Deep Learning Business Models
npbay.es
npbay.es
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.
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.
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.
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.
When the press talks about an academic paper, they tend not to add much value in terms of actually explaining what's going on. I think people drawing their own conclusions from the original works is the better way to go. Obviously not everyone will agree with me here, but so be it.
- students of Hinton/LeCun/Bengio/Ng, and/or
- members of Google/Facebook/Baidu's deep learning group.
An actual deep learning expert can have unrivalled access to infrastructure, excellent colleagues, data, and compensation, at any of Google/Facebook/Baidu etc.
Given this backdrop, it's less surprising that a significant number of the current crop of deep learning companies are unimpressive technologically - those with actual expertise have much better opportunities.
Do you feel that talk is overly optimistic/misleading? Or just that you don't think there's that much money to be made with it?
... and that human error rate is also > 0. Sometimes machine learning bests the humans.
It's not another machine learning algorithm, it's a feature learning algorithm. You can feed raw pixels into an SVM, but you aren't taking advantage of the structure of the data at all.
UPDATE: My commentary may have conflated "data analytics" with "deep learning". I probably should disentangle them.
That would be a big deal IMHO.
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?
Funny when you think about it : Deep learning is supposed to avoid that ^^'
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.
Right. We changed it. The submitted title was "Business models in the deep learning gold rush".
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.
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/