Completely agree, requirements are key and often badly defined due to the customers’ lack of knowledge of the intricacies of the system. You are correct to ask for clarity or it could come back to bite you later on.
I’ve just had a spec through from a BA which consists entirely of screenshots of an existing system with no technical definitition of any of the requested fields so relate to this right now.
Not substantially, it had a different title and was toned down.
I withdraw the word essay and apologise for any rudeness.
Have a good one.
My original message was “Wow ok chill.” then elaborated to clarify.
Where I’m from people are way ruder than anything I said here so perhaps I misjudged the room. However, calling it out seems to have had the desired response as since posting OP went and apologised for his anger.
I in turn apologise for my rudeness to them.
Did you read it before the edits? If it’s written like that I think I’m entitled to exaggerate back slightly to make a point by calling it a rage essay.
Anywho I wouldn’t choose to rant like this to my coworkers or online. A quick 🤦 in chat usually does the trick.
Completely put to task, yep really got me there. I will never see the world in the same way again.
Also, I don’t think you understand what irony means.
You too.
Wow ok chill.
The title of this post was “I hate programming, why did I choose this field”.
I responded suggesting maybe OP isn’t suited to SQL in particular. There are plenty of other languages to learn that they might pick up quicker if they are struggling with SELECTs.
It’s partially right but led OP down the wrong lines of thinking because it interpreted the prompt as a date field being missing rather than the field named date being missing.
Tbh I don’t blame it too much here as there is kind of a base level of understanding requred to use it successfully.
Ah yes here you are successfully ignoring it.
Might not be encouraging it but you seem to be defending it.
It made an incorrect inference, imagine how wrong it is on more complex questions.
There’s a difference between ranting to your coworkers at lunch about a stupid mistake and typing out a full rage essay.
Imagine the state of the sub if we all did that… Wait…
Fair play. SQL is pretty different from traditional programming and errors often aren’t very descriptive.
You’ll need to get very familiar with fields you have included or not in your queries when using more advanced stuff like group functions as including or excluding them can alter the number of rows returned.
Perhaps, but we don’t know and therein lies the problem.
You are correct I don’t know the circumstances so all we can go on is what OP wrote…
Yes of course, but it’s not the sort of thing I’d make a rant post criticising the entirety of programming about.
It’s like going to a mathematics forum and declaring “Guyz I forgot to carry a 1, screw Maths.”
Ah I see ChatGPT is being as accurate as ever making up a created_at field completely unprompted. They’ve already found the correct SQL:
SELECT task, status, id, date FROM mainWorkSpace WHERE user_id = @user_id
Although I would question the sense in calling a date field “date”.
You didn’t add the date field to your query and couldn’t work out why it didn’t return the missing field for over 2 hours?
Perhaps SQL isn’t for you as things get waaaaay more finicky than that.
Yep my sentiment entirely.
I had actually written a couple more paragraphs using weather models as an analogy akin to your quartz crystal example but deleted them to shorten my wall of text…
We have built up models which can predict what might happen to particular weather patterns over the next few days to a fair degree of accuracy. However, to get a 100% conclusive model we’d have to have information about every molecule in the atmosphere, which is just not practical when we have a good enough models to have an idea what is going on.
The same is true for any system of sufficient complexity.
This article, along with others covering the topic, seem to foster an air of mystery about machine learning which I find quite offputting.
Known as generalization, this is one of the most fundamental ideas in machine learning—and its greatest puzzle. Models learn to do a task—spot faces, translate sentences, avoid pedestrians—by training with a specific set of examples. Yet they can generalize, learning to do that task with examples they have not seen before.
Sounds a lot like Category Theory to me which is all about abstracting rules as far as possible to form associations between concepts. This would explain other phenomena discussed in the article.
Like, why can they learn language? I think this is very mysterious.
Potentially because language structures can be encoded as categories. Any possible concept including the whole of mathematics can be encoded as relationships between objects in Category Theory. For more info see this excellent video.
He thinks there could be a hidden mathematical pattern in language that large language models somehow come to exploit: “Pure speculation but why not?”
Sound familiar?
models could seemingly fail to learn a task and then all of a sudden just get it, as if a lightbulb had switched on.
Maybe there is a threshold probability of a positied association being correct and after enough iterations, the model flipped it to “true”.
I’d prefer articles to discuss the underlying workings, even if speculative like the above, rather than perpetuating the “It’s magic, no one knows.” narrative. Too many people (especially here on Lemmy it has to be said) pick that up and run with it rather than thinking critically about the topic and formulating their own hypotheses.
Lol indeed, just seen you moderate a Simulation Theory sub.
Congratulations, you have completed the tech evangelist starter pack.
Next thing you’ll be telling me we don’t have to worry about climate change because we’ll just use carbon capture tech and failing that all board Daddy Elon’s spaceship to teraform Mars.
Shame climate change has rendered it about as useful as looking out the window at predicting the weather.