When you “work through” a problem or issue that requires a decision, you likely feel as if you’re going through a linear checklist. But that’s not how the human brain operates; it processes in a non-linear pattern. And this is essentially how deep learning, a subset of artificial intelligence (AI), works too.


Copyright: weforum.org – “How deep learning can improve productivity and boost business”


Deep learning works like the human brain

Deep learning, at its essence, learns from examples — the way the human brain does. It’s imitating the way humans acquire certain types of knowledge. Because deep learning processes information in a similar manner, it can be used to do things people can do – for example, learning how to drive a car or identifying a dog in a picture.

Deep learning is also used to automate predictive analytics – for example, identifying trends and customer buying patterns so a company can gain more customers and keep more of them. You know those sections on retail sites that show items “frequently bought together” when you’re purchasing a new screwdriver? Those are based on predictive deep learning algorithms that have considered both your current search and past buying patterns to suggest additional products you might also need.

Other applications include numerous everyday encounters and activities, such as virtual assistants, fraud detection, language translation, chatbots and service bots, colourization of black-and-white images, facial recognition and disease diagnoses.

A simple example of a neural network’s application is in parsing speech. The network takes sounds from raw audio, which combine to make syllables, which combine to make words, which combine to make phrases that prompt actions. The machine learns that this particular sound means that it should pull up a credit card balance and the more times it’s asked the same thing, the more accurate it gets.

Deep learning has applications across industries

Neural networks are not new; they’ve been around since the 1940s. In 1943, two computer scientists introduced models of neurological networks, recreated threshold switches based on neurons and showed that even simple networks of this kind are able to calculate nearly any logic or arithmetic function.

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The first computer precursors were developed by a computer scientist who was tired of calculating ballistic trajectories by hand. Today, more than 70 years later, deep learning has exploded in sophistication and use, primarily because of expanded computing power (along with greatly reduced costs per unit of power), better modelling and the availability of data. Deep learning requires massive amounts of data. Currently, it’s estimated that the data we generate every day is 2.6 quintillion bytes. And it can analyse massive datasets far faster than a human. Machines don’t suffer from monotony or fatigue.[…]

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