Lori Nickel:

When the Green Bay Packers suffered an embarrassing loss to the New York Giants in which they looked overmatched and outplayed, it could have demoralized a team with too many starters and stars on the sidelines in casts and on crutches.

Three days later, almost everyone expected a verbal lambasting from coach Mike McCarthy.

“But he gave this speech,” said nose tackle B.J. Raji, “and it just threw me.”

McCarthy quoted Corinthians from the Bible: “My grace is sufficient for you, for my power is made perfect in weakness.”

“The room was in complete silence,” said Raji. “It was not a long speech. I was surprised. And shocked. And kind of impressed. Losing a four-touchdown game on prime time, that’s cause for a fire-and-brimstone type of speech. It just shows how much he understands the players he’s coaching. I think the message got across.”

The message was to learn from the loss and convert it into something useful and therefore something meaningful.

You’ll never hear McCarthy say he’s out to prove the doubters wrong. You’ll never hear him vent his defenses to critics. One of his survival tactics is that he doesn’t waste his energy on anything he perceives as negative.

“I feel the ability to grow the positive is the best way to accomplish things,” McCarthy said last week in a one-on-one interview.

Nassim Nicholas Taleb:

The point we will be making here is that logically, neither trial and error nor “chance” and serendipity can be behind the gains in technology and empirical science attributed to them. By definition chance cannot lead to long term gains (it would no longer be chance); trial and error cannot be unconditionally effective: errors cause planes to crash, buildings to collapse, and knowledge to regress.

Something central, very central, is missing in historical accounts of scientific and technological discovery. The discourse and controversies focus on the role of luck as opposed to teleological programs (from telos, “aim”), that is, ones that rely on pre-set direction from formal science. This is a faux-debate: luck cannot lead to formal research policies; one cannot systematize, formalize, and program randomness. The driver is neither luck nor direction, but must be in the asymmetry (or convexity) of payoffs, a simple mathematical property that has lied hidden from the discourse, and the understanding of which can lead to precise research principles and protocols.

MISSING THE ASYMMETRY

The luck versus knowledge story is as follows. Ironically, we have vastly more evidence for results linked to luck than to those coming from the teleological, outside physics—even after discounting for the sensationalism. In some opaque and nonlinear fields, like medicine or engineering, the teleological exceptions are in the minority, such as a small number of designer drugs. This makes us live in the contradiction that we largely got here to where we are thanks to undirected chance, but we build research programs going forward based on direction and narratives. And, what is worse, we are fully conscious of the inconsistency.

transparency.org:

Looking at the Corruption Perceptions Index 2012, it’s clear that corruption is a major threat facing humanity. Corruption destroys lives and communities, and undermines countries and institutions. It generates popular anger that threatens to further destabilise societies and exacerbate violent conflicts.

The Corruption Perceptions Index scores countries on a scale from 0 (highly corrupt) to 100 (very clean). While no country has a perfect score, two-thirds of countries score below 50, indicating a serious corruption problem.

Nabeel Qurishi:

“I suspect that when the history of the 21st century is written circa 2100, he [Girard] will be seen as one of the great intellectuals” – Peter Thiel

When Blake Masters was posting his great notes on Peter Thiel’s lectures at Stanford, I found myself fascinated by the influence of Rene Girard.

Girard’s an original thinker. I’m not fully persuaded by his worldview: it explains a lot, but not everything. I’ve summarized his views here.

1. Mimesis determines what you want: In Girard’s view, people have appetites, which are your basic evolutionary needs: e.g. hunger; and desires, which are all other wants, e.g. the desire for a diamond ring. Girard’s belief is that people form desires based on what others around them want.

This is known as the ‘mimetic mechanism’. People take their cues from the people around them. They use other people as ‘models’, and (subconsciously) want what other people want, while rationalizing the whole time. In the diamond ring example, companies like DeBeers create a want artificially, and it catches on like a virus. People want diamond rings because other people want them, but they rationalise it by saying “it shows that my partner loves me”.

Michael Zhang:

Russian photographer Andrew Osokin is a master of winter macro photography. His photo collection is chock full of gorgeous super-close-up photographs of insects, flowers, snow, and frost. Among his most impressive shots are photographs of individual snowflakes that have fallen upon the ground and are in the process of melting away. The shots are so detailed and so perfectly framed that you might suspect them of being computer-generated fabrications.

They’re not though. The images were all captured using a Nikon D80 or Nikon D90 DSLR and a 60mm or 90mm macro lens.

Daniel Kahenman:

The question becomes, is it possible to set up a system for learning from history that’s not simply programmed to avoid the most recent mistake in a very simple, mechanistic fashion? Is it possible to set up a system for learning from history that actually learns in our sophisticated way that manages to bring down both false positive and false negatives to some degree? That’s a big question mark.

Nobody has really systematically addressed that question until IARPA, the Intelligence Advanced Research Projects Agency, sponsored this particular project, which is very, very ambitious in scale. It’s an attempt to address the question of whether you can push political forecasting closer to what philosophers might call an optimal forecasting frontier. That an optimal forecasting frontier is a frontier along which you just can’t get any better.

PHILIP E. TETLOCK is Annenberg University Professor at the University of Pennsylvania (School of Arts and Sciences and Wharton School). He is author of Expert Political Judgment: How Good Is It? How Can We Know? which describes a twenty-year study in which 284 experts in many fields, including government officials, professors, and journalists and ranging from Marxists to free-marketeers, were asked to make 28,000 predictions about the future. He found they were only slightly more accurate than chance, and worse than simple extrapolation algorithms. The book has received many awards, including the 2006 Woodrow Wilson Award from the American Political Science Association and the 2008 Grawemeyer Award for Ideas Improving World Order.

God’s handiwork on a pleasant afternoon.

The Economist:

WHEN aliens seize and torture Dr McCoy in “The Empath”, an episode of the science-fiction series “Star Trek”, Captain Kirk and Mr Spock rush to his aid. They are able to assess his condition in seconds with the help of a medical tricorder—a hand-held computer with a detachable sensor that is normally used by Dr McCoy himself to diagnose others. A quick scan with the tricorder indicates that he suffers from “severe heart damage; signs of congestion in both lungs; evidence of massive circulatory collapse”.

Along with teleportation, speech-driven computers and hand-held wireless communicators that flip open, the medical tricorder was one of many imaginary future technologies featured in “Star Trek”. Ever since, researchers have dreamed of developing a hand-held medical scanner that can take readings from a patient and then diagnose various conditions. Now, nearly five decades after “Star Trek” made its debut in 1966, the dream is finally edging closer to reality.

The Economist:

LETTUCE is California’s main vegetable crop. The state grew $1.6 billion-worth of the leafy plant in 2010 and accounts for more than 70% of all lettuce grown in America—itself the world’s second-biggest exporter of the stuff. It is a fiddly business. As well as having to be fertilised and weeded, lettuce must also be “thinned” so that good plants do not grow too close to each other, inhibiting growth. Much of this is still done by hand. Labourers, who tend to be paid per acre, not per hour, have little incentive to pay close attention to what they pull from the ground, often leading to unnecessary waste.

Enter Lettuce Bot, the brainchild of two Stanford-trained engineers, Jorge Heraud and Lee Redden. Their diligent robotic labourer, pulled behind a tractor, starts by taking pictures of passing plants. Computer-vision algorithms devised by Mr Redden compare these to a database of more than a million images, taken from different angles against different backdrops of soil and other plants, that he and Mr Heraud have amassed from their visits to lettuce farms. A simple shield blocks out the Californian sun to prevent odd shading from confounding the software.

Tim Iacono:

I’ve about had it with how giddy a large portion of the U.S. population has become about rising home prices.

Don’t get me wrong, when first thinking about this, I was about as happy as anyone else to learn that property values are now rising sharply again since, after renting for six years, my wife and I finally bought a house about two years ago. So, we stand to benefit as much as anyone else.

But, when you look at what’s driving home prices higher and how unnatural and unsustainable those factors are, suddenly the headlines sound more ominous than optimistic.