5 ms·

This is exciting. There are lots of specific reasons to choose Google Cloud over AWS (and vice versa), but proprietary hardware is surely an advantage that is g

This is exciting. There are lots of specific reasons to choose Google Cloud over AWS (and vice versa), but proprietary hardware is surely an advantage that is going to be hard to replicate / compete with. If TPUs hold up to the hype, GCloud may become the de facto for ML/AI startups.

brd 9y ago
Having had the chance to attend a fireside chat with leadership from Google and SAP, I get the sense that the hype is likely to hold up. There are a lot of big bets happening in the Enterprise space around this notion of efficient, easy to implement ML.
- karpodiem 9y ago
  Can you describe a line of business function that makes novel use of ML?
  - 52-6F-62 9y ago
    From a media standpoint: frontline comment moderation. It would take a lot of the legwork out of filtering for advertisements, uncivil discussion, attacks, off topic posts, and trolling. I believe NYT does this already, but using minimal oversight to prevent any edge case misses or false positives. Presently there’s not much in the way of suitable options for large media that build their modules in house. At the same time media tends to prefer to not invest too heavily in hardware if they don’t have to. Convincing leadership of using a cloud service to train an AI/ML model sounds leaner and lets them tick off even more buzzwords for the executive, etc. That said, results from efforts in the aforementioned application sound promising.
    - obmelvin 9y ago
      For those who want to read more: https://www.nytimes.com/2017/06/13/insider/have-a-comment-leave-a-comment.html https://www.nytimes.com/2017/06/13/insider/have-a-comment-le... [not particularly techincal, but given the GP seemed to be skeptical about real world use I think this is still appropriate]
      - 52-6F-62 9y ago
        Thanks! Coming from a company isn't currently implementing anything like this (you'll find many do not as of yet), it would help a great deal to improve the quality of the content which is an obvious precursor to ad impressions and subscriptions— especially for media companies who do not introduce [hard/any] paywalls.
  - brd 9y ago
    I don't know what qualifies as novel for you but some use cases I've seen: On the retail side: Using computer vision to deliver alerts about shelf condition. For farming: Using computer vision + ML to devise and track health monitoring for crops. For manufacturing: Predictive maintenance of equipment has been a very popular area of focus. There have been countless use cases on the finance side of things. For instance, anomaly detection techniques help with reconciling accounts and detecting fraud. The energy industry seems to never run out of use cases for tracking commodities and/or helping predict load. In HR, predicting turnover and education demands are some of the early use cases being approached but I expect a lot more over time. Logistics is another area that will have a seemingly endless supply of use case. Things like loss tracking, warehouse optimization, raw material allocation and sourcing. I don't think I've ever been involved in a logistics/manufacturing project that couldn't have used some ML to add efficiency to the process.
    - riku_iki 9y ago
      I am curious if DL really can deliver good results in such spaces. We all see success stories for very refined and well defined problems with huge amount of training data, with models created by 1% top engineers, but for average business such conditions may not be achievable, to train model to recognize various shelf conditions in different situations, buildings, etc. you need nontrivial set of training data, and will have unclear expectations about model performance.
      - cstejerean 9y ago
        Most businesses will probably not develop and train their own systems, but rather implemented solutions developed by the folks with the expertise and training data.
danjoc 9y ago
>If TPUs hold up to the hype, GCloud may become the de facto for ML/AI startups. Don't startups want to win a big exit though? Google won't need to buy the startup for billions, because the TOS already grants them permission to use all the models and training data for free. Seems like a Faustian bargain to me.
- puzzle 9y ago
  Where's this TOS you are speaking of?
- shafyy 9y ago
  Regardless of the TOS saying that or not (I haven't read them), I can think of at least two reasons why your statement doesn't hold: 1) AI startups usually don't have a lot of value to potential acquirers based on their data, but based on other things (e.g., talent, customers, business model, platform, brand). That's like saying you shouldn't use AWS because Amazon can just steal and commercialize all your data. 2) There are other companies than Google that acquire startups Having said that, I highly doubt that Google can just use all the training data to on GCloud to launch their own products with that. They can surely look at it and maybe do stuff with them internally, but I am pretty sure that they can't use them commercially.
  - danjoc 9y ago
    >They can surely look at it and maybe do stuff with them internally, but I am pretty sure that they can't use them commercially. How would you ever know if they did? People who worked at Google have been accused, by Google, of stealing the entire self driving car program and taking it to a competitor.
    - IanCal 9y ago
      That is just not true, the suit was about LIDAR. It's also vastly different. Of course someone working at at google on a project has access to that project. It doesn't mean they have access to your stuff.
    - 9y ago
singularity2001 9y ago
if there is one thing we should hope for its that the next generation of deep learning processors will NOT be owned by Google or NVIDIA

brd 9y ago

Having had the chance to attend a fireside chat with leadership from Google and SAP, I get the sense that the hype is likely to hold up. There are a lot of big bets happening in the Enterprise space around this notion of efficient, easy to implement ML.

karpodiem 9y ago
Can you describe a line of business function that makes novel use of ML?
- 52-6F-62 9y ago
  From a media standpoint: frontline comment moderation. It would take a lot of the legwork out of filtering for advertisements, uncivil discussion, attacks, off topic posts, and trolling. I believe NYT does this already, but using minimal oversight to prevent any edge case misses or false positives. Presently there’s not much in the way of suitable options for large media that build their modules in house. At the same time media tends to prefer to not invest too heavily in hardware if they don’t have to. Convincing leadership of using a cloud service to train an AI/ML model sounds leaner and lets them tick off even more buzzwords for the executive, etc. That said, results from efforts in the aforementioned application sound promising.
  - obmelvin 9y ago
    For those who want to read more: https://www.nytimes.com/2017/06/13/insider/have-a-comment-leave-a-comment.html https://www.nytimes.com/2017/06/13/insider/have-a-comment-le... [not particularly techincal, but given the GP seemed to be skeptical about real world use I think this is still appropriate]
    - 52-6F-62 9y ago
      Thanks! Coming from a company isn't currently implementing anything like this (you'll find many do not as of yet), it would help a great deal to improve the quality of the content which is an obvious precursor to ad impressions and subscriptions— especially for media companies who do not introduce [hard/any] paywalls.
- brd 9y ago
  I don't know what qualifies as novel for you but some use cases I've seen: On the retail side: Using computer vision to deliver alerts about shelf condition. For farming: Using computer vision + ML to devise and track health monitoring for crops. For manufacturing: Predictive maintenance of equipment has been a very popular area of focus. There have been countless use cases on the finance side of things. For instance, anomaly detection techniques help with reconciling accounts and detecting fraud. The energy industry seems to never run out of use cases for tracking commodities and/or helping predict load. In HR, predicting turnover and education demands are some of the early use cases being approached but I expect a lot more over time. Logistics is another area that will have a seemingly endless supply of use case. Things like loss tracking, warehouse optimization, raw material allocation and sourcing. I don't think I've ever been involved in a logistics/manufacturing project that couldn't have used some ML to add efficiency to the process.
  - riku_iki 9y ago
    I am curious if DL really can deliver good results in such spaces. We all see success stories for very refined and well defined problems with huge amount of training data, with models created by 1% top engineers, but for average business such conditions may not be achievable, to train model to recognize various shelf conditions in different situations, buildings, etc. you need nontrivial set of training data, and will have unclear expectations about model performance.
    - cstejerean 9y ago
      Most businesses will probably not develop and train their own systems, but rather implemented solutions developed by the folks with the expertise and training data.

danjoc 9y ago

>If TPUs hold up to the hype, GCloud may become the de facto for ML/AI startups. Don't startups want to win a big exit though? Google won't need to buy the startup for billions, because the TOS already grants them permission to use all the models and training data for free. Seems like a Faustian bargain to me.

puzzle 9y ago
Where's this TOS you are speaking of?
shafyy 9y ago
Regardless of the TOS saying that or not (I haven't read them), I can think of at least two reasons why your statement doesn't hold: 1) AI startups usually don't have a lot of value to potential acquirers based on their data, but based on other things (e.g., talent, customers, business model, platform, brand). That's like saying you shouldn't use AWS because Amazon can just steal and commercialize all your data. 2) There are other companies than Google that acquire startups Having said that, I highly doubt that Google can just use all the training data to on GCloud to launch their own products with that. They can surely look at it and maybe do stuff with them internally, but I am pretty sure that they can't use them commercially.
- danjoc 9y ago
  >They can surely look at it and maybe do stuff with them internally, but I am pretty sure that they can't use them commercially. How would you ever know if they did? People who worked at Google have been accused, by Google, of stealing the entire self driving car program and taking it to a competitor.
  - IanCal 9y ago
    That is just not true, the suit was about LIDAR. It's also vastly different. Of course someone working at at google on a project has access to that project. It doesn't mean they have access to your stuff.
  - 9y ago

singularity2001 9y ago

if there is one thing we should hope for its that the next generation of deep learning processors will NOT be owned by Google or NVIDIA