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)

Oh yeah, it’s definitely useful for that!

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.

ChatGPT itself is usually useful. I usually asked it to explain something new as a base start for searching it myself

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.

You could use it to auto reply and delete said mails.

That’s my favorite use of ai, remembering old ass movies I have fragments of memories about from my childhood

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?”

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”

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.

Proton Mail also has AI? Just opened the app and found nothing

You click on it and enter prompts.

There’s no option hide that button.

This is the actual answer, the other replies are over thinking it. There’s a gradient on his face ffs

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.

No. Not at all. It’s about gradient descent, an optimization technique.

“Gradient descent” ≈ on a “hilly” (mathematical) surface, try to find the lowest point by finding the lowest point near an initial guess. Hopefully, the lowest point near your initial guess is low enough to pass as a solution to your problem.

“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!

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.