Links

OLPC’s $100 Laptop was Supposed to Change the World – Then It All Went Wrong – Adi Robertson at The Verge

The Father-Fuhrer – Kevin D. Williamson at National Review

The Untold Power of Investor Cliques – Leanna Orr at Institutional Investor

The More of Everything Problem – Ian Hathaway at Ianhathaway.com

Why We Haven’t Met Any Aliens – Geoffrey Miller at Seed Magazine

The Last Man Who Knew Everything – Matthew Walther at The Week

Authoritarian Gridlock? Understanding Delay in the Chinese Legislative System – Rory Truex in Comparative Political Studies

Corruption as the Only Option: The Limits to Electoral Accountability – Nara Pavão at The Journal of Politics. “When voters believe corruption to be a constant among candidate options, they are likely to overlook this aspect of government performance, basing their vote on other concerns. This attitude is particularly prevalent when corruption is more pervasive, which leads to the prediction that accountability for corruption will be weaker when it is needed the most.”

Fall of the American Empire – Paul Krugman at The New York Times

Joshua Firth | The War on Money Laundering and Why You Should Care – Jordan Harbinger with Joshua Firth on the Jordan Harbinger Show

What makes some art so bad that it’s good? – John Dyck at The Conversation

Machine Learning’s ‘Amazing’ Ability to Predict Chaos – Natalie Wolchover at Quanta Magazine

Videos

Venturing into Sacred Space | Archetype of the Magician – Like Stories of Old on YouTube

The Black Hole Bomb and Black Hole Civilizations – Kurzgesagt on YouTube

How Neutrons Changed Everything – Veritasium on YouTube

Books

Thinking in Bets: Making Smarter Decisions When You Don’t Have All the Facts, by Annie Duke (3.5/5): Well-written and well-thought-out, but not outstanding. If you’re the type of person interested in Fooled by Randomness, The Signal and the Noise, or Superforecasting, you’d be interested in Thinking in Bets, but the problem is that the book is just a shallow swim in a handful of the topics covered by those other titles, especially Superforecasting. It’s a damn shame, too, because there was a real opportunity here to use Duke’s background as a poker player to go beyond Tetlock’s question of “how do you predict under uncertainty?” and ask “how do you act under uncertainty?”, which just barely touched in the book.

Dune Messiah, by Frank Herbert (2.5/5): This was a major step down from the first book, particularly in the convoluted mess that was the first half, but thankfully enough threads untangled themselves by the end that I was able to see where everything had been going.

Ego is the Enemy, by Ryan Holiday (3.5/5): Like The Obstacle is the Way, Ego is the Enemy is more sledgehammer than scalpel, but sometimes that’s the tool for the job. With Ego, more than with Obstacle, I found myself struck by some of Holiday’s observations and personally convicted to change. I’ve never thought of myself as an egotistical person, but Holiday skillfully rips off many of the masks ego wears and shows you where it’s been hiding.

 

Music

King Charles – Loose Change for the Boatman

Madder Mortem – Fallow Season

Martin Rubashov – Hideout

The Moth & The Flame – Live While I Breathe

In every technological revolution—the first and second Industrial Revolutions and the dawn of the computer age, to name a few—some of those swept up in the ensuing disruption have dug in their heels to resist changes coming faster than people can adapt to them. The Luddites fought back in the Industrial Revolution by destroying new technology that they believed was robbing people of jobs and condemning them to a life of poverty. Life went on, however, and the economy didn’t grind to a halt; neither did it stop or even slow when “thinking machines” entered the world in the mid-20^th century. Standard economic theory predicts that some structural unemployment results from the introduction of new technology, but otherwise humans simply pick themselves back up again and move forward in a different field.

The advent of AI has led to fears that while previous kinds of technology may not have destroyed humans’ capacity to provide meaningful work, this time is different. AI could be the everything machine—better, faster, smarter than humans, with infinite adaptability. The previous adaptations that humans could make simply won’t apply, and as Nick Bostrom of Oxford puts it, “With a sufficient reduction in the demand for human labor, wages would fall below the human subsistence level. The potential downside for human workers is therefore extreme: not merely wage cuts, demotions, or the need for retraining, but starvation and death.”[1] This view has been downplayed as needlessly pessimistic about humanity’s adaptability and uniqueness. Peter Thiel, founder of PayPal and Palantir, has written that “computers are complements for humans, not substitutes.”[2] Other figures such as Bridgewater founder Ray Dalio and chess grandmaster Garry Kasparov, who have also had unique experiences working with AI, agree that humans have little to fear from an economy increasingly reliant on AI.

Dalio, for one, acknowledges in his 2017 book Principles that AI “could lead to our demise,”[3] but only after spending several pages arguing that its capabilities are so narrow that it will never truly replace humans and that human-computer teams will prove superior to computers alone. Kasparov has been hard at work promoting this view through so-called “centaur chess,” in which humans team up with computers and compete with each other. In a May 2017 interview with economist Tyler Cowen, Kasparov said there was “no doubt” that “a human paired with a set of programs is better than playing against just the single strongest computer program in chess.”

Dalio’s lack of concern primarily stems from his view that AI has lacked two key elements: evolution and the ability to determine cause-and-effect. “It’ll be decades—and maybe never—before the computer can replicate many of the things that the brain can do in terms of imagination, synthesis, and creativity. That’s because the brain comes genetically programmed with millions of years of abilities honed through evolution,”[4] says Dalio regarding the first element. But evolution is not a purposeful or intelligent process—in fact, it’s not even a single process, as Eliezer Yudkowsky points out in his essay “Evolutions are Stupid (But Work Anyway)”:

Complex adaptations take a very long time to evolve.  First comes allele A, which is advantageous of itself, and requires a thousand generations to fixate in the gene pool.  Only then can another allele B, which depends on A, begin rising to fixation.  A fur coat is not a strong advantage unless the environment has a statistically reliable tendency to throw cold weather at you.  Well, genes form part of the environment of other genes, and if B depends on A, B will not have a strong advantage unless A is reliably present in the genetic environment. […]

[…] Contrast all this to a human programmer, who can design a new complex mechanism with a hundred interdependent parts over the course of a single afternoon… Humans can foresightfully design new parts in anticipation of later designing other new parts; produce coordinated simultaneous changes in interdependent machinery; learn by observing single test cases; zero in on problem spots and think abstractly about how to solve them; and prioritize which tweaks are worth trying, rather than waiting for a cosmic ray strike to produce a good one. By the standards of natural selection, this is simply magic. […]

[…] In some ways, biology still excels over the best human technology: we can’t build a self-replicating system the size of a butterfly. In other ways, human technology leaves biology in the dust.  We got wheels, we got steel, we got guns, we got knives, we got pointy sticks; we got rockets, we got transistors, we got nuclear power plants. With every passing decade, that balance tips further.

Not only is Dalio incorrect about the nature and benefits of evolution, he neglects the fact that we can in fact replicate evolutionary processes with machine-learning algorithms—and do it better and faster, too. Random variation, natural selection, recombination—all are replicable in a virtual environment, and thanks to that foresight Yudkowsky mentioned, humans can intelligently prune dead ends, add complex improvements in a single step, and push the timescale of reproduction down from years and decades to minutes and seconds. So even if Dalio was correct about evolution providing human brains with unique capabilities, the fact that we can use evolution as a tool in our development of technology means there shouldn’t be anything stopping those capabilities from arising in our creations as well.

Those capabilities could, for instance, include the ability to assess cause and effect, of which Dalio claims machines are less capable. But again Dalio overestimates humans and underestimates machines. Humans are notoriously bad at determining cause-and-effect, particularly when it comes to false positives. Daniel Kahneman, who developed the now-dominant two-system model of cognition, found that System 1, which is our dominant method of making decisions, “automatically and effortlessly identifies causal connections between events, sometimes even when the connection is spurious…it suppresses ambiguity and spontaneously constructs stories that are as coherent as possible. Unless the message is immediately negated, the associations that it evokes will spread as if the message were true.”[5] Furthermore, there is simply no reason whatsoever to say that computers can’t be programmed to take cause-and-effect into account—or that computers can’t learn it themselves.

It’s common to throw out the platitude “correlation does not equal causation” and by extension, insinuate that since computers only measure correlation, they’re not really determining causation. But causation cannot be established without correlation, and moreover, the way to weed out false hypotheses (and assign greater validity to the true one) is by finding evidence that’s negatively correlated with those hypotheses. Bayesian inference, one of the foundational tools in modern machine learning, is a method of assigning a probability to your hypothesis (let’s call it H) based on how likely we are to see some piece of evidence (we’ll call it E) given that it’s true. If you only include that one type of evidence, it’s correct that you will only have a crude, one-dimensional understanding of the relationship between E and H. But just like human scientists can clarify their theories by introducing other variables to test, so can machines—and in practice they almost always do. Virtually any major machine learning algorithm in use today will make use of large datasets with countless variables, allowing them to test and rule out many different causal relationships. In many cases, this has made machines superior to humans in determining the likelihood of some claim being true, as they are able to digest much larger sets of possibilities.

Kasparov has echoed Dalio’s claims regarding how intelligent computers can really be. In his interview with Cowen, Kasparov also said that the Deep Blue machine that beat him in 1997 was “anything but intelligent” and simply brute-forced its way to victory. But this view of artificial intelligence is astonishingly outdated. The cutting edge of AI research today is in deep learning, in which humans rarely have direct input over the decision-making process the program uses. Rather, the program uses a network of nodes that work like neurons, and these nodes are given “weights” in the decision-making process based on how accurate previous iterations of the network have been. The human, aside from setting up the initial instructions, has very little say in how exactly the network operates, and it is even becoming increasingly common for algorithms to operate as black boxes.

“The workings of any machine-learning technology are inherently more opaque, even to computer scientists, than a hand-coded system,” Will Knight wrote in the MIT Technology Review. “This is not to say that all future AI techniques will be equally unknowable. But by its nature, deep learning is a particularly black box. You can’t just look inside a deep neural network to see how it works. A network’s reasoning is embedded in the behavior of thousands of simulated neurons, arranged into dozens or even hundreds of intricately interconnected layers.” Knight describes a medical program called Deep Patient that has proven incredibly successful at diagnosing patients, but “offers no clue as to how it does this,” and Deep Patient is far from the only example. It and other modern neural networks are nothing like what Kasparov describes; they can perform tasks like medical diagnosis that are far more open-ended than chess, adjust their internal structure without human intervention, and reach conclusions humans can’t reach, based on reasoning humans can’t understand.

Peter Thiel, whose company Palantir works on digesting massive amounts of data for business and national security applications (and who certainly can’t be dismissed as ignorant), handles this objection by making a distinction between planning (and other sorts of allegedly human-specific cognition) and “mere” data processing:

People have intentionality—we form plans and make decisions in complicated situations. We’re less good at making sense of enormous amounts of data. Computers are exactly the opposite: they excel at efficient data processing, but they struggle to make basic judgments that would be simple for any human.[6]

Notice Thiel’s use of the present tense to describe AI, however, and think about how quickly Kasparov’s dismissals of AI became outdated. While Thiel’s view has not been left in the dust like Kasparov’s just yet, it relies on stagnation nonetheless, and with the development of AlphaZero at DeepMind, it lies on the razor’s edge of obsoletion.

Consider the 2017 paper “Mastering the game of Go without human knowledge,” published by DeepMind, responsible for the creation of the programs AlphaGo and AlphaGo Zero, as well as AlphaZero:

Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: [emphasis mine] a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games…Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo.

Read: AlphaGo, with minimal human input aside from the rules, defeated the human world Go champion. AlphaGo Zero, with no human input aside from the rules, defeated AlphaGo. The gap between these two accomplishments was only two years, from October 2015 to October 2017. Humans beat horses. Then centaurs beat horses. Now horses beat both humans and centaurs. In the realm of games, at least, centaurs reached obsoletion in a timeframe that is short by the scale of a human life and vanishingly small by the scale of civilizational progress.

Two months after the defeat of AlphaGo, the DeepMind team posted a paper on the preprint site Arxiv titled “Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm.” Their announcement? A new algorithm, derived from AlphaGo Zero and simply named AlphaZero, had improved on its predecessor in two astonishing ways: first, it was able to beat AlphaGo Zero after just 24 hours of training (i.e. competing against itself to refine its network nodes), and second, its algorithm was able to generalize to chess and shogi, not just Go, beating the state-of-the-art Stockfish (chess) and Elmo (shogi) programs in an even shorter amount of time than it took to beat AlphaGo Zero.

In chess, AlphaZero outperformed Stockfish after just 4 hours (300k steps); in shogi, AlphaZero outperformed Elmo after less than 2 hours (110k steps); and in Go, AlphaZero outperformed AlphaGo Lee (29) after 8 hours (165k steps).[7]

AlphaZero deals a devastating blow to the idea that machines must be confined to the role of narrow, rigid subordinate to broad, flexible humans. Even if the algorithm did not generalize beyond Go, AlphaZero’s success at that game would have gone a long way toward establishing that computers can handle more ambiguity than previously given credit for. In contrast to chess, where establishing which player is in the lead can be estimated by simple heuristics like pawn structure and piece count, it can be “maddeningly difficult to determine who is ahead” in Go, as George Johnson put it. Cornell University’s Fellows, Malitsky, and Wojtaszczyk explain in more technical terms that “The large branching factor in the game makes traditional adversarial search intractable while the complex interaction of stones makes it difficult to assign a reliable evaluation function.” The difficulty of performing even such a basic task as determining who is winning a particular game of Go would be a significant roadblock for a machine trying to win the game itself—if the machine were dumb in the ways Thiel describes. But now a machine can beat another machine that beat another machine that beat the greatest human player in the world, demonstrating that it can, in fact, make sense of a complex and ambiguous board with only minimal instruction and a few hours of self-training.

Prediction, undirected learning, adaptability—all domains swallowed by this adolescent creation. If this is mere data processing, data processing can do a lot more than we’ve given it credit for.

Not that this even matters when it comes to Thiel’s ultimate defense—that computers will be “supplements to humans, not substitutes.” Thiel asserts: “the stark differences between man and machine mean that gains from working with computers are much higher than gains from trade with other people. We don’t trade with computers any more than we trade with livestock or lamps. And that’s the point: computers are tools, not rivals.” Thiel and his camp believe that the human economy is transitioning to a centaur economy, to put it in Kasparov’s terms.

But people are tools, too, in a contextual sense: they trade their skill, time, and effort for money in a way that is mutually productive. The entire fear is that those in a position to control automation and reap the rewards from it—quite possibly through no hard work or ingenuity of their own, just the luck of birth—will no longer need anything from any of the billions who previously had something of value to provide, and Thiel swings and misses on this softball. Naturally, from the perspective of those who are in a position to control machines, they don’t look like a replacement, but it sure looks that way for those who aren’t in such a position. AI, if not managed properly, could lead to a small group of individuals having a stranglehold on the entire world.

However much the human is crowded out of the equation now, they will be crowded out further and further the more time passes. Barring major biomedical breakthroughs, human intelligence is more or less static, while machine intelligence improves by leaps and bounds, and the assumption that humans will always be able to shift to new activities is borne out neither by the historical evidence nor by the simple reasoning that such an assumption relies on a blind search for what we know must be a finite resource.

The belief that computers can’t possibly be a meaningful substitute for humans ultimately relies on a hodgepodge of unstated assumptions about humans themselves; that computers made of meat are somehow special, that the next hundred years of our existence will look like the last ten, that there will always be a place set aside for us in the universe. Facing these assumptions and overturning them is not pleasant, but it must be done to navigate an increasingly opaque future.

It may very well be the case that the optimists are correct; no one can tell you with certainty what the future holds. But it’s precisely that uncertainty, especially mixed with vulnerability, that makes caution necessary, for while creative destruction is real, so is uncreative destruction. The answers may be unclear, but to come up with a solution, one must first face the problem as it is, not as they want it to be. Tyler Cowen, in his essay reacting to the triumph of AlphaZero over man-machine hybrids, put the problem in the bluntest terms possible: “The age of the centaur is over.”

Long live the horse.

 

[1] Bostrom, N. (2016). Superintelligence: Paths, dangers, strategies. Oxford, United Kingdom: Oxford University Press.

[2] Thiel, P., & Masters, B. (2014). Zero to one: Notes on startups, or how to build the future. New York: Crown Business.

[3] Dalio, R. (2017). Principles. New York: Simon and Schuster.

[4] Dalio, R. (2017). Principles. New York: Simon and Schuster.

[5] Kahneman, D. (2013). Thinking, fast and slow. New York: Farrar, Strauss and Giroux.

[6] Thiel, P., & Masters, B. (2014). Zero to one: Notes on startups, or how to build the future. New York: Crown Business.

[7] From the paper’s footnotes: “AlphaGo Master and AlphaGo Zero were ultimately trained for 100 times this length of time: we do not reproduce that effort here.”

Links

Milk, a symbol of neo-Nazi hate – Andrea Freeman at The Conversation

The epic mistake about manufacturing that’s cost Americans millions of jobs – Gwynn Guilford at Quartz

Israeli Operatives Who Aided Harvey Weinstein Collected Information on Former Obama Administration Officials – Ronan Farrow at The New Yorker

We read every one of the 3,517 Facebook ads bought by Russians. Here’s what we found – Penzenstadler, Heath, and Guynn at USA Today

RIP the Trans-Atlantic Alliance, 1945-2018 – James Traub at Foreign Policy

Facebook’s Cambridge Analytica problems are nothing compared to what’s coming for all of online publishing – Doc Searles at Harvard.edu

The Entire Economy is MoviePass Now. Enjoy It While You Can – Kevin Roose at The New York Times

Europe’s AI Delusion — Bruno Maçães at Politico

Code Name Crossfire Hurricane: The Secret Origins of the Trump Investigation – Apuzzo, Goldman, and Fandos at The New York Times

There’s nothing wrong with a census question about citizenship – Marc A. Thiessen at The Washington Post. A counterargument to some of the pushback against the citizenship question. I remain agnostic on the issue, but this article brings up some context to having citizenship questions on census forms and makes me inclined to believe much of the opposition is a tad hysterical.

How the fight against child porn took two men to the internet’s darkest corners – Shamsheer Yousaf at Factor Daily

The Scientific Paper is Obsolete. Here’s What’s Next – James Somers at The Atlantic

Economics renames itself to appeal to international students – The Economist

The Moscow Midterms – How Russia could steal our next election – Clare Malone at FiveThirtyEight

Maybe She Had So Much Money She Just Lost Track of It – Jessica Pressler at The Cut

Videos

A Dragon Torched My Hand (How Do VR Haptic Gloves Work?) – Smarter Every Day on YouTube

Gladiator | Turning Spectacle into a Meaningful Story – Like Stories of Old on YouTube

How a recording-studio mishap shaped ‘80s music – Vox on YouTube

Web 3.0 Explained – Siraj Raval on YouTube

The Threat of AI Weapons – Veritasium on YouTube

 

Books

The Obstacle is the Way, by Ryan Holiday (3/5): This was my third time reading this book, and I got less out of it than I did on previous readings. Holiday treats stoicism as a blunt instrument in The Obstacle is the Way and doesn’t provide much guidance about how to wield it—some sections can essentially be boiled down to “You should do X. Except when you shouldn’t do X. Then you should do Y.”  There are some writers who can successfully trade profundity for punch, however, and Holiday is one of them, enough so that The Obstacle is the Way remains on my bookshelf as a go-to resource when I’m struggling to get out of my own way.

Conspiracy, by Ryan Holiday (4.5/5): Tied with Trust Me, I’m Lying for the crown of Holiday’s best book. The Hogan-Thiel-Gawker affair seems straightforward enough from the headlines—Thiel wanted revenge on Gawker, so he funded Hogan’s lawsuit and crushed them—but such a condensation does no justice to the web of intrigue spun as those events escalated out of such humble beginnings as the outing of a gay entrepreneur. Holiday is clearly more sympathetic to Thiel than to Gawker editor Nick Denton, but not fawningly so, and he provides ample criticism for both sides. Highly recommended for its examination of both the events in question and the nature of conspiracies in general.

Music

Crystal Castles – Sad Eyes

Ghost – Rats

M83 – Un Nouveau Soleil

Tool – Forty-Six & Two

 

Movies

Avengers: Infinity War (4/5) – I enjoyed Infinity War, so I’ll start by saying it was a great movie that skillfully pulled together all the disparate threads of the MCU into a thrilling story, but I think Thanos is overrated as a villain. He’s decently-written, but his motive for killing half the population of the universe doesn’t make him some sympathetic utilitarian a la Watchmen, it makes him an asshole. There needed to be either a more fleshed-out in-universe reason for Thanos wanting to cull the herd or a different motive entirely. There were—as far as I can recall—precisely zero references in previous Marvel movies to an overpopulation problem or some metaphysical principle of “balance,” but as soon as Thanos revealed those to be his motives, edgelords everywhere apparently decided he was the second coming of Jeremy Bentham.

Solo (3.5/5) – Solo certainly wasn’t the best Star Wars film ever, but you’d have to be out of your mind to say it’s a bad movie. Most of its struggles seem to be the result of having to follow so quickly after Rogue One and The Last Jedi and being released so soon after Infinity War and Deadpool 2. I thought it was a fun, well-made film that just got drowned out by excitement of other movies.

In light of Trump’s consistent unpopularity among most of the country and the staggering losses the Republican party has already seen in special elections since the 2016 general, most seem to be anticipating a massacre for the Democrats in the 2018 midterms. This is ostensibly good news for anyone who wants to see the downfall of Trump and the GOP, but the GOP’s depraved behavior, Trump-supporting base, and expectation of closing doors will produce a toxic mix of incentives that should worry even the optimists.

Since the 2016 election, the GOP rank-and-file have shown a disturbing lack of willingness to stand up to Trump’s dismantling and reshaping of the federal government, and Trump in turn has made it clear that he will scratch their backs if they scratch his. Three of the four most outspoken Trump opponents in the Senate GOP, John McCain, Bob Corker and Jeff Flake, cannot be expected to stay in the fray for long, as McCain is dealing with a highly aggressive form of brain cancer and Corker and Flake have announced their intention to retire at the end of their current terms. In Corker’s and Flake’s cases, their decisions were made in large part due to the intense resistance they expected to face in their respective primary races, specifically resistance from far-right challengers. The message for anyone running for the Republicans in 2018 is clear: opposing Trump doesn’t pay. If you don’t back the president strongly enough (and even Corker, McCain, and Flake overwhelmingly voted in alignment with him), you will be beaten by someone who does. Even Paul Ryan, whose flaccid opposition to Trump earned him scorn from both sides, is more than likely passing the Republican torch not to a moderate, but to white supremacist Paul Nehlen.

An incumbent who doesn’t get primaried will still have to face their general election opponent, likely a Democrat. Given the stark divide between Trump’s approval among Republicans (85% as of March 25, 2018) and his approval in the nation as a whole (39% as of the same date), it’s unlikely that a Trump-supporting Republican will be able to convince many independents or conservative Democrats to join his side merely by turning on the president, especially if they were just praising Trump a few months before to avoid getting primaried. There is still no reward for a Republican wanting to reach across the aisle—that time is long gone; contrary to what one might assume, distancing yourself from an unpopular president might actually hurt your chances of getting elected. Republicans are faced with a choice of remaining radicalized (or even becoming more so) and keeping their small-but-passionate base of support, or moderating themselves and losing even the base while gaining nothing.

In light of this, even before the 2018 elections occur, I would expect to see more radicalization, not less, occurring on multiple fronts. For current Republican officeholders, there is nothing to gain and everything to lose by opposing Trump. Will Ted Cruz suddenly bring over more independents and conservative-leaning Democrats just by edging away from Trump? There is no reward for a Republican wanting to reach across the aisle—that time is long gone. Republican candidates can either remain radicalized (or become more so) and keep their 35% support, or moderate themselves and lose even that. The only real hope for someone in that position is that their base is so fired up that their participation rate swamps that of their opposition.

Additionally, in certain cases the problem may actually be worse in competitive districts than in comfortably red areas. In districts with a smooth distribution of political views across the population, even if it leans right overall, most people’s views will likely rest comfortably near the center of that distribution, meaning that a leftward shift from a right-wing candidate still could appeal to much of the population on ideological grounds, even if the candidate has already burned goodwill for supporting Trump. Compared to that kind of district, one with a more powerful left wing will probably have fewer people residing in the center (especially if the district is red overall, indicating polarization). In this instance, there will be much fewer people brought over due to ideological agreement—probably less than can be gained by pushing even harder to the right.

This pressure will also manifest itself in a sort of political FOMO—officeholders who feel that their time may be running out, especially if they moderate themselves, may push extreme legislation much harder than they would if they felt comfortable in their chances of re-election. Again, there is nothing to lose and everything to gain by extremifying yourself in a situation like this. Shifting rightward to shore up your base will increase both your chances of re-election and your chances of successfully accomplishing your agenda.

Those who are truly desperate may resort to increasingly dangerous measures to hold on, especially if the Mueller investigation threatens to bring down more than just Trump and his inner circle. Consider the decision-making process for someone who believes that they will face worse consequences from GOP losses (their own or someone else’s) than they will by using illegal or questionably-legal tactics to win. It might seem outlandish to predict such authoritarian maneuvers, but the if the incentives are aligned correctly, it’s not a ridiculous prospect at all. The Economist, in fact, has already reported that multiple Republican governors are blocking Democrat-leaning special elections until conditions are more favorable for the party:

Mr. Walker reacted [to a court ordering a special election] by asking Republican legislative leaders to recall lawmakers for an extraordinary session on April 4th, so they could pass a bill that would no longer allow special elections after the state’s spring election in even-numbered years. (This year’s spring election is on April 3rd). […]

[…] Two other Republican governors, Rick Snyder of Michigan and Rick Scott of Florida, are stalling on special elections. Mr. Snyder has decided to wait until November to replace John Conyers, a Democratic congressman who resigned in December because of allegations of sexual harassment, as well as Bert Johnson, a Democratic state senator who resigned after pleading guilty to charges of corruption. Mr. Scott, who like Mr. Snyder is term-limited, is refusing to hold special elections for two seats in Florida’s legislature.

There are still reasons to be optimistic about the 2018 election, as it seems unlikely that the handful of measures mentioned here will outweigh the overwhelming Democratic momentum, but it’s important to fight complacency. Equally important to avoid is a sense that America just needs to wait for the 2018 elections and everything will be fixed. A lot of damage can still be done between now and November, and carelessness and stupidity are unacceptable luxuries that will only speed it along.

Links

The Working Person’s Guide to the Industry that Might Kill Your Company – Hamilton Nolan at Splinter

In Praise of Conspiracies – Ryan Holiday at The Observer. A followup to Holiday’s article on Silicon Valley, which I linked to in last month’s Miscellanea.

What is Your Tribe? The Invention of Kenya’s Ethnic Communities – Patrick Gathara at The Elephant

The Myth of ‘Learning Styles’ – Olga Khazan at The Atlantic

The Real Origins of the Religious Right – Randall Blumer at Politico.

Are We Seeing the Start of a Liberal Tea Party? – Nathaniel Rakich at FiveThirtyEight

The Surprisingly Solid Mathematical Case of the Tin Foil Hat Gun Prepper – BJ Campbell

Facebook: The Cambridge Analytica thing wasn’t a ‘data breach,’ it’s just totally how our platform works – Laura Hazard Owen at Nieman Lab. I hate to keep pounding the Facebook drum so incessantly, but it cannot be emphasized enough that none of the incentives currently at play will allow Facebook—or just about any other social media company—to prioritize privacy, autonomy, or security. If knowledge is power, what does it mean to give out knowledge about yourself? And on that note:

China waging ‘psychological warfare’ against Australia, US Congress told – Ben Doherty at The Guardian. The genie is out of the bottle. This was never going to just be a vulnerability that got exploited once and then fixed immediately.

Why we should bulldoze the business school – Martin Parker at The Guardian

 

Videos

Basic Income Explained – Siraj Raval on YouTube

 

Books

Colonel Roosevelt, by Edmund Morris (4/5): This was the final volume of Morris’ three-part study of Theodore Roosevelt, collectively the best biography I’ve ever read. The trilogy peaked with the masterful first volume, The Rise of Theodore Roosevelt, but the two following volumes are well worth the reader’s time as well.

 

Music

Black Stone Cherry – Soul Machine

Chevelle – An Island

Evergrey – The Grand Collapse

Les Discrets – L’echapee

Ne Obliviscaris – And Plague Flowers the Kaleidoscope

 

Movies

A Quiet Place (5/5): Wildly suspenseful and surprisingly heartfelt. The casting of real-life couple John Krasinski and Emily Blunt was a wise choice, as was casting a deaf actress for their daughter Regan.

Truth or Dare (2/5): It’s a Blumhouse movie.

Links

It’s Time to Get Real About Power in Silicon Valley – Ryan Holiday at The Observer. This essay shook me more powerfully than anything I’ve read since Meditations on Moloch. An unflinching look at what makes the wheels of the world turn.

My Life as a Tunnel Rat – Jim Marett at The New York Times

A Quick (Battle) Field Guide to the New Culture Wars – Venkatesh Rao at Ribbonfarm

Russia ‘arming the Afghan Taliban,’ says US – Justin Rowlatt at the BBC. The circle completeth.

In the Trenches of the Facebook Election – John Herrman at The Awl. See also How Facebook plans to become one of the most powerful weapons in politics – Philip Bump at The Washington Post. Note the publication dates on both.

The desire to fit in is the root of almost all wrongdoing – Christopher Freiman at Aeon.co

I Got a Story to Tell – Steve Francis at The Players’ Tribune

Is Big Business Really That Bad? – Robert Atkinson and Michael Lind at The Atlantic

Videos

A Real Life Haptic Glove (Ready Player One Technology Today) – Smarter Every Day 190 – YouTube

Learned Helplessness – YouTube

Books

None this month.

Music

Alice in Chains – Stone

Amplifier – Matmos

Nightwish – Ghost Love Score

 

Movies

Icarus (5/5) (Netflix): Worth every bit of the hype it received. Starts out as a self-experiment in sports doping, takes insane left turn, leaves viewer feeling overwhelming sense of dread.

Links

(Don’t) Be the Gray Man – Patrick Steadman at Ribbonfarm. “While it’s fun to make fun of the dynamics of virtue signaling on social media, a society where many people have ‘gray’ identities and belief systems is quietly primed for chaos.”

Life Is Hard; Get Drunk on This – Brett McKay at The Art of Manliness

‘Never get high on your own supply’—why social media bosses don’t use social media – Alex Hern at The Guardian

How Skyscrapers Can Save the City – Edward Glaeser at The Atlantic

Heroes are not Replicable – Alex Tabarrok at Marginal Revolution

He Was a Crook – Hunter S. Thompson at The Atlantic. Hunter S. Thompson killed himself 13 years ago this February. The Atlantic published his vicious obituary of Richard Nixon, originally written for Rolling Stone. “He was a swine of a man and a jabbering dupe of a president. Nixon was so crooked that he needed servants to help him screw his pants on every morning. Even his funeral was illegal. He was queer in the deepest way. His body should have been burned in a trash bin. These are harsh words for a man only recently canonized by President Clinton and my old friend George McGovern — but I have written worse things about Nixon, many times, and the record will show that I kicked him repeatedly long before he went down. I beat him like a mad dog with mange every time I got a chance, and I am proud of it. He was scum.” Do yourself a favor and read the whole thing.

Today’s Impeach-O-Meter: Democrats Unveil Worst Campaign Idea Since “Pokemon Go to the Polls” – Ben Mathis-Lilley at Slate. Democrats: snatching defeat from the jaws of victory since 1828.

With all of the negative headlines dominating the news these days, it can be difficult to spot signs of progress. What makes you optimistic about the future? – /u/thisisbillgates at /r/AskReddit. In which Bill Gates descended upon the teeming masses of reddit to inspire optimism and change.

How Manafort’s inability to convert a PDF file to Word helped prosecutors – Timothy B. Lee at Ars Technica. Also, Paul Manafort’s Password was “bond007,’ Making Him the Worst Unregistered Foreign Agent Ever. We really are living through Stupid Watergate.

Current Affairs’ “Some Puzzles For Libertarians”, Treated As Writing Prompts For Short Stories – Scott Alexander at Slate Star Codex, demolishing an embarrassingly stupid critique of libertarianism.

With Xi’s Power Grab, China Joins New Era of Strongmen – Steven Lee Myers at The New York Times.

Deep Fakes: A Looming Crisis for National Security, Democracy, and Privacy? – Robert Chesney and Danielle Citron at Lawfare. Their colleague Herb Lin offers a more optimistic perspective here.

This Mutant Crayfish Clones Itself, and It’s Taking Over Europe – Carl Zimmer at The New York Times.

Videos

Cardinal Conversations – Reid Hoffman and Peter Thiel on Technology and Politics – YouTube

LIVE DEBATE – Swipe Left: Dating Apps Have Killed Romance – YouTube

The Incredible Sounds of the Falcon Heavy Launch (BINAURAL AUDIO IMMERSION) – Smarter Every Day 189 – YouTube

The Bayesian Trap – YouTube

Brave New World vs Nineteen Eighty-Four – YouTube. While a huge fan of both works, I continue to find 1984 a more compelling and relevant work, a fact that I’m actually somewhat puzzled by. Even I have to admit that our society more closely resembles that of Brave New World, at least on a superficial level, and it seems like the recent trend among the smart people I follow has been to regard BNW more highly than 1984. Perhaps this will be a good topic for a future essay, after I’ve examined the two works more closely and tried to figure out why I remain so closely attached to Orwell.

Deepmind AlphaZero – Mastering Games Without Human Knowledge – YouTube

Ian Morris | Why the West Rules — For Now – YouTube

Waking Up with Sam Harris #109 – Biology and Culture (with Bret Weinstein) – YouTube

Books

Outer Dark, by Cormac McCarthy (2.5/5): This was the fourth McCarthy book I’ve read, and the earliest one chronologically speaking (the others are Blood Meridian, No Country for Old Men, and The Road). I’ve concluded I’m not as much of a fan of early McCarthy. I think the later you go in McCarthy’s bibliography, the less McCarthy’s style completely dominates the experience. In Outer Dark, the opaque language, static characters, and sparse action that McCarthy is known for are such a chore to get through that I nearly gave it up. It almost read like a McCarthy parody at points. However, his strengths—gorgeous turns of phrase and a thought-provoking ending—were also present.

The Art of Learning, by Josh Waitzkin (4.5/5): This was my third reading of this book, and I got more out of it this time than any of the previous times. I’ll be writing a more thorough analysis of this book in the coming weeks.

Letters from the Earth, by Mark Twain (4/5): A short, clever deconstruction of the Bible written from the perspective of Satan after being put in the time-out corner for excessive cheekiness. I’m amazed it’s not better known among secular activists and the like; Twain puts his sharp eye and devastating wit to good use without being heavy-handed or unfair.

Music

Above and Beyond – Tri-State

Breaking Benjamin – Feed the Wolf

Grabbitz – Follow Me

LRKR – Morning Rain

A Perfect Circle – The Doomed

A Perfect Circle – TalkTalk

Pop Evil – Waking Lions

 

Movies

Black Panther (4/5): I thought it was a bit overhyped (is it really 97-percent-on-Rotten-Tomatoes good?), but still excellent—definitely better than any of the comedies-with-superheroes that Marvel’s been releasing lately.

The Shape of Water (3.5/5): I thought it kind of jumped the shark in the third act (there is such a thing as being too weird), but it was still a sweet, well-made movie.

My Scientology Movie (3.5/5) (Netflix): Less about Scientology itself, and more about the process Louis Theroux went through even trying to make the movie to begin with. Quirky and fun to watch without detracting from the gravity of the subject.

The psychology of learning is one of those fields where many have opinions, but few have facts. The good news—not that you’d know it from the way people talk—is that an enormous amount of research has been done over the last several years on the topic, and while there’s still plenty more to be done, scientists have identified many strategies the help people learn more effectively. Having delved into more than my fair share of that research, I can confidently say that Make It Stick is the single best resource for the layman wanting to improve their ability to learn. It’s practical, thorough, and concise, and it uses its own principles to help the reader internalize its message.

Top 5 Key Concepts

Page 30: Prior knowledge doesn’t hinder creativity or problem-solving, it aids them

“The frustration many people feel toward standardized, ‘dipstick’ tests given for the sole purpose of measuring learning is understandable, but it steers us away from appreciating one of the most potent learning tools available to us. Pitting the learning of basic knowledge against the development of creative thinking is a false choice. Both need to be cultivated. The stronger one’s knowledge about the subject at hand, the more nuanced one’s creativity can be in addressing a new problem. Just as knowledge amounts to little without the exercise of ingenuity and imagination, creativity absent a sturdy foundation of knowledge builds a shaky house.”

Page 55: Memorization of basic facts of a subject is necessary to advance to higher-level application of that subject

“To paraphrase a conclusion from one of these studies, recall and recognition require ‘factual knowledge,’ considered to be a lower level of learning than ‘conceptual knowledge.’ Conceptual knowledge requires an understanding of the interrelationships of the basic elements within a larger structure that enable them to function together. Conceptual knowledge is required for classification. Following this logic, some people argue that practicing retrieval of facts and exemplars would fall short as a strategy for comprehending general characteristics that are required for higher levels of intellectual behavior. The bird classification studies suggest the opposite: strategies of learning that help students identify and discern complex prototypes (family resemblances) can help them grasp the kinds of contextual and functional differences that go beyond the acquisition of simple forms of knowledge and reach into the higher sphere of comprehension.”

Page 72: Learning occurs in a three-step process of encoding, consolidation, and retrieval

“Encoding: […]

“[…]The brain converts your perceptions into chemical and electrical changes that form a mental representation of the patterns you’ve observed…We call the process encoding, and we call the new representations within the brain memory traces. Think of notes jotted or sketched on a scratchpad, our short-term memory. […]

“[…] Consolidation:

“The process of strengthening these mental representations for long-term memory is called consolidation. New learning is labile: its meaning is not fully formed and therefore is easily altered. In consolidation, the brain reorganizes and stabilizes the memory traces. […]

“[…] Retrieval:

Learning, remembering, and forgetting work together in interesting ways. Durable, robust learning requires that we do two things. First, as we recode and consolidate new material from short-term memory into long-term memory, we must anchor it there securely. Second, we must associate the material with a diverse set of cues that will make us adept at recalling the knowledge later. Having effective retrieval cues is an aspect of learning that often goes overlooked. The task is more than committing knowledge to memory. Being able to retrieve it when we need it is just as important.”

Page 76: Memory is (virtually) limitless—it’s retrieval that’s the bottleneck

“There’s virtually no limit to how much learning we can remember as long as we relate it to what we already know. In fact, because new learning depends on prior learning, the more we learn, the more possible connections we create for further learning. Our retrieval capacity, though, is severely limited. […]

“[…] Knowledge is more durable if it’s deeply entrenched, meaning that you have firmly and thoroughly comprehended a concept, it has practical importance or keen emotional weight in your life, and it is connected with other knowledge that you hold in memory. How readily you can recall knowledge from your internal archives is determined by context, by recent use, and by the number and vividness of cues that you have linked to the knowledge and can call on to help bring it forth.”

Page 141: Individual differences may help or hinder learning, but they may not be the differences you think

“Each of use has a large basket of resources in the form of aptitudes, prior knowledge, intelligence, interests, and sense of personal empowerment that shape how we learn and how we overcome our shortcomings. Some of these differences matter a lot—for example, out ability to abstract underlying principles from new experiences and to convert new knowledge into mental structures. Other differences we may think count for a lot, for example having a verbal or visual learning style, actually don’t.”

Top 5 Practical Takeaways

Page 20: Retrieving material from memory—not simply re-encountering it—aids in learning

“The act of retrieving learning from memory has two profound benefits. One, it tells you what you know and don’t know, and therefore where to focus further study to improve the areas where you’re weak. Two, recalling what you have learned causes your brain to reconsolidate the memory, which strengthens its connections to what you already know and makes it easier for you to recall in the future. In effect, retrieval—testing—interrupts forgetting.”

Page 47: Making retrieval more difficult aids in learning, despite our intuitions

“While practicing is vital to learning and memory, studies have shown that practice is far more effective when it’s broken into separate periods of training that are spaced out. The rapid gains produced by massed practice are often evident, but the rapid forgetting that follows is not. Practice that’s spaced out, interleaved with other learning, and varied produces better mastery, longer retention, and more versatility. But these benefits come at a price: when practice is spaced, interleaved, and varied, it requires more effort. You feel the increased effort, but not the benefits the effort produces. Learning feels slower from this kind of practice, and you don’t get the rapid improvements and affirmations you’re accustomed to seeing from massed practice. Even in studies where the participants have shown superior results from spaced learning, they don’t perceive the improvement; they believe they learned better on the material where practice was massed.”

Page 87: Trying to answer a question or solve a problem will solidify your memory of the material, whether you’ve encountered it before or not

“The act of trying to answer a question or attempting to solve a problem rather than being presented with the information or the solution is known as generation. Even if you’re being quizzed on material you’re familiar with, the simple act of filling in a blank has the effect of strengthening your memory of the material and your ability to recall it later. In testing, being required to supply an answer rather than select from multiple choice options often provides stronger learning benefits. Having to write a short essay makes them stronger still. Overcoming these mild difficulties is a form of active learning, where students engage in higher-order thinking tasks rather than passively receiving knowledge conferred by others.”

Page 152: Use dynamic testing to improve your weak spots

“Dynamic testing has three steps.

“Step 1: a test of some kind—perhaps an experience or a paper exam—shows me where I come up short in knowledge or a skill.

“Step 2: I dedicate myself to becoming more competent, using reflection, practice, spacing, and the other techniques of effective learning.

“Step 3: I test myself again, paying attention to what works better now but also, and especially, to where I still need more work.”

Page 160: Distill the key principles from your material and incorporate them into a structure

“If you’re an example learner, study examples two at a time or more, rather than one by one, asking yourself in what ways they are alike and different. Are the differences such that they require different solutions, or are the similarities such that they respond to a common solution?

Break your idea or desired competency down into its component parts. If you think you are a low structure-builder or an example learner trying to learn new material, pause periodically and ask what the central ideas are, what the rules are. Describe each idea and recall the related points. Which are the big ideas, and which are the supporting concepts or nuances? If you were to test yourself on the main ideas, how would you describe them?

What kind of scaffold or framework can you imagine that holds these central ideas together?”

Top 5 Disagreements

—

Connections to Other Works

Outgoing Connections:

• Deep Work, by Cal Newport
  • “Learning is deeper and more durable when it’s effortful.” (pg. 3)
• Thinking, Fast and Slow, by Daniel Kahneman
  • “In his book Thinking, Fast and Slow, Daniel Kahneman describes our two analytic systems. What he calls System 1 (or the automatic system) is unconscious, intuitive, and immediate. It draws on our senses and memories to size up a situation in the blink of an eye…System 2 (the controlled system) is our slower process of conscious analysis and reasoning. It’s the part of thinking that considers choices, makes decisions, and exerts self-control…System 1 is automatic and deeply influential, but it is susceptible to illusion, and you depend on System 2 to help you manage yourself: by checking your impulses, planning ahead, identifying choices, thinking through their implications, and staying in charge of your actions.” (pg. 105)
  • “To sum up, the means by which we navigate the world—Daniel Kahneman’s Systems 1 and 2—rely on our perceptual systems, intuition, memory, and cognition, with all their tics, warts, biases, and flaws. Each of us is an astounding bundle of perceptual and cognitive abilities, coexisting with the seeds of our own undoing. When it comes to learning, what we choose to do is guided by our judgments of what works and what doesn’t, and we are easily misled.” (pg. 123)

Incoming Connections

• Die Empty, by Todd Henry
  • “I call this state of mind the ‘curse of familiarity.’ Because of my awareness of something, I am often falsely under the impression that I understand it.” (pg. 65)
• Thinking, Fast and Slow, by Daniel Kahneman
  • “One of the dials measures cognitive ease, and its range is between ‘Easy’ and ‘Strained.’ Easy is a sign that things are going well—no threats, no major news, no need to redirect attention or mobilize effort. Strained indicates that a problem exists, which will require increased mobilization of system 2. Conversely, you experience cognitive strain. Cognitive strain is affected by both the current level of effort and the presence of unmet demands. The surprise is that a single dial of cognitive ease is connected to a lage network of diverse inputs and outputs…The figure suggests that a sentence that is printed in a clear font, or has been repeated, or has been primed, will be fluently processed with cognitive ease. Hearing a speaker when you are in a good mood, or even when you have a pencil stuck crosswise in your mouth to make you ‘smile,’ also induces cognitive ease. Conversely, you experience cognitive strain when you read instructions in a poor font, or in faint colors, or worded in complicated language, or when you are in a bad mood, or even when you frown.” (pg. 59)

Closing Thoughts

If I had to summarize Make It Stick with one sentence it would be this: active learning is more effective than passive learning, even when it doesn’t feel like it. The importance of exerting effort is hammered throughout the book; in virtually every instance cited in Make It Stick, increasing the level of cognitive effort required to understand something—short of making it literally impossible by, for example, writing it in a different language—also increased the level of long-term comprehension.

As with everything else that has to do with our minds, we have to be wary of relying on our intuition to guide us. Intuition can be a helpful servant but a terrible master, and only after a long time of deliberately using System 2 thinking—controlled decision-making—does intuition start to become dependable. Make It Stick provides ample evidence that this holds just as true for metalearning as for the rest of cognitive psychology. Many of its solutions are counterintuitive, but they’re backed by solid research going back decades.

As I’ve mentioned before, you should be suspicious of advice that enables you to do what you’d like to do anyway. It may be technically true that “creativity is more important than knowledge,” as the authors quote Einstein saying, but it’s easy to use that platitude as justification for not having to do that hard work of learning the nuts and bolts of your chosen subject. Likewise, passive strategies like rereading (which is done by 80 percent of college students, according to studies cited by the authors) allow you feel like you’re learning effectively without putting forth much effort, but in the vast majority of cases, it’s simply not a good strategy.

It’s certainly possible that there are exceptions to the rule, and that you’re one of them, but in keeping with the book’s theme of using objective measures of competency, why would you assume that you’re the exception to the rule without hard evidence? Given the mind’s innate tendency to self-deceive, even among the well-intentioned, there simply is no substitute for testing yourself against reality. Luckily, open-minded self-experimentation carries no downsides; whether you’re right or wrong, you will always either get on track or stay on track.

Final Score: 5/5

Many deep ideological fault lines have formed in the American political landscape over the decades; issues ranging from tax policy to immigration have set brother against brother to a degree unseen in recent memory. But no matter how strongly people disagree on these topics, they can disagree while still acting and arguing in good faith. Sometimes, however, such benign interpretations are simply not viable. When two factions radically diverge in behavior, it may not be out of differences in vision; it may be that one is just plain worse than the other—more authoritarian and hungrier for power, less honest and easier to corrupt. When a wave of sex scandals broke in late 2017, there was consistently more evidence of worse crimes against Republicans when compared to Democrats, though right-wing partisans relentlessly muddied the water by claiming false equivalency between the two parties. There was wrongdoing on both sides, but that certainly did not make the two sides equal.

This trend continues with one of the more frequent political spectacles of the modern era: governmental investigations. Not since Iran-Contra and possibly Watergate has the country’s attention been focused so intently on congressional committees and internal probes, with Hillary Clinton’s two investigations concerning the Benghazi attack and her private email server taking up gargantuan amounts of media attention in the early- to mid-2010’s, and the Trump campaign’s alleged collusion with the Russian government potentially turning into the biggest scandal in American history. As with the accusations of sexual misconduct, it’s easy at first glance to draw parallels between the two parties and how they’ve handled their members being investigated, but a closer look reveals a deep asymmetry in how far each party has gone to protect their own.

The ongoing investigation of Trump has raised many hackles in the Republican Party and its associated media outlets, with some saying it was an attempt to discredit the President by the Democrats, the Deep State, or other forces aligned against the Trump agenda. Trump himself went even further:

This echoes what people said in defense of Hillary Clinton during her investigations. Writers in outlets ranging from The New Republic to The New York Times to The Huffington Post used the phrase “witch hunt” to describe the inquiries into both Benghazi and the private email server. As with the accusations of sexual misconduct covered in the previous installment in this series, “where there’s smoke, there’s fire” applied to both sides. But to twist the metaphor a bit, the investigations into Clinton’s conduct produced a lot of heat and not a lot of light—and the investigation into Trump has done the exact opposite.

In the wake of the 2012 attacks on the U.S. embassy in Benghazi, Clinton accepted responsibility for the attack, saying “I’m in charge of the State Department’s 60,000-plus people all over the world.” In May 2014, the House of Representatives voted 232-186 to create a Select Committee on Benghazi chaired by Rep. Trey Gowdy (R-SC). For the next two and a half years, the Committee investigated the events surrounding the attack and placed special attention on the role played by Hillary Clinton, who appeared before the committee in October 2015 and was questioned for more than eight hours about her role in the Benghazi attacks. By the time the committee wrapped up, it had spent more time investigating the Benghazi attacks than Congress had spent investigating 9/11, Watergate, the JFK assassination, and Pearl Harbor.

However innocent or sinister Clinton’s conduct in the Benghazi affair may have been, the investigation into it was anything but a pure search for truth. House Majority Leader Kevin McCarthy (R-CA), on the friendly turf of Sean Hannity’s FOX show, let slip an ulterior motive on September 29, 2015:

“What you’re going to see is a conservative Speaker, that takes a conservative Congress, that puts a strategy to fight and win. And let me give you one example. Everybody thought Hillary Clinton was unbeatable, right?

 “But we put together a Benghazi special committee. A select committee. What are her numbers today? Her numbers are dropping. Why? Because she’s untrustable. But no one would have known that any of that had happened had we not fought to make that happen.”

McCarthy later insisted he hadn’t really meant that the committee was politically motivated, but another GOP Congressman, Richard Hanna (R-NY), agreed that the investigation was designed to attack Clinton and suggested that McCarthy was only walking back his statement because he had committed “the biggest sin you can commit in D.C.”—telling the truth.

When Clinton became embroiled in yet another scandal—this time surrounding her use of a private email server while serving as Secretary of State—it occurred in conjunction with her ferocious race for the Presidency against Donald Trump. While the FBI ultimately recommended against filing charges, the issue dogged her entire campaign, with the Columbia Journalism Review finding that her private server scandal, as well as other email-related scandals such as the DNC and Podesta hacks, accounted for more sentences of news coverage than all of Trump’s scandals combined. A particularly harsh blow was dealt when FBI Director James Comey, with less than two weeks to go before the election, publicly announced he was re-opening the investigation in light of new evidence. Clinton’s lead diminished from 11 points to 4-5 points after the announcement. When Clinton supporters complained of the effect this announcement had on the campaign, Sarah Huckabee said of them on November 3:

In the following months, Sarah Huckabee (now Sarah Huckabee Sanders) found her new boss doing exactly what she decried in that tweet. Donald Trump called the investigation into his campaign’s alleged collusion with the Russian government a “witch hunt” no less than five times on Twitter between January 10, 2017 and Jan 10, 2018, escalating that criticism on the latter date by calling it “the single greatest Witch Hunt in American history…”

While the Republican Party maintained some distance from the investigation for most of 2017, it began to circle the wagons in early 2018 with the controversy surrounding Devin Nunes’ memo alleging abuses of the national surveillance apparatus by the FBI against the Trump campaign. Nunes’ memo was privately doubted by many of his Republican colleagues and ended up containing virtually nothing of substance, but that didn’t stop a Republican (and Russian) hype campaign from pushing the narrative that federal law enforcement had been illegally and unethically attacking Trump since he had started campaigning.

Meanwhile, the FBI actually came under scrutiny during the election for supporting the Trump campaign, with multiple FBI personnel publicly backing Trump and sometimes offering inside knowledge of information that ended up damaging Clinton (such as the Comey announcement). Comey himself was a Republican who worked for administrations of both parties in various roles. Special Prosecutor Robert Mueller, the other major figure in the Russia investigation, is the last person one would expect to be a Democratic partisan—he is a registered Republican, a George W. Bush appointee, and a USMC veteran with a Bronze Star and a Purple Heart from the Vietnam War.

None of this has stopped Republicans or their media mouthpieces from undercutting the investigation at every turn. The Senate Intelligence Committee, which is controlled by the Republicans, has only assigned seven full-time staffers to the task, far fewer than were assigned to many other major intelligence investigations, including the one on Benghazi, which had 46. Newt Gingrich initially called Mueller a “superb choice” for the special counsel, only to reverse his position once the investigation made progress and call him “the deep state at its very worst” on Sean Hannity’s show. Hannity himself has attacked the Special Counsel and become one of the most commonly cited sources for Russian botnets aiming to control the narrative of the investigation.

The use of internal investigations to punish political opponents shouldn’t be a political issue—it’s not as though we’re discussing taxes or drug policy, where there may be legitimate ideological disagreements between different factions that otherwise act in good faith and agree on the importance of fairness and objectivity. This is about the abuse of a powerful tool in the governmental arsenal, one that is intended to correct injustice but can be wielded to crush enemies. One can naturally expect bias to seep into any investigation, because all investigators are human. For instance, the ongoing debacle surrounding disgraced FBI agent Peter Strzok has been framed by many conservatives as clear-cut evidence that the inquiry into the Trump campaign has been irreparably tainted by partisanship and personal antipathy toward Trump, but while Strzok’s texts certainly show his distaste for Trump, one element that’s been suspiciously absent from this whole discussion is any evidence that Strzok’s personal feelings actually influenced the investigation in a substantial way. Indeed, Strzok was removed by Mueller immediately after the texts were released, and Strzok reportedly even pushed for the reopening of the Clinton investigation, which pushed the election in Trump’s favor.

In contrast, we have clear evidence from top officials in the Republican party itself that their investigations into Hillary Clinton were designed to drag her down in the public eye, and indications that, intentionally or otherwise, the Congressional investigation into Trump has been denied the resources granted to other operations of much lesser significance. When it comes to the government’s power to investigate, the Republican Party has consistently and openly weaponized it against its enemies, and no amount of comparatively trivial examples from the Democrats can counteract that evidence of systematic abuse.

Links

Google Maps’ Moat: How Far Ahead of Apple Maps is Google Maps? – Justin O’Beirne

The Talk – SMBC Comics. “Wait. You guys put complex numbers in your ontologies?” “We do. And we enjoy it.” “Ewww.”

What is the best book about each country? – Tyler Cowen at Marginal Revolution

Artificial Intelligence is Going to Supercharge Surveillance – James Vincent at The Verge

Conflict vs. Mistake – Scott Alexander at Slate Star Codex

Which was more technologically advanced, the Roman Empire or Han China? – Hoang Nghiem at Quora. A brilliant, 18,900-word exploration of the relative technological levels of two powerful contemporary civilizations.

Videos

Unresolved: America’s Economic Outlook

Dystopian Fiction: How Stories Transform Your Mind

Caesar Crosses the Rubicon (52 to 49 B.C.E.)

Books

Deep Work, by Cal Newport (analysis here)

The Authoritarians, by Bob Altemeyer

Make It Stick, by Brown, Roediger, and McDaniel (analysis here)

Tribe of Mentors, by Tim Ferriss

A More Beautiful Question, by Warren Berger

Music

Junkie XL – Mad Max: Fury Road soundtrack

Hans Zimmer – Blue Planet II soundtrack

Harakiri for the Sky – Heroin Waltz

Avatar – King’s Harvest

Damjan Mravunac – The Forbidden Tower (from The Talos Principle soundtrack)

Skyharbor – Blind Side