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At Tesla's scale and priorities, they'd probably be less keen on using external cloud providers. Using TPUs at their scale would certainly require Google's AI c

At Tesla's scale and priorities, they'd probably be less keen on using external cloud providers. Using TPUs at their scale would certainly require Google's AI consultants to supervise which isn't ideal for Tesla. Not agreeing or disagreeing with their decisions, but if you have the resources, you can certainly design a custom chip that performs a specific type of task very well that beats other competitors. Nvidia's GPUs are have to be reasonably good at training across different NNs. You could have a chip that's exceptional good at training one/two specific types of tasks. For most companies, this would be a bad idea. However, Tesla knows how to produce hardware.

chronic739i 7y ago
> 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?
choppaface 7y ago
> At Tesla's scale and priorities, they'd probably be less keen on using external cloud providers. Not sure if it’s still the case today, but previously Tesla’s training was done on-prem and with their own in-house Tensorflow clone. And yes, if you get TPUs from GCloud, you are likely to be working with their engineers to get things working. Those engineers tend not to have much business conflict of interest, though. They want to help you because your problems are likely more interesting than what they’d otherwise be assigned.

chronic739i 7y ago

> 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?

choppaface 7y ago

> At Tesla's scale and priorities, they'd probably be less keen on using external cloud providers. Not sure if it’s still the case today, but previously Tesla’s training was done on-prem and with their own in-house Tensorflow clone. And yes, if you get TPUs from GCloud, you are likely to be working with their engineers to get things working. Those engineers tend not to have much business conflict of interest, though. They want to help you because your problems are likely more interesting than what they’d otherwise be assigned.