Jury Razumau
Contains spoilers
Let’s say there are three items A, B, and C. You can combine A with B in five different ways, and B with C in six ways. And let’s say there are thousands of people who use items every day. It would be very unusual if these people would not go through all possible ways to combine these three items and figure out which of these ways are useful.
If you introduce these items in a fast-paced novel, a reader might not do this by themselves (they don’t have direct access to this universe to test their hypotheses), and they might accept that a discovery is surprising. But now, when you think about the state of the world before this discovery, something seems wrong about it. How come nobody bothered to check this? Isn’t it your main problem? Why is nobody really studying it?
(That is to say, somebody on Canticle would have discovered how to recharge sunhearts long ago without any outside help.)
This might have been a great book about sex work (the chapter on Backpage and other classifieds is very good), but on most other topics it is very shallow (and very US-centric).
Three long articles originally published in The New Yorker.
1. If you do nothing, most of the Mississippi’s water flow would go into Atchafalaya. This would be bad for everyone near both rivers, and people have been controlling these water flows for more than a century.
2. If lava flows slowly, and it is close to a water source, and you have good pumps lying around, you can cool the lava enough to prevent it from flowing further and destroying your town and its bay. This has been done in 1973 on the island of Heimaey in Iceland.
3. If you live close to the San Gabriel mountains in Los Angeles, your house might get destroyed by a debris flow. You can dig a place for these flows to go into, but these places can turn out to be too small. And since many people move in and out, they are not ready: you couldn’t have seen that worst-in-20-years storm if you've only lived here for five years.
On the surface, this is a book about people who discovered ancient skeletons (of dinosaurs and humans).
But mostly it’s a book about epistemology, about what you can say in public, and about how these two influence each other.
Ancient skeletons are a difficult problem in 19th-century Britain: if a species dies out, does it mean that God made a mistake? Since there was only one week of creation, what do you with the fact that birds seem to grow out of dinosaurs? How much weirdness can you sweep under the flood?
In early 19th century, a suggestion of a dangerous thought might get you banned from any social and professional life, but by 1871 Darwin can publish The Descent of Man, which not only fully accepts extinctions, but also replaces “God created man in his own image” with evolution.
I would like to understand this transition better: sure, bones have helped, but other social changes have helped paleontologists to discuss their progress publicly. What drove this change?
In particular, half of the scientists in this book seem to have rejected Christianity because of the regular old “God wouldn’t have allowed this child to die from a random disease this early”. Had this sentiment been common, Christianity wouldn’t survive the Middle Ages. However, child mortality for Victorian-era upper classes was already several times lower than for anyone before the 19th century, so maybe it was fair for them to have higher expectations?
This is also a book about Richard Owen being a dick to everyone who disagreed with him.
La Maison de rendez-vous was published a year after In C premiere, and while this is a random fact I needed to start the review, the book has a strikingly similar usage of repetitions and slow changes common in minimalist music. Similarity to music probably looks lazy and standard, but Mr. Robbe-Grillet obviously doesn't go for Toscanini-like brightness of moments and is much more devoted to Adorno-praised interest in the structure of a piece.
Amazingly, one can be considered an expert and still write a book based on the it-worked-for-my-friend's-company type of argument.
Supposedly, there's the following framework: if the company has some issues, we should just switch to Scrum and then follow it strictly. If there's something messing with our switch, we should call it “impediment”, fix it, and be happily sure that these impediments were the main issues in our company. The question of why this is the case is never discussed.
Well, maybe this book isn't supposed to convert me. So it quickly goes to the description of how one should transit to Scrum (create Scrum team that will guide other teams to Scrum; also, it should fix the impediments) and some discussion of possible problems. Then, there are solutions.
Scrum master (spelled as “ScrumMaster”) isn't good enough? Replace him. Product Owner can't cope with the backlog? Replace him. Team still works slowly? Replace everyone.
Legacy code is discussed for several pages in the most obvious possible way. Proposed solution? Fix it or deal with it.
Finally, there's redundancy, more redundancy and some redundancy again. Also, redundancy.
Too many lists and too many generic descriptions like “This area has a long history of producing good wines, but lately they've become even better.”
A collection of kinda well-written New Yorker profiles of scientists. Not a book on chaos.
Errors, logistics, and infrastructure are more important than blood, sweat, and tears; trucks are better than heroism.
- It feels like non-fiction got a lot better since then at exploiting the curiosity gaps.
- We learned a lot about neuroscience since 1977, but still moved in the same direction, so the book is still correct about many things at large, although details are often important.
- Contrary to Geoffrey Miller, who tells that evolutionary psychology was extremely controversial at the moment (just two years after the Sociobiology release—which, by the way, is not mentioned at all), here's a Pulitzer winner that has a lot about how the human mind was defined by its evolution (although, admittedly, without many crucial details).
- A good book to learn something about the history of the area. If you want to learn about evolutionary psychology itself, you'd be better off starting elsewhere.
An interesting topic which deserves better treatment than a collection of Vox-style op-eds. This is not a book that wants to teach you how mathematical models can fail, it's a book that wants you to feel OUTRAGED about UNFAIRNESS.
Here's how it works. There's some area that's supposed to be improved by using a mathematical model (say, teacher evaluation in public schools). But after implementing this system there are some casualties (say, unfairly fired teacher who was well-liked and respected both by students and parents), which is bad and leads to a lengthy discussion of perils of capitalism.
Don't get me wrong, all things discussed in the book (which include recidivism, future job performance, and insurance) are indeed hard to model, but that's not a good way to discuss this models. One of the book's ideas is that you should forgo some of the model's accuracy to make it more fair. However, it's hard to talk about trade-offs without talking about how much we have in accuracy and utility. Did this teacher evaluation model improve overall school performance? If it did, would it be fair to students to make them go back to their horribly unimproved previous school performance? Or was it actually not that bad, and their test results improved simply because of better lunches (or even less lead in water)?
The chapter on credit scores grudgingly admits that human curation wasn't perfect (painting an expected picture of a banker discussing credits with his golf partners). Skip ten pages, and there's a friendly woman who helps to clean up the mess made by automated system that confused a client with a criminal namesake. Humans are winning again!
Except that they still have their own models, which are also bad (albeit in a different way). However, it is much easier to fix biases in algorithms and data if you're dealing with computers. One of the common complaints of the book is that computers can only project past data on the future, saving all those biases. It's not a problem that can't be fixed. Humans are.
More generally, it may be fun to complain about the issues of the model, but it's only useful to compare it to the alternatives. An implicit message of the book seems to be that we should ban usage of some algorithms and data (as expected, there's no discussion of second-order effects—if credits become more expensive, what will happen to the economy? Is this trade-off useful?). However, we can't simply ban things and forget about them, we can only replace them with something else.
I don't think that a book that is strictly about negative sides of something should necessarily strive to be objective. However, I would like to see less diatribes against greediness and more interviews with people who designed the models. What do they think about these problems?
(By the way, if you explain something by greediness, you‘re already wrong).
Some quotes are amazing, though.
fairness is squishy and hard to quantify. It is a concept. And computers, for all of their advances in language and logic, still struggle mightily with concepts. They “understand” beauty only as a word associated with the Grand Canyon, ocean sunsets, and grooming tips in Vogue magazine. They try in vain to measure “friendship” by counting likes and connections on Facebook. And the concept of fairness utterly escapes them. Programmers don't know how to code for it, and few of their bosses ask them to.
But I would argue that the chief reason has to do with profits. If an insurer has a system that can pull in an extra $1,552 a year from a driver with a clean record, why change it?
Spotify playlists: https://open.spotify.com/user/razumau/playlist/48iiUSUegC9oc3fDRE58CY, https://open.spotify.com/user/razumau/playlist/0hEjft9RUfmBrBEVi8Uq9L.
“Pizza” is mentioned 39 times. “Render” and “polygon” combined for 19. Would you read a book about Beethoven that only mentions how great were his symphonies and how everyone loved them without ever talking about what exactly made them great (and probably not even discussing Fidelio's plot)? So what exactly were Carmack's innovations in game engines? Oh, he was very smart and worked a lot; now let's talk instead about his Ferrari (have I already mentioned that his office was full of pizza boxes?).
- I'm afraid that extrapolating features from the currently most productive humans will in fact tell us too little. Yes, ems will be 10 percent more X, but for the most Xs other changes will modify X by a larger percentage.
2. I'm also not convinced that even pre-selected ems will be uber-rational enough to do things like all those complicated dances with the correct backup timing all the time: flossing has become a habit, but paying bills on time hasn't.
3. Moreover, if a world is mostly populated with lots of copies of only several hundred ems, individual peculiarities are more important than overall trends.
4. If the first ems are created with the destruction of their originals, they probably have some other unusual qualities; however, they probably aren't of great importance in the later society.
5. Would a charity collecting spurs and running them as micros or slower be economically feasible?
6. On the other hand, retirement even at the 1/100th of the overall em world speed seems hard. If I want to have at least ten years, I need the outside world to be stable for its one thousand years. Even leaving existential risks aside, institutions surviving this long are a new challenge.
7. Being able to copy a single great specialist many times may in fact increase opportunities for occupational licensing and certifications (assuming a clan or a single em can reliably forbid making new copies of them).
8. By the way, since piracy almost directly leads to slavery, humanity should become much better at computer security for the healthy em society to arise (or at least a single healthy em society).
9. However, I would expect that the em culture is extremely fragmented (including attitudes to enslaved ems, spurs and death).
10. Continuity and consciousness overall still seem to be the most important questions (is reversing myself to a backup a suicide?).
11. At the very least, ems would be a great boost for experimental social sciences.
I'm very interested in talking to a person who has read The Age of Em before reading any Overcoming Bias posts.
It still reads like lecture notes. Some lectures are good and full of ideas it's hard not to agree with, some are full of business book bullshit and graphs like this. Maybe, if you want to make some non-crappy string theory references, general education still has some value.
Странно, но в благодарностях Дойч упоминает сразу двух редакторов. Более того, это реальные люди.
Ещё более странно, что там же упоминаются люди, которых Дойч благодарит за «thorough, critical reading».
Конечно, ошибки вроде «Если из “Не B” следует “Не А”, то из B следует А» можно списать на переводчика (умеющего писать «Доукинс» и «“Элементы” Евклида»), но это не больше четверти логических дыр — а их больше, чем в «Dark Knight Rises».
Разделы про математику вообще смешные; возможно, про эпистемологию и эволюцию тоже смешно, но про первую я ленился придумывать все контрпримеры, а про вторую недостаточно знаю.
“Any liveliness comes solely from the ideas,” hilariously writes Mr. Egan in his review of [b:A New Kind of Science|238558|A New Kind of Science|Stephen Wolfram|https://d.gr-assets.com/books/1386925097s/238558.jpg|231083]. However, it's not an issue per se that Distress itself doesn't go further than that.
Neal Stephenson—another author famous for including lots of exposition—claims that “story is everything”; Greg Egan also “wants to tell a story”. However, while Mr. Stephenson writes great fiction, Mr. Egan tries to, and although there's an interview where he rants against standard “development” of the characters, Distress has a lot of remarkably unmotivated stuff that looks like “I was told one needs this in a novel”. Andrew Worth's supposed transformation is the best (and the main) example: he has a break-up, he's bored of his whole life, he talks to interviewees and Stateless locals (and their Ayn Rand-ish transhumanism-related speeches are probably the second main source of liveliness), he falls ill, he's on the verge of death, he's reborn as another person. Well, in fact he doesn't change—maybe because he had no identity before, maybe because all this stuff is incredibly superficial.
It would be great if one day Mr. Egan starts writing non-fiction on sociology and ethical issues of technology adoption, but sadly, this day will probably never come.