What is AI? A Primer

In this blog series, I’ll explain what AI actually means, and then move on to its little sister, ML. This first article will also illustrate ML with an example from medical diagnostics. The second article will focus on confusingly similar “imposters”, which many would label as artificially intelligent, although they’re not. In the last article, I’ll finish the series with an overview of the challenges regarding Machine Learning.

What It Is

Artificial Intelligence (AI)

Generally speaking, something is said to be artificially intelligent if it can react appropriately and optimally to new input over time, particularly complex input. A common, if extreme, example of this would be for the reactionary behavior of a robot to be indiscernible from that of a human. This behavior is also not static, meaning that just as humans grow and adapt as the world changes, so must the machines. A hallmark feature of this adaptation is a lack of manual intervention for any sort of tuning. An artificially intelligent robot would naturally learn how to react in the 21st century through experience, rather than a 21st century update patch.

Machine Learning (ML)

Machine learning is a subset of artificial intelligence that pertains to math, science, and core processing. It is the algorithm or set of algorithms that determine which reaction is appropriate and optimal given a set of inputs. Importantly, the algorithm is also in charge of updating or tuning responses based on new input and experiences. Machine learning algorithms can be used to answer questions like “Does this person have this disease?” or “What is the optimal move to make in this chess game?”

How It Works Technically

Let’s consider the disease example from above and assume we’re trying to determine if someone has the flu. There are an indescribably large number of factors that could go into making a determination. Some simple factors might be age, location of pain, duration of symptoms, etc. with more complicated factors being genetic disease history and other biomarkers. The algorithm typically starts off with some commonsense approach. For this example, let’s say that our starting rule is:

People who have a cough, congestion, and runny nose have the flu.

So far this is the thought process a doctor might take. But you might ask “Couldn’t this person just have the common cold?” So we bring more factors into the decision, such as if the person has gotten their flu shot (which would trend towards a cold diagnosis) or if they also have a fever (which would trend toward a flu diagnosis). Eventually we’ll be left with all of the input information about the patient (whether it was used to make a diagnosis or not), and the diagnosis itself, or the output information. Gather up these inputs and outputs from doctors around the world and we have ourselves a statistically significant sample data set.