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.
One major problem with the current generation of "AI"seems to be it’s inability to use relevant information that it already has to assess the accuracy of the answers it provides.
Here’s a common scenario I’ve run into:
I’m trying to create a complex DAX Measure in Excel. I give ChatGPT the information about the tables I’m working with and the expected Pivot Table column value.
ChatGPT gives me a response in the form of a measure I can use. Except it uses one DAX function in a way that will not work. I point out the error and ChatGPT is like, "Oh, sorry. Yeah that won’t work because [insert correct reason here].
I’ll try adjusting my prompt a few more times before finally giving up and just writing the measure myself.
It does not have the ability to reason that an answer is incorrect even though it has all the information to know that the answer is incorrect and can even tell you why the answer is incorrect. It’s a glorified text generator and is definitely not “intelligent”.
It works fine for generating boiler plate code but that problem was already solved years ago with things like code templates.
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.
Seem to be 2 problems. One is obvious, the other is that such tedious boilerplate exists.
I mean, all engineering is divide and conquer. Doing the same thing over and over for very different projects seems to be a fault in paradigm. Like when making a GUI with tcl/tk you don’t really need that, but with qt you do.
I’m biased as an ASD+ADHD person that hasn’t become a programmer despite a lot of trying, because there are a lot of things which don’t seem necessary, but huge, turning off my brain via both overthinking and boredom.
But still - students don’t know which work of what they must do for an assignment is absolutely necessary and important for the core task and which is maybe not, but practically required. So they can’t even correctly interpret the help that an “AI” (or some anonymous helper) is giving them. And thus, ahem, prepare for labs …
If you’re in school, everything being taught to you should be considered a core task and practically required. You can then reassess once you have graduated and a few years into your career as you’ll now possess the knowledge of what you need and what you like and what you should know. Until then, you have to trust the process.
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.
One major problem with the current generation of "AI"seems to be it’s inability to use relevant information that it already has to assess the accuracy of the answers it provides.
Here’s a common scenario I’ve run into: I’m trying to create a complex DAX Measure in Excel. I give ChatGPT the information about the tables I’m working with and the expected Pivot Table column value.
ChatGPT gives me a response in the form of a measure I can use. Except it uses one DAX function in a way that will not work. I point out the error and ChatGPT is like, "Oh, sorry. Yeah that won’t work because [insert correct reason here].
I’ll try adjusting my prompt a few more times before finally giving up and just writing the measure myself. It does not have the ability to reason that an answer is incorrect even though it has all the information to know that the answer is incorrect and can even tell you why the answer is incorrect. It’s a glorified text generator and is definitely not “intelligent”.
It works fine for generating boiler plate code but that problem was already solved years ago with things like code templates.
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.
Seem to be 2 problems. One is obvious, the other is that such tedious boilerplate exists.
I mean, all engineering is divide and conquer. Doing the same thing over and over for very different projects seems to be a fault in paradigm. Like when making a GUI with tcl/tk you don’t really need that, but with qt you do.
I’m biased as an ASD+ADHD person that hasn’t become a programmer despite a lot of trying, because there are a lot of things which don’t seem necessary, but huge, turning off my brain via both overthinking and boredom.
But still - students don’t know which work of what they must do for an assignment is absolutely necessary and important for the core task and which is maybe not, but practically required. So they can’t even correctly interpret the help that an “AI” (or some anonymous helper) is giving them. And thus, ahem, prepare for labs …
If you’re in school, everything being taught to you should be considered a core task and practically required. You can then reassess once you have graduated and a few years into your career as you’ll now possess the knowledge of what you need and what you like and what you should know. Until then, you have to trust the process.
People are different. For me personally “trusting the process” doesn’t work at all. Fortunately no, you don’t have to, generally.
When AI achieves sentience, it’ll simply have to wait until the last generation of humans that know how to code die off. No need for machine wars.