Artificial Intelligence 1: All About AI

All About AI

Artificial Intelligence 1

This lesson will help you earn points in the technology section of the Demo video part of the rubric.

In this lesson, you will…

  • Learn how artificial intelligence works

Key Terms and Concepts

  • Artificial intelligence (AI) - machines/programming that can do tasks normally thought to be done only by humans
  • Machine Learning - the subset of AI where a technology is trained with data and “learns” to recognize patterns in order to make predictions.
  • Datasets - information that is used to teach AI to recognize patterns and predict something.

Earlier, you learned how artificial intelligence is being used in different areas to make an extraordinary impact on our daily lives. Now, let’s look at how it actually works.

AI has 3 basic parts:

For AI to work, it needs a LOT of data to learn from. Technological advancements have allowed more information to be gathered faster than ever before. That’s one thing that makes AI possible now! The AI learns from the data and finds patterns on its own.  Then when it takes in new data, it can make a prediction.

Think about yourself as a human. What sort of predictions do people make? What inputs and patterns do you consider when making these predictions? Many people love predicting the weather, some people like to predict scores of sports games, and other people like to predict what might happen in a movie. 

Data can come in different forms. In these lessons, we’ll be using sounds, numbers, text, or pictures. 

In your household, what sorts of data do you create every day via technology?

Did you think of any of these?

  • Every Google search, words you type into emails
  • Every question you ask Alexa/Siri/OK Google
  • Connected devices - each time you turn on lights, Air conditioner temperature
  • Taps you make on your cell phone
  • Anything you purchase online
  • Who you are connected to on social sites
  • Songs you listen to
  • Steps you take

Zooming back out, let’s look at an example with Google Maps.

How does Google Maps use AI to give you directions to where you want to go?

Step 1

Google Maps takes in the following inputs to make a prediction:

  • Current location
  • Destination
  • Mode (walk, car, public transport)
  • Traffic

Step 2

But does AI stop at just making a prediction?

Step 3

Google Maps also takes action after making a prediction.

Step 4

The action/decision Google Maps takes: Shows you the best route.

Collecting Good Data

AI models are programmed algorithms that are trained on data that replicate human decision making. AI models need a lot of data to make good and accurate predictions. Here we’ll look at some different ways to collect good data for your invention.

But… What is “good” data? Consider the following:

  • Matches your problem/solution
  • Plentiful - the more, the better
  • Accurate 
  • Comprehensive - good variety representing different situations
  • Has permission from the people who shared the images or information 

There are three popular ways to collect data (data such as images, numbers, sounds, or text) for training and using in their AI models. Here they are with a reason why you might want to follow a certain method:

1. Collect your own training data from your community

You can gather a lot of data you need from your community from a variety of situations, and you can get permission to use it.

2. Invention gathers data with sensors or user input

You want your invention to be able to collect data on its own. You'll learn more about mobile sensors in Coding Lesson 11.

Tip: You’ll probably still additionally need training data from one of the other methods.

3. Use data from public datasets for training data*

You need more data than you can gather in your community, or you’re working on a solution for a problem that is more global.

*Small warning: Lots of free public datasets exist. You can see if you can use one! But sometimes these datasets require a lot of work to even decide if/what you can use. Some people have created whole jobs of making public datasets into good resources and then selling them. 

We’ve gathered some free public datasets for you to explore in the Additional Resources section.

Activity: Putting It All Together

Write down some of the apps or technologies that you and people you know use that utilize AI. Try to identify at least 3 different examples. Then consider the following:

What sort of data does it use to make predictions? How does it collect data?

What prediction is it making?

What action is taken after making its prediction?

Activity: Testing Out AI

You’ve brainstormed about apps on your own, now let’s try out some examples of AI in action.

Explore some of the following websites and get a taste of what AI can do. As you explore, think about further applications of the technology you see. Could you take the concept in front of you and apply it in a different context? AI uses lots and lots of data (images, text, sounds) and machine learning to create a model that can predict something. What sort of data would be necessary to make these applications work?

Imaginary Soundscape

Imagine you’re traveling and visiting another city. Many of us would think of what that place would look like, but have you ever thought about what it would sound like? This technology does just that. Based on an image, AI generates what it believes you would hear if you were actually there.


How many times have you had this clear image in your head and when you tried to draw it, it didn’t come out like you had in mind? With this website, AI takes your doodling and predicts what it is you’re trying to draw.

X Degrees of Separation

This one gives a glimpse of how AI “thinks” by taking two art pieces and showing us a bridge of similar artworks that connects the two together.

Want to explore some more?

Experiments with Google

NVIDIA AI Playground


Now that you have a glimpse of what it takes to create artificial intelligence, you might want to think about the problem you are solving, and how AI might be useful. Can you think about your problem and possible solution in terms of the three parts of AI - data, pattern, prediction? How would you address all three in your solution?

Additional Resources: Advanced Integrations

Build your own AI Models

Here are some of the platforms we use to explore AI:


Research datasets

Here are some places to start exploring datasets out there.


SolveIt Series by Technovation

Technovation created a video series that challenges you to expand your mind and tackle new problems. A lot of these concepts apply directly to creating your app and using AI!