• 3 Posts
  • 26 Comments
Joined 1 year ago
cake
Cake day: June 20th, 2023

help-circle





  • The company’s valuation in a public company reflects the price that people pay for shares, so it shows the value of the company on the open market. The employees created this value, so it does indicate how much they each created quite accurately. And you would think that they’d at least get a representative percentage of that at least. I mean if you paint a painting and someone pays $1m for it, you get $1m gross. You make the software and IP that’s sold for $100m and you only get $100k a year, that’s kinda wack.







  • My issue with generative AI is not that it doesn’t have uses, but that it seems to me that the vast majority of those uses are nefarious.

    As far as I can tell, it has the most potential for:

    • Creating sock puppet accounts on social media to sway public opinion

    • Make fake media/ identity theft

    • Plagarize various art mediums and meld them together enough to make attribution difficult

    Other positive use cases like summarization or reformatting seem to pale in comparison to the potential negative effects of the bad use cases. There are many marginal use cases like coding or law where you may save some time but the review required is likely not that much different than the time it would take for a good programmer or lawyer to just write it.








  • It’s really hard to know how this will play out. The models only have to improve a bit at this point to be reliably better than humans, as which time it probably makes sense to replace humans. It seems they will probably still hallucinate but do it little enough that it’s still a net gain to use them. Compute power needed to run them will surely come down.

    I’m as skeptical as the next guy, but I do think they will have uses, especially in examples like radiology which he he uses as a negative case. However I’m pretty sure it will eventually be able to do the initial screening to find the 95% of cases with nothing at a rate similar to existing medical diagnostic testing and then return the other 5% back to a human to review and decide further treatment. Based on my experience with speech language models, I’m pretty sure you’d be able to tweak the models to produce mostly false positives rather than false negatives and then run it through further layers of review afterwards.