I played that game!! Although I was 10 years old and barely knew any English. After some playing, I always got a "go home" note on my windshield, I never found out why.
At some point, OpenAI is going to cheat and hardcode a pelican on a bicycle into the model. 3D modelling has Suzanne and the teapot; LLMs will have the pelican.
I wanted to ask almost this question, then saw that it is on #1 right now.
My use case is ssh. I would like to stick my private key into a local Docker container, have a ssh-identical cli that reverse proxies into the container, and have some rules about what ssh commands the container may proxy or not.
I am sorry, I am not a real computer scientist and I find it difficult to find the right term.
With "sufficiently expressive", I mean things like dependent types and refinement types, that can express the constraint on a unit vector.
It seems to me that this is more or less the same thing, but Monte Carlo. Like MCMC vs symbolic Bayesian inference.
I am actually a research engineer paid by the French government. They take digital sovereignty pretty serious over here, which is sometimes good, sometimes less so.
Definitely the right call on Windows, though. Even my parents (in their mid-seventies) moved to Linux this year.
This is the first time I hear of property-based testing, and I am intrigued. What is the difference between this and a sufficiently expressive structural type system?
After the first run, you have a script and an API: the agent discovery mechanism is a detail. If the script is small enough, and the task custom enough, you could simply add the script to the context and say "use this, adapt if needed".
Yes, exactly. There are some tools that are used over and over again. But apart from that, dirt ramps are the norm in scientific computing. Once it gets you over the 2 meter wall of publication, it's disposable.
Natural language is ambiguous. If both input and output are in a formal language, then determinism is great. Otherwise, I would prefer confidence intervals.