Scott Alexander Reviews Thiel's Zero to One
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Maybe we approach limit of human cognitive abilities and another Newton is not possible for current problems? Maybe to find a cure for cancer you need to spend 10k hours studying biology, 10k studying chemistry, 10k studying machine learning and then spend 100k hours on the problem, which it's not possible for a human being.
Right now, scientific papers are a shitshow, and even refined and systematized knowledge in textbooks isn't as accessible as it could be in the digital age. Could we encode knowledge in more powerful tools allowing us to explore through free-form simulations?
The goal here would be to make discovery of promising solutions more time- and effort-efficient.
Related, I feel we need to figure out a way to systematize information in scientific papers to make mining them for cross-cutting insights possible. I suspect there are lots of discoveries hiding in plain sight, if one knew which particular papers from which disciplines touch on the same underlying phenomenon or concept.
This is interesting problem, unfortunately it's really, really hard.
I'm most familiar with mathematics, so I'll use it as an example, but this is not limited to mathematics.
If you take any new paper on research mathematics, in a hot field like algebraic geometry or partial differential equations, then unless you're an expert in that field, it will almost always be literally impossible for you to understand -- not simply hard to follow the arguments, but simply impossible to understand even what it's about. Look, I just grabbed random example from recent posts on arXiv[1]: try reading an abstract and explaining it back to me. For 99.99% English speakers, this will be indistinguishable from random gibberish in a paper written by recurrent neural network trained on arXiv papers.
However, 0.01% of people will understand something, and for probably 1% of these, the abstract will make perfect sense. However, if you ask these people to explain it, you'll either spend an hour or two on getting some very superficial understanding of what's at stake here, which won't be very useful to you -- you still won't be able to actually read and follow the paper, and use the insights for your own purposes. Alternatively, and if you're intelligent enough, they can spend a year or two teaching you required background. Then you can see the insight for yourself.
The problem here is that you need literally years of background studies to appreciate the insight. There likely is no quick and easy way around it, otherwise some of the extremely smart people involved would already have had figured it out -- assuming otherwise is hubris. This doesn't mean that the system cannot be improved upon: there's tons of ways to make things simpler, clearer, more digestible. However, you'll still be left with hard problems of hard things being hard.
Abstract. Using elliptically fibered Kummer surfaces of Picard rank 17, we construct an explicit model for a three-parameter bielliptic plane genus-three curve whose associated Prym variety is two-isogenous to the Jacobian variety of the general three-parameter hyperelliptic genus-two curve in Rosenhain normal form. Our model provides explicit expressions for all coefficients in terms of modular forms.
Oh. Oh my. Checks list of Sokal Squared spoof papers, nope. "Indistinguishable from random gibberish in a paper written by a recurrent neural network" it is then. Rather than being conservative, now I see that you were wildly optimistic in your estimate. There can't possibly be over 1,000 people in the world to whom this would make perfect sense, can there?
> The problem here is that you need literally years of background studies to appreciate the insight. There likely is no quick and easy way around it, otherwise some of the extremely smart people involved would already have had figured it out -- assuming otherwise is hubris.
That's fair; something of a scientific version of efficient market hypothesis, I guess. If following the bleeding edge of a scientific domain didn't require years of background studies, it would be easy, so scientists would quickly zoom through it, until the going got difficult again.
> This doesn't mean that the system cannot be improved upon: there's tons of ways to make things simpler, clearer, more digestible. However, you'll still be left with hard problems of hard things being hard.
Yeah, I was just thinking about ways to tackle the things that can be made "simpler, cleaner, more digestible". I don't deny that there are fundamentally hard problems we have to face directly, but right now, those problems are wrapped in a lot of irrelevant cruft that makes them bigger than they really are.
> try reading an abstract and explaining it back to me
Challenge accepted, though I know the result kind of reinforces your point. But here's what I understood from that abstract:
> Using elliptically fibered Kummer surfaces of Picard rank 17, we construct an explicit model for a three-parameter bielliptic plane genus-three curve whose associated Prym variety is two-isogenous to the Jacobian variety of the general three-parameter hyperelliptic genus-two curve in Rosenhain normal form. Our model provides explicit expressions for all coefficients in terms of modular forms
We took a particular weird abstract shape with interesting properties, and used it to describe a particular different weird abstract shape, whose properties are important to us. Abstract shapes can be written down as maths, and depending on the way you write it, they can have properties exposed directly as "knobs" to tweak - e.g. "circle of radius r" has a radius exposed directly, whereas "circle that fits in that place" hasn't. In this paper, our description of the weird abstract shape has its important knobs exposed.
sure, but if AI would do 80% of the job then we can't really compare it to another Newton.
[1] although... "thinking" can stack surprisingly well with some activities we'd consider leisure, like walking or (at least in my case) gardening. So maybe this isn't really going to be 65hrs/week at a desk...
But, all this thinking is in my experience pretty useless in terms of real world results (and I don't mean results like Stanford idolizing you). You're not going to "think" your way into curing cancer no matter how many hours of biology or chemistry you take, and I should know, because I've seen people try. It's pretty hard to "think" up a company the size of Amazon, too, especially in a world where a lot of industry have their Amazon. You need capital and a degree of self-confidence bordering on manic delusion and when you have this you still need to not do what Elizabeth Holmes did and know when you've failed and give up what may have been ten or twenty years or a lifetime and start something else.
It's not the 100k hours. It's the very large odds they will have been for nothing, and picking up whatever is left of your life after.
That's an interesting take. But my experience is that there is a non-negligible set of people who would see a lifetime spent working on their chosen problem to be well-spent, even if they don't eventually succeed.
That’s how many hours it would take today. But if we get a few fancy new libraries for ML, it could be trivial to implement. 20 years ago, if I told you a single developer could deploy a website with a db + authentication in about 20 mins, you’d call me a liar. But today, you can use a fullstack generator and deploy to heroku with the press of a few buttons.
The Bay Model is well worth visiting. While the Reber Plan to dam up the bay would have been an environmental disaster, the model is really cool. It was actually used for many years after the plan was abandoned. It was only the rise of fluid dynamics simulators that made it obsolete.
That's an interesting origin story.
I was curious to see the notes in question. Found them here, for anyone else interested: http://blakemasters.com/peter-thiels-cs183-startup
Ordering via a web catalog. Visiting a local bookseller takes a bigger commitment than opening a web browser.
Price competition. Amazon typically had a better price on any given book (and later, on most items) than any local store.
Delivery to home or office. Now you need to schedule a second visit to your local bookstore.
2, Price competition likely was less important; in Austria (and I think, Germany) there exists the "Buchpreisbindung", the price of books is mandated, no seller must offer a lower price; also, tax was always included, from day one.
They still won big, very early, very fast. I remember that an employee of a (now defunct) bookstore told me "please order via Amazon, you'll get the book faster, and I cannot offer any added value".
One contribution to Amazon's success was also the great culling of (potential) competitors after the dotcom burst. They were really lucky to rake in a 700 million investment barely a month before the crash, and most competitors afterwards had a hard time raising serious money.
Now sure you can argue that there's strategies you can pick that maximise your chances of abusing these crashes, but they're very hard to predict and if they don't come you might lose out, and I don't think Bezos ever anticipated the dotcom bubble.
So chance and timing really play a gigantic role in creating successful businesses, there are not always secrets or geniuses behind successful enterprise.
So what then is the secret sauce?
Generally big payouts don't come from a sure thing; you have to take multiple risks that could potentially have big payouts and survive long enough for one or more of them to pan-out.
Edit: for formatting
Right time and place, luck, skill and money in varying relative quantities. People focus on the skills, not unimportant by all means, but it is not the secret sauce.
Google X is a startup incubator, not blue-sky research[1] in the way we imagine Bell Labs and PARC to be.
[1] yes, Loon, I get it. Har har
If you wish to know more, I'd nominate "Who by very slow decay" [1] as his one work that everyone should read, because more people knowing it would make the world a better place. (edit: content warning: hospitals and death. It's not a pleasant read, but I think it's a necessary one.)
[1]: https://slatestarcodex.com/2013/07/17/who-by-very-slow-decay...
Edit: Doing a search for the title shows that it's been the main tradition for ~ 2 years. So that line of reasoning seems a little suspect.
But it's worth emphasizing Scott doesn't participate in that thread, and he doesn't necessarily like it either. In fact, from what I can tell he's not a huge fan of lots of the conversations that go on there, and the topics discussed.
If anyone were to judge him on comments, please only do it based on the curated comments on his website, where he deletes and bans people who are uncivil.
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But politics exists, much real stuff happens there, and also, it's great entertainment. It's also great training, if you don't succumb to bad thinking. It's better doing in company of people who try to be civil and use logic, even if it usually barely works.
Resolving this tension is part of Scott's writing from very early on. This is not just image clean-up, it's dealing with a difficult situation.
Not so much that, but to avoid repercussions in his real life. There are topics being discussed that are real sacred cows, and even if his conclusions are "correct", sometimes one is considered evil just for daring to question the conventional values. (indeed, that's much of what the "culture war" thread is about)
Since his writing is just a hobby, he doesn't want fallout from that to impact his real professional career.