dguest

I've been in a near miss in a plane: the pilot in the other plane was clearly inexperienced, not following radio protocol, and we got within O(200m) of a head-on collision which certainly would have been fatal for everyone.

Thing is, the sky is big. You can have non-fatal near-misses that everyone learns from. In trains they literately railroad you into a head-on collision when things go wrong.

I believe that aviation is safe because of standardization, but there's also just a lot more space to almost mess up.

What do you think the cause is?

I'm not trying to argue there is a single cause, either (sorry if my either-or phrasing implied that). I think attributing any general feature of an academic discipline to a single cause would be missing a dozen other contributing factors.

Of course three letter agencies are watching this carefully, and I'd believe they were weighing in somehow, as they have in cryptography in the past. My point was that we shouldn't ignore the overall sparsity of funding in pure math and science projects. A lot of (probably most) contributors to bleeding-edge research have a number of other jobs in their position.

Maybe because cryptography is funded better than the rest of math.

The big AI companies spend millions on some of these proofs (15M was one estimate for the Naiver-Stokes thing). Had anyone has spent millions on them before this? There was a 1M prize but that certainly wouldn't convince anyone to spend orders of magnitude more to solve the problem.

Meanwhile the three letter agencies are spending billions on cryptography. They have programs that recruit students starting in high school. So maybe they are sending the spooks to suppress OpenAI's cryptography breakthroughs, or maybe they just already outspent them.

That to me is the point that isn't getting enough attention: pure math isn't that well funded. I agree as much as anyone that these proofs are an unprecedented breakthrough for LLMs, but let's keep in mind that the money they are throwing around is also unprecedented.

It would be cool if they embellished on the buildings and towns a bit: realign them on the grid so that they actually look good, embrace the medium. Maybe even tweak some of the narrow valleys to be more aligned.

How does it infer what's underground? Does it include tunnels / mineral deposits / bunkers?

Direct link to article: https://arxiv.org/abs/2605.08147

That's true for theoretical physics.

Experimental physics takes it one step back: it's all about setting bounds on what is potentially possible. A model of the universe might say that the numbers are between zero and one, but there's some other model that says there could be 3s.

Note that this epistemic uncertainty is not the same thing that the article is focused on. The article is mostly about one specific model, which itself has some randomness baked on.

But seriously, it works to market anything:

> A doghnut: a snack for hungry people, and adventurers

Didn't you see the mountain?

As one friend put their endorsement of Ted Chiang: sometimes you don't need 200 pages to make a point.

In general, though, it's amazing how much hard science fiction authors managed to get right. I've read quite a few 20 year old books recently and while I might just be selecting those that aged well, someone definitely saw it all coming.

I hope so too! Unfortunately animal studies are still one of the main pillars of physiology and drug development. They were even a requirement until very recently in the US, see the rule just approved by the FDA [1].

[1]: https://public-inspection.federalregister.gov/2026-19350.pdf

I feel like "just a question of cost" is a heavy lifter here though.

Cost core to our resource allocation system, for better or worse, and if something costs a lot it usually means we can't do it as much. In this framing, cutting out animal testing would (often) mean medical research is slower. In this framing it's a question of where people are willing to put the dial on ethics vs results, and of course pumping the breaks in one place without universal buy-in just means the cruelty moves elsewhere.

I don't think the above 1-dimensional framing is the whole story though. You could imagine more standardized data sharing, so that the data that cost the lives of a few thousand rats could be used by others. And of course you could require (as drug trials do) that the study be registered before any experiment happened, and that the results be published regardless of the outcome. Some of these changes could actually make the science better, in addition to more ethical.

There are a few interesting takes on this.

- In Blindsight [1] the main character is a "synthesist", part of his job is to figure out what much more intelligent transhuman or artificial intelligence is up to and translate it to normal people.

- In Children of Time [2] the character is a "classicist", he studies ancient technology, which is far more advanced than the technology humans have in his era thanks to a technological collapse.

Ted Chiang's story came first, but these are still over a decade (or two) old.

[1]: https://en.wikipedia.org/wiki/Blindsight_(Watts_novel)

[2]: https://en.wikipedia.org/wiki/Children_of_Time_(novel)

I'm not claiming that anyone is doing this on purpose, but overly engineered codebases are a good thing for a company like Anthropic. If your code is so bloated that it is pretty much unreadable without an LLM they win on both sides.

The US is innovative but can't be bothered to get the boring stuff right.

Meanwhile Europe standardized bank transfers with IBAN, killed connector type proliferation with by standardizing on USB-C, and routed 3-phase power most homes. Oh, and the metric system. And while Europe has a wide range of cultures and functionality of government, a lot of places have simpler tax forms and a more prescriptive legal system (as opposed to the precedent-dominated Common Law tradition in the US). The German-speaking countries even rewrote spelling to make it simpler.

I view a lot of this stuff as infrastructure investment. We shouldn't have to stumble over the mundane to do interesting things. As others have commented, Europe also had the advantage of adopting some technologies later, but in some cases they just seem to be more aligned with a system of constant (if sometimes frustratingly slow) improvement.

I would imagine the problem is that there are too many of them.

For better or worse the prize can only go to 3 people. Over the years there are generally many dozens of people who make absolutely critical contributions to these kinds of experiments. In this case, though, the same guy was listed as the PI of the UW Madison group, and Madison is very clearly "the" operator of the project.

Halzen is by any measure an awesome physicist, but he's also a good "fit" for the Nobel because of this unique situation.

All of science is collaborative and this is especially true in these big experiments: the IceCube collaboration is over 400 people [1] from several dozen institutes. There are a lot of experiments where giving a Nobel prize would be impossible because there's no "principal investigator" for the experiment.

[1]: https://icecube.wisc.edu/collaboration/meet-the-collaboratio...

Photons work better for driving a car too but being able to hear is still quite useful.

In other words: we already have lots of things that measure photons. You see things on two totally different media it's a lot more compelling than just one.

It's an interesting parallel with cars: the target consumer pays a bunch of money for a new model, takes it in for service when something is wrong, and sells it before any problems come up.

The 2nd, 3rd, 4th hand buyer, on the other hand, wants all the data, and is happy to know that the internals are a bit more complicated than "full" vs "empty". I guess one explanation for this phenomena is that the 2nd hand buyers aren't the main profit stream.

A few replies explained that the machine has a good reason to ignore you. I'm sold, they seem like good arguments!

But can empathize with your frustration. It looks like a bug, and I'd like the machine to explain that it's acting intentionally rather than having to dig around to figure out what's going on. It's like having an employee who simply ignores the spec rather than pointing out where it's not going to work.

On the other hand, maybe we're not the target demographic: a lot of people are happier with clicking a box and assuming everything will go to plan. The risky part of the design is that it requires enormous trust in the brand and minimal hacking from the user (which apple mostly has these days): the moment people have reason to believe their system is buggy, they need a bit more feedback.

For what it's worth I know people with ADHD who have no evidence of childhood trauma.

I'm saying this to offer an honest counterpoint to your anecdote. I don't think you meant to imply that ADHD is entirely due to trauma, but in fairness to parents it's probably worth entertaining the idea that some kids are just born that way.

Sorry for the lazy terminology, I actually meant chicanes:

https://en.wikipedia.org/wiki/Chicane

> Norway and Sweden deliberately made their roads less safe to take advantage of this.

All over Europe you see "horizontal speed bumps": deliberate road constrictions that funnel you into oncoming traffic. To an American it looks crazy but it works.

Vermont gets occasional ice storms or heavy snow that brings down trees on the power lines, so having a battery in your house can be pretty useful.

I'm a bit nostalgic for the nights when we'd break out the kerosene lanterns. But I concede that this might be more practical and a bit safer (a lot of VT houses are tinder boxes and good luck getting a good fire response in an ice storm).

That's encouraging, about the payment.

My information is 6 months old: I borrowed an EV to travel around while visiting friends in the US. I've never had any issues paying with a credit card at a gas pump, but had an O(0.1) success rate with the EV charger. But I guess wherever you are the payment is sorted out better.

The mysterious thing to me was why they ever tried to reinvent this part of the procedure. As I said, it was one of the few things they could just copy from the working model.

EV charging needs to sort out the one thing they didn't have to innovate on: payment.

All the novel parts of supplying megawatts of power to consumers were sorted out and work perfectly, which is really impressive work! But paying with a credit card is often impossible? And forget about cash. You often need an app, and god forbid you don't have a data connection.

Sadly, it it should be simpler to charge an EV: power distribution is already ubiquitous, much more so than gas distribution.

In practice (in my experience) the EV infrastructure needs a serious central push before we get there. Beyond payment there are a few more things to sort out:

- High capacity charging is nowhere near as common as gas stations are in most of the US (and everywhere else I've been).

- Charging protocols are mostly sorted out via adapters, but residual annoyances remain, some of which won't be fixed soon (the US mostly distributing 2-phase AC while Europe distributes 3-phase drives one of these).

Recently I've reviewed a few MRs that ended in me writing more comprehensive guidelines for the project.

The author hints at the bidirectional aspect of code review, but they miss that each MR is teaching you how your contributors are getting confused.

I don't think we should let musk appropriate nerdy space dreams.

Hand him the nerds and you'll hand him the future, for better or worse.

I always thought it was about commoditization: sure a SMR might cost 10x more per watt in theory but if you can reliably roll them off the production line without having to redesign each iteration and without the cost overrunning by 10x, it pays for itself.

Yes it's a joke, but to quote some numbers in the US: something like 93% of the US graduates matched to a residency program [1], although the numbers were a lot lower for international students applying for US residency. So if you're talking about US medical students, you have to be in the bottom 10% to flunk out.

The attrition for lawyers is higher, neither has anything on PhDs where the number of "practicing" doctors drops off constantly during and after the degree, and only really levels off when O(10%) of them land faculty jobs.

[1]: https://www.ama-assn.org/medical-students/preparing-residenc...