Re:Implementation of Imagen, Google's text-to-image neural n...
by dotnet00 on Thursday May 26, 2022 @01:37AM
(#31515012)
I think there are three main reasons why:
- being open is kind of just how things in ML generally work right now, it's in stark contrast to things like chemistry or physics where paywalls are pretty common
- it's a matter of clout, ML is moving ridiculously quickly, with work from just 5 years ago being considered outdated in terms of capability, if you don't publish, someone else will and they'll get the credit. This likely also matters for the researchers since they get credit too. In a sense this is just publish or perish culture from academia.
- it's also somewhat about hiring, which is related to the clout. By putting out this kind of research, they're attracting talented engineers to consider working for them. This of course is pretty relevant to the rest of their business, especially given how heavily Google leans on AI to handle moderation.
- being open is kind of just how things in ML generally work right now, it's in stark contrast to things like chemistry or physics where paywalls are pretty common
- it's a matter of clout, ML is moving ridiculously quickly, with work from just 5 years ago being considered outdated in terms of capability, if you don't publish, someone else will and they'll get the credit. This likely also matters for the researchers since they get credit too. In a sense this is just publish or perish culture from academia.
- it's also somewhat about hiring, which is related to the clout. By putting out this kind of research, they're attracting talented engineers to consider working for them. This of course is pretty relevant to the rest of their business, especially given how heavily Google leans on AI to handle moderation.