One way an AI recognises an object is by feature extraction. These are the features that help the AI differentiate one object from another. Take one of the examples below; for example, shark, crocodile or weed-spotting or traffic light detection. Ask students to describe features of each to enable the AI to recognise one object from another.
Some students may need individual support during this activity to select and view relevant images and discuss features of each.
Now take that idea of feature extraction a step further. Ask students to think about the features of the objects they described and how they might represent these in a simple line drawing.
A fun way to do this is to go to the AutoDraw website and select Launch an experiment. Ask students to draw a shark, crocodile, weed or traffic light onscreen first, and see how well the AI guesses (predicts) what they are drawing.
You might also try out Quick draw. It will direct students to draw a specific object. The AI will guess what it is, based on known patterns in how people from all over the world draw.
Computers powered by AI also ‘look’ at and categorise objects by breaking them down into shapes. We can model this process by creating a simple representation of an object using geometric shapes.
Ask students to try representing an animal or object using common geometrical shapes. They could use shape blocks, cut out shapes from cardboard or use this interactive Picture studio. Have students work with a partner to see if they can identify their partner’s object. What were the key features that helped identify the animal or object?
Image: Picture studio screen capture: Kangaroo.
In reality, a computer sees images as individual picture elements (pixels). Use this pixel viewer to show that an image is made up of pixels. Each colour pixel in the image is made up of numbers that represent the colour as a combination of Red-Green-Blue (RGB). So the computer recognises a kangaroo, for example, based on a pattern of pixels and complex mathematical algorithms.
No wonder it is such a challenge for a computer to recognise an object accurately!
Image: Pixel viewer screen capture: Kangaroo (Right Image Zoom in showing RGB for each pixel)
Explore an AI that detects objects and classifies them. Use Google’s Vision AI which uses pre-trained machine learning models to assign labels to images and quickly classify them into millions of predefined categories. Detect objects and faces, read printed and handwritten text. Note the confidence level the AI has of its match and predicted classification. In the example the AI is 80% confident that the object detected is a kangaroo.
Have students begin by dragging a photo into the API.
Students can test a new image by selecting ‘new file’ and uploading an image from their computer. You may want to download these images, which are copyright free.
Discuss the level of accuracy of the AI and its confidence level. To help understand percentage you can draw a bar and indicate the level coloured in. 50% half coloured in, 80% as 4/5 coloured in etc. The closer to 100% the higher the confidence the AI has in its predicted match. Discuss the training data and that the AI may only have been trained on common Australian animals such as Kangaroo, Koala and that it would predict an emu as an ostrich as this is what it most likely was trained on.
Kangaroo image credit: pen_ash/pixabay
Introduce the idea of classification using the sorting of Animal picture cards. The cards can be sorted into groupings such as:
Share the different ways the animals were sorted.
Ask students to look at the features of animals and work out how these can be used to classify the animals. Create a table of data to describe the key features of four to five Australian animals. Alternatively, use a combination of images with text/numbers.
Image: Information in a Table and presented visually
A spreadsheet is a useful way to organise the information, as it can be filtered by column heading. You could use this Excel spreadsheet or this Google spreadsheet for students not familiar with setting up a spreadsheet. It has four tabs.
Use this experiment to show how an AI identifies a particular animal based on selected features. The AI has been trained to identify to identify six relevant animals by whether each animal has features such as legs, arms, wings or fins, tail or no tail, certain types of body covering and external ears or ear holes.
Note the level of black in the text box in the output layer. This indicates how sure the AI is in its prediction. Black indicates a high confidence level. Several options displayed as outputs in grey, indicate that the AI has a lower confidence level, based on the features that have been selected. https://mycomputerbrain.net/php/experiments/ai.experiment26a.php
Image credit: MyComputer brain (animal classification by an ANN)
Computer vision has come a long way but it is still a challenge for computer scientists to improve and build accurate systems. Currently, computer scientists teach computers in a similar way to the way teachers teach students at school. Artificial intelligences use the methods that you have seen in this lesson plan to form an understanding of the object – and this understanding is based on a picture of the object/thing, using its colour, shape, and features. AIs then combine these insights to form a judgement, a classification. Like humans, AIs are confident or not so confident. But a good AI always tells us how confident it is about its judgement. It won’t pretend to be confident if it isn’t.
In Digital Technologies, representing data refers to the way data is symbolised, visually treated or provided in audio. For students in years 3–4, the focus for data representation is on how the same data can be represented in different ways. An example of this is data in a table represented as text or as images or a combination of these.
Image recognition is an area of AI that has many applications. For a computer to recognise what it sees, it needs input of data through a camera and some form of processing. Classification is a supervised learning technique used to group data based on attributes or features. Humans can provide labels on the data input (for images or text, for example) that tell the machine both about the attributes or features (such as colour, size, shape, measurements) of the data input and how to group it. The machine then matches future data based on the similarity of the new data to predefined groups. An example: sorting images of kangaroos and wombats based on their number of legs, whether they have a tail and whether they have external ears.