## Simply Practical Learning

### want to know exactly what Artificial Intelligence is?

## The big reveal, don't be too disappointed....

#### Artificial Intelligence

Ai is the ability for a computer program to be created from data rather than data being created from coding an application.

#### Take the humble calculator

The program understands that when an input number is followed by a plus symbol then another input number, the result is the addition of those two numbers. Simple.

#### Ai works differently

Machine Learning seeks to understand all the data and all the connections in that data to create a predictive model instead of a program. Tis is something that would take a Human a little bit longer than a machine.

#### How do we know Ai works?

Machine Learning models typically use usually 75% of the data for training and hold back the rest of the data to test the validity and credibility of the model being used.

It checks the predicted outcomes of the new model created with the held testing data of real world outcomes to check the accuracy of the model and more importantly the predictions.

Even better, as new real world data is introduced, the model retunes itself giving more and more accurate results over time.

#### We have models, Now What?

Once a model has been created and deployed in the real world, it works like a traditional program; when new data points are fed into the model an outcome is predicted.

#### OK but still, How does it work?

So in our calculation example instead of knowing the rule that 1 + 1 = 2, instead, we build a model from the data which might look something like this

Row 1 – Input 1, Input 2, Result, Outcome.

When we have 1000’s of examples of these rows showing what inputs give what result and whether this result is correct or not, then we can, with some accuracy understand that when input 1 is “1” and input 2 is “1” then the result is (nearly) always “2”

Row 0 - 1, 1, 2, true

Row 1 - 1, 1, 2, true

Row 3 - 1, 1, 2, true

Row 4 - 1, 1, 2, true

Row 5 - 1, 1, 2, true

Row 6 - 1, 1, 2, true

Row 7 - 1, 1, 7, false - Data is rarely 100% accurate and doesn't need to be with ML.

Row 8 - 1, 9, 2, false - Inputs as well as outputs may vary.

Row 9 - 1, 1, 2, true

So instead of being programmed to understand that 1 + 1 = 2 we can now infer from the real world outcomes, that we know to be correct, in most cases where there are two inputs, 1 and 1, the expected result is 2. Simple.
This example is called a Binary Classification

#### Asking a Question of Data

All we need is a spreadsheet of historical sales data showing under what circumstances our customers actually buy – for example:

#### Getting Predictions

From here we can create a model and predict if those companies who are not yet customers will buy from us and when our customers are predicted to buy and how much they will spend. So you now have all the information you need to concentrate your efforts and resources in order to maximise your growth!