https://translate.kagi.com/?from=en&to=someone+who+keeps+say...

e: Converting back from output like this to LinkedIn Speak also returns some... illuminating results about the underlying models.

There has to be a base of knowledge available before the student can even comprehend many/most open research questions, let alone begin to solve them. And if they were understandable to a beginner, then I’d posit the LLM models available today would also be capable of doing meaningful work.

That is, until we got a new president who set a new strategic goal for being a top research school and adjusted all hiring and tenure standards for that.

* For some definitions of native. I've had to work as a translator for a Kiwi and an American, both native English speakers.

The real power I've found is using it as a tutor for my specific situation. "How do list comprehensions work in Python?" "When would I use a list comprehension?" "What are the performance implications?" Being able to see the answers to these with reference to the code on my screen and in my brain is incredibly useful. It's far easier to relate to the business logic I care about than class Foo and method Bar.

Regarding retention, LLMs still doesn't hold a candle to properly studying the problem with (well-written) documentation or educational materials. The responsiveness however makes it a close second for overall utility.

ETA: This is regarding coding problems specifically. I've found LLMs fall apart pretty fast on other fields. I was poking at some astrophysics stuff and the answers were nonsensical from the jump.

This is how big operations handle clearances today, complete with integration into the FMS. The pilot simply reviews the clearance and accepts it.

That code was also used at our (EMS) depots to secure the controlled drugs as well, as if none of us could have guessed it.

Come on, man. Have some self-respect.