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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

by 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.

by brd 9y ago

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.