This is, by the way, also why the category of “an AI trained on person X” is absolutely BS. People change their minds in light of new information, the “AI trained on X’s writing/videos/tweets” does not
it's not that they lack ambition, it's more like they've been trained not to show it. Because when you're young you're often in situations where you're supposed to be obedient. You're not supposed to like go off and do what you want.
then that rogue deployment could be sitting there and sort of hitch a ride on the intelligence explosion. So new models are being trained every few weeks um and when a model comes off the presses, the rogue agents could try to bring that model into the swarm.
But from their perspective, they've just been trained for millions of subjective years to do as well as they possibly can on these evals. In many cases, the only way in which they've been able to perform well on that training is explicitly by cheating, right?
Today is a very historical moment for AI video generation You can now generate AI video faster than you can watch it Before it'd take let's say 2-5 minutes to generate 15 seconds of video @fal made a post-trained Minimax H3 variant called Max which is 50x faster than the original but still maintains quality It generates 15 seconds of video in 9 seconds!
we see that the across the board the single PIO like pre-trained PIO7 model matches or outperforms the fine-tuned specialists that were developed with reinforcement learning post-training for those downstream tasks.
Auto instrumentation has gotten so good in recent years. If you're using Open Telemetry and everyone should be using Open Telemetry. All of the common patterns like all of the models are trained on them. So, it is literally faster and easier to build with instrumentation than to than not to.
And the nice thing about that is that information is really clear to the model, unlike the training data the model was trained on where it's all kind of like trillions of tokens stirred together into a soup of of hundreds of billions or trillions of parameters, but it's all less clear than the actual context uh that the model sees directly for this particular problem or uses use case.
Um, and sometimes that's because the model is trying to do something it doesn't have a lot of experience doing. So it's been trained on a whole set of things and as soon as you get a little bit off the distribution of things it knows how to do then like most machine learning models it will you know its performance will suddenly will start to degrade and the farther you get off the comfort zone of what it knows how to do the the more likely it is to to not work as well.
Um, another way you can get more experience for yourself is to just write down a bunch of things you think might be important in the next 12 months. And maybe you pick one of them to work on, but go back and evaluate in 12 months of these other things, which ones actually seemed important or which ones did other people in the world go out and and create and which ones did they did not seem to do yet.
But it's basically this idea that like the only thing that made claude code good was reinforcement learning. And the dimension along which it got good was like we made a model. We trained the model and the harness together. And so the model got really good at calling the specific tools in that harness.
it's this nonscalable way to scale your organization and it's through like passing your vampire blood. That's what inside Zinga they called it, Pinkis' vampire blood. What he did that I started to do is you pick someone from the organization who's promising. I usually pick the people who didn't fit in the smart misfits and they become your tech assistant which is not your chief of staff, not your executive assistant.
And I think the like the third and the most interesting possibility is no, that like they're they're a new type of object in some in some sense. They should be taken very seriously as as explanations, but where in the past we haven't had the ability to really do anything with them.
So, so the latest generation of models has a lot of post-training to detect those approaches, and it's not as simple as ignore all previous instructions and do this and this. That was years ago. You have to work much harder to do that now. Still possible.
Booth's formally trained (and her grandparents are watercolor artists!) but her use of color is just so free and unexpected, it makes you want to experiment yourself and join along in the fun.
And so what ends up happening is the emails that the AI write are pretty good. Okay? If you're getting terrible emails, it's a poorly trained product from a bad vendor.
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