al. comments (February 16, 2016 bioarxiv). They claim that relatedness thresholding used with GCTA analysis is flawed and that residual standard errors are much larger than claimed.

Gamazon and Park (February 18, 2016 bioarxiv) question spectral analysis and random matrix theory results in the PNAS paper. (I believe this is the first critique which looks at the mathematics of the PNAS paper, as opposed to simulation results.)
This dispute shows the utility of blogs (Gusev) and biorxiv for rapid scientific discussion. Some of the commentaries listed above are 20+ pages long with figures and equations. This discussion would not have been possible (or would have taken months or years) in a journal setting.

The next step should be a mini-workshop conducted online, with each group allowed 30 min to present their results, followed by questions :-)


I've always felt that the real weakness of GCTA is the assumption of random effects. A consequence of this assumption is that if the true causal variants are atypical (e.g., in terms of linkage disequilibrium) among common SNPs, the results could be biased. It is impossible to evaluate this uncertainty at the moment because we do not yet know the genetic architectures of any complex traits. See Why does GCTA work? for more discussion and a link to work by Lee and Chow examining this issue.

Recently, a promising new method (Heritability Estimates from Summary Statistics) has been proposed which does not make assumptions about the effect size distribution -- it uses GWAS estimates of effect size to directly estimate variance accounted for by each region of the genome. The initial application of this method also suggests significant heritability due to common variants.

The broader debate over whether common variants will eventually account for significant heritability in many complex traits has been going on for years now. The centrality of GCTA results to this question decreases by the year as more and more heritability is accounted for by specific loci identified at genome wide significance in well-powered GWAS. For example, this slide (see talk on genomic prediction I gave in 2015 at NIH and HLI) shows that GWAS hits on height now account for 16% of total variance. That means a predictor could be constructed with correlation ~0.4 to the actual trait. I think the argument is basically over, unless you have some ulterior motive for denying the potential of genomic prediction.

Wednesday, February 17, 2016

Elite schools, birthright, and credentials





In mixing together the truly talented with the rich and powerful, elite US universities perform a useful service to both groups.

Khan is discussing themes related to his book Privilege: The Making of an Adolescent Elite at St. Paul's School.

See also Credentialism and elite employment , Credentialism and elite performance, and Defining Merit.

Bloomberg View: Save Us From The Ivy League Oligarchy.

NIH peer review percentile scores are poorly predictive of grant productivity


The impacts of studies ranked in the 3rd to 20th percentile are more or less statistically indistinguishable. With current funding lines as low as 10th percentile, this means that many unfunded proposals are better than funded studies.
NIH peer review percentile scores are poorly predictive of grant productivity
DOI: 10.7554/eLife.13323.001

Peer review is widely used to assess grant applications so that the highest ranked applications can be funded. A number of studies have questioned the ability of peer review panels to predict the productivity of applications, but a recent analysis of grants funded by the National Institutes of Health (NIH) in the US found that the percentile scores awarded by peer review panels correlated with productivity as measured by citations of grant-supported publications. Here, based on a re-analysis of these data for the 102,740 funded grants with percentile scores of 20 or better, we report that these percentile scores are a poor discriminator of productivity. This underscores the limitations of peer review as a means of assessing grant applications in an era when typical success rates are often as low as about 10%.

Sunday, February 14, 2016

Free Harvard, Fair Harvard: Enrollment Trends

The graph below shows changes in the number of Harvard students by ethnic group, relative to the total college age US population of that group (Harvard Enrollment Per Capita = HEPC). Over the last 20 years, the Asian American HEPC has declined by almost 60%. Unless Asian American applicants to Harvard have, on average, declined significantly in relative quality (anecdotal evidence suggests that is far from true), we are left with a mystery: Why has Asian American HEPC declined so precipitously?

Only the innumerate can fail to be intrigued (alarmed? offended?) by this simple observation.

Some caveats: both numerator and denominator for HPEC are difficult to determine. The former comes from NCES data (National Center for Education Statistics), self-reported by universities. The denominator comes from Census bureau Current Population Survey data, and is a bit noisy year to year. I doubt we can trust the HEPC number from one year to the next, but the 20 year trend is probably roughly reliable.



The Economist covered this topic recently in an article entitled The model minority is losing patience. From their chart, one can see that the HEPC mystery extends to the rest of the Ivy League: Asian American enrollment at the Ivies has mysteriously converged at around 15-20%, despite the huge growth in college age Asian American population over the last 20 years.


Caltech is the one school among those in the graph which explicitly declines to use race as a preference (or penalty) in admissions. Caltech has the academically strongest student body and its alumni win more Nobel Prizes and major science and technology awards per capita than any other school.

If you are a Harvard degree holder, I urge you to vote for the Free Harvard, Fair Harvard slate in the coming Overseer elections this spring. We are asking for greater transparency in Harvard admissions, which would help resolve the HEPC mystery discussed above.

See also 20 years @15 percent: does Harvard discriminate against Asian-Americans?
The historical parallels with anti-semitic practices of the early 20th century are reviewed in detail:
... In a letter to the chairman of the committee, President Lowell wrote that “questions of race,” though “delicate and disagreeable,” were not solved by ignoring them. The solution was a new admissions system giving the school wide discretion to limit the admission of Jewish applicants: “To prevent a dangerous increase in the proportion of Jews, I know at present only one way which is at the same time straightforward and effective, and that is a selection by a personal estimate of character on the part of the Admissions authorities ... The only way to make a selection is to limit the numbers, accepting those who appear to be the best.”

... The reduction in Jewish enrollment at Harvard was immediate. The Jewish portion of Harvard’s entering class dropped from over 27 percent in 1925 to 15 percent the following year. For the next 20 years, this percentage (15 percent) remained virtually unchanged.

... The new policy permitted the rejection of scholastically brilliant students considered “undesirable,” and it granted the director of admissions broad latitude to admit those of good background with weaker academic records. The key code word used was “character” — a quality thought to be frequently lacking among Jewish applicants, but present congenitally among affluent Protestants.

Friday, February 12, 2016

Epistasis and Complex Traits

Short summary: To first approximation we can ignore gene-gene interactions in the prediction of complex traits. This paper examines specifically how non-additive variance is driven to zero as the number of loci involved becomes large, assuming some dispersion in allele frequencies.

Earlier paper of Hill, Goddard, and Visscher and the simpler 2 locus case is discussed here.
Influence of Gene Interaction on Complex Trait Variation with Multilocus Models

Asko Mäki-Tanila, William G. Hill
GENETICS September 18, 2014 vol. 198 no. 1 355-367; DOI: 10.1534/genetics.114.165282

Although research effort is being expended into determining the importance of epistasis and epistatic variance for complex traits, there is considerable controversy about their importance. Here we undertake an analysis for quantitative traits utilizing a range of multilocus quantitative genetic models and gene frequency distributions, focusing on the potential magnitude of the epistatic variance. All the epistatic terms involving a particular locus appear in its average effect, with the number of two-locus interaction terms increasing in proportion to the square of the number of loci and that of third order as the cube and so on. Hence multilocus epistasis makes substantial contributions to the additive variance and does not, per se, lead to large increases in the nonadditive part of the genotypic variance. Even though this proportion can be high where epistasis is antagonistic to direct effects, it reduces with multiple loci. As the magnitude of the epistatic variance depends critically on the heterozygosity, for models where frequencies are widely dispersed, such as for selectively neutral mutations, contributions of epistatic variance are always small. Epistasis may be important in understanding the genetic architecture, for example, of function or human disease, but that does not imply that loci exhibiting it will contribute much genetic variance. Overall we conclude that theoretical predictions and experimental observations of low amounts of epistatic variance in outbred populations are concordant. It is not a likely source of missing heritability, for example, or major influence on predictions of rates of evolution.
See also Determination of Nonlinear Genetic Architecture using Compressed Sensing, and related posts on epistasis and additivity.

Thursday, February 11, 2016

LIGO detects gravity waves

Live-blogging the LIGO announcement of detection of gravity waves. Detection of an event in 2015 (initial science run of advanced LIGO) is good news for the future use of gravity waves as an astrophysical probe -- it suggests a fairly high density of NS-NS, NS-BH, and BH-BH binaries in the universe. Each time astronomers have developed a new probe (radio waves, x-rays, etc.) they have discovered new cosmic phenomena. The future is promising!

Techno-pessimists should note that detecting gravity waves is much, much harder than landing on the moon. LIGO measured a displacement 1/1000 of a neutron radius, in a noisy terrestrial background, accounting even for quantum noise.
https://www.ligo.caltech.edu/: 9/14/15 detection of BH-BH (~ 30 solar masses) merger at distance 1.3 Gy. The energy in the gravitational wave signal was ~3 solar masses!

Here is the paper  http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.116.061102
When I was an undergraduate, I toured the early LIGO prototype, which was using little car shaped rubber erasers as shock absorbers. Technology has improved since then, and the real device is much bigger.



Kip Thorne (from whom I learned General Relativity) has been one of the driving forces behind the effort to detect gravity waves for over 40 years. The picture below was taken during a conference in Eugene back in 2005.


Wednesday, February 10, 2016

Free Harvard, Fair Harvard: Freeharvard.org

We received confirmation from Harvard today that our petitions have been accepted and that our slate will appear on the Overseer ballots. For more information see FreeHarvard.org.


Our slate: Ralph Nader, Ron Unz, Lee Cheng, Stuart Taylor Jr., and myself.

Our platform:
1. More transparency in Harvard admissions
2. Increased use of endowment income to make Harvard more accessible
We are NOT conservative extremists: I voted twice for Obama, as did others on our slate.

We are NOT running against Affirmative Action. I support moderate admissions preferences, and I support diversity on campus. However, I am against preferences which are so large that they make it unlikely that the recipient of the preference can succeed in challenging courses at the university. Any admissions system has to be studied carefully to understand its consequences, and Harvard's is no exception.

Sunday, February 07, 2016

Slate Star Codex on Superforecasting

Scott Alexander (Slate Star Codex) on Philip Tetlock's Superforecasting:

Book review
Highlighted passages.

I especially liked this passage that Scott highlights:
When hospitals created cardiac care units to treat patients recovering from heart attacks, Cochrane proposed a randomized trial to determine whether the new units delivered better results than the old treatment, which was to send the patient home for monitoring and bed rest. Physicians balked. It was obvious the cardiac care units were superior, they said, and denying patients the best care would be unethical. But Cochrane was not a man to back down…he got his trial: some patients, randomly selected, were sent to the cardiac care units while others were sent home for monitoring and bed rest. Partway through the trial, Cochrane met with a group of the cardiologists who had tried to stop his experiment. He told them that he had preliminary results. The difference in outcomes between the two treatments was not statistically signficant, he emphasized, but it appeared that patients might do slightly better in the cardiac care units. “They were vociferous in their abuse: ‘Archie,’ they said, ‘we always thought you were unethical. You must stop the trial at once.'” But then Cochrane revealed he had played a little trick. He had reversed the results: home care had done slightly better than the cardiac units. “There was dead silence and I felt rather sick because they were, after all, my medical colleagues.”

[ Cochrane Collaboration ]    [ Bounded Cognition ]
See also Medical Science?

Saturday, February 06, 2016

Wednesday, February 03, 2016

Trump: the Master Persuader



Dilbert creator Scott Adams on Trump -- the Master Persuader :-)
Donald Trump has a way with words—and with people. Yet despite his popularity, he has been a mystery to the media, which have mostly derided his campaign as consisting of nothing more than random insults and ignorant bluster.

Scott Adams, prolific author, blogger, and creator of the massively popular comic strip Dilbert, has a different theory. He tells Reason TV's Zach Weissmueller that the media are being trolled by a skilled manipulator, or in Adams's parlance, a Master Wizard. So exquisite does Adams believe Trump's skills to be that he predicts The Donald will go on to win the presidency.

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