I feel like this can be generalized to AI in general for most people. I still don’t see much usefulness or quality in output in the scenarios where I’ve been exposed to AI LLMs.
I feel the same way about AI as I felt about the older generation of smartphone voice assistants. The error rate remains high enough that i would never trust it to do anything important without double checking its work. For most tasks, the effort that goes into checking and correcting the output is comparable to the effort I would have spent to just do it myself, so I just do it myself.
Real talk though, I’m seeing more and more of my peers in university ask AI first, then spending time debugging code they don’t understand.
I’ve yet to have chat gpt or copilot solve an actual problem for me. Simple, simple things are good, but any problem solving i find them more effort than just doing the thing.
I asked for instructions on making a KDE Widget to get weather canada information, and it sent me an api that doesn’t exist and python packages that don’t exist. By the time I fixed the instructions, very little of the original output remained.
As a prof, it’s getting a little depressing. I’ll have students that really seem to be getting to grips with the material, nailing their assignments, and then when they’re brought in for in-person labs… yeah, they can barely declare a function, let alone implement a solution to a fairly novel problem. AI has been hugely useful while programming, I won’t deny that! It really does make a lot of the tedious boilerplate a lot less time-intensive to deal with. But holy crap, when the crutch is taken away people don’t even know how to crawl.
This semester i took a basic database course, and the prof mentioned that LLMs are useful for basic queries. A few weeks later, we had a no-computer closed book paper quiz, and he was like “You can’t use GPT for everything guys!”.
Turns out a huge chunk of the class was relying on gpt for everything.
Yeeeep. The biggest adjustment I/my peers have had to make to address the ubiquity of students cheating using LLMs is to make them do stuff, by hand, in class. I’d be lying if I said I didn’t get a guilty sort of pleasure from the expressions on certain students when I tell them to put away their laptops before the first thirty-percent-of-your-grade in-class quiz. And honestly, nearly all of them shape up after that first quiz. It’s why so many profs are adopting the “you can drop your lowest-scoring quiz” policy.
Yes, it’s true that once they get to a career they will be free to use LLMs as much as they want - but much like with TI-86, you can’t understand any of the concepts your calculator can’t solve if you don’t have an understanding of the concepts it can.
I feel like this can be generalized to AI in general for most people. I still don’t see much usefulness or quality in output in the scenarios where I’ve been exposed to AI LLMs.
I feel the same way about AI as I felt about the older generation of smartphone voice assistants. The error rate remains high enough that i would never trust it to do anything important without double checking its work. For most tasks, the effort that goes into checking and correcting the output is comparable to the effort I would have spent to just do it myself, so I just do it myself.
For programming it saves insane time.
Real talk though, I’m seeing more and more of my peers in university ask AI first, then spending time debugging code they don’t understand.
I’ve yet to have chat gpt or copilot solve an actual problem for me. Simple, simple things are good, but any problem solving i find them more effort than just doing the thing.
I asked for instructions on making a KDE Widget to get weather canada information, and it sent me an api that doesn’t exist and python packages that don’t exist. By the time I fixed the instructions, very little of the original output remained.
As a prof, it’s getting a little depressing. I’ll have students that really seem to be getting to grips with the material, nailing their assignments, and then when they’re brought in for in-person labs… yeah, they can barely declare a function, let alone implement a solution to a fairly novel problem. AI has been hugely useful while programming, I won’t deny that! It really does make a lot of the tedious boilerplate a lot less time-intensive to deal with. But holy crap, when the crutch is taken away people don’t even know how to crawl.
This semester i took a basic database course, and the prof mentioned that LLMs are useful for basic queries. A few weeks later, we had a no-computer closed book paper quiz, and he was like “You can’t use GPT for everything guys!”.
Turns out a huge chunk of the class was relying on gpt for everything.
Yeeeep. The biggest adjustment I/my peers have had to make to address the ubiquity of students cheating using LLMs is to make them do stuff, by hand, in class. I’d be lying if I said I didn’t get a guilty sort of pleasure from the expressions on certain students when I tell them to put away their laptops before the first thirty-percent-of-your-grade in-class quiz. And honestly, nearly all of them shape up after that first quiz. It’s why so many profs are adopting the “you can drop your lowest-scoring quiz” policy.
Yes, it’s true that once they get to a career they will be free to use LLMs as much as they want - but much like with TI-86, you can’t understand any of the concepts your calculator can’t solve if you don’t have an understanding of the concepts it can.