What Is Deep Learning AI? A Simple Guide With 8 Practical Examples

What Is Deep Learning AI? A Simple Guide With 8 Practical Examples

There’s a lot of conversation lately about all the possibilities of machines learning to do things humans currently do in our factories, warehouses, offices and homes. While the technology is evolving—quickly—along with fears and excitement, terms such as , and may leave you perplexed. 

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SwissCognitiveI hope that this simple guide will help sort out the confusion around and that the 8 practical examples will help to clarify the actual use of technology today. Adobe Stock Adobe Stock What is ?

The field of is essentially when machines can do tasks that typically require human intelligence. It encompasses , where machines can learn by experience and acquire skills without human involvement. Deep learning is a subset of where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Similarly to how we learn from experience, the algorithm would perform a task repeatedly, each time tweaking it a little to improve the outcome. We refer to ‘’ because the neural networks have various (deep) layers that enable learning. Just about any problem that requires “thought” to figure out is a problem can learn to solve.

The amount of data we generate every day is staggering—currently estimated at 2.6 quintillion bytes—and it’s the resource that makes possible. Since deep-learning algorithms require a ton of data to learn from, this increase in data creation is one reason that capabilities have grown in recent years. In addition to more data creation, algorithms benefit from the stronger computing power that’s available today as well as the proliferation of Artificial Intelligence () as a Service. as a Service has given smaller organizations access to technology and specifically the algorithms required for without a large initial investment.

Deep learning allows machines to solve complex problems even when using a data set that is very diverse, unstructured and inter-connected. The more algorithms learn, the better they perform.

8 practical examples of

Now that we’re in a time when machines can learn to solve complex problems without human intervention, what exactly are the problems they are tackling? Here are just a few of the tasks that supports today and the list will just continue to grow as the algorithms continue to learn via the infusion of data.

  1. Virtual assistants

Whether it’s or Siri or Cortana, the virtual assistants of online service providers use to help understand your and the language humans use when they interact with them.

  1. Translations

In a similar way, algorithms can automatically translate between languages. This can be powerful for travelers, business people and those in government.[…]

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