The year is 1999, but everyone unknowingly locked in a false parallel world controlled by machines believes it's 2199. Morpheus takes Neo on a journey of discovery to help fight 'The Matrix' and train's him in the martial arts, better to fight the living embodiment of violent software. Neo wakes after an injection of knowledge and exclaims:

"I know Kung Fu"

What if you could do the same for Artificial Intelligence?

Well, you be Neo, I'll be Morpheus, step into my world...

Step 1. There are 3 types of Artificial Intelligence

I sit in my office next to a huge whiteboard. A considerable amount of it is devoted to the nuances of AI, the different behaviours and the best ways to explain these technical concepts in a world where people want clear understanding. AI is a branch of computer science dealing with the simulation of intelligent behaviour in computers.

Some also define it as "the capability of a machine to imitate intelligent human behaviour", but this short-sighted view only serves to define our human limitations, not see the potential in reaching far beyond what is actually possible. But you’ve heard of Artificial Intelligence, Machine Learning and Deep Learning – what are they?

AI is most commonly used in practice to:

· Automate manual, effort intensive processes

· Engage audiences, customers and employees

· Optimise product quality and customer experiences

So, what’s the difference then?

Artificial Intelligence needs to be told what to look for, and has programmed answers to specific questions.

Machine Learning however, learns from programmed knowledge to guess an answer.

Deep Learning learns from previously learned knowledge and probability factor increases with more knowledge.

Machine Learning takes data, processes it and makes a prediction about the correct answer. It then makes a decision based on that prediction, which feeds the results. These results are fed back into the original data to enhance it, making processing and predictions more accurate next time.

This is transforming the financial world to understand spending patterns and market analysis. For example, a trading algorithm designed to process securities on the basis of quantities like historical correlations and the general economic variables is now possible, way beyond what humans or standard computing could ever hope to achieve.

Deep Learning infers common features from reference data about the subject matter, uses artificial neural networks to organise them into layers of hierarchies and relationships, and weighs those features for certainty and commonality.

If you don't have the question yet, but you do have data, then Deep Learning can bring untold advantages. Self-driving cars, face recognition, and document analysis are all enabled by using Deep Learning, and advances in this area are growing rapidly.

AI in Sentiment Analysis was used to incredible effect to understand Storytelling Arcs by the University of Vermont’s Computational Story Laboratory (CSL), by taking thousands of books and millions of words and feeding it into their Machine Learning algorithms. What transpired was a realisation that every book fell into 1 of 6 story arc categories - something posed by Aristotle but now computationally proven.

Step 2. Profit

So Neo, now you know the difference between Machine Learning and Deep Learning, you can be confident discussing the concepts of AI and be more authoritative in your reasoning, plus, now you can take on the Matrix and reign successful in the modern digital world.

As a technologist, I see how AI and the fourth industrial

revolution will impact every aspect of people’s lives.”

– Fei-Fei Li, Director of Stanford University's Human-Centered

AI Institute and the Stanford Vision and Learning Lab

To see how Machine Learning of AI was used to determine story arcs from over 51,000 books online, watch out for another blog post soon!

Have you opened your AIs?