> It's baffling that so many people insist this is not happening, or that it's too computationally expensive.
If i am not mistaken - this paper seems to look at explicit "Smart Speaker interactions" - not passive background listening which is what I believe you are alluding to.
Not arguing that I am saying that latter does/doesn't happen - but just that this paper is not proof of it. And there is a big difference.
One example I am trying to wrap my head around is if you have two adjacent polygons (say California and Oregon) and perform an interior cover of both with variable hex sizes.
It seems possible that a child hex might actually slip outside the boundary - since the 7 children don't fit squarely inside the parent (no pun intended).
In S2 it guaranteed that any child cell of the S2CellUnion representing that cover is strictly inside the polygon bounds.
This doesn't seem to be guaranteed in H3. I could have a location that is in Oregon, that depending on the child resolution could slip into to Oregon instead of California - or vice versa?
Now imagine an business application where a user must be mapped to one of 2 physically exclusive regions, (for say pricing, legal, compliance reasons) it seems like exact containment is preferred.
Perhaps there is another way to employ H3 that would mitigate this?
H3 doesn't guarantee a child hexagon at level N+1 strictly belongs to 1 parent at level N. S2 is built on this exactly this primitive, but then struggles with cell-size variability across latitude.
This lack of strict hierarchy seeming negates alot of practical benefits (e.g tree data-structure that maps well to sharding and aggregation). Whilst I haven't dug into H3 that much from a practical sense - but I have build several Geospatial systems with S2 that exploit this strict hierarchy - I can't imagine this isn't a huge pain-point with H3.
Would be interested to hear of how these approximate cases are handled at Uber or in any practical setting.
Snap Inc | Distributed Systems + Recommendation Systems | Mountain View, LA, Seattle ONSITE | https://snap.com/en-US/jobs
My team is currently building out Snaps' ML Recommendation systems which powers Snap's power Growth, Content and Ads recommendation/ranking. Our services serve billions of requests a day evaluating trillions of ML predictions.
We have several roles open across multiple sub-teams
- Backend + Distributed systems - Scalable infrastructure for services that handle billions of requests per day
- Information Retrieval - search and retrieval over several 100M+ corpus
- Performance - C++, Go, Java expertise to optimize latency and compute cost of our services (tens of millions dollars, hundreds of thousands compute cores)
Feel free to reach to me directly at [email protected] (or via linkedin link in profile) if you are interested!
Snap Inc | Distributed Systems + Recommendation Systems | Mountain View, LA, Seattle ONSITE | https://snap.com/en-US/jobs
My team is currently building out Snaps' ML Recommendation systems which powers Snap's power Growth, Content and Ads recommendation/ranking. Our services serve billions of requests a day evaluating trillions of documents and predictions.
We have several roles open across multiple sub-teams
- Backend + Distributed systems - Scalable infrastructure for services that handle billions of requests per day
- Information Retrieval - search and retrieval over several 100M+ corpus
- Performance - C++, Go, Java expertise to optimize latency and compute cost of our services (tens of millions dollars, hundreds of thousands compute cores)
Feel free to reach to me directly at [email protected] (or via linkedin link in profile) if you are interested!
I have used Numi for several years - and versus Alfred/Google variants i have found Numi to be incredibly helpful for most back of the hand calculations.
I have yet to find an alternative that is more convenient
1) Configurable keyboard shortcut to bring this up
2) Good units/percentages/bytes support
3) Variable assignments / custom functions
4) Long History
Snap Inc ML Infrastructure | ML Infrastructure Engineer | Mountain View, LA, Seattle ONSITE | https://snap.com/en-US/jobs
My team is currently building out Snaps' ML Platform which powers Snap's power Growth, Content and Ads recommendation/ranking. The platform handles the training of thousands of ML Models - evaluating trillions of predictions per day.
We have several roles open
- Performance engineers with C++/CUDA performance expertise to optimize and scale our inference
- Distributed systems infrastructure expertise to scale our 'feature store'
- Recommendation system infrastructure experience to power our retrieval and ranking services for Ads, Content and Growth objectives
Feel free to reach to me directly at [email protected] (or via linkedin link in profile) if you are interested
Snap Inc ML Platform | ML Infrastructure Engineer | Mountain View, LA, Seattle ONSITE | https://snap.com/en-US/jobs
My team is currently building out Snaps' ML Platform - handling the training of thousands of ML Models and evaluating trillions of predictions per day.
In particular we are looking for engineers with C++/CUDA performance and/or distributed systems expertise to optimize and scale our ML inference services and feature store.
Feel free to reach to me directly at [email protected]
(or via linkedin link in profile) if you are interested
If i am not mistaken - this paper seems to look at explicit "Smart Speaker interactions" - not passive background listening which is what I believe you are alluding to.
Not arguing that I am saying that latter does/doesn't happen - but just that this paper is not proof of it. And there is a big difference.