Lesson 12: AI For Oceans

Overview

Tutorial Summary: First students classify objects as either "fish" or "not fish" to attempt to remove trash from the ocean. Then, students will need to expand their training data set to include other sea creatures that belong in the water. In the second part of the activity, students will choose their own labels to apply to images of randomly generated fish. This training data is used for a machine learning model that should then be able to label new images on its own.

Checking Correctness: This tutorial will not tell students whether they completed the level correctly. It is possible to skip through the different parts of the activity quickly. Encourage students to watch the videos, read the instructions, and try different things along the way. At any time, they can share their findings with you or a classmate.

Purpose

This tutorial is designed to quickly introduce students to machine learning, a type of artificial intelligence. Students will explore how training data is used to enable a machine learning model to classify new data. Students should have a positive experience during the tutorial and more importantly should be motivated to keep learning computer science.

Agenda

Warm Up (5 min)

Activity (30 - 40 min)

Wrap Up (5 - 10 min)

Extended Learning

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Objectives

Students will be able to:

  • Discuss the role artificial intelligence plays in their lives.
  • Train and test a machine learning model.
  • Reason about how human bias plays a role in machine learning.

Links

Heads Up! Please make a copy of any documents you plan to share with students.

For the Students

Vocabulary

  • Artificial Intelligence - the ability of machines to learn and problem-solve.
  • Machine Learning - How computers recognize patterns and make decisions without being explicitly programmed

Support

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Teaching Guide

Warm Up (5 min)

Build Excitement!

Motivate: Explain to students the goals of today's activity. They are going to start using a new tool that will let them train a real machine learning model, a form of artificial intelligence.

Video: The first level of this activity is a video that gives important context around artificial intelligence and machine learning. Watch it as a class and debrief afterwards to help students build connections to the content.

Activity (30 - 40 min)

General Support

General Support: As a teacher your role is primarily to support students as they make their way through the tutorial. Here are a few tips that should help students regardless of the level they're working on

  • Collaborate with Neighbors: Encourage students to check in with a neighbor to discuss what they are experiencing. Since this tutorial includes videos and students may be wearing headphones it can get easy to "go into a bubble". Help break those barriers by actively pairing students.
  • Read the Instructions: The instructions usually provide helpful information about what is happening behind the scenes.
  • Go back and try different things: If students finish quickly, encourage them to go back to "Train More". In the last part of the activity, students can also go back and choose a "New Word". More training data tends to make the machine learning model more accurate and consistent. Students can also learn by purposefully training their model incorrectly, or not training it at all.

Level 1 - Machine Learning

Video: AI: Machine Learning - Video

Quick Share-out: Where have you seen or experienced artificial intelligence in your lives? Examples from the video include:

  • email filters
  • auto-complete text
  • video recommendation systems
  • voice recognition
  • translation apps
  • digital assistants
  • image recognition

Prompt: Based on what you saw in the video, what is machine learning?

Discuss: Beginning in small groups then moving to whole class, students share their responses.

Discussion Goal

Goal: Get students familiar with the world of artificial intelligence.

Say: Machine learning refers to a computer that can recognize patterns and make decisions on its own based on data. In this activity you’re going to give the computer data to train it. Imagine an ocean that contains creatures like fish, but also contains trash dumped by humans. What if we could train a computer to tell the difference and then use that technology to help clean the ocean?

Content Corner

Every image in this part of the tutorial is fed into a neural network that has been pre-trained on a huge set of data called ImageNet. The database contains over 14 million hand-annotated images. ImageNet contains more than 20,000 categories with a typical category, such as "balloon" or "strawberry", consisting of several hundred images. When A.I. is scanning new images and making its own predictions in the tutorial, it is actually comparing the possible categories for the new image with the patterns it found in the training dataset.

Levels 2-4 - Train AI to Clean the Ocean

Students can work through the first three levels on their own or with a partner. To program A.I., use the buttons to label an image as either "fish" or "not fish". Each image and label becomes part of the data used to train A.I. to do it on its own. Once trained, A.I. will attempt to label 100 new images on its own, then present a selection that it determined have the highest probability of being "fish" based on its training. Students who consistently label things correctly should see an ocean full of different types of sea creatures, without much (or any) other objects.

Level 5 - Training Data & Bias

Quick Share-out: How well did A.I. do? How do you think it decided what to include in the ocean?

Video: AI: Training Data & Bias - Video

Discussion Goal

Goal: Get students to reflect on their experience so far. It is important at this point that they realize the labeling they are doing is actually programming the computer. The examples they show A.I. are the "training data".

Prompt: How do you think your training data influence the results that A.I. produced?

Discuss: In small groups, students share their responses. Circulate the room and listen to student ideas. This can be followed with full class discussion, or students can jump right back into the tutorial.

Content Corner

The fish in this tutorial are randomly generated based on some pre-defined components, including mouths, tails, eyes, scales, and fins, with a randomly chosen body color, shape, and size. Rather than looking at the actual image data, A.I. is now looking for patterns in these components based on how the student classifies each fish. It will be more likely to label a fish the same way the student would have if it has matching traits.

Level 6 - Using Training Data

In the second half of the activity, students will teach A.I. about a word of their choosing by showing it examples of that type of fish. As before, A.I. doesn't start with any training data about these labels. Even though the words in this level are fairly objective, it's possible that students will end up with different results based on their training data. Some students may even intentionally train A.I. incorrectly to see what happens. If students are reflecting on how machine learning works, it should be encouraged!

Level 7 - Impacts on Society

Video: AI: Impact on Society - Video

Say: Artificial intelligence systems learn from the data we give it, but sometimes we might not give it enough data or we might give it data that makes it act strangely.

Discussion Goal

Goal: The goal of this discussion is to bring students back to the context of artificial intelligence in the real world.

Say: Think back to the examples of artificial intelligence we discussed at the beginning. Think of a time where machine learning might have got something wrong in the real world? (For example, voice recognition fails to understand you.)

Prompt: Could training data actually create problems? How?

Discuss: Beginning in small groups then moving to whole class, students share their responses.

Say: Some ways to fix this are by using a lot of training data, and making sure we understand the problem well ourselves so we give the right kinds of data. In the final part of the activity you’re going to teach A.I. a word that could be interpreted in different ways.

Level 8 - Teach A.I. a new word

Here, as before, students will use training data to teach A.I. to recognize different types of fish. The words in this list are intentionally more subjective than what students will have seen so far. Encourage students to decide for themselves what makes a fish look "angry" or "fun". Two students may choose the same label and get a very different set of results based on which fish traits were their focus. Encourage students to discuss their findings with each other or go back and choose new words. Each student will rely on their own opinions to train A.I. which means that A.I. will learn with the same biases held by the students. As students begin to see the role their opinion is playing, ask them to reflect on whether this is good or bad, and how it might be addressed.

Wrap Up (5 - 10 min)

Debrief

Open question: How could artificial intelligence be used to solve a problem in the world?

Extended Learning

Help Classify Animals at Mountain Zebra National Park

Snapshot Safari has placed hundreds of hidden cameras across southern Africa, capturing millions of images of beautiful and rare animals. Students can help protect the endangered Cape Mountain Zebra by classifying the different animals in these images. You can read about the project here or click below to give it a try!

Snapshot Mountain Zebra - Zooniverse

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Student Instructions

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Student Instructions

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Student Instructions

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Student Instructions

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Student Instructions

Standards Alignment

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CSTA K-12 Computer Science Standards (2017)

DA - Data & Analysis
  • 1B-DA-07 - Use data to highlight or propose cause-and-effect relationships, predict outcomes, or communicate an idea.
IC - Impacts of Computing
  • 1B-IC-18 - Discuss computing technologies that have changed the world and express how those technologies influence, and are influenced by, cultural practices.