Get Started with Artificial Intelligence

Learning Objectives

After completing this unit, you’ll be able to:

Explain the importance of understanding fundamental concepts of artificial intelligence.
Identify the challenges that make defining artificial intelligence difficult.
Describe the types of tasks artificial intelligence can perform.
Define the term artificial intelligence.

Trailcast

If you'd like to listen to an audio recording of this module, please use the player below. When you’re finished listening to this recording, remember to come back to each unit, check out the resources, and complete the associated assessments.

Time to Get Fluent in AI

Artificial intelligence (AI) has been a dream of many storytellers and sci-fi fans for years. But for a long time most people hadn’t given AI much serious thought because it was always something that might happen far into the future. Well, researchers and computer scientists haven’t been waiting for tomorrow to arrive, they’ve been working hard to make the dream of AI into a reality. In fact, some have said we’ve already entered the Age of AI.

A closeup of a person sitting at a typewriter, drawn in the style of fun 2D vector artwork.

The scene is in a university classroom, there’s a blackboard in the background with a sketch of a neural network. In the foreground is a college student typing on computer, drawn in the style of fun 2D vector artwork.”

[AI-generated images using DreamStudio at stability.ai. The first uses the prompt, “A closeup of a person sitting at a typewriter, drawn in the style of fun 2D vector artwork.” The second uses the prompt, “The scene is in a university classroom, there’s a blackboard in the background with a sketch of a neural network. In the foreground is a college student typing on computer, drawn in the style of fun 2D vector artwork.”]

It’s unclear just how deeply AI will become part of our daily lives. But what is certain is that for us to have meaningful conversations about AI, we need a shared vocabulary and a solid foundation of core concepts to build upon. As it stands, if you ask 10 people to define artificial intelligence, you’re likely to get 10 different answers. In this badge we try to reach an agreed-upon definition by exploring AI’s current capabilities. We also investigate how computer scientists create the AI systems that perform such incredible feats.

The Difficulty of Defining AI

The first step in defining AI is to recognize that our current notion of AI might be a little distorted. A steady diet of science fiction books and movies where AI is seen as a nefarious entity bent on conquering the world hasn’t helped.

Science fiction isn’t the only thing that’s complicated our view of AI. Generally speaking, we humans tend to think quite highly of ourselves; the benchmark by which everything else is measured. So when we speak of artificial intelligence, we can’t help but to compare it to our own intelligence. The problem is that humans aren’t the only intelligent beings out there. Animals, from crows to octopuses, use tools and problem solving to perform complex tasks. Even slime molds can solve mazes if given enough time.

And as we’ve begun to appreciate the huge spectrum of intelligence in the animal kingdom, we’ve also started to recognize the great diversity in our own human intelligence. Maybe you’ve met someone who’s fantastic at public speaking but can’t do math to save their life. Or someone who can always tell when you’re feeling a little anxious, but would trip over a soccer ball at the first opportunity. The point is that our intelligence is expressed in many, specialized forms. We need to think of artificial intelligence in the same way. There are specific kinds of AI that are good at specific kinds of tasks. So let’s bring some definition to what we mean by artificial intelligence by taking a close look at what AI can do today.

Main Types of AI Capabilities

Right now there’s no singular AI that’s good at everything. That idea, known as general AI, is still far into the future. Instead, over the years we’ve developed several specialized AI systems that are designed to perform specific tasks. The kinds of tasks they do generally fall into one of a few broader categories.

Numeric Predictions

Have you looked at a weather forecast recently? Predicting rain or shine helps you decide if you should grab an umbrella. Although we’ve made weather predictions for thousands of years, AI can do it better than any previous method.

A good prediction can help you answer all sorts of questions. Is this customer likely to renew their subscription? Are you at risk for a medical condition? Will there be high demand on the power grid this evening?

Often AI predictions take the form of a value between 0 (not going to happen) to 1 (totally going to happen). Numeric predictions include more than just percent values, they can predict any numeric value, such as dollars. Maybe your business wants to predict next quarter’s sales, or figure out the optimal pricing for your latest service: Widget+. And as a consumer you’re probably already affected by these kinds of numeric predictions, even more than you realize. Just imagine a trip overseas: the airline tickets, hotel room, ridesharing, and travelers insurance are all likely to be priced by AI to perfectly balance supply and demand.

A closeup of a friendly robot driving a taxi, in the style of flat 2D line art.

[AI-generated image using DreamStudio at stability.ai with the prompt, “A closeup of a friendly robot driving a taxi, in the style of flat 2D line art.”]

Classifications

Is a hot dog a sandwich? This question has led to countless hours of friendly philosophical debate about how we categorize things. But in the real world, the stakes can be much higher. Is this plant edible or poisonous? Is that email legitimate or a phishing attempt? Classification is often the first step in taking some kind of action, making it an incredibly valuable skill.

So it isn’t surprising that computer scientists have worked hard to create AI that’s good at classifying data. Identifying plants and phishing emails is only the tip of the iceberg. Financial institutions need to flag fraudulent transactions. Medical professionals must diagnose illnesses. Social media platforms want to identify toxic comments. All of these are examples of classification problems. AI can effectively make the first pass at classifying, and then the professionals can take it from there.

Often, AI classifiers can do the job just as well, or better, than humans. That said, each classifier is only good at one, narrow task. So the AI that’s great at detecting phishing emails would be lousy at identifying pictures of actual fish.

Some AIs excel at navigating a changing environment, and that might mean actual navigation in the case of autonomous (hands-free) driving. AI-powered cars are already quite capable of keeping centered in a lane and following at a safe distance on the highway. They adapt to curves in the road, gusts of wind from semi trucks, and sudden stops due to traffic.

AI that can adapt to changing environmental conditions have all sorts of real-world applications. For example, businesses need to produce and deliver products to their customers every day. Lots of market conditions play a role in how quickly that gets done: materials availability, manufacturing capacity, existing inventory, transportation costs, even real-time traffic. AI can optimize the supply chain even while conditions are changing.

And let’s not forget robots! Even the modest robot floor sweeper can avoid stairs and chairs. On a bigger scale, assembly lines are being fitted with robots that become faster and more efficient over time. Those same robots can adjust for changes to the production method without costly reprogramming. And researchers are creating rescue robots that can traverse disaster areas, such as a collapsed building. A robot-caterpillar that can squeeze through tiny cracks could deliver aid and hope to those trapped inside.

Language Processing

On November 30, 2022, Merriam-Webster’s word of the day was quiddity. Those who learned that word got a little better at what might be the most important skill of all: communication. On that same day, the world was introduced to ChatGPT, an artificial intelligence that demonstrated its own communication skills. It could write long responses to questions about almost any topic. And the responses seemed like they were written by a human. ChatGPT is one of the most capable AIs built to interpret everyday language and act on it in some meaningful way. This is known in the industry as natural language processing, or just NLP.

Natural language processing relies on an understanding of how words are used together, and that lets AI extract the intention behind the words. For example, you might want to translate a document from English to German. Or maybe you want a short summary of a long, scientific paper. AI can do that too.

NLP is a huge part of generative AI, a subcategory of AI that takes words and turns them into unique images, sounds, and of course other words. Generative AI is such a disruptive technology that we’ve devoted a whole badge to Generative AI Basics. Check it out when you’re done here.

In Summary

Artificial intelligence can be thought of as the ability for a computer to perform skills typically associated with human intuition, inference, and reasoning. At this time, AI skills are very specialized, and fall into some broad categories like numeric predictions and language processing.

Now that you have a sense of what AI is (and isn’t), you’re ready to explore how computer scientists and researchers create AI.

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