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Artificial Intelligence (AI), machine learning, and deep learning have been playing a ground-breaking role in the development of security and defense systems for the last few years, in fields such as robotics and unmanned systems, video analytics, intelligence, etc. However, these terms often overlap and are easily confused. Blogs.oracle.com makes the definitions of these terms clearer.
AI means getting a computer to mimic human behavior in some way.
Machine learning is a subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications.
Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems.
Artificial intelligence as an academic discipline was founded in 1956. The goal then, as now, was to get computers to perform tasks regarded as uniquely human: things that required intelligence. Initially, researchers worked on problems like playing checkers and solving logic problems.
Artificial intelligence refers to the output of a computer. The computer is doing something intelligent, so it’s exhibiting intelligence that is artificial.
In the 1980s, a new category of AI techniques started becoming more widely used is machine learning: Mimicking how humans learn. Feed an algorithm (as opposed to your brain) a lot of data and let it figure things out.
As these algorithms developed, they could tackle many problems. But some things that humans found easy (like speech or handwriting recognition) were still hard for machines. However, if machine learning is about mimicking how humans learn, why not go all the way and try to mimic the human brain? That’s the idea behind neural networks.
The idea of using artificial neurons (neurons, connected by synapses, are the major elements in your brain) had been around for a while. And neural networks simulated in software started being used for certain problems. They showed a lot of promise and could solve some complex problems that other algorithms couldn’t tackle.
However, simple neural networks with 100s or even 1000s of neurons, connected in a relatively simple manner, just couldn’t duplicate what the human brain could do. It shouldn’t be a surprise if you think about it; human brains have around 86 billion neurons and very complex interconnectivity.
Deep learning is all about using neural networks with more neurons, layers, and interconnectivity. How does it work? If I give you images of horses, you recognize them as horses, even if you’ve never seen that image before. You can recognize a horse because you know about the various elements that define a horse: shape of its muzzle, number and placement of legs, and so on.
Deep learning can do this. And it’s important for many things including autonomous vehicles. Before a car can determine its next action, it needs to know what’s around it. It must be able to recognize people, bikes, other vehicles, road signs, and more. And do so in challenging visual circumstances. Standard machine learning techniques can’t do that.
To conclude, AI refers to devices exhibiting human-like intelligence in some way. There are many techniques for AI, but one subset of that bigger list is machine learning – let the algorithms learn from the data. Finally, deep learning is a subset of machine learning, using many-layered neural networks to solve the hardest (for computers) problems.