"AI" is a buzzword ...

... the basic concept is prediction.

There are two types of people: those who think artificial intelligence (AI) is mystical, and those who know how to create it. The former, the majority of the population, have created a buzzword around the concept, and probably do not realize that AI is usually not necessary in order to successfully complete the task at hand. What should educators be teaching learners about AI?

It all lies in the definition. Artificial intelligence is just prediction, but where the model has been tested and tuned for greater accuracy. A simple prediction, using thumb-suck parameters, would yield an accurate result. In comparison, training the model takes significant resources, so this is only necessary in a long-term setting, where the model and task have been clearly defined, and there will be cost-efficiencies when running the model repeatedly.

A predictive model brings probability into the system. The primary objective of an AI algorithm is to predict, accurately (regressions focus more on causality than other algorithms). During the training step, the values for the parameters that yield the most accurate results are chosen. Hence, using these ideal parameter values when sending the algorithm out in the wild on unseen data should give better predictive results, without the heavy, process-intensive training time. This is machine learning.

Do I use AI in my work?

I can. I know how to train a predictive algorithm, tuning it so that the optimal parameter values are used. However, simply using a predictive model gives a good result, and often it is not necessary to invest time into fine-tuning the accuracy of the prediction, as the change may be small and there can be lots of other work to do in the project.

Some examples of predictive models that I have used include:

• Cross-sectional and panel regressions
  Tuning can be done by selecting significant explanatory variables.

• Time series forecasting
  Tuning can be done by choosing a model with a low AIC value.

• KNN classification
  The number of neighbours can be tuned so that accuracy is maximized when training.

• Fuzzy string matching
  The maximum distance metric between the input string and best match can be tuned to minimize false positives, when training. However, checking the matches is necessarily very manual (possibly intensive), hence training is not necessary.

I have used these models for imputation, forecasting or matching.

So, what do people mean by "AI"?

In public (outside of software development circles) laypeople tend to perceive AI models as having minds of their own. Sure, it can be difficult to describe how some models are structured due to their inherent complexity (the so-called black box) but it is important to remember that software developers know how to create AI models. 

We should appreciate the concern that AI development is proceeding too quickly by understanding what that means. It doesn't mean that androids will wage war against humans using guns, but it does mean that software can independently manipulate the complexity of the internet to influence real world outcomes.