I don't yet have numbers to back this up, but I gotta believe that someone connecting their agent to Kody in one agent and then switching their main driver to another somewhere else is suboptimal.
But keep those methods you use to investigate things and monitor things very separate from the methods you use to generate reward which is something that AI companies including open AAI have held up as a principle especially in the case of avoiding putting training pressure on the chain of thought. So you might have monitors that read the agents chain of thought in order to alert you if something is going wrong somewhere but you don't train the agents with the outputs of that monitor.
even in this incident we saw there was a lot of pressure um as a result of this incident to stop doing cyber security evaluations and I really don't think that stopping doing evaluations and like sort of blinding ourselves to the result of evaluations is the right reaction to this problem.
Like that just does not seem like the ideal artifact. We should be able to store them somewhere. We should be able to have architecture diagrams that we can review and discuss that generate that code to spec.
I would say that I expect like full automation of AR&D perhaps somewhere around like 2031 2030 and then getting to like the like beats all humans on the job milestone. Maybe I expect median around 2033
Second, I think ML is a very shallow domain relative to math. So I think in math there's much more of a you find some true deep abstraction um and then like that like if you really understand that thing which is hard to understand then you get somewhere
somewhere along the line this kind of HR ratio of like a pod popped into being. You know, every time you hire six engineers, you add a designer or you add a PM, you add an EM
if if we're if we're too ambitious and we're at the outset and too ambitious and visionary about the product we want to build, then we will probably miss product market fit because we won't start at a small enough humble enough place
it's not just like there's some factory somewhere that you can pay to produce the the data like you actually need to invent new novel scientific approaches
We made this something useful for adults and we had this user pay model that could monetize your engagement instead of trying to show you an ad and getting you to leave the game to go somewhere else. It actually the more engaged you were, the more likely you were to, you know, spend money.
Pick your ASIC team where you can say I can bet the farm of I can bet my entire business that you will be here for me every single year. Your cost, your token cost will decrease by an order of magnitude every single year. I can count on it like I can count on the clock.
But right now I think it's sitting somewhere between half million and five million lines of code, somewhere in there. Probably more on the half million side right now and with the next drop of an Anthropic model, we're probably going to see it jump up to a few million lines.
Which is, you know, you’re the game designer who’s playing God, and players are the ants in your ant farm, and you want to see what they’re gonna do, which is not the correct way to be a good multiplayer designer.
I think there's something going on that pre-training it's it's not like the process of humans learning. It's somewhere between the process of humans learning and the process of human evolution.
But I don't want to, like, pull that down because every time someone made the first pull request is a win for our society, you know? Like, it… Like, it doesn't matter how, how shitty it is, y- you gotta start somewhere.
Use red and amber lights in the evening . Bluelight is bad for sleep. You can get blue light blocking apps (f.lux), glasses and also turn red mode on your phone.
I think some of the times they are but they're certainly involved and there are people and in some sense we haven't actually removed the person we've like moved them to somewhere where we can't see them.
the only thing that we know is that models will improve. Will it be incremental? Will it be exponential? I mean somewhere in between. Who knows? But uh what you have to believe is that you get better as models get better. Your organization gets better as models get better.
it's easy to um go on vibes for too long. Uh some folks, you know, just kind of like trust the vibes and you know that'll get you somewhere, but it's not rigorous.
Mongols produced nothing. They could produce felt to make their tents, but they were not craftsmen. And so they had to get these items from somewhere, and it was through raiding.
It becomes impossible to imagine that life was not sparked somewhere else, in our Milky Way galaxy, and maybe even in our local neighborhood of the Milky Way galaxy, maybe within a few hundred light years of our solar system.
but I'm very sorry to break it to all of you, but your problem is not unique. I'm 99% sure of that. Your problem has been felt by somebody somewhere and probably many, many places
Legacy code has no test and is not written to be testable. Touching it this sphaghetti code somewhere breaks the system. And to refactor safely, we'd need to have tests first... but where to start? This book gives practical suggestions
I think it’s a failing of archeology to properly see what was happening. I think that most of those cities populations moved no more than 20 to 40 kilometers out and started their own farm, and they lived in perishable houses.
Mind candy fantasy for younger readers, in which Mr. and Mrs. Middle America's beloved adoptive daughter turns out to be from Somewhere Else, which comes calling. It's very fluffy, which I was in the mood for, and Wrede is always reliably enjoyable.
Yet lime is a huge blind spot for economic historians, despite it increasing the productivity of what was still by far the largest portion of the economy: agriculture.
The thing is, being a particularly complex or efficient agrarian economy doesn’t seem to have been the most important thing for producing the industrial revolution or it almost certainly would have happened earlier and probably not in Europe.
I can't remember when exactly I started looking into David Ogilvy – or where I got this book, probably from a secondhand bookstore somewhere – but once I started reading it I couldn't put it down. Ogilvy had such a great personality. Very spirited, opinionated guy with a great sense of occasion.
Mathigon is a very beautifully done set of interactive articles on all sorts of fun topics in math. The experience sits somewhere between reading and playing a game, with a really wonderful blend of artwork, technology and pedagogical clarity.
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