|ANN||artificial neural network|
|IoT||internet of things|
Tool used in the plugged part of the MyComputerbrain activity
This image shows the view in the MyComputerbrain application for the level 1 differentiation of the activity. On the right, we see four actions that our home automation system can execute. It can turn the lights on or off, and the fan on or off.
The left-hand side of the image depicts an artificial neural network (ANN), described here as the ‘input pattern’. The boxes in the input, hidden and output layers are called ‘perceptrons’. These can be likened to neurons in the brain. Within the input pattern, we see all the words that the ANN is able to understand. The three black boxes, ‘switch’, ‘light’ and ‘on’, represent one set of training data. You can drag the slider above the ‘input pattern’ label to see all training data.
Discuss the internet of things (IoT), which has made it possible to automate many processes we undertake in our daily lives. Now with the emergence of AI and the opportunities for voice commands, another level of automation is possible.
In pairs, ask students to sketch the floor plan of a typical house, and draw and label five ways that they could make the home a ‘smart home’ through automation. As a class, have the students share the different ideas. Which of them require AI? Which require sensors? How would digital and electrical systems interact with each other?
Discuss home automation:
Explain that computers can be programmed to be intelligent or, at least, smart. Ask the class if they think a computer could understand the meaning of something they say or write. How would it do that? At this point, students might mention their use of Siri, Alexa or similar technologies.
This step relates to the main components of common digital systems and how they may connect in the context of home automation.
Input, decision-making and output, in the context of a home automation system, are highly suitable for covering a range of content descriptors in the Australian Curriculum: Digital Technologies. Consider flow charts or any other form of diagram to conceptually explore sequences, branching and iterations.
The purpose of this step is not to write any code, but to explore and visualise possible steps and actions that might occur in a home automation system. This step also provides an opportunity to discuss scientific ideas about electrical systems and how they can be controlled by a digital system.
The plugged activity explores input and decision-making, as discussed in Step 3 above. For the output step separate activities can be undertaken with Arduino, Raspberry Pi, or BBC micro:bit. These activities demonstrate how code drives actuators.
This plugged activity allows students to interact with an AI application to recognise written commands and make meaningful decisions, such as turning the lights on or off. No coding is required by the teacher or students. Students can use a total of 12 expressions, including single words and whole sentences, to train the AI.
The underlying AI runs locally in the browser, so it’s learning speed only depends on the speed of the local processor, rather than internet speed. For example, the training process will take approximately 30 seconds on a 2012 laptop.
To increase the speed of the learning process, the lines representing the synapses can be hidden by clicking on 'Hide wires' in the menu.
To see the colour changes of the lines representing the synapses, drag the slider to the left.
Note, however, that this will slow the learning process.
The activity has been levelled to enable differentiation.
Students investigate the control required to switch lights and fans on or off. The training data for the ANN has already been provided and cannot be changed by the students. The experiment is available here.
Students are given the freedom to decide on four different outputs and to develop the training data. Students develop data sets and enter them into a table. Sample training data is provided for inspiration. The experiment is available here.
On level 2, the experiment has a special feature, which is that the ANN is constructed in real time while students enter the training data. This is intended to help students understand how the input data influences the topology of the ANN. It is an opportunity for the teacher to discuss data input, and more general human sensory perception with students.
Note: The student input is not stored. When the browser window is reloaded, the experiment is reset.
The function depicted above is a Sigmoid-function: f(x)= 1 / (1 + e^(-x)). During the learning process, the ANN also uses the following derivative of the Sigmoid function: df(x)/dx = f(x) * (1 - f(x)).
To learn more about ANNs, explore the other experiments in this course at https://mycomputerbrain.net/php/courses/ai.php. The first experiment is free and provides a good introduction to ANNs.
AI is the ability of machines to mimic human capabilities in a way that we would consider 'smart'.
Machine learning (ML) is an application of AI. With ML, we give the machine lots of examples of data, demonstrating what we would like it to do so that it can figure out how to achieve a goal on its own. The machine learns and adapts its strategy to achieve this goal.
In our example, we are feeding the machine with words. The more varied data we provide, the more likely the AI system will correctly classify the input as the appropriate output. In ML, the system will provide a confidence value. In this case, it is a decimal value between 0 and 1, and the output box is coloured white, shades of grey or black. The confidence value provides us with an indication of how sure the AI is of its classification.
This lesson focuses on the concept of classification. Classification is a learning technique used to group data based on attributes or features.