Analysis: A More Beautiful Question, by Warren Berger

Warren Berger’s A More Beautiful Question shows a variety of ways that asking better questions can provoke positive change in life, relationships, and business, and provides a broad framework for how to do so in actionable ways. It’s a flawed book, but a worthwhile read with an underappreciated thesis.

Top 5 Key Concepts

Page 8: A “Beautiful Question” changes the way we think and serves as a catalyst

“A beautiful question is an ambitious yet actionable question that can begin to shift the way we perceive or think about something—and that might serve as a catalyst to bring about change.”

Page 23: Navigating modern society requires us to retain childlike curiosity

“As expertise loses its ‘shelf life,’ it also loses some of its value. If we think of ‘questions’ and ‘answers’ as stocks on the market, then we could say that, in this current environment, questions are rising in value while answers are declining…We must become, in a word, neotenous (neoteny being a biological term that describes the retention of childlike attributes in adulthood). To do so, we must rediscover the tool that kids use so well in those early years: the question. [MIT’s Joi] Ito puts it quite simply: ‘You don’t learn unless you question.’”

Page 75: Different problems call for different mindsets and different questions

“Each stage of the problem-solving process has distinct challenges and issues—requiring a different mind-set, along with different types of questions. Expertise is helpful at certain points, not so helpful at others; wide-open, unfettered divergent thinking is critical at one stage, discipline and focus is called for at another. By thinking of questioning and problem-solving in a more structured way, we can remind ourselves to shift approaches, change tools, and adjust our questions according to which stage we’re entering.”

Page 133: You’re never quite done questioning; successful inquiry leads to more inquiry

“While the How stage is positioned here as a third and final stage of innovative questioning, there really is no final stage—because the questions don’t end, even when you arrive at a solution. Many successful questioners, having arrived at an ‘answer,’ quickly return to asking questions. Often, they’re questioning the very answers they found, which may not have been definitive. There is invariably room (and the need) to find ways to improve those solutions, to expand upon them, take them to another level.”

Page 183: People avoid questioning primarily because they’re scared they won’t find satisfactory answers

“Among the reasons people tend to avoid fundamental questioning of much of what they do in their lives (especially the important things), four stand out:

  • Questioning is seen as counterproductive; it’s the answers that most people are focused on finding, because answers, it is believed, will provide ways to solve problems, move ahead, improve life.
  • The right time for asking fundamental questions never seems to present itself; either it’s too soon or too late.
  • Knowing the right questions to ask is difficult (so better not to ask at all)
  • Perhaps the most significant: What if we find we have no good answers to the important questions we raise? Fearing that, many figure it’s better not to invite that additional uncertainty and doubt into their lives.”

Top 5 Practical Takeaways

Page 31: Effective questioning often takes the form of 1) Why?, 2) What If?, and 3) How?

“In observing how questioners tackle problems, I noticed a pattern in many of the stories:

  • Person encounters a situation that is less than ideal; asks Why.
  • Person begins to come up with ideas for possible improvements/solutions—with such ideas usually surfacing in the form of What If possibilities.
  • Person takes one of these possibilities and tries to implement it or make it real; this mostly involves figuring out How.

The Why/What If/How sequence represents a basic and logical progression, drawing, in part, on several existing models that break down the creative problem-solving process.”

Page 107: Knowledge forms the raw material that gets connected by effective questioning

“In particular, if your curiosity has been focused on a particular problem, and you’ve been doing dep thinking, contextual inquiry, questioning the problem from various perspectives and angles, asking your multiple Whys—it all becomes fodder for later insights and smart recombinations.

“So even though it can initially be beneficial to approach a problem with a beginner’s mind, as you progress to imagining What If solutions, it’s useful to have some acquired knowledge on the problem—preferably gathered from diverse viewpoints. It also helps to have a wide base of knowledge on all sorts of things that might seem to be unrelated to the problem—the more eclectic your storehouse of information, the more possibilities for unexpected connections.”

Pg. 112: Deliberately think wrong

“The idea, then, is to force your brain off those predictable paths by purposely ‘thinking wrong’—coming up with ideas that seem to make no sense, mixing and matching things that don’t normally go together.”

Pg. 146: Use questioning to impose and remove constraints

“History and routine aren’t the only things that can impede a company’s forward movement. Various real-world constraints can also inhibit a company’s ability to adapt and innovate; for example, being overly concerned with practical issues such as costs and budgets tends to limit the scope of creative thinking. That’s why some business leaders (including Steve Jobs when he headed Apple) have been known to use What If hypothetical questioning to temporarily remove practical constraints…

“By temporarily removing these restrictions, people’s imaginations are freed up to find the best idea, cost notwithstanding. You might end up with a ground-breaking possibility that can then be scaled back to make it more affordable.

“Conversely, using What If questions to impose constraints can also be effective. By challenging people to think about creating or achieving something within extreme limits—What if we could only charge ten bucks for our hundred-dollar service?—it forces a rethinking of real-world practicalities and assumptions.”

Pg. 195: Use experiments to act upon your questions

“…experimentation can be thought of as, simply, the way you act upon questions. You wonder about something new or different; you try it out; you assess the results. That’s an experiment.

“…If you randomly try things in life, it can lead to haphazard results; but if you bring thought to trying new approaches or experiences—if you take the time to consider why they might be worth trying, and what might be the best way to test them out, and then assess whether the trial was a success and worth following up on—it’s a more practical way to bring change into your life.”

Top 5 Disagreements

A More Beautiful Question has one overarching problem that appears in multiple contexts throughout the book, and it’s the way the book deals with prior knowledge. While there are some half-hearted caveats (e.g. pg. 107) to make sure the reader knows that no, knowledge doesn’t ALWAYS hamper creativity and questioning, the book frequently claims or insinuates that “the value of explicit information is dropping” and that knowledge may be “obsolete.” But if you have read Make It Stick (or my analysis of it), you know that the often-denigrated “rote memorization” is actually crucial to performing any higher-level cognitive tasks.

Think of everything you hold in your memory—everything from the little facts and figures to the broader conceptual understanding—and picture it as a giant cluster, where each bit of knowledge is a separate piece that contributes to the whole and makes it a little bit bigger. New knowledge comes flying toward it, sometimes missing but sometimes sticking, and the bigger the cluster is, the more surface area there is for new knowledge to stick to. In this way, knowledge has a snowball effect where everything you learn makes it easier to learn yet more things, but only when it’s actually kept in the snowball. If you rely on writing or computers to be substitutes for memory, you lose that extra surface area and that cumulative advantage to learning new things.

Picture your snowball of knowledge again, and picture each piece of knowledge being combined, compared, and contrasted with all the other pieces; this is what it’s like when you exercise higher-level skills like creativity and analysis. Contrary to the “wisdom” that is rapidly becoming conventional in the digital age, it is not sufficient to hold those individual bits of knowledge outside your head with the use of technology. Research has conclusively shown that students who carry more basic facts around in their memory are able to perform better at higher-level tasks. When you create, you need raw material to create with. When you analyze, you need some thing to analyze.

A More Beautiful Question does acknowledge certain circumstances where prior knowledge is helpful, but based on my reading, I’m afraid that uninformed readers will come away with the misconception that they should deprioritize memorization of facts if they want to become better thinkers on complex topics. It’s important that readers understand that the opposite is true: simply having basic, nuts-and-bolts information about a topic readily available in memory dramatically increases one’s ability to perform more complex cognitive tasks.

Connections to Other Works

Outgoing Connections

  • Make It Stick, by Brown, Roediger, and McDaniel
    • “In the current era of Google and Watson, with databases doing much of the ‘knowing’ for us, many critics today question the wisdom of an education system that still revolves around teaching students to memorize facts. One such education critic, the author Sugata Mitra, made just this point at a TED Conference by tossing out the provocative question Is ‘knowing’ obsolete? Of course, not all knowledge is mere factual information; the TED question, as worded, is overly broad. But if we zero in on a narrow kind of knowledge—stored facts or ‘answers’—then that kind of ‘knowing’ might be better left to machines with more memory.” (pg. 27)
  • Zero to One, by Peter Thiel and Blake Masters
    • “PayPal cofounder Peter Thiel believes entrepreneurs can find ideas to pursue by asking themselves, What is something I believe that nearly no one agrees with me on?” (pg. 151)
  • Man’s Search for Meaning, by Victor Frankl
    • “The author and creativity coach Eric Maisel says that when people ask, How can I find the meaning of life?, they’re asking a ‘completely useless question.’ That classic query is based on the flawed notion that ‘meaning’ is an objective truth to be found out there somewhere. Better to think of it this way, Maisel says: We have to construct meaning in our lives, based on everyday choices—and every one of those choices is a question. Why should I do X? Is it work my time and effort to do Y?” (pg. 185)
  • The Four-Hour Body and The Four-Hour Chef, by Tim Ferriss
    • “…experimentation can be thought of as, simply, the ways you act upon questions. You wonder about something new or different; you try it out; you assess the results. That’s an experiment…If you randomly try things in life, it can lead to haphazard results; but if you bring thought to trying new approaches or experiences—if you take time to consider why they might be worth trying, and what might be the best way to test them out, and then assess whether the trial was a success and worth following up on—it’s a more practical way to bring change into your life.” (pg. 195)
  • Superforecasting, by Philip Tetlock
    • “…one other question comes highly recommended from Michael Corning, a top engineer at Microsoft, who said he has relied on this in both his work and his life: What are the odds I’m wrong? As Corning points out, just pausing every once in a while to ponder this question can provide a check on our natural tendency to be overly certain of our own views.” (pg. 206)

Incoming Connections

Closing Thoughts

The question is a woefully underused tool, and Warren Berger deserves credit for exploring it in detail for a popular audience, thereby providing them with the skills to use it in a variety of potentially profound arenas, but stumbles by denigrating prior knowledge and leaving readers unequipped to integrate it into their questions.

Final Score: 3.5/5

Miscellanea: July 2018


Normcore – Jedediah Purdy at Dissent

Europe needs to start planning for a future with no U.S. – Anne Applebaum at The Washington Post

Meet the Renegades of the Intellectual Dark Web – Bari Weiss at The New York Times. I’m a bit late to the party—the article was written in May—but I’m fascinated by the both the existence of and reaction to the group. I’ve paid attention to various figures in the group for years, primarily Harris, Rogan, Sommers, and Shermer, and have seen them coalesce into what could at one point have generally been described as a center-left reaction against identity politics. I’m a fan of this in principle, but puzzled by some of the strange bedfellows the IDW has created. Ben Shapiro, for instance, strikes me as intelligent and well-spoken, but not quite enough so to warrant his apparent rock-star intellectual status, and his rarely-discussed but open bigotry would in a sane universe be enough to disqualify him from a group desperately trying to establish a third way between the insanities of racism and political correctness.

Napoleon was the Best General Ever, and the math proves it – Ethan Arsht at Towards Data Science. Many asterisks on that statement, but still an entertaining and enlightening approach to the oft-debated topic of who the greatest general in history really was.

All it takes to win McDonald’s Monopoly is a massive, country-wide criminal conspiracy – Randall Colburn at The AV Club

Basic Income, Not Basic Jobs: Against Hijacking Utopia – Scott Alexander at Slate Star Codex

Why Many Young Russians See a Hero in Putin – Julia Ioffe at National Geographic

How to Pick a Career (That Actually Fits You) – Tim Urban at Wait But Why

Not a Tea Party, a Confederate Party – The Weekly Sift

Why Being a Foster Child Made Me More Conservative – Rob Henderson at The New York Times

What Happens to the Plastic We Throw Out – National Geographic

Fork Science – Bayesian Investor Blog

The Coming Age of Special Warfare – The XX Committee

Epistemic Spillovers: Learning Others’ Political Views Reduces the Ability to Assess and Use Their Expertise in Nonpolitical Domains – Marks et al. in Harvard Law School, Public Law & Legal Theory, Research Paper Series. “We find that participants falsely concluded that politically like-minded others were better at categorizing shapes and thus chose to hear from them. Participants were also more influenced by politically like-minded others, even when they had good reason not to be. The results demonstrate that knowing about others’ political views interferes with the ability to learn about their competency in unrelated tasks, leading to suboptimal information-seeking decisions and errors in judgement.”

Artificial Neural Nets Grow Brainlike Navigation Cells – John Rennie in Quanta. “’I think with this work, we were able to give a proof of principle that grid cells are used for taking shortcuts,’ Banino said. The results therefore supported theories that grid cells in the brain are capable of both path integration and vector-based navigation. Comparable experimental proof with studies on living animals, he added, would be much more difficult to obtain.”

How to change emotions with a word – The Economist



World Models Explained – Siraj Raval at YouTube

Code vs. Data – Computerphile at YouTube



Endurance, by Alfred Lansing (5/5): Rarely has a book humbled and thrilled me as much as Endurance. Even having read it before, I felt my stomach drop with every failure and my heart soar with every success just as I did the first time I experienced this wonderful work of historical adventure. Lansing did an incredible job of painting such a rich picture of the expedition with such a short volume, always turning just the right phrase to evoke a complete Antarctic landscape and the twenty-eight men who occupied it for hundreds of days before making their escape.

Bold, by Peter Diamandis and Steven Kotler (4/5): Bold was a refreshing dose of techno-optimism, if a slightly dated-feeling one (believing tech is good is sooooo 2015). Diamandis and Kotler demonstrate a variety of ways that an enterprising individual or team can take advantage of hidden nonlinearities in the world even without the resources of big governments or corporations. It did seem as though the authors were a bit too quick to try and draw examples from Diamandis’ own life even when many of Diamandis’ entrepreneurial successes seem to only loosely connect to the specific exponential lessons of the book. This is hardly a slight against Diamandis, however, and reading Bold inspired me to read Julian Guthrie’s biography of him titled How to Make a Spaceship.



Audrey Fall – Wolmar

Delain – April Rain

Foo Fighters – The Line

Nine Inch Nails – 12 Ghosts II

Ramin Djawadi – Codex



Derren Brown: The Push (Netflix) (7/10): A month has passed and I’m still not sure what to think of The Push. Everything it says about human nature, I basically agree with—we’re easily scared, easily manipulated, easily fooled; walking murder machines when coaxed just so. That said, I still found it hard to swallow a lot of this movie. First, remember that all those characteristics apply to us, the viewers, not just the unwitting actors on screen, and realize that even given everything shown to us, the setup of The Push was a highly abnormal scenario, with participants pre-screened for high compliance and then put into a carefully crafted pressure cooker. Second, remember that we probably didn’t even see everything that truly went into the production, and that what was left out could be more telling than what was left in.


TV Shows

Westworld Season 2 (9/10): Ignore the skeptics: Westworld is still one of the best shows on TV. The second season had a few mid-season hiccups preventing it from reaching quite as high as the first, but it recovered well with several strong episodes leading into a dark, poignant finale. If season 3 happens (and it certainly looks that way), the show will be a very different animal going forward; there’s still plenty of room for more storytelling, but the end of season 2 clearly marked the end of a phase.

Miscellanea: June 2018


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

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


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


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.



King Charles – Loose Change for the Boatman

Madder Mortem – Fallow Season

Martin Rubashov – Hideout

The Moth & The Flame – Live While I Breathe

Slaying the Centaur

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-20th 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.”

Miscellanea: May 2018


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

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


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



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.


Crystal Castles – Sad Eyes

Ghost – Rats

M83 – Un Nouveau Soleil

Tool – Forty-Six & Two



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.

Extremify or Die

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.

polarization diagram
The candidate in the first box (who appeals to the population between the two vertical lines) will lose votes by shifting rightward, and may gain some by shifting leftward. The candidate in the second box has no such incentives–he will clearly lose by shifting leftward and may still gain some by shifting rightward.

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.

Miscellanea: April 2018


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



Basic Income Explained – Siraj Raval on YouTube



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.



Black Stone Cherry – Soul Machine

Chevelle – An Island

Evergrey – The Grand Collapse

Les Discrets – L’echapee

Ne Obliviscaris – And Plague Flowers the Kaleidoscope



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.