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Artificial intelligence (AI) brings with it a promise of genuine human-to-machine interaction. When machines become intelligent, they can understand requests, connect data points, and draw conclusions. They can reason, observe, and plan. Consider:

Clearly, we’re not talking about robotic butlers. This isn’t a Hollywood movie. But we are at a new level of cognition in the artificial intelligence field that has grown to be truly useful in our lives.

We get it, though. You’re still confused about how all these topics – AI, machine learning and deep learning – relate. You’re not alone. And we want to help.

In this article we’ll explore the basic components of artificial intelligence and describe how various technologies have combined to help machines become more intelligent.

The history of AI and machine learning

So where did AI come from? Well, it didn’t leap from single-player chess games straight into self-driving cars. The field has a long history rooted in military science and statistics, with contributions from philosophy, psychology, math and cognitive science. Artificial intelligence originally set out to make computers more useful and more capable of independent reasoning.

Most historians trace the birth of AI to a Dartmouth research project in 1956 that explored topics like problem solving and symbolic methods. In the 1960s, the US Department of Defense took interest in this type of work and increased the focus on training computers to mimic human reasoning.

For example, the Defense Advanced Research Projects Agency (DARPA) completed street mapping projects in the 1970s. And DARPA produced intelligent personal assistants in 2003, long before Google, Amazon or Microsoft tackled similar projects. This work paved the way for the automation and formal reasoning that we see in computers today.

Artificial Intelligence and Machine Learning

While AI is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine how to learn. Watch this video to better understand the relationship between AI and machine learning. You'll see how these two technologies work, with examples and a few funny asides.

Machine learning and deep learning are subfields of AI

As a whole, artificial intelligence contains many subfields, including:

While machine learning is based on the idea that machines should be able to learn and adapt through experience, AI refers to a broader idea where machines can execute tasks "smartly."

Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems.

How big data plus AI produced smart applications

Remember the big data hoopla a few years ago? What was that about? Advancements in computer processing and data storage made it possible to ingest and analyze more data than ever before. Around the same time, we started producing more and more data by connecting more devices and machines to the internet and streaming large amounts of data from those devices.

With more language and image inputs into our devices, computer speech and image recognition improved. Likewise, machine learning had much more information to learn from.

All of these advancements brought artificial intelligence closer to its original goal of creating intelligent machines, which we're starting to see more and more in our everyday lives. From recommendations on our favorite retail sites to auto generated photo tags on social media, many common online conveniences are powered by artificial intelligence.

Where are we today with AI?

With AI, you can ask a machine question – out loud – and get answers about sales, inventory, customer retention, fraud detection and much more. The computer can also discover information that you never thought to ask. It will offer a narrative summary of your data and suggest other ways to analyze it. It will also share information related to previous questions from you or anyone else who asked similar questions. You’ll get the answers on a screen or just conversationally.

How will this play out in the real world? In health care, treatment effectiveness can be more quickly determined. In retail, add-on items can be more quickly suggested. In finance, fraud can be prevented instead of just detected. And so much more.

In each of these examples, the machine understands what information is needed, looks at relationships between all the variables, formulates an answer – and automatically communicates it to you with options for follow-up queries.

We have decades of artificial intelligence research to thank for where we are today. And we have decades of intelligent human-to-machine interactions to come.


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