Filesystem reads in that case are 10 nanoseconds at most (the filesystem cache helps so much here) but doing any network roundtrip is 10 milliseconds at minimum. It's at least a million times slower because of how reality works.
It's hard it's harder to start a rocket startup on no money, but even a rocket startup you can start on not much money because what you do is you adjust for how much money you've got.
And so we can distribute the voting across a lot of the company for this initial review, which is essential because if you're doing anything cool, by the time you get a couple years into it, that top of funnel is overwhelming. And if you place any in any small group of employees or any one person in the way as a bottle bottleneck on this, they will absolutely bottleneck the whole rest of the organization.
That's a hell of a lot slower than knowing it in cognitive L1 cache. And you can have way more round trips in your brain than you can, you know, muttering through, you know, super whisper or typing it out or whatever.
The result is something completely worthless, but not obviously distinguishable from many other things on hep-th, which is in serious danger of going from something sad and unhealthy to something completely overwhelmed with crud.
Then you had code review which gave you another level of feedback. You could roll out internally more frequently. And everybody was using Facebook for all kinds of stuff, personal and internal business stuff. So whatever feature you developed, people would start using it immediately. So you get another round of feedback. Then we had this phased roll out process where you'd start rolling your stuff out. If there was a problem, the blast radius would be limited to a a few million people.
It becomes insufferable like as a mathematician because you you would basically be like I'm every single time I see one of these I kind of don't know if it's worth my time even if 99 out of 100 of them are right.
Look, first of all, if a company's successful, someone will knock on your door and try and give you more money. So, like almost every round is preemptive for successful companies. And when you take that much money, $300 million, the only way to spend it is to take your burn rate up. And I always thought of burn rate as a measure of risk.
these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code.
these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code.
and I don't think it's possible for one person to understand everything in depth. Lots of people understand broadly a lot of these ideas but they don't understand sort of everything in in the depth that is actually utilized. That's why there's these, you know, papers with with well over a thousand authors.
Um I think with within a decade, a lot of things that mathematicians currently do um we we spend a lot of the bulk of our time doing and a lot of stuff we put in our papers today can be done by AI. Um but we will find that that actually wasn't the most important part of what we do.
But okay, they shouldn't actually be enacting these ideas. There is a queue of ideas and there's maybe an automated scientist that comes up with ideas based on all the archive papers and GitHub repos and it funnels ideas in or researchers can contribute ideas, but it's a single queue and there is workers that pull items and they try them out.
um humanoid robots maybe start to or robotics at least start to but the main factor is going to be for reducing the number of people is modularizing things and making them in factories in Asia
It's such a pointless achievement and yet when you're at 99, all you think of is going to one more country so you can never think about the number of countries you've been to again.
Over the past few decades, journal editors and peer-reviewers have increasingly insisted that papers must present large datasets that have been treated using complex statistical methods in order to make even the mildest claims about what caused what.
If you’re stuck in a depressive cycle, even when you really, really, really, really don’t want to do anything, to do something. Try to make progress because the good feeling comes on the end of that. The whole point is to do first and then feel, not feel and then do.
I think that it's optimistically that it's actually the other way around that the robotics uh element of the equation will make all the other stuff better. And there are two uh reasons for this that that I could tell you about. One has to do with representations and focus.
So Spinosaurus is a super weird and exaggerated version of what was already a kind of super weird group of theropods. So Spinosaurus is properly strange. And then, as you kind of hinted at, super controversial as well, because various papers have claimed it's a diver or a really good swimmer, and I think the evidence for that is very weak at best.
There are certainly math results which could only have been accomplished because there was a human authentication and an AI involved, but it's hard to disentangle credit. I mean, these tools, they do not replicate all the skills needed to do mathematics, but they can replicate some non-trivial percentage of them, 30, 40%, so they can fill in gaps.
And that's a phase shift, because suddenly it makes sense when you write a paper to write it in Lean first, or through a conversation with AI, which is generally on the fly with you, and it becomes natural for journals to accept.
Worse, as the latest Apple papers shows, LLMs may well work on your easy test set (like Hanoi with 4 discs) and seduce you into thinking it has built a proper, generalizable solution when it does not.
I don’t think anyone really believes that at the event horizon you’ll find a firewall. But it did lead to things like the entangled wormholes embroidering a black hole, which was born out of an attempt to address the concerns that AMPS raised. So it did lead to progress.
Taken together, there is an argument to be made that AVs should be safer than human drivers by about a factor of 10 (being a nice round order of magnitude number) to leave engineering margin for the above considerations.
Scientists have run studies where they deliberately add errors to papers, send them out to reviewers, and simply count how many errors the reviewers catch. Reviewers are pretty awful at this.
I have reviewed before the effectiveness of peer review at figuring out "what's good" in the context of grant awards, finding that peer review as currently practiced does substantially better than chance at predicting future impact, especially for the most impactful of the papers; but at the same time is is far from perfect, leaving plenty of variance unexplained.
To read papers, I use Adobe Acrobat Reader and sync them in the cloud. This lets me read, highlight, and sync my papers across devices (work laptop, personal laptop, iPad). Instapaper does the same for online articles.
To read papers, I use Adobe Acrobat Reader and sync them in the cloud. This lets me read, highlight, and sync my papers across devices (work laptop, personal laptop, iPad). Instapaper does the same for online articles.
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