There are many different lenses through which AI can be viewed – for example, economic, social, business or technical.

Here we will focus on what AI is from a technology perspective.

Let’s start unpicking AI by building a metaphor for it. Imagine that you are driving a very smart combine harvester.

As it cuts through the crop it applies AI – or machine learning (ML) to be more precise – capabilities.

The first thing it does is classifies the crop in terms of whether it is wheat or chaff. The wheat is separated into a tank and the chaff is blown out of the back of the combine harvester.

Next, it looks for anomalies – are there other species of plants in the crop?

Then it analyses the size of the grains, and clusters them into groups – something that could be useful when combined with geographic and soil analysis data.

Finally, it can forecast ahead. It can use regression analysis to predict the grain sizes and the overall yield.

Figure 3. A combine harvester as an analogy for AI and machine learning

Driving the Combine Harvester is a diesel engine which in turn drives an electrical generator, which powers its sensors, computing and communication capabilities.

We can think of AI as the Combine Harvester and the crop operating in the field, and machine learning as the engine that drives the ’intelligence’.

It’s really important to remember that the point of machine learning is to make predictions.

In the case of a combine harvester the outcomes could be a fully automated machine, real-time information to the markets, and information to form the basis of future crop planning.

To understand what AI is, we first need to understand how it is positioned against other related domains.

WHERE IS AI ?

Figure 4. Where is AI?

Artificial Intelligence (AI) is a branch of predictive analysis within the broader world of data.

Data is the basis of reasoning or calculation.

Big Data means large data sets that may be analysed to reveal patterns, trends, and associations.

AI is a computer system able to perform tasks normally requiring human intelligence. machine learning is a method for achieving AI.

Deep Learning is a form of machine learning based on neural networks that use many layers of processing to extract features from data.

In AI Demystified we are going to focus on the ‘engine’ that drives AI – machine learning - which can be defined as a field in Computer Science that is focused on enabling computers to learn.

Figure 5. We will be focusing on AI on this part of the course