
Deep Learning Business Models - beaucronin
http://npbay.es/deep-learning-business-models.html
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motters
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.

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robert_tweed
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.

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agibsonccc
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/)

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beaucronin
Blogspam? I demand satisfaction!

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agibsonccc
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.

~~~
agibsonccc
Before I get bombarded here, I think it's best to clarify that the word
blogspam is a term thrown around a lot around here.

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.

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dave_sullivan
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
Does your company have customers?

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dave_sullivan
Yes

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BenderV
"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 ^^'

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p1esk
I think you're confusing deep learning with strong AI.

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visarga
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.

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StandardFuture
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.

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dang
> 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".

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dj-wonk
> 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
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)

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dj-wonk
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.

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arek2
The only gold I see in that gold rush is from acqui-hire.

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

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agibsonccc
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/)

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usav
kaggle++

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Irishsteve
ARGHHHHHHH

