Comment Re:Win the battle, lose the war (Score 3, Informative) 64
And do what? Write their own OS along with every integration needed?
No, they'll buy licenses for QNX or VxWorks. Or switch to BSD. A lot of car stereos run on QNX/Qt.
And do what? Write their own OS along with every integration needed?
No, they'll buy licenses for QNX or VxWorks. Or switch to BSD. A lot of car stereos run on QNX/Qt.
What planet are you on where people care more about the Internet than oil? The planet of your mom's basement?
In case you haven't noticed, Iran has already cut off its citizens from the Internet. Threatening to cut them off is like threatening to cut off cruise ships from Iowa.
Exactly. I've never heard of this person or this film.
Have you heard of Ocean's Eleven? He directed Ocean's Eleven. And a few dozen other films, one of which you're probably seen, unless you don't like going to movies.
I enjoyed it at the time, and Object Pascal was a pretty reasonable language, but outside of maintaining legacy apps,
I'm guessing it's a lot of legacy apps. My friend worked with PowerBuilder heavily in the 1990s doing a lot of custom work for niche vertical markets, like municipal water utility billing applications and industrial monitoring systems. I think a lot of that stuff is still floating around, and, similar to mainframe applications, organizations don't want to pay to overhaul the whole thing in Java/Rust/Python/whatever is fashionable at the moment.
They need to have a moral/foundations layer which does the same thing, perhaps even trained on its own very insular dataset that's been curated to meet objectives that can help it rank the value of different data.
It's all statistical connections between words. It's not a conceptual model. There is no understanding of morality, ethics, or basic reason or logic. The only way to fix it is to bolt stuff on after training.
Also, you need an enormous dataset to get enough useful weighting for the model to work. For example, they didn't use chat logs because they wanted to, but because they needed the training data to get the models to function. They are still looking for more. You could prune back sources, but the models will perform worse.
Some people carve careers, others chisel them.