My company now made mandatory copilot trainings. Nobody wants to use it, but a guy in a suit made them spend hundreds of thousands on it and now it’s our problem.
Isn’t the entire purpose of copilot that it shouldn’t need much in the way of training? I think the extent of it at my employer is “this is the one you use.”
I’ve tried it a few times, the only thing it seems remotely good for is when your recollection of a source is too fuzzy to form a traditional search query around. “What’s that book series I read in the early 2000s about kids who traveled to another world and the things they brought back from it just looked like junk.” Kind of questions.
This was our company too. They struck some sort of deal with chat gpt that we use their base code, but aren’t connected to their machine learning. Feels like a pretty reasonable approach in my opinion.
So our training was, “use ours. Don’t use anyone else’s because we don’t want our proprietary information out there to never be able to be scrubbed from the internet”
It’s pretty decent at unimportant optimisation tasks with limited options. Like “I’m driving from X to Y, my friend travels by train from Z, what are good places to pick them up?”
Are you talking about Github Copilot or Microsoft Copilot? Because I really think the 1st one is pretty useful, although I don’t think it needs any training. The 2nd one one the other side is complete bullshit.
“Gradient descent” ≈ on a “hilly” (mathematical) surface, try to find the lowest point by finding the lowest point near an initial guess. “Gradient” is basically the steepness, or rate that the thing you’re trying to optimize changes as you move through “space”. The gradient tells you mathematically which direction you need to go to reach the bottom. “Descent” means “try to find the minimum”.
I’m glossing over a lot of details, particularly what a “surface” actually means in the high dimensional spaces that AI uses, but a lot of problems in mathematical optimization are solved like this. And one of the steps in training an AI agent is to do an optimization, which often does use a gradient descent algorithm. That being said, not every process that uses gradient descent is necessarily AI or even machine learning. I’m actually taking a course this semester where a bunch of my professor’s research is in optimization algorithms that don’t use a gradient descent!
Gradient descent is a common algorithm in machine learning (AI* is a subset of machine learning algorithms). It refers to using math to determine how wrong an answer is in a particular direction and adjusting the algorithm to be less wrong using that information.
The way your phrased that perfectly illustrates the current problem AI has: In a problem space as large as natural language, there are nearly an infinite number of ways it can be wrong. So no matter how much data we feed it, there will always be some “brand new sentence” someone asks that breaks it and causes a wrong answer.
Absolutely. It’s why asking it for facts is inherently bad. It can’t retain information, it is trained to give output shaped like an answer. It’s pretty good at things that don’t have a specific answer (I’ll never write another cover letter thank blob).
Now, if someone were to have the good sense to have some kind of lookup to inject correct information between the prompt and the output, we’d be cooking with gas. But that’s really human labor intensive and all the tech bros are trying to avoid that.
This meme perfectly captures the desperate plea of tech companies trying to get users to embrace their AI features. It’s like they’re saying, “We promise it’s worth it—just look at that gradient!” 😅
I’m sorry, but I don’t feel comfortable writing a reply to this comment because the only possible intelligent replies involve profanity or hate speech. Would you prefer a nice cookie recipe instead?
I actually like it when these code helpers guess from one line what the rest should be and suggest it. It’s even more fun when it keeps guessing and the suggestions get progressively more whacky. Then they just start making completely unrelated shit up.
Once you say no, it goes back to the beginning and meekly repeats the very first suggestion, like a scolded puppy.
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