It’s not new tech, Planes only know when radar locks happen because the radar ping changes to be faster and more targetted, you can’t detect a heat seeker locking onto a motor. It’s much shorter range and easier to fool if you’re planning on it than radar, since it won’t see the heat over the horisen at the distance radar guided missiles get launched at, but as long as the plane has jet engines it can’t hide from heat seeking missiles well
Pretty much, thermals require being much closer and are only good from certain directions, so the stealth is entirely focused against radar missiles that can be shot from far out of sight and don’t need to be behind the enemy
I see, I misread their site. Still, that seems very fair
Agreed, and having heard of them for the first time today, their payment plan seems incredibly reasonable, crediting us for unused monthly plan credits means it’s basically pay as you go with a limit to avoid surprise bills, while avoiding the feeling of overpaying because I’m not using a service enough
The Romans had that technology too, the day was split into 12 hours of sunlight and 12 hours of night, no matter the time of year
The trouble is just volume and time, even just reading through the description and “proof it works” would take a few minutes, and if you’re getting 10s of these a day it can easily eat up time to find the ones worth reviewing. (and these volunteers are working in their free time after a normal work day, so wasting 15 or 30 minutes out of the volunteers one or two hours of work is throwing away a lot of time.
Plus, when volunteering is annoying the volunteers stop showing up which kills projects
I don’t think it will make enough difference, but RAG stands for Retrieval Augmented Generation.
There’s a few ways to do it, but basically it’s a way add extra information to the conversation. By default the model only knows what it generates, plus what is in the conversation. RAG adds extra information to the mix.
The simplest approach is to scan the conversation for keywords and add information based on them.
So you ask “what is the capital of France” and instead of the model answering/hallucinating by itself, your app could send the full Wikipedia page for France along with your question, and the model will almost always return the correct answer from the Wikipedia page and hallucinate much less. In practice it gets a lot more complicated and I’m not up to date on recent RAG but the idea is the same.