Google’s recent $500 million acquisition of DeepMind, and Facebook’s recent hiring of NYU professor Yann LeCun, a respected pioneer in artificial intelligence (AI) and deep learning,
has a lot of people talking about deep learning. So is deep learning a good
fit for your corporate strategy? In short, it depends on what you’re trying to
accomplish.

Deep learning: What every leader needs to know 

Think of deep
learning as cutting-edge AI that generally represents an
evolution over primitive neural networks. A key distinction between traditional
machine learning and deep learning is the amount of supervision and human
intervention the AI system requires.

Traditional machine
learning techniques, including classic neural networks, need to be supervised
by humans so they can learn. Deep learning is an attempt to have the system
learn on its own, without human intervention. It may sound like science fiction
and rather far-fetched, but we’ve actually experienced success in certain
areas using deep learning, such as image and voice recognition.

Maybe the most compelling reason to consider deep learning for your organization is if it supports a breakthrough strategy,
where you can be competitive or distinctive with traditional machine learning. 

For instance, imagine that you’re trying to decode the digital behavior of customers. You’re in a very competitive space, and your competition
is already making progress in the same pursuit with its big data analytics.
You don’t know what techniques your competitor is using, but you do know their team of data scientists is busy working on advanced algorithms, so
it’s a safe bet that machine learning is part of their arsenal. A competitive
strategy is not an option here–you need to build something fantastic that your
competition can’t match. This is a good time to roll the dice on deep learning.

Deep learning shouldn’t take deep pockets

I believe deep learning is a good
investment; however, you need to do some homework first. Because of the all the
noise about deep learning, this homework won’t be as easy as you might think; you’ll either need to
get outside advice from someone you trust or carefully process all information
through a business leader’s filter.

Whatever you do, don’t turn the decision
over to your data scientists. This is far too intellectually attractive for a
typical data scientist to give you an unbiased opinion. You need to make an
economical decision–you’re running a company, not a research facility. The
problem, though, is the subject matter gets thick pretty fast, so it really
does take a data scientist to fully understand the science and technology
that’s under consideration. So, unless you know someone who has a background in
data science and the mindset of a business person, you’ll have to rely
on your business sense to make sure you’re not getting into a bad
situation.

If you’ve done your homework at a
high level, you should already know your cost threshold for making a smart
investment on big data analytics–this will prevent you from making an
egregious mistake with your money. However, it’s not smart to dump this entire
allotment on deep learning. You should think of deep learning as a high-risk/high-reward
play.

Take five or ten percent of your budget for big data and invest in a deep
learning specialist and maybe a small, dedicated server farm. If
your budget can’t afford this type of investment, reconsider how you’re going
to use deep learning in your corporate strategy. You don’t have to entirely
abandon the idea–just don’t make it an integral part of your strategy. Maybe
use it as a development opportunity for your data scientists to boost morale
and attract top talent. They’ll love it.

Summary

Deep learning is the catnip of data
science these days, but euphoria and hallucinations don’t serve as great
innovation machines. It’s your responsibility to make
sure that you make smart investments with your big data dollars. Deep learning
is one of those areas that can become a huge money pit with very little return.
I’m not suggesting that you stay away from it–just proceed with caution.

The
first step is to understand what you’re dealing with, so take some time today
to investigate. Approach it with an open mind and a business sense. If you find
something interesting and you have the budget, take a chance.

Once in a while
when you go deep, you score a touchdown.