<h1><span style="font-family: inherit;">Deep Machine Learning; Some Numbers</span></h1>

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"Sum" numbers?

Yes, although not exactly indeterminate, the amount of currently known A.I. research dealing with deep machine learning is actually pretty sparse - even more so with tools used for sustainability purposes.

As such, I was only able to retrieve a small set of numbers dealing with the cost of developing deep learning technology. Most of which are only indirectly related to the past sustainability tools I have mentioned.

With the information I have found however, there is one thing I can say for sure - I can do a lot with that kind of money.

Now, before I continue, I would like to say that it is going to seem like I am drifting from sustainability a bit. This post is going to be about a different kind of green but trust me, the dots will connect eventually.

So how much we talking?

Let me rewind a little bit.

Back in 2014, a startup firm based in London by the name of DeepMind Technologies, was bought out by Google.

Google. (Let that sink in.)

Now get this - they acquired this startup firm for about $400 million.

Okay, but for a startup?

Yes! Not surprisingly, this firm was not bought simply for its technologies. Although DeepMind Technologies was a firm that focused on deep learning, it was mainly bought for its group of expert A.I. researchers.

As I have hinted at before, deep learning is a relatively new field of computer science. Most articles of research that have been published about deep learning and artificial intelligence date from the last few months to last year (2014). 

Therefore, the experts in this field are limited. So rare, in fact, that, 

"..the cost of a top, world-class deep learning expert was about the same as a top NFL quarterback prospect. The cost of that talent is pretty remarkable. " 

This was said by Peter Lee, who heads Microsoft Research. They are in such demand that they command the same seven-figure salaries. 

And that was only for the researchers.

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What about the other stuff?

Computational hardware needed for deep learning are definitely not going to be cheap. Even today, buying a laptop with good specs costs $1000 easily. You are going to need more than that for A.I.

Clearly only businesses like the tech giants Facebook, Microsoft and Google can afford all of this. 

Andrew Ng, a chief scientist at Baidu Research in Silicon Valley, worked  a project at Google that dealt with deep learning. The objective was to build a computerized brain that could recognize cats in videos. 

Although he was successful, the system in which he used was a roughly 1-billion connection network trained on 1,000 computers. The system cost about $1 million. Another source says that Google used 16,000 machines to build this stimulated brain. If you are not getting the picture, this was only to detect cats on videos, specifically. 

Essentially, to do anything else, you're going to need a whole lot of money.

Now what about making this stuff?

Anytime you have to make anything - there is the issue of sustainability of the materials used. With computers and A.I., how does that work? I plan to address this, as well as a more positive outlook of A.I. in my next  post.

Stay tuned.