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Deep Learning Business Models

85 points by beaucronin 12y ago

StandardFuture 12y ago
WTF is 'the deep learning gold rush'? (Also, the HN title does not match the article title, which is: "Deep Learning Business Models".) Would love a better explanation from someone than this article gives to support the notion of some kind of massive 'Deep Learning' market that is yet untapped.
- dang 12y ago
  > the HN title does not match the article title Right. We changed it. The submitted title was "Business models in the deep learning gold rush".
BenderV 12y ago
"Deep learning requires a ton of tuning and tweaking, and getting good results is as much art as science." Funny when you think about it : Deep learning is supposed to avoid that ^^'
- p1esk 12y ago
  I think you're confusing deep learning with strong AI.
  - visarga 12y ago
    No, deep learning really does automatically (read - without much art) learn good features, that's its main purpose. Problem is that deep learning itself is not trivial to use.
motters 12y ago
I didn't know there was a deep learning gold rush. Maybe this explains the crazy number of stars on my libdeep library on Github, while there being no comments or issues raised. Deep learning is not any sort of magic bullet. It may be marginally better than other machine learning methods in specific contexts, but I'm not convinced that there are going to be any deep learning tycoons or deep learning entrepreneurs (were there any SVM tycoons?). But I suppose as a buzz term "deep learning" is better than the meaningless "big data". Just replace the latter with the former in the marketing literature.
- robert_tweed 12y ago
  I've been noticing a slight trend among the same group of people that are likely to use the big data buzzword without really understanding it, to also use deep learning in the same way, to simply mean "gain business insights from [obviously big] data". As far as I know the only gold rush is around marketing surrounding this and related buzzwords, i.e., it's the latest thing that your business absolutely must be doing to keep up with your competitors. That particular usage of course has as little to do with actual deep learning as the misappropriation of big data has to do with anything. Fortunately this trend has been a bit slower to take off, presumably because whereas big data is a fairly nebulous concept, it's much easier to correct someone when they talk about deep learning quite wrongly.
  - agibsonccc 12y ago
    That's the exact market I'm cashing in on. But rather than just be someone in the space who talks about it, I made it[1]. I think at the end of the day, the stuff talked about in the media may or may not have some merit (otherwise why would google or these other companies put resources in to it?) Rather than read the blogspam, read the papers instead though. Try to understand the merits of what's going on and apply it to your use case. [1]: http://deeplearning4j.org/ http://deeplearning4j.org/
    - beaucronin 12y ago
      Blogspam? I demand satisfaction!
      - agibsonccc 12y ago
        Let me clarify: Techcrunch articles talking about papers being published. Blogspam to me is just something that talks about the stuff at such a high level, there's no meat in it. Within that subset I'm talking about academic concepts where you can learn a lot more if you just read the papers.
        12y ago
dj-wonk 12y ago
> Deep learning requires very large training sets, and these shouldn’t be shipped around a lot. To the extent that this is true, companies that offer these services may be driven to integrate more closely with customer data. This may involve custom in-house deployments or ways of getting the data in a cost-effective way from, say, Amazon S3, or wherever the data lives (HDFS, etc). This leads me to speculate on an additional business model, "Behind The Firewall" Software Deployment. This could be somewhat different from the others suggested in the article: 1. Sell hardware; 2. Open source plus services; 3. Hosted API, “Deep Learning as a Service”; 4. Individual deep learning services.
- agibsonccc 12y ago
  I think there is something to be said for the "deep learning gold rush" I'm one of the ones trying to cash in myself by being an independent player in the space with my own distributed deep learning framework[1]. The goal of data accessibility can be solved by an abstraction layer that auto vectorizes (transforms in to matrices) the needed data at runtime, trains the nets on that particular mini batch of data, and continues on. That's what I'm trying to do with a concept of a DataSetIterator[2]. This understands how to pull in the data, and handles all the logistics while the runtime only knows about DataSetIterators. I'm also partnering with a former cloudera engineer in the hadoop space to take on in process YARN deep learning[3]. Data should not be moved. It should be processed and left where it is. I'll be interested to see the innovations in this space in the coming years. I don't believe deep learning as a service is the way to go, I think behind the firewall deep learning apps will be the way to go here. [1]: http://deeplearning4j.org/ http://deeplearning4j.org/ [2]: http://deeplearning4j.org/customdatasets.html http://deeplearning4j.org/customdatasets.html [3]: https://github.com/jpatanooga/Metronome https://github.com/jpatanooga/Metronome
dj-wonk 12y ago
For many (most?) analytics tools, the application developer has to specify what gets recorded and what gets fed as an input to predictive analytics. I expect to see more tools that assume any data is fair game; e.g. all data is worth at least a quick look. These tools will probably figure out how to select what is interesting enough to examine more deeply.
mirador 12y ago
Agreed, was unaware that there was a gold rush, anyone know about specific companies in this space that are still active?
- agibsonccc 12y ago
  Ersatz Labs is in the space, and we're in the process of launching as well [wait till tomorrow ;) ] At the end of the day, companies that focus on apps will be the ones that get bought or cash in. I don't think "machine learning" appeals to businesses. 30% more revenue because of a more accurate model sounds pretty good to me though. [1]: http://www.ersatzlabs.com/ http://www.ersatzlabs.com/
deleted 12y ago
[deleted]
Irishsteve 12y ago
ARGHHHHHHH
arek2 12y ago
The only gold I see in that gold rush is from acqui-hire.
dave_sullivan 12y ago
If you believe a few assumptions: 1. Machine learning will come to play a more important role in the future (not less) 2. Machine learning is a wide field with many areas of complexity and a wide variety of applications, many not yet discovered 3. Deep learning is an exciting area in machine learning research showing promising (state of the art?) results in several domains Then I think a few conclusions follow: 1. New tools will address this market, both free and commercial. 2. Services will be a major part of this ecosystem (see history of databases, ERP, CRM) and those consultants both use and sell tools (applications) 3. Deep learning is interesting, quite possibly worth using, and changing rapidly. But it doesn't negate what came before it (or after). So the answer here is all of the above models will be important, with the application of machine learning to everything really being the larger umbrella opportunity, and deep learning currently being an interesting avenue of research?
- arek2 12y ago
  Does your company have customers?
  - dave_sullivan 12y ago
    Yes
usav 12y ago
kaggle++

85 points by beaucronin 12y ago

WTF is 'the deep learning gold rush'? (Also, the HN title does not match the article title, which is: "Deep Learning Business Models".) Would love a better explanation from someone than this article gives to support the notion of some kind of massive 'Deep Learning' market that is yet untapped.

dang 12y ago
> the HN title does not match the article title Right. We changed it. The submitted title was "Business models in the deep learning gold rush".

BenderV 12y ago

"Deep learning requires a ton of tuning and tweaking, and getting good results is as much art as science." Funny when you think about it : Deep learning is supposed to avoid that ^^'

p1esk 12y ago
I think you're confusing deep learning with strong AI.
- visarga 12y ago
  No, deep learning really does automatically (read - without much art) learn good features, that's its main purpose. Problem is that deep learning itself is not trivial to use.

motters 12y ago

I didn't know there was a deep learning gold rush. Maybe this explains the crazy number of stars on my libdeep library on Github, while there being no comments or issues raised. Deep learning is not any sort of magic bullet. It may be marginally better than other machine learning methods in specific contexts, but I'm not convinced that there are going to be any deep learning tycoons or deep learning entrepreneurs (were there any SVM tycoons?). But I suppose as a buzz term "deep learning" is better than the meaningless "big data". Just replace the latter with the former in the marketing literature.

robert_tweed 12y ago
I've been noticing a slight trend among the same group of people that are likely to use the big data buzzword without really understanding it, to also use deep learning in the same way, to simply mean "gain business insights from [obviously big] data". As far as I know the only gold rush is around marketing surrounding this and related buzzwords, i.e., it's the latest thing that your business absolutely must be doing to keep up with your competitors. That particular usage of course has as little to do with actual deep learning as the misappropriation of big data has to do with anything. Fortunately this trend has been a bit slower to take off, presumably because whereas big data is a fairly nebulous concept, it's much easier to correct someone when they talk about deep learning quite wrongly.
- agibsonccc 12y ago
  That's the exact market I'm cashing in on. But rather than just be someone in the space who talks about it, I made it[1]. I think at the end of the day, the stuff talked about in the media may or may not have some merit (otherwise why would google or these other companies put resources in to it?) Rather than read the blogspam, read the papers instead though. Try to understand the merits of what's going on and apply it to your use case. [1]: http://deeplearning4j.org/ http://deeplearning4j.org/
  - beaucronin 12y ago
    Blogspam? I demand satisfaction!
    - agibsonccc 12y ago
      Let me clarify: Techcrunch articles talking about papers being published. Blogspam to me is just something that talks about the stuff at such a high level, there's no meat in it. Within that subset I'm talking about academic concepts where you can learn a lot more if you just read the papers.
      - 12y ago

dj-wonk 12y ago

> Deep learning requires very large training sets, and these shouldn’t be shipped around a lot. To the extent that this is true, companies that offer these services may be driven to integrate more closely with customer data. This may involve custom in-house deployments or ways of getting the data in a cost-effective way from, say, Amazon S3, or wherever the data lives (HDFS, etc). This leads me to speculate on an additional business model, "Behind The Firewall" Software Deployment. This could be somewhat different from the others suggested in the article: 1. Sell hardware; 2. Open source plus services; 3. Hosted API, “Deep Learning as a Service”; 4. Individual deep learning services.

agibsonccc 12y ago
I think there is something to be said for the "deep learning gold rush" I'm one of the ones trying to cash in myself by being an independent player in the space with my own distributed deep learning framework[1]. The goal of data accessibility can be solved by an abstraction layer that auto vectorizes (transforms in to matrices) the needed data at runtime, trains the nets on that particular mini batch of data, and continues on. That's what I'm trying to do with a concept of a DataSetIterator[2]. This understands how to pull in the data, and handles all the logistics while the runtime only knows about DataSetIterators. I'm also partnering with a former cloudera engineer in the hadoop space to take on in process YARN deep learning[3]. Data should not be moved. It should be processed and left where it is. I'll be interested to see the innovations in this space in the coming years. I don't believe deep learning as a service is the way to go, I think behind the firewall deep learning apps will be the way to go here. [1]: http://deeplearning4j.org/ http://deeplearning4j.org/ [2]: http://deeplearning4j.org/customdatasets.html http://deeplearning4j.org/customdatasets.html [3]: https://github.com/jpatanooga/Metronome https://github.com/jpatanooga/Metronome

dj-wonk 12y ago

For many (most?) analytics tools, the application developer has to specify what gets recorded and what gets fed as an input to predictive analytics. I expect to see more tools that assume any data is fair game; e.g. all data is worth at least a quick look. These tools will probably figure out how to select what is interesting enough to examine more deeply.

mirador 12y ago

Agreed, was unaware that there was a gold rush, anyone know about specific companies in this space that are still active?

agibsonccc 12y ago
Ersatz Labs is in the space, and we're in the process of launching as well [wait till tomorrow ;) ] At the end of the day, companies that focus on apps will be the ones that get bought or cash in. I don't think "machine learning" appeals to businesses. 30% more revenue because of a more accurate model sounds pretty good to me though. [1]: http://www.ersatzlabs.com/ http://www.ersatzlabs.com/

deleted 12y ago

[deleted]

Irishsteve 12y ago

ARGHHHHHHH

arek2 12y ago

The only gold I see in that gold rush is from acqui-hire.

dave_sullivan 12y ago

If you believe a few assumptions: 1. Machine learning will come to play a more important role in the future (not less) 2. Machine learning is a wide field with many areas of complexity and a wide variety of applications, many not yet discovered 3. Deep learning is an exciting area in machine learning research showing promising (state of the art?) results in several domains Then I think a few conclusions follow: 1. New tools will address this market, both free and commercial. 2. Services will be a major part of this ecosystem (see history of databases, ERP, CRM) and those consultants both use and sell tools (applications) 3. Deep learning is interesting, quite possibly worth using, and changing rapidly. But it doesn't negate what came before it (or after). So the answer here is all of the above models will be important, with the application of machine learning to everything really being the larger umbrella opportunity, and deep learning currently being an interesting avenue of research?

arek2 12y ago
Does your company have customers?
- dave_sullivan 12y ago
  Yes

usav 12y ago

kaggle++