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AI stands for artificial intelligence. The words “Artificial Intelligence” are used a lot. As a result, it can be difficult to determine what AI is (and what it isn’t).  One definition that works well is this:

"AI is a computer doing something that we believe humans can do better."

What people deem as AI today, may not be what we describe as AI tomorrow. As such, AI is a moving goalpost. In other words, some people may describe a certain technology as AI, while others may choose not to describe the same technology as AI. Moreover, what people deemed as AI twenty years ago, may no longer be considered AI. However, there are some things that most people currently universally consider to be AI: “Computers drawing images from a description,” “Computers writing music given a prompt,” “Computers writing an essay,” and even “Computers driving a car.”

Note: This explanation was inspired by . For a deeper exploration of this topic, please see .

So far we know that AI is a moving goalpost, and AI encapsulates all those things that a computer does that we believe a human can do better.

Machine Learning (ML) describes one category (and a large one!) of artificial intelligence. In order to define ML, it is helpful to consider two possible approaches to getting computers to do human-like tasks. We will call these approaches (1) knowledge-based AI, and (2) example-based AI. For the sake of an example, let’s consider one task that humans complete fairly well: looking at X-ray images of bones and determining whether there is a fracture present (whether the bone is broken).

Consider Approach 1: Knowledge-Based AI

With this approach, computer-scientists provide all of the rules associated with what a fracture might look like. After speaking with many radiologists, orthopedic surgeons, and X-ray technicians, a computer scientist determines exactly what will warrant a fracture on an X-ray image. They then painstakingly program all the details of what to look for when given an image, and hope for the results of their program to produce accurate results.

The approach sounds like it should work. The thought goes, in order to learn how to do something well, talk to the experts, the people with the knowledge, and learn from them. This approach was taken by early computer scientists in the 1960s, 1970s and 1980s.

Unfortunately, this approach did not work very well. Every time computer scientists thought they had a system that could accomplish a task such as the bone-fracture classification problem, their system would make a mistake. They would speak to the experts about the mistake, and they would learn that there was something that the experts forgot to mention during interviews. The computer scientists would learn that there was an exception to the rule. There was something that they had not explicitly told the computer to look for, and, as a result, the computer would classify an item incorrectly. Artificial intelligence seemed to be impossible. Knowledge-based AI, the leading approach to making intelligent machines, had failed.

Then, in the late 1980s and early 1990s, computer scientists tried a different approach. They tried approach 2. They tried example-based AI.

Consider Approach 2: Example-Based AI

With this approach, computer scientists provided machines with lots and lots of data and hoped the machine could use mathematical feedback systems, similar to how neuroscientists believed our brains learned, to find a pattern.

Let us return to our X-ray example. Imagine thousands of X-ray images of bones were given to the computer along with the diagnosis that had been provided by physicians. It would look like this: “Here is picture 1. It is a fracture.” “Here is picture 2. It is a fracture.” “Here is picture 3. It is not a fracture.” And so on. No rules were given to the computers on how to accomplish a task. Instead many, many examples of the task being done correctly were given to the computer, and it was asked to determine the pattern itself.

Did the new approach work? Simply put, the results were astounding. The system worked; it worked really well. This new type of AI, example-based AI, seemed to be the ticket to success! In fact, it was so successful it was given a new name: Machine Learning. Machine Learning is example-based AI, the approach to building intelligent computer systems simply by giving the system data and letting it mathematically determine the patterns that exist.

Computer scientists like to use this word a lot in the world of machine learning. For example, you might hear something about the designers of ChatGPT, Claude, or Gemini having developed a new model that is more powerful than their last model, and wonder, “what even is a machine learning model?”  Simply put, a “model” is a system that has been created to take input values and produce an output prediction.

Consider the following prediction-system that may be created to help a realtor determine a house price, given the number of bedrooms that a house has. Here, the input value for the system is “number of bedrooms” and the output prediction is “house value.”

To build such a system, a realtor may collect some data and put it on a simple two-dimensional graph, with the horizontal axis representing the number of bedrooms, and the vertical axis showing the house values for houses that have sold in the past in a given market. The data might look like this:

From this data, a realtor could create the prediction system by trying to match the pattern seen in the data. One such system might look like this:

Now, this particular prediction-system is one you may be familiar with: it’s a line of best fit. This prediction-system is a model. In fact, it is one of the simplest machine learning models we can make. 

What makes a model like this useful, is that we can make predictions about new data, simply by using our previous data and pattern, and a new input variable. In this case, a realtor may want to sell a house in the market that is represented by this model, and by providing the number of bedrooms, they can predict the selling price of the house. This might look like this:

Notice that there was no previous data on the cost of house for the input number of bedrooms, but the model was still able to make a prediction. This is a simple example of a machine-learning model making a prediction. A more complex model could use two variables as inputs, say, number of bedrooms and number of bathrooms, to make a prediction about house price. An even more complex example could have ten input variables to make a prediction. Very complex models may have thousands or even millions of input variables to help make a prediction. Even though we can’t visualize this easily on a graph, we hopefully can understand how input variables, accompanied by a model, can help us make predictions.

AI and Machine Learning is used everywhere to help humans make decisions. Because computer scientists have helped computers learn patterns from large amounts of data, they can provide large amounts of data on any problem and build models to help make predictions. Below are some of the areas where AI helps inform societal decision making.

Disaster Relief and Resource Allocation

Humans can provide computers with drone footage of flood areas, where humans have labeled pictures of building structures as completely destroyed, partially destroyed, or undamaged. AI models can be built to be used after a storm hits, and feed unlabeled pictures of structures taken from drones. Models can easily predict which pictures represent the most damaged homes and prioritize sending more aid to these regions, even without humans asking for help. Click here to learn about how AI can assist with resource allocation during disasters.

Medical/Public Health

Humans can provide computers with information about patients along with diagnoses and create models that can predict a correct diagnosis for a patient simply given their presenting symptoms. Another model could be used to predict the way that diseases spread in a population given information about past diseases and the patterns that are learned. . 

Transportation

Humans can provide information from vehicles with sensors that record how cars successfully drive on a road given a set of conditions and create a driving model. This model can be used for a car to predict how to drive itself. . 

To better understand AI, it is helpful to understand the terminology used. Here are some key terms to get you started:

• Big Data: Complex datasets that exceed the processing capacity of traditional data management tools. Big data is characterized by volume (a lot of data), velocity (the high speed of data creation and processing), and variety (diverse data types like text, images, and video).
• Input: What goes into our model to help us predict, often called the features (ex. number of bedrooms in a house)
• Black box: In engineering, the term is used to describe any device whose inner workings are hidden from the user. In ML, this may be a particular algorithm or set of steps, whose inner workings are mysterious to the user. A black box allows us to hide unimportant details and is very important for human problem solving.
• Output: The prediction from our model, often called the label (ex. the price of the house)
• Model: The “machine” associating the input to a predicted output by finding the pattern in the data.
• Training: When a model is being built, and data is provided to help it learn the pattern, we say the model is being “trained”. 
• Inference: When a model is being used to make a prediction about data it has not yet seen (or has not been trained on), we say the model is making an “inference”.

For a more comprehensive list of industry terms, please refer to .

Note: While many of these are available to the general public, some of the links below require subscription accounts.

The Fourth Industrial Revolution and Internet of Things (IOT)
Case Study: AI in the Film Industry
Human Decision Making and Sports