4 Deep Learning Breakthroughs for Business Leaders

Deep learning, a set of techniques at the forefront of AI, is a great place to begin to understand how new developments can help business executives solve their specific challenges.


Gaining an understanding of deep learning is the best place to start for executives interested in keeping up with the rapid developments in AI. An exciting and powerful subfield of AI, deep learning focuses on how computers learn as opposed to how they are explicitly programmed by us.

In this method, researchers place concepts into a hierarchy. At each layer, a machine learns a concept and passes it to the next layer, which in turn uses it to build a more sophisticated concept. The more layers these models have – or the ‘deeper’ they are – the more concepts they can learn, leading to ever-expanding possibilities of applying AI to business problems.

 Here are four deep learning breakthroughs business leaders should be aware of, from the immediately applicable to the most cutting edge. Drastically different applications prove that these can be used innovatively to introduce the next great product or service based on the right set of data.

1.     Image understanding – Deep learning algorithms called convolutional neural networks, which can be trained to identify objects in an image, already do better in image classification than humans.

Applications:

·       Google Image Search

·       Self-driving cars

·       Disease diagnoses

 

2.     Sequence prediction – Recurrent neural networks can be trained to look at huge amounts of past sequences of characters or data, learn their patterns and generate future sequences.

Applications:

·       Producing human-like handwriting

·       Prediction of user demand by Uber

 

3.     Language translation – Discovered in 2014, this technology uses sequence-to-sequence architecture and recurrent neural networks to make machine translation almost as good as human translation. So far used for narrowly defined domains, this area holds significant promise.
Applications:

·       Google Translate and Apple’s Siri

·       Chatbots

4.     Generative models – Creation of models that generate complex data, like images that resemble faces but aren’t actual faces. This is possible due to architectures called generative adversarial networks, which use convolutional neural nets. Though their business applications are limited as of now, research is on to find exciting ways to deploy their power to solve practical challenges.

Applications:

·       Aiding image classification models to distinguish real images from fake ones

 

Open source implementations of the above breakthroughs make it possible to download pre-trained models to apply to your data. For example, pre-trained image classifiers can be purchased to feed your data through to classify new images.

This overview brings you closer to companies like Uber and Google that put deep learning models to good use. The next generation of business applications is yet to come, and will be driven by new ways discovered by you to apply these techniques to your own data.


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