People are treating AI like crypto, and on some level I don’t blame them because a lot of hype-bros moved from crypto to AI. You can blame the silicon valley hype machine + Wall Street rewarding and punishing companies for going all in or not doing enough, respectively, for the Lemmy anti-new-tech tenor.
That and lemmy seema full of angsty asshats and curmudgeons that love to dogpile things. They feel like they have to counter balance the hype. Sure, that’s fair.
But with AI there is something there.
I use all sorts of AI on a daily basis. I’d venture to say most everyone reading this uses it without even knowing.
I set up my server to transcribe and diarize my my favorite podcasts that I’ve been listening to for 20 years. Whisper transcribes, pyannote diarieizes, gpt4o uses context clues to find and replace “speaker01” with “Leo”, and the. It saves those transcripts so that I can easily switch them. It’s a fun a hobby thing but this type of thing is hugely useful and applicable to large companies and individuals alike.
I use kagi’s assistant (which basically lets you access all the big models) on a daily basis for searching stuff, drafting boilerplate for emails, recipes, etc.
I have a local llm with ragw that I use for more personal stuff like, I had it do the BS work for my performance plan using notes I’d taken from the year. I’ve had it help me reword my resume.
I have it parse huge policy memos into things I actually might give a shit about.
I’ve used it to run though a bunch of semi-structured data on documents and pull relevant data. It’s not necessarily precise but it’s accurate enough for my use case.
There is a tool we use that uses CV to do sentiment analysis of users (as they use websites/apps) so we can improve our ux / cx. There’s some ml tooling that also can tell if someone’s getting frustrated. By the way, they’re moving their mouse if they’re thrashing it or what not.
There’s also a couple use cases that I think we’re looking at at work to help eliminate bias so things like parsing through a bunch of resumes. There’s always a human bias when you’re doing that and there’s evidence that shows llms can do that with less bias than a human and maybe it’ll lead to better results or selections.
So I guess all that to say is I find myself using AI or ml llms on a pretty frequent basis and I see a lot of value in what they can provide. I don’t think it’s going to take people’s jobs. I don’t think it’s going to solve world hunger. I don’t think it’s going to do much of what the hypros say. I don’t think we’re anywhere near AGI, but I do think that there is something there and I think it’s going to change the way we interact with our technology moving forward and I think it’s a great thing.
Do people here actually use AI? And if so…for what?
Summarizing, drafting things, understanding complex things that are filled with jargon, etc.
People are treating AI like crypto, and on some level I don’t blame them because a lot of hype-bros moved from crypto to AI. You can blame the silicon valley hype machine + Wall Street rewarding and punishing companies for going all in or not doing enough, respectively, for the Lemmy anti-new-tech tenor.
That and lemmy seema full of angsty asshats and curmudgeons that love to dogpile things. They feel like they have to counter balance the hype. Sure, that’s fair.
But with AI there is something there.
I use all sorts of AI on a daily basis. I’d venture to say most everyone reading this uses it without even knowing.
I set up my server to transcribe and diarize my my favorite podcasts that I’ve been listening to for 20 years. Whisper transcribes, pyannote diarieizes, gpt4o uses context clues to find and replace “speaker01” with “Leo”, and the. It saves those transcripts so that I can easily switch them. It’s a fun a hobby thing but this type of thing is hugely useful and applicable to large companies and individuals alike.
I use kagi’s assistant (which basically lets you access all the big models) on a daily basis for searching stuff, drafting boilerplate for emails, recipes, etc.
I have a local llm with ragw that I use for more personal stuff like, I had it do the BS work for my performance plan using notes I’d taken from the year. I’ve had it help me reword my resume.
I have it parse huge policy memos into things I actually might give a shit about.
I’ve used it to run though a bunch of semi-structured data on documents and pull relevant data. It’s not necessarily precise but it’s accurate enough for my use case.
There is a tool we use that uses CV to do sentiment analysis of users (as they use websites/apps) so we can improve our ux / cx. There’s some ml tooling that also can tell if someone’s getting frustrated. By the way, they’re moving their mouse if they’re thrashing it or what not.
There’s also a couple use cases that I think we’re looking at at work to help eliminate bias so things like parsing through a bunch of resumes. There’s always a human bias when you’re doing that and there’s evidence that shows llms can do that with less bias than a human and maybe it’ll lead to better results or selections.
So I guess all that to say is I find myself using AI or ml llms on a pretty frequent basis and I see a lot of value in what they can provide. I don’t think it’s going to take people’s jobs. I don’t think it’s going to solve world hunger. I don’t think it’s going to do much of what the hypros say. I don’t think we’re anywhere near AGI, but I do think that there is something there and I think it’s going to change the way we interact with our technology moving forward and I think it’s a great thing.
I use it to summarize things for me. Or rewrite something I’ve written a bit better. I usually need to spot check it, but it’s still nice to have.