Tesla AI Day 2022
youtube.com66 pointsby dmartinez86 comments
dd if=file.iso of=/dev/usb_drive
- Clone a drive to another drive with 4MB block and ignore error: dd if=/dev/source_drive of=/dev/dest_drive bs=4m conv=noerror
- Generate a file of 100 random bytes by using kernel random driver: dd if=/dev/urandom of=random_file bs=100 count=1
- Benchmark the write performance of a disk: dd if=/dev/zero of=file_1GB bs=1024 count=1000000
[0]: https://github.com/tldr-pages/tldr
In-person work has higher bandwidth and lower latency than remote work, so for certain roles it makes sense you wouldn't want to farm it out to remote workers. The quality of the work can degrade in subtle ways that some people find hard to work with.
Similarly, handing a task to a human versus an LLM probably comes with a context penalty that's hard to reason about upfront. You basically make your best guess at what kind of system prompt an LLM needs to do a task, as well as the ongoing context stream. But these are still relatively static unless you have some complex evaluation pipeline that can improve the context in production very quickly.
So I think human workers will probably be able to find new context much faster when tasks change, at least for the time being. Customer service seems to be the frontline example. Many customer service tasks can be handled by an LLM, but there are probably lots of edge cases at the margins where a human simply outperforms because they can gather context faster. This is my best guess as to why Klarna reversed their decision to go all-in on LLMs earlier this year.