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:
Leaving for a business trip tomorrow? Your intelligent device will automatically offer weather reports and travel alerts for your destination city.
Planning a large birthday celebration? Your smart bot will help with invitations, make reservations and remind you to pick up the cake.
Planning a direct marketing campaign? Your AI assistant can instinctively segment your customers into groups for targeted messaging and increased response rates.
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
Automating Analytics with Artificial Intelligence
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 Defence took interest in this type of work and increased the focus on training computers to mimic human reasoning.
For example, the Defence 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.
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 analyse 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 favourite retail sites to auto generated photo tags on social media, many common online conveniences are powered by artificial intelligence
Real-world benefits of artificial intelligence
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.
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