Having driven in the US and UK, this is a significant difference between the two. In the UK, you might sometimes drive 30 under on a road that is nominally 60 mph. In the US, that road would have a specific posted speed limit that is safe to drive. US roads are also more consistently designed for constant speed or have additional advisory speed limits for curves. You can nearly always drive as fast as the number on the sign unless there is some additional hazard.
I'm not the author, but I think you could by using UNION ALL instead of temp tables. You could also make a view that just calls this function. I'm not sure why it would matter though.
I think the sports analogy would be passing the ball back and forth in front of the goal instead of shooting. Nearly every time, an athlete with the ball in front of an open goal will take the shot themselves instead of passing. Even in a 2v1 situation, passing is a huge risk and taking the shot has a high likelihood of scoring, so you only want to pass if the defender is leaving your teammate open (usually they split the difference, so it's complicated). Too many passes in a 2v1 guarantees that more defenders will show up and you lose your advantage.
The paper is based on an estimate of productivity increases per industry due to AI. The highest increases are around 0.2% and most industries are <0.1%. In that world, AI isn't transformative and doesn't accelerate.
In some ways I think this is probably realistic, but it's not compatible with outcomes touted by boosters. They estimate 28 PJ/year, which is only about 0.9 GW. Stargate was planned to build 10 GW of capacity alone, so they can't both be true
"lawful" seems like an enormous loophole that makes this seem vacuous. If the government makes what you are doing unlawful, then it can be restricted. How would the government restrict you from doing something lawful in the first place? A bill of attainder? That's already illegal.
It gives a legal foothold to those who would challenge later laws, akin to the bill of rights. Believe it or not, courts will honor that kind of thing, and many legislators act in good faith (at least at the state level).
The difference is that while they can restrict the what, they can't restrict the how. Yeah they could make training LLMs illegal, but they can't for example put a quota on how much training you can do. Passing a law to ban something completely is a lot harder than passing a law that puts a "minor restriction" in place.
Ultimately any law can be repealed, so the loophole of changing the law in the future always exists. The point is that any future change to the law will take time and effort, so people can be confident in the near term that they won't be subject to the whims of a regulator or judge making decisions in a legal grayzone which may come down to which side of the bed they woke up on.
Yes, rights are real in the way ideas are real, for what that’s worth. They’re not guarantees, as many tend to view them.
They only become tangibly real when those in power allow it. More of a temporary gift, quickly taken away when those in power are supplanted by a tyrant.
The interesting angle to me is that the same ideas seem to be sort of “inevitably re-emergent”. They return, even after generations of tyranny, where no one alive in society has been handed these ideas we call rights.
So it’s more of a temporary gift that we should appreciate while we have it, which is forever at risk of being taken away, but which will always re-emerge as long as there are conscious beings capable of suffering.
That's as strong a rule as you can put in a normal law. If you want it to restrict what laws the government can pass, you need to put it in the constitution.
This is essentially the "strict scrutiny" standard, which governments have to achieve in order to violate your strongest constitutional rights (e.g. 1A). If you don't spell it out, then it might be delegated to a lower standard like "rational basis".
No. AIME is a test for advanced high schoolers that mostly tests higher level math concepts like algebra and combinatorics. The arithmetic required is basic. All the answers are 3-digit numbers so that judging is objective and automated while making guessing infeasible. You have 12 minutes on average for each question, so even if you are terribly slow at arithmetic, you should still be able to calculate the correct answer if you can perform all the other math.
That's probably a great test for high schoolers but it doesn't really test what we want from AI, no? I would expect AI to be limited by the far greater constraints of its computing ability, and not the working memory of a human high schooler.
There are a ton of details on the page that go into why it is so hard. One reason is that there are a lot of fundamental things to build and deploy before anything can be automated. E.g. before ADS-B (equipment on planes that detects its own location with GPS and automatically broadcasts it), ATC needed to talk to pilots and ask them where they were in a lot of cases. ADS-B has only been required on commercial flights since 2020.
Then it also suffers like every large government software project where a bunch of $100+ million contracts get paid out to private companies while nothing gets built. And it's part of annual appropriations, so funding was unpredictable. It's like working at a software company that had a major layoff or hiring spree every year for the last 20 years. If we could figure out how to run major project, the value to humanity would be enormous.
There is a GitHub issue that details what's blocking stabilization for a each feature. I've read a few recently and noticed some patterns:
1. A high bar for quality in std
2. Dependencies on other unstable features
3. Known bugs
4. Conflicts with other unstable features
It seems anything that affects trait solving is very complicated and is more likely to have bugs or combine non-trivially with other trait-solving features.
I think there is also some sampling bias. Tons of features get stabilized, but you are much more likely to notice a nightly feature that is unstable for a long time and complex enough to be excited about.
> It seems anything that affects trait solving is very complicated and is more likely to have bugs or combine non-trivially with other trait-solving features.
Yep and this is why many features die or linger on forever. Getting the trait solving working correctly across types and soundly across lifetimes is complicated enough to have killed several features previously (like specialization/min_specialization). It was the reason async trait took so long and why GAT were so important.
I think they meant on unstable features which might yet change their semantics. A stable API relying on unstable implementation is common in Rust (? operator, for example), but that is entirely dependent on having a good idea of what the eventual stable version is going to look like, in such a way that the already stable feature won't break in any way.
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