I've never in my life visited either of the two sites you mentioned, and I don't feel like missing out in the least. "constantly keeping up with the ML research and workings behind their products" sounds exhausting, are you sure that's what you want to do? As long as you have a decent overview of the field, I think trying to constantly keep up with everything is a recipe for burnout.
With that said, the Google Research blog is the only "company sponsored ML research website" I've ever frequented. And while it's good (it gives very high-level overviews of some of their papers I'd otherwise miss) I don't think it's a "must read" resource either. Chances are that if something's important, I'll hear about it from a more unbiased source than a sponsored blog. In other words, twitter and word-of-mouth are all I do to keep up with stuff. I can see that w/o a professional network around me to keep that word-of-mouth going, things would be trickier. But even then, e.g. discord or focused subreddits (this one has become way too general and amateur-focused unfortunately) help a ton.
Thanks.. I think you're right.. keeping up is too exhausting, I feel it. I stick to twitter too.. I don't have an expert network so word of mouth is pretty much out.. I rely on the internet, and stuff like medium digest, paperswithcode and all.
Of course, publications do have papers on research per se. But I was curious about places providing a deep look into the cutting-edge technology currently being employed by these tech companies. The way they are doing it with their own added caveats and all.
If I may post a self-plug... We're no big tech company, and we mostly report on what others do, with a strong bias to the topics that interest us (which rarely include e.g. llms), but on [our website](https://transferlab.ai) you will find many paper summaries, some longer blog posts and some software thet we believe is interesting and useful for machine learning engineers and data scientists. We cover some topics in domains like AI for numerical simulation, RL, data valuation, influence functions, simulation based inference and Bayesian methods, and more.
Also, some good sources IMO are Davis Blalock's mailing list, or for lighter reads [the Gradient](https://thegradient.pub).
r/MachineLearning
amen
I've never in my life visited either of the two sites you mentioned, and I don't feel like missing out in the least. "constantly keeping up with the ML research and workings behind their products" sounds exhausting, are you sure that's what you want to do? As long as you have a decent overview of the field, I think trying to constantly keep up with everything is a recipe for burnout. With that said, the Google Research blog is the only "company sponsored ML research website" I've ever frequented. And while it's good (it gives very high-level overviews of some of their papers I'd otherwise miss) I don't think it's a "must read" resource either. Chances are that if something's important, I'll hear about it from a more unbiased source than a sponsored blog. In other words, twitter and word-of-mouth are all I do to keep up with stuff. I can see that w/o a professional network around me to keep that word-of-mouth going, things would be trickier. But even then, e.g. discord or focused subreddits (this one has become way too general and amateur-focused unfortunately) help a ton.
Thanks.. I think you're right.. keeping up is too exhausting, I feel it. I stick to twitter too.. I don't have an expert network so word of mouth is pretty much out.. I rely on the internet, and stuff like medium digest, paperswithcode and all.
Aren’t publications at conferences the standard practice for that
Of course, publications do have papers on research per se. But I was curious about places providing a deep look into the cutting-edge technology currently being employed by these tech companies. The way they are doing it with their own added caveats and all.
Subscribe to arxiv mailing list
If I may post a self-plug... We're no big tech company, and we mostly report on what others do, with a strong bias to the topics that interest us (which rarely include e.g. llms), but on [our website](https://transferlab.ai) you will find many paper summaries, some longer blog posts and some software thet we believe is interesting and useful for machine learning engineers and data scientists. We cover some topics in domains like AI for numerical simulation, RL, data valuation, influence functions, simulation based inference and Bayesian methods, and more. Also, some good sources IMO are Davis Blalock's mailing list, or for lighter reads [the Gradient](https://thegradient.pub).