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?"
I'm a self-taught C# dev, I've found tremendous success specifically just describing what I want to do in dumb language that I'd feel stupid asking people IRL about and that aren't googleable without knowing what both the terms "null-coalescing" and "non-merchandise supergroup" are describing.
There are a lot of patterns that don't have obvious names and that aren't easily described without describing a specific scenario in a way that might only make sense institutionally, or with additional context that your average person might not have. ChatGPT is fairly good at being the "buddy that you have a bunch of in-jokes with that can remember things better than you". I can skip a lot of explaining why I need to do a thing a certain way like I can with my coworkers (who all aren't programmers), and I can get helpful answers for programming questions that my coworkers don't know the answers to.
It's frustrating to see this incredibly advanced context-aware autocorrect on steroids get used in ways that don't acknowledge the inherent strengths of what LLMs are actually great at doing. It's infuriating to have that potential be actively misused and packaged as a service and have that mediocre service sold to you once a month as a necessity by idiots in suits watching a line on a chart.
Dude, they flubbed this so damn hard by over reaching. A few years ago, when they mentioned there would be a button in word that you could use to make a slide deck of your word dock, I was so excited. The teams meeting part where it will summarize meetings is honestly fantastic in doing Roberts rules of order type stuff. My response was "I hate what this means in terms of privacy, but godamn that sounds useful".
In turning into an everything all or nothing they massively screwed up. I have a self hosted instance of llama-gpt that I use to solve the "blank page" problem that AI was actually great at.
I have a lot of issues with AI on principle, like a lot of folks. But it blows my mind how hard they screwed up delivery (and I don't just mean the startups, that's to be expected). There's plenty to be said about uber at a principle level, but it's still bloody convenient. The entire roll out of a AI-ecosystem reeks of this meme: "but we made plans!".
My company is all in on GitHub Copilot. They have very unrealistic expectations for how much it will increase productivity. I suspect they were sold on data from junior developers, who I think it helps the most. Anyways, now they are measuring how much engineers use it, so there is some amount of pressure to use it more often.
The training was a little worrisome and disingenuous. The internal team advocating for it aren't strong coders and kept showing examples of it automating antipatterns, like writing useless tests that duplicate an if statement in the tested function, writing very verbose and vague comments (meaningless), or taking an example function and making a new one in a boiler plate way (that cut/pastes common code rather than extracting it into a shared function).
Really, I think it's helpful -- sometimes. Especially to new engineers or when dealing with an unfamiliar library. But I do worry it will lower the bar, and feel over using it can be a waste of time.
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.
Hi! Copilot has detected informal language in your response. Are you stressed by any chance? I have scheduled a priority meeting with your allocated HR during your lunch break to sort things out. Please let me know if you need anything else. Happy coding!
I thought it meant that all the icons/interfaces for AI seem to have a graphical gradient between colors, usually cool colors like blue/purple/pink. (Like the face in the meme)
Yes this is the correct answer. The words in the meme are written to a hypothetical end user. They would not reference technology like the other person said.
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 you 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.
"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!
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 have never once found an "AI" feature integrated by a corporation useful.
I have only ever found "AI" useful when it's unobtrusive, and something I chose to use manually. Sometimes an LLM is useful to use, but I don't need it shilled to me inside a search bar or in a support chat that won't solve my problem until I bypass the LLM.
I find customer support service Chatbots useful, they tend to ask the right questions before connecting me to an actual human, so I don't have to explain myself over and over. They also categorize your problem so you won't be forwarded 3 times till you finally reach the right department. They're essentially like the "press 1 to..., press 2 to..." shtick during a service call, except the customer support person has access to your chat history.
I find those kinds of chatbots useful, but those aren't the ones I encounter 90% of the time. Most of the time, it's a chatbot that summarizes the help articles I just read, giving faulty interpretations of the source material, that then goes on to never direct me to a real person unless I tell it multiple times that the articles it's paraphrasing aren't helping. (and sometimes, they have no live support at all, and only an LLM + support articles)
I have occasionally found the Google search AI handy in pointing me in the right direction, like when I can't remember or don't know a particular term for something, it's decent at giving me the term I'm actually searching for. Can't trust it for shit as it's intended to be used though.
Since LLMs are essentially just very complicated probabilistic links between words, it seems to be extremely good at picking the exact word or phrase that even a thesaurus couldn't get me.
I primarily end up using LLMs through DuckDuckGo's private frontend alongside a search, so if my current search doesn't yield the correct answer to my question (i.e. I ask for something but those keywords only ever turn up search results on a different, but similar topic) then I go to the LLM and ask a more refined question, that otherwise doesn't produce any relevant results in a traditional keyword search.
I also use integrated LLMs to format and distill my offhand notes, (and reformat arbitrary text based on specific criteria repeatedly for structured notes,) learn programming syntax more at my own pace and in my own way, and just generally get answers on more well-known topics a lot faster than I would scrolling past 5 pages of SEO-"optimized" garbage just designed to fill time for the ads to load before actually giving me a good answer.
At work I use the summary function in edge to generate code since all tohet llm are blocked. It is really helpful to burp templates of programs when you tell it your grand mother is dying
In my country we don't have an AI button either. What it's supposed to do?? It's the AI talking for you? Or it's just another chatbot trying to get your data?
I don’t even use LLMs to generate code because all we ever do anymore is migrate the horde of microservices with one or two endpoints that was going to fix software development forever three years ago to the latest hype hosting and devops platform that will somehow balance out the maintenance cost of having all those services this time for real.
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.
"Why would you need AI in a toaster firmware? Uhhhh don't think about it! Yeah, just use the damn thing! It will make your toast so much better trust me!"