Readit NewsReminds me of that story from probably 5-7y ago. Someone wanted to use AI to classify photos of tanks as soviet vs US. So he went to a US tank museum and took lots of pictures of the tanks under every angle. Did the same in a soviet tank museum. The resulting model worked great on that training dataset. Then he tried on photos outside of the training dataset. Turned out that it was cloudy the day he visited the US museum and sunny for the soviet museum, and the model used the color of the sky to classify.
Which is perfectly fine IMO - that stuff can stay below ground.
Any examples? Which side is "wrong"?
I like to use chatgpt for enhancing productivity for rote tasks but I keep finding I can't rely on it. Is there a reliable generative text AI out there?
I have a Django app for building topical maps for content marketing, and I'd like to be able to visualise the topic maps in terms of interconnected nodes.
The only reason that's not the case on iOS devices is that they don't have that option.
I use FreeBSD for everything from my colocated servers, to my own PC. By no means am I developer; seasoned Unix Admin at best. Bare-metal forever but welcome to the future. Especially anything that contributes to the OS.
However I hear buzz words like Lambda and Firecracker and really have no idea where the usage is. I get docker, containers, barely understand k8s but why do you need to spin up a VM only to tear it down compared to where you could just spin up a VM and use it when you really need to. Always there, always when.
Is it purely a cloud experience, cost saving exercise?