4 ms·

> At Tesla's scale and priorities, they'd probably be less keen on using external cloud providers. By hardware-hours, Tesla is hardly one of the top companies

> At Tesla's scale and priorities, they'd probably be less keen on using external cloud providers. By hardware-hours, Tesla is hardly one of the top companies training deep networks. Planet Labs (satellite imaging), Netflix, Pornhub, to name a few. What's the info they'd leak to the Google consultants? How much data or TPUs they're using? This is practically public information.

Udik 7y ago
What does Netflix do with NNs?
- SloopJon 7y ago
  Their own page says, "our recommendation algorithms ... learning characteristics that make content successful ... optimize the production of original movies and TV shows ... optimize video and audio encoding, adaptive bitrate selection, and our in-house Content Delivery Network ... and advertising". https://research.netflix.com/research-area/machine-learning https://research.netflix.com/research-area/machine-learning Here's another article on the subject: https://becominghuman.ai/how-netflix-uses-ai-and-machine-learning-a087614630fe https://becominghuman.ai/how-netflix-uses-ai-and-machine-lea...
  - Udik 7y ago
    At a first glance, they don't seem to be problems in which ML learning can have a huge impact. Netflix in the US has a catalogue of about 4000 movies plus a few hundred tv series. It's tiny- compare with 153707 items on a niche recommendation website (criticker.com). Characteristics that make content successful.. here again, it's mostly decent quality content plus marketing. I doubt the scripts are reviewed by NNs. I have no idea about the network and delivery stuff, but I guess a well designed network can take care of most of it. My strong impression is that, similar to other well known cases (Uber, WeWork) Netflix is a mostly traditional company (a media company) that very strongly wants to be seen as a tech company.
  - dzdt 7y ago
    Netflix put a lot of work into recommendation back in the dvd delivery days when their catalog was absolutely massive. Now in the streaming space the catalog they license is much smaller so recommendation is less important; basically they just advertise the popular stuff for your demographic. Its a bit odd that legally one can rent out physical disks, but there is no corresponding way to legally get permission to rent out streaming content without negotiating with the rightsholder. But thats how it is...
    - mlyle 7y ago
      > Its a bit odd that legally one can rent out physical disks, but there is no corresponding way to legally get permission to rent out streaming content without negotiating with the rightsholder. But thats how it is... It's hard to think of a good fair regime to do this under. At least with a physical disk, there's a maximum reasonable rate that you can turn the disk around between users and you need to have enough copies for whatever the lifecycle peak demand is. We do have an audio compulsory licensing system for things that are purely songs. But with video works, there's not a clear boundary for "how big" the work is-- how do you treat 30 hour anime series vs a 5 minute Pixar short? How do you treat continuing medical education videos vs. fluff amateur made content? Etc.
millettjon 7y ago
Do those really take more than 70,000 gpu hours to train a model?

Udik 7y ago

What does Netflix do with NNs?

SloopJon 7y ago
Their own page says, "our recommendation algorithms ... learning characteristics that make content successful ... optimize the production of original movies and TV shows ... optimize video and audio encoding, adaptive bitrate selection, and our in-house Content Delivery Network ... and advertising". https://research.netflix.com/research-area/machine-learning https://research.netflix.com/research-area/machine-learning Here's another article on the subject: https://becominghuman.ai/how-netflix-uses-ai-and-machine-learning-a087614630fe https://becominghuman.ai/how-netflix-uses-ai-and-machine-lea...
- Udik 7y ago
  At a first glance, they don't seem to be problems in which ML learning can have a huge impact. Netflix in the US has a catalogue of about 4000 movies plus a few hundred tv series. It's tiny- compare with 153707 items on a niche recommendation website (criticker.com). Characteristics that make content successful.. here again, it's mostly decent quality content plus marketing. I doubt the scripts are reviewed by NNs. I have no idea about the network and delivery stuff, but I guess a well designed network can take care of most of it. My strong impression is that, similar to other well known cases (Uber, WeWork) Netflix is a mostly traditional company (a media company) that very strongly wants to be seen as a tech company.
- dzdt 7y ago
  Netflix put a lot of work into recommendation back in the dvd delivery days when their catalog was absolutely massive. Now in the streaming space the catalog they license is much smaller so recommendation is less important; basically they just advertise the popular stuff for your demographic. Its a bit odd that legally one can rent out physical disks, but there is no corresponding way to legally get permission to rent out streaming content without negotiating with the rightsholder. But thats how it is...
  - mlyle 7y ago
    > Its a bit odd that legally one can rent out physical disks, but there is no corresponding way to legally get permission to rent out streaming content without negotiating with the rightsholder. But thats how it is... It's hard to think of a good fair regime to do this under. At least with a physical disk, there's a maximum reasonable rate that you can turn the disk around between users and you need to have enough copies for whatever the lifecycle peak demand is. We do have an audio compulsory licensing system for things that are purely songs. But with video works, there's not a clear boundary for "how big" the work is-- how do you treat 30 hour anime series vs a 5 minute Pixar short? How do you treat continuing medical education videos vs. fluff amateur made content? Etc.

millettjon 7y ago

Do those really take more than 70,000 gpu hours to train a model?