A better question is why is the US the only developed country that’s seen pedestrian deaths increase over the past 10-15 years. Every other developed country has seen both occupant and pedestrian deaths decrease over the same time period, and has seen a larger combined drop in deaths than the US. And to be clear, I’m talking about deaths per mile driven, not absolute counts, so the size of the US is already factored into the numbers.

You should tell that to the families of the 8000 that are killed each year. I’m sure it’ll help them accept the accept the loss of their loved ones.

The US is the only developed country that has seen a steady increase in the number of pedestrian deaths per 1000km driven over the past 10-15 years. And 10-15 years ago it was one of the worst performing developed countries for pedestrian deaths. Every other developed country has seen decreases in pedestrian deaths over the same time period, which means the US is an extreme outlier when it comes to pedestrian safety, or lack there of.

Is that the argument the paper is making? In my reading they seem to primarily be making the point that assigning anthropomorphic concepts to LLM is dangerously misleading, and more importantly, not needed to properly study and evaluate LLMs.

I don’t think you have to make the assumption that humans are unique for that argument to hold up. I would argue that really it’s a comment on how loose and poorly defined all anthropomorphic attributes are. At the end of the day we have to make the assumption that other humans feel and experience broadly the same mental activity as each other, because we’ll never directly experience anyone else conscience, we can only experience our own.

We can barely link our own mental experiences to concrete empirical measurements. The vast majority of the measurements we make are entirely self-reported, and we simply assume strong correlation between self-reported measurements and the individuals actual experiences. We also have to assume that somehow all of our self-reported measurements are “calibrated” to some reasonable degree. Even measuring anthropomorphic properties in humans is pretty fuzzy and inaccurate, the only reason accept such poor data is because it’s the best we’ve got, and there enough signal in there for us to develop useful tools like talking therapy, physiological profiles, mental health scores etc which have some level of predictive and healing power when applied to _humans_.

It’s honestly amazing that what we have works for measuring and predicting humans, and we only know that works through decades of empirical measurement and study. But to then try and directly apply that fuzzy mess to a completely different system, and just assume the same level of predictive power, strikes me as kinda crazy. It requires huge assumptions, which effectively can never be tested (because even the human mind is a total mystery to us), to be made, and if we can study these systems without making those assumptions, then why make the assumptions at all?

If they were cheap or easy to solve, don’t you think US refineries would have already converted to support domestic crude? Domestic crude is cheaper than imported crude, the only reason to import is because it so expensive to convert a refinery. My, admittedly very limited, understanding is that you generally don’t convert refineries, it’s cheaper and easier to just build a new one that targets a new type of crude. Building refineries takes a few years, they’re not something you throw together in a few months when oil markets go crazy.

But we’re seeing lots of open weight models that are either pretty close to SToA, or more importantly, perfectly capable of doing all the low level token insensitive grunt work when writing code. Pairing them with SToA models for long horizon task management, and you’ve got a very cost effective system.

The frontier labs have put little effort into cost efficient inference, they don’t need to, but folks like DeepSeek clearly are, and have achieved some impressive cost improvements. Given DeepSeeks models give you 70% of the capabilities for 30% of the cost, expect people to start moving lots of workloads to providers that provide cheap inference for open models, and huge competition to appear to provide that cheap inference. It’s truly commodity LLM inference.

In turn expect more companies to focus on building inferences efficient models, because someone that can build a model that provides 70% of SToA capabilities for 10% of the token cost, immediately eats up huge amounts of the available inference market.

Another factor in all this, is it’s becoming increasingly clear that building custom agents/workflows for LLM to operate in, is required to get the best out of these models. That means people are implicitly building the infra needed to use multiple model types and evaluate workflow performance end-to-end. Which in turn means they have everything they need to plugin in future, cheaper, inference providers and quickly evaluate if they can change their model provider.

Unless we’re producing data on the order of an entire new internet every couple of years, then it’s hard to see how LLMs can achieve further huge leaps in capability compared to training on effectively 0% of the internet vs 100% of the internet.

AWS isn’t going to risk their reputation, and thus huge chunks of their business, just so a few AI labs can get some extra training data. That’s an insane risk with zero upside for AWS. AWS knows full well they will make insane quantities of cash without breaking legal contracts with companies who pay them billions each year for infra.

There’s plenty of other more valuable things that are prototyped using 3D printers, such as high end commercial machines, or components that go into those machines.

I suspect that getting hold of STLs from US defence manufacturers would be extremely valuable. Why bother trying to capture a copy of your enemies technology, when they’ll happy just send you all the prototype STLs. Even if it’s not defence, don’t you think access to prototype components from EUV machines from ASML would be crazy valuable to Chinese companies trying to close the gap between Chinese and Western chip fabrication technologies?

CO2 is bad for us long term. But there are plenty of other nasty combustion products that are extremely bad for humans in the short term. Which is why we have pollution and air quality regulations.

Portable generators don’t meet any of the stronger requirements that utility scale systems have to meet, because it’s assumed they’re only operated in small numbers for short periods of time. They’re not designed to safe to operate in large numbers over long periods of time in the same place. For that you need proper pollution controls

Comments URL: https://news.ycombinator.com/item?id=48043149

Points: 3

# Comments: 1

During the investigation of the accident the Admiral that built and ran the Navy nuclear program was asked how the Navy had managed to operate accident free, and what others could learn. This was his response:

> Over the years, many people have asked me how I run the Naval Reactors Program, so that they might find some benefit for their own work. I am always chagrined at the tendency of people to expect that I have a simple, easy gimmick that makes my program function. Any successful program functions as an integrated whole of many factors. Trying to select one aspect as the key one will not work. Each element depends on all the others.

So recreating that accident free operating environment requires a lot more than just training. It would require wholesale adoption of the Navy’s approach across the entire industry. Which probably doesn’t scale very well. Not to mention the Navy operates much smaller nuclear reactors compared to utility scale reactors, and has extremely easy access to lots of cooling water, which probably gives them a little more wiggle room when dealing unexpected reactor behaviour.

[1] https://jackdevanney.substack.com/p/tmi-lessons-what-was-lea...

They don’t want the App Store filled with app that can’t be used by the vast majority of people that might see and download it.

I’m sure Apple could do everything that box does and more. But why bother designing, building and manufacturing your own specialist device when someone else already sells a perfectly good tool that does the job.

Don’t forget this is for use in a retail store by people who will have been given 5mins training on how to use the device. You want something that just requires a person to plug two phones in and hit a big “go” button. And it needs to work 99% of the time with zero messing around.

But if you have strong end-to-end encryption for messages, then you don’t have to care about the transport anymore, you assume they’re all compromised. At that point you might as well use the push notification system as your transport, given both OSs allow applications to intercept the push notification locally and decrypt it before it’s displayed to the user.

Apple support applications sending encrypted notifications, where the OS launches the app the decrypt the notification body locally and pass it back to the OS for display.

As for L3 packets going into the void. Yeah they’re gonna get lost, can’t be helped. But the server also isn’t going to get any L4 acks for those packets. So when a new L3 connection is created, and the L4 session recovered, the lost packet just get replayed over the new L3 connection.

iOS vs Android isn’t relevant when discussing hardware. It’s Apple vs Samsung etc. iOS doesn’t need majority market ownership for Apple to completely dominate their hardware competitors in a market.