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The beginners guide to understanding artificial intelligence, machine learning and deep learning.

Artificial Intelligence (AI) has made the transition from once being a glorious manifestation of sci-fi imagination to today emerging as a technological reality capable of disrupting industries. However, technologies—including machine learning, deep learning and artificial intelligence have become buzzwords used far too interchangeably. This quick jargon buster will help ensure you aren't using any AI terms interchangeably anymore!

What is Artificial Intelligence?

A branch of computer science dealing with the

simulation of human level intelligence in machines. 


How Does Machine Learning Relate to AI?

Machine learning (ML) is a subset of AI and refers to a machine’s ability, through the use of algorithms, to consume data and make predictions in a similar way to humans.

Machine learning and deep learning are everywhere. It’s how Netflix knows which show you’ll want to watch next or how Facebook knows whose face is in a photo.

As far as human involvement is concerned it can do this in a supervised, unsupervised and/or reinforced way.

Deep Learning

In practical terms, deep learning is just a subset of machine learning. It technically is machine learning and functions in a similar way (hence why the terms are sometimes loosely interchanged), but its capabilities are different.

A deep learning model is designed to continually analyze data with a logic structure similar to how a human would draw conclusions. To achieve this, deep learning uses a layered structure of algorithms called an artificial neural network (ANN). The design of an ANN is inspired by the biological neural network of the human brain.

This makes for machine intelligence that’s far more capable than that of standard machine learning models.


Neural networks

The neural networks we hear so much about these days are a novel way of processing large sets of data by teasing out patterns in that data through repeated, structured mathematical analysis. The method is inspired by the way the brain processes data, 

Different types of neural networks are widely used to train algorithms via methods of supervised and unsupervised learning.

Definitions of artificial intelligence begin to shift based upon the goals that are trying to be achieved with an AI system. Generally, people invest in AI development for one of these three objectives:
1. Build systems that think exactly like humans do (“strong AI”)
2. Just get systems to work without figuring out how human reasoning works (“weak AI”)
3. Use human reasoning as a model but not necessarily the end goal

What is the difference between Artificial Intelligence, Machine Learning and Deep learning?

Artificial intelligence is an umbrella term machine learning is the technique that powers AI technology.
While traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction.

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