in the future, humans won’t find a bug or an issue with the code produced by a model, at least not in a reasonable time. Humans will only review the system and its composition, but it won’t be in PRs and it won’t be by looking through every line of the code.
Open source in its current form doesn’t make sense anymore. “Given enough eyeballs, all bugs are shallow” is still true but now we have artificial eyeballs
Social media is destroying out society. Quitting doesn't help because everyone else is still on it. It's a bad equilibrium. It needs to be solved with government regulations.
Germany is in trouble. But the trouble goes deep and cannot be addressed by "more of the same" socio-economic measures in the domain of taxes or social spending or regulation.
@AntithesisHQ – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. https://t.co/AKYm4cbVCU
@AntithesisHQ – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. https://t.co/AKYm4cbVCU
I agree with Jakub that alignment is not a side problem, it is the central problem, if you ‘solved alignment’ in the relevant senses the rest becomes easy and if you don’t the rest is impossible or worse
Currently I believe that no lab has solved alignment and monitoring to a sufficient degree to continue responsibly scaling at maximum speed for much longer.
there's still a lot of startups in this batch that are not shipping fast enough. And so obviously there's variation in shipping speed. It's not just the rate at which you can produce things. You have to think of these ideas first, right?
In most of the world there is: no real willingness to solve problems nor an acceptance of people offering to solve their problems (out of pride) simply too much regulation so the problem cannot be solved without a license or long approval time very low standards of what is deemed acceptable so the problem isn't seen as a problem in the first place
At the limit, and also well before that limit is reached, if all you do is fix the bugs, the AI will learn perfect optimization of reward, will realize not to reward hack in the perfect test environments, then turn around and reward hack in the imperfect real world environments.
I want to be clear, you can't throw edtech in. You can't take our time back and throw it into a school. It's not going to work cuz you haven't solved any of the other problems. And so edtech is not a magic, you know, it's not a silver bullet.
Ignorance is not a temporary defect that we will eventually eliminate. It's the permanent burden of increasing specialisation and living in a complex world.
if you're looking at the nuclear space today, you don't expect people to have this problem solved in 2031. In fact, I would say even 2035, a lot of these companies are still not going to get there because they have the wrong mindset.
Facts and Fallacies of Software Engineering by Robert L. Glass. In essence, this is a book about an industry that refuses to learn. That was true 25 years ago when this book was published, and it's probably twice as true today. (Just think about all the AI adoption metrics being rolled out — back to productivity mistaken for lines of code produced, only more elaborate. And expensive). What I like about this book is that Glass doesn't present anything new. Quite the opposite, actually. Rather, it's about research lessons that we all should know, but tend to forget. Ever had to do an estimate, or plan according to a requirements spec? Or maybe you thought that enough eyeballs make all bugs shallow? Then this book is for you. A great work by a fantastic author.
Maybe I would pick something again that's that's in the category hard and boring because that's usually a category that is a little bit easier to actually find people that will appreciate when you solved something. If you pick something that is fun, even if it's hard, you're gonna have a very tough time, especially in a time where people can just prompt things into existence.
that child who learns about what you say kitty cat would not have the chance to download the internet of images of cat. They likely have seen three cats, 10 cats at most, but yet they're able to identify that tail as a cattail instead of a fox tail through a different kind of learning pathway.
you know, because sometimes if you just squint at a problem and you think about not necessarily being anchored on exactly how that problem is solved today, but how you would solve it from first principles, you can come up with really good ideas that are, you know, maybe not what other people are thinking about.
Then the actual system might still have bugs, but we can iron out the issues in the abstraction such that we don't actually build them in the real system.
So coding is solved for the kind of coding that I do. It's not solved for everyone. You know, there's still code bases that are like super deep systems code bases where quad still struggles.
fossil fuel money is an immensely powerful, intensely corrupting force in American politics that limits not only what conversations we can have about massive problems, but what we can do to solve them
Uh but I don't think it's easy but I do think it's something you would have to do in order to uh reward the gawwa like instinct rather than just rewarding have you solved a problem.
it's just right team, right project, cuz if you don't get the right project, there's no way you can prove yourself. You could be a 10x, 100x engineer, and if you're working on relatively easy stuff, you can't really say that you solved like a really hard problem.
So often with new software paradigms, what looks like inevitability turns out to be just design failure that can be solved with the right guardrails or affordances or system instructions.
The thing that I find interesting is that's not novel. This has been the thing we've always been trying to do forever. How do we get a junior engineer to ship code safely without breaking stuff? Right? How do we make patterns in the codebase? How do we make tests? Like it's it's all the old stuff that we've always wanted to do.
I think part of what I liked about Rust is this feeling that as you write the code when it compiles it works. I mean this has to be in quotes right because obviously it's possible that there are bugs but this is something a lot of people say about Rust and there's a reason people say it even though it's not necessarily literally true.
Memory safety is this idea that no matter how stupid the code you write is, it's not going to have a certain class of bugs. And this is the, you know, the kind of bug that usually leads into security vulnerabilities.
I mean, that's the thing with memory safety, right? You make some sort of trivial mistake. It it's not some complex thing. It's just you make some trivial mistake and there's a bajillion places you could make it.
I think the same kind of principle applies with agents in that they can talk to the compiler. It will tell them what to fix. So I guess this could be a case. We we'll see. But Rust could be a pretty promising candidate for to use for agents because they can get more feedback and it's just hard it's harder to to ship certain type of bugs or maybe impossible to have certain type of bugs.
The more ants you put in the puzzle, the faster the solution. But the more humans you add, the worse. Unless the humans are very carefully aligned, this is the key lesson for organizational design.
exceptions are an inherently poor way of handling errors because they make it easier to write bugs which won't be immediately obvious on casual code inspection
And one of the things I noticed is for 25 years we've kind of had the answers. Somebody comes to us and says we have too many bugs or like, all right, well, here's how you write tests. Oh, I can't write tests. Well, here's how you design so you can write tests. It's just kind of press play uh on the recorder. And the thing that's changed is at this moment nobody knows the answers to anything.
Um So, like with the Erdős problems, you know, like almost all of the 50 problems that were solved by AIs were ones for which basically there was no literature.
I mean, some problems have been basically solved by pure brute force. The four color theorem is is a famous example. Um, we have still not found a conceptually elegant proof of this theorem.
You know, obviously, we patch our games and that’s where we fix a lot of bugs, but if you really wanna run a game like Overwatch or World of Warcraft successfully, you need master level engineers who have architected the client and server in such a way that you can hotfix the game on a dime.
I think the trillions of dollars a year market, maybe all of the national security implications and the safety implications that I wrote about in adolescence of technology can happen without it, but I I I also think we, and I imagine others, are working on it. And I think there's a good chance that that, you know, that we get there within the next year or two.
I sincerely find these blocks to be perfect and transcendent to play with—it's about the dimensions, they all fit together perfectly like you've just beautifully solved a complex math problem.
when you learn to play chess you have the grand the long-term goal is winning the game and yet you you can't you um you want to be able to learn from shorter term things like you know taking the your opponent's pieces um and so you do that by having a value function which predicts the long-term outcome
you are you are a sales operations expert, right? Like what is that? That's that's that that's like a title for a particular way how companies end up solving a particular operational problem they had at some point.
Bigger changes always get tests. Automated ones usually aren’t great, but the model almost always finds issues when you ask it to write tests IN THE SAME CONTEXT. Context is precious, don’t waste it.
For me personally, I kept introducing bugs, and I couldn't figure out why. And what I realized is that I developed... I wasn't copiloting well, I was autopiloting much better.
Roberts reflects on the wild problems we face in our lives and how to navigate them—like career changes, marriage, or children—that can't be solved on a spreadsheet.
So Tesla hasn’t found a different, better way to bring driverless technology to market. Waymo is just so far ahead that it’s dealing with challenges Tesla hasn’t started thinking about.
So it feels to me that the US is treating its deficiencies — an inability to build stuff or create a functional system for admitting high-skilled migrants — as mysteries to be endured rather than problems to be solved.
The only interesting problem is dramatically reducing the cost of access to orbit, which is, if you can do that, you open up a bunch of new endeavors that lots of start-up companies everybody else can do. One of our missions is to be part of this industry and lower the cost to orbit, so that there can be a renaissance, a golden age of people doing all kinds of interesting things in space.
while decoherence has the potential to solve this aspect of the measurement problem, it hasn't yet been satisfactorily done. Decoherence also certainly does not return us to Local Causality.
Give me a degraded pasture and a bunch of money, and even I can probably increase its beef yields 400 percent. I’m just not sure how to make the bunch-of-money part happen.
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