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The Future of Deep Learning for Your Business

Tom Hoblitzell | | August 6, 2019

The future growth of deep learning holds many exciting possibilities for businesses. Deep learning is a type of machine learning where artificial neural networks mimic the structure and learning processes of the human brain. This type of learning provides some unique advantages over other types of machine learning.

Unlike other forms of machine learning, deep learning algorithms can process data that comes in the form of text, images, or sounds – areas that until recently were difficult for computers to understand. Previous methods of teaching computers how to understand these datasets required extensive human supervision, yet still produced poor results. Deep learning can learn to recognize and read this data better and requires little or no human supervision. Once set up, the deep learning algorithm’s main requirement is data: the more it receives, the better it gets.

Deep learning is already being used in many different ways. Existing applications that make use of deep learning include chatbots, facial recognition software, and translation software, including Google Translate. Autonomous vehicles also use deep learning to help them process and understand the video images they receive.

What Does Deep Learning Mean for Your Business?

The proliferation of open-source deep learning frameworks (Caffe, TensorFlow, PyTorch) and the diminishing costs and wide availability of distributed computing mean that deep learning is becoming available to a wider range of businesses. You no longer need to be a Google or Facebook to start using deep learning, although there are still a few challenges.

One of the key barriers holding smaller businesses back is the massive requirement for data. Google and Facebook’s domination of deep learning isn’t just because of their deep pockets – they also have huge data sets to draw on. It doesn’t matter how sophisticated your algorithm is if you don’t have enough data to feed it.

Another challenge is the high demand for machine learning expertise. The huge leaps made in this field have substantially increased its complexity. Consequently, it has become increasingly difficult for the average developer to jump in and get started. Nurturing this expertise on your own teams will be essential.

What Does Deep Learning Mean for the Future of Analytics?

Recent advances in deep learning have illustrated both the huge upside and potential challenges of using deep learning. In particular, recent work has shown just how important it is that the data provided to the learning algorithm is high-quality and free of bias.

A recent study found that autonomous cars were less accurate at recognizing pedestrians with darker skin tones, putting some people at increased risk of being hit by a vehicle. This is a clear example of how a flaw in a dataset could have real-world consequences, including loss of life. Now more than ever, businesses must be aware of what data they are feeding their algorithms and how the quality of that data affects the results they receive.

What Benefits Can We Expect from Deep Learning in the Future?

Despite the challenges, deep learning holds huge potential. The ability for computers to read and understand text, images, and audio offers the possibility of many exciting new technologies.

Possibilities include:

  • Superior descriptive technologies will enable the blind to “see” both the real world and the web better.
  • Increasingly accurate real-time language translation will unlock barriers to communication and make global business easier.
  • Facial recognition software will advance considerably – will we see a time where cameras will track our shopping, recognize our faces, and charge our account automatically?
  • Improved diagnostic systems will help doctors treat patients more effectively.

What’s Next for Your Business?

The diminishing cost and increasing availability of cloud computing puts deep learning within reach of many more businesses than before. Businesses should act now to research potential applications within their field.

The relatively high cost (now coming down) means that many industries have yet to tap the advantages of deep learning and there is an opportunity to get a first-mover advantage over competitors.

What possibilities are there for creating value in your industry? And how will you need to improve your infrastructure and personnel to take advantage of them? If you don’t consider these questions soon, your competitors may beat you to it.

 

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