Yeah, those bs ones pop up everywhere. If only there was some model to sort between those and the good ones… And I’m kind of giving up on being caught up, seeing g all the answers.

Wow, that is a lot of work. It’s awesome that you manage to have the latest and the pulse of AI as you said. That is the kind of discipline I cannot follow. Just one hour at work in the morning would destroy the rest of my day ^^

Hugging face is for sure a godsend, even though I’m still at a semi-loss with their API. It changed so much, and there is so much more now that it has become a little confusing. Nothing a little work can’t fix ! But that raises the question to me : how do these people manage to get out every model so fast ?

You managed to put into words what bugs me with the field nowadays. What kills me most is that third paragraph you said : no-one cares what the model does IRL but how it improves a metric on a benchmark task and dataset. When the measure becomes the objective, you’re not doing proper science anymore.

That helps narrow it down. Though, many discoveries are not published anymore. Reminds me of Mikolov, who was rejected pretty much everywhere and word vectors ended up being such a big deal. Or that OpenAI does not publish their models.

Thanks ! When I get back (soon) in a full-time ML position I’ll be sure to check it out.

Yes, it seems from all the answers that I just try to go too deep. Unfortunately it feels like nowadays it’s just tweaking and trying architectures, but there is no “red line” or big mechanism to know about, like there was kernels or attention.

Then it’s kind of sad, because a lot of discoveries have been made by looking at what other disciplines were doing and cross-pollinating (genetic algorithms, attention, etc.). Plus then how does one know of they want to branch to another domain? But you’re right there is too much…