ylai@lemmy.ml to Technology@lemmy.worldEnglish · 1 year agoVR Headsets Are Approaching the Eye’s Resolution Limitsspectrum.ieee.orgexternal-linkmessage-square34fedilinkarrow-up112arrow-down14
arrow-up18arrow-down1external-linkVR Headsets Are Approaching the Eye’s Resolution Limitsspectrum.ieee.orgylai@lemmy.ml to Technology@lemmy.worldEnglish · 1 year agomessage-square34fedilink
minus-squaredrislands@lemmy.worldlinkfedilinkEnglisharrow-up3arrow-down1·1 year ago…because they frequently do? Glaring errors are like, the main thing LLMs produce besides hype.
minus-squareKairuByte@lemmy.dbzer0.comlinkfedilinkEnglisharrow-up5arrow-down1·edit-21 year agoThey make glaring errors in logic, and confidently state things that are not true. But their whole “deal” is writing proper sentences based on predictive models. They don’t make mistakes like the excerpt highlighted.
minus-squaredrislands@lemmy.worldlinkfedilinkEnglisharrow-up1·1 year agoY’know what, that’s a fair point. Though I’m not the original commenter from the top, heh.
minus-squareKairuByte@lemmy.dbzer0.comlinkfedilinkEnglisharrow-up2·1 year agoAh apologies, I’m terrible with tracking usernames, I’ll edit for clarity.
minus-squaredrislands@lemmy.worldlinkfedilinkEnglisharrow-up1·11 months agoNo worries mate. I appreciate the correction regardless.
minus-squareGarbanzo@lemmy.worldlinkfedilinkEnglisharrow-up1·1 year agoI’m imagining that the first output didn’t cover everything they wanted so they tweaked it and pasted the results together and fucked it up.
minus-squareGlitterInfection@lemmy.worldlinkfedilinkEnglisharrow-up1·11 months agoPretty soon glaring errors like this will be the only way to identify human vs LLM writing. Then soon after that the LLMs will start producing glaring grammatical errors to match the humans.
…because they frequently do? Glaring errors are like, the main thing LLMs produce besides hype.
They make glaring errors in logic, and confidently state things that are not true. But their whole “deal” is writing proper sentences based on predictive models. They don’t make mistakes like the excerpt highlighted.
Y’know what, that’s a fair point. Though I’m not the original commenter from the top, heh.
Ah apologies, I’m terrible with tracking usernames, I’ll edit for clarity.
No worries mate. I appreciate the correction regardless.
I’m imagining that the first output didn’t cover everything they wanted so they tweaked it and pasted the results together and fucked it up.
Pretty soon glaring errors like this will be the only way to identify human vs LLM writing.
Then soon after that the LLMs will start producing glaring grammatical errors to match the humans.