I've heard about this, in different contexts. What it mainly comes down to is that incremental improvements can have massive impacts when you can apply them at a scale available at FAANG. I first read about this outside the context of machine learning, but it certainly would apply here.
For those of us who don't work at such scale, can you (maybe with a little fuzziness to avoid telling too much about an internal project) give a few examples of the kind of projects where a fairly simple model can have a 1M+ impact?
Here's a ~5 minute talk with 5 such examples (where relatively simple ML models made a 1M+ impact at a FAANG) :) https://youtu.be/zyOEOd1HkSY?t=946
Happy to talk about more details if you message me through my profile!
Thank you for the link! They were all interesting, and yes, all the result of having a high scale. For anyone curious and thinking about watching the video (I recommend watching it), the topics were 1) should you immediately re-run a failed ad payment (getting paid vs transaction costs/flagged for repeated billing), 2) should you send an IM immediately after a login failure (cost of text message vs possibility user will give up and not reset password), 3) should you fetch data for pre-loading in a web page (higher engagement with page vs cost of unnecessary loading), 4) video upload quality, 5) taking screen real estate for less commonly used UI features.
Interesting examples, and yes, they're all the kind of thing that might not justify the effort for an ML model (and might not have enough data to train) for a small website or operation, but can easily justify the cost and effort when you have a huge number of transactions.
On another note, this is why I often like lightening talks. So many people think that what they're doing falls below the threshold for what is an interesting presentation, when in fact it's the most relevant thing a lot of people will see at a conference.
I’m a consultant. I actually do a very different kind of thing. Yeah big tech hyper optimized content so that a tiny boost of engagement improves a billion users by a tiny amount so the net effect is huge. I’m skeptical of it tbh. I think it forgets emergent effects and externalities over time.
What I refer to for my work is the low hanging fruit. Old problems that businesses solve with manpower or overly unspecific rules. Something where just a little clarity can help them hone their efforts on the 80/20 of it all. I made a slightly more detailed post in an adjacent response
For those of us who don't work at such scale, can you (maybe with a little fuzziness to avoid telling too much about an internal project) give a few examples of the kind of projects where a fairly simple model can have a 1M+ impact?