31–40 of 152 posts
This is some dystopian shit right here. I don't care what fancy models you train on it, or even what funny jokes you make of it. I'm just so done with this.
Just to clarify, it's not really no-code: pseudocode is the new bytecode it would seem and this is just compiling that into usable code. You still need to be able to code and understand what you're doing. You can't just ask simple questions and get complex answers. You still have to be capable of asking complex questions. A common scenario I can think if is where I struggle to remember the name or API of the exact th…
Note that in part of the process, Copilot was the one asking complex questions when the human programmer didn't know how to proceed. Copilot adds tremendous value for someone who knows what they want, but not how to do it. For example, I'm not a great programmer. I'm also a lazy programmer. I had to convert a time to a specific format, in a specific timezone in JS, and I couldn't be bothered looking up documentation…
How do you know it was what you were after? Like you said, it could be .toLocaleTimeString or .toLocaleString (or something else).
How do you verify that the AI isn't giving you broken/incorrect code? I guess you could check the docs, or run the code yourself, but at that point what's the value add for copilot?
I'm now imagining a very frustrated junior developer a few years from now trying to argue with Copilot to write code for a classifier for chemical compounds, but it just spits out code for classifying text.
But also ... what do you need to know to recognize that the concept of a 'toxicity classifier' is likely broken? We can do _profanity_ detection pretty well, and without a huge amount of data. But with 1000 example comments, can you actually get at 'toxicity'? Can you judge toxicity purely from a comment in isolation, or does it need to be considered in the context in which that comment is made?
Maybe you don't need to know about python, but if you're building this, you should probably have spent some time thinking and grappling with ML problems in context, right? You want to know that, for example, the pipeline copilot is suggesting (word counts, TFIDF, naive Bayes) doesn't understand word order? Or to wonder whether it's tokenizing on just whitespace, and whether `'eat sh!t'` will fail to get flagged b/c `'shit'` and `'sh!t'` are literally orthogonal to the model?
More people should be able to create digital stuff that _does_ things, and maybe copilot is a tool to help us move in that direction. Great! But writing a bad "toxicity classifier" by not really engaging with the problem or thinking about how the solution works and where it fails seems potentially net harmful. More people should be able to make physical stuff too, but 3d-printed high-capacity magazines don't really get most of us where we want to go.
Impressive BUT. Who is defining toxic speech? Where is that data being taken from? This is the definition of using AI to set what the edges of “speech” should be based on potentially flawed data. This is a clown world.
I think you missed the forest for the trees. It isn't the model that matters, it's that copilot is building the classifier from intent (comments). It wouldn't matter if it was classifying flowers instead.
My problem is with the dataset and datasets like this overall that sets the tone through AI of what is acceptable and what is not.
That said, this isn't the robot that replaces us, obviously. Making the process of getting to 80% faster is better for everyone, but the last 20 is tough and anything further needs real expertise. I like how promising this is for the masses.
I'm out almost immediately. The first comment is more text than the code that it produces.
I think you're missing the point: this is starting to open the door to people who can't code.
Earlier quoted context omitted.
Note that in part of the process, Copilot was the one asking complex questions when the human programmer didn't know how to proceed. Copilot adds tremendous value for someone who knows what they want, but not how to do it. For example, I'm not a great programmer. I'm also a lazy programmer. I had to convert a time to a specific format, in a specific timezone in JS, and I couldn't be bothered looking up documentation…
> I wrote a comment outlining exactly what I wanted, and immediately Copilot generated the code I was after. How do you know it was what you were after? Like you said, it could be .toLocaleTimeString or .toLocaleString (or something else). How do you verify that the AI isn't giving you broken/incorrect code? I guess you could check the docs, or run the code yourself, but at that point what's the value add for copilot…
Think of it like a snippet engine on steroids. It's a huge value add.